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Before hiring an AI consulting firm, ask it to define the business problem and measurable outcome, explain why AI is appropriate, identify the people who will do the work, and show how it will handle your data and test the system. Then put deliverables, security and data terms, ownership, monitoring, knowledge transfer, and exit arrangements in writing. Ask every firm the same questions and judge its answers by the evidence behind them—not by a polished demo alone.
Start with the problem, not the proposed AI
Before you discuss models or features, make sure the firm can explain the business problem in plain language: who experiences it, what happens today, and what better result would matter to the people affected. A proposal should connect the work to a business outcome, not simply promise to build a chatbot, automate a workflow, or add AI to an existing product.
Ask: “What problem are you solving, for whom, and how will we know the engagement worked? Why is AI appropriate, and what simpler or non-AI approaches did you consider?”
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A useful proposal describes outputs and performance requirements while leaving room to revise the approach as the firm learns more. It should not require you to specify a particular model before the problem and constraints are understood. The UK government’s AI procurement guidance recommends clearly stating the challenge, explaining why AI is relevant, and remaining open to alternatives. NIST’s AI procurement guidance similarly emphasizes defining the problem and assessing risks before procurement.
Find out who will actually deliver the work
Ask for the proposed team by name, not just the firm’s general capabilities. You need to understand who is accountable for delivery and whether the people assigned have the technical and domain expertise the work requires.
- Roles and time: Who leads the engagement, who makes technical decisions, who handles data and security, and how much time will each person commit?
- Relevant experience: Which projects resemble your problem, users, operating conditions, or constraints? Ask what the team itself did, rather than treating the firm’s entire client list as evidence of the proposed team’s experience.
- Outside parties: Will subcontractors, cloud services, model providers, or other suppliers be involved? Ask what each will do and who remains responsible for the work.
- References and setbacks: Request references you can contact. Ask for an example of a project that missed its target, what caused the shortfall, and what the firm changed as a result.
Evaluate the team against the capabilities your project actually needs. A project involving sensitive data, regulated decisions, or integration into an existing operation may call for different expertise than a limited internal prototype. The reviewed procurement guidance supports multidisciplinary evaluation; it does not establish a universal credential requirement or a minimum number of references. Treat a reference as one piece of evidence, not a substitute for examining the proposed plan and contract.
Check data readiness and set boundaries for data use
Ask the firm to assess whether the necessary data exists and can lawfully and practically be used before it presents implementation as a certainty. The assessment should consider availability, quality, completeness, provenance, rights, access, and known limitations. If the project depends on data that is missing, inconsistent, inaccessible, or of uncertain origin, ask how that affects scope, timeline, feasibility, and the success measures.
Then make the data flow explicit. Ask: “What information do you need, for what purpose, where will it be stored or processed, who can access it, and what happens to it when the work ends?” Have the firm identify whether information will go to subcontractors or model providers, how long it is retained, and how it will be deleted or returned. Clarify whether prompts, outputs, logs, or copies used for testing are included in the same rules as source data.
For sensitive information, ask which protections apply and how you can verify them. Ask how access is limited, how staff are trained, what security evidence the firm can provide, and how it will notify you if an incident affects your information. The Federal Trade Commission advises businesses to ask service providers how they secure data, who has access, and how staff are trained; it also recommends writing expectations into contracts and checking that they are followed. Its 2017 guidance puts the point plainly: “Before bringing service providers on board, spell out what you expect in terms of security.” See the FTC’s service-provider security guidance.
Data discovery should also address bias and other limitations that could affect the intended users or outcomes. The UK procurement guidance recommends considering data quality, bias, and limitations before going to market. Ask the firm to explain what it can establish from the data it has inspected and what remains an assumption; a promise to “use your data” does not answer whether that data is suitable.
Understand the proposed system and how it will be tested
Ask the firm to describe the proposed system in enough detail for you to understand its important components and dependencies. This does not mean you must prescribe the technical design. It means you should know what the firm proposes to build or configure, what information the system will use, what third parties it depends on, and what factors could make its outputs unreliable.
- Which models, algorithms, data sources, and third-party services are proposed?
- What training data or other data informs the approach, and what is known about its origin and limits?
- What conditions, user inputs, or operating changes could lead to poor or unsafe results?
- How can a user or reviewer understand the basis for an output, and when should a person check, override, or escalate it?
- What would trigger a material change to the system or require reassessment?
Require a test plan that reflects your users, data, and actual failure risks. Ask what cases will be tested, how results will be measured, who will review failures, and what must be true for you to accept the work. A demonstration can show that a system produces an output in one setting; it does not establish how it performs on your data or in your operating environment. For higher-impact uses, ask whether independent review or audit is appropriate and what evidence it would examine.
Testing should not stop at handover if the system will remain in use. Ask who monitors performance after deployment, what signals they will watch, how often results will be reviewed, and who decides what to do if quality changes. Agree how the firm will handle a material model, data, or service change, including whether it will retest affected requirements. The UK guidance raises questions about models, training-data origin, limitations, explainability, possible independent audits, and continued efficacy monitoring.
NIST’s AI Risk Management Framework is voluntary risk-management guidance, not a certification that a supplier or system is safe. NIST says AI RMF 1.0, released January 26, 2023, is being revised; its Generative AI Profile was released July 26, 2024. Consult the NIST AI Risk Management Framework page to understand the framework’s status and materials. If a firm claims that a framework or profile proves its system is safe, ask what specific evidence supports that claim instead.
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A sales proposal is not a substitute for written commitments. Before signing, make sure the agreement and its attachments describe what will be delivered, how progress will be assessed, and what happens when assumptions or requirements change.
