The failure mode of corporate AI is rarely the model. It is a portfolio of impressive demos that never reach a customer, a plant or a P&L. The cause is almost always the same: the programme started from the technology — 'we have a chatbot, where can we use it?' — rather than from a business problem with an owner and a number attached. A useful strategy inverts that. It begins with the handful of workflows where the company already measures something it cares about — downtime, days of inventory, cost to serve, time to quote — and asks where an AI system could move that number. The model becomes a means, not the point. This reframing also resolves the build-versus-buy anxiety: with frontier capability converging and prices falling fast, betting the strategy on any one lab is a mistake. The durable asset is your proprietary data, the workflow you wrap around it, and the trust you earn — not the model of the month.

The model is not the moat

Any competitor can rent the same frontier model you can, often by next quarter and for less. Your advantage is the data only you have, the workflow you've integrated it into, and the relationship of trust around it. Strategise on those, treat the model as a swappable part.

1. 1.The most common reason enterprise AI programmes stall is:

   The models aren't capable enoughThey start from the technology rather than a business problem with an owner and a numberThere aren't enough vendors to choose fromEmployees refuse to use any AI tools

2. 2.In a converging, falling-price model market, durable advantage comes mainly from:

   Owning the largest modelProprietary data, integrated workflow and trustSigning an exclusive deal with one labHaving the most GPUs