What is touchless rate, and what AP quality KPIs should sit beside it?

Honest ranges, not vendor-slide numbers: many organizations sit below 30% truly touchless; well-run automated teams with strong PO discipline commonly reach 50 - 70%; the highest published claims concentrate in PO-heavy, EDI-heavy environments and often use looser definitions of "touch." Two cautions: definitional games (counting "human approved the AI's work" as touchless) inflate comparisons, and your invoice mix - PO share, vendor data quality, entity complexity - bounds what's achievable more than the software does. Demand the definition before believing any number, including your own.

Categorize every exception at creation - price variance, quantity variance, missing PO, coding failure, vendor-data mismatch, duplicate suspect - then Pareto by vendor, category, and entity monthly. The distribution is always concentrated: a handful of vendors or one entity's process typically drives the bulk. Fix at the source (vendor invoicing instructions, PO discipline in one department, a tolerance setting), not in the queue. Track exception rate *and* average exception age - a low rate with slow resolution still wrecks cycle time.

Exception rate is the share of invoices that fall out of the standard flow and require investigation. Standard categories: price variance, quantity variance, missing or unmatched PO, missing receipt, coding failure, vendor master mismatch, duplicate suspicion, and policy violations. Consistent categorization at creation is what makes the metric actionable.

First-pass match rate is the share of PO-backed invoices that match without human help on the first attempt. Well-run 2-way matching commonly reaches the high 80s to low 90s percent; 3-way runs lower because receiving data adds a failure mode. Below ~75%, the problem is usually upstream - PO quality and receiving discipline - not the matching engine.

Sample posted invoices monthly and score fields against source documents: amounts, dates, coding, vendor. Field-level error rates above 1 - 2% on financial fields, or any recurring systematic error (same field, same vendor type), justify process or tooling change - systematic errors compound at scale in ways random ones don't.

Track three numbers: duplicates blocked before payment (control effectiveness), duplicates paid and recovered (detection effectiveness), and net loss. Industry recovery audits commonly report duplicate payments in the range of a few hundredths of a percent up to roughly half a percent of disbursements depending on control maturity - your blocked-vs-paid ratio is the trend that matters.

Decompose the touched population: invoices touched for data correction (capture/data constraint), for coding (rules/learning constraint), for matching failures (vendor and PO-quality constraint), or waiting on people (approver constraint). The plateau almost always has one dominant cause, and it's frequently vendor-side invoice quality - which is fixed with vendor instructions, not software settings.

Late payment rate is invoices paid after due date as a share of payments. Split it by cause: invoices that were approval-stuck past due date (process-caused) versus invoices approved in time but scheduled late (treasury decision). Reporting them as one number lets a cash-management choice masquerade as an AP failure - or vice versa.

Stampli's framing is deliberate: coverage with human validation, not fragile precision. Stampli AI evaluates 100% of structured, ERP-aligned invoice fields and flags duplicates, variances, and compliance risks proactively - the goal is to eliminate manual entry and chasing so humans concentrate on the exceptions that need judgment. Stampli does not market "touchless" finance; every suggestion remains subject to human review and approval before posting to the ERP, which is precisely what keeps the quality KPIs defensible in an audit.