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12 questions to ask an AI development agency
Use these questions in a written request and in conversations with the team proposed for your project. Ask for clear answers in plain language as well as technical detail where needed. A confident pitch is not a substitute for evidence; an incomplete answer is a reason to investigate further, not automatic proof that a vendor is unsuitable.
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1. What problem and user outcome are you solving—and why is AI appropriate?
Ask the agency to define the problem, the people affected, and the result you should be able to observe. Then ask why AI is a better fit than a simpler workflow, conventional software, or an off-the-shelf product. The U.S. National Institute of Standards and Technology (NIST) procurement workbook recommends describing the problem and focusing on outcome criteria rather than prescribing an AI system too early.
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- How will we measure that change, and what baseline will we compare it with?
- What simpler approaches did you consider, and why did you rule them out?
2. What exactly are you proposing to build or integrate, and what are its limits?
Establish whether the proposal uses existing software, custom development, or a combination. Ask which components come from the agency, your organization, or third parties; what the system does; and what it does not reliably do. Request an explanation that both technical staff and business stakeholders can understand.
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- Which tasks will be handled by AI, and which remain ordinary software or human work?
- What are the likely failure cases, constraints, and unsupported uses?
- What happens when the system is uncertain, unavailable, or produces an unsuitable result?
3. What data does the solution need, and have you checked that we can use it?
Ask for a data assessment before committing to a build. Identify what data exists, who controls it, whether it is suitable for the intended users and task, and what gaps or quality issues may affect the result. Establish what data would be shared with the vendor and under what arrangements. UK government AI procurement guidance recommends assessing data and defining data-sharing arrangements before procurement; it also notes that data is the basis of most AI-powered solutions.
- What data is required for development, evaluation, and operation?
- Who has authority to provide it, and are there restrictions on its use or transfer?
- How will the parties address missing, inaccurate, outdated, or unrepresentative data?
- What data will the vendor retain, for how long, and for what purposes?
4. How will you test the system, and what result counts as success?
Request a written evaluation plan before a proof of concept or delivery milestone. It should identify the intended use, relevant test data, measures, comparison point, and acceptance criteria. The measures should match the consequences of errors in your use case, rather than rely on a broad claim such as “high accuracy.” Ask how the agency will check performance after launch and what happens if results fall short.
- What test set represents the actual users, inputs, and operating conditions?
- What baseline or benchmark will the system be compared against?
- Who approves the acceptance criteria, and how are failed tests handled?
- How will post-launch results be measured and reported?
5. What risks could affect users, and how will you manage them?
Ask the agency to identify risks relevant to your application and explain the controls it proposes. Depending on the use, this may include privacy, security, unfair or inconsistent outcomes, lack of transparency, misuse, and overreliance on automated output. If people need to review or correct results, define how that process works and who is accountable for it.
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NIST’s AI Risk Management Framework can help structure a risk discussion, but it is voluntary guidance—not a certification or guarantee. NIST has said version 1.0 is being revised, so check the framework’s current status before using it as a procurement reference.
6. Who will do the work, and what will each person own?
Meet the proposed delivery lead and the technical contributors, not only the sales team. Check that the team collectively covers the domain knowledge, data, engineering, model, security, delivery, and commercial skills your project needs. Ask what work will be subcontracted, who will supervise it, and how much access the named people will have to your project.
- Who is accountable for delivery decisions and escalation?
- Which named people will contribute, and how will their roles change across stages?
- What work is subcontracted, and what approval or oversight applies?
7. How do you develop and secure software, including third-party components?
Ask for a concrete description of development and security practices that fits the sensitivity of your project. Discuss testing, dependency management, vulnerability reporting and remediation, and how the agency will handle components maintained by other parties. NIST software procurement guidance describes requesting information from producers about secure development practices; ask what evidence the agency can provide that is relevant to your deal.
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8. Who owns the deliverables, data, and rights to use the components?
Get a written account of rights for custom code, documentation, your data, pre-existing tools, third-party software, models, and libraries. Ask what licenses apply, whether you can use and modify the deliverables, and what happens if you move to another supplier. NIST’s procurement workbook raises deliverable ownership and licensing as procurement questions; the allocation must be agreed in the contract for your specific project.
