TL;DR:

  • AI process mining combines event log analysis with language models to automatically identify which workflows are worth automating — and explain why in plain English
  • Unlike traditional RPA projects that start with a consultant mapping processes manually, AI-driven process mining starts with your actual system logs and surfaces candidates automatically
  • The shift in 2026 is from “process mining as reporting” to “process mining as an autonomous agent that suggests and sometimes executes improvements”

Traditional automation projects start the same way: someone decides a process needs automating, brings in consultants or an internal team to document it, maps the current state, designs the future state, builds the automation, and deploys it. The whole cycle takes months. And it assumes you already know which process to automate.

That last assumption is the problem. Most organisations have dozens, sometimes hundreds, of repetitive workflows that nobody has formally documented because nobody’s had time to. The obvious candidates get automated first. The second and third tiers sit in the backlog indefinitely, and the people doing them just carry on doing them manually because raising a proper automation project feels disproportionate to the effort.

AI process mining is the answer to this. It starts with what you already have — the digital traces that every system interaction leaves behind — and works backwards to surface what’s worth automating.

What Process Mining Actually Does

At its core, process mining analyses event logs: the timestamped records of who did what in your systems. Your CRM logs every deal stage transition. Your ERP logs every purchase order step. Your helpdesk logs every ticket state change. These logs contain a complete record of how your processes actually run — not how someone thinks they run, and not how a consultant modelled them, but the real thing.

Classic process mining (tools like Celonis, UiPath Process Mining, SAP Signavio) takes those logs and creates process flow visualisations. You can see that your invoice approval process has 14 variants, that 23% of invoices loop back through approval twice, and that Thursday afternoons have a processing delay. Useful, but it requires a human to look at the visualisations and decide what to do.

The AI layer changes the output. Instead of charts for a human to interpret, you get prioritised recommendations in plain language. “Your accounts payable team processes 340 invoices per week that match three specific patterns — same vendor, same amount range, pre-approved vendor list. These represent 61% of volume and take an average of 4.2 minutes each. Automating this cohort would save approximately 24 hours per week.” That’s a recommendation an operations manager can act on without a data analyst intermediary.

Where Agents Enter the Picture

The more interesting development in 2026 is process mining agents that don’t just recommend — they execute. Once a high-confidence automation candidate is identified (high confidence meaning the process is consistent, the volume is significant, and the exceptions are well-defined), an agent can draft and deploy the automation rather than handing it back to a human project queue.

This is sometimes called hyperautomation, though that word gets used for a lot of things. The specific capability here is the closed loop: discover → recommend → build → monitor, with human approval at the critical step (the build-to-deploy transition) rather than at every stage.

Tools like UiPath, ServiceNow, and Automation Anywhere have all added AI layers to their process mining modules in the last 18 months. The pattern is similar: feed in event logs, get back automation candidates ranked by expected ROI, optionally have the platform generate automation code for review and deployment.

Practical Starting Points

You don’t need a full enterprise platform to experiment with AI process mining. Here’s what a lightweight version looks like.

Start with any system that generates structured event logs — a helpdesk ticket system is ideal because the logs are clean and the process is reasonably consistent. Export 90 days of ticket state changes: ticket ID, timestamp, status, assignee, category.

Run that through an LLM with a prompt that asks it to identify: which ticket categories follow the most consistent resolution paths, where the most time is spent waiting rather than actively worked, and which resolution paths could plausibly be automated with predefined responses or routing rules.

You’ll get rough output, but it’ll likely surface at least one or two candidates you hadn’t prioritised. The point is that the discovery happened in minutes rather than weeks of process mapping.

For more rigorous analysis, tools like PM4Py (Python library, open source) give you proper process mining algorithms you can run against your logs before the LLM interpretation layer. Feed the conformance checking results and bottleneck analysis into the LLM for the plain-English summary.

The Honest Limitations

Process mining is only as good as your event logs. If a process involves significant manual steps that don’t leave digital traces — a phone call, a physical signature, someone popping their head round a door — that process is invisible to mining. Hybrid processes that cross the digital/physical boundary need human input to complete the picture.

There’s also a risk of over-optimising for the metric you can measure. A process that looks like an automation candidate because it’s repetitive might be repetitive because it requires judgment on each case that isn’t obvious from the log data. An agent that automates it without understanding the edge cases creates a different kind of problem.

The safeguard is keeping a human in the loop at the approval stage. AI process mining is excellent at discovery and reasonable at recommendation. Deployment decisions should still have a human reading the generated automation logic before it goes live, at least until you’ve built enough confidence in the specific system’s outputs to raise that threshold.

The broader shift is real, though. In 2026, the question has moved from “how do we automate this?” to “which thing should we automate next?” — and increasingly, AI agents are answering the second question without being asked.