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Ask an AI vendor to prove that its system works for your specific task, users and operating conditions—not merely to show a polished demonstration. Require the method used to test it, the measured result, the conditions behind that result, the person responsible and the remedy if performance or safety falls short.
A useful vendor review covers six areas: contextual performance, data and access controls, model and supplier dependencies, ongoing monitoring, incident readiness, and contract commitments. The questions below are written for an RFP, security review, vendor meeting and final negotiation.
1. Define what “reliable” means for your deployment
Reliability is not a universal percentage. A model that is adequate for drafting internal summaries may be unacceptable for a customer-facing decision or a safety-sensitive workflow. The National Institute of Standards and Technology (NIST) lists validity and reliability among AI trustworthiness characteristics and notes that the characteristics can involve trade-offs. Start by describing the intended purpose, users, decisions, data and operating environment in writing.
Questions to ask about intended use
- What exact use and user population was the system designed for?
- Which uses, users, geographies, languages or decisions are outside its intended scope?
- What input quality, data freshness, connectivity, latency and volume assumptions does the system make?
- What human review is expected, and which decisions must never be automated?
- What conditions would make the vendor advise us not to deploy it?
Ask the vendor to map each answer to your proposed workflow. A general product description is not evidence that the system is suitable for your context.
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Questions about evaluation design
- Which evaluation datasets, held-out test sets, metrics and testing tools do you use?
- How do those sets represent our languages, terminology, edge cases, expected input quality and likely user behavior?
- What are the measured results, confidence intervals or uncertainty information, and known failure modes?
- Can you show results by relevant subgroup, scenario, task type or language where that distinction matters?
- Were the tests run on the same model version, configuration and connected services that we would use?
Request the test-set description, dates, model version, configuration, exclusion rules and raw or independently verifiable results. If a vendor supplies only an average accuracy figure, ask what the average conceals: rare but severe errors, subgroup differences, unsupported inputs or failures under load.
Turn results into an operating threshold
Agree the performance level your workflow requires before deployment. The threshold might concern error rate, response time, abstention rate, citation accuracy, escalation coverage or another task-specific measure. Ask:
- What threshold is acceptable for our workflow, and who on each side approves it?
- How will the threshold be measured in production?
- What happens when the threshold is missed: warning, human review, traffic reduction, rollback, suspension or termination?
- How quickly must the vendor investigate and provide a corrective plan?
Record the threshold, measurement window, sample requirements, owner and remedy in the statement of work or service agreement. Without those details, a promise to be “highly accurate” is not an enforceable operating requirement.
Check scale and technical limits
NIST’s AI Procurement in a Box workbook prompts buyers to examine scale limits, data coverage, processing sensitivities, accuracy, maintenance and support. Ask the vendor to state:
- Maximum document size, context length, batch size, concurrent users and daily or monthly volume.
- Supported file types, languages, character sets and data formats.
- Expected latency, rate limits, regional availability and behavior during peak demand.
- Whether performance changes when prompts, retrieval sources, tools or integrations are added.
- Which functions degrade first when data quality, volume or connectivity falls.
Run acceptance tests at your expected scale, not only on a small demonstration sample.
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2. Make monitoring part of the purchase, not a post-launch promise
A pre-launch demonstration is evidence about one point in time. It is not a monitoring plan. Require a production measurement plan with documented thresholds, drift detection, a named owner and a defined response when performance degrades.
Questions about maintenance and drift
- How do you maintain accuracy and integrity after launch?
- Which signals indicate data drift, concept drift, quality degradation, abuse or unusual output patterns?
- How often are metrics reviewed, and who receives the reports?
- What minimum sample size and review period are needed before declaring a change significant?
- Can we access the measurements, alerts, evaluation sets and model/version history?
- What is the rollback or safe-mode procedure if a new version performs worse?
Define a joint monitoring register: metric, target, alert threshold, data source, review frequency, owner, escalation path and remediation deadline. Include business outcomes as well as technical metrics; a stable response-time graph does not prove that generated answers remain useful or safe.
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Specify who supplies monitoring data, who validates samples, who investigates an alert, who can pause traffic and who authorizes restart. A vendor may monitor infrastructure while your team must assess business accuracy or regulatory impact. Put both duties in writing and test the escalation route during a pilot.
3. Ask exactly what happens to your data
Control starts before procurement: confirm that the data needed for evaluation and operation exists in a usable, lawful form. UK Government guidance for AI procurement advises buyers to establish data availability, define how data will be shared and ask about model choices and system limitations.
