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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Before signing a multi-year AI agreement, define the business outcome and acceptable risk, test the supplier’s evidence on your own use case, and turn data rights, performance, security, change control, remedies, and exit into enforceable terms. A persuasive demo is not evidence of durable performance. Requirements vary with your jurisdiction, sector, data sensitivity, and the consequences of an error, so have local counsel map this checklist to the final contract.
1. Define the problem, users, and measurable outcome
Start with the service you need, not a model name. Describe the business or public-service problem, who will use the system, which decisions it may support, and which uses are prohibited. State the outcome in measurable terms: for example, a response-time target, a document-classification recall threshold, or a reduction in manual work that can be verified from your own records. The UK Government’s Guidelines for AI procurement recommend output-based requirements and an early assessment of whether suitable, lawful data exists.
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- Specify intended users and affected people. Include operators, reviewers, customers, citizens, employees, and anyone who may be affected by an output.
- List acceptable and prohibited uses. A system approved for drafting internal text may not be approved for eligibility, employment, credit, safety, or other high-consequence decisions.
- Check the data before choosing a supplier. Confirm that the data is relevant, lawful to use, sufficiently complete, representative of the populations and conditions you will encounter, and available in a form the service can process.
- Consider a non-AI alternative. A rules-based workflow, search system, human service, or existing software may meet the outcome with less uncertainty and less contractual exposure.
- Set a baseline and acceptance test. Record current cost, speed, error rate, and quality so that a vendor claim can be compared with the result of doing nothing or using an alternative.
Do not sign a long term based only on a promise to improve later. Make the initial use case, acceptance test, and prohibited uses an attachment to the agreement so the parties have the same definition of success.
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2. Demand evidence about the model and the supplier
Ask for enough technical and operational detail to evaluate the exact configuration you will buy. Australia’s Reliability and safety guidance identifies model development, data sources, known limitations, versioning, testing, documentation, privacy, intellectual-property, security, supply-chain, and support capability as relevant third-party selection questions.
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Model and data provenance
- Identify the model family, version, deployment mode, context or input limits, tools, retrieval components, safety filters, and any customer-specific fine-tuning included in the price.
- Ask what data trained or otherwise informed the system, how licensing and provenance were assessed, and whether the supplier can provide documentation or a summary suitable for your legal and risk review.
- Request known failure modes, prohibited content behavior, language or domain limitations, and conditions under which performance is expected to decline.
Testing and operational capability
- Request evaluation methods, test-set descriptions, benchmark results, confidence information, red-team or adversarial testing, and user-testing results. Ask which results are independently verified and which are supplier claims.
- Confirm how the supplier releases versions, records changes, supports rollback, handles outages, and communicates incidents.
- Assess support coverage, escalation contacts, response targets, training materials, documentation updates, and the supplier’s ability to support your whole contract term.
- Trace critical subcontractors and hosting providers. A contract with the front-end supplier does not remove risk from a model host, data processor, security provider, or other dependency.
Put the evidence you relied on in a schedule or repository with a date and version. If a sales statement matters to your decision, make it a warranty, service level, or other contractual commitment rather than leaving it in presentation material.
3. Set accuracy, fairness, and transparency requirements before procurement
Define acceptable performance for the actual task and population before comparing bids. Generic benchmark scores rarely predict results on your documents, languages, edge cases, or workflow. The Information Commissioner’s Office (ICO) advises organizations to request evidence of accuracy, fairness, explainability, limitations, and user testing, and not to procure where bias or discrimination cannot be mitigated. See its contracts and third parties guidance for artificial intelligence.
Build a use-case test pack
- Assemble representative, lawful test data, including rare cases, difficult inputs, relevant languages, accessibility needs, and the groups most likely to be affected.
- Define measures before seeing vendor results: precision, recall, false-positive and false-negative rates, abstention or escalation rate, latency, availability, and human-review quality as applicable.
- Test the same scenarios on every shortlisted configuration. Record prompts, model version, settings, retrieved material, outputs, evaluator, and date so results can be reproduced.
- Assess disparate error patterns and whether users can understand, challenge, and correct an output. Document mitigations and the residual risk.
