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Buy an AI tool only after you can name the business task it will improve, the data it may process, the controls the supplier must provide, and the evidence that a limited pilot creates useful results. A convincing demonstration is not proof of workflow fit. Use the sequence below to test need, data, system behavior, supplier risk, contractual protection, and ongoing cost before making a broad commitment.
1. Define the business problem before looking at products
Start with a process, not a category such as “an AI assistant” or “automation.” Write down the work that is difficult, expensive, slow, inconsistent, or hard to scale. Identify where the work begins and ends, who performs it, which systems are involved, and what a successful result looks like.
Name the task and users
Describe the task in operational terms: for example, classifying incoming support requests, extracting fields from invoices, drafting internal summaries, searching a controlled knowledge base, or helping analysts prepare a first draft. Record the people who will use or review the output, their current tools, and any decisions that remain with a human.
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Include the volume and timing of work if those facts affect value. A tool that helps with occasional research may need a different design from one used continuously by a service team. If the process crosses departments or suppliers, note those handoffs and the systems that store the source records.
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Set a measurable outcome
Choose measures that reflect the purpose of the purchase. Depending on the workflow, useful measures may include time per item, turnaround time, error or rework rate, coverage of a queue, response quality judged against a rubric, or the percentage of cases that still require escalation. Establish a baseline using your existing process where practical.
Define a minimum acceptable result and a point at which the purchase is not worthwhile. Treat any proposed improvement as a test hypothesis, not a guaranteed benefit. Include the cost of human review, integration, training, monitoring, and exception handling in the business case.
Keep the requirement vendor-neutral
The U.S. General Services Administration’s “Buy AI” guidance says: “Focus on mission requirements rather than specific technologies or vendor solutions.” Apply that principle by writing the requirement before a sales demonstration. A product should be evaluated against the named workflow, data, controls, and outcome; the workflow should not be reshaped merely to fit a product’s headline feature.
2. Check whether the necessary data is ready and permitted
Many AI purchases fail because the organization has not established what information the task actually needs or whether it can lawfully and contractually be used. Perform a data assessment before selecting a service.
Inventory inputs and sources
- List every input the system would receive, such as documents, messages, images, audio, records from a business application, or user prompts.
- Identify the system of record, owner, format, volume, update frequency, and access method for each input.
- Separate essential fields from information that is convenient but unnecessary. Reducing data can reduce exposure and simplify testing.
- Document the outputs that will be written back to another system or shared with a customer, employee, partner, or decision-maker.
Test data quality and coverage
Check whether the data is complete, current, consistently labeled, and representative of the cases the tool will encounter. Note missing values, duplicate records, language or formatting variation, and rare but consequential cases. If the model will rely on a knowledge base, identify stale, conflicting, or inaccessible documents before blaming the AI for an answer that the source material cannot support.
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Confirm permission and purpose
For each data class, record why it may be used for this task, who authorized that use, and whether contractual, confidentiality, employment, sector, or jurisdictional rules restrict it. The UK government’s procurement guidance recommends assessing data before procurement and defining if and how data will be shared with vendors. GSA guidance likewise emphasizes understanding data flow, storage, protection, and limits on data types.
Do not put live sensitive information into an evaluation account until the permitted use, access controls, and vendor terms are understood. Create a representative test set with redacted, synthetic, or otherwise approved records when that is sufficient to measure performance.
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A privacy statement or an “enterprise” label does not by itself answer how a service handles your information. Ask specific questions and preserve the answers in the order form, data-processing agreement, security addendum, or other binding contract as appropriate.
Questions about collection and access
- What prompts, files, telemetry, account details, usage logs, and support materials are collected?
- Which supplier personnel, subcontractors, hosting providers, or support systems can access the information?
- Are administrators able to restrict access by role, workspace, geography, or data type?
- Can the business disable logging, history, connectors, or other features that are not needed for the use case?
Questions about use and sharing
- Is customer content used to train, tune, evaluate, or improve a shared model or service?
- Is content shared with affiliates, subprocessors, model providers, analytics services, or other third parties?
- Can the supplier change those uses or subprocessors without notice or a meaningful objection process?
- Does the service combine your data with information from other customers, and can your administrators prevent that?
Questions about storage, retention, and deletion
- Where are inputs, outputs, backups, and logs stored, and what regions or transfer mechanisms apply?
- How long is each category retained by default, and can retention be shortened?
- What happens to data when a user, workspace, or subscription is closed?
- What is the deletion process for primary systems, backups, caches, and subprocessors, and what evidence is supplied?
- Can you export your content and configuration in a usable format before termination?
Verify rather than accept assurances
The Federal Trade Commission’s small-business cybersecurity guidance puts the point plainly: “Don’t just take their word for it.” Ask for current documentation, test relevant settings, and determine how compliance with agreed controls will be demonstrated. The FTC also recommends putting security expectations in writing, specifying vendor data handling, verifying compliance, and updating controls as threats change.
