Governments should contract for a defined public outcome, not just a named AI product. Before award or deployment, the agreement should make the system’s permitted use, data handling, testable performance, human controls, security duties, change notifications, remedies, and exit arrangements clear—and scale those protections to the consequences of failure.
Start with the public need and intended use
Describe the service problem the purchase is meant to solve, who will use the system, who may be affected by it, and what outcome the agency expects. A contract built around a product label such as “AI assistant” or “automated screening” leaves too much room for the supplier and agency to understand the scope differently.
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Define the boundary of permitted use
- Specify the tasks the system may perform and the decisions or actions it may support.
- Identify prohibited uses and foreseeable adjacent uses that require a new approval or assessment.
- State whether the supplier or subcontractor may use AI as part of a broader service, and require disclosure of relevant AI components and material subcontractors.
- Name the agency owner responsible for approving deployment and ongoing use.
The UK government’s AI procurement guidance recommends transparency about the project, tools, data, and algorithms, and the UK procurement policy note offers optional tender questions about supplier AI use. That policy note applies to specified UK central government bodies; it is not a universal procurement rule.
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Check that the agency can operate the system
Before specifying a technical solution, establish that relevant data is available and that the agency has people, processes, and authority to supervise the service. Clarify the workflow around the AI: what staff do before using its output, what they do when it is uncertain or wrong, and who can halt use.
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Scale contract controls to risk and impact
There is no single risk score or clause set that fits every public-sector AI purchase. Assess the decision’s consequences, affected rights and services, data sensitivity, security environment, degree of automation, and the likely harm if the system fails. A tool that helps draft internal text and a system that influences access to an essential service should not automatically receive the same controls.
Use that assessment to set the strength of testing, human review, monitoring, audit access, and remedies. Record the assessment and revisit it if the use, data, model, or service changes materially.
Set rules for data, privacy, and intellectual property
Do not leave the handling of government information to general vendor terms. The agreement should identify the information the supplier can access and the purposes for which it can use it.
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- Inventory: Identify data types, sources, sensitivity, and material assumptions about data quality.
- Limit use: Define permitted processing, access, retention, disclosure, transfer, and deletion. State explicitly whether government data may be used to train, fine-tune, or otherwise improve a model.
- Protect information: Set applicable security safeguards, access controls, subcontractor obligations, and breach-notification procedures.
- Allocate rights: Clarify ownership and licence scope for government data, supplier materials, outputs, and derived data.
- Enable oversight: Require the documentation, traceability, and access the agency needs to review data use and investigate problems.
- Close out data: Specify what is returned or deleted at the end of the contract and how the supplier will evidence deletion.
AI-specific model clauses do not replace ordinary contract terms. The European Commission Public Buyers Community’s proposed clauses are voluntary drafting aids and do not cover matters including intellectual property, acceptance, payment, delivery times, applicable law, or liability. The buyer must address those subjects in the full agreement.
Turn performance claims into acceptance tests
Claims such as “highly accurate” are not useful obligations unless the parties define the task, conditions, measure, and threshold. Specify the system’s operating context and evaluate it with data and workflows representative of actual use, including relevant populations, languages, and environments.
Before launch
- Agree on acceptance criteria, error thresholds, service levels, availability, and response times appropriate to the use case.
- Require evidence of capability and known limitations, not just general product descriptions.
- Where practicable, test the proposed solution before award or deployment and define retesting and correction procedures if it fails.
After launch
Set monitoring responsibilities, evaluation frequency, reporting, and the process for investigating a deterioration in performance. State who performs and pays for retesting after a material change. The US Government Accountability Office’s summary of federal acquisition guidance describes testing proposed solutions where practicable and contract provisions for ongoing testing, monitoring, and performance; those federal requirements should not be treated as binding outside their applicable US federal context.
Require transparency, records, and audit cooperation
The agency needs enough information to oversee the system, verify contractual compliance, maintain the service, and explain decisions to affected people where appropriate. Specify which documentation the supplier must deliver and keep current, what logs and records must be retained, and how the agency can obtain evidence during an investigation or audit.
