Here’s a number I want you to sit with: Gartner predicts that through 2026, organizations will abandon 60% of their AI projects. Not because the AI didn’t work. Because the data underneath it was unusable.
Sixty percent. That’s not a failure rate — that’s a pattern. And it’s a pattern that should alarm every executive who just signed off on an AI budget without asking a single question about the data infrastructure it’s supposed to run on.
The Electric Train Problem
There’s a metaphor I keep coming back to when I talk to companies about this. They want the electric train — the cutting-edge AI tools, the automation, the intelligent agents that are going to transform their business. And they should want those things. The capabilities are real.
But their infrastructure is a horse-drawn carriage.
Their data lives in seventeen different systems that don’t talk to each other. Their CRM hasn’t been cleaned since 2019. Their marketing data is in one platform, their sales data is in another, and their customer service data is in a third, and nobody’s reconciled them. Half their analytics still run on spreadsheets that one person maintains and nobody else understands.
And into this environment, they’re deploying AI and expecting it to deliver insights.
Here’s what the maturity data actually shows: fewer than one in five organizations report high maturity in any aspect of data readiness. Only 4% — four percent — have high maturity in both data governance and AI governance together. The foundation that AI needs to work doesn’t exist in most organizations. It’s not that it needs a tune-up. It fundamentally isn’t there.
And yet the AI budget got approved. The data infrastructure budget didn’t.
Meanwhile, Your Employees Built Their Own Railroad
Here’s where this gets worse. While leadership has been debating which AI platform to buy, evaluating vendors, running pilots, and building PowerPoint decks about their “AI roadmap” — the workforce didn’t wait.
Seventy-five percent of workers are already using AI at work. And 78% of them brought their own tools to do it. Nearly half are accessing AI through personal accounts, completely bypassing every security control, every data governance policy, and every compliance framework the company has in place.
This isn’t hypothetical risk. Ninety percent of IT leaders say they’re concerned about shadow AI from a privacy and security standpoint. And here’s the part that should keep you up at night: 80% have already experienced negative AI-related data incidents. Not “might experience.” Have experienced. Past tense.
So while leadership was still working on the strategy, the data was already flowing through tools and accounts that nobody in IT even knows about. Your employees didn’t wait for the electric train. They built their own railroad. And it runs through your proprietary data, your customer information, and your intellectual property.
This Isn’t a Technology Conversation
I know exactly what happens next in most organizations when they hear these numbers. They buy something. A data quality tool. A governance platform. An AI security solution. Another vendor, another contract, another implementation timeline.
And it won’t fix the problem. Because this isn’t a technology problem. It’s a leadership problem.
The reason most organizations have garbage data infrastructure isn’t because the right tool doesn’t exist. It’s because nobody wanted to do the boring work. Data governance is not glamorous. Data cleanup is not exciting. Reconciling seventeen systems into a coherent architecture doesn’t make for a great board presentation.
This is the 10/20/70 reality again. Ten percent algorithms, 20% technology, 70% business process and people. The infrastructure crisis isn’t in the 10% or the 20%. It’s in the 70% that nobody budgets for, nobody staffs for, and nobody wants to present at the all-hands because it doesn’t have a good demo.
You know what does have a good demo? A generative AI tool running on clean data. The problem is, nobody wants to do the 12 months of invisible work that makes that demo possible.
What the Foundation Actually Looks Like
If this sounds familiar, it should. I wrote a few weeks ago about AI training programs failing because companies skip the assessment work. This is the same pattern, just applied to data instead of people.
The fix isn’t complicated to understand. It’s just hard to do.
Audit before you buy. Before you sign another AI contract, figure out what data you actually have, where it lives, who owns it, and whether it’s usable. This is the equivalent of getting a building inspection before you renovate. Almost nobody does it because they don’t want to hear the answer.
Govern before you glamour. You need data governance that people will actually follow — not a 60-page policy document that lives in SharePoint and nobody reads. Governance that works is governance that’s built into the workflow, not bolted on after the fact. And it has to address the shadow AI problem directly, because your people are already using tools you don’t know about.
Accept the timeline nobody wants to hear. Most organizations need 6 to 12 months of infrastructure work before their AI investments will actually pay off. Nobody wants to hear that. Every vendor in the market is telling you that you can deploy AI in weeks. And you can — you can deploy AI in weeks. You just can’t deploy AI that works on data that doesn’t exist.
Stop treating data readiness as IT’s problem. This is a business problem. It requires business decisions about what data matters, how it should be structured, and who’s responsible for maintaining it. When data readiness gets delegated entirely to IT, it becomes a technical project. It needs to be a strategic priority with executive sponsorship and business ownership.
The Museum Metaphor
You can put a touchscreen in a museum. You can add digital signage and interactive displays and a really nice app. And when visitors use the app, it’ll work. But the building is still a museum. The infrastructure is still old. The plumbing still leaks. The electrical can’t support the load.
That’s what most companies have done with AI. They’ve put a touchscreen on a museum and called it a digital transformation.
The organizations that will actually win with AI — not in 2026, but in 2028 and beyond — aren’t the ones buying the most tools right now. They’re the ones doing the foundation work that nobody wants to talk about at conferences because it’s boring and it takes a long time and you can’t put it in a press release.
They’re the ones whose leadership looked at the shiny AI demos and said, “Great. Now show me the data architecture.” And when the answer was unsatisfying, they had the discipline to fix the foundation before building on top of it.
That’s not a technology decision. That’s a leadership decision. And right now, most leaders are choosing the demo over the foundation. They’ll figure out the cost of that choice in about two years — when 60% of their AI projects are abandoned, and they’re wondering what went wrong.
The data was never ready. Nobody wanted to look.