Jessica Caresse White identifies the gap between AI strategy and execution and the five forces that explains why AI initiatives fail to yield returns on investment.
Executive summary
Nearly nine in ten organizations now use AI in at least one function, yet fewer than one in ten report meaningful financial returns. The gap is not technology. It is leadership, governance, and organizational design.
Enterprise AI adoption has reached an inflection point. Nearly nine in ten organizations now use AI in at least one business function, yet fewer than one in ten report meaningful financial returns. The gap between strategy and execution is not a technology problem. It is a leadership, governance, and organizational design problem. This paper examines five converging forces that explain why most AI initiatives stall after an initial proof of concept: the failure to treat AI as an operating model transformation, a widening leadership readiness deficit, the trap of waiting for perfect data, the inability to scale beyond early wins, and emerging questions about the long-term cost sustainability of AI infrastructure. Drawing on current research from McKinsey, BCG, Deloitte, Gartner, EY, PwC, KPMG, MIT Sloan Management Review, and Harvard Business Review, we argue that the enterprises most likely to capture AI’s value in the next 12 to 18 months are those that redesign how they operate, not those that simply add AI to what already exists. For PE-backed and high-growth companies, where speed and capital efficiency are existential, the cost of getting this wrong is compounding daily.
The convergence
Something unusual is happening in the enterprise AI conversation. After two years of breathless investment, pilot programs, and vendor promises, a more sober set of questions is emerging. Not whether to adopt AI (that debate is settled) but how to make it work at a scale that justifies the cost, the organizational disruption, and the leadership attention it demands.
The numbers tell a stark story. BCG surveyed 1,803 C-suite executives in early 2025 and found that 75% rank AI among their top three priorities, but only 25% report realizing significant value. Sixty percent generate no material value despite meaningful investment (Source: BCG, “From Potential to Profit: Closing the AI Impact Gap,” 2025). McKinsey’s parallel finding is equally striking: 88% of organizations use AI in at least one function, but only 39% report any positive impact on earnings, and for most, that impact is less than 5% of EBIT (Source: McKinsey, “The State of AI in 2025,” 2025).
These are not early-stage adoption numbers. These are the returns from organizations that have already invested, already hired, already built. The problem is not awareness or enthusiasm. It is execution. And execution, as practitioners consistently report, breaks down not in the technology layer but in the human and organizational layers that surround it.
Key points include:
- The operating model problem
- Leadership as the barrier to AI value creation
- The data readiness trap
Read the article, The execution gap: why most enterprise AI strategies fail to deliver and what leaders can do about it., on JCaresse.com.