Let Your Event Logs Talk: Process Mining With an AI Assist

Hello community. A question for your next team meeting: how does work actually flow through your organisation? Not the version in the onboarding slides. The real version, with the loops, the skipped steps and the cases that sit untouched for weeks.

The answer is usually sitting in your databases already. Process mining asks the data, and AI makes it far more practical for teams without data scientists.

What Process Mining Is

Every time someone creates an order, approves an invoice or closes a ticket, a system usually writes a record. Process mining collects those records and reconstructs the flow of work from them.

To do that, each event needs at least three things:

• A case identifier, such as an order number or claim ID.
• An activity name, such as "invoice approved".
• A timestamp.

With enough events, mining tools rebuild the paths cases took, show how often each path occurs and highlight where time disappears. There is even a standard format for this data, IEEE XES, which open-source tools use.

Why It Beats Another Workshop

Workshops capture what people believe happens. Logs capture what did happen. The two rarely match. Mining regularly surfaces rework loops nobody mentioned, approvals that get bypassed under pressure and waits that dwarf the actual effort.

That evidence matters a lot if you plan to adopt BPM in software, because automating the imagined process just moves the real one into email.

Where AI Genuinely Helps

AI does not replace process mining. It removes the friction that used to stall it.

1. Making sense of cryptic data. The hardest part of mining has always been extraction. Tables with names like TBL_STAT_HIST and status codes nobody remembers. Language models are good at proposing how those fields map to business activities. An analyst still confirms every mapping, but the starting point arrives much faster.

2. Capturing work that lives in email and chat. A lot of real process work never touches a structured system. Models can read messages and label them with a likely case ID and activity, turning conversations into events. Sample and review these labels before trusting the resulting map, because errors compound.

3. Explaining variants in plain language. Mining tools can show hundreds of process variants, which overwhelms most readers. An AI summary layer can point to the handful that matter, such as which region or customer segment drives most rework, in terms an operations lead can act on.

4. Spotting automation candidates. High-volume, rule-heavy, predictable steps suit straight automation. High-volume steps that involve reading and judging documents suit an AI agent working inside a governed step, with a rule or a person making the final call.

Pitfalls to Watch

• History includes bad habits. A mined map faithfully records workarounds and delays. Treat it as evidence for redesign, not a template to automate as-is.
• Missing events distort everything. If a key system does not log a step, the map will show a gap or a false shortcut. Check coverage first.
• Case IDs change mid-journey. An order becomes a shipment becomes an invoice. Linking those identifiers correctly is often the real work.
• Privacy still applies. Logs and messages can contain personal data. Minimise, pseudonymise and follow your data policies before analysis.

From Map to Running Process

Mining shows what is happening but runs nothing. Next, redesign the process and execute it on a process engine, so every new case leaves a clean history and future mining gets easier.

If you want to see how mining, BPMN models and AI agents fit together end to end, the full guide to BPM in software walks through it. For help putting AI to work on your own process data, our AI development services team is a good place to start.

Curious whether anyone here has tried mining their own ticketing or ERP data. What surprised you most?

Frequently Asked Questions

What data do I need to start process mining?

At minimum, events with a case identifier, an activity name and a timestamp, usually exported from ERP, CRM or ticketing systems.

Is process mining the same as BPM software?

No. Mining analyses how processes ran in the past. BPM software executes processes going forward. They work best together.

Can AI build the process map on its own?

AI can speed up data mapping, labelling and summarising, but humans should validate mappings and sample AI-generated labels before trusting results.

How long does a process mining pilot take?

Typically a few weeks, depending mostly on how accessible and clean the event data is. As a planning range, pilots often cost roughly $15,000 to $50,000.

Should we automate the process exactly as mined?

No. The mined process includes workarounds and delays. Use it to guide a redesign before automating.

Are there privacy concerns with mining emails?

Yes. Messages often contain personal data, so apply minimisation, pseudonymisation and your organisation's data protection rules first.