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Before signing a multi-year AI contract, prove that the system solves a defined problem, set measurable acceptance criteria, and make the supplier’s data use, security, model changes, service levels, audit rights, liability, and exit duties enforceable. The right checks depend on your jurisdiction, industry, data sensitivity, and the harm a failure could cause. Treat government guidance as a diligence framework, then have counsel map it to your specific contract.
Start with the problem, not the model
A long commitment is difficult to unwind if the business case is vague. Write a short use-case specification before contacting vendors. It should identify the process being improved, the people who will use the system, decisions the system may support, decisions it must never make, and the result that would justify the expenditure.
Define permitted and prohibited uses
Describe intended users, workflows, data sources, jurisdictions, and the level of human review. List prohibited uses explicitly—for example, making a final employment, credit, safety, medical, or eligibility decision without an accountable human process where your law or policy requires one. Include foreseeable “scope creep” cases so a later team cannot quietly repurpose the service for a higher-risk task.
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Use outcome-based requirements rather than a generic request for the “best” model. Examples include a response-accuracy threshold on a named test set, a maximum false-negative rate for a particular class of cases, a response-time percentile, a human-review rate, or a reduction in handling time without increasing complaints. Define how each measure will be calculated, who supplies the test data, and what happens when results fall below the threshold.
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Check that AI is necessary
Assess whether a rules-based process, search tool, conventional software, or additional staff would meet the need with less risk. The UK Guidelines for AI procurement recommend an early assessment of the problem, available data, and alternatives. Do not sign a long term merely because a supplier offers a familiar model.
Confirm data fitness before supplier selection
Inventory the data the system would require and test whether it is relevant, lawful to use, sufficiently complete, current, representative, and technically accessible. Record ownership, retention rules, sensitive fields, cross-border constraints, and any rights held by people represented in the data. A supplier cannot compensate for data that is unavailable, biased, or prohibited from the proposed use.
Demand evidence about the model and supplier
Marketing descriptions are not a substitute for evidence tied to your task. Ask for a technical description of the exact service configuration you would buy, not only the name of a model family.
Model identity and provenance
- Record the model, release or version, hosting arrangement, supporting components, and any retrieval, fine-tuning, filtering, or orchestration used in your configuration.
- Ask what data sources trained or otherwise informed the system, how provenance is documented, and what known gaps or restrictions apply.
- Request documentation of limitations, intended use, excluded use, safety controls, and material dependencies.
- Require the supplier to identify subcontractors, infrastructure providers, and other components that could affect confidentiality, availability, or intellectual-property rights.
Australian government reliability and safety guidance treats model provenance, versioning, testing, monitoring, and supplier capability as procurement considerations. Ask for the underlying evidence and preserve it with the procurement record.
Testing that resembles production
Request benchmark definitions, test-set composition, sample sizes, confidence intervals where available, and a description of excluded cases. Re-run representative tests yourself or through an independent assessor. A high general benchmark score does not establish performance on your language, documents, users, or operating conditions.
Supplier capacity and continuity
Examine the supplier’s support model, staffing, financial and operational resilience, release process, incident history, and ability to maintain the service for the full term. Ask how the supplier handles a discontinued model, a major infrastructure failure, an acquisition, sanctions or export restrictions, and loss of a critical subcontractor. Require notice and a workable alternative rather than accepting an unqualified right to substitute a materially different service.
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Set accuracy, fairness, and transparency requirements
Decide what level of error is acceptable before reviewing offers. The threshold should reflect the consequence of an incorrect or missing output, not the vendor’s marketing target.
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Test relevant populations and scenarios
Build an evaluation set covering languages, regions, accents, document formats, accessibility needs, edge cases, and other groups affected by the service. Measure false positives and false negatives separately where their consequences differ. Include adversarial, ambiguous, incomplete, and out-of-distribution inputs. Have representative users review outputs for usefulness and harmful failure modes.
