Ask an IT leader about their AI portfolio and you’ll hear the same list: three pilots running, one promising, zero in production. The pilots aren’t failing in the technical sense. The model works. The demo impresses. The stakeholders nod. Then the project quietly becomes a spreadsheet and the team moves on. We’ve seen this often enough to give it a name: pilot purgatory. Here’s what lands projects there, and how we keep them out.
Why Pilots Die
Pilot purgatory rarely has anything to do with the model:
- The pilot optimized for the demo, not the business. If success means a demo that impresses, you’ve built a demo. A pilot needs a production-shaped metric from day one: error rate on real data, time saved per transaction, exceptions that need a human.
- Integration came last. The pilot ran on exported spreadsheets. Production needs live systems, permissions, and data governance. When the pilot team realizes that’s another project, the pilot ends.
- Nobody owned it past the pilot. A champion sponsors the pilot. Nobody owns the production system. When the champion’s priorities shift, the pilot evaporates.
- The handoff was never defined. “It works, now let’s hand it to IT” is not a plan. IT has 47 other things to do and no context on your model.
The Production Contract
Before any AI engagement, we write a production contract with the client. Four clauses, agreed up front:
1. The metric is defined before the model is trained. Accuracy on a validation set doesn’t count. What counts is a measurable change in the business process: fewer manual touches, lower error rate, faster turnaround, on real data.
2. The data path looks like production. The pilot runs against the same systems, permissions, and data the production system will use. If that means integration work first, we do integration work first. There is no spreadsheet phase.
3. There’s a named production owner. From day one, the person who will run this system in production is in the room, reviewing the work weekly. No handoff at the end. The operator grows up with the system.
4. Exit criteria are explicit. The pilot ends in one of three ways: it ships, it dies with a documented reason, or it’s extended with a new question. What never happens is “let’s see how it goes.”
What Running in Production Looks Like
Production AI doesn’t look like a robot overlord. It looks like a workflow: a document arrives, a model extracts and classifies, a human approves the exceptions, the system logs everything. The boring parts are what let it survive contact with a busy department. Our clients’ AI systems that are still running a year later are the ones designed around a process, owned by a person, and measured against a number.
If you have a pilot that’s been in the lab too long, or you’re about to start one and want it to skip purgatory entirely, book a discovery call. We’ll tell you honestly whether it’s ready to ship, and what’s missing if it isn’t.