Why Most AI Pilots Never Make It to Production
November 14, 2023
The enterprise AI landscape in late 2023 looked promising from the outside. Every firm had a pilot running somewhere. Most of them would never ship.
The problem was not the technology. GPT-4 was capable enough to deliver real value in a dozen obvious use cases. The problem was how organizations were approaching the work. Pilots were being run by whoever was most enthusiastic, not whoever owned the process being automated. Success criteria were vague. The path from proof of concept to production was undefined before the pilot started.
The result was predictable. Pilots produced impressive demos, generated internal excitement, and then stalled when it came time to deploy. The integration work was harder than expected. The legal and compliance review took longer than anyone budgeted. The original champion moved to a different role. The initiative quietly died.
What makes a pilot different from a project is the word eventual. A pilot is a test. A project has a shipping date. Organizations that were actually getting AI into production in 2023 were treating it as a project from day one. They defined the production environment before they wrote the first line of integration code. They identified the stakeholders who could block deployment and included them from the start.
The other thing that separated successful deployments was choosing the right first problem. Not the most exciting problem. Not the one most likely to impress the CEO. The one with the clearest ROI, the best-defined inputs and outputs, and the least regulatory complexity. Boring problems deployed. Interesting ones languished.
If your organization is sitting on a successful pilot right now, ask one question before the end of the quarter: what, specifically, would have to be true for this to be in production in sixty days? If you cannot answer that clearly, the pilot is not actually successful. It is just not yet failed.