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Choose an AI development company by testing its proposal against a business outcome you can measure—not by judging a polished demo or broad AI claims. Ask every candidate the same questions about expertise, testing, data, security, integration, ongoing operations and contract terms, then compare their answers against your use case.
How do you decide whether AI is the right solution?
Start with the problem, not a model or feature list. Ask each company to restate who has the problem, what should improve, and how you will tell whether the work succeeded. A vendor should also explain why AI is appropriate and what a simpler, non-AI approach would look like.
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Evaluate the proposal across business value, user experience and technical feasibility. Include likely risks and the change-management work needed for people to use the system. Microsoft’s AI use-case evaluation guidance treats implementation and maintenance as ongoing concerns, not tasks that end at launch.
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- What outcome should improve, and how will you measure it?
- What assumptions does the proposal make about data, users or existing systems?
- What simpler alternative could achieve the same outcome?
What questions should you ask an AI development company?
Who will do the work, and what relevant evidence can they show?
Ask for the names or roles of the people assigned to the project, their relevant technical and domain expertise, a proposed architecture, and a delivery plan. Request examples of comparable work, then ask what those examples demonstrate and how the prior systems differed from yours. A credential or case study alone cannot establish that a company can deliver your specific system. NIST’s AI Procurement in a Box: Workbook and Guidelines for AI Procurement include supplier questions about specialist expertise, implementation evidence, output interpretation, security and testing.
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How will you test the system and decide whether to accept it?
Define acceptance criteria before signing. Ask what test data and evaluation methods fit the intended use, how the team will assess performance and failure conditions, and whether bias or unequal outcomes are relevant. Clarify when a person reviews outputs and how test results will be documented. NIST’s procurement guidance includes questions on acceptance criteria, accuracy, bias and maintenance.
If you are hiring a red-team provider, ask about its threat models, testing rigor, tooling, governance and evidence behind reported issues. OWASP’s 2026 vendor evaluation criteria for AI red teaming provides a framework for assessing providers and tools; a handful of jailbreak screenshots alone does not show that an assessment was meaningful.
What data and external services will the system use?
Request a clear account of the proposed data flow and dependencies: data sources, models, APIs, libraries, cloud services and other vendors. For each external provider, ask what information it receives, who can access it, how it is protected, whether prompts or outputs are retained or used for training, and how deletion works. Establish your rights to project data and outputs, and make sure you can access the records needed for monitoring and future maintenance.
NIST’s workbook addresses access and ownership for input, training, processed and enriched data. AWS advises buyers to review model-provider terms and data-use policies in its security-readiness checklist. Microsoft’s AI governance guidance also flags risks involving external data, models, libraries, APIs and integrations.
How will security and operational responsibilities be divided?
Ask how authentication and authorization work, how least-privilege access is enforced, and how environments and user sessions are isolated. Ask how the company handles vulnerabilities, incidents, logging and monitoring. Have it distinguish controls it owns from those your organization or cloud provider must supply.
AWS’s security checklist covers governance, provider terms, access controls, input and output risks, data protection, perimeter security, monitoring and incident response. Microsoft’s governance guidance highlights integration concerns such as dependency cascades, data incompatibilities, performance bottlenecks and security gaps.
Who will integrate, monitor and maintain the system after launch?
A working prototype is not necessarily an operable product. Agree who will connect it to your systems, prepare and train users, monitor behavior, respond to incidents and maintain dependencies. Ask what happens if a model or API changes, performance degrades, or your internal processes change; clarify whether model updates or retraining are included.
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NIST’s procurement materials address deployment training, transparency, monitoring and maintenance. AWS recommends monitoring and incident response, while Microsoft’s use-case guidance recognizes that product changes and maintenance continue after release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should be in an AI development contract?
Make the proposal and agreement specific enough to test what is promised and identify who is responsible. Cover scope and assumptions, deliverables, milestones, acceptance criteria, change control, data and intellectual-property rights, external services, support responsibilities, service levels where appropriate, and exit or handover expectations.
Ask how the estimate is calculated and which costs may continue after the initial work, including cloud or model usage and support. Agree how success and return on investment will be measured. AWS Prescriptive Guidance recommends AI-specific project and contract templates, service-level agreements, partner performance measures, cost allocation, ROI tracking and flexible engagement models in its AI-powered operating-model guidance.
There is no universal price range established by these sources. Cost depends on scope, geography, data readiness, integration, risk and support needs, so ask candidates to state the assumptions behind their estimates and what could change them.
How do you compare shortlisted companies?
Give every candidate the same use-case description and compare their answers on the same criteria. The right emphasis will depend on your project; the sources do not prescribe universal weights or a single scoring formula.
Quick Recap
- Fit with the business problem and users
- Relevant domain and technical expertise
- Transparency about architecture and dependencies
- Data rights and security controls
- Evaluation method and acceptance plan
- Integration and operational readiness
- Ownership, contract clarity and handover
- Total cost assumptions, including ongoing use and support
Shortlist interview questions
- What business outcome are you proposing to improve, and how will we measure it?
- Why is AI appropriate here, and what simpler alternatives did you consider?
- Which roles and skills will perform the work, and what comparable evidence can you share?
- Which models, data sources, APIs, libraries and cloud services will the solution depend on?
- What information will be sent to each external provider, and what are its retention and deletion terms?
- What are the acceptance criteria, and how will you test quality, failure modes and relevant bias risks?
- What systems must this integrate with, and how will you manage failures or dependency changes?
- Who owns and can access the data, code, configurations and other project outputs?
- What monitoring, incident response, maintenance and support are included after launch?
- What assumptions drive the estimate, and which ongoing costs are outside the initial fee?
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