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Before buying AI software, make the vendor prove that it solves a defined work problem, can use your data lawfully and safely, performs on tasks your staff actually do, fits existing workflows, and remains affordable after implementation. Ask these questions before a contract, then verify the answers in a controlled pilot with agreed success and stop criteria.
The process below covers problem definition, data, evidence, security, total cost, integration, contracts, exit, and post-launch oversight. It is designed for teams evaluating any AI application, from a narrow assistant to a system that influences decisions about people. Government guidance from the U.S. General Services Administration, UK Government, NIST, the UK Information Commissioner’s Office, and the Open Contracting Partnership emphasizes a problem-first, risk-based procurement process; legal and regulatory duties still depend on your country and use case.
What problem are we solving, and why is AI suitable?
Start with the work problem, not a product demo. A purchase is justified only when the task, users, desired outcome, and acceptable risk are clear enough to compare AI with simpler approaches.
Write a one-sentence problem brief
Describe the current process, who performs it, what is failing, and what measurable improvement you want. For example: “Customer-support agents spend too long finding approved answers; we want faster first responses without increasing factual errors or exposing customer data.” This wording identifies the people affected, the task to test, and the outcomes to measure.
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Define what the system must not do as well. A tool might draft text but not send messages automatically, or summarize records but not make an eligibility decision. Boundaries prevent a broad promise such as “use AI to improve productivity” from becoming an untestable purchase.
Compare AI with non-AI options
Ask whether process redesign, better search, a rules-based workflow, a database change, additional staff training, or an existing feature would solve the same problem with less risk and cost. Official procurement guidance recommends centering the problem rather than prescribing an AI solution. Keep alternatives in the evaluation so a compelling demonstration does not become the decision by default.
Identify the people and consequences
List direct users, people whose information enters the system, downstream recipients, and anyone who could be harmed by an incorrect result. Classify the use as low, medium, or high consequence for your organization. If outputs can affect rights, access to services, employment, health, safety, or money, involve the relevant legal, privacy, security, data, and subject-matter experts before testing.
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Data readiness can determine whether an AI product is viable. Establish what data enters the system, who owns it, whether its use is permitted, how it moves, and what happens to it after processing.
Inventory the inputs
Ask the vendor to list every required and optional input: files, messages, databases, telemetry, user prompts, images, audio, or connected applications. For each source, check whether the data exists in usable form, is relevant to the task, is sufficiently accurate, and is current enough for the intended decision. A model cannot compensate for records that are missing, contradictory, or outside their permitted purpose.
Map the complete data flow
Require a plain-language diagram or written description covering collection, transmission, processing, storage, backups, support access, subprocessors, retention, deletion, and export. Confirm the storage locations and the security applied in transit and at rest. Ask whether prompts, uploaded files, generated outputs, logs, or feedback are reused to train or improve a shared model, and whether that use can be disabled.
Confirm ownership and permission
Clarify who owns each data set and whether contracts, licenses, confidentiality duties, copyright rules, employment policies, or data-protection law allow the proposed use. Do not assume that an employee’s access to a record gives the software permission to copy it into another service. Assign an internal owner accountable for approving data sources, documenting the lawful basis or other permission, and handling changes.
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Check data minimization and quality controls
Ask whether the product can restrict fields, redact identifiers, separate tenants, set retention periods, and prevent users from submitting prohibited content. Determine how the system signals missing, stale, or conflicting information. If the vendor cannot explain what is retained or reused, treat that uncertainty as a procurement blocker for sensitive data.
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What evidence supports the product’s performance?
Marketing examples are not evidence that a system will work on your tasks. Ask for intended uses, limitations, data provenance, evaluation methods, known failure modes, and results under conditions resembling your work, then verify the claims yourself.
Ask how the vendor measured quality
Request the evaluation task definitions, data characteristics, sample sizes or test coverage, scoring method, and date of the results. Ask whether the examples were selected by the vendor, whether a human reviewed them, and which errors were counted. Find out what training or other data provenance the vendor can disclose and what uses the product is not designed to support.
Demand failure information, not only success rates
Ask for representative examples of hallucinations, omissions, unsafe recommendations, biased outputs, prompt-injection susceptibility, and performance degradation on unusual inputs. Learn how often the system refuses a task, produces an answer with no reliable source, or changes its behavior after a model update. A limitation that is visible and manageable is safer than one the supplier will not describe.
