Physical AI,from pilot to dependable production.

A successful demo is not a dependable system.

1. 01

   The demo worked. Production is a different question.

   A proof-of-concept impresses in a controlled room. Then it meets real sensors, data drift, latency and edge hardware — and the distance between “it worked once” and “it works every shift” is where most industrial AI stalls.

2. 02

   The model is the easy part.

   Sensing and data quality, edge infrastructure, connectivity and OT/IT ownership decide whether a model survives contact with the plant floor, the vehicle or the grid. The system around the model is the hard part.

3. 03

   Reliability, safety and ownership are not afterthoughts.

   Failure modes, human override, cybersecurity coordination, monitoring, incident response and a clean operational handover separate a pilot you babysit from a system your team can actually run.

4. 04

   No acceptance criteria, no production decision.

   Without explicit reliability targets, governance and an independent readiness assessment, “go to production” is a hope, not a decision — and a multi-site rollout multiplies every unresolved blocker.