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The safest way to evaluate AI tools is to define the work first, test every candidate against the same representative cases, and make portability a requirement from day one. Judge the complete lifecycle—quality, risk, integration, rights, operating cost, monitoring and exit—not a polished demonstration or introductory price.
Public-sector procurement guidance from the UK Government, OECD and NIST provides a useful foundation. Its legal duties do not automatically apply to private organisations, but its questions expose the practical decisions any team must make before committing to an AI supplier.
Start with the job, not the vendor
Write a concise use-case statement before attending demonstrations or accepting a trial. Identify the people who will use the system, the inputs it will receive, the outputs or actions expected, and the human review that will occur. Describe the consequence of a wrong, late, biased or unavailable result.
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NIST procurement guidance recommends describing the problem and opportunity rather than prescribing a solution. That prevents a familiar product category—such as a chatbot, model API or document assistant—from defining the problem for you.
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Record the minimum requirements
- Required tasks and acceptable output quality.
- Data types, volumes, retention limits and geographic constraints.
- Identity, access-control, audit and logging requirements.
- Systems that must connect, including file stores, business applications and identity providers.
- Maximum tolerable latency, downtime, error rate and human-correction effort.
- Accessibility, language, explainability and approval requirements.
- Deal-breakers that remove a candidate before scoring.
Set these criteria before vendors demonstrate products. Otherwise, a persuasive demo can cause the team to change the goal after seeing what one supplier happens to offer.
Build a representative test set
Use real examples where permitted, or safely de-identified and synthetic cases that preserve the difficulty of the work. Include routine requests, edge cases and conditions likely to trigger failure. The same prompts, files, integrations, user roles and acceptance rules should be applied to every candidate.
What to record for each test
| Observation | What to capture |
|---|---|
| Task result | Whether the output met each pre-set acceptance criterion. |
| Error type | Fabrication, omission, unsafe advice, bias, formatting failure, privacy exposure or integration error. |
| Repeatability | Whether materially different answers appear when the same case is rerun. |
| Human effort | Time and expertise required to check, correct or rework the result. |
| Workflow friction | Latency, file limits, access steps, context loss and failures at hand-offs. |
| Evidence | Logs, screenshots, configuration details and the test date or model version. |
Do not present this exercise as an independently validated benchmark. It is your acceptance test: a practical way to understand capabilities and limitations on the work that matters to your organisation.
Evaluate six dimensions, not just output quality
The following dimensions synthesise official UK, OECD and NIST guidance. They are not a universal scorecard. Decide the relative importance of each dimension for your use case, document the reason for the weighting, and record uncertainty rather than turning unsupported claims into precise scores.
| Dimension | Questions to ask every candidate |
|---|---|
| Task performance | Does it meet the pre-set criteria on representative examples? Which cases fail, how often, and how consistently? |
| Risk and governance | What privacy, security, bias, transparency and safety risks arise? Who monitors them, investigates incidents and approves changes? |
| Interoperability | Which formats, APIs, identity systems, hosting arrangements, data stores and custom components are required? Can another provider use the same inputs and continue the workflow? |
| Data and intellectual-property rights | Who may use, retain, export, delete or reuse inputs, outputs, derived products and newly developed or adapted material? Which rights survive termination? |
| Lifecycle burden | What integration, training, hosting, support, maintenance, monitoring and migration work is required, and who pays for it? |
| Exit readiness | Can you recover useful data and derived work, document the workflow, transfer knowledge and terminate cleanly? Are the exit steps testable? |
Demand evidence through demonstrations and tests
Ask each supplier to demonstrate the stated task, not a generic showcase. Require explanations of capabilities, limitations, dependencies, governance controls, support arrangements and known failure modes. Separate a polished demonstration from evidence produced on your representative cases.
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OECD’s 2025 account of US federal AI acquisition guidance describes market research, detailed demonstrations and tests, performance-based requirements, contract oversight and testing before award. Those practices can be adapted by other organisations without assuming that US federal rules govern them.
Questions that expose a demo’s limits
- Which parts of the result were generated automatically, retrieved from a source, or supplied by a human operator?
- What happens when the input is incomplete, contradictory, adversarial or outside the supported language?
