What Is AI Process Mining?
Process mining is a technology that automatically extracts process flows from the event logs generated by your IT systems (ERP, CRM, MES, EHR). Every time someone creates an order, completes a task or updates a record, a timestamp is created. Process mining reconstructs the actual process flow from these timestamps — showing you exactly how work happens, not how you think it happens.
Traditional VSM vs AI Process Mining
Both tools serve the same purpose — understanding process flow to identify improvement opportunities. But they work very differently.
Created in workshops. Based on team knowledge. Snapshot in time. Takes 1-3 days. Shows the agreed process.
Extracted from system data. Based on actual events. Continuously updated. Takes hours. Shows what actually happens.
The best approach combines both: use process mining to discover the actual current state, then use traditional VSM to design the ideal future state — giving your kaizen workshops a far richer foundation to work from.
Process Mining Tools: What's Available
Enterprise Tools
- Celonis: Market leader. Deep SAP/Oracle integration. Enterprise pricing.
- UiPath Process Mining: Strong for IT and service processes. Integrates with UiPath RPA.
- IBM Process Mining: Good for financial services and regulated industries.
Mid-Market & SME Tools
- Disco (Fluxicon): Excellent entry-level tool. Accepts CSV exports from any system.
- Minit: User-friendly interface, good visualisations.
- ProM: Free, open-source, academic-grade. Steep learning curve.
Disco offers a free tier that accepts event logs in CSV format. If your ERP can export order history as a CSV with timestamps, you can run your first process mining analysis this week — no budget required.
Automated Waste Detection: AI Identifies the 8 Wastes
Process mining doesn't just map the process — it automatically quantifies waste across all eight categories.
- Waiting: Time between process steps is measured automatically — every queue, delay and idle period is visible and quantified.
- Rework: Process loops (cases that return to earlier steps) are automatically identified and their frequency and cost calculated.
- Over-processing: Steps that add no value (unnecessary approvals, redundant checks) show up as high-frequency activities with no output impact.
- Defects: Cases that deviate from the ideal path are flagged — revealing where errors and exceptions occur most frequently.
A B2B manufacturer used process mining on their order-to-cash process. The AI identified 23 process variants where the standard was 1. The most common non-standard path — which affected 34% of orders — included an unnecessary manual credit check that added 2.4 days to average order fulfillment. Automating the credit check for approved customers eliminated 2.1 days of waste.
Implementing AI Process Mapping in Your Organisation
- Identify a target process: Choose a high-volume process with a clear start and end event — order management, invoice processing, customer onboarding, patient discharge.
- Extract the event log: Work with your IT team to extract a CSV of: Case ID, Activity Name, Timestamp. That's all you need to start.
- Load into Disco or Celonis: Upload the CSV and let the tool automatically generate the process map.
- Analyse the variants: Look at the top 5 process paths by frequency. What percentage of cases follow the ideal path? What do the deviating paths have in common?
- Quantify the waste: Use the tool's time analysis to calculate how much time is lost in waiting steps and rework loops.
- Take findings into a Kaizen: Use the AI-generated process map as the foundation for a focused improvement workshop.