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Where feasibility is uncertain, consider separating discovery, a bounded proof of concept, integration, and deployment into decision gates. For each stage, agree what evidence is due, who decides whether to proceed, and what you owe if you stop. UK government guidance describes discovery and proof-of-concept work as ways to test whether a system is likely to meet wider requirements.
- What question is each stage intended to answer?
- What deliverables and acceptance criteria apply at that gate?
- Can work stop without committing to later stages, and what are the financial and operational consequences?
10. What happens after launch?
Put operational responsibilities on both sides in writing. Agree who monitors efficacy, handles incidents, supports integrations, maintains dependencies, and communicates material changes. UK government guidance says ongoing testing is necessary to maintain model accuracy and recommends agreeing with the supplier how efficacy will be monitored.
11. How will you price and contract the work, including uncertainty and change?
Ask the agency to make its assumptions and exclusions visible. The proposal and contract should explain milestones, acceptance criteria, change control, recurring charges, and support costs, as well as how uncertainty discovered during delivery will be handled. There is no universal pricing model or standard set of contract clauses established by the reviewed procurement guidance; tailor the terms to the project and get legal or commercial advice where appropriate.
12. What evidence supports your relevant experience?
Ask for references and case studies close to your use case. Verify what the proposed team actually delivered, what was measured, and whether results were compared with a baseline. Treat logos, demonstrations, and general expertise claims as starting points for questions rather than proof on their own. Official procurement guidance supports evidence-based evaluation, but it does not certify named agencies or prescribe one universal reference-check method.
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How to compare proposals fairly
Give each bidder the same brief and compare responses against a shared scorecard. Weight the criteria to reflect your goals and the consequences of failure; a project involving sensitive data or consequential decisions may need more emphasis on risk, security, and oversight than a lower-risk internal workflow. NIST recommends outcome-based procurement tailored to the buyer’s needs, and UK guidance likewise emphasizes output-based requirements.
| Criterion | Evidence to compare |
|---|---|
| Problem and outcome fit | Defined users and desired result; rationale for AI; consideration of simpler alternatives. |
| Technical rationale and limits | What is being built or integrated, component sources, expected failure cases, and stated limitations. |
| Data readiness | Data access and control, quality and representativeness, gaps, and sharing arrangements. |
| Evaluation plan | Relevant test data, measures, baseline, acceptance criteria, and post-launch checks. |
| Risk and security | Controls suited to the use case, including relevant privacy, security, fairness, transparency, and oversight practices. |
| Team and delivery ownership | Named contributors, accountability, required skills, subcontracting, and access to the work. |
| Rights and dependencies | Ownership and licenses for deliverables and components, data rights, and supplier transition terms. |
| Integration and operation | Integration responsibilities, support, monitoring, incident handling, and maintenance. |
| Total costs and assumptions | Milestones, exclusions, recurring and support costs, acceptance terms, and change process. |
A practical selection process is to request the same short written answers from every bidder, interview the proposed delivery team, and check the evidence behind claims. If a material feasibility question remains unanswered, use a bounded discovery or proof-of-concept stage with explicit decision criteria. Do not choose solely on a polished demo, an unsupported accuracy promise, a framework name, or a low initial price: each leaves important questions about outcomes, data, risk, delivery, or ongoing cost unresolved.
What the public guidance can—and cannot—tell you
The available guidance is useful for framing procurement questions, not for ranking private AI agencies or proving that a particular vendor meets a standard. A 2026 U.S. Government Accountability Office review covered 13 AI acquisitions across four selected federal agencies—DOD, DHS, GSA, and VA—and recommended systematically collecting lessons learned, including practices involving contract terms for data rights or testing requirements. That is evidence about those federal agencies, not a representative statistic about private-sector AI development firms.
Requirements and contract terms vary by jurisdiction, sector, and use case. Verify a candidate’s current team, references, security evidence, prices, and subcontractors directly, and confirm that the contract reflects the specific allocation of rights and responsibilities you intend.
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