The reason a working capability fails to become a working number is recorded in the program itself: in the staffing history, in the rollout plan, in what the steering committee stopped asking for.
The sequence is familiar to anyone who has sat through the reviews. A pilot clears its acceptance criteria inside a protected team, the sponsor presents it, the rollout gets funded, and two years later the benefits case has been restated twice while the line owners drift back to the old way of working. The models performed. What failed sits around them: a leadership team that funded the tool without re-underwriting the operating model, roles that kept their old shape, a process that treats the system's output as a suggestion. These factors multiply rather than add, which is why the strongest of them cannot compensate for the weakest and a well-funded program can settle near zero.
The engagement works from your delivery record, the steering-committee minutes, the staffing decisions, and the benefits-case revisions rather than from a maturity survey, and it names the binding constraint with evidence a board can act on. The work ends with the constraint removed and the capability owned by your own team. It is led by the operator who built four enterprise AI functions at market leaders, on programs whose first production quarters were measured in EBITDA.