- Set a minimum acceptance threshold and a review interval. Include accuracy or quality KPIs, measurement method, reporting format, and a remedy if the threshold is missed.
Explainability can mean different things: a reason code, source excerpts, a decision log, confidence information, or a human-review trail. Specify what your users and regulators actually need instead of accepting an undefined promise of an “explainable” model.
4. Control data use, privacy, and intellectual property
The contract should identify every category of data that enters or leaves the service and every purpose for which it may be processed. The UK procurement guidance, the ICO guidance, and Australia’s guidance all point to these issues; the applicable legal roles and duties still depend on your facts and jurisdiction.
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| Contract question | What to specify |
|---|---|
| Inputs and prompts | Permitted data classes, sensitive-data restrictions, customer instructions, encryption, access controls, and whether prompts, files, logs, or feedback can be retained. |
| Training and improvement | Whether your inputs, outputs, feedback, or telemetry may train, fine-tune, evaluate, or serve other customers; obtain a contractual prohibition where required. |
| Roles and locations | Controller/processor or equivalent roles, processing instructions, hosting regions, international transfers, subprocessors, approval and notice procedures, and assistance with data-subject or regulatory requests. |
| Retention and deletion | Retention periods by data type, backup handling, deletion deadlines, deletion certificates or other verification, and exceptions required by law. |
| Rights and licences | Ownership or licence rights in customer inputs, generated outputs, prompts, custom models, connectors, configurations, documentation, and newly developed work; state which rights survive termination. |
| Records and audit | Access to relevant logs, processing records, model and version information, security evidence, and the practical audit method, frequency, notice, confidentiality, and cost allocation. |
Do not assume that paying for an output automatically gives you unrestricted rights to it, or that the supplier’s standard privacy notice matches your required processing instructions. Have counsel address copyright, database rights, confidentiality, regulated data, cross-border transfers, and any sector-specific retention rule.
5. Specify security, incident response, and dependency controls
Security obligations should match the sensitivity of the data and the harm a compromise could cause. Require safeguards for identity and access management, least privilege, tenant isolation, encryption, key management, secure development, vulnerability management, logging, backup and recovery, and physical or cloud infrastructure as relevant to the service.
- Incident notice: define what counts as an incident, the initial notification deadline, required facts, update cadence, customer and regulator cooperation, and who leads communications.
- Investigation and evidence: require preservation of logs and other evidence, root-cause analysis, corrective action, and access needed for your investigation.
- Subcontracting: require a current dependency list, notice or approval for material changes, equivalent flow-down obligations, and responsibility for subcontractor failures.
- Continuity: set recovery objectives, backup testing, outage communications, alternative processing or manual fallback, and assistance when a critical dependency fails.
- Assurance: specify acceptable independent reports, penetration-test summaries, certifications, questionnaires, or customer audits, and the frequency at which they must be supplied.
For U.S. federal information-technology contracts covered by it, FAR Part 39 calls for risk analysis and privacy safeguards. That is a federal procurement requirement in its specified context, not a universal private-sector rule; other buyers must map security terms to their own law and policy.
6. Make model changes and performance drift controllable
A multi-year contract must assume that models, hosting, safety systems, prices, and subcontractors will change. Require advance notice of any material model, provider, hosting location, interface, data-use, or service-level change. Define “material” with examples so a supplier cannot avoid notice by describing a consequential change as routine maintenance.
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Agree change triggers and responses
- Retesting when a model, tokenizer, retrieval index, safety filter, training method, data source, or key dependency changes.
- Customer approval, a pilot, or a rollback option for changes that affect a high-consequence use or a contractual KPI.
- Remediation plans, service credits, suspension of a use case, price adjustment, or termination if testing shows unacceptable degradation or harmful new behavior.
- Notice and assistance if the supplier is acquired, becomes insolvent, discontinues a model, changes a processing location, or loses a critical licence.
Operate continuous evaluation
Set monitoring measures, owners, review intervals, alert thresholds, and escalation paths. Track drift in accuracy, fairness, latency, availability, refusal behavior, security events, data quality, and user complaints. The UK guidance says ongoing model testing is needed to maintain accuracy, while Australia’s guidance highlights versioning, drift, testing, and monitoring. Store enough input, output, configuration, and reviewer information to investigate an alert without collecting data you do not need.
