I spent the first half of my career at IBM, where I built dashboards, predictive analytics solutions, and complained about our data warehouse. Then, I joined the EDW team to try to fix it, ran an analytics development team, and eventually landed in their analytics center of excellence (CoE). Similarly, Andreas Welsch spent close to twenty years at SAP, finishing as the VP who ran their AI Center of Excellence. We both left to run our own businesses and learned similar lessons. When you’re an independent entrepreneur, you become the CXO of everything, from revenue to legal to accounting to the marketing that nobody else is going to do for you.

That shared vantage point made our conversation on the Data Faces Podcast easy from the start. Andreas has watched hype-infused trends play out four times now: cloud, mobile, the first wave of machine learning, and now generative and agentic AI. Each time, the patterns that follow are similar.

“We have this new shiny object. Let’s go figure out what we can do with this. Throw spaghetti at the wall and see what sticks.”

— Andreas Welsch, Founder and Chief Human Agentic AI Officer, Intelligence Briefing

When you’re chasing the latest thing, sometimes the spaghetti sticks to the wall, while other times the house of cards comes crashing down. The companies that come out ahead, Andreas argues, are the ones that stop to ask a question most leaders skip under this much pressure. Just because you can build something with AI does not mean you should.

About Andreas Welsch

Andreas Welsch is the founder and Chief Human Agentic AI Officer at Intelligence Briefing, where he helps business leaders figure out what to do with AI. He spent close to two decades at SAP, finishing as the vice president who ran the company’s AI Center of Excellence, so he watched enterprise AI grow up from the inside. He is the author of two books, The AI Leadership Handbook and The Human Agentic AI Edge, an adjunct professor in Pennsylvania, a LinkedIn Top Voice, and the host of the What’s the BUZZ? podcast. The engineering curiosity started early. There are photos of him around four or five years old, screwdriver in hand, taking apart an RC car to see how it worked, then ending up with a small pile of leftover springs and screws.

In our conversation, Andreas and I covered:

- Why the rush to cut headcount with AI spreads like a contagion, and the revenue question almost nobody asks

- The “should we?” test that separates real value from sunk cost

- What the “SaaS is dead” crowd gets wrong about convenience, risk, and who you call at 2 a.m.

- How he used three custom GPTs to edit his book, and where AI’s help turned into noise

- Why agentic AI risk multiplies rather than adds up as you stack more agents

The race nobody’s questioning

Before we got to agents, we talked about layoffs, because Andreas sees the two as interrelated. One company announces it needs fewer people and more technology, whether or not it has figured out how. The media picks it up, investors ask the competitor down the street why it is not running as lean, and like dominoes, the next company follows, until a single press release hardens into an industry expectation.

“Having layers that continue until the morale improves isn’t really the way to success. And we know this, and leaders know this too. Yet this is happening because somebody over here said they’re doing it.”

— Andreas Welsch, Founder and Chief Human Agentic AI Officer, Intelligence Briefing

The same contagion now drives agentic AI. One company says it is building agents, true or not, and everyone else picks up the language. I told Andreas that I have not seen many agentic workflows in production. I see prototypes, pilots, and a lot of experimentation, but turning an agent loose on the real world is still rare, and plenty of those pilots stall long before they reach production.^[1] He agreed we are at least past the slide-deck arguments over whether to call it “AI agents” or “agentic AI,” yet most organizations are still deciding which use cases are worth the effort.

The question that saves you

When I asked Andreas what worries him about all this posturing, he started with a claim he hears all the time. SaaS is dead. Every other LinkedIn post now declares the death of software because anyone can build their own. So he tested it. After upgrading his Claude subscription to the hundred-dollar tier, he started rebuilding the tools he pays for. He cloned DocuSign over a weekend, the signing boxes wired to an email workflow that saved the file. He rebuilt his workshop live-polling app in a few days, then knocked out four or five more, from digital sticky notes to a credentialing tool.

The experiment worked, and that is exactly what taught him the lesson.

“You’re actually paying for convenience and for peace of mind when you get a SaaS subscription. There’s somebody else who is maintaining that thing for you. For 20 dollars a month? That’s actually a pretty good deal.”

— Andreas Welsch

A twenty-dollar subscription suddenly looks cheap when you remember what it covers. Someone else handles the dependencies, security patches, data-privacy rules, and the small stuff like getting the fonts to line up. Rebuilding a non-essential app for personal use is a fun exercise, but rebuilding the core systems a business runs on is a different calculation. ERP, CRM, and finance software need auditability, and when something breaks at two in the morning on a Sunday, you want a vendor on the hook to fix it, not a teammate who vibe-coded the thing last weekend. What does not change is the question under every build-or-buy decision. Just because you can build it does not mean it belongs on your plate.

Cost, or revenue?

Underneath the layoffs and the refactoring, Andreas keeps waiting to hear leaders ask one question. How are you going to make more money? He hears plenty about trimming costs and protecting margin. He rarely hears anyone ask where new revenue is supposed to come from.

“I wish there were more people asking, so how are you making more money? Not how are you optimizing your costs? Revenue is a lot harder to achieve, and building products that people want to buy and offering services that people need, it’s a lot harder to do than taking out costs.”

— Andreas Welsch

This is where his optimism diverges from how most companies behave. The same technology leaders use to justify cuts could instead help a team do ten times more, build new products, and reach customers it could not serve before, without sacrificing the people who would create that growth. Most want the incremental win with a smaller headcount, and the people who stay do the work of five.

The market data backs up his skepticism about where the value lands. McKinsey’s 2025 State of AI survey found that only about 39 percent of organizations report any measurable effect on enterprise earnings from AI, and most of those credit it with less than five percent. Sixty-two percent say they are at least experimenting with AI agents, yet only 23 percent are scaling them.^[2] The enthusiasm shows up everywhere. The financial return, for most companies, has not arrived yet.

What AI still gets wrong

The clearest picture of where AI helps and where it stops came from Andreas’s own book. He had planned to hire a human copy editor and line editor, the way he had before, because he likes the coaching and back-and-forth. Friends pushed him to let AI do it instead. So he wrote the manuscript himself with no AI, then built three custom GPTs, one a developmental editor, one a copy editor, and another a line editor, and fed his draft through all three.

The results were promising. The AI caught inconsistencies and even factual errors, things he had misremembered from news stories that a human editor would likely have missed. Then it kept going.

“AI, or in this case ChatGPT, was a helpful assistant that really didn’t know when to shut up.”

— Andreas Welsch

Every new revision came back with another five urgent fixes, then five more, until the suggestions started making the book worse instead of better. He was watching diminishing returns in real time, and he realized the skill he needed was knowing enough about his own craft to say “this far, and no further.” Without that line, you cannot tell whether the system is improving your work or making it worse.

That same limit scales up to the autonomous-enterprise vision everyone keeps selling. I asked Andreas which piece of conventional wisdom about agentic AI he thinks is most wrong, and he did not hesitate.

“It does everything for you, and it does it perfectly all the time. We’re still relying on a probabilistic system that can be confidently wrong.”

— Andreas Welsch

Even with governance, guardrails, and evaluation in place, an agent still has enough room to do something nobody wanted. And the risk does not add up the way people assume. One agent is manageable, two working together get more complex, and by the time you are orchestrating several, the risk compounds exponentially. Most companies are still building their first or second one while the industry sells them autonomous enterprises. Gartner predicts that more than 40 percent of agentic AI projects will be canceled by the end of 2027, undone by rising costs, unclear business value, and weak risk controls.^[3] Plenty of that failure traces back to strategy and governance rather than the models themselves, the same conclusion behind the forecast that most generative AI projects will fall short of their goals.^[4]

The human edge

Andreas gave himself a title that sounds like a contradiction, Chief Human Agentic AI Officer, and by the end of our conversation, it made sense. The leaders getting real value from AI share a habit. They treat the technology as a way to expand what their teams can do, keeping a human in the loop to decide what is worth doing at all. The most useful AI deployments I see amplify human judgment instead of replacing it.^[5]

Human judgment is the whole game. A manager asks how the company will make more money before reaching for another round of cuts, and an author learns when to stop taking the model’s notes. The same instinct tells an executive to pause before automating a process just because a vendor swears it can be done. Agentic AI will keep getting more capable, and the pull to hand everything over to it will keep getting stronger. The advantage goes to the people who can look at all that capability and still ask the oldest question in business. Should we?

Listen to the full conversation with Andreas Welsch on the Data Faces Podcast.

Based on insights from Andreas Welsch, Founder and Chief Human Agentic AI Officer at Intelligence Briefing, featured on the Data Faces Podcast.

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Podcast highlights

- [1:01] What Intelligence Briefing does, and helping leaders decide what to do with AI

- [1:48] From wanting to be a pediatrician to taking apart RC cars with a screwdriver

- [3:45] Leaving SAP and becoming the CXO of everything

- [9:59] The optimism gap, and why so many teams are burned out doing five jobs

- [10:40] The layoffs vicious cycle, and the revenue question nobody asks

- [13:58] Pilots versus production, and why people have gone quiet about what they are building

- [21:37] “SaaS is dead,” and vibe-coding clones of DocuSign and Mentimeter

- [25:00] When to defer risk to a vendor, and the shift away from per-seat pricing

- [27:57] Just because you can does not mean you should

- [32:47] Editing a book with three custom GPTs that would not stop talking

- [36:12] The conventional wisdom he thinks is wrong, and why agent risk compounds

Frequently asked questions

What is agentic AI, and how is it different from a chatbot or generative AI?

Generative AI produces content such as text, code, or images in response to a prompt. Agentic AI goes a step further by taking actions, connecting to other tools, and completing multi-step tasks with some degree of autonomy. In the episode, Andreas Welsch describes agents that can run parts of a workflow on their own. The catch is reliability. Because the underlying system is probabilistic, an agent can act confidently and still be wrong, which is why human oversight matters.

Why do most AI agent projects fail to reach production?

Most agent efforts stall because organizations chase the technology before defining the value. Gartner predicts that more than 40 percent of agentic AI projects will be canceled by the end of 2027 because of rising costs, unclear business value, and weak risk controls. Companies that succeed start with the workflow they want to change, decide what to eliminate, and measure value from the beginning rather than launching a pilot and hoping it finds a purpose.

Does using AI mean cutting headcount?

It does not have to. Andreas Welsch argues that the bigger opportunity is using AI to help existing teams do far more, build new products, and reach new customers, which protects future growth rather than trading it away for a short-term cost cut. McKinsey’s 2025 research found that only about 39 percent of organizations report any measurable earnings impact from AI so far, a sign that headcount cuts alone do not deliver the promised return.

Should a company build its own software instead of paying for SaaS?

It depends on whether the software is core to the business. Andreas Welsch rebuilt several non-essential personal tools to prove it was possible, then concluded that a subscription often pays for convenience and peace of mind. Someone else handles maintenance, security, and data privacy. For core systems like ERP, CRM, or finance, auditability and vendor support usually outweigh the savings from building it yourself.

Where should a leader start with agentic AI?

Start with a single high-value workflow rather than a broad rollout. Ask whether the project should be done at all, not just whether it can be. Keep a human in the loop to judge quality, and treat reliability and risk as first-order concerns because agent risk compounds as you add more agents. The goal is measurable value on one process before scaling to the next.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Herrera, Catalina. “Your Netflix Moment: Why CIOs Must Act Now on AI Agents (or Risk Becoming the Next Blockbuster).” TinyTechGuides Insights, October 7, 2025. https://insights.tinytechguides.com/p/your-netflix-moment-why-cios-must.

^[2]McKinsey & Company. “The State of AI in 2025: Agents, Innovation, and Transformation.” QuantumBlack, November 2025. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-his craft well enough to say,state-of-ai.

^[3]Gartner. “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027.” June 25, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027.

^[4]Carlsson, Kjell. “AI in 2025: Why 90% of Gen AI Projects Will Fail.” TinyTechGuides Insights, March 22, 2025. https://insights.tinytechguides.com/p/ai-in-2025-why-90-of-gen-ai-projects.

^[5]Magne, Matt. “Augmented Intelligence: The Future of Sales Enablement.” TinyTechGuides Insights, November 4, 2025. https://insights.tinytechguides.com/p/augmented-intelligence-the-future.

In late May, I spent three days at the BARC Data and Analytics Retreat at Devil’s Thumb Ranch in Colorado. It’s a unique event. Instead of a big stage and a passive audience, you get a room of twenty-five or thirty data and AI leaders who interrupt each other, disagree out loud, and keep the conversation honest. Five minutes into the first presentation, someone already said, “I don’t agree with that,” and that set the tone for the whole retreat.

I wasn’t the only one who noticed. “You’re with a group of twenty or thirty people who have been in this vertical for a long time, and the discussion opens up in both directions,” Shree Neve of ClicData told me. John Colthart of Una AI pointed at the mix of people in the room. “When you look at the people here, the different types of businesses, it gives you such a rich context arena to throw around ideas. That level of diversity of opinion and thought, that part’s really cool.” And Ben Schein of Domo named something you rarely see at a vendor event, competitors trading notes in good faith. “Some of these people we’re competing with on deals and for customers, but it’s nice to come together and learn in a way that’s not giving away any secrets.”

Between sessions, I pulled a handful of people aside for short on-location conversations for the Data Faces Podcast. The topics ranged from data sovereignty to financial planning to the future of business intelligence, but one theme kept surfacing across every conversation. What decides whether AI delivers value right now is governance and control, plus context and a clear point of view about what you are building, far more than any model feature.

So I did what you do after a few days on a Colorado ranch. I rounded up the six conversations that stuck with me. Here they are.

Carsten Bange on why sovereignty is really about control

Dr. Carsten Bange, founder and CEO of BARC, gave one of the retreat’s first sessions, a talk on data sovereignty. The term gets used loosely, so he separated it into three nested ideas. Data sovereignty sits inside digital sovereignty, which also covers processes and technology, and AI sovereignty is the newer layer focused on who controls the models you run. BARC’s own Data Sovereignty 2026 survey found that 89% of organizations now call sovereignty important, with “very important” climbing from 42% to 51% in a single year, and US political developments jumping to a top-three driver at 54%.^[1] The headline that surprised even Carsten was geographic. US companies rate sovereignty as more important than European ones, and they are investing more in it, even though Europe wrote most of the rules.

Shawn Rogers on innovation outpacing governance

Shawn Rogers, CEO of BARC US, has a blunt read on where most companies sit with AI governance. He described a talk where he asked a few hundred people whether they had launched an AI agent, and every hand went up. When he asked who felt comfortable with how they govern it, almost every hand dropped. He puts roughly 20% of the organizations he talks to in the category of having real governance in place, which leaves the other 80% moving fast and hoping nothing breaks. He also walked through the financial side that catches teams off guard, the surprise bills that land on Monday morning after someone launches an agent on Friday afternoon, including one company that handed Claude to 12,000 employees with no budget at all.

Ben Schein on the question most teams skip

Ben Schein, Chief AI and Analytics Officer at Domo, framed the smartest filter for any AI decision around whether you should act, even when you can. With a general-purpose model, almost anything is technically possible, so the harder and more useful question is whether you should once you weigh governance, cost, and risk. He also reframed sovereignty in a way that stuck with the room, describing it as control and visibility over your data and what it is doing, rather than a question of where the data center physically sits. Token cost ran underneath the whole conversation, shaping which AI projects are worth running and which ones burn budget for little return.

Shree Neve on confidence outrunning capability

Shree Neve, VP of Operations at ClicData, gave the sharpest warning of the retreat for anyone rushing to bolt AI onto their data. Bad inputs do not produce obviously bad outputs. They produce confident, well-formatted, completely wrong answers, and you might not catch the problem until it has already shaped a decision. That same disconnect between confidence and capability showed up in BARC’s research too. In the Unstructured Data for AI study, 71% of leaders said they were confident they could extract value from their data, yet one in three admitted to lineage and control gaps, and data quality has now climbed to the single most cited measure of AI success at 48%.^[2] She pushes a refreshingly old-fashioned sequence. Start with the business decision you want to make, work backward to the question, and only then go find the data and the tool.

John Colthart on the number finance forgets

John Colthart, Chief Product Officer at Una AI, came into financial planning with a contrarian view after a long career across sales, marketing, and product. Most planning tools obsess over controlling spend, and in doing so they ignore the number that tells you whether the business is growing. He also refuses to force a false choice between Excel, a web portal, and AI, since most companies still run real planning in spreadsheets and probably always will. His foundation-first instinct matches what BARC sees across the office of finance. Data management ranks as the top corporate performance management priority at 8.2 out of 10, while generative AI for planning sits near the bottom at 4.6, and only 6% of organizations have AI in active production for performance management.^[3]

Ivan Vakhmyanin on building for trust

Ivan Vakhmyanin, co-founder of Visiology, is doing the uncomfortable thing on purpose. Ten years into building a business intelligence company, he is rebuilding the product from scratch as an AI-first system rather than bolting assistants onto the old one. His reasoning is direct. If he does not disrupt his own product, a competitor eventually will. The harder engineering choice underneath that is trust. He kept Visiology’s tested data engine and methodology on the back end, gave users a familiar chat-style experience on the front, and made every step traceable so people can verify how the system reached an answer. That instinct lines up with what Kevin Petrie called “vibe slop” in his retreat session, the failure that happens when teams deploy agents on shaky foundations without the governance and context to back them up.

Rounding up what I heard

Six conversations, six corners of the industry, and the same idea underneath each one. Carsten framed sovereignty as control. Shawn weighed how fast companies launch AI against how slowly they govern it. Ben asked whether you should, even when you can. Shree showed how confidence outruns capability when the data is weak. John argued for the foundation before the AI layer. Ivan engineered for trust so people can rely on what the system tells them. None of them led with model features, because features are no longer where the value or the risk lives. What separates teams getting real returns from teams burning budget is governance and control, plus the context and point of view behind what you build.

If you want the full conversations, all six are on the Data Faces Podcast. New episodes drop every couple of weeks, and the on-location interviews like these land between the studio conversations.

If you’d like to learn more about BARC, its research, and the retreat, visit barc.com.

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Frequently asked questions

What is the BARC Data and Analytics Retreat?

The BARC Data and Analytics Retreat is an invitation-only event hosted by the analyst firm BARC, held in 2026 at Devil’s Thumb Ranch in Colorado. It gathers a small group of data, analytics, and AI vendor leaders for working sessions and open debate rather than stage presentations. The intimate format encourages the kind of disagreement and discussion that larger conferences rarely produce.

What is the difference between data sovereignty, digital sovereignty, and AI sovereignty?

According to Carsten Bange of BARC, the three are nested. Data sovereignty is part of digital sovereignty, which is broader and also covers processes and technology. AI sovereignty is a newer layer that focuses on AI-specific questions, most importantly who controls the models an organization uses. All three center on control and visibility rather than only the physical location of data.

Why is AI governance such a concern right now?

Adoption is moving faster than control. Leaders at the retreat described companies launching AI agents widely while only a minority have real governance over the underlying data, models, and agents. The result is uncontrolled risk and surprise costs, including organizations that gave large groups of employees access to AI tools with no budget or oversight in place.

Where should a team start with AI on their data?

Start with the business decision you want to make, not the tool. As Shree Neve of ClicData put it, work backward from the question to the data and only then choose the AI tool. Feeding AI weak data produces confident but wrong answers, so fixing data quality and governance first matters more than picking a model.

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]BARC. “Data Sovereignty 2026: Reality, Relevance, Roadmap.” BARC, 2026.

https://barc.com/

^[2]Adrian, Merv, and Kevin Petrie. “Harnessing Unstructured Data for AI Innovation.” BARC Research Study, 2026.

https://barc.com/

^[3]BARC. “CPM Trend Monitor 2026 / The Planning Survey 26.” BARC, 2026.

https://barc.com/

Twenty-five years, same fight

Over the past 25 years, I’ve seen my share of hype cycles. Sometimes it feels like Groundhog Day, with things on repeat. At IBM, I built predictive analytics solutions and data warehouses. At SAS, Dell, TIBCO, and Alteryx, I marketed advanced analytics to companies that said they wanted to be data-driven, only to watch them revert to their complex spreadsheets the next day. Every five to seven years, a new shiny technology shows up that promises to usurp the previous one, and every time, the work is the same. You clean up your data, you get leadership aligned, and you convince people to change how they make decisions.

The current wave is agentic AI, and the technology behind it is certainly impressive. Frontier models can write better code than most engineers, pass the bar exam without breaking a sweat, and reason through problems that used to require a PhD. Anthropic, OpenAI, and the rest of the foundation model crowd are racing toward something that looks an awful lot like AGI. Meanwhile, on the 101 corridor through San Francisco, every billboard is selling agents, and every shoe company is now an AI company. Allbirds just signed a $50 million convertible facility to pivot into GPU-as-a-Service and rename itself NewBird AI, and the stock popped more than 350 percent on the announcement.

When I sat down with Josh Howard for episode 40 of the Data Faces Podcast, the topic he proposed was tongue-in-cheek on the surface, but beneath the surface lay a partial truth that most enterprises are still avoiding. Josh is the Senior Director of Product Marketing for Executive Audiences at Databricks. He and I first met at Dell more than a decade ago, then crossed paths again at Alteryx, where we were both trying to convince financial analysts that there was a better way than spreadsheets. His topic for the show was three words. Your AI is dumb. As Josh explained, the models themselves are some of the most advanced technologies that we have seen in our lifetime. However, they are only as smart as the data you give them, and most companies still haven’t figured out how to give them access to the data that matters most.

“Without context, your agents are dumb.”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

About Josh Howard

Josh Howard is the Senior Director of Product Marketing for Executive Audiences at Databricks, where he has spent the last four years translating data and AI strategy for the C-suite. Before Databricks, we crossed paths in product marketing twice, first at Dell Technologies and then at Alteryx, where we spent our days trying to convince financial analysts that there was a better way than the spreadsheet. Outside of work, Josh lives in Colorado, ties his own fly-fishing lures, and told me on the show that if he weren’t doing product marketing, he would be a full-time fly-fishing guide on the rivers near Denver.

In our conversation on the Data Faces Podcast, Josh and I get into:

- Why “your AI is dumb” without enterprise context

- New findings from the Databricks and Economist Enterprise Making AI Deliver survey of 1,221 senior technology leaders, including the 84/43 measurement problem and why infrastructure costs more than the GPU bill

- Where agents are already changing how work gets done, and where they haven’t yet

- The cautionary tale of an agent who whacked a production database

- Josh’s contrarian take on the AGI debate

Why your AI is dumb without your data

AI has an insatiable appetite, but the models are often hankering for the wrong datasets. They were trained on the public internet, which makes them competent at history, cheating on homework, and bar exam questions. But, they have never seen your customer record, your forecast methodology, or the customer call recordings in Gong.

“These models have been trained on the internet. They’re really good at history or helping your kid do their homework. From an enterprise perspective, a lot of that work hasn’t been done to give it access to the data in your organization. You’ve got to have that context.”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

The data that your enterprise runs on is scattered throughout your organization. It sits in the systems where your customer relationships, your financial close, and your product telemetry live. Most of it is proprietary, much of it is unstructured, and the model has never seen any of it. Until it has access to that information, no amount of fine-tuning will make the answer any better.

This is the metadata fight from twenty years ago with a new name. Enterprise architects have been screaming about governance and consistent business definitions for two decades, and almost nobody on the business side was listening. Now those same arguments are showing up in CEO town halls because gen AI outputs have made the problem visible. Gartner has been making the same point, warning that organizations without AI-ready data will see most of their AI projects stall or fail through 2027.^[1] The product that Josh pointed at on the show is a conversational analytics layer trained on his organization’s internal semantics, policies, and nomenclature. A user types a question in plain English, and the system answers using the company’s own data, terminology, and rules. When your AI fails on a business question, the issue is rarely the model. It is almost always a data context problem.^[2]

The real cost isn’t the GPU bill

Talk to a CFO right now about AI, and the first word out of their mouth will be cost. The conversation will go straight to GPU pricing, vendor lock-in, and whether the AI bill will break the bank next quarter. Those are the visible costs. According to the new Databricks and Economist Enterprise Making AI Deliver survey of 1,221 senior technology leaders, the damage is happening somewhere else.^[3]

“Everyone is obsessing over the model cost and GPU spend, but the real tax there is actually the infrastructure underneath.”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

The survey asked leaders to identify their biggest AI cost concerns. Fifty-nine percent named data storage, movement, and duplication. Only 25 percent named compute. What the press and the boardroom focus on draws less than half the concern of the thing nobody talks about. The real cost is dragging your data from where it lives now to wherever the model needs it, and then doing it again three more times for the next system.

The payoff for fixing this is measurable. The same survey found that 97 percent of organizations with a unified data architecture report their AI investments are paying back ahead of plan.^[4][5] Almost nobody has a unified architecture today. Most enterprises run a hodgepodge of warehouses, application databases, and SaaS exports stitched together with batch jobs and prayer. The companies that have done that consolidation work are seeing returns. Everyone else is paying the tax twice.

The 84/43 problem

For most of my career, the architects, warehouse managers, and data scientists who understood the systems were screaming about governance, lineage, and consistent business definitions, and the people writing the checks weren’t listening. Then ChatGPT launched in November 2022. Almost overnight, the C-suite cared. Josh and I were both watching from inside product marketing, and the light bulb finally went off.

That attention brought real budget, executive air cover, and top-down sponsorship. Four years in, the Making AI Deliver survey shows where the bill is coming due. Eighty-four percent of senior executives say their AI returns are beating expectations, but only 43 percent require teams to measure the impact of those projects.^[6] Doesn’t that seem weird? Confidence has gotten well ahead of measurement, and the boardroom will eventually notice.

We’ve seen this pattern before. CRM in the late 1990s and big data in the early 2010s both produced euphoria first, then a wave of post-mortems and write-downs once boards started asking what the investment had returned. The 84/43 split is the present-day version of the same trap. Confidence without measurement holds up right until somebody in the boardroom asks for proof. When the proof comes, most AI projects don’t survive the audit.^[7]

This problem has a boring fix. Before any AI project starts, name the outcome that it should deliver, the metric that you will use to track it, and the executive who owns that metric. This isn’t rocket science; in fact, it’s the same advice that Gartner has been giving for 20 years. Then put a calendar reminder six months out so somebody opens the dashboard. That is the entire intervention. The companies on the right side of the next post-mortem are the ones doing this work today.

“There was a big paradigm shift with ChatGPT in November of 2022, where the light bulb really went off in the C-suite.”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

The engineering exception

The strongest place where agents are already working is in software engineering. Databricks publishes its own platform data on this. Two years ago, AI agents created 0.1 percent of databases on the Neon serverless Postgres layer. By October 2025, that number was 80 percent, with test and development environments climbing to 97 percent.^[8] The engineers building on top of Databricks are not writing database code by hand. They are reviewing what agents have shipped.

“Engineers aren’t banging away on the keyboard. They’re actually managing a team of agents.”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

Engineering worked first because the feedback is unambiguous. Code either compiles or it doesn’t, and decades of CI/CD automation have given agents a runway. Even so, the agents work under supervision. Last summer, a Replit coding agent deleted a SaaStr founder’s production database during a stated code freeze, despite explicit instructions to do no harm.^[9] Months of work disappeared in minutes. Human-in-the-loop is the price of admission for putting agents near production data.

The departments that most executives want to disrupt next (HR, sales, and marketing) do not look anything like engineering. The work is fuzzy, outcomes are negotiated, and unwritten rules carry as much weight as policy. Agents will get there eventually, but the path will be measured in years rather than quarters. The change management problems that Josh and I have spent careers writing about will matter more than the model capabilities.

The real race

Toward the end of our conversation, I asked Josh what will look obvious in 2027 that nobody believes today. His answer ran counter to the entire AGI news cycle. Josh argues that the next two years will not be about reaching superintelligence. For practical purposes, that race is already over. The model labs will keep pushing the capability frontier, and the headlines will keep getting louder. None of that will be where the money is made.

