Before adopting an AI tool, a business should confirm that it solves a defined problem better than the current workflow, can use the required data appropriately, performs reliably enough for its intended task, and comes with supplier, contract, security and oversight arrangements the business can live with. Treat adoption as a continuing operational decision—not simply a software purchase—and compare an AI option with a non-AI alternative where that could meet the need.
The checks below are general guidance. Legal duties vary by jurisdiction, sector, use, affected people and whether the organization is the system’s provider or deployer.
1. Is AI the right fit for the business problem?
Describe the task before reviewing product features. Be specific about the workflow, intended users, people affected, and whether the tool will draft content, make recommendations, or influence a decision. A vague aim such as “use AI to improve efficiency” is not enough to assess whether a tool is suitable.
Set a baseline using the existing process and define what measurable improvement would justify the tool’s cost and operational changes. Depending on the task, that might mean fewer errors, shorter processing time, a specified quality level, or reduced workload without worsening service. Set acceptance criteria before procurement so that a pilot can be judged against the same standard as the current process.
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Compare the AI tool with the present workflow and realistic alternatives, including a non-AI process. Consider performance and limitations, required data, privacy and security controls, human review, effects on different groups, integration effort, supplier dependencies, contract and exit terms, and total cost relative to the expected benefit. Give greater weight to error consequences and data sensitivity than to novelty or a supplier’s feature list. UK Government procurement guidance notes that data availability can be a prerequisite for an AI solution; do not proceed if the data needed for the task is unavailable or not appropriately governed.
NIST’s AI Risk Management Framework (AI RMF) is voluntary, not a certification or a guarantee that a particular product is safe or compliant. NIST’s Generative AI Profile offers a way to consider distinctive generative-AI risks in light of an organization’s goals and priorities.
2. What data will the tool handle, and on what terms?
Trace the information that enters and leaves the system, why it is processed, who is affected, and which parties in the supplier chain handle it. Include prompts, uploaded files, generated outputs, logs, backups and retained copies—not just the data visible in the user interface.
- Identify whether the information is personal, confidential, regulated, copyrighted or otherwise restricted.
- Ask whether the vendor retains inputs or outputs, uses them to train or improve models, shares them with subprocessors, or stores them in another jurisdiction.
- Clarify retention periods, deletion procedures, backup treatment, access permissions and the vendor’s ability to confirm deletion.
- Map the organization’s and vendor’s roles for each processing activity, and make sure the agreed roles and purposes appear in the contract and relevant privacy information.
The UK Information Commissioner’s Office (ICO) advises organizations to document controller and processor roles across processing activities and formalize the agreed position. The Federal Trade Commission (FTC) advises businesses to define vendors’ permitted data use, sharing, sale, retention and deletion in writing, then verify that vendors comply. These checks do not by themselves determine whether a particular use of data is lawful. Involve privacy, legal and security specialists when the proposal involves personal or regulated information, sensitive decisions or cross-border processing.
3. Can the tool meet an agreed quality threshold?
Decide before a pilot what level of accuracy and quality is acceptable for the intended task. Ask the supplier how the system was evaluated, what data or models underpin it, what its known limitations are, and under which conditions its performance claims were established. A result demonstrated on one task or dataset is not proof that the tool will perform equally well in your workflow.
Test representative cases using data and conditions that reflect actual use, including likely edge cases and relevant groups of people. Record where the tool succeeds, fails, or produces plausible but incorrect output. Ask for evidence of fairness testing and look for performance differences that could affect groups subject to the system. The ICO recommends setting acceptable accuracy before procurement and assessing accuracy, bias, discrimination and trade-offs. UK Government procurement guidance recommends that suppliers explain limitations and testing under a range of conditions.
Set rules for what users may do with outputs. For example, decide whether an output can be used as a draft, may only inform a recommendation, or must be checked by a competent person against another source. The greater the potential harm from an error, the stronger the review, escalation and approval controls should be. NIST identifies accountability, transparency, explainability, validity, reliability, safety, security, privacy and fairness as trustworthiness characteristics to consider throughout the AI lifecycle.
