The SOP
- Invoice received
- PO matched
- Approved
- Paid
On paper, the process looks linear.
AI Workflow Workshop example
Example: Finance workflow review
A simplified example of how one anonymized workflow case can become a before/after AI review and a practical experiment recommendation.
Example output, not a client case study
This is a simplified example of the kind of output the AI Workflow Workshop is designed to create. It starts with one real or representative case, not a generic idea list, and turns that case into a structured workflow review.
SOP vs reality
Most teams have a process for how work is supposed to happen. The useful question is how the work actually happens when exceptions, missing context, approvals, handoffs, and judgment calls appear.
In this example, the clean process looks simple. The real workflow is where the AI opportunity shows up.
The reality
In reality, exception invoices move through more steps, more handoffs, and more judgment. A PO mismatch or unclear cost center can send the invoice through finance, procurement, department owners, vendor communication, controller review, and manual follow-up.
This is an illustrative example, not a client benchmark.
The gap
- Context is scattered across systems, emails, approval rules, and vendor history.
- Exceptions follow repeatable patterns, but the patterns are often not documented.
- Humans spend time gathering context before they can make the real decision.
- Delays create avoidable rework, missed discounts, or slower payment cycles.
- AI can help prepare the review, but humans still own approval, policy, and risk decisions.
Example workflow
Invoice exception triage
A B2B SaaS finance team receives vendor invoices through email, PDFs, and finance tools. Most invoices are straightforward, but exceptions create delays: missing POs, amount mismatches, first-time vendors, unclear GL coding, and approvals stuck between finance, procurement, and department owners.
The case
A vendor invoice arrives with a PO mismatch and unclear cost center. The finance analyst needs to decide whether it can move forward, who must approve it, and what information is missing.
Current workflow replay
- Invoice arrives through email or finance system.
- Finance analyst checks vendor, amount, PO, entity, cost center, and due date.
- Analyst searches ERP records, approval matrix, procurement notes, prior emails, and vendor history.
- Analyst classifies the issue.
- Analyst asks vendor or internal owner for clarification.
- Controller reviews high-risk or unclear cases.
- Approved invoice enters payment queue.
AI-assisted attempt
AI could prepare:
- Extracted invoice summary.
- Likely exception type.
- Missing information.
- Suggested internal owner.
- Draft clarification message.
- Confidence note.
- Controller review note.
Sample AI-assisted review note
- Exception type
- PO mismatch
- Missing information
- Confirmed cost center and approval owner
- Suggested owner
- Department budget owner
- Risk level
- Medium
- Recommended next step
- Request cost-center confirmation before controller review
What improved
- Faster first read of the invoice.
- Clearer exception classification.
- Less manual rewriting.
- Better preparation for the controller.
- More consistent clarification message.
What failed or felt risky
- AI may infer the cost center without enough evidence.
- AI may sound confident when approval context is missing.
- Vendor history may be incomplete.
- High-value or policy-sensitive exceptions cannot be auto-approved.
- Final payment decision must remain human-controlled.
Human control model
AI can suggest
- Exception type.
- Missing information.
- Likely owner.
- Draft clarification message.
- Evidence summary.
Human must approve
- Payment release.
- New vendor approval.
- Unusual GL coding.
- Large variance handling.
- Policy exceptions.
System must log
- Source invoice.
- Extracted fields.
- Confidence level.
- Human correction.
- Final decision.
Never automate
- Payment approval.
- Policy exceptions.
- High-value exceptions.
- Low-confidence cases.
Recommended experiment
- Name
- Invoice exception review assistant
- Duration
- 2 weeks
- Users
- 1 finance analyst, 1 controller
- Scope
- 30 to 50 exception invoices
- AI role
- Pre-classify exceptions, gather context, draft the reason, and suggest the next owner.
- Human checkpoint
- Controller reviews high-risk, high-value, or low-confidence cases.
- Success metric
- Reduce manual triage time by 30% without increasing review risk.
- Expected business impact
- Less manual prep work, faster exception routing, fewer avoidable delays, and clearer evidence for whether this workflow deserves deeper automation or system integration.
- Verdict
- Diagnostic recommended if the team wants to connect ERP, approval matrix, procurement notes, and vendor communication paths into a proper experiment design.
What this shows
This example does not redesign the finance department. It shows how one workflow case can reveal where AI is useful, where it is risky, and what experiment is worth testing.
Where this can lead
Workshop
Review one real case and choose the experiment.
Diagnostic
Design the experiment properly with owners, metrics, risks, data needs, and implementation options.
Sprint
Help the team prototype, test, document, and operationalize the workflow change.
Want to review one workflow from your team?
Bring one anonymized case. I will prepare an AI workflow review, then we will decide what worked, where it failed, what humans must control, and what experiment is safe enough to test.