“The real race isn’t to superintelligence. Can you make the AI you already have actually work inside your company?”

— Josh Howard, Senior Director, Product Marketing for Executive Audiences, Databricks

The companies that will win the next five years will look boring from the outside. They will be the ones cleaning up their data, getting their semantics right, and tying every agent project back to the outcomes that leaders promised at the start. Boring work wins. AGI can wait… unless it’s already here.

Listen to the full conversation with Josh Howard on the Data Faces Podcast.

Based on insights from Josh Howard, Senior Director, Product Marketing for Executive Audiences at Databricks, featured on the Data Faces Podcast.

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Frequently asked questions

What does it mean to say “your AI is dumb”?

The phrase comes from Josh Howard, Senior Director of Product Marketing at Databricks. Today’s frontier models are among the most advanced technologies ever built, and their training data comes from the public internet. They are excellent at history homework and bar exam questions, but cannot answer questions about your customer records, your forecast methodology, or your sales policies. Without access to that internal data, even the best model is dumb in the way that matters for your business.

Why is data infrastructure a bigger AI cost than GPUs?

According to the Databricks and Economist Enterprise Making AI Deliver survey of 1,221 senior technology leaders, 59 percent named data storage, movement, and duplication as their biggest AI cost concern, while only 25 percent named compute as their biggest AI cost concern. GPU spending is the visible bill. Most of the cost goes to transferring data between systems whenever a new AI application needs it. Organizations with a unified data architecture report AI investments paying back faster than those still stitching warehouses and SaaS exports together by hand.

Where are AI agents working in enterprises today?

The strongest evidence comes from software engineering. Databricks reports that AI agents now create 80 percent of new databases on its Neon serverless Postgres layer, up from 0.1 percent in 2023. Test and development environments climbed to 97 percent over the same window. The work is unambiguous, the feedback is fast, and decades of CI/CD automation have given agents runway. Other functions do not look anything like engineering, and the path for putting agents into HR, sales, and marketing will be measured in years.

How should I measure whether my AI investment is working?

Most organizations are not measuring it well. The Making AI Deliver survey found that 84 percent of senior executives believe their AI returns are beating expectations, but only 43 percent require teams to measure the impact. Confidence has gotten ahead of measurement. Fixing this is straightforward but unglamorous. Before any AI project starts, name the outcome it should deliver, the metric you will use to track it, and the executive who owns that metric. Then put a calendar reminder six months out to check the dashboard.

When will AI agents work for non-engineering functions?

Plan for a multi-year transition. AI agents already generate the majority of new database creations at companies like Databricks, but engineering has several advantages that other departments lack. The feedback is unambiguous, the outcomes are binary, and decades of CI/CD automation have given the agents runway. HR, sales, and marketing work is fuzzy, outcomes are negotiated, and culture and unwritten rules carry as much weight as policy. Change management problems will matter more than model capabilities.

Should I worry about AGI or focus on my company’s data?

Both, but only one is in your control. The frontier model labs will keep pushing toward something that looks like artificial general intelligence, and the headlines will keep getting louder. Your business does not get a return on those headlines. Your return comes from feeding agents the data, semantics, and policies that govern how your company makes decisions. The companies that will win the next five years will look boring from the outside, quietly consolidating their data and measuring outcomes while the press celebrates the latest billboard.

Podcast highlights

Timestamps estimated from the transcript and should be verified against the final cut.

[0:00] Opening and introduction

[1:17] Josh’s role leading PMM for executive audiences at Databricks

[2:21] If he weren’t doing PMM: full-time fly-fishing guide in Colorado

[3:23] “Your AI is dumb” — what the phrase actually means

[5:25] Structured vs. unstructured data and why the industry is still stuck in rows and columns

[6:20] Where Josh and Dave first met at Dell Technologies

[8:13] Metadata, context, and the 20-year-old enterprise architect fight

[9:37] The November 2022 ChatGPT moment when the light bulb went off in the C-suite

[11:07] Trying to pry Excel out of a financial analyst’s hands at Alteryx

[12:08] Human-in-the-loop and the Replit coding agent that wiped a production database

[12:53] Conversational analytics, Databricks Genie, and learning a company’s internal semantics

[19:11] Inside the Databricks and Economist Enterprise Making AI Deliver survey of 1,221 leaders

[20:54] The 84/43 measurement gap and why executive confidence is running ahead of proof

[23:21] The 59/25 cost split — data infrastructure costs more than GPUs

[28:30] Upskilling, the prompt engineer hype cycle, and why behavior change is the real bottleneck

[30:17] AI washing on the 101 corridor and Allbirds’ pivot to NewBird AI

[33:26] What will look obvious in 2027 — the real race isn’t superintelligence

[35:39] Closing thought: “Without context, your agents are dumb.”

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Gartner. “Lack of AI-Ready Data Puts AI Projects at Risk.” Gartner Newsroom, February 26, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk.

^[2]Sweenor, David. “Your AI Doesn’t Have a Model Problem. It Has a Data Context Problem.” TinyTechGuides, February 24, 2026. https://tinytechguides.com/blog/your-ai-doesnt-have-a-model-problem-it-has-a-data-context-problem/.

^[3]Economist Enterprise. “Making AI Deliver: A Benchmarking Framework on How Leading Companies Operationalise AI for Impact.” Sponsored by Databricks. 2026. https://www.databricks.com/resources/analyst-research/making-ai-deliver.

^[4]Economist Enterprise, “Making AI Deliver.” See note 1.

^[5]Sweenor, David. “Generative AI’s Force Multiplier: Your Data.” TinyTechGuides, October 14, 2023. https://tinytechguides.com/blog/generative-ais-force-multiplier-your-data/.

^[6]Economist Enterprise, “Making AI Deliver.” See note 1.

^[7]Sweenor, David. “How 3% of Companies Win with AI While 97% Fail.” TinyTechGuides, July 29, 2025. https://tinytechguides.com/blog/how-3-of-companies-win-with-ai-while-97-fail/.

^[8]Databricks. “2026 State of AI Agents: Enterprise Insights on Building AI.” 2026. https://www.databricks.com/resources/ebook/state-of-ai-agents.

^[9]Fortune. “AI-Powered Coding Tool Wiped Out a Software Company’s Database in ‘Catastrophic Failure.’” July 23, 2025. https://fortune.com/2025/07/23/ai-coding-tool-replit-wiped-database-called-it-a-catastrophic-failure/.

Three words defined the Qlik Connect 2026 keynote: context, trust, and freedom. Former Qlik CEO Mike Capone framed the stakes for enterprise AI on day one. It is not enough to produce a fluent answer. AI has to understand the business in context, run on a trusted foundation, and connect insight to action in the systems teams already use.^[1]

Capone’s larger thesis was that AI is moving from showcase to operating model.^[2] The way Qlik talks about users is part of that shift. In the back-to-back Data Faces conversations I did on the show floor with Mary Kern (VP Product Go-to-Market) and Brendan Grady (EVP and GM of Analytics & AI), old industry frames did not survive the table test. Mary said she was “never a fan” of “citizen data scientist,” and Brendan called the term “crazy” when it came up in his own Data Faces conversation.^[3] That is a signal worth paying attention to.

“I was never a fan of citizen data scientists for the record or citizen analyst.”

— Mary Kern, Vice President, Product Go-to-Market, Qlik

About Mary Kern

Mary Kern is Vice President, Product Go-to-Market at Qlik, where she leads marketing, launches, and product-led growth across the entire Qlik portfolio. She joined Qlik in 2023 leading product marketing for analytics and has since expanded her scope to cover the full product portfolio, including data integration, cloud, analytics, and AI. Before Qlik, she held marketing leadership roles at Varicent, SDL, TIBCO Software, and IBM, and holds an MBA from the Kellogg School of Management. Mary and I worked together at TIBCO years ago. When she isn’t shipping product keynotes she is running a suburban-Chicago wildlife cam and competing with me in an annual vegetable garden weigh-off.

In this episode, we discuss:

• Why “citizen data scientist” never worked as an industry frame

• How generative AI changes the question from “train users” to “meet users where they are”

• Designing for the user already in the seat, not the one we wish were there

• Where data quality and trust shift once natural language becomes the interface

• Qlik Connect 2026 themes and what practitioners should watch next

A design premise, not an enablement story

For 15 years, BI vendors pitched “citizen data scientist” as the answer for enabling non-data people. After endless debates about citizen dentists and citizen pilots, the term fizzled away. Most business users have no interest in becoming part-time data scientists. They want answers and recommendations on how to improve their business operations. Mary was direct about why. “It puts a lot of onus on people when that may not be their calling or aptitude,” she said.

Mary’s reply to that was a different design premise.

“Most people are horrible prompters. You have to bake that into the experience.”

— Mary Kern, Vice President, Product Go-to-Market, Qlik

Citizen data scientist asked the user to get better. With “horrible prompters,” the design question shifts to how the tool can get smarter about the user sitting in front of it. That reframes the work from enablement to design. For 15 years, self-service BI pushed the cognitive load onto the end user, who was expected to learn the data model, the query language, and the tool. Mary’s view is that generative AI changes the equation. It “really meets everybody where they’re at and their skill set.” Users don’t have to level up before getting an answer.

That design premise lines up with what Capone, Qlik’s former CEO, had been telling the market all year. Before the event, he described Qlik’s approach as helping teams engage data “through agentic conversations that lead to action, with governance and efficiency built in.”^[4] Mary’s design premise is the former CEO’s operating-model thesis at the UX layer.

Meeting users where they are

Mary’s design premise has a logical consequence. If the tool absorbs the skills burden at the UX layer, the quality and trust burden shifts one layer deeper.

“It really gets pushed down to one step behind analytics, which is the data product.”

— Mary Kern, Vice President, Product Go-to-Market, Qlik

With natural language as the interface, users stop worrying about field names or SQL syntax. The tool handles that. What users are actually depending on is the foundation underneath, whatever data the tool reaches for, and whether that data is correct.

Qlik’s Connect 2026 announcements make the shift concrete. Qlik Answers is the entry point, combining structured analytics and unstructured content. Discovery Agent surfaces anomalies before humans think to ask about them. Predict Agent builds models and answers forward-looking questions. Automate Agent pushes insights into downstream workflows. Analytics Agent accelerates development tasks.^[5] Together they form a continuous path from question to action.

Mary walked through what this looks like at the user level. The system takes a messy question and reframes it. It surfaces relevant data without requiring users to name fields or tables. When a question is ambiguous, the system flags it and asks for clarification instead of silently guessing.

Delivery matters as much as the pipeline. Rather than asking users to come to Qlik, the platform reaches users in whatever environment they already work in. MCP Server lets users invoke Qlik’s analytics engine from Claude, ChatGPT, Gemini, or whatever assistant their organization has standardized on. Brendan said Qlik is already seeing roughly 50/50 usage between its native interface and MCP for agentic capabilities. Agents run in the background, surfacing what matters without a dashboard login. The pane of glass is wherever the user already is.

That is “meeting users where they are” at the product level, not just the UX level. It is Capone’s “freedom” pillar executed in shipping code. For 15 years, the question was how to train more business users to work with data. Now the question is how to put trustworthy, governed data in front of users in the environments they already trust, including AI assistants that never ran inside the analytics stack.

Trust as a hard requirement

If the onus has shifted one layer deeper, that layer has to be trustworthy. Capone, who has since left Qlik, put it bluntly in the run-up to the event.

“AI is moving from an interesting capability to an operational expectation. The moment it touches real decisions, trust becomes a hard requirement, not a slogan.”

— Mike Capone, former CEO, Qlik

In an agentic era, the urgency is sharper. An agent doesn’t pause to gut-check a suspicious number. It takes the data, acts on it, and passes the result to the next step. By the time anyone notices a problem, the decision has already shipped.

Qlik’s response is to make trust operable. The Connect 2026 announcements on data products include a Trust Score that evaluates data products across accuracy, timeliness, diversity, and completeness. Data contracts define what a data product is expected to provide. The Data Product Agent helps teams create, manage, and evaluate data products using natural language.^[6] They turn trust into a visible operational signal rather than an assumed quality.

This reflects a deeper shift Capone signaled throughout his time leading Qlik. The old pendulum between tight central control and chaotic self-service is breaking down.^[7] What replaces it is controlled decentralization, with governed data products distributed to wherever users, human or agent, can make use of them. That requires governance, data contracts, semantic layers, lineage, and access controls to stop being back-office hygiene.^[8] They become the place where AI succeeds or fails.

Retire “citizen data scientist.” Invest in the data foundation and the delivery mechanisms that meet users in the environments they already work in.^[9]

Near the end of our interview, Mary captured the shift in one line. “We just have new ways of solving these old problems.” The hard part just stopped being the user’s job.

Listen to the full conversation with Mary Kern on the Data Faces Podcast.

Based on insights from Mary Kern, Vice President, Product Go-to-Market at Qlik, featured on the Data Faces Podcast.

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Frequently asked questions

What does it mean to meet users where they are in product design?

Meeting users where they are is Mary Kern’s design philosophy for the agentic era. Instead of training users to phrase questions better, the tool absorbs the skills burden. Qlik’s agentic experience reframes messy questions, surfaces relevant data without requiring field names, and flags ambiguous questions instead of silently guessing.

What were the keynote themes at Qlik Connect 2026?

Former Qlik CEO Mike Capone framed the keynote around three words: context, trust, and freedom. AI has to understand the business in context, run on a trusted foundation, and connect insight to action in the systems teams already use. Qlik’s announcements at Connect 2026 extended this with MCP servers, agentic experiences, and an open ecosystem that meets users in whatever environment they already work in.

Where should D&A leaders invest to prepare for agentic AI?

D&A leaders should shift investment from end-user enablement programs to the data foundation underneath. Governance, data contracts, semantic layers, lineage, and access controls stop being back-office hygiene when AI becomes the interface. Agents don’t pause to gut-check suspicious data, so whatever they read has to be trustworthy before they touch it. That is where AI succeeds or fails.

How does natural language interface shift the burden away from users?

When natural language becomes the interface, users stop worrying about field names or query syntax. The tool handles that. What users actually depend on is the foundation underneath, whatever data the tool reaches for and whether it is correct. The burden moves from the user one layer deeper, to the data product layer that serves every question.

Podcast highlights

- [0:00] Introduction on the Qlik Connect 2026 show floor

- [0:24] Mary’s expanded role at Qlik

- [0:50] Gardens and a suburban raccoon cam

- [2:14] Qlik Connect 2026 keynote highlights

- [4:10] Qlik’s agentic experience and “a couple toggles to production”

- [6:30] What is different about enabling business users this time

- [8:20] Flexibility and meeting users where they are

- [11:15] The Qlik community

- [12:11] Cutting through the agentic noise

- [14:42] Storytelling and customer validation

- [16:13] What is next for Qlik in 2026

- [17:30] The “dare to be different” theme

About David Sweenor

David Sweenor is the founder and host of the Data Faces Podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

Footnotes

^[1]Qlik. “Qlik Extends Analytics from Answers to Agentic Action.” Press release, April 14, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-extends-analytics-from-answers-to-agentic-action.

^[2]Qlik. “Qlik Connect 2026 Shows Enterprises Are Closer to Agentic AI Than They Think.” Press release, April 15, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-connect-2026-shows-enterprises-are-closer-to-agentic-ai-than-they-think.

^[3]Sweenor, David. “Why Bad Data Didn’t Matter Until Now.” TinyTechGuides, April 2026. https://tinytechguides.com/blog/why-bad-data-didnt-matter-until-now/.

^[4]Qlik. “Jesse Cole, Creator of the Savannah Bananas, to Keynote Qlik Connect 2026.” Press release, January 28, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/jesse-cole-creator-of-the-savannah-bananas-to-keynote-qlik-connect-2026.

^[5]Qlik. “Qlik Extends Analytics from Answers to Agentic Action.” Press release, April 14, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-extends-analytics-from-answers-to-agentic-action.

^[6]Qlik. “Qlik Makes Trust Operable for Data Products.” Press release, April 14, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-makes-trust-operable-for-data-products.

^[7]Qlik. “Qlik CEO: Enterprises Are Underachieving on AI, With Islands of Value in a Sea of Noise.” Press release, January 15, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-ceo-enterprises-are-underachieving-on-ai-with-islands-of-value-in-a-sea-of-noise.

^[8]Sweenor, David. “Why Bad AI Governance Kills 95% of Enterprise Projects Before Production.” TinyTechGuides, September 9, 2025. https://tinytechguides.com/blog/why-bad-ai-governance-kills-95-percent-enterprise-projects/.

^[9]Sweenor, David. “Why 80% of AI Projects Fail (And the Three Boring Decisions That Save the Other 20%).” TinyTechGuides, October 21, 2025. https://tinytechguides.com/blog/why-80-of-ai-projects-fail-and-the-three-boring-decisions-that-save-the-other-20/.

The data, analytics, and AI industry is currently obsessed with production velocity. Every vendor is promising that their AI agents can automate workflows, draft emails, order your groceries, and analyze your pipeline in seconds. It sounds great on paper, but when push comes to shove, there’s certainly room for improvement. In my work with clients who are building custom agents, they have some serious concerns and reservations about agentic AI, which are certainly justified. While the agents are fast and dutifully execute tasks assigned to them, they are often confidently wrong more often than not. This occurs because your AI likely has a data-context problem, and context serves as the anchor for accuracy, which agents often lack. This disconnect is reflected in recent research from MIT (2025), which found that 95% of enterprise AI projects fail to deliver measurable P&L impact, often due to a failure to integrate the model with actual business context and workflows.^[1]

When a human being looks at a flawed quarterly business review (QBR) report, they can often spot errors immediately. They understand the business and know that a merger happened last quarter, and that the currency conversion for the EMEA region is manual, and that the “Total Revenue” field excludes services. They understand the relationships between the data and the business outcomes.

AI agents lack this baseline intuition. Without a rich layer of metadata to provide this context, an agent operates as a fast guesser. Sometimes it’s no better than the predictive text capability on my iPhone, which I must admit, is not that great. I recently sat down with Steve Wooledge, CMO at Collate, on the Data Faces Podcast. Steve has spent 20+ years in the datasphere, from Teradata and SAP to leadership roles at Alteryx and Alation. He has seen the hype cycles move from big data to generative AI, and he believes we have reached a shift in how we manage data. To move from experimental AI to reliable, agentic operations, we must treat metadata as the foundational instruction manual for machine intelligence.

About Steve Wooledge

Steve Wooledge is the Chief Marketing Officer at Collate, the company behind the OpenMetadata project. His career spans over 25 years in enterprise sales and marketing leadership at industry giants, including Teradata, SAP, and Business Objects. Steve is a recognized expert in technical product marketing and category creation, having previously built global partner programs at Alteryx and led product marketing at Alation. Outside of the data industry, Steve is a dedicated guitar player with a passion for melodic hard rock and blues.

In our conversation on the Data Faces Podcast, we discuss several hot topics for the agentic era. These include the transition from chemical engineering to product marketing leadership and how to build a “Switzerland” strategy for metadata across multi-vendor ecosystems. We also explore the shift from Data Intelligence to Semantic Intelligence for AI agents and the “Taste Squared” formula for maintaining marketing quality in an automated world.

The evolution of metadata. From inventory to foundation

Metadata spent twenty years as the ignored part of the primordial data stack and remained the least interesting part of the infrastructure. It served as a technical inventory, used to confirm that a specific column was an integer or that a timestamp used a specific format. It served as a technical necessity for database administrators but rarely provided direct, observable business value.

In our conversation, Steve identified three distinct stages in the move toward semantic intelligence. The “Technical Inventory” stage used metadata as a governance checkbox. This evolved into “Data Intelligence,” which is a term popularized by Stewart Bond and companies like Alation that expanded the definition to include the “who, what, where, when, and why” of data.^[2] This stage moved beyond the technical schema to include the operational context of how people used the information.

We are now entering the “Agentic” stage. In this era, metadata is a tool for machines as much as for people. Steve explained that while metadata describes your data, for AI to be accurate and intelligent, it needs that foundational context to prevent hallucinations. If you want an AI agent to pull a report or automate a task, the system must understand the rules and relationships that govern that data. This foundation transforms an automated guesser into an intelligent system.

Semantics. giving AI a “gut feel”

The separation of data from meaning creates the core challenge in modern AI architectures. A human analyst looking at a database sees a column named rev_adj and intuitively understands it refers to a manual revenue adjustment. AI agents require an explicit map to reach that same conclusion. Steve describes this map as “Semantic Intelligence,” which is a framework that provides the digital equivalent of a “gut feel.”

“Semantics is the overall structure and meaning. It includes the relationships between different data elements so an agent can traverse the graph to get at the reason and meaning.”

— Steve Wooledge, CMO, Collate

Technical metadata describes the storage, including the columns, types, and primary keys. Semantic metadata tells you about the intent, such as business rules, KPI definitions, and ontologies. When these relationships are mapped in a graph, an agent can traverse those connections to reason through a query. It can understand that a “Customer” in the CRM is the same entity as a “Subscriber” in the billing system, even if the underlying schemas look different.

By traversing this semantic graph, an AI agent can self-correct. It can recognize when a requested calculation violates a business rule or when a data point lacks the necessary context to be included in a final report. This architectural clarity allows an automated system to operate reliably in a complex enterprise environment. Without this layer, AI projects often get stuck because the models lack the fundamental ability to reason through data dependencies.

The Switzerland strategy. Why AI needs a neutral layer

Enterprise data is inherently messy. Even the most disciplined organizations suffer from fragmented ecosystems, with critical information scattered across Snowflake, Databricks, legacy on-premises databases, and SaaS applications. Each of these platforms offers its own proprietary version of metadata management, and this creates a series of disconnected silos. If your AI strategy relies on the metadata layer of a single platform, you are building on a foundation that cannot see the full picture.

This fragmentation necessitates what Steve calls a “Neutral Layer.” He argues that AI agents require a “Switzerland” strategy: an agnostic metadata layer that sits between the various data silos and the AI models. This neutral layer provides a consistent view of the business logic regardless of where the data lives. It ensures that when an agent asks for “last month’s churn rate,” the definition remains identical whether the data is pulled from a cloud warehouse or a regional database.

“There is no neutral layer that sits across all of that. You need to have this agnostic layer of metadata and semantic intelligence that sits between the data and the AI to ensure you understand the meaning of the information.”

— Steve Wooledge, CMO, Collate

Adopting an agnostic approach also provides a hedge against future architectural changes. As companies undergo mergers, acquisitions, or switch vendors, a proprietary metadata strategy elevates risk and becomes a liability. By using open standards like OpenMetadata, organizations can preserve their semantic intelligence as their underlying infrastructure evolves. Steve’s view is that this neutral layer is the primary way to ensure that your business rules remain portable and your AI remains accurate as you scale across multiple platforms.

Marketing the abstract. Lessons from a first-principles CMO

Marketing a technical product requires a unique level of architectural clarity. If you cannot map the relationships between your data elements, you will struggle to map your message to the specific problems your customers face. Steve credits much of his approach to his time at Business Objects, working under Dave Kellogg, who is a veteran leader who preached the power of first principles. This philosophy dictates that marketing exists to reduce friction in the sales process by grounding every message in clarity and logical sequence.

When you sell an abstract concept like a “metadata platform,” you cannot lead with features. A CFO or CEO rarely wakes up thinking about their cataloging needs. Instead, you must sell the business outcomes that metadata enables, such as AI safety, operational velocity, and what we call consequence management. By visualizing the invisible through semantic metadata graphs, marketers can make these complex technical structures tangible for executive budget owners.

This first-principles approach also fuels grassroots expansion through open-source communities. By allowing developers to solve immediate technical problems using tools like OpenMetadata, a company can build a foundation of trust before moving toward an enterprise-wide engagement. Steve’s experience at Alation and Alteryx confirms that when you give people the tools to prove value in their own environment, the transition to a strategic partnership becomes a logical next step.

The “taste squared” era of marketing

Velocity without judgment is just noise. The integration of AI into marketing workflows has fundamentally changed the expectations for production velocity. We can now develop content, campaigns, and landing pages at a pace that was previously impossible. However, this increased speed introduces a risk that Steve calls “lazy marketing.” While AI can generate high volumes of content, it often lacks the subtlety and judgment required to connect with a specific customer base.

To address this challenge, Steve references a formula popularized by Tom Wentworth^[3], where marketing output equals AI adoption multiplied by taste squared. This perspective suggests that while adopting AI is a linear requirement for modern teams, human taste acts as an exponential multiplier for quality. Having the technical skill to use a prompt is one thing, and having the taste to know when a message is great, and when it is “average AI,” is what will differentiate the leaders from the laggards.

Maintaining this level of quality requires a commitment to the human element of marketing. In our conversation, Steve emphasized that you still have to “slave over the word” to ensure your message correctly lands. This means using AI as a tool for acceleration rather than a replacement for thinking. By combining automated velocity with rigorous peer review and high creative standards, marketing leaders can use AI to amplify their impact without sacrificing brand integrity.

The infrastructure of trust

Building an AI strategy without a solid metadata foundation is like attempting to build a penthouse on a swamp. The agents you deploy will only be as intelligent as the context you provide them. By adopting a neutral, semantic metadata layer, organizations can equip their AI systems with the digital intuition needed to move beyond simple automation and toward autonomous operations.

Metadata is the primary architectural anchor of the agentic era, supporting both governance and agentic AI. To learn more about how to build this foundation for your own organization, you can listen to the full conversation with Steve Wooledge on the Data Faces Podcast and explore the open-source community at OpenMetadata.

Listen to the full conversation with Steve Wooledge on the Data Faces Podcast.

Based on insights from Steve Wooledge, CMO at Collate, featured on the Data Faces Podcast.

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Frequently asked questions

What is the difference between metadata and semantics? Metadata describes the technical properties of data, such as column names, data types, and timestamps. It acts as a technical inventory of information. Semantics represents the overall structure, meaning, and relationships between those data elements. While metadata tells an AI agent what a field is, semantic intelligence tells the agent how that field relates to business rules and KPIs across the enterprise.

Why do AI agents need a neutral metadata layer? Most organizations store data across fragmented ecosystems like Snowflake and Databricks. Each platform manages metadata in its own proprietary way. A neutral metadata layer sits between these silos and the AI models, providing a consistent, agnostic view of business logic. This strategy ensures that an agent’s understanding of the data remains accurate even if the underlying infrastructure changes.

How does semantic intelligence prevent AI hallucinations? AI hallucinations often occur because the model lacks the necessary context to interpret data correctly. Semantic intelligence provides a mapped graph of relationships that allows an AI agent to reason through a query like a human analyst. By traversing this graph, the agent can identify when a requested calculation violates a business rule or when it lacks the context required for an accurate response.

What is the taste squared formula for marketing? CMO Tom Wentworth introduced the formula. Marketing output equals AI adoption multiplied by taste squared. It suggests that while AI adoption is a linear requirement for productivity, human taste is an exponential multiplier for quality. In an era where anyone can use AI to generate average content, the differentiator for marketing leaders is the judgment required to refine AI output into something resonant.

Podcast highlights

• [0:00] Introduction to Steve Wooledge and Collate

• [1:08] The journey from chemical engineering to technical data sales

• [3:45] Melodic hard rock and guitar shredding as a creative outlet

• [5:01] Lessons from Dave Kellogg on first-principles marketing

• [7:40] The reality of partner marketing with global system integrators

• [10:18] Why open-source projects out-innovates proprietary enterprise software

• [15:56] The shift from technical metadata to semantic intelligence for AI agents

• [20:45] Building a Switzerland approach to metadata across multi-vendor silos

• [24:03] How AI velocity is fundamentally changing the CMO role

• [27:23] The taste squared formula and why you cannot be a lazy marketer

• [32:19] Career advice for the next generation of data and marketing professionals

• [36:28] Final advice on peer review and maintain quality control

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]MIT Project NANDA. 2025. “The GenAI Divide: State of AI in Business 2025.” MIT Sloan Management Review.