4. Is the supplier and its security posture trustworthy enough?
Assess the vendor as an ongoing supplier, not just as the provider of a feature. NIST’s finalized SP 1326 due-diligence guide, published July 8, 2026, identifies supplier ownership and control, provenance, resilience, foundational cybersecurity practices and supply-chain tiers as assessment components.
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Request evidence proportionate to the importance of the system. Relevant questions include who owns or controls the supplier; what third parties and dependencies the service relies on; how access is restricted; how incidents are handled and reported; what continuity arrangements exist; and how the supplier manages data retention, deletion and subprocessors. Ask how material model, product or data-practice changes will be communicated and what information the business will receive to evaluate them.
Do not treat a marketing claim as proof of a security control. The FTC recommends specifying security requirements in vendor contracts, verifying them rather than relying on assurances alone, and keeping vendor security current as threats change. NIST’s July 8, 2026 announcement puts the procurement point plainly: “Acquirers who make procurement decisions need to be informed about potential supplier risks before those decisions are executed.”
5. Does the contract cover the actual use case?
Make the agreement reflect the task and restrictions the business assessed. Where feasible, specify measurable service and quality expectations, including how performance will be evaluated and what happens if agreed requirements are not met.
Check that the written terms address:
- Permitted purposes, data types and vendor uses of prompts, inputs, outputs and logs.
- Processing roles and instructions, security requirements, subprocessors and incident-notification commitments.
- Retention, deletion, records and documentation, including what happens to copies on termination.
- Notice of material changes to the service, model, data handling or relevant dependencies.
- Review or audit rights appropriate to the risk, plus a workable way to export data, end the service or switch suppliers.
The ICO recommends documenting processing purposes and roles, considering the full supply chain, using accuracy-based KPIs or service-level agreements where appropriate, and reviewing outsourced services as risks or circumstances change. FTC small-business cybersecurity guidance says: “If there are specific security standards you want your vendor to follow, be specific in your contract and make the terms non-negotiable.” Contract language should be reviewed against the organization’s actual obligations and negotiating position.
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Name an internal owner before the tool goes into use. That owner should know who can approve use, monitor performance and risk, respond to errors and complaints, review supplier changes, and pause or change the system when necessary. Users should know how to report a problem and when they must escalate rather than rely on an output.
Keep an inventory of AI tools, including AI features embedded in ordinary business software. Establish a review cadence suited to the use and its risks, and revisit the tool when performance changes, incidents occur, data practices shift, or new legal requirements affect the use. NIST organizes risk work into four functions—Govern, Map, Measure and Manage—which can help structure ownership, context assessment, testing and response without prescribing a single process for every organization.
7. Which legal requirements apply to this use?
Do not assume that a general procurement checklist establishes compliance. Requirements depend on the organization’s location and role, the tool’s purpose, the people affected and any sector-specific rules. Employment, credit, health, safety, access to essential services and other consequential uses warrant specialist review before deployment.
For the EU, the European Commission’s July 20, 2026 guidance says that specified AI Act Article 50 transparency obligations apply from August 2, 2026. Duties differ by role and context: provider obligations include direct AI interactions and machine-readable marking of AI-generated or manipulated content; deployer disclosure duties cover specified contexts involving emotion recognition or biometric categorisation, deepfakes, and certain AI-generated public-interest text without human review or editorial control. This is not a blanket duty for every business to disclose every use of any AI tool. Determine whether the particular system and use fall within the relevant provision, which party is provider or deployer, and whether other local or sector rules apply. Because the AI Act timeline and guidance can change, verify the current official text and guidance before relying on them operationally.
The ICO marks its AI contracts and third-party guidance as under review following the UK Data (Use and Access) Act, and NIST says revision of AI RMF 1.0 is in progress. Check current official guidance for the relevant jurisdiction and use when making a procurement or compliance decision.
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