Why most AI initiatives die between proof-of-concept and proof-of-value — and the strategic shift CEOs need to bridge the gap.
Axel Tombereau, Odyssey
AI ROI
No technology in recent memory has generated such a cascade of spectacular announcements in such a short span of time. Every week brings a new headline about an AI breakthrough: conversational chatbots promising frictionless interactions, automated assistants claiming to reinvent entire workflows, bold declarations of exponential productivity. Yet behind the media frenzy, the reality is more complex. Many companies remain stuck in perpetual pilot mode or engage in AI mainly for its marketing glow.
The real question is no longer whether to experiment with AI, but how to turn it into a strategy that creates value.
Recent McKinsey research underscores this gap between ambition and impact. More than 80 percent of large enterprises report having launched at least one AI initiative. But barely 15 percent say they have observed any meaningful effect on profitability or growth. AI, in other words, is everywhere in corporate narratives, but still rare in tangible results.
The reasons are well known. Too many IT projects are run in silos, disconnected from the company’s strategic priorities. Budgets are scattered across dozens of experiments with no clear line of sight to scale. Talent is insufficient, and change-management capabilities even more so. In these conditions, AI is often reduced to a gadget—another “proof of concept” destined to be celebrated in a slide deck and forgotten soon after. The technology may be cutting-edge; the impact is not.
Creating value with AI requires a shift in perspective.
Instead of asking which AI use case to test next, leaders need to pose a more fundamental question: which value-creation levers matter most for the business, and how can AI reinforce them? Seen through that lens, three paths consistently emerge. First is operational efficiency, where AI acts as a measurable engine of productivity—automating repetitive tasks, optimizing logistics flows, and elevating predictive maintenance to new levels of precision. Second is customer differentiation, where AI enables hyper-relevant experiences, more intelligent recommendations, and product offers that adapt in real time to individual needs. The result is not only stronger loyalty but a real revenue lift. Third is the creation of entirely new business models powered by data: AI-enabled services, dynamic pricing engines, and platforms in which intelligence is not an add-on but the foundation of the value proposition.
The companies that succeed with AI are not the ones dazzled by the latest model release. They are the ones that embed AI as a logical extension of their core strategy, not as a side project in search of a purpose.
Across industries, the most compelling examples reflect this alignment. In aerospace, several players now use AI to dramatically reduce fuel consumption by optimizing flight paths—creating immediate, measurable, and sustainable value. In retail, AI drives personalized promotions while fine-tuning stock management, a combination that simultaneously boosts sales and cuts costs. In insurance, AI is reshaping the customer relationship itself: simple claims are handled automatically, fraud is detected proactively, and policyholders enjoy a smoother, more transparent experience. In all of these cases, the technology is not a disconnected experiment; it is a natural expression of the company’s strategic intent.
It is time to move beyond technological fetishism. AI is not an end in itself, nor a miracle solution to be sprinkled across the enterprise in hopes that innovation will magically materialize. Poorly integrated, it disperses resources and fuels disappointment. But when treated as a strategic lever—designed, sponsored, and governed with intention—it becomes a source of competitiveness and renewal.
Leaders now face a choice: continue multiplying proofs of concept that shine for a moment in corporate communications, or commit to a proper AI strategy aligned with their most fundamental value priorities.
The history of technological waves is unequivocal: the winners are not those who experiment the earliest, but those who deploy the smartest.
AI is not just a technical issue. It is a matter for the executive suite. It must be steered from the top, with clarity of purpose and long-term commitment. Because in a world where global competition accelerates every quarter, value is not created in the labs. It is made in a strategy.