7. Allocate responsibility and remedies explicitly
Buying a supplier’s system does not transfer responsibility for your data, instructions, or decisions. The UK’s Data and AI Ethics Framework says public bodies remain responsible for their data and algorithm-assisted decisions and should allocate supplier responsibilities clearly. Use a responsibility matrix that names an accountable owner for each risk.
| Risk or activity | Questions the contract should answer |
|---|---|
| Use and decisions | Who approves use cases, sets human review, handles appeals, and stops use when harm appears? |
| Data and transfers | Who supplies lawful data, manages permissions, responds to requests, and pays for required transfer or deletion work? |
| Performance and quality | Who measures KPIs, validates updates, investigates errors, and supplies corrective action? |
| Security and incidents | Who protects each environment, notifies whom and when, preserves evidence, and coordinates remediation? |
| Legal and third-party claims | Who handles allegations involving privacy, intellectual property, discrimination, product defects, or regulatory action? |
With counsel, negotiate warranties, indemnities, liability caps and carve-outs, insurance, service credits, refunds, suspension rights, and termination rights. A low headline price can be poor value if the buyer carries unlimited exposure for a risk the supplier can control. The UK Government’s procurement guidance puts it plainly: “Correct risk allocation is crucial to the long term viability of the service, but also to achieving best value.”
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Lock-in is often created by undocumented prompts, proprietary schemas, embedded workflows, and inaccessible logs rather than by the model alone. Write the transition plan into the initial agreement.
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- Define exportable data, outputs, configurations, prompts, evaluation sets, logs, documentation, and audit records.
- Specify machine-readable formats, export frequency, delivery deadlines, integrity checks, encryption, and who pays reasonable extraction costs.
- Require transition assistance, knowledge transfer, named technical contacts, and continued read or service access for an agreed migration period.
- State what happens to customizations, connectors, licences, and records when the relationship ends.
- Require return or verifiable destruction of data, including backups and subprocessors, with a certificate or equivalent evidence.
- Define continuity measures for supplier failure, model discontinuation, acquisition, or a disputed termination.
Open standards and interpretable outputs generally make it easier to appoint another supplier, but they do not guarantee portability. Test an export and restore process during due diligence, not after termination notice.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Compare vendors with a use-case scorecard
Set criteria and weights before reviewing offers. Weight the consequences of error and data sensitivity; do not let a generic “best model” ranking decide a high-risk purchase. Distinguish vendor assertions from independently verified evidence and record why a score was awarded. The guidance does not prescribe universal weights, so your governance team must justify them.
| Criterion | Evidence to compare | Decision questions |
|---|---|---|
| Task performance | Results on your test pack, failure modes, latency, availability, and human-review burden. | Does it meet the acceptance threshold on ordinary and edge cases? |
| Data provenance and terms | Training or informing data description, licences, retention, training-use controls, and subprocessors. | Can the supplier lawfully process your data for only the purposes you allow? |
| Privacy and security | Roles, locations, controls, incident history and process, assurance reports, and audit access. | Are safeguards proportionate to the harm of exposure or misuse? |
| Fairness and explainability | Group-level testing, mitigation evidence, explanations, appeal support, and limitations. | Can affected people and reviewers understand and challenge outputs? |
| Change control | Version policy, notice period, testing, rollback, and discontinuation commitments. | Can you detect and control a material change? |
| Support and continuity | Service desk, escalation, recovery objectives, dependency transparency, and financial stability information. | Will the service remain usable and supportable for the full term? |
| Portability | Export formats, documentation, transition assistance, and deletion verification. | Can you move without losing essential records or operational knowledge? |
| Liability and total cost | Fees, usage and overage rules, implementation, monitoring, migration, insurance, liability allocation, and remedies. | What is the cost and exposure over the entire term, including exit? |
10. Questions to put to every shortlisted supplier
Ask the questions in writing and carry material answers into the contract, a service schedule, or a signed data-processing document.
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- What exact model, version, configuration, hosting arrangement, tools, and limits are included, and how will you notify us of changes?
- What data trained or informed the system, what data will our use send to you or subprocessors, and where will it be processed?