- Can the supplier reproduce the exact configuration, model version and prompts used in the demonstration?
- What independent logs or audit records will your organisation receive?
- Which features are generally available, and which are experimental, preview or dependent on a particular model?
- What changed in the last year, and how are customers notified before a change affects quality or compatibility?
Make portability a technical requirement
Vendor lock-in is not limited to a proprietary model. It can arise from undocumented prompts, custom workflow logic, provider-specific data structures, identity dependencies, storage formats, fine-tuning artefacts, monitoring dashboards or staff knowledge that exists only inside one supplier.
Check interfaces and formats
- Prefer documented, stable APIs and ordinary export formats where they meet the use case.
- Identify which components are portable and which must be rebuilt elsewhere.
- Keep prompts, evaluation cases, configuration, schemas, workflow definitions and runbooks in your own controlled repository.
- Separate business data from provider-managed indexes, embeddings, logs and other derived stores so you understand what can actually be exported.
- Confirm whether another provider can authenticate users, receive the same inputs and return results to the same downstream systems.
NIST’s 2021 procurement paper recommends interoperability and open licensing terms to help avoid lock-in. It discusses open standards, royalty-free licensing, intellectual-property arrangements and independent maintenance where systems may become obsolete. These are negotiation options, not blanket legal advice; technical feasibility and local law still determine what is appropriate.
Secure rights to data, outputs and new work
“We own our data” is too vague for an AI contract. Separate the categories and ask what each party may do with them.
| Category | Questions for the agreement |
|---|---|
| Inputs | Can the supplier use them to train or improve a service? How long are they retained? Where are they processed? Can you retrieve and delete them? |
| Outputs | Who may use, modify, export and commercially exploit generated material? Are there restrictions caused by third-party components or source material? |
| Derived products | Who controls indexes, embeddings, classifications, evaluations, fine-tuning data and other artefacts created from your information? |
| New or adapted intellectual property | Who owns software, prompts, workflows, documentation, models or integrations created specifically for your organisation? What licence is granted if ownership is not transferred? |
| Termination rights | What remains available after the contract ends, for how long, in which formats and at what cost? When must deletion be certified? |
Have legal and security specialists review these terms for the jurisdictions and data involved. Public procurement examples can highlight issues, but they do not substitute for advice on your own contract.
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Calculate lifecycle value instead of headline price
A low subscription price can hide substantial operating and exit costs. Build a total-cost view that includes:
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- Integration, custom development and identity work.
- Hosting, usage, storage, network transfer and model or feature changes.
- Training, accessibility support and process redesign.
- Human review, correction and escalation.
- Security assessments, compliance work and monitoring.
- Incident response, rework and the cost of incorrect decisions.
- Migration, parallel running, data conversion and contract close-out.
- Knowledge transfer and documentation needed for another supplier or an internal team.
OECD’s 2025 discussion of US acquisition practice calls for performance-based requirements, contract oversight, periodic value evaluation and possible sunset criteria. Establish in advance what evidence will show that the system still delivers enough value to continue.
Use decision gates
- Discovery gate: stop if the supplier cannot meet a non-negotiable security, legal, data or integration requirement.
- Pilot gate: continue only if representative tests meet the agreed acceptance criteria with an affordable human-review burden.
- Scale gate: require operating procedures, monitoring ownership, support commitments and an export test before wider deployment.
- Renewal gate: compare measured benefits, incidents, usage, total cost and alternatives before extending the agreement.
- Sunset gate: define the evidence or event that triggers replacement, redesign or retirement.
Match review depth to risk
A tool that drafts low-risk internal notes does not need the same controls as one that influences eligibility, employment, health, finance, safety or access to essential services. The sources do not provide a universally valid set of weights or a single risk threshold. Your assessment should consider the affected people, reversibility of mistakes, sensitivity of data, scale, likelihood of misuse and ability of a human to detect an error.
Higher-risk uses generally warrant
- More diverse and adversarial test cases.
- Documented human authority to override or stop the system.
- Stronger access controls, audit trails and segregation of duties.
- Independent review of bias, security and explainability.
- Shorter monitoring intervals and explicit incident escalation.
- A tested fallback process that works when the service is unavailable.
