A blueprint for automating accounts payable end to end — extraction, validation, matching and exception routing — with honest handling of the cases the automation should not attempt.
This is a blueprint, not a client case study. The situation described is a composite of the conditions we design for, not a named engagement. Targets below are measures we would baseline and report against — not results already achieved.
A shared services finance function processes somewhere around 14,000 supplier invoices a month across two entities. Invoices arrive by email as PDFs, as scanned paper, and through a supplier portal that exports a weekly file in a format nobody chose.
Six people key invoice data into the ERP. A seventh handles the exceptions — mismatched purchase orders, missing GRNs, invoices for entities that no longer exist. Month-end accrual is a scramble because nobody knows what is sitting unprocessed.
A previous automation attempt built four bots that worked until the ERP was upgraded. Two were repaired, two were quietly abandoned, and the team's confidence in automation went with them.
An invoice paid at the wrong amount costs more to unwind than it cost to key manually. Confidence thresholds have to route uncertainty to a person rather than guess.
Roughly a third of invoices have something wrong with them. An automation that handles only the clean two-thirds and dumps the rest changes very little.
Brittle UI automation against an upgrading ERP is exactly what failed last time. Integration must prefer APIs and must be monitored for silent failure.
An automated process that creates and approves its own payments fails the control. The automation posts; it does not approve.
Credibility has to be rebuilt with a parallel run on real invoices, not with a demo on curated samples.
The shape of the solution, before any of the detail below.
Confidence-gated invoice processing
These are the calls that determine whether the engagement works — most of them made before any build starts.
We measure the real distribution first — volume by channel, exception rate by cause, actual handling time per invoice type. That produces a ranked view of where automation pays back and, just as importantly, which invoice categories should stay manual because their variability makes automation a maintenance liability.
A meaningful share of exceptions are caused upstream: suppliers sending invoices without PO references, a portal export format that loses data, tolerance settings that were never revisited. Fixing those removes exceptions rather than automating them, and it is cheaper than any bot.
Layout-aware document AI extracts header and line-level fields with a per-field confidence score. Fields above threshold pass through; anything below routes to a review queue with the extraction pre-populated and the source document beside it. Thresholds are tuned per field, because a wrong supplier name and a wrong total carry very different costs.
Every exception is classified by cause — PO mismatch, missing receipt, tolerance breach, unknown supplier, duplicate. Each class gets a defined handling path, and volume by class drives the improvement backlog. Without this, the exception queue is just a different inbox.
ERP posting goes through APIs wherever they exist, with UI automation as a last resort and explicitly flagged as fragile. Synthetic transactions run daily so a broken integration is detected by monitoring rather than by a supplier chasing payment.
The automation runs alongside the manual process on live invoices for a defined period, with variance analysed and explained. The business case is then built on the team's own data, which is also what earns the team's confidence back.
Durations are indicative for an engagement of this shape. Yours would be sized during assessment.
Measure the real process — volumes, channels, exception causes and handling times — and produce a ranked automation scope with an explicit do-not-automate list.
Activities
Remove the exceptions that are caused upstream, then design the target process with its exception taxonomy and control points.
Activities
Develop extraction, matching and posting, then run in parallel with the manual process on live invoice volume.
Activities
Phased production rollout by invoice category and entity, with monitoring live from the first transaction.
Activities
Steady-state operation with exception causes reviewed on a cadence and the top drivers fixed rather than serviced indefinitely.
Activities
Every one of these is baselined before work starts, so the comparison at the end is defensible rather than flattering.
| Measure | Target | How it's measured |
|---|---|---|
| Straight-through processing rate | Baselined in pilot, improved quarterlyShare of invoices posted with no human touch, tracked by invoice category rather than as a single blended figure. | Share of invoices posted with no human touch, tracked by invoice category rather than as a single blended figure. |
| Extraction accuracy above threshold | Validated against manual keying in parallel runField-level comparison between automated extraction and manual entry across the full parallel-run sample. | Field-level comparison between automated extraction and manual entry across the full parallel-run sample. |
| Exception volume by cause | Top three causes reduced, not just routedException taxonomy counts tracked monthly, with upstream fixes prioritised by volume. | Exception taxonomy counts tracked monthly, with upstream fixes prioritised by volume. |
| Handling time per invoice | Measured on a consistent definitionBefore and after comparison using the same task boundaries, so the improvement figure is defensible. | Before and after comparison using the same task boundaries, so the improvement figure is defensible. |
| Automation availability | Failures detected by monitoring, not by suppliersSynthetic daily transactions plus alerting on queue depth and posting failures. | Synthetic daily transactions plus alerting on queue depth and posting failures. |
Named up front. A risk register written after something goes wrong is just a post-mortem.
API-first integration with UI automation minimised and explicitly flagged. Upstream change impact assessment is part of ongoing support, not an afterthought.
Thresholds are derived from parallel-run data per field, and reviewed monthly against actual error rates rather than set once at go-live.
Exception taxonomy drives an improvement backlog targeting causes. Queue depth is monitored and alerted, and the top causes are fixed upstream.
Parallel running on live data with transparent variance reporting, and the processing team involved in threshold tuning rather than presented with a finished system.
It never does. Tell us where yours differs and we'll tell you honestly whether we're the right people for it.
Or email us directly at reachus@segakiv.com