Where industrial AI is actually heading.
Field notes on deployment, governance and the gap between pilots and production.
Show delivered scope before claiming business outcomes
A project can be concrete and valuable before ROI is verified. The key is to separate what was built, what was tested, and what changed in the business.
Turning ISO/IEC 42001 into daily AI work
Governance works when ownership, evidence, risk and review live inside the initiative workflow, not in a separate binder.
Reporting automation people can actually check
The goal is not a faster spreadsheet. It is a repeatable path from raw exports to reconciled totals, visible exceptions and a decision-ready report.
Build the operating system before adding AI agents
AI cannot repair missing ownership, inconsistent masters and invisible handoffs. Connect the work first, then add intelligence where it changes a decision.
Why most manufacturing AI pilots stall at POC, and how to design past it
A working demo is not a working deployment. The pilots that cross the gap share three habits, and they are all decided before any model is trained.
Governance-first AI: shipping under the EU AI Act and China's rules
For manufacturers selling into Europe and operating in China, governance is not paperwork you add at the end. Built in early, it is faster, not slower.
The forward-deployed model: putting engineers on the factory floor
The fastest way to deploy useful AI in a factory is not a better model. It is an engineer standing next to the machine, watching how the work actually happens.