In a separate warning about AI commitments, the FTC notes that a company’s incentive to ingest more data can conflict with privacy promises or expose competitively significant business information. Treat data minimization, training use, and deletion as negotiated requirements rather than assumptions.
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4. Evaluate the system on the work you actually need done
AI quality is more than an impressive answer to a prepared demo prompt. Test representative tasks, ordinary cases, edge cases, and failure modes that could harm customers, finances, safety, privacy, or reputation.
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Use a fixed set of approved examples covering normal variation and difficult cases. Include ambiguous requests, incomplete records, unusual formats, adversarial or misleading content, and cases that should be refused or escalated. Keep the inputs and scoring rubric the same when comparing candidates.
Have people who understand the work judge outputs for correctness, completeness, clarity, and suitability for the next step. Record both unacceptable answers and acceptable answers that require excessive correction. If the tool changes over time, retain dated test results so a later update can be investigated.
Use NIST’s trustworthiness dimensions
The National Institute of Standards and Technology’s AI Risk Management Framework describes several characteristics to consider. They are evaluation dimensions, not a guarantee that a particular product meets them.
| Dimension | What to examine in your use case |
|---|---|
| Validity and reliability | Whether results are accurate enough for the defined task, repeatable under similar conditions, and robust to expected variation. |
| Safety | Whether foreseeable failures could cause physical, financial, operational, or other harm, and what safeguards or human intervention limit that harm. |
| Security and resilience | Resistance to unauthorized access, manipulation, prompt injection, service disruption, and recovery failures. |
| Accountability and transparency | Whether responsibilities, system boundaries, limitations, incidents, and material changes are documented and communicated. |
| Explainability and interpretability | Whether users can understand enough about an output and its basis to review it appropriately for the task. |
| Privacy enhancement | Whether collection, processing, access, retention, and disclosure are limited and controlled for the intended purpose. |
| Managing harmful bias | Whether performance and error patterns differ across relevant groups or conditions, and whether mitigations and escalation routes exist. |
Match human review to consequence
Specify which outputs may be used automatically, which require approval, and which are prohibited. A drafting aid might allow a trained employee to edit every result; a system influencing eligibility, employment, safety, credit, healthcare, or legal outcomes may require substantially stronger controls and specialist review. This general guide cannot determine the legal obligations for a particular sector or jurisdiction.
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5. Assess the supplier and its dependencies
The model is only one part of the service. Review the company that operates it, critical subcontractors, hosting arrangements, update process, support capability, and ability to continue or exit the service.
Supplier questions for a material purchase
- Who provides the application, model, hosting, identity service, storage, and important connectors?
- Which functions are supplied by subcontractors, and how are changes communicated?
- What is the incident-notification process, including timing, contact points, and customer cooperation?
- How are vulnerabilities discovered, prioritized, fixed, and communicated?
- What backup, recovery, availability, and service-continuity arrangements support the workflow?
- What happens if a model, feature, region, or supplier is discontinued?
- What evidence can the supplier provide about provenance, security practices, testing, and material changes?
Use a proportionate supply-chain review
NIST Special Publication 1326, a final quick-start guide dated 8 July 2026, addresses ICT supplier assessment. It identifies provenance, resilience, foundational cybersecurity practices, supply-chain tiers, and foreign ownership, control, or influence as due-diligence components. Its scope is ICT suppliers, so adapt the questions to the service and potential impact rather than treating every item as mandatory for every buyer.
For a low-impact experiment, a lightweight review and clear exit plan may be sufficient. For a service handling sensitive information or supporting a critical process, involve security, procurement, privacy, and business owners; request evidence proportionate to the risk; and document unresolved dependencies.
6. Run a bounded pilot before a broad rollout
A pilot turns claims into evidence while limiting exposure. Use a sandbox or a small, trained user group, approved data, a defined time window, and an owner who can stop the test.
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- Write the pilot charter. State the workflow, users, approved data, prohibited data, success measures, review method, duration, and decision date.
- Prepare the test set and baseline. Capture representative tasks and the current process’s time, quality, escalation, and cost measures where feasible.
- Configure controls first. Set roles, authentication, logging, retention, connector permissions, spending limits, and human-approval rules before users begin.
- Train participants on boundaries. Explain what may be entered, how outputs must be checked, how incidents are reported, and when the tool must not be used.
- Run normal and adverse cases. Include routine work, edge cases, ambiguous inputs, unavailable data, malicious instructions, and service interruptions relevant to the workflow.
- Score outputs consistently. Use the same rubric for every candidate and record corrections, escalations, refusals, latency, and unusable results.
- Review operational burden. Count review time, integration work, support questions, duplicate entry, training, and exceptions rather than measuring only generated output.
- Make a documented decision. Expand, revise, pause, or reject the purchase based on the predefined thresholds. Record open risks and the owner for each mitigation.
