The real problem with AI isn't the technology. It's that leadership doesn't understand it.

Three of the world's most rigorous research firms measured the same thing in 2025. They reached the same uncomfortable conclusion I reached, and it has nothing to do with technology.

The next competitive gap will not be only between companies that use AI and those that don't.

It will be between leadership teams that understand how AI changes operating models, and those that still treat it as a technology layer.

Most still don't distinguish that difference well. That is the strategic risk.

The data worth facing head-on

When three independent studies, with different methodologies, converge on the same figure, it pays to pay attention.

McKinsey, in its State of AI 2025 (1,993 organizations), found that 88% of companies already use AI in at least one function, but only 39% report any effect on EBIT, and in most cases below 5%.¹

BCG, in a survey of 1,250 executives, was blunter: only 5% of companies achieve value from AI at scale (the ones it calls future-built), while 60% reap hardly any material value despite substantial investment.²

And MIT, in its The GenAI Divide report, found that 95% of generative AI pilots produced no measurable impact on the P&L.³

Three firms. Three methodologies. One conclusion: adoption is massive, but value is rare.

The right question is not "are we using AI?" Almost everyone is. It is "why do so few capture real value?"

The bottleneck is not the technology

Here is the most important finding, and the most ignored. MIT was explicit: the divide is explained not by model quality or regulation, but by tools that do not learn or integrate into workflows.³ In my experience, AI is 20% algorithms and 80% organizational redesign. BCG puts it as its 10/20/70 rule: only 10% of the value of an AI transformation comes from the algorithms, 20% from technology and data, and 70% from people and processes.⁵

The most revealing data point in the MIT study is indirect: while only 40% of companies purchased official AI subscriptions, workers at more than 90% of the companies surveyed regularly use personal tools like ChatGPT or Claude for their tasks.³ There is a "shadow AI economy" that works better than corporate systems, and that most leaders neither see nor govern.

Meanwhile, 42% of companies abandoned most of their AI initiatives in 2025, up from only 17% the year before.⁴ That is not a slowdown. It is a sign of investing without understanding.

Why the delegation model breaks

For years, leadership operated on the basis of delegation: executives set the direction, technology teams executed, and business units adopted.

With AI, especially GenAI and agentic AI, that model starts to break.

Leaders can still delegate execution. But they can no longer delegate understanding.

What leadership needs to know (and it isn't Python)

This is not about learning to code or tuning neural networks. It is about knowing:

• Where AI creates value and where it creates risk
• What should be built versus bought
• What should be centralized versus federated
• What must be governed
• What should never be automated without human accountability

When leadership cannot ask those questions, trade-offs fail in predictable ways:

• Overinvesting in tools while underinvesting in data
• Launching pilots without a real path to deployment
• Underestimating governance
• Centralizing everything and losing speed, or decentralizing too much and fragmenting

A data point from the same MIT study illuminates the problem: about half of generative AI budgets go to sales and marketing, even though back-office automation often yields better returns.³ It is exactly the kind of wrong trade-off that happens when decisions are made without fluency.

Where value really lives

What does the 5% that captures value do differently? According to BCG, the winners, the future-built companies, stand out for redesigning their workflows and raising their talent's capabilities, not for having better models.² The real value of enterprise AI lives in the dependencies most decisions omit: data quality, process redesign, governance, adoption, accountability and operational discipline.

A team can approve an AI strategy. But if it cannot distinguish between a chatbot, a RAG architecture, a predictive model and an autonomous agent, it will struggle to ask the right questions, and it may assume that tools like ChatGPT or enterprise copilots will solve complex business problems on their own.

They won't.

Gartner frames the same blind spot from the top: executive AI literacy does not require coding, but it does demand the strategic intelligence to question assumptions, evaluate risks and align AI with long-term priorities.⁶ Without that literacy, initiatives get approved that are not fully understood.

What I have seen in practice

Leading data and AI transformations in environments as different as pharmaceuticals, retail and technology services, the pattern that repeats most is the same. When I implemented predictive models for oncology and vaccines, what separated a successful pilot from one left by the wayside was almost never the algorithm: it was whether the business had redesigned its decision process around the model. The projects that captured value were not the most technically sophisticated, but those where leadership understood that AI changed how the business operated, not just which tool it used.

It is the same conclusion McKinsey, BCG and MIT reached separately. I saw it project by project.

That is the gap that will define the next decade.

Most leadership teams believe they understand AI. The results, often, say otherwise.

Where do you see the biggest gap today: boards, C-level, business units or technology leadership?

References

1. McKinsey & Company (2025). The State of AI in 2025: Agents, Innovation, and Transformation. Global survey, November 2025 (1,993 participants). mckinsey.com
2. Boston Consulting Group (2025). The Widening AI Value Gap: Build for the Future 2025. September 2025, survey of 1,250 executives. bcg.com
3. MIT NANDA (2025). The GenAI Divide: State of AI in Business 2025. July 2025. PDF report
4. S&P Global Market Intelligence (2025). Survey of more than 1,000 companies in North America and Europe, cited in CIO Dive, March 14, 2025. ciodive.com
5. Boston Consulting Group. AI at Scale, 10-20-70 approach (corporate page, accessed October 2026). bcg.com
6. Gartner (2025). How CIOs Can Build AI Literacy at the Executive Level. Webinar, October 27, 2025. gartner.com