Before adopting a high-risk AI system, confirm what it will do and where it will be used, determine your organization’s legal role, and obtain evidence for the specific model and configuration under consideration. Then assess risks in your own operating context, test performance and impacts on affected people, and put human oversight, incident response, monitoring, and change controls in place before launch. Legal duties depend on jurisdiction, use, and organizational role; voluntary frameworks can help structure the work but do not replace legal analysis.
1. What exactly will the system do, and where will it be used?
Start with the business decision, not the product label. Record the system’s intended purpose, who will use it, whose data or interests may be affected, and whether it recommends, ranks, or makes decisions. Map the process around it: what information goes in, how staff use the output, and what happens when the output is wrong or unavailable.
- List deployment countries and any relevant sector or local rules.
- Identify user groups and people affected by decisions, including groups who may be vulnerable or need accessible ways to interact with the process.
- Describe intended use and foreseeable misuse. A tool used for a different purpose or in a different setting may present different risks.
- Record the model, version, configuration, connected data sources, and planned integrations you are evaluating.
For EU deployments or other situations within the EU AI Act’s scope, assess whether the system is high-risk under the Act rather than relying on a vendor’s marketing description. The European Commission’s classification-guidance page has described its guidance as draft and subject to consultation; check its current status before treating it as authoritative. Classification is a fact-specific legal assessment, not a conclusion this checklist can make for a particular system.
2. Which organization has which legal and operational role?
Determine whether your organization is acting as a provider, deployer, importer, distributor, or another operator under each applicable regime. Roles can affect duties and the evidence you need; do not assume that buying a system makes your organization only a customer with no obligations.
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For an EU AI Act assessment, Article 16 sets out provider obligations, including compliance, a quality-management system, technical documentation, logs under provider control, and conformity assessment. Establish what the vendor must do and what your organization must do, then assign named owners for the work that remains yours.
- Who supplies and maintains compliance evidence?
- Who informs you about updates, incidents, or changes in intended purpose?
- Who monitors system performance and keeps the records needed to investigate outcomes?
- Who can restrict use, require corrective action, or suspend the system?
- How will responsibilities transfer or be handled if the vendor relationship ends?
3. What evidence should you require from the vendor?
Request material that lets your team assess the actual system, not general promises about responsible AI. Check that the evidence matches the model version, configuration, intended purpose, and operating conditions you plan to use.
| Evidence to request | What to check | Why it matters to the adoption decision |
|---|---|---|
| Purpose and operating instructions | Intended uses, prohibited or unsupported uses, user guidance, and known limitations | Helps determine whether the proposed deployment stays within the system’s stated purpose and whether staff can use it safely. |
| Technical and performance documentation | Evaluation methods, results, test conditions, relevant populations, and version or configuration covered | Shows what the evidence actually establishes and where it may not generalize to your workflow. |
| Data-governance information | Data sources and handling, quality controls, and relevant limits or gaps | Supports assessment of data suitability, provenance, and risks arising from the data used or processed. |
| Risk and change records | Known risks, mitigations, change history, and how material updates are communicated | Helps identify residual risks and decide when a change requires further review or testing. |
| Support and escalation commitments | Incident notification, response contacts, investigation support, and corrective-action processes | Clarifies whether the vendor can support your operational controls when something goes wrong. |
The European Union’s AI Act Article 16 assigns documentation and quality-system duties to providers, but the material available to a buyer depends on the organization’s role, the product, and applicable law. If evidence is incomplete, out of date, or unrelated to the configuration you will deploy, treat that as an unresolved procurement risk rather than filling the gaps with assurances.
4. How should you assess and test the risks?
Build a documented assessment around harms that could arise from intended use and reasonably foreseeable misuse. EU AI Act Article 9 describes risk management for high-risk AI systems as a continuous, iterative process across the system lifecycle, addressing risks to health, safety, and fundamental rights. Use that lifecycle approach even when a different legal regime applies, while separately checking the rules for each jurisdiction.
- Identify harms and failure modes. Consider incorrect, inconsistent, unavailable, or misleading outputs; misuse; and harms caused by people over-relying on the system.
- Estimate severity and likelihood. Consider who could be affected, how often an error could occur, how difficult it would be to detect, and whether the harm could be reversed.
- Choose mitigations. Decide whether to change the workflow, add human review, limit the system’s permitted use, improve data controls, or reject the system.
- Document residual risk. Record which risks remain, who accepts them, and what conditions or safeguards are necessary for use.
- Set review triggers. Specify what changes in the model, data, business process, or external context require reassessment.
