No hype cycles, no vendor pitches. Just honest thinking about where AI adds real value in operational and enterprise environments — written by someone who has worked both sides of the equation.
Decades of process data sit locked in proprietary historians. Here's how modern AI unlocks that value without touching production — and why the data quality problem is almost never what you think it is.
Not every enterprise is ready for autonomous AI agents — and deploying the wrong pattern in an operational environment can be worse than doing nothing. An honest field assessment.
It's not "what AI should we use?" It's something more fundamental — and getting it wrong is the most common, most expensive mistake in enterprise AI adoption.
Most OT/IT integration failures aren't technology failures. They're the result of one foundational architecture decision made too early, without enough operational context.
When an AI model makes a recommendation that an operator doesn't understand, they'll ignore it. Explainability isn't a nice-to-have; it's the difference between a deployed model and a shelf project.
Some of the most valuable data in your facility is coming from hardware that predates the internet. Here's the practical engineering playbook for connecting it to modern ML pipelines.
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