Most AI pilots that stall do so for organizational reasons, not technical ones: no single owner accountable for the outcome, no agreed definition of success, and no plan for what happens when the model gets something wrong in front of a customer. Fix those three before writing more code.
Assign an owner before the first prototype
Pilots without a named, accountable owner drift indefinitely — everyone contributes, nobody decides when it's ready to ship or when to kill it. The owner doesn't have to be an engineer; they have to have the authority to say "ship it" or "stop."
Define success before you measure it
A pilot judged on "does it feel impressive in a demo" will always feel impressive and never ship, because there's no threshold to clear. Define the metric — resolution rate, time saved, error rate ceiling — before the first line of code, and revisit it against real usage, not curated examples.
- Name one accountable owner with authority to ship or kill the pilot
- Set a numeric success threshold before building anything
- Design the fallback path for when the model is wrong, not just the happy path
- Put the pilot in front of real users and real data within the first two weeks
A pilot with no failure plan isn't a pilot — it's a demo that never has to work twice.
What percentage of AI pilots reach production?
Industry estimates commonly put it around 20%. In our experience, pilots with a named owner and a defined success metric from day one clear production at a substantially higher rate.
Hayden Jackson
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