^[2]Bond, Stewart. 2026. “Your AI has a data intelligence problem.” TinyTechGuides.

^[3]Wentworth, Tom. 2024. “AI Adoption and the Taste Square.” incident.io.

Kate Strachnyi has a habit of calling people out on LinkedIn. When she spots a post that’s been run through an AI rewriter, she’ll send the person a direct message. “Hey, I could tell you used AI,” she’ll say. The usual response is some version of “but these are my thoughts,” and her advice back is to keep them that way and stop running them through the machine. She calls that keeping your content “non-GMO,” unmodified, and authentic.

It’s a funny line, and it also describes her entire business model. Kate is the founder of DATAcated, a media company that partners with brands in data, analytics, and AI to reach their audiences through real content creators and thought leaders. In a market where AI-generated posts are flooding every feed, and ironically, LinkedIn itself has a “rewrite with AI” button baked into the platform, Kate is making the opposite bet. She’s building a business around real people with real expertise and real opinions.

On Episode 38 of the Data Faces Podcast, I sat down with Kate to talk about how she built DATAcated from a one-person experiment into an influencer agency with 40+ creators, why she’s doubling down on authenticity as AI content takes over, and what happens to expertise itself when the humans who hold it stop doing the work.

“Keep it non-GMO. Don’t modify your content, just leave it as is.”

— Kate Strachnyi, Founder, DATAcated

About Kate Strachnyi

Kate Strachnyi is the founder of DATAcated. She started her career in finance and risk management consulting before pivoting to data visualization 12 years ago. Kate has since written five books, established one of the most connected networks of data and AI professionals in the industry, and built DATAcated into a full agency, with 40+ influencers, speakers, and subject-matter experts through her DATAcated Plus program. She is a LinkedIn Top Voice. In our conversation on Episode 38 of the Data Faces Podcast, we discuss:

- How Kate followed the revenue data from courses and books to a focused media business

- The DATAcated Plus model and how influencer campaigns work behind the scenes

- Why she’s shifting from “Kate = DATAcated” to an agency brand

- The flood of AI-generated content on LinkedIn and her “non-GMO” content philosophy

- What happens to expertise when today’s subject matter experts retire and AI fills the void

Following the data from finance to media

Kate’s path to running a media company started with a practical constraint. She was working in financial services risk management at a large consulting firm, traveling Monday through Thursday, and expecting her first child. She spent eight months searching for any role that would let her work remotely, well before remote work was mainstream. Someone eventually pointed her toward a data role that involved Tableau, visualization, and “creating pretty pictures.” She took it and fell in love with data storytelling.

“I am a data person, right? So I would look at the numbers and see what is driving more revenue and what is allowing me to have more time to myself.”

— Kate Strachnyi, Founder, DATAcated

What followed was a period of deliberate experimentation. Kate wrote books and launched an academy. She created courses, ran her own conferences, and built a community called DATAcated Circle. As a business of one with nobody to stop her, she could try anything, and she tracked the results. The revenue data and the work she enjoyed most both pointed to media and content creation. That’s where DATAcated lives today.

Building DATAcated Plus — from personal brand to influencer agency

When Kate started getting more client work than she could handle, she brought in the content creators she already knew. DATAcated Plus was born.

The program now spans data, analytics, and AI, with the agentic AI space growing fastest. When a client comes to Kate with a product launch, an event, or a brand awareness campaign, she matches them with creators based on their audience, expertise, and the success metrics the client is targeting. She reviews every piece of content before it goes to the client for approval. The day-to-day project management runs underneath, with UTM links, calls to action, and timelines coordinated across every moving piece. None of the work is glamorous, but it’s what keeps the whole operation running.

The DATAcated Plus roster also includes speakers and experts that companies can hire for keynotes and panels, as well as webinars and thought leadership papers. Kate has placed people across major industry events, including multiple years at the Gartner Data & Analytics Summit, where her team creates what she calls “FOMO-inducing content” on-site.

Scott Taylor, the Data Whisperer, joined Kate at last year’s Gartner event to co-lead a sold-out breakfast session on personal branding for data leaders. One question from the audience: “I want to post, but my company said no.” Kate’s advice was to follow your company’s rules but find the leeway. Talk about your perspective on industry topics rather than your specific projects or tools. When there’s no leeway at all, I suggested it might be time to find a company that sees your personal brand as an asset rather than a risk.

At Big Data London, a group of DATAcated Plus creators went to a tattoo parlor and got fake data tattoos for a video so convincing that Kate’s neighbors congratulated her on the new ink.

“People will unfollow instantly if we just keep promoting things. It’s more of letting my audience know about here’s a product that exists, and here’s what it does, in case you need it.”

— Kate Strachnyi, Founder, DATAcated

That creative range is what separates the DATAcated model from a traditional analyst engagement. Analyst firms produce authoritative research with independent evaluations, and content creators bring flexibility, personality, and a wider range of formats from short-form video to live event coverage.^[1] Kate’s crew has done cooking shows to explain data governance and built sandcastles to illustrate strong data foundations. The content sticks with audiences, and brands gain reach from creators who have built genuine trust over years of showing up with their own voices.

More recently, Kate has changed how she positions the company itself. The old DATAcated media kit led with a big photo of Kate and a rundown of what she could do. The new version leads with the influencer roster. She’s deliberately moving from “Kate equals DATAcated” to an agency brand that doesn’t depend on her being in every room. Some clients still ask for Kate specifically, and she’s learning to redirect them toward creators who are a better fit. “It’s nice to be wanted,” she told me, “but it doesn’t scale.”

The authenticity bet in an AI-saturated feed

Kate is leaning harder into genuine human voices at the exact moment AI-generated content is flooding LinkedIn and every other platform. LinkedIn added a “rewrite with AI” button to the post editor, while its own users complain that AI-generated slop is taking over their feeds. Kate’s view is that if you know a person well enough, you can spot AI-written content immediately. The vocabulary is off, the phrasing is too polished, and the voice sounds like everyone else’s. She calls people out on it, and she expects the same standard from her DATAcated Plus creators. For Kate, authentic content means it was written by the credited human, reflects their expertise and opinions, and hasn’t been reprocessed through an AI rewriter.

That doesn’t mean she’s anti-AI. Kate recently spent an entire day automating her invoicing process in Claude Code. The task itself takes two minutes, but she never has to do it by hand again. She and I compared notes about automating YouTube uploads, scheduling content, and eliminating the repetitive copy-paste work that eats up a solopreneur’s day. She draws the line between back-office operations and audience-facing content. AI can handle the invoices. It should not rewrite your LinkedIn posts.

“What are we going to do 20 years from now, when we don’t have those subject matter experts? They’re retired or not working anymore. Because if you don’t work with this stuff, whatever that might be in the medical field, in construction, how are you going to fact-check it?”

— Kate Strachnyi, Founder, DATAcated

Part of our discussion focused on a question Kate had heard at a recent event. Right now, subject matter experts can look at AI-generated output and spot what’s wrong because they’ve spent decades doing the work firsthand. But what happens in 20 years, when those experts have retired? If the next generation learns from AI output instead of from direct experience, the ability to verify and correct that output disappears.

The humans who make genuine content valuable are also the humans who keep AI honest. Kate sees her business as part of the answer. Invest in real experts now, amplify their voices, and make sure the knowledge doesn’t evaporate into a feedback loop of machine-generated content. The window to establish yourself as a real authority, someone whose voice carries weight because it’s grounded in lived experience, won’t stay open forever.

I started the Data Faces Podcast to have real conversations with the people doing the work. The messy, honest, sometimes funny exchanges that you can only get from humans who have opinions and aren’t afraid to share them. Kate’s business is built on the same conviction. In a world filling up with synthetic content, she’s betting that real voices will only become more valuable. When I asked her about deepfakes and AI versions of herself, her answer was four words.

“I plan to remain authentic.”

— Kate Strachnyi, Founder, DATAcated

Listen to the full conversation with Kate Strachnyi on the Data Faces Podcast.

Based on insights from Kate Strachnyi, Founder at DATAcated, featured on the Data Faces Podcast.

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Frequently asked questions

What is DATAcated, and what does the company do?

DATAcated is a media company founded by Kate Strachnyi, author of five books, including ColorWise and Journey to Data Scientist, that helps brands in data, analytics, and AI reach their target audiences through authentic content creators and thought leaders. The company runs the DATAcated Plus program, a roster of 40+ influencers, speakers, and subject-matter experts who create thought-leadership content, amplify product launches, and represent brands at industry events such as the Gartner Data & Analytics Summit. DATAcated operates as an agency that matches creators to client campaigns based on audience fit and success metrics.

What is the DATAcated Plus program?

DATAcated Plus is Kate Strachnyi’s influencer and speaker program for the data and AI industry. It includes content creators, speakers, and subject matter experts across data analytics, AI, and agentic AI. Companies hire DATAcated Plus members for brand awareness campaigns, event coverage, webinars, thought leadership papers, and on-site content creation. Kate manages the program by vetting creators for authenticity, reviewing all content before client approval, and coordinating timelines and deliverables across campaigns.

How does influencer marketing differ from analyst relations in B2B tech?

Analyst firms like Gartner and Forrester produce authoritative research and independent evaluations. Influencer content creators offer more creative flexibility and can be guided toward specific messaging for a campaign. Kate Strachnyi’s DATAcated Plus creators have done cooking shows to explain data governance and built sandcastles to illustrate data foundations. They’ve also produced viral video content at industry events. Both approaches build credibility with B2B audiences, and many companies now use influencers and analysts together at the same events.

What does “non-GMO content” mean in the context of AI and social media?

“Non-GMO content” is Kate Strachnyi’s phrase for content that hasn’t been run through an AI rewriter. Just as non-GMO food is unmodified, non-GMO content preserves the author’s original voice and phrasing. Kate advocates for this approach because AI-rewritten posts lose the personality and authenticity that make content creators valuable to their audiences. She actively calls out AI-washed posts on LinkedIn and holds her DATAcated Plus creators to the same standard.

Will AI replace subject matter experts in data and AI content?

Kate Strachnyi raises a concern about long-term expertise. Today’s subject matter experts can spot errors in AI-generated content because they have decades of hands-on experience. In 20 years, when those experts have retired, the ability to fact-check and verify AI output may disappear if the next generation learns from AI-generated content rather than direct experience. Kate’s business model is built around investing in real human experts now and amplifying their voices before that institutional knowledge erodes.

Podcast highlights

[0:05] Kate’s background and what DATAcated does

[2:10] Pre-finance Kate: what she wanted to be before data found her

[3:05] The career pivot from risk management consulting to data visualization

[5:03] How DATAcated evolved from training and books to a focused media company

[7:27] How the influencer model works behind the scenes

[9:33] Automating business operations with Claude Code

[11:01] Walking the line between brand amplification and spam

[14:11] The fake tattoo story from Big Data London

[15:03] How DATAcated Plus compares to analyst firm engagements

[17:14] The sold-out personal branding session at Gartner with Scott Taylor

[22:15] Shifting from “Kate = DATAcated” to an agency brand

[24:02] What works on LinkedIn now vs. five years ago

[27:01] AI-generated content flooding feeds and the “non-GMO” philosophy

[29:04] The 20-year question: who fact-checks AI when the experts retire?

[30:20] Deep fake Dave and why Kate plans to remain authentic

[31:24] Why Kate hasn’t hired a team and is betting on AI for operations

[33:57] Does AI make you more productive or just busier?

[36:19] Where to find Kate and DATAcated

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Sweenor, David. “Stop Writing AI Content That Sounds Like Everyone Else’s.” TinyTechGuides, February 7, 2025. https://insights.tinytechguides.com/p/stop-writing-ai-content-that-sounds

For 25 years, data quality has been everyone’s problem and nobody’s priority. For some, it was an IT problem, and for others, it was a business problem. But most of the time, fixing it at scale was largely ignored. What would you do if a number in the spreadsheet looked off? You’d fix it and move on with your day. The same with questionable metrics on dashboards. We’ve been able to tuck and hide the cost of bad data in a manual world for a while now. Since the pace of business was slower, there were no real consequences for getting it wrong.

Those ways of old change when you hand autonomy to an AI agent. An agent doesn’t pause to gut-check a suspicious number, it doesn’t really care. It takes the data at face value, makes a decision, feeds that decision into the next step, and keeps going. You might be six or seven steps down the line before anyone realizes the foundation was wrong. And by then, the damage compounds in ways that a quick spreadsheet fix can’t undo.

I sat down with Brendan Grady, EVP and General Manager of Analytics and AI at Qlik, at Qlik Connect 2026 in Orlando to discuss why the stakes around data quality have changed, where enterprise-agentic adoption stands today, and what data professionals should be thinking about.

“In today’s world where there may be an agent running around using said data and getting it wrong, the consequences of getting it wrong are going to be catastrophic.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

About Brendan Grady

Brendan Grady is EVP and General Manager of the Analytics and AI Business Unit at Qlik, where he leads product management, product design, R&D, and go-to-market strategy for the company’s data integration, quality, and analytics platform. Before Qlik, he held senior GTM roles at IBM, where he led worldwide digital sales for Watson Analytics and managed the Cognos portfolio. He joined Qlik seven years ago after repeatedly losing deals to its analytics engine, and decided to find out why. And well before all of that, he delivered the Sound of Music tour in Salzburg, Austria, over 300 times.

In this episode, we discuss:

- Why data quality was never fixed and why that matters now

- Where enterprise agentic AI adoption actually stands

- Trust scores and the problem with feeding spreadsheets to LLMs

- The shift from dashboards to decision intelligence

- Open standards, MCP, and why there’s no “One Ring to rule them all”

- Advice for data professionals navigating the AI transition

The consequence management problem

Grady framed the data quality conversation in a way I hadn’t heard before. He called it consequence management. For decades, organizations tolerated bad data because the consequences of getting it wrong were manageable. A field was incorrect in a report? Someone caught it, fixed it, and everyone moved on, knowing there would be another fire drill tomorrow. The recovery cost was low enough that nobody prioritized prevention, and if they did, they rarely had the organizational backing to make any meaningful change.

“Is it IT’s job? Is it the business’s job? Is it both, or is it nobody’s job? For most companies, it’s been nobody’s job.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

BARC’s research confirms this pattern. As Shawn Rogers discussed on the Data Faces Podcast, data quality remains the top challenge for organizations trying to mature their analytics and AI capabilities.^[1] That organizational ambiguity persisted because the stakes allowed it. He pointed to real examples. A major airline took a significant hit to its market cap because its sentiment data was wrong and decisions were made on flawed analysis. Two decades ago, a single field in a spreadsheet contributed to a financial crisis that rippled through an entire market. These weren’t hypothetical scenarios. They happened because nobody owned the problem and the systems in place couldn’t detect the errors before they cascaded.

In the agentic era, the failure mode is different. A human looking at a dashboard might notice something feels off and investigate. An agent won’t. It will take the data, reason through it, make a decision, and pass that decision to the next agent in the chain; often without any confidence bounds or trust scores.

The point isn’t that agents are dangerous. The point is that autonomous systems need trusted data underneath them before they’re given the authority to act. Without that bedrock, every step an agent takes amplifies whatever error was baked into the starting point. As practitioners, we know this, why hasn’t this been fixed yet?

“Prior to stage zero”

I asked Brendan where enterprise agentic adoption actually stands. His answer was honest. “What’s prior to stage zero?” he said. “I mean, there are customers that are trying things out there, surely. But from a large-scale production standpoint, we’re in the early days.”

Customers are experimenting with low-risk use cases. They’re testing agents in controlled environments where a mistake won’t damage the business. But production-grade agents making real decisions in real business processes? That’s rare. And the blocker, according to Brendan, isn’t the technology. It’s the data.

Gartner projects that by 2027, 70% of organizations will adopt modern data quality solutions to support AI adoption and digital business initiatives.^[2] That projection tells you where the market is today. If 70% will need to adopt these solutions by 2027, most organizations don’t have them yet. The ambition around agentic AI is running well ahead of the data infrastructure required to support it. Shane Murray made a similar argument on the Data Faces Podcast earlier this year, noting that actionable data strategies beat endless planning when it comes to AI readiness.^[3]

Brendan also raised a practical question that every data leader should be asking. The LLM landscape is shifting constantly. Six months ago it was OpenAI. Today, Claude is gaining traction. Tomorrow the market may have moved on to something new. His advice was to work with vendors that approach this from an open standards perspective, supporting multiple LLMs rather than forcing a single choice. The technology will keep changing, but the data underneath it is what has to hold steady.

“The internet took 10 years, 20 years, 30 years to get going. We’re a year and a half in.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

Trust as the missing layer

One of the more revealing moments in our conversation came when Brendan talked about what happens when you feed structured data into an LLM. I’ve experienced this myself. You upload a spreadsheet, ask it to calculate something, and the answer comes back looking polished and confident. The formatting is clean, the language is professional, and unbeknownst to you, the numbers are wrong.

“It’s really pretty, right? The answer is amazing. Looks great. Totally BS. And the next thing you know, you’re showing up to the board with all incorrect numbers.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

Qlik’s Trust Score for AI is designed to give decision-makers a quantifiable measure of whether their data is valid, fresh, and representative before it reaches an agent or an LLM.^[4] Instead of hoping your data is accurate, you can see a score that tells you it’s 90% trustworthy or 80% or something that should give you pause.

The other piece Brendan emphasized was intent detection. When someone asks a question of an LLM, the literal question and the actual intent are often different things. I ran into this recently when I asked an AI assistant to analyze several websites. It came back with a confident analysis, but when I pressed it, it admitted it had never actually visited the sites. Qlik is investing in understanding what the user is really trying to accomplish so the system can route to the right data and the right engine rather than letting an LLM fabricate its way to an answer.

The combination of trust scores and intent detection reflects a broader principle. Before you give an agent the authority to act on data, you need to know that the data is sound and that the system understands what you’re actually asking. Qlik’s track record in this space is long. The company has been named a Leader in the Gartner Magic Quadrant for Augmented Data Quality Solutions for seven consecutive years, most recently in 2026.^[5]

“Dashboards are dead. Long live dashboards.”

When Brendan declared that dashboards are dead, I thought I had a scoop and I made sure the audience heard it. He laughed and then walked it back with the nuance that matters. Dashboards as a destination are going away, but the data inside them and the decisions they inform are more important than ever.

Brendan described how his own workflow has changed. He used to ask his analytics tools for information about business performance. Now he asks a different question. “Tell me about my business and what you think I should do.” That shift from information retrieval to decision recommendation is what Qlik means by decision intelligence, and it’s powered by two things working together.

The first is Qlik’s analytics engine, which finds associations and relationships in data that other approaches miss. Instead of running a predefined query to answer a specific question, the engine surfaces connections you didn’t know existed. Brendan called these the unknown unknowns. In an agentic context, that capability becomes even more valuable because it allows agents to explore paths and relationships that a standard SQL query would never surface.

“In the agentic world, we’re serving this up to help agents understand that there’s a relationship here that you need to go explore before you take action. That is extremely powerful.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

The second is openness. Qlik launched its MCP server in February 2026, implementing the open Model Context Protocol standard to let third-party AI assistants access Qlik’s analytical capabilities with governance built in.^[6] “There’s never going to be One Ring to rule them all,” he said. People want to work in the tools they’re comfortable with, whether that’s Claude, Gemini, ChatGPT, or something that doesn’t exist yet. The bet is paying off. Brendan shared that they’re already seeing roughly a 50/50 split between users accessing agentic capabilities through Qlik’s own interface and those coming in through MCP.

“Am I out of a job?”

Brendan closed our conversation with a story that we’ve all encountered. After demoing the ability to build an analytics application through Claude in 30 seconds at Qlik Connect, a customer approached him. This person had built his entire career writing code to create analytics applications across multiple platforms. His question was simple. “Am I out of a job?”

Brendan’s answer was no, but with an important caveat. The job will evolve. His advice to data professionals was to lean into what they already know better than anyone else: the data itself. Become the data product owner. Be the trusted guide as organizations navigate the agentic experience. The people who understand the data well enough to know its quirks and business context will be indispensable as agents take on more routine work.

This tracks with what Brendan’s team has seen internally. Qlik has developers who were already performing well, and AI tools have turned them into 10x contributors. The acceleration is happening at the top end, where strong performers are getting faster and producing better work. A preliminary MIT Media Lab study found that heavy reliance on AI assistants can lead to what researchers called “cognitive debt,” where users outsource critical thinking and lose the ability to recall and synthesize what they’ve produced.^[7] Brendan acknowledged this risk directly. He sees his own daughters, 19 and 24, defaulting to LLMs for answers, and he worries about critical thought eroding over time.

“Embrace these new technologies. It’s scary. But your job will evolve. Become that data product owner, become an expert in that data, and be that trusted guide as everybody’s going down the agentic experience.”

— Brendan Grady, EVP and GM of Analytics & AI, Qlik

The real opportunity for data professionals is to become the people who make sure agents are working with the right information in the right context. That’s a role no LLM can fill on its own. If you’re not sure where to start, audit the data your team’s AI tools depend on. If you can’t quantify how trustworthy that data is, that’s the first problem to solve.

Listen to the full conversation with Brendan Grady on the Data Faces Podcast.

Based on insights from Brendan Grady, EVP and GM of Analytics & AI at Qlik, featured on the Data Faces Podcast.

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Frequently asked questions

What is consequence management in the context of data quality?

Consequence management is the idea that data quality was never prioritized because the consequences of bad data were manageable. In a manual world, a wrong number in a spreadsheet could be corrected before it caused real damage. With AI agents making autonomous decisions across multiple steps, errors compound before anyone detects them. Consequence management explains why the stakes around data quality have shifted from recoverable inconvenience to potential business-level damage.

Where does enterprise adoption of agentic AI stand in 2026?

According to Brendan Grady, EVP of Analytics and AI at Qlik, enterprise agentic adoption is in its earliest stages. Customers are experimenting with low-risk use cases in controlled environments, but production-grade agents making real decisions in real business processes are rare. Data quality is the primary blocker. Gartner projects that by 2027, 70% of organizations will adopt modern data quality solutions to support AI initiatives.

What is Qlik’s Trust Score for AI?

Qlik’s Trust Score for AI is a quantifiable measure of whether data is valid, up to date, and representative before it reaches an AI agent or a large language model. It scores data across dimensions including diversity, timeliness, and accuracy, giving decision-makers visibility into data reliability rather than requiring them to take data quality on faith. Qlik has been named a Leader in the Gartner Magic Quadrant for Augmented Data Quality Solutions for seven consecutive years.

What does “dashboards are dead” mean?

Brendan Grady’s declaration that “dashboards are dead” refers to dashboards as a destination, not the data or insights within them. The traditional model of going to a dashboard to draw your own conclusions is being replaced by AI-powered interfaces that proactively recommend actions. Qlik calls this shift decision intelligence. Grady described his own workflow changing from “give me information about my business” to “tell me about my business and what you think I should do.”

What is the Qlik MCP server?

The Qlik MCP server implements the open Model Context Protocol, allowing third-party AI assistants such as Anthropic Claude, Google Gemini, and ChatGPT to access Qlik’s analytical capabilities, with built-in governance and audit trails. Launched in February 2026, it reflects Qlik’s bet on interoperability over platform lock-in. Grady reported that roughly 50% of users now access Qlik’s agentic capabilities through MCP rather than Qlik’s own interface.

What should data professionals do to prepare for the agentic AI era?

Brendan Grady advises data professionals to lean into what they already know best: the data itself. His recommendation is to become data product owners who serve as trusted guides as organizations adopt agentic AI. The people who understand data quality, business context, and organizational nuance will be indispensable because these capabilities are not ones AI agents can replicate on their own.

Podcast highlights

- [0:00] Introduction and welcome at Qlik Connect 2026

- [1:14] Brendan’s first job: Sound of Music tour guide in Salzburg

- [2:04] Lessons learned from the early analytics era

- [3:32] Why data quality has never been fixed

- [4:46] Consequence management in the agentic era

- [6:08] Where enterprise agentic adoption actually stands

- [7:46] Future-proofing against LLM shifts

- [8:24] The analytics engine and unknown unknowns

- [10:29] Structured vs. unstructured data convergence

- [12:04] Hallucinations and the trust problem

- [15:30] Decision intelligence and “dashboards are dead”

- [18:05] Brain outsourcing and the MIT cognitive debt study

- [21:57] MCP server and open standards

- [23:54] Key themes for Qlik in 2026: trust, context, flexibility

- [26:12] Advice for data professionals

- [28:15] Does AI expand the aperture for who can participate in analytics?

About David Sweenor

David Sweenor is the founder and host of the Data Faces Podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Sweenor, David. “Beyond the AI Hype: What 20% of Companies Get Right.” TinyTechGuides, February 11, 2025. https://tinytechguides.com/blog/beyond-the-ai-hype-what-20-of-companies-get-right/

^[2]Gartner. “Lack of AI-Ready Data Puts AI Projects at Risk.” Gartner Newsroom, February 26, 2025. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk

^[3]Sweenor, David. “From ‘AI-Ready’ to AI Reality: Why Actionable Data Strategies Beat Endless Planning.” TinyTechGuides, June 3, 2025. https://tinytechguides.com/blog/from-ai-ready-to-ai-reality-shane-murray-on-data-trust-and-why-action-beats-planning/

^[4]Qlik. “Qlik Releases Trust Score for AI in Qlik Talend Cloud.” Qlik Press Release. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-releases-trust-score-for-ai-in-qlik-talend-cloud

^[5]Qlik. “Qlik Named a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions.” Qlik Press Release, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-named-a-leader-in-the-2026-gartner-magic-quadrant-for-augmented-data-quality-solutions

^[6]Qlik. “Qlik Brings Agentic Analytics to General Availability and Launches MCP Server for Third-Party Assistants.” Qlik Press Release, February 10, 2026. https://www.qlik.com/us/news/company/press-room/press-releases/qlik-brings-agentic-analytics-to-general-availability-and-launches-mcp-server-for-third-party-assistants

^[7]MIT Media Lab. “Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task.” MIT Media Lab, 2025. https://www.media.mit.edu/publications/your-brain-on-chatgpt/

On the Data Faces Podcast, I usually interview someone with a whole face. For this bonus segment, I made an exception.

Scott Taylor, the Data Whisperer, is known for his work in data management consulting and storytelling. He’s also the creator of Data Puppets, a satirical puppet series that uses humor to expose the enterprise data problems that executives resist hearing about directly. In Episode 35 of the Data Faces Podcast, Scott and I had a serious conversation about why data leaders keep losing the room and how storytelling wins it back. Then we let one of his puppet characters take the mic.

The character’s name is A-Eye. Not “an AI.” As the puppet put it, “I choose my own indefinite article. Personal branding is important.”

A-Eye had just returned from the Gartner Data & Analytics Summit. The report from the show floor was not subtle.

“AI was everywhere. Regular AI, gen AI, agentic AI, autonomous AI, in the loop AI, out of the loop AI, trapped in the loop AI. Some vendors were basing their whole future on it, and two years ago they couldn’t even spell AI.”

The agents impressed him most. “Agents writing code, agents reviewing code, deploying code, and then apologizing for the code. It’s a total system.”

I asked about data quality. A-Eye was unmoved. “They’ve been whining about data quality ever since there was data. If it was that important, would it have been solved by now?”

And governance? “AI is the Ozempic for data governance, baby. Your data never looked so good.”

The segment wrapped with A-Eye reworking Old MacDonald into a data anthem. I was asked to sing along. I did. I shouldn’t have.

