Three of the most-cited 2025 reports on enterprise AI — Stanford HAI's AI Index, McKinsey's State of AI, and MIT NANDA's *GenAI Divide* — agree on one uncomfortable picture: almost everyone has adopted AI, and almost no one is making money from it.
These three reports come from very different vantage points. Stanford's index is an academic survey of the whole field. McKinsey's is a practitioner study built on responses from roughly 2,000 organisations. MIT's is a closer look at what happens once generative AI is actually inside a business. When studies with different methods and different incentives all land on the same conclusion, it's worth taking seriously.
Adoption is near-universal
- 78% of organisations used AI in at least one function in 2024, up from 55% a year earlier (Stanford); McKinsey's 2025 survey of ~2,000 firms puts it at 88%.
- Generative AI use more than doubled — 33% → 71% of organisations.
- Global corporate AI investment hit $252.3 billion in 2024.
Adoption that fast is historically unusual. For comparison, the cloud took most of a decade to reach the share of enterprises that generative AI captured in about two years. Part of why is that this technology arrived as a consumer product first — employees were already using it before procurement, security or finance had a say. That bottom-up arrival is also why the returns look thin: a lot of "adoption" is people pasting text into a chat window, not a redesigned process.
...but impact is rare
- Only ~one-third of organisations have scaled AI past pilots ("pilot purgatory").
- Just 39% report any EBIT impact — and mostly under 5%.
- Only 6% qualify as high performers; MIT found 95% of GenAI pilots deliver no measurable P&L impact.
The story of enterprise AI in 2025 isn't capability. It's the chasm between buying AI and getting value from it.
The bottleneck is consistent across all three reports — and it isn't the models. It's integration, workflow redesign, measurement and senior ownership. A model that can draft a customer email is not the same thing as a support process that gets measurably cheaper. The first is a demo; the second requires wiring the model into your systems, defining what "good" looks like, and giving someone accountability for the number.
Why this matters
The danger is reading these headlines as "AI doesn't work." That's the wrong lesson. The technology plainly works — it's the operating model around it that's missing. Firms stuck in pilot purgatory tend to share a pattern: a flashy proof-of-concept, enthusiastic early users, no baseline measurement, no owner with a P&L stake, and no appetite for the unglamorous integration work. The pilot impresses everyone in the room and then quietly never ships.
A short scenario makes it concrete. A mid-sized insurer runs a six-week pilot using AI to summarise claims notes. Adjusters love it; leadership approves a wider rollout. Then it stalls — the summaries live in a separate tool, nobody reconciled them with the system of record, and no one ever measured whether claims actually closed faster. Twelve months on it counts as "AI adoption" in a survey and changed nothing on the income statement. Multiply that by thousands of firms and you get exactly the gap these reports describe.
What the minority do differently
MIT's report is the most useful here because it didn't stop at the failure rate — it looked at the 5% that succeeded. The pattern is consistent and, frankly, a little deflating for anyone hoping the answer is a smarter model. The successful minority tended to buy rather than build for non-core capabilities (vendor partnerships succeeded markedly more often than internal builds), pushed ownership of each initiative out to the line managers who actually run the workflow rather than parking it in a central innovation lab, and chose tools that integrated deeply into existing systems instead of bolting a chat window onto the side. None of that is about the model. It's about organisational design — who owns the outcome, how deeply the tool is wired in, and whether the workflow itself was redesigned.
The flip side is the failure mode McKinsey and MIT both circle: the "learning gap." Tools don't adapt to how people actually work, organisations don't change how they work to suit the tools, and the result is software that technically functions and practically goes unused. Capability was never the constraint. The constraint is the much harder, much less glamorous work of changing how an organisation operates — which is precisely why so few clear it.
What this means for your team
- Measure the before, not just the after. If you can't state the current cost, time and quality of a workflow, you can't prove AI improved it — the discipline behind measuring AI ROI.
- Give every initiative an owner with a number. Diffuse benefits with no accountable owner are how pilots die.
- Redesign the workflow, don't bolt the model on. The value is in the process change, not the model call.
- Treat integration as the real project. The model is the easy 10%; connecting it to your data, systems and escalation paths is the other 90%.
The firms in the winning minority aren't the ones with the best model — they're the ones that changed how work gets done around it. If you'd rather get past the pilot stage than add to the 95%, that's exactly the conversation worth having with our team.
Sources
- Stanford HAI — 2025 AI Index
- McKinsey — The State of AI 2025
- MIT NANDA — The GenAI Divide