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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchBefore adopting an AI tool, confirm that it solves a defined business problem, can use the necessary data appropriately, performs well enough on representative cases, and can be governed through a clear contract and accountable owner. Compare it with your existing or another non-AI workflow; buying an AI product is not itself a business case.
1. What business problem will the tool solve?
Describe the task before reviewing product features. Identify the users, the people affected, where the tool fits into the workflow, and whether it will draft, recommend, classify, or make decisions. The consequences of an error should shape how much evidence and human oversight you require.
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Set a baseline using the current process and define measurable acceptance criteria before procurement. Decide what improvement would justify the software cost, integration work, training, and operational change. Check that the data needed for the task is available and governed. UK government procurement guidance treats data availability as a frequent prerequisite for an AI solution; if the prerequisite is missing, pause rather than assuming the tool will fix it.
Compare the AI option with the current workflow and plausible non-AI alternatives. The National Institute of Standards and Technology’s AI Risk Management Framework (AI RMF) is voluntary; it can help organize trustworthiness considerations across design, development, use, and evaluation, but it is not a certification or a guarantee that a particular tool is safe or compliant. NIST’s Generative AI Profile can help organizations identify generative-AI risks and consider responses in light of their goals.
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2. What data goes in, and what happens to it?
Map the information the tool receives and produces, why it is processed, who is affected, and which parties handle it—including vendors and subprocessors. Decide whether confidential, personal, regulated, copyrighted, or otherwise restricted information is appropriate for this use. Do not assume that a tool’s general availability means your business may submit every kind of information to it.
Ask the supplier, and get the answers reflected in the agreement, for each relevant category of data:
- Are prompts, uploads, outputs, logs, or retained copies stored, and for how long?
- Are they used to train or improve models, or shared with other companies? Can those uses be disabled?
- Where is data processed and stored, and which subprocessors can access it?
- How can your organization retrieve or delete data, including copies held by subprocessors?
The UK Information Commissioner’s Office (ICO) advises documenting controller and processor roles for the processing activities involved and formalizing the agreed position in contracts and privacy information. The Federal Trade Commission (FTC) likewise advises small businesses to put permitted vendor data use, sharing, sale, retention, and deletion in writing and verify that the vendor follows the terms. These checks do not by themselves determine whether a particular use of data is lawful. Involve privacy, legal, and security specialists when personal or regulated data, sensitive decisions, or cross-border processing are involved.
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3. How will you test quality, limitations, and fairness?
Set acceptable accuracy and quality thresholds before purchase, for the actual task and the cost of getting it wrong. Ask the supplier how the model and its data were sourced, what limitations are known, and under what conditions performance and fairness were evaluated. Then test the tool yourself on representative cases, including likely edge cases and relevant groups affected by its outputs.
Record the results and trade-offs, including any tension between accuracy, explainability, and data minimization. ICO guidance recommends setting acceptable accuracy before procurement and assessing accuracy, bias, discrimination, and trade-offs. UK government procurement guidance advises asking suppliers to explain their approach and limitations and to demonstrate practices such as testing across conditions, defined performance, accountability, fairness, and proportionate security.
Specify how outputs may be used. Some may be suitable as drafts or recommendations; others may require review by a competent person, escalation, or confirmation from an independent source before action. NIST identifies characteristics to consider throughout the AI lifecycle, including accountability, transparency, explainability, validity, reliability, safety, security, privacy, and fairness.
4. How do you assess the supplier and its security?
Assess the vendor as an ongoing supplier, not just as a product demo. Ask who owns or controls the supplier, what is known about the product’s provenance and dependencies, and how the service would cope with disruption or a material change. Request evidence appropriate to the importance of the system, such as security assurance materials, access-control practices, incident response, continuity arrangements, subprocessor information, and retention and deletion controls. A marketing claim is not evidence of a control.