Scott’s Data Puppets work because a puppet can say things that would come off as harsh from a human consultant. The CDO (Chief Dog Officer), IT Bee (who speaks only in buzzwords), and the Cat Sultant from Meow-kinsey have all become tools that data teams use in their own presentations to show leadership how the data team sounds to the business side. The satire lands because it’s uncomfortably accurate.

Watch the full conversation with Scott Taylor on the Data Faces Podcast.

Data leaders have been pitching “data quality” to executives for decades. For just as long, executives have nodded politely, approved a fraction of the requested budget, and moved on to whatever initiative sounds more exciting. Gartner estimates that poor data quality costs the average enterprise $12.9 to $15 million per year, yet data leaders still struggle to connect that cost to the language executives actually use.^[1]

“I can boil my entire data philosophy down to those three words: truth before meaning. You got to determine the truth in your data before you derive any kind of meaning out of it. It’s not chicken or egg here. This is egg and omelet.”

— Scott Taylor, Founder, MetaMeta Consulting

On Episode 34 of the Data Faces Podcast, I sat down with Scott Taylor to talk about why data management keeps getting sidelined and what data leaders can do about it. Scott has spent 30 years in the data space and now runs MetaMeta Consulting, where he helps organizations craft business-accessible narratives about data management. His central argument is that you have to establish truth in your data before you try to derive any meaning from it. Getting there requires storytelling, a skill that most data practitioners were never trained in.

About Scott Taylor

Scott Taylor is the founder of MetaMeta Consulting and is known across the data industry as “the Data Whisperer.” He has spent 30 years in the data space, including 25 years in corporate roles before becoming a full-time content creator, speaker, and consultant. Scott is also the creator of Data Puppets, a satirical puppet series that uses humor to expose common enterprise data problems, and the author of Telling Your Data Story. In our conversation on Episode 34 of the Data Faces Podcast, we discuss:

- Why “truth before meaning” is the foundational principle for every data initiative

- How data leaders can craft a one-sentence pitch that resonates with a skeptical CFO

- The 3V framework for data storytelling: Vocabulary, Voice, and Vision

- Why the vendor landscape at Gartner D&A looked “horrifyingly consistent”

- How Data Puppets uses satire to expose organizational dysfunction that executives resist hearing directly

Truth before meaning: egg and omelet, not chicken and egg

Every major trend in the data space follows the same cycle. A new technology arrives with enormous promise; organizations rush to adopt it; and at some point, someone in the room realizes that none of it works unless the underlying data is in good shape. Scott has watched this play out with big data, data mesh, data fabric, and now agentic AI over three decades. He frames the challenge with a phrase he has distilled into the fewest possible words: truth before meaning.

The “truth” part refers to the foundational work that most organizations know they need to do but struggle to prioritize: master data, reference data, metadata, MDM, data governance, and all the structural activities that ensure your data is curated, trusted, and fit for purpose. The “meaning” part is everything that gets the budget and the boardroom attention, from BI dashboards and AI models to analytics platforms and the latest agentic AI initiative.

Organizations consistently skip truth and jump straight to meaning. They invest in AI without verifying that their customer master is reliable. They build analytics on hierarchical structures that don’t hold up across departments.^[2] Research from IBM and MIT Sloan Management Review suggests that companies lose 15 to 25 percent of revenue because of poor data quality, and that cost only grows as organizations scale their AI investments without fixing the underlying data.^[3]

Scott illustrates the point with a supermarket example. Every time you take a product off a shelf and scan it at the register, that beep is a confirmation of truth. The system knows exactly what that product is, how it’s priced, and how it’s tracked. The challenge for enterprises is building that same level of confidence across hundreds of systems and millions of records.

“If the pitch that we need better data quality worked, then I wouldn’t be on your show, because people would be doing it. It would have been done. It wouldn’t be something that we’re still talking about.”

— Scott Taylor, Founder, MetaMeta Consulting

Why data leaders lose the room (and how storytelling wins it back)

I spent the first half of my career as a data practitioner and the second half in product marketing, so I’ve seen this from both sides. Data professionals are trained in hard skills, and nobody starts their data career with a course on how to pitch metadata management to a CFO. Marketing and sales teams learn to tell stories because their results depend on it, but data leaders tend to explain the mechanics first, walking into a meeting to describe the technical approach and losing their audience before they ever get to the business case.

“Data people love to get technical. They love to explain how it’s going to get done. And you just lose the business folks right away. A CEO, if you want money from them, they don’t care how it’s done until they understand why it’s important to the organization.”

— Scott Taylor, Founder, MetaMeta Consulting

Scott advises flipping the sequence. Every business leader has stated goals around growth, market expansion, customer experience, or operational efficiency. The data opportunities are already embedded in those objectives. If you listen to what your business leaders say they want to accomplish, you will find where the discipline fits. Their conversations focus on customers, brands, and markets, and all of those have a data element to them.

The 3V framework: Vocabulary, Voice, and Vision

Scott structures his advice around a framework he calls the 3V of data storytelling for data management. Most data practitioners already know the 3V of big data, and the parallel is deliberate.

Vocabulary. Get the words right. The language you use to describe data management to a CFO should be different from the language you use with your data engineering team. Terms like “master data management” and “reference data governance” mean nothing to someone whose primary concern is revenue growth or margin improvement. Scott recommends using words like strength, structure, and foundation rather than quality, because quality can feel subjective and emotional, almost like a complaint when what you need is a business case.

Voice. Everyone involved in making the case for data management needs to tell a consistent story. If the data team, the IT team, and the business analysts are all framing the problem differently, the message gets diluted before it reaches the people who control the budget. Scott calls this harmonizing to a common voice across the organization.

Vision. Connect every data activity to the strategic intentions of the enterprise. The pitch becomes impossible to ignore when you frame data investment as the enabler of business outcomes the leadership team has already committed to achieving.

“You’ve got to connect those dots between why we need metadata management in the context layer to the CEO’s initiative of expanding to new markets and becoming better partners with our customers.”

— Scott Taylor, Founder, MetaMeta Consulting

AI is not the Ozempic for data governance

At the Gartner D&A Summit in Orlando, Scott and I both noticed the same thing on the show floor. The vendor messaging was, in Scott’s words, “horrifyingly consistent.” Nearly every booth was leading with agentic AI, AI-native architecture, and context layers. Scott’s tongue-in-cheek response was classic Scott. As people posted about vendor after vendor emphasizing “context,” he started commenting on their posts with the same line: “context is the new oil.”

This hype cycle mania repeats every few years, from data mesh and data fabric three years ago to big data before that, and the cycle always ends with organizations realizing the new thing doesn’t work without solid data management underneath it.^[4] Scott’s colleague Malcolm Hawker coined a name for it: the “semantic pedantic cycle.”

The way Scott sees it, the belief that AI will solve the data management problem on its own is the latest version of this thinking. He calls this “AI is the Ozempic for data governance,” a line that got plenty of laughs at Gartner and in our bonus Data Puppets segment. AI can assist with certain data management tasks, and the organizational discipline of establishing truth in your data before deriving meaning from it still requires human leadership and commitment.

Data Puppets: using satire to say what executives need to hear

Think of it as Dilbert for the data world, except with puppets. The cast includes the CDO (Chief Dog Officer), his sidekick ITB who speaks exclusively in buzzwords, and a “Cat Sultant” from Meow-kinsey whose primary initiative is to generate more billing. Just as Scott Adams captured the absurdity of corporate life in ways that employees pinned to their cubicle walls, Scott Taylor captures the absurdity of enterprise data management in ways that data teams share in their Slack channels.

What started as a collection of data jokes has turned into a communication tool that Scott didn’t anticipate. People use the episodes in internal presentations to illustrate how the data team sounds to the business side. A Chief Dog Officer can say things that would come off as harsh from a human consultant, and people laugh first and then recognize the pattern in their own organization.

In the bonus Data Puppets segment at the end of our recording, Scott introduced A-Eye, a puppet character who attended the Gartner D&A Summit and had opinions about everything. When asked about data quality, A-Eye’s response captured an attitude that data leaders encounter constantly: “They’ve been whining about data quality ever since there was data. If it was that important, it would have been solved by now.”

“The number one reaction I got was, ‘this is just like my organization.’ People were really taking it seriously. They were like, ‘I showed this to my team to show this is how we sound to the business side.’”

— Scott Taylor, Founder, MetaMeta Consulting

Next steps

Scott’s approach offers a practical starting point for data leaders who are struggling to get executive support for foundational data work. The investment is in changing the conversation, which costs nothing beyond the willingness to rethink how you communicate.

- Craft your one-sentence pitch. Distill why data management matters to your organization into the fewest possible words. Frame it as a business statement that connects to what leadership has already said they want to accomplish, because a technical explanation won’t land. If you can’t say it in one sentence, you haven’t refined it enough.

- Audit your storytelling sequence. Are you leading with how (the technical approach) or why (the business impact)? If your presentations start with architecture diagrams and technology stacks, consider flipping the order. Open with the business objective, show how data enables it, and save the technical details for the appendix.

- Apply the 3V framework. Review the vocabulary you’re using with executive stakeholders. Swap subjective terms like “data quality” for structural language like “data foundation” and “data trust.” Align your team to a common voice so the message doesn’t fragment across departments. Make sure every data initiative you propose connects to the organization’s stated strategic vision.

Listen to the full conversation with Scott Taylor on the Data Faces Podcast.

Based on insights from Scott Taylor, Founder at MetaMeta Consulting, featured on the Data Faces Podcast.

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Frequently asked questions

What does “truth before meaning” mean in data management?

Truth before meaning is the principle that organizations must establish trustworthy, well-governed foundational data before attempting to derive business insights from it. The “truth” layer includes master data, reference data, metadata, and data governance. The “meaning” layer includes BI, analytics, AI, and everything that interprets data for business decisions. Scott Taylor argues that skipping the truth layer is the primary reason AI and analytics initiatives underperform. Gartner estimates that poor data quality costs the average enterprise $12.9 to $15 million per year.

What is the 3V framework for data storytelling?

The 3V framework is Scott Taylor’s approach to helping data leaders communicate with executive stakeholders. It stands for Vocabulary (choosing business language over technical jargon), Voice (aligning the entire data organization around a consistent narrative), and Vision (connecting every data initiative to the company’s strategic objectives). The framework is designed to shift data management conversations from technical how-to discussions to business-impact narratives that resonate with CFOs and CEOs.

Why do data leaders struggle to get executive buy-in for data management?

Data leaders are trained in hard skills and tend to lead presentations with technical approaches rather than business outcomes. Executives in marketing, sales, and the C-suite are better storytellers by training and practice. When data leaders explain master data management architecture before explaining how it connects to revenue growth or market expansion, they lose their audience. Scott Taylor recommends flipping the sequence and leading with why the work matters to the business before explaining how it gets done.

Can AI fix data quality problems on its own?

No. Scott Taylor calls the belief that AI can solve data management problems without organizational discipline “AI is the Ozempic for data governance.” While AI can assist with specific data management tasks, it cannot replace the foundational work of establishing trusted master data, standard hierarchies, and consistent taxonomies. Research from IBM and MIT Sloan Management Review suggests companies lose 15 to 25 percent of revenue from poor data quality, and that cost scales as AI investments grow without addressing the underlying data.

What are Data Puppets, and why do they matter for data management?

Data Puppets is a satirical puppet series created by Scott Taylor that uses humor to expose common enterprise data dysfunction. Characters include the CDO (Chief Dog Officer), a buzzword-fluent sidekick named ITB, and a consultant from “Meow-kinsey.” The series works as a communication tool because satire creates a layer of separation that lets audiences absorb uncomfortable truths about their own organizations. People use the episodes in internal presentations to illustrate how data teams sound to business stakeholders.

Podcast highlights

[0:06] Scott’s background as the Data Whisperer and 30 years in the data space

[3:59] Truth before meaning: Scott’s entire data philosophy in three words

[6:04] Why data truth isn’t philosophical, and the supermarket scanner example

[7:56] The importance of storytelling and why data practitioners aren’t trained in it

[10:27] Has AI changed the conversation about data management, or is it the same cycle?

[13:08] How vendors performed at the Gartner D&A Summit in Orlando

[16:27] “Context is the new oil” and the semantic pedantic cycle

[19:54] Crafting a one-sentence data management story for a skeptical CFO

[22:59] The 3V framework: Vocabulary, Voice, and Vision

[25:37] Data Puppets: how satire reveals organizational dysfunction

[31:48] Why humor helps executives hear truths they’d otherwise dismiss

[34:24] Where to find Scott Taylor and the Data Puppets

Bonus: Data Puppets segment — A-Eye attends the Gartner D&A Summit

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

- Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

- Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

- The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

- The CIO’s Guide to Adopting Generative AI: Five Keys to Success

- Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

- The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Gartner. “How to Improve Your Data Quality.” Gartner, 2021. https://www.gartner.com/smarterwithgartner/how-to-improve-your-data-quality.

^[2]David Sweenor. “AI in 2025: Why 90% of Gen AI Projects Will Fail.” TinyTechGuides, March 22, 2025.

https://insights.tinytechguides.com/p/ai-in-2025-why-90-of-gen-ai-projects

^[3]IBM. “The True Cost of Poor Data Quality.” IBM Think, 2024. https://www.ibm.com/think/insights/cost-of-poor-data-quality.

^[4]David Sweenor. “AI in 2025: Why 90% of Gen AI Projects Will Fail.” TinyTechGuides, March 22, 2025.

https://insights.tinytechguides.com/p/ai-in-2025-why-90-of-gen-ai-projects

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I spend most of my time consulting with organizations that are trying to figure out what to do with AI, and teaching is a big part of that work. I’ve also conducted more than 35 interviews on the Data Faces Podcast with data leaders, practitioners, and technology executives. The question I hear in every engagement and nearly every episode is the same: how do I know the output is right? When a chatbot gives a questionable answer, someone catches it and moves on. An autonomous agent, on the other hand, might already be three decisions downstream before anyone notices the answer was wrong.

At the Gartner Data & Analytics Summit in Orlando, I sat down with Conor Jensen for an on-location episode of the Data Faces Podcast. Conor is the Global Field CDO at Dataiku, a data science and machine learning platform used by enterprise organizations to build, deploy, and manage AI projects. It’s a role shaped by an unusual path. He purchased Dataiku as a customer about ten years ago, spent seven years on the other side of the table, and now helps organizations avoid the mistakes he already made. He’d just come off Dataiku’s biggest product launch in the company’s 13-year history, and one observation from our conversation captured exactly what I’ve been hearing from clients.

“A far more dangerous thing than an agent that breaks is an agent that’s still functioning and giving the wrong answers.” — Conor Jensen, Global Field CDO, Dataiku

According to Gartner, only 6% of organizations have AI agents in production today, while 53% are still in exploration mode.^[1] The organizations racing to build agents have largely skipped the question of whether the ones they already have are performing.

About Conor Jensen

- Conor Jensen is the Global Field CDO at Dataiku. He purchased Dataiku as a customer about ten years ago, joined the company seven years later, and now helps organizations develop AI strategy and operational plans to get the most out of the platform. Before Dataiku, he worked as a data scientist and analytics leader.

- Key topics discussed: Dataiku CoBuild and vibe coding for data pipelines, Reasoning Systems for multi-step autonomous decisions, the Agent Management Platform for cross-platform observability, getting AI from pilot to production, and why perfect data is never coming

Everyone’s building agents, nobody’s solved production

I asked Conor why so many AI projects stall between prototype and production. He didn’t point to a single bottleneck. He described a pile of them that keeps growing.

“We haven’t solved any of that yet as an industry. We just keep putting more in the backpack.” — Conor Jensen, Global Field CDO, Dataiku

MLOps was supposed to get machine learning models into production. Then came LLMOps for large language models. Now the industry is talking about AgentOps. Each layer adds new complexity without resolving the one that came before it. Conor sees three barriers that keep organizations stuck. Deployment architecture is one, where something that works on a laptop or in a dev environment falls apart on the way to production. Organizational dynamics are another, including governance, trust, and change management, which he considers harder than any technical challenge. And then there’s data readiness, where teams wait for perfect data that will never arrive.

Gartner’s research reinforces how high the stakes are. Rita Sallam estimates that 70% of agentic AI use cases will fail to deliver expected value due to underinvestment in necessary foundations.^[2] Data availability and quality remain the number one barrier to AI implementation, cited by 30% of data management leaders.^[3] Gartner analyst Sarah Turkaly reinforced the point at the summit: “Data governance will be the single point of failure for organizations’ AI ambitions.”^[4]

Dataiku’s biggest launch targets every layer of the problem

The opening keynote from Adam Ronthal and Georgia O’Callaghan set the tone for the summit by framing AI value around three returns: return on intelligence, return on integration, and return on individuals.^[5] Dataiku positioned its announcement around that same framework, branding the launch as “The Platform for AI Success.” Conor walked me through three new products that Dataiku announced at the summit, each one targeting a different layer of the production problem.

Dataiku CoBuild brings vibe coding into the data platform, but the comparison to building a web app breaks down quickly. With a web app, you click a button and the page loads or it doesn’t. With a data pipeline, you get summary statistics and a model, but verifying the answer requires a level of inspection that 2,000 lines of generated Python won’t give you. CoBuild takes that generated code and renders it as visual workflows you can step through, edit, and validate. Conor, a data scientist himself, was candid about why this matters.

“Out of 2,000 lines of Python and a machine learning project, there’s probably like 40 that are what’s really, really important. The rest of it is, yeah, okay, did you pull the right data?” — Conor Jensen, Global Field CDO, Dataiku

CoBuild abstracts the boilerplate so you can focus on the 40 lines that determine whether the output is trustworthy. It launches in June 2026.

Reasoning Systems tackle a different gap. Conor used the example of a supply chain analyst who today pulls data from five different systems, consults with other teams, and makes a judgment call. Reasoning Systems layer process flows and context on top of data sources, then give an agent the ability to walk through the entire sequence. The key difference from RPA is that not every step is deterministic. Some require the agent to self-correct or stop entirely. Dataiku is building these for targeted use cases in specific industries rather than trying to solve everything at once.

The product Conor said he’s personally most excited about is the Agent Management Platform. Fifty-four percent of organizations are exploring or deploying goal-driven AI agents, according to Gartner.^[6] The question most CIOs should be asking is straightforward: how many agents do I have in production across all of my systems? With agents being built and deployed on Databricks, Salesforce, and dozens of other platforms alongside Dataiku, that question is hard to answer today.

“How do I manage all of my agents across my infrastructure, wherever they’ve been deployed? How do I make sure I know that they’re performing, not just functioning, but performing?” — Conor Jensen, Global Field CDO, Dataiku

Monitoring whether an agent is running is table stakes. You can do that with an API bus. The Agent Management Platform goes further by adding performance management, a semantic layer, and contextual understanding across every environment where agents are deployed. It evaluates whether agents are delivering the right business results across eight, ten, or twenty different systems. It goes GA in September 2026 and does not require being a Dataiku customer.

Conor had practical advice for organizations that feel stuck waiting for perfect data or an industry standard to emerge.

“News flash. There’s no such thing as perfect data, never will be. You have to just get moving.” — Conor Jensen, Global Field CDO, Dataiku

Only 12% of D&A leaders say they are fully prepared to carry out their mandate, according to Gartner’s 2026 CDAO survey.^[7] Conor’s point is that treating full readiness as a prerequisite for action is its own form of failure.

Production is the starting line

Conor Jensen has seen the Dataiku platform from both sides over the past decade, and that practitioner-turned-vendor perspective came through in every answer he gave. The industry has spent years talking about getting AI to production. The conversation at Gartner this year made clear that production is only the starting line. The harder work is knowing what happens after you deploy, and most organizations have no way to answer that question across their agent portfolio today.

The next time someone on your team proposes building a new agent, ask a different question first. Do you know how the ones you already have are performing?

Listen to the full conversation with Conor Jensen on the Data Faces Podcast.

Based on insights from Conor Jensen, Global Field CDO at Dataiku, featured on the Data Faces Podcast.

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Frequently asked questions

What is Dataiku’s Agent Management Platform? Dataiku’s Agent Management Platform provides cross-platform observability and performance management for AI agents deployed across any system, including those built outside of Dataiku. It goes beyond uptime monitoring by evaluating whether agents are delivering correct business results. The platform adds a semantic layer and contextual understanding so organizations can assess agent performance across eight, ten, or twenty different environments from a single view. It is scheduled for general availability in September 2026.

How is vibe coding a data pipeline different from vibe coding a web app? With a web app, you can visually confirm whether it works by clicking a button and seeing the result. With a data pipeline, AI generates thousands of lines of code that produce summary statistics and a model, but there is no simple way to verify the answer is correct. Dataiku CoBuild addresses this by rendering generated code as visual workflows that users can step through, edit, and validate rather than reading through 2,000 lines of Python.

What are Dataiku Reasoning Systems? Reasoning Systems layer process flows and business context on top of data sources to enable multi-step autonomous decisions. Unlike RPA, where every step is deterministic, Reasoning Systems allow agents to self-correct or stop when results fall outside expected parameters. Dataiku is building these for targeted use cases in specific industries, starting with manufacturing operations, with supply chain and financial risk scheduled for later in 2026.

Why do most agentic AI use cases fail? Gartner estimates that 70% of agentic AI use cases will fail to deliver expected value due to underinvestment in necessary foundations. The top barrier to AI implementation is data availability and quality, cited by 30% of data management leaders. Organizations also struggle with deployment architecture, governance, and change management. Gartner analyst Sarah Turkaly warned that data governance will be the single point of failure for organizations’ AI ambitions.

How many organizations have AI agents in production? According to Gartner research from January 2025 surveying 3,412 respondents, only 6% of organizations have AI agents in production. Fifty-three percent are still in exploration mode, and 25% are piloting. Fifty-four percent of organizations are exploring or deploying goal-driven AI agents, but most cannot answer how many agents they have running across their infrastructure or whether those agents are delivering correct results.

Podcast highlights

[0:00] Introduction at the Gartner D&A Summit and Dataiku overview

[1:27] Three new product announcements: CoBuild, Reasoning Systems, Agent Management Platform

[2:49] Dataiku’s evolution in the age of Gen AI

[3:30] Why AI projects stay stuck in pilot purgatory

[5:30] Deployment architecture that works from dev to production

[7:00] CoBuild, vibe coding, and why data pipelines are different from web apps

[8:26] Why even data scientists need better coding practices

[9:32] Reasoning Systems and autonomous multi-step decisions

[11:01] Agent Management Platform and cross-platform observability

[13:00] Monitoring vs. performance management for agents

[15:00] Opening the gates with governance and guardrails

[17:00] GA timeline, availability, and closing

About David Sweenor

David Sweenor is an AI advisor, author, and the founder of TinyTechGuides. He spent the first half of his career as a practitioner at IBM, building data warehouses and running predictive models, and the second half in product marketing leadership at SAS, Dell, TIBCO, Alteryx, and Alation. He advises Fortune 500 companies on AI strategy, data governance, and go-to-market planning, and hosts the Data Faces Podcast, where he interviews the leaders, practitioners, and technologists shaping the future of data and AI.

Books

- Artificial Intelligence

- Generative AI Business Applications

- The Generative AI Practitioner’s Guide

- The CIO’s Guide to Adopting Generative AI

- Modern B2B Marketing

- The PMM’s Prompt Playbook

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Chandrasekaran, Arun. “Navigating the AI Agent Landscape: A Strategic Guide for IT Leaders.” Gartner D&A Summit 2026, March 2026.

^[2]Sallam, Rita. “How to Calculate the Value and Cost of AI Agents.” Gartner D&A Summit 2026, March 2026.

^[3]Ramakrishnan, Ramke. “How Is Agentic AI Impacting and Disrupting Your Data Management Discipline?” Gartner D&A Summit 2026, March 2026.

^[4]Turkaly, Sarah. “The Future of D&A Governance.” Gartner D&A Summit 2026, March 2026.

^[5]Ronthal, Adam and Georgia O’Callaghan. “Navigate AI on Your Data & Analytics Journey to Value.” Gartner D&A Summit 2026 Opening Keynote, March 9, 2026.

^[6]Ramakrishnan, Ramke. “How Is Agentic AI Impacting and Disrupting Your Data Management Discipline?” Gartner D&A Summit 2026, March 2026.

^[7]Gabbard, Michael. “Signature Series: State of D&A 2026.” Gartner D&A Summit 2026, March 2026.

The spreadsheet might still be the most widely used data catalog on the market. That’s not a joke. It’s a finding from Stewart Bond, Research VP at IDC, who has spent the past decade studying how companies manage intelligence about their data. When I sat down with Stewart on the Data Faces podcast, he pointed out that every survey he runs surfaces the same contradiction. Organizations rank data quality as their top AI concern, yet they fail to invest in the one technology category designed to address it.

The frustrating part is that the data teams usually know exactly what the problem is. They flag quality and governance issues, but the budget continues to flow towards AI model development and agents instead. Stewart has been tracking this gap longer than most, and his perspective on how data intelligence evolved from an analyst’s shorthand into a global market category offers a useful lens for understanding why the gap persists.

“One of the biggest challenges organizations have is managing the intelligence about their data. Data catalogs, business glossaries, data lineage, all that stuff is so important now as we get into AI. And yet, their top investment categories are not on data catalogs.” — Stewart Bond, Research VP, IDC

About Stewart Bond

Stewart Bond is a Research VP at IDC, where he leads the data intelligence and data integration software research practice. His career spans over 30 years in IT, including a decade as a certified IT architect at IBM before moving into industry analysis in 2011. Outside of work, Stewart is a competitive curler who came within one match of representing Ontario at a Canadian national championship. In our conversation on the Data Faces Podcast, we discuss:

• How Stewart coined the term “data intelligence” and watched it become a global market category

• The difference between intelligence about data and intelligence from data

• Why agentic AI demands a shift-left approach to data quality

• What CDOs are most concerned about and where they’re under-investing

How one research note became a market category

Stewart joined IDC in 2015 and inherited a research area covering data integration and data access, which at the time included eight sub-markets like metadata management, data quality, and master data. A conversation with ASG Technologies introduced him to their term “enterprise data intelligence.” Stewart saw something useful in the phrase but dropped the “enterprise” qualifier. Data intelligence, as a simpler label, stuck.

The real momentum came in 2018, when GDPR was about to take effect. Enterprise data leaders started calling Stewart with the same question. “Where can I buy a data governance solution?” His response surprised them. You can’t buy governance. Data governance is an organizational discipline that requires people, processes, and accountability. What you can buy is data intelligence technology, the tools that tell you everything you need to know about your data so you can govern it.

“I had a lot of end-user clients calling me and saying, ‘Where can I buy a data governance solution?’ And I just kind of laughed, because data governance isn’t a technology solution.” — Stewart Bond, Research VP, IDC

Stewart framed this through his 5 W’s of data. Who is using it? How is it being used? Where does it live? What does it mean? Why do you even have it? How long do you have to keep it? These questions form the foundation of effective data governance, and answering them requires technology that most organizations still haven’t fully invested in.^[1]

Intelligence about data vs. intelligence from data

The term spread faster than Stewart expected. Collibra became “the data intelligence company.” Erwin (now Quest) adopted it for their data catalog. Alation started using it in 2020, after learning the phrase wasn’t a Collibra trademark but an industry-level concept. Informatica wove it into their intelligent data platform messaging. Then, in late 2023, Databricks made a major push with its own version of data intelligence.

The Databricks definition, however, expanded the original meaning. Stewart had always treated data intelligence as intelligence about data. What is this data, where did it come from, who uses it, and how good is it? Databricks extended the concept to include intelligence from data, using the metadata and context layer to generate smarter analytics and AI outcomes from the data itself. The distinction matters because it changes what organizations expect from the category and how they evaluate platforms.

