Pilots are easy to start and hard to finish. A demo that impresses in a meeting can still fail to reach production because of integration complexity, unclear ownership, security review or simply a lack of user adoption.
Design the pilot for production
Agree up front what production success looks like: the metric, the threshold and who signs off. Use realistic data and involve the people who will actually use the system.
The operating model
- A central AI enablement team owning platforms, standards and reusable components.
- Embedded champions in business units who understand workflows and drive adoption.
- A clear intake and prioritisation process so the best use cases get resourced.
- Shared governance covering risk review, monitoring and incident response.
Measure what matters
Accuracy is necessary but not sufficient. Track usage, time saved, quality outcomes and cost. A slightly less accurate system that people use every day beats a perfect one nobody opens.
The last mile of enterprise AI is not technical. It is trust, workflow fit and ownership.
Key takeaways
- Define production success criteria before the pilot starts.
- Plan for integration, security and support from day one.
- Measure adoption and business impact, not just model accuracy.
- A small central team plus embedded champions scales best.
