Eight out of ten AI pilots we are asked to review never reach production. The models usually work. What fails is everything around them: ownership, data contracts, and the boring operational detail nobody budgeted for.
The pilot trap
A pilot is designed to prove that something is possible. Production needs to prove that something is reliable, affordable and owned. Those are different questions, and most teams only plan for the first one.
Three reasons pilots stall
First, nobody owns the outcome after the demo. Second, the data the pilot used was hand-cleaned and will not arrive that way again. Third, the cost per decision was never measured, so finance cannot approve the scale-up.
What production teams do differently
The teams that get through the gap treat the pilot as the first sprint of a product. They name an owner on day one, write the data contract before the model, and track cost per decision from the first week.
A 90-day path out
None of this is glamorous. It is also the difference between a slide that says “AI” and a line on the P&L that says “saved”.
“A pilot proves it can work. Production proves it can work on a Tuesday, at 3 a.m., for the price you agreed.”
Key takeaways
Name a business owner before you write a line of code.
Agree the data contract, not just the dataset.
Measure cost per decision from week one.
Hannah Ross
Managing Partner, Strategy. Fifteen years running data and automation programmes in finance and logistics.