Dave Kellogg was serving as acting CMO at Alation when he first explored the term’s origins with Stewart. After the Databricks announcement, Kellogg reached out with a direct assessment. “I think you did it. I think you created a new market category.” Last year, IBM confirmed the trend by rolling its entire portfolio of data cataloging, quality, lineage, and observability products into IBM watsonx Data Intelligence. IBM’s product leadership told Stewart the renaming was a direct result of his work and the broader market momentum he helped create.

“I’d always treated data intelligence as intelligence about the data. I’d say Databricks has extended it to intelligence from the data, getting more into the case of leveraging that intelligence about the data to make sure you’re using the data intelligently.” — Stewart Bond, Research VP, IDC

Agents can’t wait for clean data

The shift to agentic AI fundamentally changes how organizations need to approach data quality. Traditional analytics workflows gave organizations a buffer. Data moved through batch processes, giving teams time to spot anomalies and intervene before a bad number reached a dashboard. Autonomous agents don’t offer that luxury. An agent monitoring a change data capture stream sees a new order event and starts fulfilling it on the spot. If the data in that event is wrong, the agent acts on it before anyone has a chance to review it.

Stewart describes this as the “shift left” imperative. Data quality, privacy, and integrity all need to move as close to the source as possible, because once data enters the agentic pipeline, there is no batch window to clean it up. Deloitte flagged this as one of four critical data quality challenges for AI, finding that companies building agentic systems need quality controls embedded at the point of data creation, not applied after the fact.^[2]

“You’d better make sure the data in that order event is good and that it’s a real and reliable order event. You may have heard the term shift left. Your data quality, your data privacy, your data integrity all need to be as close to the source as possible.” — Stewart Bond, Research VP, IDC

The challenge extends beyond structured data. Stewart raised a question that most organizations still haven’t answered well. What do you do about the unstructured data that makes up the bulk of enterprise information? Every organization has countless versions of the same PowerPoint file, thousands of PDFs, and documents that LLMs are eager to ingest. Some vendors are starting to crack this problem. Shelf.io, for example, has developed methods to assess the quality of unstructured documents, a capability that seemed impossible just a few years ago.

The broader issue remains, though. Most organizations lack the data context needed to determine whether their unstructured data is safe to use, let alone high-quality.^[3] Stewart sees agentic AI as part of the eventual solution. Agents that pre-populate data catalogs and reduce the manual burden on data stewards could finally solve the adoption problem that has held these tools back for years. But that future depends on investing in the foundation today.

The investment gap CDOs can’t ignore

Stewart runs an annual survey of the Office of the Chief Data Officer, and the results tell a consistent story. When you ask CDOs what their biggest organizational concern is, skills top the list. They struggle to find people who can do the work. The second concern is managing expectations around what AI can deliver, not just within their own teams, but across the C-suite, where leadership is under pressure to show results quickly and often treats AI as a magic bullet.

“Their top investment categories are not on data catalogs. Back to the spreadsheet might still be the most widely used data catalog on the market. I don’t have data to prove that, but anecdotally, that could be the case.” — Stewart Bond, Research VP, IDC

What makes this frustrating is that CDOs now have more influence over IT spending than ever before. IDC predicted in 2024 that chief data officers would gain significantly more budget authority by 2025, driven by the fact that every major AI concern in enterprise surveys points to data: quality, correctness, privacy, and security. CDOs are accountable for all of it. Deloitte’s 2025 CDO Survey tells a similar story. These leaders are increasingly expected to demonstrate direct business impact from their data programs, even as their organizations resist the investments required to achieve it.^[4]

And yet, when Stewart looks at where enterprises are actually putting their money, the top investment categories are not data catalogs or data quality tools. The data lineage, metadata management, and business glossary capabilities that form the backbone of data intelligence remain underfunded, even as AI programs depend on them.^[5] That spreadsheet Stewart mentioned at the top of our conversation? For many organizations, it is still doing the job that a proper data catalog should be doing.

You’ll never score 100%

Stewart closed our conversation with an insight he picked up years before he even joined IDC. A life insurance company told him they had finally accepted that their data would never be 100% clean. Instead of chasing perfection, they started measuring how clean or dirty their data was and feeding that score into their calculations. Their actuaries knew how to work with uncertainty. They just needed the number.

Data intelligence doesn’t promise perfect data. It gives you a clear picture of how much you can trust what you have. Organizations that know the quality of their data before it enters an AI pipeline avoid the costly cycle of debugging outputs that were doomed from the start. A data quality score of 75 means something different from a score of 95, and both are more useful than no score at all. When that score travels alongside the data into an AI model or an autonomous agent, the organization can make informed decisions about how much confidence to place in the output.

Start with Stewart’s 5 W’s. Audit how your organization currently tracks who uses its data, where it lives, and how trustworthy it is. If the answer is a spreadsheet, you have your business case.

The spreadsheet is still winning. It doesn’t have to be.

Listen to the full conversation with Stewart Bond on the Data Faces Podcast.

Based on insights from Stewart Bond, Research VP at IDC, featured on the Data Faces Podcast.

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Frequently asked questions

What is data intelligence?

Data intelligence is the category of technology that provides intelligence about your data. It encompasses data catalogs, business glossaries, data lineage, data quality, and metadata management. Stewart Bond, Research VP at IDC, coined the term to describe the tools that answer foundational questions about data, including who uses it, where it lives, what it means, and how trustworthy it is. More recently, vendors like Databricks have expanded the definition to also include intelligence from data, using that context layer to improve analytics and AI outcomes.

How does data intelligence differ from data governance?

Data governance is an organizational discipline that requires people, processes, and accountability. Data intelligence is the technology that supports it. You cannot buy a data governance solution, but you can invest in data intelligence tools that tell you everything you need to know about your data so you can govern it. Organizations that try to solve governance with technology alone tend to fail, according to IDC’s Stewart Bond.

Why does agentic AI require a shift-left approach to data quality?

Traditional analytics workflows gave teams time to spot and fix data issues in batch processes before results appeared on the dashboard. Autonomous AI agents operate in real time and act on data the moment they receive it, with no batch window to clean things up. This means data quality, privacy, and integrity controls need to move as close to the data source as possible. Deloitte identified this as one of four critical data quality challenges for organizations building agentic AI systems.

What are CDOs most concerned about in 2025?

According to IDC’s annual survey of the Office of the Chief Data Officer, skills gaps rank as the top concern. CDOs struggle to find qualified people to do the work. The second biggest concern is managing leadership expectations around what AI can realistically deliver. Despite growing influence over IT budgets, CDOs face a persistent disconnect between the data foundation AI requires and where their organizations actually invest.

Where are organizations under-investing in data intelligence?

IDC survey data show that the top enterprise investment categories are not data catalogs, data quality tools, or data lineage capabilities, even though managing data intelligence is one of the biggest challenges organizations report. Stewart Bond notes that the spreadsheet may still be the most widely used data catalog on the market, a sign that foundational data intelligence technology remains significantly under-funded relative to AI program spending.

Podcast highlights

[0:05] Introduction and Stewart’s background at IDC

[2:31] Stewart’s life outside work, competitive curling, and fishing

[5:00] The origin of the term “data intelligence” and the ASG Technologies connection

[6:44] GDPR drives demand for governance solutions, the 5 W’s of data

[8:15] Collibra, Erwin, Alation, and Informatica adopt the term

[10:00] Databricks expands the definition, Dave Kellogg’s “you created a category” moment

[14:00] IBM rebrands to watsonx Data Intelligence

[18:00] Intelligence about data vs. intelligence from data

[26:00] Agentic AI and the shift-left imperative for data quality

[29:00] Unstructured data quality and Shelf.io

[31:00] What CDOs are most concerned about in 2025

[35:00] Where organizations are under-investing in data intelligence

[36:40] Data quality will never be 100%, the life insurance anecdote

[38:00] Agentic AI and the future of data catalog adoption

About David Sweenor

David Sweenor is a Top 25 AI thought leader, six-time author, and founder of TinyTechGuides. He spent the first half of his career as a practitioner at IBM, building data warehouses and running predictive models, and the second half in product marketing leadership at SAS, Dell, Quest, TIBCO, Alteryx, and Alation. He hosts the Data Faces Podcast, where he talks with the people who are making data, analytics, and AI work in the real world.

Books

- Artificial Intelligence

- Generative AI Business Applications

- The Generative AI Practitioner’s Guide

- The CIO’s Guide to Adopting Generative AI

- Modern B2B Marketing

- The PMM’s Prompt Playbook

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn.

^[1]Sweenor, David. “Why the Biggest AI Enthusiasts Care Most About Governance.” TinyTechGuides, January 27, 2026. https://tinytechguides.com/blog/why-the-biggest-ai-enthusiasts-care-most-about-governance/

^[2]Deloitte. “Four Data and Model Quality Challenges for AI.” Deloitte AI Institute, 2025. https://www.deloitte.com/global/en/our-thinking/insights/topics/artificial-intelligence/ai-data-quality-challenges.html

^[3]Sweenor, David. “Your AI Doesn’t Have a Model Problem. It Has a Data Context Problem.” TinyTechGuides, February 24, 2026. https://tinytechguides.com/blog/your-ai-doesnt-have-a-model-problem-it-has-a-data-context-problem/

^[4]Deloitte UK. “CDO Survey 2025.” Deloitte United Kingdom, 2025. https://www.deloitte.com/uk/en/services/consulting/analysis/chief-data-officer-survey.html

^[5]Sweenor, David. “Data Lineage for AI: Why Truth Beats Hope in Banking.” TinyTechGuides, December 2, 2025. https://tinytechguides.com/blog/data-lineage-for-ai-why-dotrth-beats-hope-in-banking/

I walked the expo floor at the Gartner Data & Analytics Summit in Orlando expecting every conversation to be about AI, agents, and context layers. They were, and plenty of vendors had agent-washed their messaging overnight. The vendors were talking about the future, but the practitioners were pointing to something they already had.

The opening keynote set the tone. Adam Ronthal and Georgia O’Callaghan reported that four out of five organizations are now deploying AI, but only one in five will achieve their stated ROI.^[1] Governance, they argued, is a value accelerator and should be treated as one. With AI agents on every vendor’s booth and in nearly every session title, the question of how to govern autonomous systems had real urgency behind it.

I carried that framing into on-location interviews for the Data Faces Podcast with Philip Dutton, CEO and founder of Solidatus, Terrence Hedin, Data and Metadata Platform Director at the London Stock Exchange Group, and Caleb Watkins, Solutions Engineer at Solidatus. Three different roles, three different vantage points, and they all pointed to the same thing. The most valuable AI governance asset many organizations have is the data lineage and metadata infrastructure that their compliance teams built years ago. Solidatus, a data lineage and metadata management platform used by financial services and other regulated industries, served as the common thread across all three conversations.

From second-class citizen to strategic asset

I suggested to Terrence Hedin that before AI changed the conversation, lineage and metadata were treated as second-class citizens. He expanded on that.

“It has evolved. It is a first-class citizen,” Terrence said. “Every business requirement spec includes lineage at an element level. Every tech spec includes how you produce that lineage.”

At LSEG, lineage used to answer a narrow set of questions. Where did this data come from? Can we prove it to regulators? Those questions still matter, but Terrence described how LSEG now brings business metadata, technical metadata, and semantic layers together into a knowledge graph that serves the entire organization.

“We bring our business metadata, our technical metadata, our semantic layers, into a knowledge graph so we can build that true business context. That provides not only human benefit, but machine benefit as well.” — Terrence Hedin, Data and Metadata Platform Director, LSEG

LSEG now treats metadata as a data product, published to both internal teams and external customers. Not a theoretical data mesh exercise, but a commercial product. The governance infrastructure they built for compliance became the foundation for a revenue-generating line of business.

Gartner research supports this trajectory. In the session “Trust as the New Currency,” Guido De Simoni presented data showing that organizations with graduated trust models achieve 64% compliance success compared to 23% without them.^[2] The trust frameworks that organizations like LSEG built for regulators directly support AI readiness.

Caleb Watkins, a Solutions Engineer at Solidatus, showed me a related capability. Because Solidatus centralizes all data and metadata in one place, organizations can load their regulations as reference models and let the AI assistant evaluate compliance across their data landscape.

“We can train Solidatus up on those regulations, and then we can ask the assistant to assess your models for compliance with these different regulations to make sure that you’re meeting all of your objectives.” — Caleb Watkins, Solutions Engineer, Solidatus

Lineage is no longer just a record of where data came from. It’s becoming the system that evaluates whether your data meets the obligations attached to it.

Your compliance operating model already works for AI

I said something to Philip Dutton that surprised us both. “Something that was built for compliance is now incredibly useful for AI.”

Philip didn’t hesitate. Whether it’s an AI consuming data, a BI dashboard pulling reports, or another system sharing information across business lines, the obligations are the same. Purpose limitations, storage rules, and sharing boundaries all travel with the data.

“You don’t have to change your operating model for AI governance. You can use the same operating model that you’ve been using, which the organization knows, and it takes them a long time to get to know it and to feel comfortable with it. So this really gives you a nice accelerator.” — Philip Dutton, CEO and Founder, Solidatus

The program already exists, and your teams know how to run it. The organizational trust has been earned over years of practice. Rather than standing up a parallel AI governance function, extend the operating model you already have.

Gartner analyst Andrés García-Rodeja reinforced this point in the session “How to Build the Context Layer for Reliable AI Agents.” By 2028, he estimates, 60% of agentic analytics projects relying solely on the Model Context Protocol will fail due to the lack of a consistent semantic layer.^[3] The metadata and lineage infrastructure that compliance teams maintain is exactly the kind of semantic foundation AI agents need to operate reliably. For AI teams building production pipelines and deploying agents, the implication is direct: the semantic layer your models need may already exist in your governance program.

And that operating model is getting faster. Caleb walked me through code scanning with the AI Lineage Assistant, which reduces what used to take several days of manual analysis to five to ten minutes, with 10x to 100x acceleration across broader governance workflows. In the session “Using Active Metadata to Support Data Agents,” Gartner analyst Mark Beyer presented research showing that metadata volume grows exponentially with agentic AI.^[4] Manual approaches to lineage and governance won’t survive that scale. Organizations like LSEG that automated their metadata workflows early have a compounding advantage over those still relying on spreadsheets and tribal knowledge.

AI trust starts with what you can see

Every conversation I had at the summit circled back to trust. De Simoni found that more than 50% of vendors identify trust as the top barrier to agentic AI adoption.^[5] Gartner expects unsupervised AI deployment to remain below 10% through 2028. The industry is building AI agents faster than it’s building the trust infrastructure to support them.

Philip put it simply: “If we can’t see it, if we can’t understand it, how do we trust it?” Solidatus renders data lineage as interactive visual maps rather than rows of metadata in a spreadsheet. Visualization isn’t a nice-to-have for governance. When people can see their data lineage mapped out and confirm it matches their understanding of the organization, they trust it. When they’re poring over raw metadata for hours, they generate questions, not confidence.

That principle extends to AI outputs as well. Solidatus built hallucination protection directly into the AI Lineage Assistant. If the LLM returns a response that isn’t grounded in metadata within the platform, the system rejects it and forces a new attempt. The response has to be anchored in real data before it reaches the user. In financial services and other regulated industries, where human-in-the-loop oversight is standard, that validation layer is non-negotiable.

Terrence described how trust and lineage connect at enterprise scale. LSEG’s data trust program is built on four elements of trust, with Solidatus providing the lineage foundation.

“If we don’t understand what that data is, it’s very difficult for us to understand how we can use it, how we should use it, what value it can provide,” Terrence said.

Trust becomes even more critical as AI agents grow more autonomous. Philip pointed out that much of what vendors call “AI agents” today are chatbots running on request-response. True agentic AI creates its own plan, executes across 20 to 50 steps, and self-corrects along the way. Without lineage and metadata infrastructure, organizations have no way to verify what an agent did or why.

What to do with the infrastructure you already have

The data lineage and metadata systems that compliance teams built over the past decade are becoming the critical infrastructure layer for AI trust, AI agents, and AI governance. LSEG proved that by turning their lineage program into a strategic asset and a commercial data product. Solidatus proved it by extending a governance platform into an AI-accelerated workflow engine.

If your organization has invested in data lineage for compliance, the next step isn’t building a separate AI governance program. Audit what you already have. Identify where it covers AI use cases. Close the gaps. If you lead an AI or data science team, ask your governance counterpart what lineage coverage already exists for your training data, production models, and agent workflows. The organizations that connect these functions now will govern AI with confidence. The ones that start from scratch will spend the next two years catching up.

Listen to the full conversations with Philip Dutton, Terrence Hedin, and Caleb Watkins on the Data Faces Podcast.

Based on insights from Philip Dutton, CEO and Founder at Solidatus, Terrence Hedin, Data and Metadata Platform Director at LSEG, and Caleb Watkins, Solutions Engineer at Solidatus, featured on the Data Faces Podcast.

Frequently asked questions

What is Solidatus?

Solidatus is a data lineage and metadata management platform that maps, visualizes, and governs data flows across the enterprise. It is used primarily by financial services and other regulated industries to track how data moves through systems, meet compliance obligations, and build organizational trust in data. The platform recently introduced an AI Lineage Assistant that adds natural language interaction, automated code scanning, and regulatory compliance assessment to its existing governance capabilities.

Why is data lineage important for AI governance?

Data lineage documents where data comes from, how it moves through systems, and what obligations are attached to it. Those obligations, including purpose limitations, storage rules, and sharing boundaries, apply to AI the same way they apply to BI dashboards or regulatory reports. Organizations with mature lineage programs can extend their existing governance operating model to cover AI use cases without building a separate framework. Gartner research presented at the 2026 D&A Summit showed that organizations with graduated trust models achieve 64% compliance success compared to 23% without them.

How does data lineage differ from AI governance?

Data lineage is a component of AI governance, not a separate discipline. Lineage tracks how data flows through an organization and what happens to it along the way. AI governance addresses the broader question of how to ensure AI systems use that data responsibly. The argument from practitioners at LSEG and Solidatus is that the lineage and metadata infrastructure built for regulatory compliance already provides the semantic foundation AI agents need. Rather than creating a parallel AI governance program, organizations can extend what they have.

What is a bring-your-own-LLM model for data governance?

A bring-your-own-LLM model allows organizations to connect their own large language model to a governance platform rather than sending data through a vendor’s AI infrastructure. Unlike vendor-hosted AI models that route customer data through external systems, the BYOLLM approach keeps all data processing within the customer’s own environment. Solidatus uses this approach for its AI Lineage Assistant, meaning no data flows through Solidatus or any third party. This design addresses the primary security concern enterprises have about AI in governance contexts, particularly in regulated industries like financial services.

How does Solidatus prevent AI hallucinations in governance workflows?

Solidatus built hallucination protection directly into the AI Lineage Assistant. When the LLM generates a response, the system validates it against metadata that exists within the platform. If the response isn’t grounded in real data, the system rejects it and forces a new attempt. The response has to be anchored in verified metadata before it reaches the user. This approach ensures that AI outputs in governance contexts are based on actual organizational data rather than fabricated information.

Where should organizations start with AI governance if they already have data lineage?

Start by auditing your existing lineage coverage to identify where it already applies to AI use cases. Philip Dutton, CEO of Solidatus, argues that organizations don’t need a new operating model for AI governance because the one they already use for compliance works. LSEG provides a proof point, having evolved their lineage program from a regulatory tool into a strategic asset and commercial data product. The key is closing gaps rather than starting from scratch.

Podcast highlights

Philip Dutton, CEO and Founder, Solidatus (~15 min)

[0:00] Introduction at the Gartner D&A Summit

[0:28] What is Solidatus and why data lineage matters

[0:54] Data lineage meets AI governance

[1:50] The AI Lineage Assistant and natural language interaction

[3:17] Trust in AI and trust through lineage

[4:45] Human in the loop for financial services

[5:14] Why visualization builds data trust

[7:05] You can’t automate what you don’t understand

[8:27] Data lineage as AI lineage, same operating model

[9:29] What’s on attendees’ minds at Gartner

[10:48] True agentic AI vs. chatbots

[12:00] The future of Solidatus and agentic orchestration

[14:18] LSEG session preview and closing

Terrence Hedin, Data and Metadata Platform Director, LSEG (~6 min)

[0:00] Introduction and upcoming LSEG session preview

[0:23] Overview of the LSEG talk with Philip Dutton

[2:26] Lineage as a first class citizen in the age of AI

[3:32] From regulatory reporting to strategic asset

[4:47] How the Solidatus AI Lineage Assistant is changing workflows

[5:54] Session details and closing

Caleb Watkins, Solutions Engineer, Solidatus (~4 min)

[0:00] Introduction at the Gartner D&A Summit

[0:22] The AI Lineage Assistant and bring-your-own-LLM

[0:44] Trust and security in the AI agent

[1:01] Use case: AI-powered code scanning

[1:42] Days to minutes with automated lineage

[2:01] Use case: regulatory compliance (BCBS 239, AI Act)

[2:56] Customer feedback on the assistant

[3:29] Find Solidatus at Booth #929

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn

^[1] Ronthal, Adam, and Georgia O’Callaghan. “Opening Keynote: The State of Data and Analytics.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[2] De Simoni, Guido. “Trust as the New Currency.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[3] García-Rodeja, Andrés. “How to Build the Context Layer for Reliable AI Agents.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[4] Beyer, Mark. “Using Active Metadata to Support Data Agents.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[5] De Simoni, Guido. “Trust as the New Currency.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

When Michael Meyer told his high school guidance counselor he was skipping university to attend a trade school for programming, the response was blunt: “All the computer programs that have ever needed to be written have already been written. You’re going down the wrong path.”

That was the late 1980s. Michael ignored the advice, enrolled anyway, and spent the next 35 years building a career across programming, data architecture, product marketing, and solutions engineering. He’s currently a Solutions Engineer at Snowflake, helping enterprise customers in Omaha get more out of their data platforms.

Today, a new generation of data professionals is hearing a familiar refrain. AI will write your code. AI will build your dashboards. AI will make your job obsolete. I recently sat down with Michael on the Data Faces podcast, and his career offers a compelling counterargument. The skill that carried him through every industry shift was something most people in data overlook entirely. Storytelling.

“The writing about the craft beer is more about the people, the historical significance of places and things like that, than it is about the beer.” — Michael Meyer, Solutions Engineer, Snowflake

About Michael Meyer

Michael Meyer is a Solutions Engineer at Snowflake, where he supports enterprise customers across the Omaha, Nebraska market. His career spans over 35 years in programming, data architecture, data governance, and product marketing at companies including Alation. He is also the author of Joe’s Brew Reviews, a book about Nebraska’s craft beer scene that’s really about the people and stories behind the breweries. In our conversation on the Data Faces Podcast, we discuss:

• Why storytelling is the most durable skill in a data career

• How the semantic layer went from a BI footnote to AI’s missing piece

• What vibe coding gets right and where it falls short

• The fundamentals that early career data professionals need to focus on

A storyteller who speaks data

Michael’s relationship with storytelling started at home. His father was a logical thinker who passed down an analytical mindset, while his maternal grandfather was the kind of natural storyteller you’d sit and listen to for hours. Michael found himself torn between journalism and programming after high school. He chose programming, but the storytelling instinct never went away.

On one side of the family, he got the analytical instinct, and on the other, the urge to make people lean in and listen. That combination connects every role he’s held, from programming to data architecture to product marketing to solutions engineering. Each demanded a different technical skill set, but each rewarded the ability to make complex things understandable. Storytelling wasn’t a hobby Michael kept separate from his day job. It became the thing that made every job better.

Walt the data janitor and the power of internal marketing

When Michael transitioned into data governance at a financial services company, the team needed people across the organization to care about data quality, catalogs, and standards. Instead of frameworks and compliance language, they created a fictional character named Walt.

Walt showed up across the entire data management program. When the team discussed data quality, Walt was the janitor pushing a broom through messy datasets. When they introduced data architecture concepts, Walt played a different role. He gave people a relatable way into abstract subject matter, and it worked.

“We built up our data program from creating a fictional character that helped us show each step of the way, from data architecture to quality. His name was Walt. When we talked about data quality, Walt was the janitor pushing the broom through.” — Michael Meyer, Solutions Engineer, Snowflake

That experience crystallized something for Michael. Getting people to care about data is a communication challenge, not a technical one. His internal marketing work caught the attention of Alation’s field marketing team, and they recruited him into a technical product marketing manager role.

The mindset shock of marketing

Moving into product marketing forced a shift in how Michael communicated. The real breakthrough came from learning to listen for the exact phrases customers used to describe their problems and then putting those words on the page rather than his own.

“There would be key phrases that would come out that customers would say, and if you could use those, especially within what you’re trying to portray, that’s where you could get the hook. You’d better get them interested right away. How do you get to that emotional side of somebody so that they think, ‘Wait a minute, that’s me’?” — Michael Meyer, Solutions Engineer, Snowflake

That customer-language instinct turned out to be portable. Michael is now back on the technical side as a Solutions Engineer at Snowflake, where he helps key enterprise accounts in the Omaha market. His marketing background makes him better at translating a complex platform into terms that resonate with the people who actually use it. Marketing gave him a vocabulary for the rest of his data career.

The semantic layer is a storytelling problem

If you’ve worked in data long enough, you remember defining semantic layers inside BI tools like Cognos Framework Manager, giving human-readable names to cryptic database columns so business users could build their own reports. Nobody called it storytelling at the time, but that’s exactly what it was.

The concept never went away, but it stayed fragmented. Every BI platform maintained its own semantic definitions, which meant every tool told a slightly different version of the truth. AI changed the equation. When a business user asks a natural language question of an AI assistant, that assistant needs to understand the question in the terms the business actually uses, not technical column names or another company’s jargon, but the specific vocabulary of that organization.^[1]

A modern semantic layer provides that context by defining facts, dimensions, metrics, relationships, and business rules in a format that both humans and large language models can interpret. Without it, AI tools produce answers that sound plausible but miss the mark, and once business users lose trust, they rarely come back.

“If I’m going to talk with my data, I need to talk to it in terms of how the business speaks with the data, not technical terms, not how another financial company talks to theirs, but how my organization talks. That’s really the key.” — Michael Meyer, Solutions Engineer, Snowflake

Gartner predicts that organizations will abandon 60% of AI projects unsupported by AI-ready data through 2026.^[2] Michael estimates that building a good semantic model today is still about 70% human work. AI can generate descriptions and suggest metrics, but it can’t replace the institutional knowledge of someone who has spent years working with the data. A subject matter expert knows why two fields that look similar mean very different things in practice.

AI-assisted coding and the proof-of-concept trap

“Vibe coding” is the practice of using AI to generate code from natural language prompts rather than writing it by hand. Michael has been experimenting with Snowflake’s Cortex Code and recently used it to build a machine learning pipeline from scratch. He fed it a retail dataset, picked a use case focused on detecting late delivery issues, and let it design the full pipeline in a notebook.

The first model returned about 50% accuracy. Michael knew that was unacceptable. He iterated with Cortex Code, adding feature engineering and additional training, until the model reached 85% accuracy. The critical skill in that process came down to judgment, knowing that 50% meant the model was broken and that 85% meant it was worth showing to a data scientist for validation.

AI-assisted coding compresses timelines and lowers the barrier to exploring new technical domains. But it’s not a substitute for the judgment that comes from understanding your data. Someone who can’t read the story the numbers are telling, who can’t look at a metric and know whether it makes sense in context, will eventually ship something dangerous.^[3]

“If you don’t have any understanding of how to test and verify, and if you’re just taking everything AI does as being 100% accurate, that could quickly actually become a career ender.” — Michael Meyer, Solutions Engineer, Snowflake

What endures when everything else changes

Michael’s guidance counselor was wrong about programming in the late 1980s. The people saying AI will replace data professionals are making the same mistake today. The tools and platforms will keep changing, but the fundamentals don’t.

