The Tool Desk
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1. Define the system’s purpose, users, and legal scope
Start with the decision the AI will support or make—not the vendor’s product label. Write down the intended purpose, who will use the system, who may be affected, where it will be deployed, and how its output will enter the business process. Include foreseeable misuse and the consequences of an incorrect or unavailable output.
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Then assess whether the system is high-risk or otherwise regulated in each relevant jurisdiction. Classification can depend on the system’s use and context, so a vendor’s description alone is not a legal determination. The European Commission’s classification page is intended to help providers and deployers assess high-risk status; it described draft guidance and consultation. Check the page’s current formal status before relying on it.
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2. Establish who is accountable
Determine your organization’s role under each applicable regime: for example, provider, deployer, importer, or distributor. The role affects which duties apply and what evidence you may need. Under Article 16 of the EU AI Act, provider duties include compliance, a quality-management system, technical documentation, logs under the provider’s control, and conformity assessment.
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Translate role allocation into contract terms and operating procedures. Name who is responsible for providing compliance evidence, handling updates, monitoring performance, retaining and reviewing logs, investigating incidents, and taking corrective action. Make sure responsibility does not fall between your organization and the vendor.
3. Require evidence for the exact system you will use
Ask for documentation that matches the specific model, version, configuration, and intended use being procured. Evidence for a different release or a broad product family may not establish how the system you plan to deploy performs.
- Intended-purpose statement, operating instructions, and known limitations.
- Technical and performance documentation, including the evaluation conditions and results.
- Information about training, validation, and testing data governance, where available and relevant to your role.
- Change history and notice of updates that could affect behavior or compliance.
- Support, escalation, and incident-response commitments.
The material available to a buyer will depend on the product, the parties’ roles, and applicable law. Treat assurances without supporting documentation as unresolved procurement questions, not proof of suitability.
4. Assess risks across the lifecycle
Identify possible harms from intended use and reasonably foreseeable misuse. Consider severity and likelihood, select mitigations, record residual risks, and set a schedule for review as the system or operating environment changes. The EU AI Act’s Article 9 describes risk management for high-risk systems as a continuous, iterative process throughout the lifecycle, covering risks to health, safety, and fundamental rights. It states: “A risk management system shall be established, implemented, documented and maintained in relation to high-risk AI systems.”
Consider who may be affected, including children or other vulnerable groups where relevant to the intended purpose. Assess accessibility and whether errors could fall disproportionately on particular groups. Decide what remedy is available when the system produces a harmful or incorrect result, such as a route to human review, correction, appeal, or service recovery where appropriate.
5. Test performance in the intended setting
Evaluate the system using data and operating conditions that represent the actual deployment—not only vendor demonstrations or aggregate results. Examine performance by relevant population and use case, data quality and provenance, robustness, security, and foreseeable failure modes. Choose measures and acceptance thresholds that fit the decision and potential harm; the cited sources do not establish one universal performance threshold.
Before launch, define what happens if results fall short: reject the system, limit its use, require more evidence, or revalidate it after changes. Testing should connect to the risk assessment, so that findings change mitigations and deployment decisions rather than remaining an isolated report.
6. Make human oversight operational
Identify the people responsible for oversight and ensure they understand the system’s intended use, limitations, and escalation path. Specify when a user must question, override, or escalate an output, and what authority they have to pause a decision. A nominal human sign-off is not a control if staff lack the time, information, training, or authority to intervene.
Decide what records are needed to investigate outcomes and how they can be accessed. Set incident triggers, assign investigation owners, and ensure the organization can suspend or roll back the system when necessary. Under the EU AI Act, providers have obligations concerning automatically generated logs when those logs are under provider control, as well as necessary corrective actions; agree how relevant records and responses will be handled across the vendor relationship.
7. Set monitoring and change controls before deployment
Define what will be monitored, who reviews it, and how often. Relevant signals can include performance, complaints, overrides, incidents, and changes in the system or business context. Establish thresholds that trigger investigation, revalidation, restricted use, or shutdown. Article 9 calls for regular review and updating of risk management; the Act also includes provider post-market and corrective-action processes.
Agree in advance how vendor updates are disclosed and assessed. A change to the model, configuration, data, or workflow may alter risk or invalidate earlier testing. Route material changes through review before they reach production, and retain a record of the decision and any new controls.
8. Compare alternatives against the actual decision
Compare candidate systems—and the option of not using AI—against the same intended purpose and risk profile. Tailor the importance of each factor to the potential harm; there is no official universal scoring formula in the cited sources.
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- Fit to intended purpose and quality of version-matched evidence.
- Performance and error distribution in representative conditions, including effects on affected groups.
- Residual risk, explainability needs, and practical human oversight or contestability.
- Data governance, security, robustness, and resilience.
- Monitoring and change controls, vendor accountability, and support.
- Integration burden, total cost, and applicable regulatory duties.
9. Use governance frameworks without confusing them with law
Frameworks can give teams a structure for documenting and improving responsible AI practices, but they do not determine whether a system is legally high-risk or certify that a particular deployment complies with law.
| Resource | What it contributes | Status and qualification |
|---|---|---|
| EU AI Act | Binding requirements, including high-risk risk-management and provider obligations. | Applies where its scope and requirements cover the system and organization. The EUR-Lex text used here is consolidated through 2026-07-27; verify current law and local requirements for a specific deployment. |
| NIST AI Risk Management Framework (AI RMF) | A structure for incorporating trustworthiness considerations into AI design, development, use, and evaluation. | NIST describes it as intended for voluntary use and reports that it is being revised. The NIST AI RMF Playbook offers voluntary implementation suggestions based on AI RMF 1.0, released January 26, 2023. |
| OECD responsible-business-conduct guidance | Adapts due diligence for multinational enterprises to businesses in the AI value chain. | OECD’s 2026 guidance is a governance resource, not a substitute for applicable legal obligations. |
ISO/IEC 42001 may also serve as an optional AI-management-system reference; OECD’s 2026 guidance maps due-diligence practices to it, including risk-treatment and data-management provisions. Purchasing the standard does not by itself establish compliance.
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