A familiar story, echoed by professionals across industries, goes as follows: you may not have deployed AI, but it is already in your organization. It is built into your HR systems, analytics tools, and customer platforms. You may not have planned for AI, but your organization has bought it and bundled it into everyday software. This shift in the technological landscape changes what procurement means. It is no longer about price, features, or compliance. Procurement is now a strategic control point and one of organizations’ first lines of defense for managing AI risk.
Organizations need a practical roadmap to get procurement right. The following discussion, grounded in global frameworks such as the National Institute of Standards and Technology (NIST) Artificial Intelligence (AI) Risk Management Framework (RMF), International Organization for Standardization (ISO)/ International Electrotechnical Commission (IEC) 42001/42005, and the EU AI Act, provides guidance designed to help organizations build AI oversight into every step of the procurement life cycle, from evaluating vendors to writing contracts and monitoring use.
7 Phases of the AI Procurement Life cycle
Purchasing technology used to be simple: compare features, negotiate price, sign the deal. But with AI, procurement is no longer simply about making a purchase. It is about deciding which systems are safe to bring into your organization, how they should be evaluated, and what guardrails need to be in place from the start. The stakes are higher with AI. A misstep does not just cost organizations money, it can create bias, expose sensitive data, or damage trust. This is why procurement cannot work in isolation. IT, cybersecurity, legal, and governance teams all need to play a role in the procurement life cycle.
The 7 phases of AI procurement lay out a practical roadmap to manage risk. The approach works no matter how AI shows up, whether as a product, a subscription service, a feature embedded in tools an organization already uses, or a system built in-house, these phases are designed to help organizations get procurement right.
Phase 1: Define Scope — Start With the Problem
The biggest mistake in AI procurement is chasing shiny tools without asking if they solve the right problem. A chatbot might look impressive, but if the real issue is slow customer response times, it will not solve the problem.
Start by framing goals in terms of results, not features. For example, “reduce service response time by 20%” instead of “deploy a chatbot.” This focus keeps the conversation grounded in business value. If the system affects sensitive areas such as eligibility, safety, or public-facing services, start an AI impact assessment early. It is crucial to not do this in isolation. Bring in IT, procurement, legal, privacy, ethics, and audit, as AI will influence them all.
Quick Tip: Define the problem yourself. If vendors define it for you, you will get the solution that suits them, not the organization.
Phase 2: Market Scan — Test for Transparency
Before any request for proposal (RFP) is written, procurement teams should scan the market to understand which vendors are willing to provide meaningful transparency. Ask not just what the system does, but how it was trained and governed. If a vendor cannot answer clearly, that should be a red flag.
Inquiries should be made regarding how models were trained, what data they used, and how they are updated. For more detailed information, request a “transparency pack” which includes documentation, training data sources, benchmarks, explainability notes, and any third-party dependencies. For generative AI, inquire about guardrails: How does the vendor reduce hallucinations, prevent leaks, or stop prompt injection? You are not just comparing features here. You are checking whether a vendor is trustworthy long term.
Quick Tip: If a vendor cannot explain how their AI works, that is a red flag.
Phase 3: RFP — Put Guardrails in Writing
The RFP is where governance is put into action—by turning requirements into enforceable commitments.
Ask vendors for a software bill of Materials (SBOM) and an AI bill of Materials (AIBOM). Think of them as ingredient lists that show what is inside the system before you put it into production. An SBOM shows the software components, libraries, and licenses, while an AIBOM outlines the data sources, models, and algorithms. Together, they provide transparency, help surface security and compliance risk, and reduce the chance of adopting a black-box solution. Be sure to insist on evidence of fairness testing, explainability methods, and independent audits.
When designing guardrails, ensure the requirements are precise. For instance, a statement such as, “The system should be fair” is too vague to include in a contract. Instead, write requirements as measurable commitments. For example: “require each decision to include a SHAP-based explanation of the top contributing features, delivered to users and auditors within an agreed timeframe.”
Similarly, fairness can also be defined in measurable terms— for example, by requiring error-rate disparities across defined demographic groups to remain below a specified threshold. Likewise, extend service-level agreements (SLAs) beyond uptime; require transparency, and clear timelines for incident response.
Quick Tip: If it is not in the RFP, do not expect it to show up later.
Phase 4: Due Diligence — Push System Limits
A slick demo proves nothing. Real diligence means testing AI with your own examples, under real-world conditions, and actively trying to make it fail. This means pushing the system with edge cases, errors, and bad inputs. Check whether human-in-the-loop controls actually work, whether override mechanisms trigger when they should, and whether audit logs hold up.
The goal is not to embarrass the vendor. It is to see how the system behaves when things go wrong because sooner or later, they will.
Quick Tip: Do not test to confirm success. Test to uncover failure.
Phase 5: Contract — Lock in Oversight
A handshake is not governance. If something truly matters, it must be written into the contract. Define ownership clearly: who controls the data, the outputs, and any models that are fine-tuned on the organization’s behalf. Secure explicit audit rights so you can verify fairness, performance, and security rather than simply taking the vendor’s word for it. Establish clear timelines for incident reporting and spell out the consequences if the vendor fails to meet those obligations.
Because AI systems evolve, vendor agreements need to evolve as well. Build in flexibility by including reopener clauses that allow you to renegotiate terms if regulations shift, risk changes, or new requirements emerge.
Quick Tip: If it is not written down now, it cannot be enforced later.
Phase 6: Deploy — Launch With Guardrails
Go-live is not the finish line. In AI, it is when the real risk begins. Deployment should include technical safeguards such as authentication, monitoring, and rollback protocols. However, it is important to remember that the organization’s employees matter just as much. Train staff to know what the AI can and cannot do, when to question its outputs, and how to escalate issues.
The system is not safe just because it is live. It is only safe when employees understand the system’s limits and know how to step in when needed.
Quick Tip: A system is only as safe as the people trained to manage it.
Phase 7: Govern — Remain Vigilant
AI is not static. Models drift as data shifts; vendors roll out updates, often without notice; and regulations keep evolving. This is why governance cannot be treated as a one-time project; it is a long-term commitment.
That commitment means building a routine for monitoring and maintenance: scheduling regular checks for bias, drift, and accuracy; keeping SBOMs and AIBOMs up to date; and staying alert to regulatory changes. Equally important, ensure contracts give you the flexibility to adapt when the environment inevitably shifts.
Quick Tip: Think of governance not as bureaucracy but as the reason your organization can use AI confidently, year after year.
AI Procurement Is a Governance Imperative
AI procurement is not just a back-office task; it is a front-line responsibility for digital trust. Each step in the procurement process is a chance to influence how AI enters your organization—ensuring clarity, accountability, and alignment with organizational goals, risk tolerance, and values. It is not just about picking the right system; it is about putting the right guardrails in place from day one.