Make fairness and explainability concrete
Ask for bias and discrimination testing, the populations tested, metrics used, remediation steps, and residual limitations. Require a way to explain the factors or sources that materially influenced an output when people need to challenge a decision. If a foreseeable discriminatory effect cannot be mitigated, the Information Commissioner’s Office advises against proceeding with procurement in its AI contracts and third parties guidance.
Turn results into contract measures
Attach the agreed test protocol, accuracy and fairness metrics, review interval, sampling method, and reporting format to the contract. Define a cure period, retest procedure, and escalation route for a failed measure. Avoid an SLA that measures only uptime when the principal risk is incorrect or unsafe output.
Control data, privacy, and intellectual property
Write data rules in operational terms. Identify every category of information that may enter the service, the purpose for each processing activity, and the legal role of each party.
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- List prompts, files, telemetry, feedback, logs, outputs, support tickets, and diagnostic data the supplier may receive.
- State whether any of those categories may be used for training, fine-tuning, evaluation, product improvement, or another customer’s service. If not permitted, prohibit it expressly rather than relying on a dashboard setting.
- Set retention periods, deletion triggers, backup treatment, geographic storage and processing locations, and the process for responding to data-subject requests.
- Identify subprocessors and require notice, approval or objection rights, equivalent obligations, and a current subprocessor list.
- Document controller, processor, or equivalent roles where applicable, along with instructions, confidentiality duties, and assistance with impact assessments.
The ICO guidance above and the UK Data and AI Ethics Framework emphasize clear allocation of data responsibilities. Your privacy team should map those principles to the law governing your organization; UK and EU terminology does not automatically apply elsewhere.
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Allocate rights in inputs and outputs
State who owns, licenses, or may otherwise use customer inputs, generated outputs, prompts, feedback, custom instructions, fine-tuned artifacts, evaluation data, connectors, and documentation. Address third-party material that may appear in outputs and the supplier’s responsibility to provide notices or licenses. Specify which rights survive termination and whether the supplier may retain de-identified or aggregated information.
Require return and verifiable destruction
Define an exportable data set, format, delivery method, deadline, and cost. Include logs, configuration, prompts, evaluation results, records needed to explain decisions, and documentation—not merely the original files. Require written confirmation or other evidence of deletion from production systems, backups, and subprocessors, subject to narrowly defined legal-retention exceptions.
Set security, incident, and dependency obligations
Security requirements should match the sensitivity of the data and the consequences of compromise. Do not accept a generic statement that the supplier follows “industry standards” without identifying controls and evidence.
Minimum control areas
- Identity, least-privilege access, privileged-account management, multifactor authentication, and segregation of customer environments.
- Encryption in transit and at rest, key-management responsibilities, secure software development, vulnerability handling, and patch timelines.
- Logging, monitoring, retention, administrator access records, backup integrity, disaster recovery, and tested recovery objectives.
- Physical and cloud-infrastructure controls, personnel screening where appropriate, and restrictions on subcontractor access.
- Controls against prompt injection, data exfiltration, malicious files, unauthorized tool calls, and abuse of connected systems when those risks apply.
Incident response
Define what constitutes an incident, the initial notification deadline, required contents, update frequency, investigation cooperation, evidence preservation, customer communications, and cost allocation. Require the supplier to notify you of incidents involving a subprocessor or shared dependency even when the supplier believes your data was not accessed. Preserve the right to direct containment, suspend interfaces, and obtain root-cause and corrective-action reports.
For U.S. federal information-technology contracts, FAR Part 39 addresses risk analysis and privacy safeguards in its specified federal context. It is not a universal private-sector rule; use it as a reminder to identify the rules that actually govern your procurement.
Control model changes and performance drift
A multi-year contract is also a change-management agreement. Models, safety filters, hosting regions, prices, APIs, training practices, and subprocessors can change while the service remains branded the same.
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Define a material change
List changes that require advance notice, impact assessment, customer approval, or a new acceptance test. Include model replacement, major version upgrades, altered data use, new subprocessors, hosting-location changes, materially different output behavior, removal of an integration, and changes to support or service-level commitments. Set notice periods long enough for testing and migration.
