The Numbers Don’t Lie
MIT’s 2025 State of AI in Business report dropped a statistic that should terrify every executive: 95% of generative AI pilots fail to deliver measurable impact on the P&L. Read that again. Ninety-five percent.
S&P Global Market Intelligence makes it worse: 42% of companies abandoned most of their AI initiatives in 2025, up from just 17% the previous year. The average organization scrapped 46% of AI proof-of-concepts before they reached production.
These aren’t startups experimenting with bleeding-edge tech. These are enterprises with dedicated AI teams, consultants on retainer, and budgets in the millions.
McKinsey’s November 2025 Global Survey confirms the pattern: while 88% of organizations now use AI in at least one function, only 39% report any measurable EBIT impact. And among those, most attribute less than 5% of their organization’s EBIT to AI.
So what’s killing these projects?
Most AI Rollouts Die in One of Two Places
Death #1: They Move Fast…But Security/Governance Can’t Sign Off
Picture this: Your engineering team builds a brilliant AI workflow that could save 20 hours per week. It works in the demo. Users love it in testing. Then it hits the enterprise approval gauntlet.
Security asks: “What data is it accessing? How is PII being handled?”
Legal asks: “Where’s the audit trail? Can we prove compliance?”
GRC asks: “What happens when the model hallucinates? Who’s accountable?”
The answers? Often buried in vendor documentation that contradicts itself, or worse, “trust us, the black box handles it.”
The 2025 data is stark: IBM’s Cost of Data Breach Report found that 97% of organizations experiencing AI-related security breaches lacked proper AI access controls. Among all breached organizations, 63% had no AI governance policies in place.
The EU AI Act, enforced starting 2026, carries fines up to €35 million or 7% of global revenue for non-compliance. NAVEX’s 2025 research shows only 18% of organizations have an enterprise-wide council authorized to make decisions on responsible AI governance.
When governance teams can’t verify what an AI system is doing, they can’t approve it. Period.
Death #2: They Move Safely…But Everyone Loses Momentum
The flip side is equally deadly. Some organizations react to the governance challenge by building elaborate approval processes, vendor evaluation frameworks, and compliance checkpoints.
Six months later, they’re still in “pilot purgatory.”
WorkOS’s July 2025 analysis reports that Gartner predicts over 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, or inadequate risk controls.
McKinsey’s 2025 survey shows that nearly two-thirds of organizations haven’t begun scaling AI across the enterprise. They’re stuck experimenting or piloting – testing AI in isolated pockets without deep integration into workflows.
The result: By the time approval comes through, the team has moved on, the business need has evolved, and the technology has been superseded. Air India’s success with AI.g (processing over 4 million queries with 97% automation) came from identifying a specific constraint and building AI they could understand and control – not buying speed and hoping for the best.
The Tradeoff That Looks Inevitable (Until It Isn’t)
If you’re feeling this tension right now, you’re not alone. The modern enterprise AI stack is full of tradeoffs that seem inevitable:
- Speed vs. Control: Move fast and break compliance, or move carefully and lose competitive advantage?
- Innovation vs. Auditability: Use cutting-edge models that security can’t inspect, or stick with legacy systems they trust?
- Flexibility vs. Governance: Give teams autonomy to experiment, or enforce standards that strangle creativity?
These feel like fundamental tensions because that’s how the market has positioned them. Every vendor tells you to pick your poison:
- “Use our managed AI service – it’s fast!” (but you can’t inspect it)
- “Build your own AI stack – it’s controlled!” (but it takes 18 months and $2M)
- “Deploy these point solutions – they solve specific problems!” (but now you have 12 ungoverned tools)
Here’s What Nobody Tells You
You don’t actually have to choose.
The problem isn’t AI. It’s not even governance. The problem is that most enterprise AI tools treat governance as a bolt-on feature – something you retrofit after the fact, if you’re lucky. They optimize for either speed (and leave you exposed) or safety (and leave you paralyzed).
But what if an AI platform was built from the ground up with both in mind?
What if you could:
- Switch between models mid-conversation without rebuilding workflows?
- See exactly what data was retrieved, what tools were run, and why?
- Deploy agents with pre-configured guardrails that automatically enforce policy?
- Give your GRC team the audit trails they need without slowing down your engineers?
- Self-host the whole thing if your compliance team requires it?
That’s not a hypothetical. It’s a design choice.
In our next post, we’ll break down the six specific problems that create this false choice between innovation and governance – and why none of them are actually about AI.
The bottom line: When 42% of companies are abandoning AI initiatives (S&P Global, March 2025), the problem isn’t the technology. It’s the infrastructure around it. The winners aren’t the ones with the best models. They’re the ones who figured out how to get both velocity and governance without compromising either. Part 2 of this series: “The Actual Problem Isn’t ‘AI.’ It’s Everything Around AI.” explores the six systemic issues that create these failure modes – and what you can do about them.