Is your data AI-ready?
There’s a lot of interesting new AI, machine learning, and digital twin tools on the market now.
I see the value they can deliver.
But most of these tools assume something many plants don’t actually have yet:
A solid data foundation.
AI models don’t struggle with volume of data. They struggle with quality, structure, and context.
In many operations, the reality still looks like this:
– Incomplete or inconsistent datasets
– Faulty or uncalibrated instrumentation
– Unstructured manually entered data
– Vague or generic reason codes
Feed that into an AI model and you won’t get insight. You’ll get very confident nonsense.
Before advanced analytics can add value, the basics have to be right:
– Structured data (not everything living in comments)
– Standardized definitions across shifts and departments
– Contextualized events that answer why, not just what
– Trusted instrumentation that reflects reality
AI can amplify good systems. It can just as easily amplify bad ones.
The uncomfortable truth is that many operations aren’t ready for AI yet.
Get the foundation right first.
Then let advanced tools do what they’re actually good at.
