How does Process Mining work in ServiceNow?

Process mining has been sitting in the ServiceNow platform for a while, but you’d be forgiven for not noticing. It has quietly evolved from an optional add-on into one of the most underrated sources of insight available to any organisation running workflows on the platform. As of the Yokohama release, it’s installed by default. Even better: you can start mining your processes without purchasing anything extra, thanks to built-in “projects” that analyse up to 3,600 records from the last seven days across Incident, CSM Case, or HR Case.

How Process Mining works

At its core, process mining is a way of reading the digital breadcrumbs your workflows leave behind.

Every time a record moves through a lifecycle, i.e. an incident created, assigned, put on hold, reactivated, resolved, closed, ServiceNow logs an “event.” These event logs are rich, structured, timestamped clues that reveal how work actually flows in the real world, not how the process diagram on the wall claims it flows.

Process mining uses a machine learning model to analyse these logs and reconstruct the end-to-end journey for thousands of records at once. Instead of a theoretical process map, you get a model built directly from observed behaviour. The algorithm identifies:

• The common paths

• The slow or inefficient paths

• Loops and rework patterns

• Variants in behaviour between teams, channels, priorities or locations

• Outliers that dramatically inflate cycle time

By comparing these patterns across all records flowing through a table, for example, incident, the system detects where work deviates from the intended path and where time is being lost. It effectively converts raw lifecycle history into a view of your operational reality.

Once this reconstruction is done, everything else like insights, variants, bottlenecks, root cause patterns all becomes possible.

Why Process Mining matters

Most organisations see their processes from the outside-in: dashboards, KPIs, SLA trends, and executive reports. These tell you what is happening, but rarely why. A rising MTTR, for example, might trigger debate, opinion, and a few well-intentioned guesses… but rarely a confident conclusion.

Process mining flips that dynamic. Instead of relying on assumptions, you get a data-driven map of how work actually flows through your organisation. No whiteboards. No sticky notes. No collective memory of “how it usually works.” You see the real process; warts, loops, rework and all.

This is why it’s so powerful. And this is why it’s surprising how few teams know they already have access to it.

What the capability gives you

At its core, Process Mining Workspace gives you three key capabilities:

You can see the process as it truly runs. The interactive flow map surfaces every route work takes: the happy path, the loops, and the dead ends. You can see, for example, how many incidents bounce between assignment groups before resolution, or how many cases repeatedly end up in "Awaiting Caller Info".

You can drill down into the data behind the behaviour. Every node, every transition, every anomaly is backed by real operational data. That means you’re not looking at an abstract model; you’re looking at live patterns from your environment. You can then segment that by channel, assignment group, priority, location, category to drill further into your data model.

You can analyse patterns that humans miss. This is where clustering and AI-driven insights come in. The platform can detect contextual themes in the data that aren’t immediately obvious. It’s one thing to see that 8,000 incidents hit a waiting state. It’s another to see that a huge chunk of them are users trying to change the email address in their profile, an insight you’d normally only get from going through records one by one.

How Process Mining connects to the rest of the platform

Process mining doesn’t live in isolation. Its real value is in how it anchors and enhances other platform capability around it.

Performance Analytics tells you what changed; Process Mining tells you why

If your KPIs tell you MTTR is climbing, or first-contact resolution is dropping, PA gives you the trend. But it can’t explain the drivers. Process mining fills that gap by giving a clear explanation: repeated reassignment loops, specific channels causing delays, steps that consistently add days of lag.

It’s the difference between monitoring and understanding.

Continual Improvement Management (CIM) connects insight to action

One of the biggest failings in process-improvement programmes is the gap between “we found something interesting” and “we actually fixed it.”

From inside the Process Mining Workspace you can create a Continual Improvement initiative on the spot, linked directly to the inefficiency you’ve discovered. You don't need to export findings or create separate documents offline. That way insights become part of your structured improvement backlog, with ownership, tracking, and governance built in.

AI, automation and workflow teams benefit too

A clearer understanding of process reality helps everyone. AI / automation teams can build new catalogue items, create targeted virtual agent topics or intelligent flows where they’ll have the biggest impact by knowing exactly which step to streamline or eliminate. Service owners can quantify value, rather than just guessing or creating complex calculations from dashboard data.

Final thoughts

In my opinion, Process mining is one of the most overlooked capabilities in the platform. If you’re running ServiceNow and not using it, you’re leaving valuable insight on the table, or burning unnecessary hours to get to the same conclusion. And as organisations look for faster, more confident ways to improve operations, reduce cost, and build AI-ready processes, tools like this are becoming increasingly essential.

Start with the free out-of-the-box projects. Explore your Incident or Case workflow for just seven days’ worth of data. And hopefully you’ll quickly see the real flow of work, the friction points, and the opportunities you didn’t know were hiding in the system.