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Set acceptance criteria before a pilot
Choose measures tied to the problem brief: factual accuracy, completeness, time saved, escalation rate, consistency, false positives, false negatives, or user correction time. Define minimum thresholds, unacceptable failure types, and who decides whether a result passes. Include a comparison with the current process or a non-AI alternative so improvement is not assumed.
Test representative work with real users
Use a small, protected sample that reflects the languages, formats, edge cases, and workload your team encounters. Have intended users perform normal tasks, record errors and near misses, and review both successful and unsuccessful outputs. Separate model quality from workflow quality: a good answer that arrives too slowly, cannot be edited, or lacks an approval path may still fail the business need.
Use additional expertise for high-impact applications
When an output could affect a person’s rights, safety, access, or livelihood, involve domain experts and the people responsible for legal, privacy, security, and risk decisions. Test for unequal error patterns across relevant groups and document how a human can challenge or override an output.
How will security, privacy, and oversight work?
Vendor assurances are one input, not a substitute for your own risk assessment. Match controls to the data and use case, and require evidence that the people who can access or change the system are controlled and accountable.
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| Area | Questions for the vendor | Evidence to request |
|---|---|---|
| Access control | Can roles limit who views data, configures models, exports results, or approves actions? | Role descriptions, administrative controls, and the process for adding and removing users. |
| Logging and auditability | Which prompts, inputs, outputs, changes, and administrative actions are logged, and for how long? | Sample audit records, export capability, retention settings, and time synchronization details. |
| Incident handling | How are suspected breaches, harmful outputs, outages, and model defects reported and investigated? | Response process, notification commitments, escalation contacts, and post-incident reporting. |
| Third parties | Which subprocessors, model providers, hosting services, or support contractors can access the data? | Current subprocessor list, change-notice process, and access restrictions. |
| Human oversight | Where can a person review, correct, reject, or stop an automated result? | Workflow controls, approval settings, override records, and user guidance. |
Bring the right internal reviewers
Identify the security, privacy, legal, data, procurement, business, and accessibility owners who must approve the product. Their checks should reflect the sensitivity of the data and the consequence of an error; a generic vendor questionnaire cannot establish fitness for every deployment.
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Plan for misuse and abnormal behavior
Ask how the service handles malicious prompts, attempts to extract other customers’ data, compromised accounts, unsafe generated content, and a connected system that returns corrupted information. Confirm how users report a problem and how quickly administrators can disable access or pause an automated action.
What will the AI software cost over its full life?
Budget beyond the advertised subscription. AI usage can create variable charges, while implementation, training, support, maintenance, and exit work continue even when the initial license looks affordable.
| Cost category | Questions to ask before approval |
|---|---|
| Subscription or license | What users, features, environments, and support levels are included, and what renews automatically? |
| Usage | Are charges based on requests, tokens, storage, processing time, records, or another meter? What are the rates and billing periods? |
| Implementation | What configuration, data preparation, migration, custom development, or professional services are required? |
| Integrations | Are connectors, API calls, sandbox environments, or additional security tools billed separately? |
| Support and maintenance | Which support tier, response times, upgrades, model changes, and administrator work are included? |
| Training and change management | Who trains users, creates guidance, reviews outputs, and handles adoption or rework? |
| Exit and transition | What will data export, migration, contract termination, replacement, and deletion cost? |
Control consumption
Ask for dashboards or reports that show usage by team, user, feature, and period. Set alerts, quotas, approval thresholds, and a named owner who reviews spend. The GSA warns that AI consumption costs can grow without monitoring, so include a process for pausing a workload or changing configuration when usage exceeds the approved envelope.
Model the total cost of ownership
Estimate the cost of a pilot, first year, renewal periods, peak usage, and a plausible exit. Include staff time spent checking outputs and correcting errors; a nominally cheap tool can be uneconomic if every result needs extensive rework. Record assumptions so finance can compare products on the same workload rather than on list prices.
How will the product fit the team’s work?
A technically capable system fails when it disrupts responsibilities, lacks an approval path, or leaves users without training. Map the proposed workflow before purchase.
