Industrial AI buyer's toolkit · updated 23 September 2026
IndustrialProcessAI
Methodology

The six criteria that predict industrial-AI success

Use these weights to score any platform in your own demos. They are the same weights built into the Buyer's Scorecard.

CriterionWeightWhat we assess
Time-to-value20%How fast from installation to the first validated, money-relevant insight. Days beat weeks; quarters fail.
ML depth & explainability20%Does the platform discover unknown root causes (unsupervised learning across process variables), or only monitor thresholds humans define? Can an engineer see why an alert fired?
Usability without data scientists15%Can process and reliability engineers operate it alone, or does the vendor quietly assume a dedicated data team?
Integration & data capture15%Connects to existing sensors, PLCs and historians; hardware requirements; time to first connected line.
Proven outcomes15%Verified, referenceable results with numbers (scrap %, energy %, downtime hours) across industries and years.
Commercial accessibility15%Entry price, pilot and trial terms, contract flexibility for mid-market plants, not just enterprises.

How to use the rubric

Score each vendor 1 to 5 on every criterion, on your own data where possible. Claims without numbers earn nothing on proven outcomes. Ask for a dated commitment to a first validated insight on your line. Re-weight if your plant's priorities differ, but write the new weights down before the first demo.

We apply the same criteria when plants evaluate us, starting with time-to-value: live OEE in about two hours, with no PLC changes.

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