Generative AI has triggered the largest wave of enterprise technology experimentation in a decade. Yet less than one in ten organizations move beyond isolated pilots into industrialized impact. The difference is rarely a technology choice — it is an operating model choice.
1. Anchor every use case to a P&L line
The fastest way to lose CFO sponsorship is to chase technical novelty. Every candidate use case must be traceable to a revenue, cost or risk line. Build a portfolio view and prioritize by feasibility and economic value, not buzz.
2. Industrialize the platform, not the prototype
Pilot teams reach for whatever stack ships fastest. That is fine — until you have six pilots on six stacks and no path to production. Invest early in shared infrastructure for data access, model serving, evaluation and observability.
3. Build a federated operating model
Centralize platform, governance and standards. Federate use-case delivery to business units. This avoids the twin failures of an ivory-tower CoE or uncoordinated business-unit experiments.
4. Make adoption a first-class workstream
Most AI value is unlocked at the last mile — when a frontline employee actually changes their workflow. Plan adoption with the same discipline as engineering.