Define roles and human review
Specify who may use the system, who reviews drafts, who approves an action, and who is accountable for the final result. Decide which tasks remain manual and how users escalate an uncertain or harmful output. Access roles should match these responsibilities rather than giving every user administrative control.
Check integrations and workflow changes
Confirm that the product connects to the systems your team actually uses, with the required permissions and update frequency. Ask what happens when an integration fails, a source record changes, or a user leaves the organization. Document new steps, expected response times, and any duplicate entry that could erase the promised efficiency.
Provide training and knowledge transfer
Ask who creates onboarding, safe-use rules, examples of unacceptable inputs, and troubleshooting instructions. Ensure your administrators can change settings, inspect logs, manage users, and export data without depending on one vendor employee. Include training time and refresher work in the implementation plan.
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What should the contract and exit plan protect?
Put data rights, change controls, service commitments, portability, and termination assistance in writing. A pilot or favorable demo does not protect you from a later model change or an unworkable exit.
| Contract topic | Points to clarify |
|---|---|
| Inputs and outputs | Who owns or controls submitted data, generated material, prompts, configurations, and custom work? What uses are permitted? |
| Retention and deletion | How long are production data, backups, logs, and support copies retained, and how do you verify deletion? |
| Product and model changes | How much notice is provided for model replacements, feature removals, pricing changes, or material behavior changes? |
| Service levels | What availability, support response, maintenance notice, remedies, and reporting obligations apply? |
| Portability | Can you export data, prompts, configurations, audit logs, and outputs in usable formats, with documentation? |
| Termination and transition | What notice, fees, assistance, migration window, and deletion certification apply when the agreement ends? |
Reduce lock-in before signing
Prefer documented interfaces and export formats that let you move data and workflows. Keep copies of your own prompts, evaluation sets, configuration decisions, and operating procedures. Ask whether a replacement model can be tested before a forced change and whether you can decline a material change without losing access to your data.
How will the product be monitored after launch?
Buying an AI tool is the start of governance, not the end of evaluation. Name owners, watch performance and risk, and define when to scale, change, pause, or retire the system.
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Give specific people responsibility for product administration, data permissions, performance review, incident response, supplier management, and user feedback. Record who can stop the system immediately and who must approve a restart.
Monitor meaningful signals
Track the acceptance measures from the pilot, correction and escalation rates, user complaints, security events, data-quality changes, availability, latency, and spend. Review outputs after vendor updates or changes in source data. Look for drift: a system can remain available while becoming less accurate for the work it was approved to support.
Create a feedback and incident loop
Give users a simple route to report errors, unsafe content, bias, privacy concerns, and workflow failures. Triage reports, preserve relevant logs, investigate root causes, and communicate corrective actions. Update training and controls when the same failure recurs.
Set scale and retirement conditions
Define the evidence required to expand to more users or data, the trigger for a focused re-evaluation, and the conditions for pausing or retiring the product. Revisit the business case when costs, regulations, data sources, or the vendor’s model materially change.
How should we compare two or more AI products?
Compare candidates on the same use case, data sample, acceptance criteria, and evidence standard. Weight each axis according to the consequence and sensitivity of the intended use.
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| Comparison axis | What to compare consistently |
|---|---|
| Task performance | Accuracy, completeness, speed, consistency, failure modes, bias indicators, and human correction effort on representative tasks. |
| Data requirements and protections | Required sources, permissions, retention, reuse of inputs and outputs, storage locations, and deletion controls. |
| Security and governance | Access roles, logging, incident response, auditability, third parties, reporting channels, and oversight features. |
| Integration and workflow | Connectors, role design, approval steps, training needs, support, and knowledge transfer. |
| Full lifecycle cost | Usage, implementation, integrations, support, maintenance, training, monitoring, and exit expenses. |
| Contract and portability | Rights to data and custom work, change notices, service levels, export formats, termination terms, and transition support. |
| Monitoring and retirement | Available reports, feedback handling, update controls, and practical ability to pause, replace, or retire the system. |
How do we run a useful AI software pilot?
A pilot should be small enough to control and realistic enough to reveal operational problems. Complete these steps before approving a broad rollout.
- Write the problem brief. Name the users, task, baseline process, desired outcomes, prohibited uses, and consequence level.
