TinyTechGuides: Data & AI

AI's 3 Ws are killing enterprise ROI

David Sweenor — Fri, 24 Apr 2026 13:30:59 GMT

Gartner Orlando 2026 - Photo by Author David E Sweenor

Four out of five organizations are deploying AI. Only one in five is hitting its ROI targets.^[1] I’ve been to the Gartner Data & Analytics Summit eight or nine times over the years, and I’m not sure if it’s déjà vu or a movie I’ve already seen.

When big data was en vogue, we saw the same thing. Everyone rushed to adopt the technology, even though few understood it or could articulate the returns. The pay was always “next year.” Big data had its 3 Vs of Volume, Velocity, and Variety. Some people stretched it to 7, because apparently three wasn’t enough to describe the problem. The AI era already has its own shorthand, and I’m calling them the 3 Ws – washing, wishing, and waiting.

I spent three days at the summit in Orlando this March, and the 3 Ws were everywhere. They explain why that 1-in-5 number is so low, and they’re the same symptoms that dragged big data down a decade ago.^[2]

Washing

The vendor side and the practitioner side might as well have been at two different conferences. On the expo floor, every booth was a native “AI agent” company. I couldn’t figure out what half of them did because the messaging was identical. Big data washing became AI washing, which became agent washing, and the label keeps rotating while the behavior underneath stays the same.

A Gartner analyst warned against this pattern in a session on AI agents in analytics and BI, recommending evidence-based evaluation with real-world scenarios instead of trusting vendor marketing claims.^[3] Good advice, but the expo floor didn’t get the memo. Booth copy sounded like it was written by the same LLM, and vendor presentations felt forced. The AI slop is feeding itself, and Gartner predicts a $58 billion market shakeup as GenAI and AI agents challenge mainstream productivity tools.^[4]

“Big data washing became AI washing, which became agent washing.”
— David Sweenor, Founder/CEO, TinyTechGuides

Wishing

The hallway conversations told a different story compared to the keynotes. Practitioners weren’t talking about what’s working, but about what isn’t working yet. Session after session pointed to 2027 and 2030 as the years when AI will finally achieve nirvana and deliver on its promises.^[5] The finish line keeps moving, and big data had the same timeline problem. The returns were always coming “next year,” just around the corner, right after you got your data sh*t in order.

Six out of ten IT leaders are worried about AI agent cost overruns, but only two out of ten D&A leaders share that concern.^[6] That disconnect should worry everyone. The people closest to the data aren’t worried about the costs, and the people paying the bills are.

“The returns were always coming next year, just around the corner.”
— David Sweenor, Founder/CEO, TinyTechGuides

We might be one bad quarter away from CFOs redirecting AI budgets to other mission-critical priorities, or worse yet, replacing agents with actual employees. When results don’t materialize, the panacea that is AI dissipates, which is what happened to big data. The enthusiasm evaporated the moment CFOs started asking tough questions about returns, and while the technology survived, most of the budgets didn’t.^[7]

Share this with your buddy, they’ll thank you.

Waiting

The “vendor conference” sold AI-first futures, while the practitioners who need to make all of this work understood that the foundations aren’t there. Everyone is waiting, and the waiting comes in three flavors.

“Organizations are building AI on top of foundations they don’t trust.”
— David Sweenor, Founder/CEO, TinyTechGuides

Waiting on the data

Most catalog and context layer vendors are still built for structured, tabular data. Today’s AI strategies depend on documents, images, and code, which current tooling wasn’t designed to handle, and no one seems to be addressing. Meanwhile, the conversation has moved to metadata, semantic layers, and context layers as the connective tissue that makes AI work. As Scott Taylor puts it, context is the new oil.^[8] By 2027, Gartner expects 40% of IT spending on data management to target multistructured data, and AI data readiness spending will increase sevenfold from 2025 to 2029.^[9] The money is apparently coming. Who’ll build the plumbing? Agents or people?

Waiting on governance

Everyone is pro-governance, and for most companies in the US, that’s about as far as they’ll go. AI governance, the kind that covers model risk management, bias testing, usage controls, and agentic oversight, is barely on the radar.^[10] Only 14% of IT leaders said they were confident their data and content assets are secured and governed.^[11] Organizations are building AI on top of foundations they don’t trust.