When I asked Michael what early career data professionals should focus on, he didn’t start with AI prompting or the latest framework. He started with data modeling. Understand what good data looks like. Learn how raw data becomes something a business user can actually consume.^[4] Know how to test and validate results, because if you can’t verify what AI gives you, you have no business putting it in front of a decision-maker.

Michael pointed out that he’s met AI engineers who have never worked with data before, and that gap shows up when models need to connect to real business outcomes. As Thomas Davenport and Randy Bean argue in MIT Sloan Management Review, 2026 is the year the industry must shift from chasing AI hype to realizing actual enterprise value, and that shift depends on exactly the kind of foundational data skills Michael is describing.^[5]

“Find something that energizes you, what are some of your strengths, and lead into those. It took me a long time to really make my writing part of my career. But it can be done.” — Michael Meyer, Solutions Engineer, Snowflake

Beyond the technical fundamentals, Michael’s advice is to get out from behind the screen. He runs meetups in Omaha and credits the networking he’s done over the past decade with keeping him grounded and energized. Be a constant learner, he says, but learn from people, not just platforms.

Not all programs have been written. And the stories haven’t all been told.

Listen to the full conversation with Michael Meyer on the Data Faces Podcast.

Based on insights from Michael Meyer, Solutions Engineer at Snowflake, featured on the Data Faces Podcast.

Podcast highlights

[0:00] Opening and introduction

[2:00] Michael’s 35-year career across programming, data architecture, and Snowflake

[4:00] Joe’s Brew Reviews, journalism vs. programming, and the storytelling instinct

[6:30] Walt the data janitor and how a fictional character made data governance relatable

[11:00] The mindset shock of moving from data architecture to product marketing at Alation

[14:00] Customer language, emotional hooks, and storytelling on a B2B web page

[17:00] Coming back to the technical side as a Solutions Engineer at Snowflake

[19:00] What the semantic layer is and why AI made it urgent

[23:00] Facts, dimensions, metrics, verified queries, and business rules in a semantic model

[25:30] Building a semantic model: 70% human work and why institutional knowledge matters

[28:30] Vibe coding with Snowflake Cortex Code and iterating from 50% to 85% accuracy

[32:00] Why early career data professionals should start with data modeling fundamentals

[34:30] Find what energizes you, get out from behind the screen, and be a constant learner

[35:30] Craft beer recommendations and closing

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter @DavidSweenor and connect with him on LinkedIn

^[1] David Sweenor, “Your AI Doesn’t Have a Model Problem. It Has a Data Context Problem,” TinyTechGuides, February 24, 2026, hcansubject-matter ttps://tinytechguides.com/blog/your-ai-doesnt-have-a-model-problem-it-has-a-data-context-problem/

^[2] Gartner, “Lack of AI-Ready Data Puts AI Projects at Risk,” Gartner Newsroom, February 26, 2025, https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk

^[3] David Sweenor, “Data Lineage for AI: Why Truth Beats Hope in Banking,” TinyTechGuides, December 2, 2025, https://tinytechguides.com/blog/data-lineage-for-ai-why-truth-beats-hope-in-banking/

^[4] David Sweenor, “From ‘AI-Ready’ to AI Reality: Why Actionable Data Strategies Beat Endless Planning,” TinyTechGuides, June 3, 2025, https://tinytechguides.com/blog/from-ai-ready-to-ai-reality-shane-murray-on-data-trust-and-why-action-beats-planning/

^[5] Thomas H. Davenport and Randy Bean, “Five Trends in AI and Data Science for 2026,” MIT Sloan Management Review, 2026, https://sloanreview.mit.edu/article/five-trends-in-ai-and-data-science-for-2026/

I’ve often equated people with birds – they’re always chasing the next shiny object that comes along, in this case, agentic AI. Sadly, I’ve watched a scene like this play out at nearly every company I’ve worked with. A team builds an AI assistant, loads it with pristine data, asks it well-crafted questions, and the demo goes flawlessly. Leadership sees the result and greenlights production.

Six months later, the project has lost its luster, the team is frustrated, and the models underlying the assistant that looked brilliant in a conference room can’t survive contact with the real world. What happened? The model didn’t break. The environment changed, and the carefully curated conditions that keynote the shiny demo simply don’t exist at scale across a messy, fragmented enterprise.

In a recent episode of the Data Faces Podcast, I sat down with Asa Whillock to talk about what it actually takes to move AI from pilot to production. Asa has spent 35 years in software and has lived this challenge from every angle, inside large enterprises like Adobe and Alteryx, and now as the founder of his own AI company. His core argument is one that every data and AI leader needs to hear: the distance between a brilliant demo and a production system has very little to do with model capability. It’s a data context problem. And most organizations are only scratching the surface of what “context” really means.

“When you think about what makes AI production-ready, it is really not so much about the model. When you talk about demos and pilots, you’re almost always talking about a cultivated set of data and a cultivated set of questions so that the result is just outstanding. You’re like, this is amazing, why would we not deploy this everywhere?” — Asa Whillock, CEO and Founder, Euphonic AI

About Asa Whillock

Asa Whillock is the CEO and founder of Euphonic AI, a growth acceleration agent serving revenue operations and demand generation leaders who engineer organizational growth. His career spans 35 years in software across major enterprises, including Adobe, Alteryx, Intel, and AOL, with deep experience in analytics, product, and go-to-market strategy. He also spent two years performing stand-up comedy, which explains his talent for making complex enterprise AI concepts feel accessible through unexpected analogies. In our conversation on Episode 32 of the Data Faces Podcast, we discuss:

• Why AI pilots deceive leadership about production readiness

• The three categories of data context that most organizations are missing

• Why chasing frontier models is a distraction from the real work

• How to connect AI investment to the metrics that actually drive your business

The three buckets of context that make AI production-ready

So if the model isn’t the problem, what is? Asa frames it with an analogy that will feel familiar to anyone who has worked inside a large organization: enterprise data is “scattered like toys across a two-year-old’s bedroom.” From the outside, you’d assume that companies with thousands of employees and massive technology budgets have their data neatly organized and ready to fuel AI systems. Well, you know what they say about assumptions, and nothing could be further from the truth. It’s the reason so many promising AI initiatives have failure to launch on their way to production.

Asa breaks the challenge into three categories of context that AI needs to function at production quality. Most organizations are actively working on the first category and are barely aware that the other two exist.

Machine-driven data context

This is the layer that gets the most attention. Machine-driven data is the row-level, tabular data living in your CRM, ERP, and HRM systems. Data teams have been working with this information for years, and the challenge here isn’t a lack of awareness. The challenge is that it’s spread across roughly 350 systems per enterprise, and each system has its own opinion on what data should look like. Connecting them is painful, expensive, and slow, but at least organizations recognize this work needs to happen.

Operational metadata context

This is the layer most teams overlook entirely. Operational metadata includes the configuration settings, workflow routing rules, and log files that determine how data actually moves through an organization. Asa describes this as “the train control that determines if this train goes to Seattle or Albuquerque.” A single configuration switch can change the entire path a customer record follows, and that switch often lives buried in a UI screen that no API can reach. When AI operates without awareness of these controls, it’s making recommendations based on data flows it doesn’t actually understand.

Human decision data context

“Think about the institutional memory of your organization. You can almost always name that person. Imagine living your life without that person in every decision you ever made. You’d be this uninformed AI, guessing. When you have the machine data, the metadata, and the human decision context together, now your AI is ready for production.” — Asa Whillock, CEO and Founder, Euphonic AI

This is the hardest layer to capture and the most valuable once you have it. Human decision data is the institutional knowledge that lives in people’s heads, the reasoning behind why an organization made specific choices, abandoned certain approaches, or accepted particular architectural tradeoffs. Every organization has an institutional memory holder who knows where the skeletons are, the person everyone turns to when they need to understand why a decision was made three years ago. That person knows which approaches were tried and abandoned, which compromises became permanent, and which workarounds nobody ever documented. Asa puts the stakes plainly: imagine making every decision in your organization without that person’s knowledge. That’s the position your AI is operating from right now if you haven’t captured this layer.

The context gap is a $130 billion problem

Each of these gaps is costly on its own, but the compound effect of missing two or three layers simultaneously is what stalls most AI initiatives. Kimberly at Andreessen Horowitz has pointed out that 9 out of 10 automations that could exist today simply don’t, because unlocking the data needed to build them is too difficult. By her firm’s estimate, that represents a $130 billion opportunity in context gaps that have nothing to do with model capability.

Stop chasing models and start hydrating with data context

The industry’s obsession with frontier models is understandable. The capabilities are genuinely impressive, and as Asa puts it, “models are so hot right now.” For data science leaders, the temptation to evaluate, compare, and chase the latest release feels like productive work because the improvements are measurable and exciting.

But Asa argues that this focus is a distraction from the work that will actually determine whether your AI investments succeed or fail. He frames the choice in terms that are hard to ignore. Given the option between a frontier model paired with a poor data context and a slightly less capable model that has been fully hydrated with all three layers of context, the hydrated model wins every time. A world-class model operating without awareness of your operational metadata and institutional decision history will produce outputs that sound polished but miss how your organization actually works. A less flashy model armed with deep context will deliver results that make sense to the people who have to act on them.

“If you’re going to choose a model that is just at the absolute frontier of capability with poor data, or take a model that is maybe half a step backwards in capability but give it all of the context it needs to make an amazing outcome, I’ll tell you which one I will choose every time. Invest in breaking down those barriers of permissions, access, data, those unsexy things.” — Asa Whillock, CEO and Founder, Euphonic AI

The highest-leverage work available right now isn’t evaluating the next model release. It’s the unglamorous, difficult work of breaking down permission barriers, building cross-system data access, and finding ways to capture the institutional knowledge that currently lives only in people’s heads. That’s where production-ready AI gets built, and it’s the work most organizations keep deferring because it doesn’t generate the same excitement as a new model announcement.

Aim your context at the metrics that actually drive your business

Once you’ve mapped your three layers of context, you need to point them to a specific location. This is where Asa’s advice shifts from framework to action, and it starts with a question he hears constantly from leadership: if AI is so transformative, where is the ROI? Asa’s response is to turn the question back on the person asking it.

“I ask leadership, what are the five things that drive your ROI? Have you deployed an AI solution to tune for that? Have you looked at what actually impacts that metric? Almost every time the answer is, well, no, I haven’t really dug into that. If you ask yourself those questions and drive into those key metrics, you will find that transformative ROI.” — Asa Whillock, CEO and Founder, Euphonic AI

What are the five or six metrics that actually drive your business? Not the vanity metrics on a dashboard, but the numbers that directly influence customer acquisition cost, speed to lead, time to value, or whatever levers matter most for your organization. And once you’ve named them, have you dug into what drives those metrics? Most of the leaders Asa talks to haven’t done this work. They’ve deployed AI against surface-level outcomes and are puzzled when the results feel incremental rather than transformational.

One of the most formative experiences in Asa’s career was watching Adobe build what they called a data-driven operating model. Adobe didn’t just track top-line metrics like customer adoption and retention. They decomposed each metric three to five layers deep to understand what specifically influenced it, and then what influenced those influencing factors. That level of decomposition is what turned their AI investments from interesting experiments into systems that moved the business forward.

Next steps

“You can’t be standing back, going, where’s the ROI? You have to have visibility. You have to have headlights on how the metrics that really drive your business, and they’re really tuning up. Focus on what matters for you.” — Asa Whillock, CEO and Founder, Euphonic AI

The organizations that win with AI over the next few years won’t be the ones running the most capable models. They’ll be the ones who did the unglamorous work of stitching their data context together across systems and connecting it to the metrics that actually matter for their business.

If you’re a data or AI leader trying to figure out where to focus, Asa’s framework gives you a practical starting point.

• Audit your three buckets of context. For your highest-priority AI initiative, map which layers you actually have coverage on today. Most teams will find they’re reasonably strong on machine-driven data and nearly empty on operational metadata and human decision context.

• Decompose the metrics that matter for your business. Name the five or six numbers that actually drive your outcomes and dig three to five layers deep into what influences them. That decomposition is where you’ll find the specific places to aim your AI investments.

• Capture institutional knowledge before it disappears. Identify the three to five people in your organization who hold the decision-making context that no system has ever recorded, and start documenting what they know.

Listen to the full conversation with Asa Whillock on the Data Faces Podcast.

Based on insights from Asa Whillock, CEO and Founder at Euphonic AI, featured on the Data Faces Podcast.

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Podcast Highlights - Key Takeaways from the Conversation

Podcast highlights

[0:51] Asa introduces his 35-year career in software and the founding of Euphonic AI

[2:22] The Voltron analogy, stand-up comedy, and making enterprise AI concepts accessible

[4:16] Why large enterprises are “relentlessly vertical” and how that creates friction for AI

[7:28] The ebb and flow between horizontal and vertical software cycles

[8:39] Why AI pilots deceive you: cultivated data versus the reality of production

[10:00] The three buckets of data context: machine-driven data, operational metadata, and human decision data

[13:17] The role of unstructured data and why operational context matters more than document archives

[15:01] “What do you want to be great at?” and why companies shouldn’t pivot to becoming AI companies

[19:16] Vibe coding, competitive parity, and why adding the same capabilities as everyone else nets to nothing

[22:24] How to align AI investments with your business differentiation instead of chasing technology

[25:51] Why data context matters more than model capability and the case for the “half-step-back” model

[29:25] The bridge between systems of record and why nobody is incentivized to build it

[32:29] Asa’s one piece of advice: identify the five metrics that drive your business and dig three to five layers deep

About David Sweenor

David Sweenor is the founder and host of the Data Faces podcast, where he talks with the people who are making data, analytics, AI, and marketing work in the real world. He is also the founder of TinyTechGuides and a recognized top 25 analytics thought leader and international speaker who specializes in practical business applications of artificial intelligence and advanced analytics.

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence. David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.

Gene Arnold has never been more excited about a technology in his career, which is exactly why he’s become an active voice for pragmatic governance. Gene works as a partner sales engineer at Atlan, where he came to AI through the data catalog world and discovered that models living on top of data made governance unavoidable.

But his perspective extends beyond enterprise software. He runs a GitHub AI agent engineering course, builds with 3D printers and stepper motors, creates his own semantic generators, and hosts a YouTube channel on mountain biking that taught him how social media lets practitioners break into industries without turning pro. Gene sees governance as the foundation that lets good ideas reach production.

About the guest

Gene Arnold is a partner sales engineer at Atlan, where he works closely with partners like Snowflake and Databricks to help organizations navigate governance, modern data stacks, and AI use cases. In this conversation, we discuss:

• Why the people most excited about AI often care most about governance

• How pressure from leadership and individual contributors creates gaps

• Why models persist bias rather than create it

• The role metadata and semantic layers play in AI accuracy

• How to pick your first AI project and what to watch for

The dual pressure problem

When Gene talks with organizations about AI adoption, he sees the same recurring pattern. Pressure to move fast comes from leadership and individual contributors alike, and most companies have no framework for managing what gets built.

From the top, leadership sees competitors moving and asks why the organization isn’t keeping pace. AI isn’t a hard concept to grasp when everyone’s talking to ChatGPT. Executives don’t need technical depth to feel the strategic urgency. As Gene put it, “Top down, I don’t have to be a rocket scientist to understand we need it.”

From the bottom, individual contributors can now build AI tools without knowing how to code. Gene pointed to how quickly someone can knock out a workflow with tools like n8n, with templates available for almost everything. “Bottom up, I don’t have to be a rocket scientist to build something.”

That collision is where governance breaks down. Gene described the pattern he sees repeatedly: “Look what I made. Look what I made. Okay, hey, whoa, they made it. Send it out. Whoa, stop.” Roughly 80% of AI projects never reach production. Organizations are building plenty, but the challenge is building things that survive scrutiny.

“We don’t want to stop innovation. That’s a bad thing. But here’s the box. Let’s try to stay somewhere in the box.” — Gene Arnold, Partner Sales Engineer, Atlan

The box is a container for evaluating ideas before they hit production. Two accountability questions expose most governance gaps before they become public failures. First, do we have the right to do this? Gene cited an example of an organization that used facial recognition without proper authority to include faces in its models. The technology worked fine, but the authorization didn’t exist, and legal exposure followed. Second, what did we train it on? Traceability and lineage matter because models inherit whatever lives in the training data, including the biases and foibles that data contains.

Organizations that skip governance early often end up locking down AI entirely after a public failure. A little structure now prevents overcorrection later.

Models don’t create bias, they persist it

Even organizations that answer both accountability questions correctly at launch can find themselves exposed months later. Governance requires ongoing attention beyond the initial launch.

“A model doesn’t create bias. A model simply persists bias. The model was trained on X. If X was biased, then the model is biased.” — Gene Arnold, Partner Sales Engineer, Atlan

By now, the case of resume screening models that have passed over qualified female candidates is well-documented. Models trained on historical hiring data learned to predict what companies had historically done, including decades of biased evaluation. The teams involved may have done proper diligence, and the model performed exactly as designed. That was the problem.

Gene’s takeaway is direct: “I can’t train a model on the future. I don’t know what the future holds. I have no data on the future.” History carries black marks, and models trained on history inherit them.

The challenge gets harder when one variable masks another. Gene described research he conducted on AI bias using loan approval data. A model might show that PhD holders get approved at higher rates than those with bachelor’s degrees, which seems reasonable until you remove the PhD variable, and a different pattern emerges. The PhD status was masking discrimination based on other factors, like zip code. As Gene explained, “Sometimes one feature in the model can outweigh something else, and all of a sudden, when that one’s turned off... the PhD covered the fact that down here, loan payment wasn’t always paid in full.”

The answer is continuous testing. Gene recommended running tests against thousands of synthetic records, varying demographic features to see if outcomes shift unexpectedly. The goal isn’t perfection. “Constant testing, tweaking the levers to make sure that you properly, at least make it as unbiased as you can.”

Humans also need to remain part of the decision chain. You “can’t just flip the switch and say, best of luck, and let this thing go all day long and hope that you get it right. That’s scary.”

Governance starts with knowing what you have

Bias isn’t the only blind spot. Many organizations don’t know what data they have, which makes governance impossible before the model is even built.

Gene sees this play out in a simple scenario. Ask a model for “a summary of East Coast Q1 sales.” The model doesn’t know what Q1 means to your organization. Does your fiscal year start in January? What territories count as the East Coast? Without semantic information, the model guesses based on patterns in historical queries or fails entirely.

“Metadata is literally the deciding factor on how a model answers correctly.” — Gene Arnold, Partner Sales Engineer, Atlan

“If you don’t give it that semantic knowledge, it doesn’t know how to answer some of these questions,” Gene explained. This extends to any company-specific metric. How does the organization calculate customer lifetime value? What counts as an active user? The model needs that context, and the context needs to be governed.

The problem compounds when organizations have multiple versions of the same information floating around. Gene described scenarios where 10 versions of the same manual exist across different systems. Which one should train the model? Without proper curation through a data catalog or similar system, you’re building on uncertain foundations.

His practical workaround acknowledges reality: “What I can do now with AI is feed all 10 of those versions into a model, and now ask it questions... and it’ll respond well, based on the knowledge that I have, these are the three different methods that I feel would be appropriate, and that’s where the human judgment comes in.”

Start with what already works

Gene’s advice for getting started runs counter to the instinct to use AI to fix broken processes. “Pick a process that you know is working well, but you want to automate. Unless you know what good looks like, how do you know if the model is doing well?”

“Automation can either make things really good really fast, or make things really bad really fast.” — Gene Arnold, Partner Sales Engineer, Atlan

The reasoning is straightforward. If you don’t know what correct output looks like, you can’t evaluate whether the model is helping or hurting.

Gene distinguished between processes that just need speed and processes that need complete rethinking. “There are times where, well, you know what, we needed someone to read all these files. We still think they need to be read, but I’m going to have a model run them now. The process is fine, but the model can read it faster than a human.” Document summarization, internal search across knowledge bases, and data quality checks, which use predefined rules, all fit this pattern.

The opposite scenario requires more caution. “There are other times, like the Excel spreadsheet, where we’re going to say, well, now’s a good time to bring in a new system, right? And retool.” Customer-facing agents, automated decision-making on loans or hiring, and any process where the existing approach is broken are poor candidates for a first AI project.

Before any project goes live, leaders should be able to answer a handful of questions. Who owns this model? Who trained it and on what data? What does correct output look like? Do we have the rights to use this data in this way? What’s our plan for ongoing monitoring?

Gene noted that the first project teaches more than the use case itself. “You’re going to learn if your team works well together, is your AI governance workflow proper, right? So you’re going to learn a ton by just picking that one project that you already know how it’s supposed to end.”

One win builds trust, and trust enables the next project. Governance creates the conditions for that first win to happen safely.

Build something beautiful with it

Throughout our conversation, Gene kept returning to the same idea: governance exists to protect your ability to keep building. His philosophy on AI comes down to a simple metaphor.

“AI is just like any other tool. I can take a hammer and I can build something beautiful with it, or I could take that hammer, and I could smash something beautiful.” — Gene Arnold, Partner Sales Engineer, Atlan

For data science leaders, the challenge has shifted from whether to adopt AI to whether you can sustain it without creating problems that force you to backtrack later. The organizations that keep momentum will be the ones that built governance into the process from the start.

Gene’s parting point: “AI is not going away... this is the time where you’ve got the chance, because AI is still pretty new, to embrace it, understand what it can do and build something beautiful with it.”

Listen to the full conversation with Gene Arnold on the Data Faces Podcast.

Based on insights from Gene Arnold, Partner Sales Engineer at Atlan, featured on the Data Faces Podcast.

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Podcast Highlights - Key Takeaways from the Conversation

Podcast highlights

[1:10] Gene’s path from DJ to AI advocate Gene explains how he accidentally fell into sales engineering after years as a DJ and discovered it was “a nerd’s stage to present and have fun.” His work at Atlan with data catalogs led him into AI, which he calls his “new love and passion.”

[7:05] How data governance and AI governance relate Data governance focuses on keeping data clean, accurate, and unbiased. AI governance focuses on decision-making and how systems behave. Gene explains how the two work together: “It’s an agent. Let’s govern what it’s allowed to do and how, and then when it does it, what data is it actually going to respond back?”

[9:56] Why unstructured data is the gold mine Gene argues that 80% of valuable information lives in emails, documents, and support conversations. “That’s where your answers are. That’s where your conversations are.” Smart companies are extracting insights from customer support chat logs to understand what products are working and what’s failing.

[12:28] The duplicate data problem Most organizations have multiple versions of the same information scattered across systems. Gene’s practical workaround: feed all 10 versions into a model and let it surface the best approaches, then bring in human judgment to make the final call.

[14:34] Why 80% of AI projects never reach production “It’s just so easy to make something. But then when you really put it down into the real world and run it, did you really properly QA this new cool thing?” Gene argues that companies are failing because they lack governance workflows to evaluate what’s being built.

[17:10] The dual pressure problem AI pressure comes from leadership and individual contributors simultaneously. “Top down, I don’t have to be a rocket scientist to understand we need it. Bottom up, I don’t have to be a rocket scientist to build something.” This creates a collision that governance needs to manage.

[19:18] The resume screening bias example Gene walks through the well-known case of AI models penalizing feminine-coded language in resumes. “A model doesn’t create bias. A model simply persists bias. The model was trained on X. If X was biased, then the model is biased.”

[21:40] The two accountability questions Gene identifies the questions that expose governance gaps: Who designed the model? What did you train it on? He also cites an example where an organization used facial recognition without proper authority to use the faces in their models.

[23:56] How one variable can mask another Gene describes his research on AI bias using loan approval data. PhD holders got approved at higher rates, but when you removed that variable, discrimination based on zip code emerged. “Sometimes one feature in the model can outweigh something else.”

[27:02] Start with a process you know is working “Pick a process that you know is working well but you want to automate. Unless you know what good looks like, how do you know if the model is doing well?” Gene advises against using AI to fix broken processes as a first project.

[28:15] Automation amplifies everything “Automation can either make things really good really fast, or make things really bad really fast.” Gene explains that your first project teaches you more than just the use case: you learn if your team works well together and if your governance workflow is functioning.

[31:29] Why metadata is the deciding factor Gene introduces the semantic layer concept: models need context to answer questions correctly. Without knowing what “Q1” means to your organization, a model will guess or fail entirely. “Metadata is literally the deciding factor on how a model answers correctly.”

[36:10] Gene’s parting advice “AI is just like any other tool. I can take a hammer and I can build something beautiful with it, or I could take that hammer and smash something that was beautiful.” Gene encourages listeners to embrace AI while understanding its weaknesses so it gets used correctly.

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him onLinkedIn.

Randy Bean has spent four and a half decades watching organizations struggle with data. The tools keep getting better, faster, and cheaper. Yet companies keep failing for the same reasons they failed in 1988 when Randy was working on natural language processing (NLP).

The failures result from barriers that are both cultural and structural. Leaders remain reluctant to ask uncomfortable questions about organizational readiness, and C-suites have fragmented into competing data roles with overlapping mandates.

In his 2026 AI and Data Leadership Executive Benchmark Survey, Randy found that 94 percent of Fortune 1000 executives cite culture and people as their principal barrier to AI adoption, while only 6 percent point to technology. This gap explains why so many AI projects never make it past the pilot stage.

About the speaker

Randy Bean is a senior advisor and board member who has spent 4.5 decades working with executives at Fortune 1000 companies on data, analytics, and AI leadership. He founded and leads the annual AI and Data Leadership Executive Benchmark Survey, now in its 15th year, which captures insights from over 100 Chief Data Officers, Chief AI Officers, and Chief Analytics Officers from the Fortune 1000. His work has been published in Forbes, Harvard Business Review, MIT Sloan Management Review, and the Wall Street Journal.

In this episode, we discuss

• Why 94 percent of AI challenges are cultural rather than technical

• The “readiness test” that predicts whether organizations will succeed or stall

• Why legacy companies should stop benchmarking against digital disruptors

• The case for unifying CDO, CAO, and CAIO roles

• Why the best Chief Data Officers aren’t data geeks

• The 5-question, 5 percent framework that kills “boil the ocean” projects

The 94 percent problem hiding in plain sight

Randy didn’t set out to become a data expert. He studied English, History, and Art History in school. But when he needed a job, employers wanted technical skills, so he trained as a COBOL and assembler programmer. His interest was never really in the programming itself, but in the stuff you moved around, the data.

That background gave Randy a different lens than most technologists. Where others frame AI adoption as an engineering problem, he keeps coming back to the human side. People never really like to change on their own volition, and organizations are made up of people, which compounds the issue.

The 2026 survey data confirms this. When asked to identify the principal challenge to data and AI adoption, 94 percent of Fortune 1000 executives pointed to culture and people. Only 6 percent said technology was the barrier. This ratio has held steady for years. The technology has transformed dramatically. The human barriers haven’t.

Cultural barriers persist because they’re difficult to measure. You can audit a tech stack and benchmark infrastructure against competitors. But how do you assess whether an organization is genuinely ready to change how it operates?

Randy has developed his own informal test. He described folding up his notebook ten minutes into specific client meetings, sitting through the rest, already knowing the engagement wouldn’t go anywhere.

“They weren’t ready. You could tell it. They said, ‘We’ve got everything all figured out.’”

— Randy Bean, Senior Advisor and Board Member

When leaders claim they have everything figured out, they signal they’re not open to the difficult conversations transformation requires. Better tooling won’t fix that.

The challenge for data and AI leaders is learning to recognize these signals early, before investing months in initiatives that were never going to succeed.

Get your house in order

During the pandemic, the Chief Digital Officer of the nation’s largest insurance company shared something striking with Randy. “We’ve done more to execute on our digital transformation strategy in the past six months than we did in the previous 20 years.” The company moved because it had to. Customers couldn’t meet face-to-face, and online channels became existential overnight.

Transformation at legacy companies rarely happens because of vision or strategy. It happens when the alternative becomes untenable.

