Intelligent automation consulting

• A process that runs. One manual, critical process, rebuilt and live on real volume.
• A clear split of work. The routine cases decided by the system, the judgment calls kept by people, and a written record of which is which.
• Evidence. A baseline from before the build and measures from the pilot, so the result can be checked.
• An operator. A team that monitors accuracy, clears blockers and adapts the system when inputs or upstream systems change.

An intelligent automation effort can stall at three points. An opportunity assessment with no owner for the build stays a document. A build tested only on clean examples can fail on the unusual cases real operations produce. And a system that goes live with nobody assigned to run it decays as the input mix shifts.

Manthle's method is shaped around those three points. Consider a shared services center that reviews employee expense exceptions for a company with tens of thousands of staff. In the See step, the team reads the expense system's audit trail alongside the policy, which is where process intelligence comes in, and spends time with the reviewers to learn which policy questions they settle by habit. Rethink decides which receipts the policy should never route to a person at all. Engineer builds the system with the reviewer's exception screen designed from the first day. Harden tests it on duplicate receipts, foreign currencies and claims split to stay under a limit. Run keeps it working through policy changes, and it is the reason the pilot becomes an operations contract instead of a handover.

The engagement takes one process at a time and goes deep on it. A company-wide automation strategy program, an inventory of every bot in the company or the selection of a platform for all teams is a different kind of work. If your need is a portfolio view across every function, a different engagement fits better.

In the expense example, depth means the engagement covers that one process completely: how claims arrive, which policy rules settle each type, which claims the system can approve or return on its own and what a reviewer sees when it cannot decide.

Consulting buyers often have an automation center of excellence or an IT automation team already. That team keeps its platform standards, its release process and its say over access to the systems of record, and decides with Manthle which parts of the new process it will own. The business process owner keeps the outcome and the final say on how the process works. The specialists keep the exceptions, which are the cases where their experience pays.

1. What will be running in production at the end of the engagement?
2. How will you find out how our process works in practice, beyond interviews?
3. Which decisions do you propose to give to models, and why those?
4. What happens to a case the system is unsure about? Show us the screen a person will see.
5. How do you test for the cases that only show up a few times a month?
6. Who operates the system after launch, and how is that paid for?
7. What would make you tell us not to automate this process?

Ask the last one early. A consultant with no example of advising against automation is selling volume.

If you need to choose and roll out an automation platform across many teams, a platform implementation partner fits better. If the work is mostly fixed steps on structured data, plain RPA consulting will be quicker and cheaper, and the RPA vs intelligent automation comparison explains the distinction. If the question is still whether a process needs AI at all, read AI vs automation first. If the board wants a multi-year transformation plan, a strategy firm writes those. The business process automation guide sets out why Manthle starts from one process instead.

A simple rule for choosing: if the deliverable you need is a recommendation, hire an adviser. If it is a process that has to work next quarter and keep working, ask who will still be running it in a year.