- Approve the data plan. List sources, owners, permissions, minimization rules, retention, storage, subprocessors, and deletion requirements.
- Choose representative tasks. Build a test set that includes routine work, edge cases, different formats or languages, and known difficult examples. Protect personal or confidential data.
- Set pass and fail criteria. Specify measurable thresholds, unacceptable errors, review effort, cost limits, response-time expectations, and who makes the decision.
- Configure safeguards. Set roles, logging, human approval, output handling, usage limits, alerts, and an emergency pause procedure before users begin.
- Train a representative group. Explain permitted inputs, verification duties, escalation, incident reporting, and how to record corrections. Include the people who will use and supervise the system.
- Run the baseline and pilot. Compare the AI workflow with the current or non-AI alternative using the same tasks. Record successful outputs, errors, refusals, bias indicators, latency, support needs, and actual consumption.
- Review and decide. Have business, data, security, privacy, legal, and subject-matter owners review the evidence. Approve scale only if the criteria are met and residual risks have an owner; otherwise revise, pause, or stop.
When should we buy, pilot, or decline?
Use the evidence, not enthusiasm, to choose among the three outcomes.
Buy or scale when
- The problem and intended users are specific, and AI performs better than a practical alternative on agreed tasks.
- Data permission, quality, protection, and accountability are documented.
- Security, privacy, legal, and business owners accept the remaining risks.
- Total lifecycle cost, consumption controls, contract rights, and exit arrangements fit the approved budget.
- Human review, training, monitoring, and a retirement path are ready.
Pilot when
- The potential value is credible but performance, workflow impact, data quality, or consumption is still uncertain.
- The vendor can support a bounded test with representative data and clear safeguards.
- You can define a stop condition and avoid exposing sensitive data unnecessarily.
Decline or stop when
- The vendor cannot explain data reuse, retention, subprocessors, or deletion for the information you need to process.
- Claims cannot be tested, failure modes are hidden, or results do not meet the acceptance criteria.
- The product requires permissions, costs, or workflow changes that exceed the problem’s value.
- The contract prevents practical export, gives inadequate notice of material changes, or leaves no workable transition.
- No accountable owner can monitor the system or intervene when it fails.
What should be on the vendor question checklist?
Send a written questionnaire before demonstrations so every candidate answers the same core questions.
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- What data is required, where does it travel and reside, who can access it, how long is it retained, and is it reused for training or improvement?
- Who owns or controls inputs, outputs, prompts, configurations, and custom work?
- What evaluations, limitations, provenance information, known failure modes, and update practices can you document?
- How will we test representative tasks, measure errors and bias, and obtain support during the pilot?
- Which roles, logs, audit exports, incident channels, safeguards, and human approval controls are available?
- Which third parties process our data, and how are subprocessor changes communicated?
- What are the subscription, usage, implementation, integration, support, training, monitoring, and exit costs?
- How are usage limits, alerts, reporting, service levels, and price changes handled?
- What notice will we receive for model or product changes, and can we reject a material change?
- How do we export data, outputs, configurations, prompts, and logs in usable formats?
- What termination assistance, deletion evidence, and transition window are provided?
- Who will own ongoing performance, security, privacy, user feedback, incidents, and the decision to scale or retire?
Frequently Asked Questions
Can a free trial replace a formal AI pilot?
Usually not. A free trial may reveal interface usability, but it often uses a different data set, support level, usage limit, or model configuration than production. Treat it as an early feasibility check, then run a controlled pilot with approved data, representative tasks, and documented pass and fail criteria.
What if the vendor refuses to disclose its training data?
Ask for the provenance information it can provide, the intended-use boundaries, evaluation evidence, and contractual promises about your inputs and outputs. Full disclosure may be commercially or technically limited, but inability to explain data handling, limitations, or reuse of your content is a material risk for sensitive deployments.
Who should have authority to pause an AI system?
Assign that authority before launch to a named operational owner, with an escalation path to security, privacy, legal, and business leaders. The person should be able to disable users or automated actions quickly, preserve relevant logs, and restart only after the triggering issue is assessed.
How can a small team make this process manageable?
Use one written problem brief, one shared test set, and a short cross-functional review rather than separate informal demos. Prioritize the controls that match your data and the consequences of error, and document decisions so the team can repeat the evaluation when the product or use case changes.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