Explainability matters for portability as well as accountability. The UK Government says interpretable outputs make it more likely that an organisation can engage another supplier to continue or build on an AI system, limiting vendor lock-in.
Write flexibility into the agreement
Contract terms should reflect the architecture and the exit plan. Consider requirements for documented interfaces, usable exports, access to data and derived products, clear intellectual-property rights, knowledge transfer, technical documentation and transition support.
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Clauses and schedules to discuss
- Service descriptions that identify model, feature and dependency changes.
- Notice periods and customer remedies for material changes or retirement.
- Performance measures tied to your acceptance tests rather than vague “best efforts”.
- Security, privacy, subprocessor, incident-notification and audit obligations.
- Export formats, extraction assistance, retention windows and deletion evidence.
- Access to configuration, prompts, schemas, evaluation results, logs and documentation that you supplied or paid to create.
- Knowledge-transfer sessions and named responsibilities at transition.
- Pricing for migration, professional services and continued read-only access.
- Termination, suspension and close-out rights if quality, risk or value falls below agreed levels.
Whether a clause is enforceable or commercially sensible depends on jurisdiction, bargaining position and the service. Treat NIST’s recommendations as issues to examine with counsel, not as a ready-made contract.
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Selection is not the end of evaluation. Assign an accountable owner and establish review points for quality, emerging risks, cost, user experience and stakeholder impact. Continue testing after model, data, prompt, workflow or policy changes.
Maintain an evidence file
- Approved use-case statement and risk assessment.
- Versioned test set, acceptance criteria and results.
- Model, configuration and integration inventory.
- Incident, complaint, override and correction records.
- Usage, cost, latency and availability measures where material.
- Training records, operating procedures and escalation contacts.
- Export and restore test results.
- Review decisions, including reasons to continue, change or retire the system.
UK guidance calls for lifespan testing and asks buyers to define end-of-contract responsibilities and data processes. Build those responsibilities into normal operations rather than waiting for a termination notice.
Test the exit before you need it
An exit plan is credible only when someone has rehearsed it. Choose a non-production window or a limited dataset and verify that the organisation can perform the following without the incumbent supplier’s goodwill:
- Export inputs, outputs, derived work, metadata and configuration in the agreed formats.
- Reconstruct the workflow sufficiently for a replacement provider or internal team to understand it.
- Run a sample through an alternative implementation and identify what must be rebuilt.
- Maintain user access to records during the transition.
- Revoke credentials, remove connectors and confirm deletion or retention obligations.
- Preserve audit evidence and communicate changes to affected users.
Record the time, staff effort, missing fields, conversion errors and supplier charges. If an export cannot be tested, treat that uncertainty as a material procurement risk.
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Use frameworks as prompts, not verdicts
NIST describes the AI Risk Management Framework as voluntary and intended to improve the incorporation of trustworthiness considerations across the design, development, use and evaluation of AI products, services and systems. NIST’s current page says the framework is being revised, so check the current version before adopting it operationally.
A framework can organize questions about validity, reliability, safety, security, accountability, transparency, explainability, privacy and fairness. It cannot tell you whether a particular tool fits your data, workflow, budget or exit requirements. Combine framework prompts with your own representative tests and contract review.
What the public-sector figures do—and do not—show
OECD’s Digital Government Outlook 2026, analysing the 2025 Digital Government Index, reported that 10 of 36 OECD countries (28%) had measured any financial or non-financial impact of government AI use cases. The same analysis found that 21 of 36 countries (58%) provided central support for procuring AI goods and services.
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A practical decision record
Before signing, keep a short record that another decision-maker can audit:
- The job, users, affected people and failure consequences.
- Minimum requirements, deal-breakers and context-specific weights.
- Representative cases and the results for every candidate.
- Known limitations, unresolved uncertainties and compensating controls.
- Integration map, dependencies and portable artefacts.
- Data, output, derived-work and intellectual-property rights.
- Lifecycle cost assumptions and value measures.
- Monitoring owner, review dates and change-management process.
- Export rehearsal, transition responsibilities and deletion process.
- The evidence and authority required to renew, replace or retire the system.
This record protects the organisation from a decision based only on a demo and gives a future team enough context to switch providers without starting from zero.
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