Choosing the Right AI Platform for Your Plant

Manufacturing AI vendor pitches tend to converge on the same broad claims — reduce downtime, improve quality, automate compliance. Underneath the similar-sounding pitches, the actual fit for your plant depends on a narrower set of questions than most evaluations ask.

Start with your bottleneck, not the platform's feature list

The most useful evaluation starts with your own ranked list of costliest problems, and asks which platform actually solves the top one well — not which platform has the longest feature list. A platform with a hundred capabilities that doesn't deeply solve your actual bottleneck isn't the right fit, however impressive the demo.

Questions worth asking every vendor

• ✓Does this connect to our existing PLC/SCADA/ERP systems, or does it require replacing them?
• ✓How is the model tuned to our specific equipment and processes, not a generic dataset?
• ✓What does a pilot actually look like — timeline, scope, and what "success" is measured against?
• ✓Where does our data live, and who has access to it?
• ✓What happens after the pilot — is there a clear path to production, or does scope keep expanding?

Fit over feature count

A platform built specifically for manufacturing — understanding PLC data, shift patterns, and regulatory requirements out of the box — will generally outperform a generic AI platform retrofitted with manufacturing use cases, even if the generic platform's feature list looks longer on paper.

The pilot is part of the evaluation

The best way to evaluate fit isn't a longer sales process — it's a scoped pilot against your actual data and your actual bottleneck. A vendor confident in their platform will typically welcome that; one who resists a scoped pilot is worth a second look.