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Specify monitoring measures, sampling frequency, alert thresholds, review meetings, and records each party must keep. Define what happens when accuracy, fairness, latency, safety, or availability deteriorates: remediation, rollback, temporary suspension, service credits, price adjustment, or termination. Require a controlled test environment or parallel run where a change could affect high-consequence decisions.
Keep testing throughout the term
Initial acceptance is not permanent proof. Retest after material updates, data-distribution changes, incidents, or a sustained metric decline. Australian guidance highlights version control, drift detection, testing, and monitoring, while UK procurement guidance calls for ongoing model testing to maintain accuracy. Put those activities, owners, and costs into the statement of work.
Allocate responsibility and remedies
A supplier’s system does not transfer responsibility for your decisions, data, or user communications. Create a responsibility matrix covering data preparation, configuration, human review, output use, security, model performance, regulatory notices, and quality assurance.
Contract terms to negotiate
- Warranties that the service will conform to the documented specification, security commitments, and agreed test protocol.
- Clear liability caps, exclusions, and carve-outs for confidentiality breaches, data-protection violations, intellectual-property infringement, fraud, wilful misconduct, and other risks your counsel identifies.
- Indemnities or defense obligations where the supplier controls a relevant risk, with procedures for notice, control of defense, settlement, and cooperation.
- Insurance types and limits appropriate to the service, including cyber, professional, and technology errors coverage where justified.
- Service credits, corrective work, retesting, suspension, and termination rights that apply to quality failures as well as downtime.
- Obligations to preserve records and cooperate with regulators, auditors, investigations, and customer complaints.
The UK procurement guidance describes product, data, algorithmic-harm, security, and contract-performance responsibilities as matters to allocate. Its central principle is that “Correct risk allocation is crucial to the long term viability of the service, but also to achieving best value.” Allocate each risk to the party best able to prevent, detect, insure, or remedy it.
Design the exit before signing
Lock-in is created by undocumented configurations, proprietary formats, dependent workflows, and a rushed termination. Treat portability as a deliverable from the first day.
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Specify the transition package
- Export formats for source data, outputs, metadata, prompts, configurations, logs, evaluations, and audit records.
- Export frequency, data volume limits, transfer method, encryption, and fees.
- Transition assistance, knowledge-transfer sessions, technical documentation, and reasonable access to personnel.
- Continued read-only or limited service during migration, with defined pricing and security controls.
- Deletion certification after the transition and a process for retrieving data held by subprocessors.
Prefer open, documented interfaces and interpretable output formats where they fit the use case. Test an export before renewal rather than discovering that a promised download omits key records. Include termination assistance for supplier breach, unacceptable performance, regulatory change, insolvency, acquisition, and a planned end of term.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare vendors with a use-case scorecard
Set criteria and weights before reading offers. Weight error consequences and data sensitivity more heavily than superficial feature counts. Record whether each answer is a contractual commitment, independently verified evidence, a supplier assertion, or an unresolved gap.
| Criterion | Evidence to request | Decision question |
|---|---|---|
| Task performance | Use-case test results, methodology, failure analysis, and retest rights | Does it meet the threshold on our representative cases? |
| Data provenance and terms | Training sources, permitted uses, retention, locations, and subprocessors | Can we lawfully use the service with our data? |
| Privacy and security | Control descriptions, audit reports, incident process, and access records | Are safeguards and response duties enforceable? |
| Fairness and transparency | Population testing, limitations, explanations, and user-review results | Can we detect and address harmful disparities? |
| Change control | Version policy, notice periods, approval triggers, and rollback plan | Can a material change be tested before it affects users? |
| Auditability | Logs, documentation, evidence access, and regulator cooperation | Can we investigate an output or incident later? |
| Support and continuity | Support hours, recovery objectives, staffing, and dependency plan | Will the service remain usable through disruption? |
| Portability | Export schema, sample export, transition assistance, and deletion proof | Can another supplier take over? |
| Risk and remedies | Warranties, liability, indemnities, insurance, credits, and termination | Who bears the cost when the service causes harm? |
| Total term cost | Usage fees, storage, support, testing, migration, integrations, and exit costs | What will the full contract lifecycle cost? |
Do not invent universal weights. A system handling public benefits or clinical information should receive a different risk weighting from an internal drafting assistant. Document why your weights reflect the use case.