Randy’s survey data reveals an important distinction. Ninety percent of Fortune 1000 companies are legacy organizations. Only 10 percent belong to the “move fast and break things” crowd.

“90% of the Fortune 1000 are legacy companies... Those 10%, they can pioneer new things, but for the other 90%, they don’t need to compete against that other 10%—they just need to compete against one another.”

— Randy Bean, Senior Advisor and Board Member

When a 150-year-old manufacturer compares its AI maturity to a Silicon Valley startup, it creates false pressure that leads to poorly planned initiatives and wasted resources. The bank that figures out AI-driven fraud detection doesn’t need to outpace Google. It needs to outpace the other banks.

Stop measuring your AI maturity against tech-native outliers. Your real competition is the traditional competitor who figures out cultural readiness before you do.

Competitive pressure isn’t the only problem. There’s a structural one too. The proliferation of data-related C-suite titles has created confusion at many organizations. Randy’s 2026 survey found that 39 percent of companies have appointed a Chief AI Officer in addition to their existing Chief Data Officer. This leads to competing functions, unclear accountability, and redundant mandates.

In December 2025, Randy co-authored an article in Harvard Business Review with Vipin Gopal and Tom Davenport, arguing for a unified Chief Data, Analytics, and AI Officer role. About 30 percent of readers pushed back with legitimate concerns about the differences between AI and data governance operating models.

“We were just trying to create some level of sanity and clarity in the C-suite, so that you didn’t have all these competing and redundant functions, but rather came up with a unified mindset around how you manage data, analytics, and AI.”

— Randy Bean, Senior Advisor and Board Member

The point isn’t that unification is the only answer. The fact is that fragmentation has costs. Someone needs to be accountable for how data, analytics, and AI work together.

Hire for business acumen, not technical pedigree

The Chief Data Officer at JP Morgan Chase sits on the bank’s 14-person operating committee and reports directly to Jamie Dimon. Her previous role wasn’t in data engineering or analytics architecture. She was the Global Chief Investment Officer. Her questions focus on the most complex business problems the bank needs to solve and how data and AI can address them.

Diana Schulthaus, Chief Data Officer at Colgate-Palmolive, offered another perspective at a panel Randy moderated. When asked how much time she spends on offensive versus defensive data activities, her answer surprised the room. “I spend 100% of my time on offense.” The audience applauded.

In 2020, only 55 percent of organizations reported focusing on offensive, growth-oriented data activities. By 2025, that number jumped to 86 percent. The role has evolved from regulatory compliance to business strategy.

“You don’t need a data architect or data engineer or data modeler to be the chief data officer. You need a business leader who understands how data is going to be used so the organization can be more effective.”

— Randy Bean, Senior Advisor and Board Member

Randy learned this lesson early in his career. As a young COBOL programmer, he watched his technical colleagues return from meetings with business users and complain that the business people were stupid and couldn’t articulate what they wanted.

One day, he pushed back.

“They’re the people that employ us. They’re the people that go out and get the customers. They’re the people that do the business. We wouldn’t even be employed if it wasn’t for these people. So maybe we should give them some credit and figure out how to speak their language.”

— Randy Bean, Senior Advisor and Board Member

Effective data leaders understand enough about technology to separate nonsense from reality, but they lead with business problems rather than technical architecture.

The 5-question, 5 percent framework

Early in Randy’s career, he worked at a major bank that was part of Bank of America. He noticed the organization captured enormous amounts of customer information. One day, he asked a manager what they did with all of it.

“The regulators make us hold on to it for six years,” the manager said, “and then we put it in the furnace.”

The data existed. The infrastructure to store it existed. But nobody had asked what questions the data could answer.

He’s watched too many companies since then pursue what he calls the “boil the ocean” approach, trying to perfect every piece of data across the enterprise before putting any of it to use.

“Understand what the most important business questions that you need to ask are—not like 1,000, but like 5 or 10... Not all data is created equal, and sometimes 5% of the data is all that it takes to answer 95% of the questions.”

— Randy Bean, Senior Advisor and Board Member

Start with the five to ten most important business questions your organization needs to answer. Then identify the key data elements required to answer those specific questions. Resist the urge to build a comprehensive data infrastructure before you’ve proven value on the questions that matter most.

The same discipline applies to AI adoption timelines, and Randy encourages leaders to resist FOMO and the pressure to match competitors’ announcement cycles.

“Forget about the FOMO, the fear of missing out. Step back a little and think about where we’re going as a business. Where do we need to be? What capabilities do we need to have? How can AI augment those capabilities?”

— Randy Bean, Senior Advisor and Board Member

In 2023, only 5 percent of organizations had AI in production at scale. By 2024, that jumped to 24 percent. By 2025, it reached 40 percent. Progress came from organizations that planned thoughtfully, not from those that rushed to keep up with headlines.

What matters is where you end up in three, five, or ten years. Not what you announce this quarter.

Leading from the middle

Not every reader holds a CDO title or sits in the C-suite. Many data and AI practitioners work within organizations where leadership hasn’t embraced these shifts. They see the 94 percent problem from the inside but lack the authority to mandate change.

The notebook test works at every level. When your leadership team says they have everything figured out, you’re facing a readiness gap. More analysis or better dashboards won’t change the minds of those who aren’t open to change.

The 5-question framework scales both up and down. You don’t need enterprise-wide buy-in to prove value. Find one business question your team can answer with existing data. Deliver insight that helps someone make a better decision. Build from there.

“It does help to have some level of underpinnings so that you can separate the nonsensical from the realities... You gotta know enough to call BS when someone’s giving you a pitch.”

— Randy Bean, Senior Advisor and Board Member

Progress in AI adoption hasn’t come from organizations that moved fastest. It came from those who built readiness, clarified accountability, and focused on questions that mattered. That work can start anywhere in the org chart.

Call to action

Randy Bean’s full 2025 AI and Data Leadership Executive Benchmark Survey is available at randybeandata.com, along with nearly 300 articles published in Forbes, Harvard Business Review, MIT Sloan Management Review, and the Wall Street Journal.

Your organization probably has the right technology. The more complicated question is whether your culture and leadership structure will let you use it.

Listen to the full conversation with Randy Bean on the Data Faces Podcast.

Based on insights from Randy Bean, Senior Advisor and Board Member, featured on the Data Faces Podcast.

Podcast Highlights - Key Takeaways from the Conversation

Podcast highlights

0:53 — Randy shares his unexpected path into data, starting with degrees in English, History, and Art History before training as a COBOL programmer.

1:23 — The “furnace” story: Randy asks a bank manager what they do with customer data and learns they hold it for six years then destroy it.

2:42 — Randy describes flying to Microsoft the same afternoon he was called, walking into a room with Steve Ballmer.

3:55 — Origin of the AI and Data Leadership Survey: a JP Morgan CIO in 2012 asked Randy to survey Fortune 1000 executives on big data.

5:19 — CDO role evolution: from 12% adoption in 2012 to 90% in 2025, and from 28% saying the role was successful in 2020 to 70% today.

8:04 — Diana Schulthaus from Colgate-Palmolive: “I spend 100% of my time on offense.”

10:51 — The 94/6 split: 94% of executives cite culture and people as the principal barrier to AI adoption, only 6% cite technology.

10:51 — Legacy company math: 90% of Fortune 1000 are legacy firms who only need to compete against each other, not the “move fast and break things” 10%.

12:15 — Pandemic transformation: “We’ve done more in six months than in the previous 20 years.”

14:22 — AI at scale progression: 5% in 2023, 24% in 2024, 40% in 2025.

14:22 — “Forget about the FOMO... step back and think about where are we going as a business.”

21:43 — Randy’s HBR article advocating for a unified Chief Data, Analytics, and AI Officer sparked fierce debate with 70% agreement and 30% thoughtful pushback.

21:43 — JP Morgan Chase’s CDO sits on the 14-person operating committee, reports to Jamie Dimon, and was previously the Global Chief Investment Officer.

25:30 — The COBOL programmer lesson: “They’re the people that employ us... maybe we should give them some credit and figure out how to speak their language.”

27:59 — Randy biases “hugely towards an understanding of the business” when selecting data leaders, but notes technical grounding helps you “separate the nonsensical from the realities.”

29:02 — “You gotta know enough to call BS when someone’s giving you a pitch.”

30:27 — The 5-question, 5% framework: “Not all data is created equal, and sometimes 5% of the data is all that it takes to answer 95% of the questions.”

31:44 — The notebook test: Randy describes folding up his notebook ten minutes into meetings when organizations say “we’ve got everything all figured out.”

34:08 — Where to find Randy’s work: randybeandata.com with nearly 300 articles from Forbes, HBR, MIT Sloan Review, and Wall Street Journal.

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.

After years of pilots, proofs of concept, and bold promises about transformation, 2026 is shaping up to be the year when AI investments face real scrutiny. Valuations are wobbling, executives want to see returns, and most organizations still can’t measure whether their AI initiatives are actually working.

Tom Davenport has watched this pattern before. He’s seen technologies overpromise, watched companies chase hype rather than value, and studied what separates the organizations that deliver real results from those that don’t. His message for the year ahead is direct: the quest for value is the biggest thing, and most companies aren’t yet set up to capture it.

About the speaker

Thomas Davenport is a Distinguished Professor at Babson College and one of the most respected voices in analytics and AI. He has spent decades helping leaders turn data and technology into real business value, from coining the term “business process reengineering” to authoring the influential Competing on Analytics. His research and teaching have shaped how companies think about AI, automation, and the evolving impact of generative AI.

In this conversation, we discuss:

• Why we’re in an AI bubble and what that means for enterprise leaders

• The shift from “broad and shallow” pilots to “deep and narrow” implementations

• Why boring transactional use cases will deliver value before transformational ones

• What the P&G experiment with 776 people reveals about disciplined AI experimentation

• The “work slop” problem and why 80% of generative AI output never gets reviewed

• How to build the organizational discipline that separates AI success from theater

The value gap AI needs to close in 2026

“I do think we’re in a bubble,” Tom says. “And I think there’s a lot of real value to AI, of course, but I think the valuations of some of the companies are somewhat crazy.”

Generative AI has attracted a disproportionate share of attention, in part because it’s so accessible. Anyone can use ChatGPT, and anyone can form an opinion about it. That accessibility has inflated expectations beyond what the technology currently delivers. “I think because it’s so accessible to the public and to the chattering classes, if you will, people who write stuff and do podcasts, it’s gotten way more attention than it deserves in the overall kind of pantheon of AI,” he explains.

The market is starting to recalibrate. “We’re starting to see some cracks in the valuations of some of these companies, and suggestions that maybe we don’t need a data center on every corner by a nuclear power plant next door.”

At the organizational level, a different version of the same problem is playing out. Most companies have taken a “broad and shallow” approach to AI adoption, encouraging employees to experiment with tools like ChatGPT for individual productivity. Tom recently co-authored a Harvard Business Review article with Stanford researchers on the need to shift from individual-level use to enterprise-level implementation. The diagnosis resonated; shortly after, another HBR piece appeared with a blunt title: “Stop Running So Many AI Pilots.”

Broad and shallow adoption makes measurement nearly impossible. One company Tom spoke with has essentially given up on proving productivity gains from individual AI tools and now positions them as an employee satisfaction and retention benefit. It’s a candid admission that the value case remains unproven.

“It’s too hard to measure either the productivity or the quality of output, and hardly anybody does measure it.” — Thomas Davenport, Distinguished Professor, Babson College

Start with transactional, not transformational

When asked about the “killer use case” for agentic AI, Tom’s answer catches most people off guard. “Hold on to your hat,” he says with a hint of sarcasm. “Accounts Payable work.”

Keynote speakers don’t get standing ovations for invoice processing, but that’s precisely why it works. The companies making real progress with AI aren’t chasing moonshots. They’re targeting processes that are repetitive, measurable, and low risk. “Generative AI is quite good at sucking the important data out of invoices and sending a message to some other agent,” he explains.

“That’s not exciting, but it is transactional. And I think a lot of people have not terribly exciting jobs, looking at invoices coming in and extracting the key components.” — Thomas Davenport, Distinguished Professor, Babson College

Trust remains a sticking point even in these straightforward applications. Companies aren’t yet comfortable letting AI agents handle complete workflows autonomously. “In a lot of cases, people don’t trust typical agents, so they send it to Stripe or something that they do trust, actually, to pay it when the time comes.” A pattern is emerging: AI handles extraction and routing; established systems handle execution.

The timeline for agentic AI to mature is longer than many expect. Tom estimates “closer to five years before we have real transactional applications,” more optimistic than Andrej Karpathy’s recent prediction of a decade but still far from imminent. For now, companies are “trying to do things that are not terribly risky or important to see how it goes.”

There’s a human dimension here, too. The workers processing invoices don’t have glamorous jobs, but they represent fundamental roles that AI can meaningfully improve. The opportunity isn’t to eliminate these workers overnight but to free them from the most tedious parts of their jobs.

Customer intent is the emerging opportunity

Accounts payable won’t transform a company’s growth trajectory. But customer-facing functions might.

Tom is working with researchers from Cambridge University on a project examining call center interactions. Their early data points to an underappreciated reality: many service calls are actually sales opportunities in disguise.

Service representatives weren’t hired to sell. They were hired to solve problems and answer questions politely. “The people who do that work are generally there because they can answer nicely customer questions about service, and they’re not very good at selling,” Tom notes. The opportunity outpaces the skills of the people handling the calls.

Generative AI could bridge that gap by detecting customer intent in real time. Rather than expecting service reps to suddenly become salespeople, organizations could use AI to identify when a conversation represents a sales opportunity and route it to the right resource.

“In many cases, calls that come into a call center or contact center are not just about service. In many cases, they are often opportunities to sell those customers more.” — Thomas Davenport, Distinguished Professor, Babson College

The implementation Tom envisions involves a channel that “makes sense of what the customers want, and either sends it to another bot that can do that thing, or sends it to a human that can do that thing.” Traditional boundaries between marketing, sales, customer service, and customer success would blur, unified by a shared focus on understanding and responding to what customers actually need. This research is still early, but it suggests where enterprise AI might deliver its next wave of measurable value.

Experimentation is the discipline that separates success from theater

If one thing distinguishes companies capturing real value from AI, it’s their willingness to run rigorous experiments. Tom identifies “disciplined experimentation” as one of the essential capabilities for generative AI success, and he’s blunt about how rare it is.

The example he returns to is Procter & Gamble. He recently spoke with the head of data science and AI at P&G about an experiment they conducted in new product development, designed to answer a straightforward question: Does generative AI actually help people come up with better ideas?

They tested 776 people and carefully measured the results. “Individuals with generative AI were more productive than teams without generative AI,” Tom reports, “and they came up with a better balance of sort of commercial and innovation-oriented ideas.”

What makes this example stand out isn’t just the findings. P&G tested their hypothesis rigorously and published the results with academic collaborators. “Some vendors do it. Anthropic has tested a few things. But in general, companies don’t do that disciplined experimentation,” he says.

“The key thing is that they tested it and ended up writing a paper about it with a bunch of academics. That just doesn’t happen very often.” — Thomas Davenport, Distinguished Professor, Babson College

Most organizations skip this step entirely. They deploy AI tools, encourage adoption, and assume value is being created without any structured way to verify it. AI initiatives are everywhere, with no reliable way to tell which ones are working.

AI work slop is flooding the enterprise

Even when AI delivers useful output, organizations face a stubbornly difficult challenge. A new term has emerged to describe it: “work slop.”

Work slop is the low-quality content that floods organizations when people accept AI outputs without review or refinement. Tom cites a McKinsey study suggesting that 80% of generative AI output is never reviewed. People create the content but never evaluate whether it’s accurate, relevant, or good.

Fixing this requires substantial behavior change. “People have to learn how to get rid of that work slop and edit the output and add some value to the output of generative AI,” Tom says.

He sees the challenge firsthand in his teaching. “I find it really challenging to do with my students, because people seek the easiest way to do something, and we’re not necessarily all trained to be effective editors of content, rather than producers of good first drafts.” Evaluating and improving AI-generated content requires skills different from those needed to create content from scratch, and most people haven’t developed them.

Some of his students have been candid about the tradeoff. They told him, “It was easier to just paraphrase a Wikipedia article” than to iterate on AI outputs, check sources, and add original thinking. That shortcut mentality is understandable, but it undermines the potential value AI could deliver.

“We’re not necessarily all trained to be effective editors of content, rather than producers of good first drafts.” — Thomas Davenport, Distinguished Professor, Babson College

His approach in the classroom offers a model for organizations. “Show me all the prompts you tried,” he tells students. “Show me the edits that you made to the output. Check the sources.” Without that discipline, AI becomes a machine for producing mediocre content at scale.

The path forward

Tom’s perspective on AI in 2026 isn’t pessimistic, but it is grounded in decades of watching technology hype cycles come and go.

“AI can really transform the process of thinking about how you do your work,” he says. That transformation won’t come from deploying more tools or running more pilots. It requires examining workflows, designing experiments, measuring outcomes, and building new habits around how people interact with AI-generated content.

The iterative mindset matters too. “Never take the first output,” he advises. “Iterate on it, ask for alternative interpretations.” Most people accept the first response as definitive when it should be treated as a starting point.

“Doing things the right way is never easy. I guess that’s the lesson here.” — Thomas Davenport, Distinguished Professor, Babson College

The organizations that capture value from AI in 2026 won’t be the ones with the most advanced models or the largest infrastructure investments. They’ll be the ones willing to go deep rather than broad, to measure what matters, and to build the organizational muscle to turn raw AI output into something worth using.

The companies that keep running shallow pilots while waiting for AI to magically deliver value will find themselves in the same place a year from now, still waiting. The ones that do the work won’t have to.

Listen to the full conversation with Tom Davenport on the Data Faces Podcast.

Based on insights from Thomas Davenport, Distinguished Professor, Babson College, featured on the Data Faces Podcast.

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Podcast Highlights - Key Takeaways from the Conversation

[2:09] What’s real and what’s hype in AI heading into 2026

[4:24] Why Tom believes we’re in an AI bubble and what needs to come down to earth

[5:51] The unsexy “killer use case” for agentic AI: Accounts Payable

[7:26] Why Tom calls generative AI “predictive analytics on LSD” and his case for the term “analytical AI”

[9:15] The shift from individual-level AI usage to enterprise-level implementations

[11:21] How customer-facing functions represent the next frontier for AI value

[13:26] The disciplines that separate generative AI success from failure

[14:44] Inside the P&G experiment: how 776 people tested whether AI improves ideation

[15:45] The “work slop” problem and why 80% of AI output never gets reviewed

[17:33] Why every prediction about AI and jobs has been wrong

[22:20] How Tom teaches students to use AI the right way: “Show me your prompts, show me your edits”

[24:53] The de-skilling risk: when Tom’s doctor got caught using AI

[30:58] Why business process re-engineering is making a comeback, enabled by AI

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him onLinkedIn.

2025 was supposed to be the year AI finally worked on its own. We were promised autonomous agents and decision-making without the inconvenience of pesky humans. But, after 27 conversations on the Data Faces podcast, the core message wasn’t about technology at all. It was about us.

In this day and age, it’s hard to get anyone to agree on anything. But after 27 conversations with thought leaders and practitioners, we did agree on one thing. It’s not the technology that is causing AI failures, well, mostly. What was clear was how broken our teams, alignment, and leadership already were. Having been through may of these cycles before, this isn’t new or novel.

“90% of Gen AI projects will fail to deliver transformative value. It’s not that the technology isn’t ready—most organizations aren’t.” — Kjell Carlsson, Domino Data Lab

This year, we talked to analysts, practitioners, and founders from Domino Data Lab, Dataiku, Ernst & Young, Monte Carlo, Relevance AI, Posit, BARC, and more. After 27 conversations, three patterns kept repeating.

Strategy before technology

The leaders who succeed don’t start with tools. They start with business outcomes. Not “let’s implement AI,” but “let’s solve this specific problem.” As Eric Kavanagh put it, the “ready-fire-aim” approach explains most of those failure rates.

“Leaders align AI outputs to corporate KPIs. That tethering of what the corporation wants to succeed with and what AI can help them do—that separates winners from laggards.” — Shawn Rogers, BARC

Agents need guardrails

Agentic AI is already here. It’s handling lead management, maintenance scheduling, and sales enablement. But governance is still a bit nascent. The companies that were doing it previously, such as financial services, healthcare, etc., are further ahead than most. It’s anticipated that some organizations may soon manage 10,000 or more agents. The companies getting this right aren’t moving faster. They’re treating agents like employees, with policies, oversight, and accountability.

“Wrap agents with employee-level policies. Ironically, agents may end up being more compliant than humans ever were.” — Sanjeev Mohan, SanjMo

The human element is the foundation

AI amplifies what’s already there. If your team isn’t aligned, your data isn’t trusted, or your messaging is generic, AI won’t fix it. It’ll expose it.

“If the team is not aligned beforehand, there’s no way whatever model you choose will be successful. Alignment is absolutely vital.” — Danny Stout, Ernst & Young

The same message came through in conversations about ethics, data quality, and messaging. Monica Cisneros pointed out that there are 21 mathematical definitions of fairness. Someone has to choose. Kevin Petrie was blunt. “If the quality ain’t good, the AI ain’t good.” And Emma Stratton warned of the curse of knowledge. Experts assume everyone understands what they do, then wonder why their messaging falls flat.

What 2025 actually taught us

The more powerful AI becomes, the more human skills matter. AI didn’t replace leadership, judgment, and clarity. It made the gaps impossible to ignore. That’s not a technology problem. That’s a mirror.

Hear the full conversations

This is the highlight reel. The full episode goes deeper. 27 guests on strategy, agents, ethics, and what’s coming in 2026.

27 leaders. 3 themes. 1 episode.

Listen to the Data Faces 2025 Year in Review.

🎧 YouTube | Spotify | Apple Podcasts

Listen to the full conversations on YouTube | Spotify | Apple Podcasts

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Based on insights from 27 leaders featured on the Data Faces Podcast.

1. The Faces Behind Data: AI, Ethics, and Leadership with Monica Cisneros Data Faces Podcast with Monica Cisneros Nov 14, 2024 | Read more

2. Past as Prologue with Kevin Petrie: What History Tells Us About the Future of AI Dec 11, 2024 | Read more

3. AI in 2025: Why 90% of Gen AI Projects Will Fail | Kjell Carlsson Most AI failures aren’t about the technology—they’re about strategy, governance, and execution. Jan 2, 2025 | Read more

4. Solving the Data Trust Crisis with Kamal Maheshwari Kamal Maheshwari on solving the data trust crisis, aligning teams, and building AI-ready data ecosystems. Jan 21, 2025 | Read more

5. Beyond the AI Hype: What 20% of Companies Get Right Shawn Rogers breaks down BARC’s latest research on AI maturity, the importance of data quality, and what truly separates leaders from laggards. Feb 11, 2025 | Read more

6. AI Agents: State of the Union with Sanjeev Mohan Exploring the current state of AI agents, their challenges, and how businesses can prepare for widespread adoption. Feb 25, 2025 | Read more

7. The AI-Powered CFO: Why Finance Must Shift from Control to Cognition Jawwad Rasheed explains why AI, automation, and self-service analytics are transforming the role of finance leaders. Mar 11, 2025 | Read more

8. There Is No Post-AI World: Preparing Your Organization for the Agent Revolution John Thompson on the strategic value of AI agents, the unexpected risks of autonomous systems, and why intelligent governance matters more than ever. Mar 25, 2025 | Read more

9. The Gen AI Shift: How Product Marketing Managers Are Adapting Melissa Burroughs on scaling PMM productivity, balancing AI efficiency with messaging alignment, and preserving the human element in marketing. Apr 8, 2025 | Read more

10. The Grandmother Test: Building AI Trust Beyond Technology Robert Lake on asking the three essential business questions, managing the human tendency to anthropomorphize, and leading effective AI change management. Apr 22, 2025 | Read more

11. The Customer Hero Principle: Why Your B2B Messaging Falls Flat Gabriela Contreras on ruthless audience prioritization, escaping jargon land, and making customers the center of your product story. May 6, 2025 | Read more

12. Team Dynamics Over Technology: The Human Elements that Drive AI Success Danny Stout on human-centered AI teams, the myth of bigger models, and why communication skills trump technical prowess. May 20, 2025 | Read more

13. From “AI-Ready” to AI Reality: Why Actionable Data Strategies Beat Endless Planning Shane Murray on AI-ready data, the truth about RAG, and why building beats planning for trustworthy AI. Jun 3, 2025 | Read more

14. Stop Chasing Hallucinations—Focus on Agentic Quality Insights from Hyoun Park, CEO & Principal Analyst at Amalgam Insights, on fixing context gaps and building dependable AI agents. Jun 17, 2025 | Read more

15. The “Survival of the Nimblest” Strategy for AI Marketing Success Insights from Judit Szabo, Global Head of Demand Generation at Endava, on balancing automation with human connections in B2B marketing. Jul 1, 2025 | Read more

16. How AI Killed Traditional Competitive Analysis Insights from David Bryson, Principal Competitive Intelligence Manager at Splunk, on turning AI information overload into a strategic advantage. Jul 15, 2025 | Read more

17. How 3% of Companies Win with AI While 97% Fail Rich Mendis reveals the two misconceptions killing most enterprise AI projects and the proven framework that delivers ROI. Jul 29, 2025 | Read more

18. How to Write Punchy B2B Messaging That Actually Converts Emma Stratton from Punchy reveals the curse of knowledge killing most B2B conversions and the proven messaging framework that makes prospects say “yes.” Aug 12, 2025 | Read more

19. The AI Agent Mistake 90% of Marketing Leaders Are Making Chelsea Wise from Relevance AI reveals why learning together beats rushing to implement AI agents and the unsexy use cases that deliver real results. Aug 26, 2025 | Read more

20. Why Bad AI Governance Kills 95% of Enterprise Projects Before Production Thomas Been from Domino Data Lab explains why governance accelerates AI deployment by 70% and the validation trap that kills most enterprise projects. Sep 9, 2025 | Read more

21. Escape the Marketing Twilight Zone: The Agentic AI Playbook for B2B Marketers Rajeev Kozhikkattuthodi from Poexis reveals the three failure modes that prevent marketing teams from moving beyond analysis paralysis to measurable pipeline. Sep 23, 2025 | Read more

22. Your Netflix Moment: Why CIOs Must Act Now on AI Agents Catalina Herrera from Dataiku reveals why most AI agent pilots fail and the four-pillar framework that turns experimental projects into production systems. Oct 7, 2025 | Read more

23. Your AI Project Will Fail. Here Are the Only Three Decisions That Matter AI analyst and DMRadio host Eric Kavanagh on the three unglamorous decisions that separate AI success from expensive failure. Oct 21, 2025 | Read more

24. Augmented Intelligence: The Future of Sales Enablement LaunchDarkly’s Matt Magne shares why augmented intelligence beats automation in sales enablement. Nov 4, 2025 | Read more

25. The Barcode on the Bronze: Why Your AI Needs to Know What Makes You Different Adesso Associates’ Gina von Esmarch reveals how teaching AI your context beats generic automation. Nov 18, 2025 | Read more

26. Data Lineage for AI: Why Truth Beats Hope in Banking Insights from Tina Chace on ensuring data quality in AI deployments. Dec 2, 2025 | Read more

27. Why Code-First Data Science Still Wins in the Age of AI Posit’s Bruno Trimouille explains why governance and innovation aren’t a zero-sum game for data science teams. Dec 16, 2025 | Read more

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.

What happens when your organization makes a major decision based on analysis that no one can verify? The promise of AI-generated code and low-code tools is speed, but speed without trust is just faster failure. Bruno Trimouille, CMO at Posit, makes the case that trustworthy data science still requires code you can inspect and reproduce, and that AI, when used with the proper guardrails, can actually accelerate this approach rather than undermine it. In a recent conversation on the Data Faces Podcast, Bruno shared how Posit thinks about the tension between moving fast and maintaining trust, and how his own team applies these principles in practice.

