In procurement, generative AI creates content: it drafts RFP questions, summarises a supplier's security questionnaire or answers a policy question. Agentic AI takes action: it moves forward the buying process, such as flagging inconsistencies in intake, routing requests with clear recommended actions, and records what it did. Most procurement AI uses both, with generative models doing the reading and writing inside agents.

The difference is important  because almost every procurement software vendor now says it has AI. Knowing which kind you're looking at tells you how much work it will actually take off your team.

What is generative AI in procurement?

Generative AI in procurement is AI that produces text or content for a person to use. It’s what most people have tried through ChatGPT or Microsoft Copilot.

Typical procurement uses include drafting RFP questions, summarising long supplier responses, explaining a clause in a contract, answering “who approves software over £10k?” and searching past contracts in plain English. Each one saves someone time. In each case, a person still has to take the output and do something with it.

What is agentic AI in procurement?

Agentic AI in procurement is AI that carries out steps in the process. It works inside the workflow, with permission to change a request, and its actions are logged. What it produces is work that moves a request forward, such as a request ready to submit, a routed contract or a risk escalation, rather than text for someone to copy somewhere else.

Typical uses include checking a request for missing information and sending it back, spotting a duplicate supplier and redirecting the requester, clearing a low-risk contract against the legal playbook, starting a sourcing event and scoring the responses, and recommending or making an approval decision within limits the team has set.

How do agentic AI and generative AI compare?

Do you need generative AI to have agentic AI?

In practice, yes. Agents use generative models to read requests, documents and policies and to write their summaries and recommendations. The difference is what wraps around the model: access to the workflow, rules on what it can do, and a log of what it did.

That’s why the two often show up in the same product. A tool can summarise a contract (generative) and then rate it against the playbook and route it (agentic).

What the agent can see matters as much as the model. An agent with no access to your request history, policies and past decisions doesn’t fail loudly; it produces plausible, generic output that your team then has to check. And agents built on a fragmented process, with intake over email and supplier data spread across several systems, speed up bad outcomes instead of fixing them.

What are the levels of agentic AI in procurement?

Agentic AI isn’t a single setting. Omnea’s guide to AI in procurement describes four levels, in the same way cars moved in stages from manual driving to self-driving:

• Level 1, assisted intelligence: AI makes suggestions and a person acts on them. It drafts an email, summarises a contract or suggests a supplier. This is generative AI.

• Level 2, semi-autonomous agents: the agent runs a multi-step task, such as pre-filling a risk form or routing an approval, and a person confirms before anything happens.

• Level 3, autonomous agents: the agent completes the task end to end, every action is logged, and people review outcomes rather than approving each step.

• Level 4, fully autonomous: end-to-end work with minimal human involvement. For most regulated businesses running high-risk workflows, this isn’t the place to start.

The right level is a decision for each workflow, not for the whole platform. A low-value software renewal and a high-risk supplier assessment shouldn’t run with the same autonomy. Autonomous procurement orchestration covers where the human checkpoint should sit.

Why does the difference matter when choosing procurement software?

Ask vendors four questions about their AI:

• What can it actually do: generate text only, or trigger multi-step work across your systems?

• Does it run inside the procurement workflow, or in a separate portal people have to remember to open?

• Does it learn from your own approvals and decisions, or start from scratch from every prompt?

• Where can a person check its work, and can you see afterwards exactly what it did and why?

• Is it included in the platform, or charged per agent or per credit?

A tool that only generates content saves individual people time. A tool with agents in the workflow takes whole steps off the team, and gets better as it sees more of your decisions.

How does Omnea combine agentic and generative AI?

Omnea AI uses generative AI where people need help writing or understanding something, and agents where a step can be taken off them. On the generative side, AI Form Builder creates forms from a plain-language chat, reviewers get AI summaries written for their role, and Workflow Diagnosis Chat explains in plain English why a workflow step ran.

On the agentic side, the Intake Quality Agent checks requests before they reach an approver, the Duplicate Supplier Agent flags existing suppliers at the point of request, and the Approval Agent prepares recommendations with cited sources in Omnea approval workflows. Because Omnea runs the workflow, its AI learns from what was requested, who approved it and what changed, as described in AI intake management and orchestration.

“Most tools bolt AI on as a feature. With Omnea it’s embedded in the process itself: flagging weak requests before they’re submitted, surfacing existing suppliers before you go out to market, recommending how to improve forms based on what’s actually slowing requests down. You feel it at every step.”

Alex Pilsl, VP Procurement Strategy, The Adecco Group

For a wider view of where AI fits across the function, read Omnea’s guide to AI in procurement.

FAQs

Is ChatGPT agentic AI or generative AI?

On its own, ChatGPT is generative AI: it answers and writes. It becomes part of an agentic setup when it’s connected to systems where it can take actions, such as creating or updating a procurement request, with permissions and a log. Without that connection, a person still has to act on what it produces.

Is agentic AI riskier than generative AI in procurement?

It carries different risk. Generative AI can produce a wrong answer that a person then acts on. Agentic AI can act on a wrong decision itself, so it needs clear limits, human review above set thresholds and a full audit trail. Those limits should differ by workflow: a low-value renewal can run with a review afterwards, while a high-risk supplier assessment needs a person to approve it first. With those controls in place, agents are often more consistent than manual processes.

Which should procurement teams start with?

Most teams start with generative features, such as summaries and drafting, because they carry little risk. The bigger time savings come from agents on high-volume steps like intake checks and duplicate supplier detection. Start agents in recommend-only mode, then give them more autonomy as they prove reliable on each type of request.