Most AI proofs of concept succeed in a notebook and fail at integration. Four habits separate the pilots that ship from the ones that quietly disappear.
The pilot trap
A pilot is built to prove that something is possible. Production has to prove that it is reliable, affordable and owned by someone. Teams that treat the first as a stepping stone to the second without changing how they work are the ones that stall.
The usual symptoms are familiar: a model that performs well on a curated dataset but degrades on live data, an integration nobody budgeted for, and no named owner once the sponsor moves on.
Start from the decision, not the model
Every use case should begin with the business decision it improves and the metric that proves it. Measure the current process first. Without a baseline, a good result cannot be told apart from an average one.
Agree the kill criteria on day one. A pilot that can be stopped cleanly is cheaper than one that drifts for a year.
Treat data readiness as the first milestone
Fragmented, ungoverned data caps accuracy long before model choice matters. Fix lineage, quality and access first, and keep a short list of the fields the model actually needs.
Build for operations from week one
Versioned data, reproducible training, automated evaluation and drift monitoring make a model safe to change. Add a human review path for low-confidence cases, which also creates the feedback loop that improves the model.
Finally, document the governance that boards now ask about: explainability, bias testing and mapping to the EU AI Act risk tiers. Shipping it with the model is far cheaper than retrofitting it.
Working through this in your organisation?
Talk to the team behind our Artificial Intelligence practice about what a practical first step looks like for you.