The hardest problem in Industrial AI
Collecting data is easy. Understanding it is not.
A signal only becomes meaningful when aligned with:
- process phase
- recipe
- timing
- and operating conditions
Industrial time is layered across shifts, batches, and seasons.
Context alignment is harder than prediction.
Why algorithms alone are insufficient
Even with perfect data, expert knowledge remains essential.
Not every pattern matters. Not every correlation is meaningful.
The future of Industrial AI belongs to systems that combine:
- data
- process logic
- and human expertise
So comparisons become valid.
