AI initiatives rarely fail for one clean reason. They fail because strategy, operating model, delivery, and adoption were never actually moving in step, even when each one looked fine in isolation.
Strategy can be sound, a real business case, a clear use case, and still go nowhere if the operating model underneath it hasn't changed. New workflows still routed through old approval chains. New tools sitting next to old ones, doing the same job twice.
Delivery can hit every technical milestone and still not matter, if adoption was treated as the last step instead of a parallel track from day one. A model that works perfectly and that nobody actually uses has delivered nothing.
The organizations that get real value from AI aren't the ones with the most sophisticated models. They're the ones who kept checking, at every stage, whether strategy, operating model, delivery, and adoption were still connected to each other, and treated it as a problem the moment any one of them drifted.