NIST’s finalized SP 1326, the C-SCRM Due Diligence Assessment Quick-Start Guide, was published July 8, 2026. It identifies supplier ownership and control, provenance, resilience, foundational cybersecurity practices, and supply-chain tiers as areas for assessment. The FTC recommends contractual security provisions, verification rather than reliance on assurances alone, and keeping vendor security current as threats change.
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5. What should the AI software contract cover?
Make the intended use and restrictions explicit, then ensure the agreement supports that use in practice. Where feasible, specify measurable service and quality expectations, including accuracy-based KPIs or service-level agreements suited to the task. Clarify the parties’ processing roles and instructions, permitted data uses, security commitments, subcontractors, incident-notification timing, records and documentation, retention and deletion, and notice of material product or model changes.
Also agree on review rights and a workable exit: how you can retrieve your data, end the service, and switch workflows if quality, security, or business needs change. ICO guidance recommends documenting processing purposes and roles, considering the full supply chain, setting suitable accuracy-based measures, and reviewing outsourced services as risks or circumstances change. FTC small-business guidance recommends putting security requirements and vendor data handling into written terms.
6. Who will own the tool after adoption?
Name an internal owner before launch. Establish who may approve use, monitor performance and risk, review user feedback, handle errors and complaints, assess changes, and pause or replace the system. Maintain an inventory that includes AI features embedded in ordinary business software, not only separately purchased AI products.
Review the system when its model, product, data practices, or intended use changes, and after significant incidents or changes in legal requirements. NIST’s four AI RMF functions—Govern, Map, Measure, and Manage—can help structure ownership, context assessment, testing, and response without prescribing a single process for every organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare candidate tools?
Score each real option—including the current workflow—on the same criteria. Weight the criteria according to the consequences of error and the sensitivity of the data; a low-impact drafting task and a consequential decision should not receive the same weighting.
| Comparison area | What to establish |
|---|---|
| Task performance | Results against your acceptance criteria, limitations, and behavior on representative and edge cases. |
| Data handling | Data access, uses, retention, sharing, processing location, and deletion. |
| Privacy and security | Controls supported by evidence, including access, incident response, and subprocessor practices. |
| Explainability and review | What users can understand about outputs and which uses require human review or escalation. |
| Fairness and impact | Potential effects on relevant groups and how the supplier and your organization test and address them. |
| Integration and operations | Workflow changes, staff training, monitoring, support, and ongoing ownership required. |
| Supplier resilience | Supplier control, dependencies, service continuity, and how material changes are communicated. |
| Contract and exit | Whether the terms cover intended use, data, security, review, change notification, and a practical way to leave. |
| Total cost and benefit | Whether expected measurable improvement justifies purchase and the costs of integration and ongoing operation. |
This comparison is a practical synthesis of NIST risk-management functions and cited procurement and vendor guidance, not an official checklist.
What rules may apply to your use?
Obligations depend on jurisdiction, sector, purpose, affected people, and your organization’s role. For consequential uses involving employment, credit, health, safety, access to essential services, or similar decisions, obtain appropriate specialist review before deployment; general procurement checks do not settle the rules for every use.
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For the EU, European Commission guidance dated July 20, 2026 says certain AI Act Article 50 transparency obligations apply from August 2, 2026. Duties vary by role and context: provider obligations include requirements concerning direct AI interactions and machine-readable marking of AI-generated or manipulated content; deployer disclosure duties include specified situations involving emotion recognition or biometric categorisation, deepfakes, and certain AI-generated public-interest text without human review or editorial control. These are not blanket disclosure requirements for every business using every AI tool. Determine whether the system and use fall within the relevant provision, which party is the provider or deployer, and whether other local or sector-specific rules apply.
Official guidance can change. Check current EU AI Act text and Commission guidance before operational reliance. The ICO’s AI contracts and third-party guidance is under review following the UK Data (Use and Access) Act, and NIST says revision of AI RMF 1.0 is in progress; verify the current versions relevant to your decision.
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