Getting AI features past the pilot stage
Most AI pilots work in a demo and then quietly die before production. The gap usually isn't model quality — it's the unglamorous work of data pipelines, evaluation, and failure handling that pilots skip.
A striking number of AI initiatives produce an impressive demo and then never ship. The pattern is familiar: a small team builds a proof of concept with a curated dataset and a favorable set of test cases, leadership is impressed, and then the project stalls indefinitely somewhere between "pilot" and "production." The model itself is rarely the reason.
What actually stalls these projects is the unglamorous infrastructure around the model — the parts that don't show up in a demo. Production AI features need a reliable data pipeline feeding them current information, not a one-time export someone pulled for the pilot. They need monitoring that can tell you when the model's outputs start drifting from what users actually need, which happens gradually and silently unless you're watching for it.
Evaluation is the piece most pilots skip entirely, and it's the piece that matters most once something is user-facing. A demo succeeds if it works on the five examples someone tried. A production feature needs a real evaluation set covering edge cases, ambiguous inputs, and adversarial attempts — and it needs that evaluation to run continuously, not once before launch, because model behavior shifts as underlying data and usage patterns shift.
Failure handling is the other quiet gap. What happens when the model is uncertain, or wrong, or simply unavailable for a few seconds? Pilots rarely answer this because pilots rarely fail in front of a real user with real consequences. Production features need a defined fallback — a graceful degradation path, a clear signal to the user, a way for a human to step in — designed before launch, not patched in after the first embarrassing failure.
The teams that reliably ship AI features aren't the ones with the most sophisticated models. They're the ones who treat the pipeline, the evaluation, and the failure handling as first-class parts of the build — not afterthoughts bolted on once the demo gets approval to go further.