Quote extraction is a great place to introduce agentic AI because the "unsafe" part is not the extraction.
The unsafe part is letting AI make the commitment (award decision, purchase order issuance, and payment) without controls.
See:
The control model (use this as a rollout checklist)
Define autonomy per action. A simple model that works:
- Assist: AI drafts, humans review
- Execute: AI performs low-risk actions automatically
- Approve: humans must approve before money/commitment actions
The key is to label actions explicitly and keep the audit trail attached.
Safe to automate (execute)
These are typically low-risk because they are reversible and reviewable:
- Extract vendor name, totals, and terms from PDFs into a structured draft
- Normalize obvious fields (units, currency codes, dates) when deterministic
- Assemble a comparison pack and flag missing evidence (not fill it in)
- Create a review queue for exceptions and missing fields
Related product workflows:
Keep draft-first (assist)
These are useful, but should stay reviewable because they can introduce subtle errors:
- Line item matching across vendors (similar-but-not-equal items)
- Substitution suggestions
- Price normalization when terms differ (delivery, warranty, taxes)
- "Best vendor" recommendations
Use assist mode to move faster without giving up authority.
Never silent (approve)
These actions should require explicit approval and an evidence trail:
- Award decisions that commit budget
- Issuing purchase orders
- Policy-bound approvals and escalations
Related product workflows:
The auditability rule
If a procurement workflow cannot answer:
- Who approved this?
- What evidence was attached?
- What changed between draft and final?
Then it is not "automation." It is hidden risk.
What to do next
- Start with the agent: Procurement Agent
- Connect it to your chain: RFQ management -> Quote comparison -> Purchase orders
- Lock governance first: Trust and governance