Data-use and retention questions
- What data must we provide, in what format and at what quality, before the system can be evaluated?
- If we send prompts, documents, telemetry or customer records, for which purposes may you use them?
- Are our inputs or outputs used to train, fine-tune, evaluate or improve any shared model? Is that use disabled by default or by contract?
- How long are inputs, outputs, embeddings, backups and support copies retained?
- How do we request deletion, and how do you verify deletion from active systems and backups?
- Where is data processed and stored, and which cross-border transfers occur?
Access and connected-service questions
- Which vendor employees, contractors, subprocessors and connected services can access our data?
- What authentication, least-privilege, encryption, segregation and privileged-access controls apply?
- Do support personnel see content, metadata or only redacted diagnostics?
- How are access events logged, reviewed and disclosed to us?
- Which subprocessors can change, add or remove access without our approval?
Request a current subprocessor list, data-flow diagram, retention schedule and access-control description. Make material changes subject to notice and, where appropriate, objection or termination rights.
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4. Establish controls you can actually operate
Ask for a live demonstration of administrative controls using a role that matches your team. “Configurable” is meaningful only when you know who can change a setting, how quickly it takes effect and whether the change is logged.
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- Which permissions, use restrictions, rate limits, human-review gates, escalation rules and suspension controls can we configure ourselves?
- Can we restrict use by user, task, risk level, geography, data type or business unit?
- Can we block particular tools, destinations, prompts, files or actions?
- Can we require approval before an output triggers an external action?
- Can we pause one workflow, tenant or integration without taking down unrelated services?
- What logs, prompts, retrieved sources, tool calls, approvals and outputs can we export, and how long are they retained?
Test permissions with ordinary and administrator accounts. Verify that a disabled integration cannot be re-enabled through an API, cached credential or vendor support action without an authorized approval.
5. Understand the model, components and supplier chain
Do not evaluate a branded interface as if it were a single model. The service may include a foundation model, fine-tuning, retrieval, classifiers, guardrails, orchestration code, hosting and third-party tools.
Questions about system composition
- Which model and other material components power the service?
- Which components are supplied by third parties, and where are they operated?
- How are models, variables, prompts and retrieval sources selected, adapted and evaluated for our task?
- What are the material limitations and inappropriate use cases?
- Which dependencies could affect availability, latency, data location or output behavior?
Ask for version identifiers and a plain-language architecture description. For high-impact uses, request enough technical documentation to reproduce the relevant acceptance tests without exposing protected intellectual property.
Change-management questions
- How will you notify us about significant model, data, feature, infrastructure or subprocessor changes?
- How much notice applies to planned changes, and what happens for urgent security fixes?
- Could a change invalidate our test evidence or agreed performance threshold?
- Will you provide a test environment, release notes and re-evaluation results before production use?
- Who owns maintenance, model updates, security fixes and customer communications?
Require a change record that identifies the affected component, effective date, expected behavior change, test evidence and rollback option. A general statement that a vendor follows a framework is not proof that this system performs adequately in your deployment.
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6. Test incident readiness before an incident occurs
Reliability includes the ability to detect, contain and recover from harmful output, data exposure, service failure or unauthorized change.
Notification and investigation questions
- What events count as reportable incidents, including harmful outputs, privacy events, security compromise, material degradation and supplier outages?
- How quickly will you notify us, through which channels and with what minimum facts?
- Who is our operational contact and our incident-response contact, including outside business hours?
- How will you preserve prompts, outputs, logs, model versions and other evidence?
- What investigation, root-cause analysis, customer communication and remediation support will you provide?
Define severity levels, notification clocks, update cadence and the information needed for your own legal, regulatory and customer obligations. OECD’s 2026 due-diligence guidance emphasizes monitoring, documented tests and metrics, incident communication, clear roles and cooperation in remediation.
Containment, recovery and exit questions
- What can we pause or disable immediately, and who has that authority?
- How do we restore service safely after a model or security failure?
- What backup, continuity and degraded-mode options exist?
- At termination, how do we retrieve data, configurations, prompts, logs and evaluation records?
- How are records deleted, including backups, and what evidence of deletion is supplied?
- How much transition support is included, for how long and at what cost?
