Start with one workflow that costs the most manual time. Prove value there before expanding.
Why approval processes are a good automation target
Approval processes tend to follow patterns. Most expense reports fall within policy. Most purchase requests are routine and expected. Most leave requests do not conflict with anyone else’s schedule. When the outcome is predictable most of the time, the process is a strong candidate for automation, because the system can handle the predictable cases and route the rest to a person.
The real cost of a slow approval process is rarely obvious until you measure it. A purchase that sits waiting for sign-off delays a project. An expense report stuck for two weeks frustrates an employee who already paid out of pocket. None of these delays come from a manager making a hard call. They come from a request sitting unseen, which is a routing problem, not a judgment problem. Add up that delay across every request in a month and the total is usually larger than most owners expect.
nd clears the requests that do not need a decision at all, freeing a manager’s attention for the ones that genuinely do.
What actually gets automated
It is important to be precise here. What gets automated is the routing and the first-pass check, not the judgment behind a genuinely difficult decision. A system can check a purchase request against budget, policy, and past spending patterns, and either approve it automatically if everything checks out, or send it to the right manager with the relevant context already attached, instead of a blank request waiting for someone to look up the numbers themselves.
This distinction matters because it changes how you should think about the project. You are not building a system that decides things for you. You are building a system that gathers the facts a decision needs and clears the requests that do not need a decision at all, freeing a manager’s attention for the ones that genuinely do.
Working on something similar?
Let's talk →Where AI fits in the approval chain
Working on something similar?
Let's talk →AI adds value beyond simple rule-based routing by reading the request itself and flagging anything unusual: a request that is a significant outlier compared to the requester’s history, a duplicate submission, or a request that touches a category with recent policy changes. This is the difference between a basic if-then workflow and a system that actually reduces the number of things a manager has to think about.
A simple rules engine can check whether a number falls under a limit. An AI layer can also notice that this particular employee has never submitted a request this large before, or that three similar requests came in from the same department in one week, patterns a fixed rule would never catch on its own.
What to keep human
Anything involving a genuine exception, a first-time request of a new type, or a decision with real financial or people implications should stay with a person. The goal of automating approvals is not to remove accountability. It is to make sure the requests that genuinely need a manager’s attention reach them quickly, with the information they need already gathered, instead of buried under routine requests that did not need their attention at all.
It is worth being explicit with your team about where this line sits, so nobody assumes the system is quietly making calls it was never meant to make. A clearly documented rule set builds more trust in the automation than a vague assurance that the AI is handling it.
What it looks like in practice
In a well-set-up system, an employee submits an expense report and it clears automatically within policy limits, posting straight to the finance system. A purchase request above a threshold routes to the relevant department head with a one-line summary of why it was flagged. Nobody is chasing approvals over email, and managers spend their attention on the requests that actually need it.
Businesses that get this right usually start with one approval type, expenses or purchase requests are common starting points, prove it works reliably, and expand to other approval chains once the first one is running smoothly. Trying to automate every internal approval at once tends to create more confusion than it solves, and it makes it much harder to tell which part of a new problem, if one appears, actually caused it.
If you want a clear next step after reading this, start with an AI readiness assessment to map where automation fits your operations.