Define access and cooperation rights proportionately, while protecting legitimate confidential information and security-sensitive material. OECD public-sector analysis warns that limited transparency can undermine independent maintenance and monitoring capability. If a supplier cannot provide the information needed to assess a serious failure or maintain the service, that constraint can become a practical dependency.
Keep people and the agency in control
Human oversight should match the decision being supported and the consequences of error. The contract should identify who monitors day-to-day performance, who reviews consequential outputs, and who can override, restrict, or suspend use when needed. It should also cover complaints, appeals, escalation, and corrective action where those processes are relevant to the service.
Assign responsibilities between supplier and agency for approvals, configuration, data quality, human decisions, and third-party dependencies. Require the supplier to support investigation and remediation rather than leaving the agency to diagnose failures without access or assistance.
Cover security, incidents, and material changes
Set security requirements for the service and any subcontractors handling government information or performing material AI functions. Require timely notice of security incidents, harmful or discriminatory outcomes, data problems, unauthorized use, and significant performance changes. Define the information the supplier must provide, response times, investigation support, and conditions for safe resumption.
Require advance notice and review of material changes to models, training or reference data, hosting, subprocessors, functionality, or performance characteristics. Specify which changes trigger approval, retesting, an updated impact assessment, or a right to reject the change or terminate. Avoid a blanket right for the supplier to change the service where those changes could alter risk or performance.
Where applicable, incorporate agency policy and security authorization requirements. GAO’s summary of US federal guidance notes that systems operated as agency information systems require authorization to operate before deployment; the exact obligation depends on the relevant federal rules and system classification.
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Allocate responsibility according to what each party controls. The supplier’s responsibilities for model components, updates, and subcontractors should be distinguishable from the agency’s responsibilities for configuration, data quality, and operational decisions.
Specify practical consequences for noncompliance: correction, retesting, agreed financial or service remedies, suspension, or termination. The agency also needs continuity provisions if the supplier fails, the service is no longer suitable, or the contract ends.
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- Define export formats, interfaces, licences, and access to records and documentation needed to continue the service or move to another provider.
- Set transition assistance, handover responsibilities, wind-down roles, and associated costs.
- Require return or deletion of government data and derived materials, with an auditable method to verify disposition.
- Address access to the information and evidence necessary to investigate failures and maintain the service during transition.
UK government guidance advises defining end-of-life roles and processes and using auditable methods for data cleaning and collection. These details help turn “avoid vendor lock-in” into obligations that can be checked and enforced.
Use model clauses carefully
Model clauses can speed drafting, but they are aids rather than complete contracts or universal legal rules. The European Commission Public Buyers Community describes its proposed AI clauses as voluntary and calls for case-by-case decisions about sufficiency and proportionality. Its materials distinguish fuller clauses for high-risk systems from a lighter approach for non-high-risk systems.
A separate record for MCC-AI-Light describes a February 2025 working document for non-high-risk procurement. Its listed topics include risk management, data governance, transparency, human oversight, accuracy, robustness, cybersecurity, and dataset rights. That record says the document does not reflect an official European Commission position, so buyers should verify its current status before using it.
UK procurement guidance, EU clause materials, and US federal acquisition requirements operate in different legal and administrative settings. Apply the rules that govern the buyer, sector, and procurement, and have the final agreement reviewed under the relevant jurisdiction rather than importing another jurisdiction’s language as if it were binding.
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A common evaluation framework makes it easier to compare offers without pretending there is a universal scoring formula. Score each bid against the actual public-service need and the controls required for that deployment.
- Impact on individuals, rights, and essential services.
- Data sensitivity, provenance, quality, and permitted reuse.
- Performance in the operating context and clarity about limitations.
- Documentation, auditability, and ability to explain relevant decisions.
- Human review and intervention options.
- Security, resilience, and subcontractor exposure.
- Governance of model and service changes.
- Portability, transition time, data disposition, and exit cost.
- Whole-life cost, including integration, maintenance, monitoring, and retirement.
These comparison dimensions reflect themes in UK, EU, OECD, and US federal guidance; those sources do not establish a single scoring formula for all government AI procurements.
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