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.
| Criterion | Weight | What we assess |
|---|---|---|
| Time-to-value | 20% | How fast from installation to the first validated, money-relevant insight. Days beat weeks; quarters fail. |
| ML depth & explainability | 20% | 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 scientists | 15% | Can process and reliability engineers operate it alone, or does the vendor quietly assume a dedicated data team? |
| Integration & data capture | 15% | Connects to existing sensors, PLCs and historians; hardware requirements; time to first connected line. |
| Proven outcomes | 15% | Verified, referenceable results with numbers (scrap %, energy %, downtime hours) across industries and years. |
| Commercial accessibility | 15% | 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.
Free Excel scorecard
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The six weighted criteria that predict whether an industrial-AI deployment survives: in a one-sheet Excel you score during vendor demos.
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Before you scale
Twelve questions between a pilot and a line that depends on it
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