AI workflow automation for one critical process

The symptoms are familiar to anyone who runs a large operation. A queue that is always a few days behind. Experienced people spending most of their day copying data between systems and a small part of it on the cases that need their judgment. Escalations that arrive without the history. A backlog that grows every time volume spikes, because the only way to add capacity is to add people.

Consider supplier onboarding at a national distributor. Each new supplier sends a tax form, bank details, an insurance certificate and a signed agreement, by email, in whatever format they have. A team reads every document, checks it against policy, chases what is missing, screens the company and keys the record into the ERP's vendor master. Buyers wait to place first orders. Suppliers wait to be paid. And a mistyped or altered bank account is a fraud risk that nobody wants to catch late.

In the rebuilt workflow, a model reads each incoming document, extracts the fields, checks them against the supplier's other documents and the onboarding policy, and writes back to the supplier for anything missing. Complete, consistent packets go straight into the vendor master through the ERP's own interface, with a record of every check. A packet with conflicting names, an expired certificate or bank details that do not match the company's other records goes to a specialist, with the documents, the conflict and a suggested next step on one screen.

The workflow is an engineered system: models where judgment on messy input is needed, plain rules where a rule will do, integrations into the systems you already run and a log of every decision. The intelligent workflow automation entry defines the idea in general terms; the rest of the work is building one that holds up, and running it.

People keep the exceptions, and in onboarding the exceptions carry the risk: the bank change that looks wrong, the supplier whose ownership is unclear, the contract term that falls outside policy. They also keep authority. Specialists review samples of automated approvals, adjust thresholds with the engineers and decide when the system is ready to take on a new class of supplier. The design goal is that a person sees only what needs a person, with everything required to decide it already in front of them.

See. For onboarding, that means following a few dozen suppliers from first email to first payment, reading the ERP's history of vendor records, and noting which documents arrive late, which fail checks and how long each stage waits.

Rethink. Some checks exist twice because two teams never compared notes. Some documents could be requested in a structured form at the start. The redesign decides which decisions the model will own, which stay with specialists and which disappear.

Engineer. The team builds the system around the redesigned workflow: extraction and matching models, the policy rules, the ERP integration and the exception screen, with specialist review of outputs built into the pilot design.

Harden. Real onboarding includes scanned faxes, documents in other languages, suppliers who reply in fragments and the occasional deliberate fraud attempt. Hardening adds confidence thresholds, a rule that bank details never change without a person's approval, retries for slow systems and a full audit trail.

Run. Manthle operates the workflow with your team: watching accuracy, clearing what blocks the exception queue, adjusting to new document types and reporting against the baseline.

A pilot runs on one workflow, on real cases, against measures agreed before the build:

• the share of suppliers onboarded with no person touching the packet;
• the accuracy of automated checks, confirmed by sampling;
• elapsed time from first contact to an active vendor record, against the See-step baseline;
• the age and size of the exception queue;
• errors found later in the vendor master or in payments;
• specialist time per exception.

When the pilot meets its measures, the workflow stays live on full volume, and the work shifts from proving it to improving it.

Be honest about which kind of workflow you have.

The RPA vs intelligent automation comparison covers the line between bots and judgment. If your workflow is mostly a queue of back-office cases that need triage, exception management is the closer fit. The business process automation services page and the business process automation guide cover the wider engagement and the thinking behind it.

A quick test before you decide: count how many of last month's cases a new hire could have handled from a written checklist. If nearly all, buy a tool or a bot. If a meaningful share needed someone experienced to read, compare and judge, the workflow is a candidate for an engineered one.