Building Trust in Industrial AI: From Pilot to Production
Week 31 covered what regulation demands. Week 32 asks the quieter question underneath: even where the rules are clear, why do most industrial AI pilots never scale? The honest answer has less to do with the model and more to do with trust.
The gap is bigger than steering committees assume — RAND found ~80% of enterprise AI projects fail to deliver promised value, and Gartner puts full ROI success near 28%. The usual culprit is data: pilots run on curated datasets that don’t exist at production volume across every plant’s real equipment. Pilot performance then tears along four seams — system, data, integration, and governance. Trust isn’t a UX add-on; it’s earned through transparency, demonstrated reliability, and training that builds the judgment to override.
What separates scaled deployments from stalled ones is rarely better AI — it’s better ownership: a named accountable owner before scale-up, finance engaged before benefits are claimed, and AI folded into existing production governance (as Siemens did at Erlangen). Your action this week: take a pilot you’re proud of and ask whether people would trust it enough to act on it at full volume, on real messy data. Wherever the answer is shaky, that’s the work. Full framework at renegrywnow.com.
Reflection questions
Would your best pilot survive full production volume on real, uncurated data — and would people act on it?
Which of the four seams (system, data, integration, governance) is most likely to break your next scale-up?
Is there a single named, accountable owner for each initiative moving from pilot toward production?
Keywords: Industrial AI, Pilot to Production, AI Trust, Data Quality, AI Governance, Ownership, Scale-Up, Manufacturing AI, ROI, Physical AI
Series: Energy Dominance · Week 32 · Part I
Next: Part II — The Leadership Risks of Autonomous Systems and How to Mitigate Them.
