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What should I look for in an enterprise AI implementation partner?
Assess each candidate against the same use case and evidence requirements. A strong proposal should explain what the system will do, how it fits your existing environment, what the partner will deliver, and how you can verify the result. Use a weighted scorecard if it helps your team compare options, but agree on the criteria before reviewing proposals.
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Relevant delivery evidence
- Request examples of comparable business processes and user groups—not just a list of industries or AI tools.
- Ask for a proposed architecture showing integrations, data flows, migration work, and dependencies.
- Set measurable acceptance criteria and ask for references you can contact about delivery, handover, and ongoing operation.
Data and intellectual-property controls
Establish what information the partner and its service providers receive, where it is processed, how long it is retained, and how deletion is verified. Ask whether any customer data, prompts, outputs, or derived information may be used to train or improve models or services. Clarify ownership and licensing for your inputs, generated outputs, partner materials, implementation code, and third-party components; address third-party content and its licensing as well.
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Review access controls, personnel access, incident response, vulnerability management, data provenance, resilience, and continuity evidence. Look beyond the prime contractor: identify subcontractors, cloud and model providers, software components, and other organizations that can access data or affect service delivery. NIST’s SP 1326, published in July 2026, offers a useful supplier-review lens: foreign ownership, control, or influence; provenance; resilience; foundational cyber practices; and supply-chain tiers. It is guidance for ICT supplier due diligence, not a universal contract checklist.
Testing and governance
Require evaluations tied to the actual use case, including how errors and unacceptable outcomes will be measured and handled. Decide who reviews results, when human oversight is needed, what gets monitored in operation, and what changes trigger retesting or approval. Ask what logs, evaluation results, and monitoring reports your team will receive.
Contract, economics, and lock-in
Compare the full delivery and operating picture: scope and exclusions, milestones, acceptance, change requests, ongoing service levels where relevant, and remedies if requirements are missed. Include training and knowledge transfer. Identify implementation assumptions, continuing operating costs, consumption-based dependencies, portability, termination assistance, and the cost of switching providers—not just the initial project fee.
What questions should I ask an AI consulting firm before signing?
Put these questions to the proposed delivery team, not only to the sales contact. Request written answers and supporting evidence for material claims.
- Which exact business process and user group will the system support? How will success, errors, and unacceptable outcomes be measured?
- Which models, data providers, cloud services, software components, and subcontractors are in scope? Which entities can access our data?
- What information leaves our environment, how long is it retained, can it be used for model training or service improvement, and how is deletion verified?
- What evidence can you provide for security controls, incident response, vulnerability management, data provenance, resilience, and continuity?
- What tests will you run before acceptance and after material changes? Can our staff or an independent assessor inspect relevant records and results?
- What happens if a model, data source, or third-party service fails or becomes unsuitable? What is the fallback, and who operates it?
- After termination, what deliverables, documentation, configuration, prompts, evaluations, and integration code will we own or be licensed to use?
- How will our staff be trained, and what must be handed over so the enterprise can operate, monitor, and change the system without the implementation partner?
How do I evaluate an AI implementation vendor’s security and data practices?
Start with the intended use and the data involved. A supplier’s controls that are adequate for a low-impact internal assistant may not address the risks of a system handling sensitive information or influencing consequential decisions. Have your security, privacy, legal, procurement, and technical teams set requirements for the specific use case and applicable jurisdiction.
Trace the data and access paths
- Map what data enters the system, where it travels, which services process it, and which people or organizations can access it.
- Check retention, deletion, reuse, and training terms across the partner and relevant subprocessors—not just the prime contractor’s headline policy.
- Ask for evidence that access is controlled and that incident response, vulnerability handling, and continuity arrangements cover the relevant services.
Examine assurance in context
Treat certifications, framework mappings, and assurance reports as evidence to assess, not as proof that your proposed implementation meets your requirements. Check what system, entity, scope, and period the evidence covers, and identify gaps against your use case. Microsoft’s Supplier Security and Privacy Assurance materials illustrate one organization’s supplier program; its conditions are specific to Microsoft and should not be treated as universal requirements.
Review dependencies and failure plans
Ask for the supplier chain, the roles each party performs, and the data each can access. Establish how you will be notified of material supplier or system changes. For important third-party data or AI services, require a documented fallback and specify who activates and operates it. NIST’s Generative AI Profile recommends due diligence that addresses intellectual property, data privacy, security, and other risks; use-case-based supplier assessment; ongoing monitoring; and attention to third-party processes and contingencies.
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What should an AI implementation contract include?
Use the following as negotiation topics for counsel and procurement, not as prewritten legal clauses. The contract should make the intended use, responsibilities, evidence, and consequences of change clear enough to manage throughout delivery and operation.
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- Purpose and scope: intended and prohibited uses, systems and data in scope, party roles, deliverables, exclusions, and measurable outcomes.
- Data and security: confidentiality, permitted processing, security controls, retention and deletion, and restrictions on training or reuse of customer information.
- Subprocessors and dependencies: named or otherwise identifiable material providers, disclosure obligations, and approval or notification for changes calibrated to risk.
- Incidents: notice, cooperation, investigation, remediation, and evidence obligations.
- Evaluation and access: workable rights to assess relevant third-party AI processes and standards, with appropriate access to records, results, logs, provenance information, and monitoring reports. NIST specifically recommends contract clauses that allow organizations to evaluate third-party generative AI processes and standards; negotiate practical evidence, audit, reporting, and remediation rights while accounting for legitimate confidentiality and security constraints.
- Acceptance and changes: testing methods, performance thresholds, limitations, change control, retesting triggers, and remedies for missed requirements.
- Intellectual property: ownership and licenses for customer data, partner materials, generated outputs, code, and third-party components.
- Continuity and exit: fallback arrangements, portability, termination assistance, deletion, and knowledge transfer.
- Ongoing review: a process for reassessing risk and suitability as the use case, data, models, services, or suppliers change; a pre-signature review alone cannot establish that a changing system remains suitable.
How can NIST frameworks help structure supplier review?
NIST’s AI Risk Management Framework is a voluntary organizing framework for trustworthiness considerations across AI design, development, use, and evaluation. NIST says AI RMF 1.0 is being revised, so confirm the current version before incorporating references to it into procurement language. The AI RMF Playbook suggests actions and documentation practices across Govern, Map, Measure, and Manage; it, too, is voluntary.
Use frameworks to organize questions and evidence, not as a substitute for requirements tailored to the system, business process, data, and risk. NIST’s supplier guidance and GenAI Profile support due diligence, monitoring, and third-party evaluation, but they do not prescribe one contract template or establish that a particular firm is qualified.
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