/ THE SHORT ANSWER
- 01Evaluate the workflow and failure modes before model performance.
- 02Require a measurement plan that uses representative operating conditions.
- 03Make integration, rollback, documentation, and training contractual outputs.
/ dotSuper point of view
A strong industrial AI proposal makes uncertainty inspectable. Confidence without a test design, fallback path, or accountable operator is a sales signal, not production evidence.
What the evidence says
NIST’s manufacturing AI program emphasises fit-for-purpose method selection, human-AI teaming, operator understanding, interoperability, and evidence-based metrics.
These are practical evaluation dimensions for a buyer.
A practical decision framework
It is designed to make the decision inspectable, not to imitate a platform ranking formula, certification checklist, or legal test.
- Define the operating decision and the consequence of a wrong output.
- Inspect source coverage, data rights, refresh paths, and representative edge cases.
- Require acceptance criteria for quality, latency, uptime, escalation, and recovery.
- Confirm who maintains prompts, rules, connectors, evaluations, and documentation.
| Step | Decision to record |
|---|---|
| 01 | Define the operating decision and the consequence of a wrong output. |
| 02 | Inspect source coverage, data rights, refresh paths, and representative edge cases. |
| 03 | Require acceptance criteria for quality, latency, uptime, escalation, and recovery. |
| 04 | Confirm who maintains prompts, rules, connectors, evaluations, and documentation. |
How to put it into practice
Ask the vendor to walk through a normal case, an ambiguous case, a missing-data case, and a harmful-action case.
Turn answers into a dated decision record.
Any unknown that could change safety, cost, or adoption becomes a pre-pilot dependency rather than an assumption hidden in the proposal.
- Name the accountable owner and the decision this work must enable.
- Record the current evidence, assumptions, exclusions, and next review trigger.
- Measure a useful outcome rather than treating publication or deployment as success.
What this page cannot conclude
- 01The checklist must be adapted to the process criticality and applicable sector rules.
- 02A vendor’s documentation does not replace independent validation under local operating conditions.
- 03Publication, technical eligibility, or good practice cannot guarantee ranking, referral traffic, citation, adoption, or a business outcome.
Sources
- 012026 Roadmap on Artificial Intelligence and Machine Learning for Smart ManufacturingNational Institute of Standards and Technology · accessed Aug 30, 2026
- 02Artificial Intelligence for ManufacturingNational Institute of Standards and Technology · accessed Aug 30, 2026
- 03AI Risk Management FrameworkNational Institute of Standards and Technology · accessed Aug 30, 2026
Our editorial standard · Found an error? Send a correction with its source.
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dotSuper Research Desk. (August 30, 2026). The Industrial AI Vendor Evaluation Checklist: 18 Questions Before a Pilot. dotSuper. https://dotsuper.net/feeds/market-intelligence/industrial-ai-vendor-evaluation-checklist