Strategy
6 min read
Why most AI pilots stall, and how to avoid it
Pilots rarely fail on technology. They fail on ownership, scope, and a missing path to production. Here is how to design pilots that scale.
The pilot trap
Most organizations now have a handful of AI pilots. Few of them reach production. The pattern is familiar: a promising demo, a small team, and then a slow fade as priorities shift and nobody owns the next step.
The cause is rarely the model. It is the absence of a business owner, a clear success metric, and a plan for what happens if the pilot works.
Design for the second step
Before a pilot starts, agree on three things: who owns the outcome, what number has to move, and what it would take to roll out. If you cannot answer those, you are running an experiment, not a pilot.
Name a business owner, not just a technical lead
Pick one metric and a target before you build
Budget for integration and change, not only the prototype
Pilots designed this way are smaller and less glamorous. They are also far more likely to become something your business relies on.
YOUR NEXT CHAPTER STARTS WITH A CONVERSATION.
Make your next move an intelligent one.
Start a conversation