Waiting on people

Gartner reports that only 6% of D&A and AI leaders consider themselves fully AI-ready when it comes to people, skills, and change management.^[12] I’m not sure I agree with that number. People are using AI all over the place, whether their organizations are ready or not. The formal readiness programs haven’t caught up yet, but informal adoption has already happened. Change management alone can require up to twice the effort of the implementation itself.^[13] Scott Brinker calls this pattern Martec’s Law, where technology changes exponentially while organizations change logarithmically.^[14] That’s why most companies are still staring at the empty shelf long after the technology has moved three aisles over.

Big data had all three of these problems. Organizations bought the tools before fixing the plumbing, skipped governance, and underinvested in people. The AI era is repeating the same mistakes.

Same plot, different city

The Hangover Part II was essentially the same movie in a different city, with a new hotel and another bachelor party leading to the same bad decisions. That’s what this feels like.

The reasons four out of five organizations aren’t hitting their AI ROI targets are the same reasons big data underdelivered. Only the buzzword changed. The organizations that closed the distance last time invested in the boring stuff, the blocking and tackling.^[15] They focused on governance before it was fashionable, fixed data quality before the next platform purchase, and developed people instead of cutting headcount. None of it was exciting, but all of it worked.

The technology works, and the big data technology worked too. What didn’t deliver was everything around it,^[16] which is where the washing, wishing, and waiting did their damage. No new generation of AI agents will fix that for you.

Frequently asked questions

What are the 3 Ws of AI?

The 3 Ws of AI are Washing, Wishing, and Waiting. Washing is vendors rebranding existing products as “AI agents” without real AI underneath. Wishing is that practitioners are deferring AI ROI to 2027 and 2030, while only one in five organizations hits current ROI targets. Waiting is the shortfall in data readiness, governance, and change management. Together, the 3 Ws explain why most AI investments fall short.

What is AI washing?

AI washing is the practice of rebranding existing products as “AI” or “AI agent” without meaningful AI underneath. At Gartner D&A Summit 2026, nearly every expo booth marketed “AI agent” products with near-identical messaging. The pattern mirrors big data washing from a decade ago. The label keeps rotating while the behavior underneath stays the same.

Why are so few AI deployments hitting their ROI targets?

The 2026 Gartner CIO Survey shows that four in five organizations are deploying AI, but only one in five hits ROI targets. The failure pattern repeats big data’s mistakes. Organizations bought tools before fixing data foundations, skipped governance (only 14% of IT leaders feel confident), and underinvested in people. The technology works; the execution around it keeps failing.

Why are IT and D&A leaders divided on AI cost concerns?

At the 2026 Gartner D&A opening keynote, six out of ten IT leaders said they were worried about AI agent cost overruns, but only two out of ten D&A leaders shared that concern. The people closest to the data aren’t worried about costs, and the people paying the bills are. AI budgets may come under CFO scrutiny before D&A teams expect it.

Where should D&A leaders focus to improve AI ROI?

Focus on the three foundations most organizations are waiting on: data readiness, governance, and people. Gartner expects 40% of IT spending on data management to target multistructured data by 2027, with AI data readiness spending growing sevenfold from 2025 to 2029. Only 14% of IT leaders are confident in governance, and only 6% of D&A leaders are AI-ready on people and change management.

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]2026 Gartner CIO Survey, n=2,437.

^[2]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/.

^[3]Macari, Edgar. “Activating AI Agents in Analytics and BI Platforms.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[4]Sallam, Rita. “Top Data and Analytics Predictions for 2026.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL. See also Gartner, “Gartner Announces Top Predictions for Data and Analytics in 2026,” press release, March 11, 2026, https://www.gartner.com/en/newsroom/press-releases/2026-03-11-gartner-afromnnounces-top-predictions-for-data-and-analytics-in-2026.

^[5]Sallam, Rita. “Top Data and Analytics Predictions for 2026.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL. See also Gartner, “Gartner Announces Top Predictions for Data and Analytics in 2026,” press release, March 11, 2026, https://www.gartner.com/en/newsroom/press-releases/2026-03-11-gartner-announces-top-predictions-for-data-and-analytics-in-2026.