Test the system with data and conditions representative of the planned deployment. Examine performance for relevant populations and use cases, robustness, security, data quality and provenance, and foreseeable failure modes. Define acceptance criteria before reviewing results, along with a process for rejecting, restricting, or revalidating the system if it falls short. The cited sources do not establish one universal performance threshold; suitable measures depend on the use case and sector.
5. How will the system affect people subject to its outputs?
Map who could bear the consequences of an error, including people who may be underrepresented in evaluation data or face greater difficulty correcting a bad outcome. Consider accessibility and vulnerability where relevant to the intended purpose; EU AI Act Article 9 specifically calls for attention to children and other vulnerable groups where relevant.
Decide what meaningful human review looks like in practice. Staff need enough authority, information, time, and training to question or override outputs—not merely a button to approve them. Where appropriate to the decision and applicable rules, provide a route for affected people to seek review, correct information, appeal, or recover a service. Document who handles each route and how a concern changes the outcome.
6. Is the business ready to operate the system safely?
Before launch, turn safeguards into operating procedures with accountable owners. A policy that does not specify who acts, when, and with what authority is not an effective control.
- Train users on intended use, limitations, escalation criteria, and when to override or stop relying on an output.
- Define incident triggers, escalation contacts, investigation steps, and decision authority for restricting or suspending use.
- Retain records needed to understand how the system was used and investigate outcomes, consistent with applicable law and the organization’s retention requirements.
- Agree with the vendor how updates, incidents, and corrective actions will be communicated and handled.
- Prepare a rollback or alternative workflow so essential operations can continue if the system must be paused.
EU AI Act provider obligations include keeping automatically generated logs when they are under the provider’s control and taking necessary corrective actions. Buyers should establish which records they can access and preserve, and ensure contracts and operating processes support investigations without assuming control over logs the provider retains.
7. What should ongoing monitoring and change control cover?
Risk management continues after procurement. Monitor signals that the system or the surrounding process is no longer behaving as assessed, and decide in advance what response each signal triggers.
- Track performance, errors, complaints, user overrides, incidents, and signs of data or performance drift.
- Review vendor updates and changes to model versions, configuration, integrations, and intended use.
- Watch for changes in the business context that could affect risk, including new users, affected groups, or decision consequences.
- Set thresholds for investigation, revalidation, use restrictions, or shutdown, with an owner and response timeframe for each.
- Review the risk assessment regularly and update it when monitoring or changes show that assumptions no longer hold.
Article 9 requires regular review and updating of risk management under the EU AI Act; the Act also includes provider post-market and corrective-action processes. Set out the information flow between your organization and the vendor so relevant changes and incidents reach the people responsible for reassessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.8. How should you compare competing systems?
Compare options against the same intended decision and operating conditions. Weight the criteria according to potential harm and business purpose; there is no official universal scoring formula in the sources cited here.
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| Comparison area | Questions to ask |
|---|---|
| Fit and evidence | Does the system fit the intended purpose, and does the evidence cover the exact version, configuration, and conditions proposed? |
| Performance and impact | How does it perform on representative cases and relevant populations? What residual risks or uneven error patterns remain? |
| Oversight and recourse | Can staff meaningfully review or override outputs, and can affected people seek appropriate review or correction? |
| Data, resilience, and security | Are data practices suitable, and are robustness and security risks addressed for the planned deployment? |
| Operations and vendor accountability | Can the vendor support monitoring, incident investigation, updates, and corrective action? Can your organization suspend or replace the system? |
| Integration and obligations | What operational burden does integration create, and which regulatory duties apply to your organization and the vendor? |
| Total cost and risk | What are the costs of implementation, oversight, monitoring, and remediation relative to the system’s benefits and residual risks? |
9. Which frameworks can help—and what do they not establish?
NIST’s AI Risk Management Framework is intended for voluntary use to help incorporate trustworthiness into AI design, development, use, and evaluation. NIST has reported that the framework is being revised. Its Playbook offers voluntary implementation suggestions based on AI RMF 1.0, released January 26, 2023. These resources can help organize governance; they are not, by themselves, law or certification.
The OECD’s 2026 guidance adapts responsible-business-conduct due diligence for multinational enterprises in the AI value chain. It can help structure attention to impacts and due diligence across business relationships, but it does not determine whether a particular system meets the law in a given jurisdiction. ISO/IEC 42001 may also serve as an optional AI management-system reference; using or purchasing a standard alone does not establish legal compliance.
For a specific procurement, verify current legal requirements and formal guidance for every relevant jurisdiction, sector, and organizational role. The EU AI Act text cited for this checklist is consolidated through July 27, 2026; dates, definitions, national enforcement, and other requirements may depend on the facts and may change. Obtain jurisdiction-specific legal advice where needed.
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