Most AI projects don’t fail because the strategy was wrong. They fail because no one could execute it.
The number gets cited a lot — 74% of enterprises fail to capture value from AI. But the stat alone doesn’t tell you much. The interesting question is why.
The Strategy-Execution Gap
Here’s what typically happens. A company brings in a consulting firm. The firm spends 8 weeks interviewing stakeholders, mapping processes, and building a strategy deck. The deck looks great. The roadmap is ambitious but “achievable.” The business case projects strong ROI.
Then the consultants leave. And nothing happens.
Not because the strategy was bad. Because the strategy was disconnected from the reality of building software. The people who wrote the strategy have never shipped an AI product. They know the theory. They don’t know the pain.
What Actually Goes Wrong
After building 20+ companies at a venture studio and consulting with dozens more, the failure patterns are remarkably consistent:
The data problem nobody mentioned. Every AI strategy assumes clean, accessible data. The reality is messy, siloed, and politically fraught. The strategy deck skipped this because it’s not exciting.
The “AI” that doesn’t need to be AI. Half the use cases in the roadmap would be better served by a well-designed rule engine or a simple workflow automation. But “we built a rules engine” doesn’t justify the consulting engagement.
The integration tax. Connecting an AI capability to existing systems takes 3-5x longer than building the AI itself. Strategy decks don’t account for this. They show the model. They don’t show the plumbing.
The talent gap. The strategy assumes you’ll hire an ML team. You won’t — at least not quickly. The market is brutal and your employer brand in AI is nonexistent.
What Works Instead
The companies that succeed with AI share a few traits:
They start small and specific. Not “AI-transform the enterprise.” Instead: “reduce false positives in fraud detection by 30%.” A specific problem with a measurable outcome.
They embed practitioners, not advisors. People who have built and shipped AI products working alongside the internal team. Not above them. Not in a separate room.
They accept ugly wins. The first version doesn’t need to be elegant. It needs to work. Polish comes later. Shipping comes first.
They’re honest about trade-offs. Sometimes the right answer is “don’t use AI for this.” The best consultants will tell you that. The worst ones will sell you more AI.
The Bottom Line
AI projects fail when strategy gets disconnected from execution. The fix isn’t better strategy — it’s practitioners who can close the gap between the deck and the deploy.
If 74% of AI projects fail, the question isn’t whether your strategy is right. It’s whether the people executing it have ever shipped an AI product before.