^[6]Ronthal, Adam, and Georgia O’Callaghan. “Opening Keynote: Navigate AI on Your Data & Analytics Journey to Value.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[7]Sweenor, David. “The AI-Powered CFO: Why Finance Must Shift from Control to Cognition.” TinyTechGuides, March 11, 2025. https://tinytechguides.com/blog/the-ai-powered-cfo-why-finance-must-shift-from-control-to-cognition/.

^[8]Sweenor, David. “Truth Before Meaning — The Three-Word Fix for Data Management.” TinyTechGuides, April 7, 2026. https://tinytechguides.com/blog/truth-before-meaning-the-three-word-fix-for-data-management/.

^[9]Showell, Nina. “Unstructured Data Is the Missing Ingredient to Prepare AI-Ready Data.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[10]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/.

^[11]2025 Gartner GenAI Enterprise Survey, n=360.

^[12]2025 Gartner Survey, n=353.

^[13]Ronthal, Adam, and Georgia O’Callaghan. “Opening Keynote: Navigate AI on Your Data & Analytics Journey to Value.” Gartner Data & Analytics Summit, March 9-11, 2026, Orlando, FL.

^[14]Brinker, Scott. “Martec’s Law: Technology Changes Exponentially, Organizations Change Logarithmically.” Chiefmartec, June 13, 2013. https://chiefmartec.com/2013/06/martecs-law-technology-changes-exponentially-organizations-change-logarithmically/.

^[15]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/.

^[16]Sweenor, David. “The $40B Reason Enterprise AI Projects Fail: It’s Not the Tech.” TinyTechGuides, September 6, 2025. https://tinytechguides.com/blog/the-40b-reason-enterprise-ai-projects-fail-its-not-the-tech/.

The $40B Reason Enterprise AI Projects Fail: It's Not the Tech

David Sweenor — Sat, 13 Sep 2025 12:42:23 GMT

Just as people need a place to relax, they also need a place to experiment. Photo by author David E. Sweenor

MIT NANDA's latest research delivers a brutal reality check. Despite $30-40 billion in enterprise AI investment, 95% of organizations failed to move their initiatives from pilot to production. Dually noted, 90% of employees quietly use personal AI tools for work tasks while only 40% of companies provide official subscriptions.^[1]

Your AI strategy isn't failing because of technology. Tech is rarely the problem. You're solving the wrong problem.

During my decade in ASIC yield characterization at IBM, I watched this pattern play out over and over again. Some teams obsessed with discovering the perfect process recipe before production consistently got outpaced by those who shipped early silicon at 5% yields, characterized failures, and iterated. My job was to find the signal in the noise. One of the many lessons I took away from this experience is that experimentation and iteration are the ultimate keys to progress.

Enterprise AI adoption follows similar patterns. After analyzing hundreds of AI initiatives across several companies, interacting with many clients, 200+ articles, 20+ podcasts, and 10+ books on the subject, the successful minority share one trait. They learned from small, incremental experiments instead of engineering away all risk.

The "AI-ready" fallacy creates the problems it claims to solve

Have you heard the term “AI-ready”? Or is your data “AI-ready”? Well, you need to have both, and I’m not underselling their importance, but it’s not what you think. Having a monolithic AI-readiness program isn’t the answer. Corporate AI-readiness programs indefinitely delay value by ignoring how learning actually works. Organizations pursue comprehensive data readiness initiatives, while employees solve real problems with $20/month ChatGPT subscriptions. And they’re doing it without the data being perfect – go figure. It’s not pristine and never will be.

This represents strategic blindness.

Gartner confirms that AI-ready data "is not something you can build once and for all nor that you can build ahead of time."^[2] Yet CIOs and tech leaders spend months in vendor evaluation cycles while their teams discover what works through daily experimentation.

Shane Murray, Field CTO at Monte Carlo Data, sees the pattern clearly. "The teams making the most progress deploy prototype and production AI products, learn where it breaks, learn where it's biased."^[3] Risk management through controlled experimentation beats comprehensive preparation.

Smart organizations apply right-sized oversight. AI for credit decisions needs high governance rigor. AI for marketing copy and coupons probably doesn't need to be that stringent. Scale governance oversight appropriate to the use case rather than treating every initiative or project like you’re launching nuclear missiles.^[4]

I know someone who would like this, I’d better share.

