Enterprise AI Procurement in the US: A Practical Buyer's Guide
A US-focused framework for evaluating enterprise AI suppliers, security, data use, employment risk, contracts, operating cost and exit.
By Leila Haddad, Women in AI Editorial Fellow · 26 August 2026
Enterprise AI procurement is a decision about a changing system. Performance can depend on data, prompts, retrieval, model versions, permissions and human review. A vendor may replace a model after the contract is signed. A workflow may become more autonomous as teams connect additional tools.
US buyers should evaluate the use case, supplier and operating model together.
Define the job and baseline
Document the current process, users, cost, failure modes and accountable owner. State what the AI system may and may not do. Define success before a pilot, including quality, severe failures, performance across relevant groups, latency, security, accessibility, cost and human-review workload.
NIST's AI Risk Management Framework provides a useful structure through its govern, map, measure and manage functions. The Generative AI Profile adds risks and actions specific to generative systems.
Map the legal surface
US requirements vary by sector, state, use and affected person. Privacy, consumer protection, employment, financial, health, civil rights, intellectual property and contractual rules may all apply.
The EEOC has addressed algorithmic fairness in employment, while the Federal Trade Commission has warned businesses that unsupported AI claims may be deceptive. Legal counsel should map actual obligations. “Compliant AI” is not a meaningful supplier assurance without a named law, use and jurisdiction.
Follow data and dependencies
Ask what data enters the system, where it is processed, how long it is retained and whether it is used for model training. Identify subprocessors, model providers, retrieval services, human reviewers and open-source components.
Contract terms and technical configuration must agree. A dashboard setting is insufficient if the agreement allows broader data use or unilateral changes.
For agents, list every permitted action. Can the system send messages, change records, execute code or make purchases? Define human approvals, spending and access limits, logging, rollback and emergency revocation.
Test representative cases
Evaluate the supplier against the current process and a credible alternative. Use ordinary and difficult cases from the intended environment. Measure failure severity, not only average quality. Test prompt injection, data leakage and unsupported output where relevant.
A pilot should end with a decision to proceed, constrain, redesign or stop. It should not become production by inertia.
Contract for change and exit
Address data use, model substitutions, security incidents, audit rights, service levels, intellectual property, regulatory cooperation, accessibility, human oversight, indemnity, liability and termination.
Calculate integration, monitoring, inference, human review, training and switching costs. Confirm that data, configurations, prompts, evaluations and logs can be exported in usable formats. An exit path is part of the purchase.
Keep a concise decision record covering the owner, evidence, limitations, dependencies, controls, exceptions, expected value and review date. Procurement cannot eliminate uncertainty, but it can make the organisation's reasoning testable.
Compare the UK procurement edition and global enterprise AI procurement guide. Continue through our enterprise AI hub and AI impact assessment guide.