Questions to put to the vendor
Send questions in writing and require answers from the people who can make contractual commitments:
- What exact model, version, configuration, hosting location, tools, and subcontractors are included?
- How will you notify us of model, provider, hosting, data-use, pricing, or service changes?
- What data trained or informed the system, and what information will our use send to you or your subprocessors?
- Will prompts, files, outputs, feedback, logs, or support data be used for training, fine-tuning, evaluation, or another customer? Can those uses be prohibited in the contract?
- Which evaluation methods, test data, benchmarks, accuracy results, limitations, and fairness assessments support the claims for our use case?
- What security controls, incident timelines, subcontractor obligations, audit evidence, recovery objectives, and evidence-preservation duties will you accept?
- Who owns or licenses inputs, outputs, custom work, prompts, evaluations, and documentation, and what rights survive termination?
- What happens if accuracy degrades, a model changes, the provider is acquired, a dependency fails, or a required processing location becomes unavailable?
- How will we export data and records, receive transition assistance, maintain continuity during migration, and verify deletion at expiry?
Adapt the questions to the actual service and applicable law. Any answer that influenced selection should appear as a measurable obligation, schedule, warranty, or other enforceable term.
Build accountable governance around the contract
Include procurement, the business owner, technical staff, security, privacy, legal, risk, and representatives of affected users in evaluation. Assign one internal owner with authority to pause use, demand remediation, and approve changes. Maintain a decision log showing the intended use, evidence reviewed, accepted residual risks, and approval route.
Separate supplier monitoring from organizational accountability. Your team remains responsible for the data it supplies, the instructions it gives, the human decisions made with assistance, and notices or appeals owed to affected people. Define escalation paths for unsafe outputs, suspected discrimination, privacy complaints, security events, and unexplained performance changes. Review access and use periodically so the deployed service does not silently exceed its approved purpose.
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The cited materials combine UK procurement and data-protection guidance, Australian government guidance, U.S. federal oversight, and the U.S. Federal Acquisition Regulation. They are not interchangeable legal rules. Requirements may differ by country, sector, public-versus-private buyer, data location, employment context, and contract value.
Have qualified counsel and relevant compliance specialists map the checklist to your governing law, procurement rules, records obligations, accessibility duties, sector regulation, and cross-border transfer requirements. For perspective, the U.S. Government Accountability Office’s 2026 review examined 13 AI acquisitions at four federal agencies and reported that those agencies were not systematically collecting lessons learned, including practices involving data rights and testing. That scope is not a market-wide failure rate or a measure of typical contract cost; it is a reason to maintain your own acquisition records and lessons learned.
Quick Recap
A practical approval sequence
- Frame the use case: document users, permitted and prohibited uses, alternatives, data needs, and measurable outcomes.
- Classify risk: assess impact of error, affected people, sensitive data, legal duties, and operational dependency.
- Design the test: prepare representative data, fairness scenarios, security cases, acceptance thresholds, and a monitoring plan.
- Issue evidence requests: ask each vendor the same core questions and require model, data, security, support, and portability documentation.
- Run due diligence: validate claims, test the configured service, inspect subcontractors and dependencies, and record unresolved limitations.
- Score and negotiate: apply pre-set criteria, convert relied-on answers into contract language, and allocate risk to the party best able to manage it.
- Pilot with controls: limit users and data, require human review, log outputs, and establish a stop procedure before broad rollout.
- Operate and review: monitor agreed metrics, retest material changes, investigate incidents, and exercise an export or transition test before renewal.
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