Your employees already cracked the code

While enterprise initiatives wallow in planning phases, your workforce has built a functioning innovation ecosystem using consumer tools. This isn't a rebellion. They have a job to do, and they’re smart. Plus, it's also market validation.

That 90% employee adoption rate represents massive, unsolicited research happening within your organization. Domain experts became power users regardless of official programs, discovering through trial and error what delivers actual value.^[5]

Personal AI experimentation reveals what enterprise demos miss. Immediate iteration is preferred compared to cumbersome corporate processes and approval workflows. Learning through use beats comprehensive training. Leaders need to enable low-friction testing of ideas without career consequences.

Consumer tools work because they embrace experimental adoption. False starts and course corrections teach more than standing from afar and watching..

Forward-thinking leaders recognize shadow AI usage as a source of competitive intelligence, not a compliance risk. Give them a place to safely experiment, and your employees will solve the adoption problem you're spending millions to crack.

People are your real AI strategy

NewVantage Partners research shows 92% of executives identify cultural change as the biggest impediment to data-driven transformation.^[6] This statistic hasn't budged in years because organizations keep attacking symptoms instead of causes.

Dr. Danny Stout from EY's Intelligence Layer puts it bluntly. Teams must align beforehand, or "there's no way that whatever model you choose is going to be successful."^[7] Robert Lake, who advises companies on AI strategy, observes that business leaders often "paper over their business problems and hope that AI will fix them magically."^[8]

Culture beats technology.

Three elements separate successful AI cultures from the 95% failure rate: 1) safe experimentation, 2) aligned incentives, and 3) internal talent development.

Safe experimentation spaces formalize what your employees have already created behind IT’s back. Teams need risk-mitigated freedom to test ideas without threatening their job security.

Aligned incentives fix the fundamental problem where productivity tools become productivity threats. When finding efficiency might eliminate roles, adoption stalls regardless of the technology’s quality.

Internal talent development leverages existing domain knowledge and expertise. People who understand your customers and problems create more value than external "AI specialists" when given basic AI literacy.

Stop investing primarily in platforms. Empowered, incentivized employees who can experiment safely turn grassroots AI usage into a sustainable advantage.

How to join the successful 5%

Shawn Rogers' BARC research shows only 20% of companies achieve AI maturity benchmarks.^[9] These leaders align AI outputs with business KPIs rather than chasing technology trends. The gap between intention and execution separates winners from the 95% failure statistics.

Based on patterns I've documented across hundreds of successful implementations, here's what works.

Map existing AI usage immediately. Survey what tools teams actually use and problems they solve. Look for results, not compliance violations. This reveals where innovation exists and what business value looks like in practice.

Enable rather than restrict. Build governance frameworks that facilitate experimentation, not prevent it. Create sandbox environments where failure has a limited downside but learning has a high upside. Most corporate AI policies optimize for preventing mistakes rather than maximizing learning velocity.

Start with known problems using available data. Launch focused projects addressing real business pain points with existing information. Deploy, measure, iterate. This matches the exact approach your shadow AI users figured out.

Scale what works. Successful companies "amplify what already works within your organization" rather than pursuing wholesale reinvention.^[10] Connect AI initiatives to existing operational strengths and measurable outcomes.

Enterprise AI failure isn't about technological complexity. It's organizational. Your employees already demonstrated that rapid iteration beats comprehensive preparation. The successful minority learned from them instead of fighting them.

Join the 5% who figured it out.

About David Sweenor

David Sweenor brings 25+ years of hands-on experience implementing AI and analytics solutions across Fortune 500 organizations, including IBM, SAS, Dell, TIBCO, and Alteryx. During his 11-year tenure at IBM, he specialized in yield engineering and predictive analytics, developing systems to optimize semiconductor manufacturing and identify yield loss patterns—experience that revealed how iterative improvement outperforms perfection-first strategies. He has authored six books on AI and generative AI applications, written 200+ articles analyzing AI adoption patterns, and co-developed four patents in semiconductors and SaaS. His marketing leadership has generated $350M+ in attributed pipeline and achieved Gartner Magic Quadrant Leader rankings at multiple companies. David hosts the Data Faces podcast and is recognized as a top 25 AI & analytics thought leader.