Navigating Enterprise AI: Beyond the Hype to Business Value

The demo-to-production gap

Almost every organisation I advise has an impressive prototype. Far fewer have an AI capability their auditors, their security team and their finance department all agree on.

The gap is rarely the model. It is data readiness, evaluation, and the absence of a value hypothesis anyone would defend in a steering committee.

A four-question filter

1. 1.What decision changes? If no decision or workflow changes, the value is zero regardless of accuracy.
2. 2.What does being wrong cost? This sets your evaluation bar and your human-in-the-loop design.
3. 3.Where does the grounding data live? If it is not governed and current, the assistant will confidently mislead.
4. 4.Who owns it in 12 months? Unowned AI systems decay faster than any other software.

Build the boring parts first

Retrieval quality, evaluation harnesses, prompt and model versioning, cost telemetry, and access control aligned to existing data governance. These determine whether a use case survives its first audit.

Enterprise AI value comes from compounding a governed data foundation — not from the newest model release.