Answer framing: A credible recommendation must be reversible. Agree the conditions for rejection, a narrower deployment or a return to the internal model before the pilot starts.
Why it matters to buyers: NIST’s AI RMF includes production monitoring and assessment against deployment conditions. A successful demonstration is therefore only one stage in a continuing evaluation process—not the end of the evidence requirement. (airc.nist.gov)
Put this to: every contributor, including suppliers.
A good answer should address these proposed decision gates:
| Gate | Evidence needed to proceed | Reason to pause or reject |
|---|---|---|
| Technical quality | Meets pre-agreed material-error and evidence standards | Material errors worsen or cannot be independently assessed |
| Economics | Comparable cost per accepted file with retained work included | Apparent savings depend on omitted review or transition costs |
| Service | Acceptance and turnaround hold across the intended file mix | Results depend on excluding ordinary difficult cases |
| Control | Authority, governance and accountable review are documented | Unclear adjusting authority or unmanageable third-party risk |
| Integration and exit | Tested operational hand-off and reconstructable export | Critical evidence, versions or configurations cannot be recovered |
Ask experts to specify what is unknown, what can be resolved through a bounded test and what is an immediate stop condition. Thresholds should be agreed by the carrier; this research does not propose a universal acceptable error rate or guaranteed return.
Sharp follow-up: “Which finding would make you recommend better inspections, a stronger internal estimating team or a smaller automation deployment instead?”
Artifact to request: a signed decision memorandum containing results, limitations, unresolved risks, rollout boundaries and rollback ownership.
Expert quote: [Reserved for approved quotation, contributor, role and interview date.]
Sources cited on this page
- airc.nist.govairc.nist.gov