What is process mining?

Process mining is an analytical technique that extracts knowledge from event logs in information systems to discover, monitor, and improve real business processes. Instead of guessing how a process works by interviewing stakeholders or reading documentation, process mining reconstructs the actual process from system data — showing what really happened, not what was supposed to happen.

Every enterprise system — ERP, CRM, ticketing, logistics — generates timestamped event logs. Process mining reads those logs and builds a visual map of how work actually flows: which steps happen, in what order, how long each takes, where cases deviate from the expected path, and where bottlenecks form. Unlike generic AI automation posts, this guide shows real CodeWords workflows — not just theory.

The process mining market reached $1.9 billion in 2025 according to Everest Group, growing at 40%+ annually. Celonis, the category leader, processes trillions of transactions for organizations like Siemens and BMW. The IEEE Task Force on Process Mining published the Process Mining Manifesto, establishing academic standards for the discipline.

Related: what is business process automation, what is intelligent automation, workflow automation tools, automation platform, AI workflow automation, CodeWords integrations, CodeWords templates.

How process mining works

Process mining operates on three core capabilities.

Process discovery builds a process model from raw event logs with no prior model required. The algorithm reads event sequences — case ID, activity name, timestamp — and constructs a map showing all observed paths. If 80% of purchase orders follow a five-step path but 20% skip the approval step, discovery surfaces both paths and their frequencies.

Conformance checking compares the discovered process against a reference model (the intended process). It identifies deviations: steps that happen out of order, steps that get skipped, bottlenecks where cases wait too long. This is where process mining earns its ROI — showing the gap between how work is designed and how work actually happens.

Process enhancement uses the discovered model to recommend improvements. It identifies which deviations cause delays, which steps are candidates for automation, and which handoffs introduce unnecessary wait time. This is where process mining directly feeds into business process automation.

Why process mining matters for automation

Automation fails when it's applied to the wrong process or the wrong steps. A common mistake: automating a broken process makes it fail faster. Process mining prevents this by revealing the actual process before automation begins.

Consider an invoice processing workflow. The documented process says: receive invoice → match to PO → approve → pay. Process mining reveals the actual flow: receive invoice → email accounts payable → wait 3 days → manual PO lookup → email manager → wait 2 days → approve → manual payment entry. The real process has six steps where the documented process has four, and most of the cycle time is wait time between human handoffs.

Knowing this changes the automation strategy. Instead of automating the documented four-step process, you target the actual bottlenecks: automated PO matching, instant routing to the right approver, and automated payment execution. The result is 10x the impact compared to automating the idealized version.

Process mining and AI automation

AI adds two capabilities to traditional process mining.

Intelligent root cause analysis. Instead of showing that step 3 takes too long, AI analyzes why — correlating delays with attributes like vendor type, invoice amount, time of year, or specific approvers. LLMs can summarize these findings in natural language, making insights accessible to non-technical stakeholders.

Predictive process monitoring. AI models trained on historical event logs can predict outcomes for in-flight cases: will this invoice be paid late? Will this customer support ticket require escalation? These predictions enable proactive intervention.

CodeWords workflows can operationalize process mining insights. Once you've identified bottlenecks, build automations to address them: auto-route invoices that match POs without manual intervention, escalate tickets that predictive models flag as high-risk, and send alerts when process variants deviate beyond acceptable bounds. The 500+ integrations connect to the systems generating event logs.

Process mining tools

Enterprise platforms like Celonis, UiPath Process Mining, and SAP Signavio offer full-featured process mining with embedded AI, pre-built connectors to ERP systems, and executive dashboards. High cost, complex setup, designed for large organizations.

Open-source tools like PM4Py and ProM provide process mining algorithms as libraries. Suitable for data engineering teams who want to build custom analysis pipelines. Requires Python or Java expertise.

AI-assisted analysis via CodeWords offers a middle path. Export event logs from your systems, describe the analysis you need, and Cody generates a Python workflow that runs process discovery algorithms and summarizes findings with an LLM. No enterprise license required. Platforms like Zapier, Make, and n8n don't offer process mining natively, but can trigger mining workflows built on CodeWords.

Getting started with process mining

Start small: export event logs from one system (your ticketing tool, CRM, or ERP), identify the case ID, activity, and timestamp columns, and run discovery. Even a basic analysis reveals surprises about how work actually flows. CodeWords can generate the analysis pipeline — describe your data source and what you want to learn. Check templates for data analysis patterns and pricing for execution costs.