- Will our prompts, files, outputs, feedback, or telemetry be used for training, fine-tuning, evaluation, or other customers? Can those uses be prohibited contractually?
- What evaluation methods, test data, benchmarks, accuracy results, fairness assessments, user tests, and known limitations can you provide for our use case?
- What security controls, incident timelines, subcontractor controls, continuity measures, and audit evidence are contractual commitments?
- Who owns or licenses inputs, outputs, custom work, prompts, configurations, and documentation, and which rights survive termination?
- What happens if accuracy degrades, a model or provider changes, you are acquired, or a key dependency becomes unavailable?
- How will we export data and records, obtain transition assistance, keep operating during migration, and verify deletion at expiry?
11. Put governance around the contract
Include procurement, business, technical, security, privacy, legal, and user representatives in evaluation and approval. Name an internal owner with authority to pause use, require remediation, and escalate incidents. Maintain a decision record covering the use case, alternatives considered, test results, residual risks, approvals, and review dates.
At each review, examine KPI and fairness results, incidents and complaints, model or dependency changes, data-use compliance, security evidence, open remediation, spend, and exit readiness. Reconfirm that the system is still being used only for approved purposes. Supplier management should be continuous; a signature is not a substitute for oversight.
12. Apply the right jurisdictional lens
| Material | What it contributes | Limit |
|---|---|---|
| UK Government AI procurement guidance | Output-based requirements, data assessment, supplier evidence, risk allocation, ongoing testing, and exit planning. | Guidance for UK procurement; it does not replace the law or rules applicable to another buyer. |
| UK Data and AI Ethics Framework | Accountability for public bodies’ data and algorithm-assisted decisions and clearer supplier responsibility. | Its public-sector framing is not a universal contract rule. |
| ICO AI contracts guidance | Practical data-protection questions on accuracy, fairness, transparency, roles, processing, and third parties. | Relevant data-protection duties depend on the processing and jurisdiction. |
| Australian Digital Transformation Agency guidance | A diligence framework covering reliability, safety, versioning, drift, testing, monitoring, and third-party model selection. | It does not determine obligations outside its context. |
| U.S. FAR Part 39 | Risk-analysis and privacy-safeguard requirements for covered U.S. federal information-technology acquisitions. | It applies in its specified federal context, not to every commercial contract. |
Map the checklist to your procurement rules, sector regulation, privacy regime, records obligations, cross-border requirements, and contract facts. The cited materials come from different governments and regulators and are complementary guidance, not interchangeable legal rules.
13. A practical pre-signing workflow
- Write the use-case brief: outcome, users, affected people, approved and prohibited uses, alternatives, data sources, and consequences of failure.
- Run the data assessment: legality, quality, representativeness, sensitivity, location, retention, and required permissions.
- Build the test pack: representative and edge cases, metrics, fairness slices, explainability needs, and acceptance thresholds.
- Issue evidence requests: model and data provenance, evaluation, limitations, security, support, dependencies, pricing, and exit capability.
- Score comparable offers: apply pre-set weights, verify important claims, document residual risks, and reject a supplier that cannot meet a non-negotiable threshold.
- Conduct a controlled pilot: use approved data, record configuration and results, involve users, and test failure and fallback procedures.
- Negotiate the contract schedules: data processing, IP, KPIs, change control, security, incidents, audit, liability, insurance, remedies, and exit.
- Assign owners and launch gates: name accountable people, complete training, confirm human review, and obtain documented approval before production use.
- Monitor and review: measure drift, fairness, incidents, security, spend, and supplier changes at agreed intervals.
- Exercise portability periodically: test an export, restore, documentation handoff, and deletion evidence before you need to leave.
What the available oversight evidence does—and does not—show
The U.S. Government Accountability Office’s 2026 review covered 13 AI acquisitions at four federal agencies. GAO reported that the selected agencies were not systematically collecting lessons learned from AI acquisitions, including practices involving data rights and testing; that finding is limited to the agencies and acquisitions in the report, not a market-wide sample. Read the full scope in Artificial Intelligence Acquisitions.
No general statistic establishing how often long-term AI contract problems occur, or what they cost, is established by these materials. The practical implication is to create your own evidence trail: define success, test it, assign each risk, and preserve the records needed to enforce or revise the deal.
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