A buyer needs something. Requisition-to-PO captures the request, checks it against budget and policy, routes it through tiered approval, sources quotes for non-catalog items, converts the approved requisition to a purchase order, sends it to the supplier, and handles the exceptions.
The 8 tasks — the nature of each, and the oversight it needs
Tag each task in plain terms — what kind of work it is and how hands-off it can run — before any mention of AI. The kind of work is what later decides which tool, if any, fits.
A structured, approval-driven workflow: most steps are deterministic orchestration that bots can run end-to-end. There is no reading bottleneck — the binding constraint is the approval. Spend controls and tiered sign-off keep a human in the loop, because the risk is committing money against budget and policy, not interpreting a document.
Commits spend against budget and policy — tiered approval and segregation of duties keep a human accountable.
The question isn’t only “is there savings” — it’s can I run this better: cheaper, faster, higher quality, better service? Here’s what best-in-class looks like, and how teams get there. (How much of it AI specifically drives — and how proven that is — is Section 04.)
Process discipline first, then automation — AI is one slice of the second column, not the whole answer.
- Guided buying & punch-out catalogs
- Approval-policy & threshold design
- Preferred-supplier / contracted pricing
- No-PO-no-pay discipline
- RPA requisition-to-PO orchestration
- Automated approval routing
- Auto PO creation & distribution
- GenAI free-form intake
“Who runs the work” is its own question, separate from AI. AI shows up across these options — sometimes heavily, sometimes not at all. Vendor-neutral; the real options mapped to PR01.
Recall the tasks and their nature from Section 01. AI is one lever, not the whole story — the mix below is simply the result of matching the right kind of solution to each kind of work, weighted by where the work concentrates.
Agentic/RPA leads (~40%) because requisition-to-PO is a structured, approval-driven workflow — capture, check budget, route, create POs — where bots orchestrate the end-to-end flow. GenAI (~20%) handles free-form intake; Document AI and ML support catalog and quote comparison. (Everest IDP PEAK 2024, McKinsey State of AI 2025.)
The right lever fits your volume, variability, control needs, and appetite to operate a system. Start here.
The autonomy question: agent or copilot?
Whichever delivery model you pick, one choice cuts across them — who presses enter.
AI agent
Runs the steps end-to-end, completes the clean cases on its own, and routes only the exceptions to a person.
AI copilot
Sits beside the person and speeds up each step; the human acts on every decision.
What to evaluate — whichever you choose
- Accuracy on your own inputs — vendor benchmarks are on clean data; test your messiest cases.
- Straight-through / touchless rate — the real efficiency number, not “AI-powered.”
- Exception-handling experience — most of your team's time goes here, not the happy path.
- ERP write-back & integration depth — does it post cleanly to your system of record?
- Data residency — does data leave your environment, and is that acceptable to compliance?
- The accountability surface — what happens, and who owns it, when the model is confidently wrong?
See every lever across your processes
Run your portfolio through the assessment — work profile, improvement potential, confidence, and executor options across all your blocks, scored against 127 enterprise subprocesses.
Open the AI Value Assessment →