Rank #4
- Scope and acceptance: List deliverables, milestones, acceptance criteria, dependencies on your staff or systems, and the process for approving changes. Make acceptance criteria observable enough that both sides can tell whether a milestone is complete.
- Rights and permitted uses: Specify rights for your data, prompts, outputs, code, documentation, pre-existing intellectual property, and newly developed intellectual property. Clarify which materials you may use, modify, share, or retain after the engagement.
- Security and privacy: Write down required controls, confidentiality duties, incident notification, access and audit evidence, subcontractor conditions, and retention, return, or deletion of information.
- Operations: Assign responsibility for monitoring, maintenance, model or service changes, and response to performance issues. Set out any continuing support obligations and their commercial terms.
- Portability and exit: Establish how you can export data and work products, what formats or documentation you will receive, and what transition assistance is included if you move to another provider.
Ask the firm to show where each promise appears in the contract. The UK procurement guidance specifically raises ownership of new IP and ways to limit vendor lock-in. The FTC recommends putting security expectations, performance standards, and monitoring methods into provider contracts. If the proposal and contract differ, identify the controlling commitment before you sign.
Plan for operating the system after the engagement
Ask what your staff will be able to do when the project ends. A handover should be more than a final presentation: clarify what documentation, training, and practical knowledge transfer are included, who receives them, and how you will know the team can perform the tasks it is expected to own.
Ask who will handle routine monitoring, maintenance, user questions, and changes to the system. If the firm expects your staff to take over, request a clear description of the skills, access, tools, and time required. If independent operation is not realistic, identify the continuing service or internal capacity the project will need and make its cost and responsibilities visible before approval.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare proposals on the same evidence
Use one comparison framework for every firm. Record not just what each bidder says, but what kind of evidence supports the answer: a conversation or reference, written policy, contract commitment, inspected configuration, or observed test. This evidence ladder is a practical way to organize diligence, not a formal standard. Seek stronger evidence in proportion to the potential consequences of failure.
| Decision area | What to compare | Evidence to request |
|---|---|---|
| Problem and outcome | Problem definition, reason to use AI, success measures, and alternatives considered | Written scope, proposed measures, assumptions, and acceptance criteria |
| Delivery capability | Named people, skills, time commitments, relevant experience, and responsibility for outside parties | Team plan, project examples, references, and subcontractor details |
| Data and security | Readiness assumptions, access boundaries, data flows, retention, and protections | Data assessment, written security commitments, and verifiable control evidence |
| Technical quality and risk | System components, limitations, failure conditions, testing, monitoring, and change handling | Technical explanation, test plan, acceptance measures, and monitoring responsibilities |
| Control and continuity | Data and IP rights, interoperability, portability, documentation, training, and exit support | Contract language, handover plan, and export or transition commitments |
| Commercial terms | Scope, milestones, dependencies, acceptance, and continuing obligations | Complete proposal and contract, including the terms for ongoing work |
Do not let a highly polished demonstration substitute for evidence about your conditions. Ask for proof that fits the risk: a reference may help assess delivery experience; a written policy may explain general practice; a contract clause makes a promise binding; an inspected configuration can show how a control is set up; and a bounded test can show behavior under specified conditions. No one form proves everything, so match the evidence to the question and record what remains unverified.
The available guidance does not establish a universal scoring formula for choosing a consulting firm. If your organization uses a scorecard, define the criteria and their importance before reviewing bids so that each firm is judged on the same basis. Record both the score and the reason for it, especially where an attractive result depends on an unverified assumption.
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Bring a short description of the problem, the people affected, the current process, the constraints you already know, and the result you would consider useful. You do not need to arrive with a model choice. You do need enough context for firms to respond to the same problem rather than each reframing it to fit its preferred service.
- Set the decision scope. Identify what decision the engagement should support—such as whether to proceed, what to build, or how to test feasibility—and who will approve it.
- List known constraints. Note data access or sensitivity, existing systems, relevant operating requirements, internal expertise, and any limits on deployment or use.
- Send a common question set. Ask every candidate to address the same outcomes, team, data, technical approach, testing, contract, and handover topics.
- Separate discovery from delivery claims. Ask firms to mark which estimates or commitments depend on information they have not yet examined, and how they propose to validate those assumptions.
- Keep an evidence record. For each important claim, note whether it is supported by a reference, written material, a contract term, an inspection, or a test—and what still needs confirmation.
- Review with the right people. Involve the business owner and the people responsible for relevant technical, data, security, legal, privacy, and procurement decisions. The right mix depends on the use case and your organization.
What the available guidance does—and does not—settle
The UK and U.S. sources cited here provide practical procurement and security guidance, not a universal rulebook for every jurisdiction, industry, or use case. Check applicable legal, privacy, security, and procurement obligations with qualified reviewers for your circumstances; general guidance does not replace review of a specific engagement.
NIST Special Publication 1326, published July 8, 2026, is a due-diligence guide for ICT suppliers. Its supplier considerations include foreign ownership, control, or influence; provenance; resilience; foundational cybersecurity practices; and supply-chain tiers. It can inform questions about relevant technology suppliers and dependencies, but its stated scope should not be treated as a universal certification or a requirement that every consulting engagement meet a single checklist. See NIST SP 1326.
The reviewed sources do not establish a defensible market-wide statistic for AI consulting success, typical fees, or return on investment. Do not use an unattributed industry average to forecast your project. Ask each firm to explain its own scope, assumptions, commercial terms, and how the proposed success measures would be evaluated.
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