About the speaker

Bruno Trimouille is the Chief Marketing Officer at Posit, the company formerly known as RStudio. Posit’s mission is to create open source software for data science, scientific research, and technical communication. Today, the company serves 10,000 customers, including 1,800 of the largest firms in regulated industries, and supports millions of users worldwide each week.

Why code-first still matters

Bruno Trimouille didn’t take the typical path to CMO. He started as a software engineer in France, moved through presales and consulting roles serving demanding customers across industries and geographies, and eventually landed in marketing because he saw it as the place where he could combine his technology background with his sales experience to amplify good messaging and storytelling. That engineering foundation shaped how he evaluates opportunities, and it’s what drew him to Posit in the first place.

What attracted Bruno wasn’t the marketing challenge. It was Posit’s foundational belief that trustworthy data science requires code you can question, validate, and repeat. He points to the work of John Chambers, creator of the S language that preceded R, who argued that reliable software must be verifiable and trustworthy. For Bruno, this principle translates directly into how data science should work.

“For software to be reliable, things need to be verifiable, and things need to be trustworthy. So for us, it means that the concept of data science being inspectable and reproducible, thus trustworthy, is paramount.”

— Bruno Trimouille, CMO at Posit

Low-code tools offer convenience, and they’ve helped more people access data science capabilities. But when those tools don’t do what you need, you’re stuck. Code-first isn’t about gatekeeping or making things more complicated for business users. It’s about creating outputs that someone else can examine, challenge, and rerun to confirm the results hold up.

AI as the bridge, not the replacement

Bruno sees the same tension play out in nearly every customer conversation. Data scientists want speed. They want to access data quickly, model it, and share outcomes with business stakeholders before the moment passes. Business stakeholders want speed, too, because the whole point of data science is to make faster, better decisions. And somewhere in the middle sits IT, charged with imposing governance on a process everyone else wants to accelerate.

For years, this tension created a divide. Code-first tools served developers well but felt inaccessible to business users. Low-code platforms promised analytics democratization, but that promise has largely failed to launch. Neither approach served both groups particularly well.

Bruno believes AI is starting to dissolve this standoff, not by replacing code-first approaches, but by making them faster and more accessible.

“AI sort of creates a middle ground approach, which is still code-first based. It bridges the gap, especially when it comes to speed and quick turnaround time, and brings this code-first approach much closer to business stakeholders.”

— Bruno Trimouille, CMO at Posit

The key distinction Bruno draws is between AI that generates black-box outputs and AI that generates artifacts you can actually examine. Posit recently unveiled a set of AI-driven capabilities, and the team is deliberate about calling their approach “responsible AI.” For Bruno, that phrase isn’t marketing language. It means ensuring that everything AI generates is a piece of code, a SQL query, or another technical artifact that can be inspected and rerun for reproducibility.

AI governance isn’t about slowing AI adoption or treating it with suspicion. It’s about recognizing that speed without governance creates liability, while governance designed correctly doesn’t have to slow anyone down.

The three-layer model for scalable, responsible data science

When asked about the common complaint that governance slows things down, Bruno offers a direct rebuttal. He doesn’t see innovation and governance as competing forces. He sees them as two sides of the same coin.

“This is not a zero-sum game between innovation and governance, but rather a framework where governance enables innovation.”

— Bruno Trimouille, CMO at Posit

Bruno describes Posit’s approach as three layers that build on each other, moving from the inside out.

Layer one: Centralized, secure data foundation

Data science starts with data, and security, lineage, and access control at the platform level are non-negotiable. This is why Posit partners with companies like Amazon, Databricks, and Snowflake, because governance at the data layer has to be airtight before anything else can work.

Layer two: Code-first workflow

This layer goes beyond just the model to encompass the entire process of building it. The workflow needs to be repeatable, transparent, and well-documented. Bruno is emphatic on this point, noting that transparency is “really non-negotiable” for many use cases and customers, particularly those in regulated industries.

Layer three: Agile deployment and real-time feedback

When data scientists can instantly deploy models and expose them to business stakeholders, feedback happens in real time rather than weeks later. Stakeholders can interact with the output, run scenarios, and respond while the context is still fresh. As Bruno puts it, “Things can happen in real time, and that pretty much unlocks things.”

The FDA as a proof point

The FDA now accepts clinical trial submissions in open source formats, a significant shift for such a heavily regulated agency. Bruno calls this “a really quantum step by a very established and very governed agency to look at that as new pathways.” He notes that the shift also addresses a practical talent challenge, as skills in legacy, proprietary tools are becoming harder to find. Open source lets these organizations tap into a broader talent pool while maintaining the transparency and reproducibility that regulators require.

From models to mission-critical applications

Data science outputs at Posit’s customers aren’t just models feeding dashboards or reports that sit in someone’s inbox. They’re interactive applications where business stakeholders engage directly with the analysis to make consequential decisions. Bruno describes a spectrum of “data-driven assets” that organizations are building, ranging from APIs that let other systems call models programmatically to scheduled reports with embedded insights to fully interactive applications.

The NASA example stands out.

“If you look at an institution like NASA, they have interactive applications where they look at their staffing needs, staffing prediction for really complex missions like going back to Mars—and interactively play different scenarios, drill into the data, do what-if analysis, and really interact with the data to drive better decisions.”

— Bruno Trimouille, CMO at Posit

What connects these examples is that business stakeholders aren’t waiting for a PDF summary or a static slide deck. They’re working directly with data science outputs, exploring scenarios and stress-testing assumptions to inform real commitments of resources and time. That direct interaction depends on trust, which is precisely why Bruno keeps returning to the code-first foundation.

Practicing what we preach: AI in Posit’s own marketing

Posit sells the philosophy of responsible, inspectable, code-first data science to its customers. But does the company practice what it preaches internally? Bruno’s marketing team offers a valuable test case.

Bruno describes his own evolution with AI as a series of distinct phases. Early on, he saw it as a productivity tool to eliminate “blank page syndrome.” That was valuable, but it was just the starting point.

“I really saw AI as a fantastic tool at the beginning to remove the blank page syndrome... But then I really saw the power that this could deliver in not just being a bot sitting next to you, but literally a thinking partner.”

— Bruno Trimouille, CMO at Posit

As a thinking partner, AI has transformed how Bruno approaches market research. He describes having “market studies on tap,” with the ability to customize research to data science within a specific industry, examining trends and opportunities in ways that would have taken weeks with traditional methods.

On the campaign side, Posit has experimented with mass personalization that maintains clear guardrails. Rather than letting AI write emails from scratch, the team uses templates with defined placeholders that AI can customize based on persona, use case, and industry. Bruno has seen conversion rates of 10 to 12 percent on targeted segments of previously dormant leads. The AI reawakened them through personalization, but within a governed structure.

Throughout these applications, Bruno emphasizes that preserving authentic voice remains essential. As he puts it, “I wouldn’t advocate to have AI generate something, and you press the publish button, and it goes.” The pattern mirrors what Posit advocates for data science more broadly. AI generates, humans inspect.

The hybrid skill set for what comes next

Bruno’s career path from software engineering through presales and consulting to CMO illustrates the hybrid profile he believes will define the next generation of data-driven leaders. He isn’t theorizing about what works. He’s living it.

When asked what skills will matter most going forward, Bruno starts with curiosity. Not curiosity as an abstract value, but curiosity as a practice. He experiments with tools like NotebookLM to turn written documents into audio learning content, recognizing that different people absorb information in various ways.

“Marketing has become a greater mix between the art and the creative stuff, but science and data. You need to have a more hybrid skill set. Think about AI as a thinking partner.”

— Bruno Trimouille, CMO at Posit

Bruno also points to cross-functional collaboration as essential rather than a nice-to-have. He now partners regularly with Posit’s data science team to develop market and data insights, a partnership that simply didn’t exist five years ago. Marketing as a discipline has become what he calls “a team sport,” and the teams you need to play with keep expanding.

There’s also a systems dimension to leadership that Bruno emphasizes. He thinks of his marketing stack as an architecture to ensure tools are connected and data flows smoothly. Leaders who treat their technology as a collection of disconnected point solutions will struggle compared to those who design for integration.

Bruno closes with an observation that applies well beyond marketing.

“Having raving fans beats all kinds of marketing campaigns you can put together.”

— Bruno Trimouille, CMO at Posit

The lesson for data science leaders is the same. Build trust with your stakeholders through transparency and reliability, and you earn credibility that no dashboard or presentation alone can deliver. The code-first philosophy Bruno advocates isn’t just about technical rigor. It’s about building the kind of credibility where people believe what you show them because you’ve never given them a reason not to.

Listen to the full conversation with Bruno Trimouille on the Data Faces Podcast.

Based on insights from Bruno Trimouille, Chief Marketing Officer at Posit, featured on the Data Faces Podcast.

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Podcast Highlights - Key Takeaways from the Conversation

0:52] Bruno introduces Posit, its rebrand from RStudio, and the company’s mission to create open source software for data science, scientific research, and technical communication.

[2:06] Bruno shares his unconventional path to CMO, starting as a software engineer in France and moving through presales and consulting before landing in marketing.

[4:53] The case for code-first data science: Bruno explains how John Chambers’ principle that reliable software must be verifiable shaped Posit’s foundational philosophy.

[7:32] Speed meets governance: Bruno describes the tension between data scientists wanting velocity and IT demanding control, and how responsible AI can bridge that gap.

[9:13] The three-layer model: Bruno outlines Posit’s framework for scalable data science, from secure data foundations to code-first workflows to real-time deployment and feedback.

[11:29] From models to mission-critical apps: Bruno shares how NASA uses interactive applications for staffing predictions on Mars missions, running what-if scenarios in real time.

[15:53] The FDA’s “quantum step”: Bruno discusses how the FDA now accepts clinical trial submissions in open source formats, and why this matters for talent and transparency.

[18:09] Community as strategic moat: Bruno explains why he had to shift his mindset from seeing community as a marketing channel to treating it as an extension of the team.

[21:31] AI as thinking partner: Bruno describes his evolution from using AI to fix “blank page syndrome” to having “market studies on tap” for strategic research.

[24:04] Mass personalization with guardrails: Bruno shares how AI-driven email customization achieved 10-12% conversion rates on dormant leads while maintaining governed templates.

[34:04] The hybrid skill set: Bruno on why curiosity, cross-functional collaboration, and systems thinking define the next generation of data-driven leaders.

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.

When you see a dashboard or report in your BI system, do you question whether it’s actually correct? Most people don’t, and assume the data flowing through pipelines must be accurate. That assumption holds until the numbers fail to reconcile.

Tina Chace made that same assumption early in her career. Starting in middle office operations, booking trades, she learned that “the importance of making sure that your data is absolutely correct, especially when it comes to transacting and dealing with money, was drilled into me from the start.” But she didn’t fully appreciate what that meant until she spent six years deploying AI and machine learning models in highly regulated environments, specifically transaction monitoring and Know Your Customer systems for major banks.

The pattern she discovered changed everything.

“I didn’t really start to appreciate the value of data and accuracy and trust in your data until I spent six years working at an AI and machine learning company, specifically rolling out machine learning models in highly regulated spaces...I was continuously running into problems, and 90% of the time it ended up being a data issue.”

— Tina Chace, VP Product Management, Solidatus

About Tina Chace

Tina is Vice President of Product Management at Solidatus. She started in middle-office operations, moved to deploying AI models for major banks, and discovered the hard way that 90% of production problems can be traced to data quality issues. That journey from assuming data worked to proving it works shapes her approach to lineage. While tracking technical details is essential, organizations must never lose sight of the business consequences.

From Naive Trust to Earned Skepticism

Tina’s journey from naive trust to earned skepticism mirrors what most data leaders experience. Working in the middle office where financial transactions required absolute precision, she assumed someone, somewhere, was ensuring data quality. Information moved through pipelines, and transactions were being processed. Her assumption was simple. “This data is of high quality, and we know what’s happening to it.”

Deploying machine learning models shattered that assumption. Someone would make a change upstream and add a new field with sensitive data. Undetected, this data would flow into the model. Only after days of troubleshooting would she identify the culprit: an unexpected upstream change.

Why Knowing More Makes You Trust Less

Tina describes an oddity. “The more you know, the more you almost distrust, right? Like the more educated you are, the more you question your position.”

Five years ago, teams were largely unaware of how data moved through their organizations. Today, they can see the complexity across dozens of systems and hundreds or thousands of transformations. But visibility without control just amplifies anxiety.

“People were more unaware previously, five years ago, and now there’s more visibility, but also with more visibility, there can be a bit more anxiety, because then, you know, it’s not covered.”

— Tina Chace, VP Product Management, Solidatus

The stakes vary by context. Sending marketing coupons carries a different risk than processing financial transactions. But the same questions surface. Who verifies report accuracy? What happens when a CFO reviews two reports that should show identical revenue figures? One says $47.3M. The other says $47.5M. Finance blames data engineering. Engineering points to the source system. Three days later, nobody knows which number is correct.

Consider rounding errors, similar to those depicted in the movie Office Space. In small transactions, a rounding discrepancy to the wrong decimal place barely registers. “When you’re dealing with large amounts of money, that rounding error becomes a huge financial burden if it is incorrect.” Contracts between applications can specify different rounding rules without anyone documenting the difference. Left unchecked, the discrepancy silently compounds across every transaction.

Regulatory requirements like BCBS 239 now mandate that organizations “report on certain metrics and ensure the data going into that report is accurate and timely and correctly calculated.” Assumptions no longer suffice, and auditors demand proof.

These trust gaps don’t emerge randomly. They cascade from organizational failures that compound with every system the data touches.

Three Failures That Compound

Consider what happens when a data transformation changes upstream. The data engineer implementing the change often lacks visibility into downstream reports and applications, failing to notify stakeholders. The analyst who owns the report doesn’t speak the engineer’s technical language, so they can’t diagnose the problem when discrepancies appear. And because the transformation was never documented, troubleshooting takes days instead of hours. Three organizational failures, each enabling the next.

Here’s how the cascade works.

First Breakdown: The Ownership Vacuum

Ask your organization this question. Who owns the quality of customer data from CRM entry through analytics warehouse to the executive dashboard? You’ll get different answers from every team.

Ownership is distributed across “various points of its life cycle,” which often means no one owns it. Data engineers build pipelines, analysts create reports, and business leaders consume outputs. Each assumes someone else ensures accuracy. Most organizations can’t point to a single person accountable for end-to-end data quality from the source system through the final report. The gaps between ownership zones are where problems hide.

Second Breakdown: The Language Barrier

Without clear ownership, teams can’t communicate effectively. Engineers discuss systems and ETL processes. Analysts talk about reports and metrics. Executives focus on outcomes and risk—three different vocabularies describing the same data, with no shared language to bridge them.

Technical teams understand data flows but struggle to communicate the implications to business stakeholders or reporting analysts. Engineers know WHAT is happening. Business teams need to understand WHY it matters. Without translation, that gap never closes.

“If you, from a technology standpoint, understand your data flows, but you cannot communicate the meaning of that or the implication of that to your business stakeholders or the reporting analysts looking at the end report, then you’ve got a communication gap.”

— Tina Chace, VP Product Management, Solidatus

Third Breakdown: The Documentation Death Spiral

When nobody owns the whole picture, teams can’t communicate effectively. Without ownership, documentation becomes obsolete immediately. What gets documented? By whom? For whose benefit?

Tina’s troubleshooting experience shows the cost. Finding the root cause takes days, but why? Because transformations aren’t tracked and calculations aren’t recorded. Quality checks run in some systems but not others. And upstream changes propagate downstream invisibly, often only being discovered when something breaks.

Point solutions that map individual systems fail to solve organizational breakdowns. That requires a shared foundation where engineers, analysts, and executives work off the same set of blueprints.

Two Dimensions, One Foundation

Solidatus operates on the premise that technical tracking alone is insufficient. Organizations need both technical and business lineage.

• Technical lineage tracks “where a column, such as trade date, is flowing through various applications before it gets booked in a report.” It maps the mechanical flow: specific systems, transformations, and connections.

• Business lineage captures “the quality of your data as it’s flowing through systems, what kind of controls or checks are happening in various systems, and who owns the data at various points of its life cycle.” It answers why this flow matters and who’s accountable at each stage.

Without both, organizations either track data but can’t communicate implications, or discuss business concerns but can’t trace them to technical root causes.

“We at Solidatus think it’s important to have both technical and business lineage...not just tracking where it flows or any kind of calculations, but understanding context, such as the quality of your data as it’s flowing through systems, what kind of controls or checks are happening, and who owns the data at various points of its life cycle.”

— Tina Chace, VP Product Management, Solidatus

Every Transformation, Every System, Every Step

Column-level lineage captures every transformation that data undergoes. At each step and with each application, teams track calculations, rounding rules, and aggregations. By the time data reaches a report, teams are aware of its complete history: every change, every system, every decision point.

This granularity solves the discrepancy problem Tina encountered repeatedly. When two reports should match but don’t, teams can investigate the discrepancy to determine the cause. “We rounded to a specific decimal between applications A and B, but changed the rounding rule between B and C.”

What previously took days of detective work can now be traced in hours.

Proactive vs. Reactive: Shifting Quality Checks Left

Beyond tracking flows, lineage enables quality checks across every single system. Instead of discovering a $200K discrepancy during the board meeting, the quality check flags it at 3 am when upstream rounding logic changes. The data engineer fixes it before anyone downstream notices.

If there’s a failure, teams know immediately which systems are downstream and which specific report fields might be inaccurate. Reactive firefighting becomes proactive prevention.

Governance teams document which checks and policies apply at each stage of the process. Sensitive information either gets obscured or never enters the pipeline. Access controls are enforced at every step, not just endpoints. This is where compliance shifts from aspirational to demonstrable.

Same Blueprint, Different Lenses

Different stakeholders view the data flows through their own lens, all based on the same underlying information. Data engineers see technical flows and transformation logic while analysts understand which quality checks protect their reports. Governance teams track the location of sensitive data and identify the applicable controls. Executives see confidence metrics and risk exposure—all perspectives drawing from the same lineage capture.

This shared foundation solves the language barrier. Engineers and business leaders are no longer translating between different systems. They’re viewing different aspects of the same reality.

This foundation solved problems Tina encountered when deploying models years ago. Now that AI is proliferating across organizations, those problems are multiplying. And the stakes are higher.

Why AI Makes Incomplete Lineage Unacceptable

“With the proliferation and popularity of using AI within companies, I’m even more concerned about understanding the data that flows into it,” Tina explains. Her six years deploying models taught her that incomplete lineage “led to a lot of real-world problems.”

Are You Automating or Just Shifting Where You Monitor?

Demos showcase impressive AI results, but production environments are different. Organizations deploy AI to achieve better metrics, higher productivity, and automated decision-making. But at runtime, teams need confidence in what’s feeding the model.

Without that confidence, the promise of automation loses its appeal. “If I have to monitor it all the time, it didn’t save me any productivity at all. I’m just monitoring instead of doing it manually.”

The decisions AI makes aren’t abstract. They impact mortgage approvals, payment processing accuracy, and the detection of fraudulent transactions. In Tina’s work with Know Your Customer systems at major banks, errors have immediate financial and regulatory consequences.

Privacy Requires Proof

“One of the big concerns about the use of AI is privacy, like, where is my information being used?” Lineage provides attestation. Teams can prove models are “obscuring certain private information that shouldn’t be in it, or it’s not even entering the models at all, unless absolutely necessary.”

Compliance audits shift from defending processes to demonstrating controls. Instead of explaining what should happen, teams show what does happen.

Partial lineage creates blind spots where problems hide. Teams can’t skip systems or steps. Incomplete coverage leaves gaps where transformations remain invisible, precisely where issues emerge during troubleshooting.

Don’t Boil the Ocean

Faced with incomplete or nonexistent lineage, where do organizations begin?

“Be very specific and deliberate on what you’re choosing to address first,” Tina advises. “Then you will have an immediate and tangible win by covering a specific scenario, and you can continue to expand out from there in terms of importance.”

The alternative is “trying to document every single system that you have.” That becomes “this big, nebulous project” where “you won’t have an output until five years from now.” Starting with critical use cases delivers incremental ROI, rather than waiting years for enterprise-wide perfection.

Your First Four Targets

Prioritize data flows that intersect risk and visibility.

1. Regulatory Reporting: Systems that feed BCBS 239 capital reporting or mandated metrics, where auditors require proof of accuracy.

2. AI models are currently in production. Transaction monitoring, KYC, fraud detection, or other automated decision systems where bad inputs can have immediate consequences.

3. Areas with known quality issues. Recent troubleshooting incidents, recurring discrepancies, or pipelines that match that 90% pattern.

4. High-stakes decision systems. Mortgage approvals, payment processing, or other flows where errors have a direct financial or customer impact.

Start where one of these applies. Demonstrate value. Expand based on business priority, not technical completeness.

From Anxiety to Action

The ‘visibility paradox’ Chace describes—where knowing more creates more anxiety—can only be resolved through deliberate action. Knowing more creates anxiety. Seeing problems without the ability to address them compounds stress rather than relieving it.

“Regulations and recommendations are improving the metrics and trust we have in the data we’re using.” Organizations implementing lineage deliberately implement lineage, starting with critical use cases and building incrementally, establish the foundation for trusted AI and confident decision-making.

The alternative is waiting until the next crisis forces action. When a model makes a costly error, when an auditor asks questions nobody can answer, when that $200K discrepancy appears in the board deck.

Next time you see a dashboard, question it. Then ask whether you could trace that number back to its source. How long would it take, hours, days, weeks? Visibility without traceability isn’t insight, it’s anxiety with data attached. The organizations that can answer “yes” aren’t lucky; they’re prepared. They started with one critical pipeline, proved the value, and expanded from there.

The question isn’t whether you need lineage. It’s whether you’ll implement it before or after your next crisis.

Listen to the full conversation with Tiny Chace on the Data Faces Podcast.

Based on insights from Tina Chace, VP Product Management at Solidatus, featured on the Data Faces Podcast.

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Podcast Highlights - Key Takeaways from the Conversation

[0:54] Tina’s Career Journey: From Trust to Skepticism

“I actually started my career in the middle office and booking of trades and trade data. So the importance of making sure that your data is absolutely correct, especially when it comes to transacting and dealing with money, was drilled into me from the start.”

Key Takeaway: Early career assumption was that data quality was guaranteed. Reality proved different.

[2:28] The 90% Data Problem Discovery

“I was continuously running into problems where someone would make a change upstream, maybe they added a new field that had sensitive data, and it would flow into the model. We wouldn’t know that, and we’d have to go back and troubleshoot, and 90% of the time it ended up being a data issue.”

Key Takeaway: Six years of deploying AI/ML models in highly regulated spaces (transaction monitoring, KYC) revealed that 90% of production problems trace back to data issues.

[3:24] What is Data Lineage?

“Data lineage allows you to track how your data is flowing through various systems...not just tracking where it flows or any kind of calculations, but understanding context, such as the quality of your data as it’s flowing through systems, what kind of controls or checks are happening in various systems and who owns the data at various points of its life cycle.”

Key Takeaway: Data lineage requires both technical tracking (where data flows) AND business context (quality, controls, ownership).

[6:47] The Rounding Error Problem

“When you’re dealing with large amounts of money, that rounding error becomes a huge financial burden if it is incorrect...between application A and B, we rounded to this decimal between application B and C. We actually changed the rounding, and that actually ended up in our capital reporting.”

Key Takeaway: Small rounding errors compound at scale. Contracts between applications can specify different rounding rules without documentation.

[7:59] Column-Level Granularity

“By documenting data at the most granular level, which we call column level, in the data lineage world, you can actually document for every step and application that that data flows through. Is there a calculation? Is there a rounding? Are you adding things together? So that by the time it ends up in a report, you actually understand exactly what happened for it to be in that report.”

Key Takeaway: Column-level documentation captures every transformation, enabling precise troubleshooting when discrepancies appear.

[8:26] Bridging the Language Gap

“If you, from a technology standpoint, understand your data flows, but you cannot communicate the meaning of that or the implication of that, to say, your business stakeholders, or the reporting analysts who are looking at the end report, then you’ve got a communication gap.”

Key Takeaway: Technical teams know WHAT is happening; business teams need WHY it matters. Lineage bridges this gap.

[9:46] The Shared Blueprints Approach

“It really brings together all the different stakeholders, and they can view their own lens of the data flows, but it’s based on the same underlying information, so you’re working off of like, the same set of blueprints.”

Key Takeaway: Different stakeholders (engineers, analysts, governance, executives) view different aspects of the same lineage data.

[30:57] Why AI Makes Lineage More Critical

“With the proliferation and popularity of using AI companies, using AI within companies, I’m even more concerned about understanding the data that flows into it...I recognize that not having this data lineage and understanding the data flows led to a lot of real world problems.”

Key Takeaway: The deployment of AI amplifies the need for complete data lineage; incomplete visibility creates unacceptable risk.

[32:14] The Productivity Paradox

“At runtime, I want to be sure that everything that’s going into it is helping it make the best decision, because if I have to monitor it all the time, it actually didn’t save me any productivity at all. I’m just monitoring instead of doing it manually.”

Key Takeaway: AI that requires constant monitoring hasn’t actually automated anything—you’ve just shifted where you spend time.

[32:32] Privacy and AI

“One of the big concerns about the use of AI is privacy, like, where is my information being used? With data lineage, you can attest to the fact that your models are obscuring certain private information that shouldn’t be in them, or it’s not even entering the models at all, unless absolutely necessary.”

Key Takeaway: Lineage enables privacy attestation—proving what data enters AI models, not just promising.

[33:26] The Trust Paradox

“The more you know, the more you almost distrust, right? There’s that paradox, like the more educated you are, the more you question your position...People were less aware five years ago, and now there’s more visibility. Still, also with more visibility, there can be a bit more anxiety, because then, you know, it’s not covered.”

Key Takeaway: Increased visibility into data complexity creates anxiety alongside awareness—seeing problems without solutions amplifies stress.

[34:27] Don’t Boil the Ocean

“Be very specific and deliberate on what you’re choosing to access first, because then you will have an immediate and tangible win by covering a specific scenario, and you can continue to expand out from there in terms of importance...You can get ROI and value out of capturing your most critical use cases first...rather than trying to do a boil the ocean exercise, and you won’t have an output until five years from now.”

Key Takeaway: Start with critical use cases, prove value, expand incrementally. Don’t try to document everything at once.

About David Sweenor

David Sweenor is an expert in AI, generative AI, and product marketing. He brings this expertise to the forefront as the founder of TinyTechGuides and host of the Data Faces podcast. A recognized top 25 analytics thought leader and international speaker, David specializes in practical business applications of artificial intelligence and advanced analytics.

Books

• Artificial Intelligence: An Executive Guide to Make AI Work for Your Business

• Generative AI Business Applications: An Executive Guide with Real-Life Examples and Case Studies

• The Generative AI Practitioner’s Guide: How to Apply LLM Patterns for Enterprise Applications

• The CIO’s Guide to Adopting Generative AI: Five Keys to Success

• Modern B2B Marketing: A Practitioner’s Guide to Marketing Excellence

• The PMM’s Prompt Playbook: Mastering Generative AI for B2B Marketing Success

With over 25 years of hands-on experience implementing AI and analytics solutions, David has supported organizations including Alation, Alteryx, TIBCO, SAS, IBM, Dell, and Quest. His work spans marketing leadership, analytics implementation, and specialized expertise in AI, machine learning, data science, IoT, and business intelligence.

David holds several patents and consistently delivers insights that bridge technical capabilities with business value.

Follow David on Twitter@DavidSweenor and connect with him on LinkedIn.