/ THE SHORT ANSWER
Ask for evidence across seven areas: the exact workflow and boundary, data provenance, performance measurement, failure handling, human authority, integration requirements, and ownership after launch. A credible vendor should describe where the system will not be trusted, how outputs will be checked, what happens when upstream information changes, and what the customer team must be able to operate without the vendor.
- 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
The NIST smart-manufacturing roadmap highlights data complexity, heterogeneous sensing and control systems, interoperability, reliability, explainability, and safety as central industrial AI challenges.
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
The following framework is dotSuper’s operating synthesis of the cited guidance. 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
Run one structured vendor session with operations, IT, quality, and the future system owner present. 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
Test the workflow before funding the solution.
The AI Readiness Sprint turns one operational constraint into a ranked decision, an accountable owner, and an implementation-ready first move.
Explore the readiness sprint