7. Convert answers into enforceable contract terms
Use the vendor meeting to discover facts, then place the critical answers in the agreement, service-level schedule, data-processing terms and change-control procedure. NIST’s procurement workbook specifically prompts buyers to request maintenance processes, support contracts, suitability for third-party support and a SaaS service agreement.
| Operational area | Contract detail to require |
|---|---|
| Performance | Use-case definition, test method, metric, threshold, measurement window, reporting duty and remedy. |
| Availability and support | Service levels, support hours, severity-based response and restoration targets, maintenance notice and escalation path. |
| Data | Permitted purposes, retention, deletion, training restrictions, locations, subprocessors and audit evidence. |
| Customer controls | Permissions, restrictions, human approval, logging, pause, suspension and export capabilities. |
| Changes | Notice periods, material-change definition, re-evaluation rights, rollback and termination if suitability changes. |
| Incidents | Reportable events, notification clock, evidence preservation, cooperation, remediation and communications. |
| Exit | Data and log format, retrieval deadline, deletion certificate, transition support and post-termination access. |
Ask the vendor to identify the remedy for each commitment: service credit, corrective action, suspension, replacement, termination right or another remedy appropriate to the impact. A promise without an owner, deadline and consequence remains an assurance rather than a control.
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8. Compare vendors with the same evidence request
Give every bidder the same use case, sample characteristics, operating assumptions, threshold definitions and evidence template. Score the answers only after checking whether the evidence is comparable.
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| Comparison axis | What to examine |
|---|---|
| Contextual performance | Relevance of test sets, measured results, robustness, thresholds and drift monitoring. |
| Transparency | Clarity about purpose, limitations, dependencies, model changes and failure modes. |
| Customer control | Data use and retention, access controls, restrictions, logs, pause and exit options. |
| Operational readiness | Support coverage, maintenance, incident notification, escalation and remediation. |
| Accountability | Named owners, documented responsibilities, evidence access and usable contract commitments. |
| Fit and proportionality | Whether safeguards and evidence match the potential impact and business context. |
Do not collapse the result into one headline score. NIST’s FAQ states: "Addressing AI trustworthiness characteristics individually will not ensure that AI systems are trustworthy." Reliability, security, privacy, transparency and fairness can pull in different directions, so document the trade-offs and why the selected controls are proportionate to your use.
9. Use a staged procurement process
- Write the use-case brief. Define users, decisions, data classes, languages, volume, latency, human review and unacceptable outcomes.
- Issue an evidence-based RFP. Require test-set descriptions, results, limitations, architecture, data terms, controls, support model and incident procedures—not marketing claims alone.
- Run a controlled pilot. Use representative and difficult cases, record model and configuration versions, test permissions and simulate an outage or harmful output.
- Set acceptance gates. Approve only when agreed thresholds, data protections, monitoring, support contacts and pause procedures work in practice.
- Negotiate the operating contract. Attach the threshold, evidence schedule, change process, incident duties, service levels and exit arrangements.
- Review after launch. Re-test after material changes, investigate drift alerts, review incidents and reassess whether the use remains within scope.
10. Put the frameworks in their proper place
NIST AI RMF 1.0, released January 26, 2023, is a voluntary, rights-preserving, non-sector-specific and use-case-agnostic framework. Its four functions—Govern, Map, Measure and Manage—help organize responsibilities and evidence. The NIST AI RMF Playbook offers suggested actions; it is not a certification or performance guarantee. Use it to structure questions, then demand system-specific records and tests.
The OECD’s 2026 Responsible AI due-diligence guidance applies to enterprises across the AI value chain and describes risk-based due diligence. It is guidance, not universally binding law. Check the laws, sector rules and contractual duties that apply in your jurisdiction and use case.
Red flags that should stop or narrow a purchase
- The vendor offers a single accuracy number without test conditions, version, subgroup or failure-mode information.
- It cannot state what data is retained, who can access it or whether inputs train shared models.
- It promises continuous improvement but has no drift metric, threshold, owner or rollback plan.
- Material model or subprocessor changes can occur without notice or re-evaluation.
- Support is limited to a generic ticket queue with no severity targets or incident contact.
- You cannot export logs and evaluation records or pause the system without vendor intervention.
- Framework alignment is presented as proof of suitability instead of system-specific evidence.
The strongest vendor is not necessarily the one with the most impressive demo. It is the one that can show relevant evidence, disclose limits, give you workable controls, assign accountable people and accept clear remedies when the system does not meet the agreed use-case standard.
Quick Recap
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