OECD guidance dated 19 February 2026 describes risk-based due diligence for enterprises in the AI value chain: map AI uses and relevant business relationships, then prioritize deeper assessment for the most significant risks. That approach is useful for deciding which pilot findings require specialist review.
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7. Calculate the full operating cost and control consumption
Do not treat the subscription price as the cost of the workflow. Depending on the service, costs may include seats, tokens or other usage, storage, connectors, implementation, integration, data preparation, training, human review, security assessment, support, and migration or exit work.
Cost categories to record
| Category | Questions to answer during the pilot |
|---|---|
| Access and usage | Is billing per user, request, token, document, minute, storage unit, or another measure? What happens when limits are exceeded? |
| Implementation | Who configures integrations, permissions, prompts, workflows, testing, and data preparation, and how many hours are required? |
| Review and correction | How much employee time is needed to verify, edit, reject, or escalate outputs? |
| Operations | What ongoing work is needed for monitoring, incident response, model changes, retraining of users, and access reviews? |
| Exit | What does export, migration, deletion verification, and replacement cost if the service no longer fits? |
GSA warns that AI services billed like software-as-a-service can accumulate cost without monitoring. Set user, workspace, or account limits where available; assign an owner for usage review; and examine consumption on a regular schedule. Alerts are useful, but also investigate unusual usage, low-value activity, and workflows that create repeated or unnecessarily large requests.
Use the pilot to replace assumptions with local measurements. Do not publish or approve a savings estimate until the organization has accounted for review, integration, and exception work.
8. Compare candidates with the same evidence
When two or more services remain viable, run the same approved tasks, data conditions, scoring rubric, security questions, and cost period for each. A comparison table prevents a polished demo or a familiar brand from overshadowing material differences.
| Comparison area | Evidence to record |
|---|---|
| Workflow fit | Required steps supported, integrations available, user roles, data prerequisites, and gaps against the defined process. |
| Observed performance | Results on representative and edge-case tasks, correction rate, escalation behavior, latency, and failure severity. |
| Data handling | Collection, training or improvement use, sharing, storage locations, retention, deletion, export, and administrative settings. |
| Security and resilience | Access controls, logging, incident response, vulnerability handling, continuity, recovery, and service dependencies. |
| Accountability | Named internal owner, supplier contacts, documentation, change notices, audit or verification evidence, and human-review controls. |
| Supply-chain exposure | Critical subcontractors, provenance information, relevant ownership or control concerns, and concentration or replacement risk. |
| Operating cost | Measured usage, review labor, implementation effort, support burden, limits, alerts, and exit requirements. |
Mark each item as verified, contractually committed, observed only in the pilot, unanswered, or not applicable. “Not stated” is more useful than silently treating an unknown as a positive result.
9. Assign ownership and maintain controls after purchase
Buying approval is not the end of evaluation. Name a business owner accountable for the workflow, a technical owner for configuration and access, and the appropriate privacy, security, legal, procurement, or compliance reviewers for the use case.
Keep a decision record
- The business problem, users, baseline, target outcome, and approved scope.
- The data inventory, permitted data classes, prohibited inputs, and retention decision.
- Pilot tasks, scoring method, results, known failure modes, and unresolved limitations.
- Supplier commitments, subprocessors, security evidence, incident contacts, and exit conditions.
- Contract terms covering data use, sharing, retention, deletion, security, changes, and cooperation.
- Usage limits, cost owner, review cadence, and escalation path.
Review when circumstances change
Reassess the service when the vendor changes a model or material feature, adds a subprocessor, changes data practices, expands the workflow, introduces a new user group, or suffers a security or availability incident. Re-run relevant tests after significant changes. Monitor actual outcomes and usage rather than assuming that a successful pilot remains valid indefinitely.
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- The proposed benefit is described only through a demonstration, with no named task, baseline, or success measure.
- The supplier cannot clearly explain whether customer content is used for training or shared with other parties.
- Retention and deletion are vague, unavailable, or inconsistent with the organization’s requirements.
- Users cannot restrict access, export data, control connectors, or apply human approval where needed.
- The supplier provides no practical incident contact, change-notice process, or service-exit path for a material workflow.
- The pilot uses sanitized examples that omit the difficult cases the production process actually contains.
- Usage-based charges have no limits, alerts, owner, or regular consumption review.
- No one is accountable for approving data, reviewing outputs, responding to incidents, and reassessing the service.
Make the decision
Proceed only when the tool addresses a named workflow, the required data is available and permitted, representative testing shows acceptable behavior, supplier and contractual controls match the potential impact, and measured operating cost fits the business case. If any of those conditions remains unknown, narrow the pilot, obtain the missing evidence, or choose not to buy yet. For consequential or regulated uses, obtain advice from the responsible legal, privacy, security, and business owners because applicable obligations depend on the jurisdiction, sector, data, and use case.
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