BCG measured this with numbers: companies that lead in AI value capture grow their revenue 1.7 times faster than the laggards. It’s not a model advantage — almost all of them use the same vendors, the same platforms, the same access to technology that anyone can buy today. The advantage lies elsewhere.
Two weeks ago I wrote that only between 5% and 6% of the companies capture a significant impact of AI on their financial results (McKinsey). The question I left open was obvious: what makes those 5-6% different? The answer is not a secret — it’s a measure that repeats itself in every study that looks at it.
It's not luck. It's a pattern that repeats itself.
Five practices separate companies that capture value from those that stay in control forever.
The first is committed executive leadership from the outset, not delegated to IT: almost 100% of the high-performing leaders have their C-suite involved, compared to just 8% of the laggards (BCG). The second is that they redesign the workflow rather than just slapping on a tool: nearly 3 out of 4 leaders do so, compared to 1 out of 4 of the rest (McKinsey). The third is shared governance between business and IT — they are 1.5 times more likely to share responsibility in this way, and 3 times more likely to appoint a formal AI leader (BCG). The fourth is that they measure value rigorously: more than 60% of the leaders do so, compared to 17% of the laggards. And the fifth is that they invest in their people knowing how to use it: more than half of their staff are trained in AI, compared to 20% in the laggards.
None of the five are new practices. They are disciplines that companies already dominate in other areas—portfolio management, data governance, change management—applied with the same seriousness to AI.
How it looks in a real case
One of the projects that illustrates this most clearly in our own practice is Factory 360, our operational intelligence offering for manufacturing: it connects OEE, quality, maintenance, and the supply chain into a single data model, with a success story that generated more than $ $2 million in annual savings. It wasn’t a tool that was installed on top of an existing process—it was a redesign of the entire decision flow in the plant, with executive sponsorship from day one and value measured from the beginning, not reported afterwards as a story.
How it is institutionalized, instead of depending on the right leader being assigned to you
The problem with the five practices is that, without structure, they depend on the company having the luck of having the right leader at the right time. An AI Center of Excellence turns these practices into routine: a board committee with formal sponsorship, a shared responsibility model between business and IT, and a ROI dashboard that reports quarterly rather than relying on someone to remember to measure.
Next step
Before asking you what AI tool your company needs, the real question that separates those who will capture value from those who won’t is whether the method already exists—leadership, shared governance, measurement—or whether each project depends on the good fortune of the person driving it. A AI Value Discovery maps exactly that in two weeks, with a GO, CONDITIONAL, or NO GO verdict at initiative. Schedule yours
Español 
English