What is Process Mining? Discover how your processes really work.
Process mining is a data-driven discipline that uses event logs from enterprise information systems to discover, monitor and optimise business processes. Unlike traditional analysis based on interviews and assumptions, process mining reveals how processes actually execute — including every variation, bottleneck and inefficiency that manual methods miss.
Six pillars of process intelligence
From discovery to AI-driven automation, these techniques form the modern process mining toolkit.
Process Discovery
Automatically reconstruct process models from raw event data without any prior knowledge. Reveals the actual flow, including all paths and variations.
Conformance Checking
Compare actual process execution against a reference model. Identify deviations, policy violations and non-compliant paths across every single case.
Process Enhancement
Enrich discovered models with performance data such as throughput times, costs and resource utilisation to pinpoint bottlenecks and improvement opportunities.
Predictive Analytics
AI models trained on historical process data forecast delays, predict outcomes for in-flight cases and estimate completion times before issues arise.
Task Mining
Capture user-level desktop interactions — clicks, keystrokes and screen changes — to reveal detailed steps within individual activities that event logs cannot see.
AI-Powered Automation
Combine process mining insights with AI to automatically identify automation candidates and recommend where RPA, agentic AI or workflow redesign delivers the greatest impact.
Process Mining Pipeline
From raw event logs to continuous optimisation — the five stages of process intelligence.
Five steps to process intelligence
A pragmatic roadmap to implement process mining in your organisation.
Define Objectives & Scope
Identify the business questions that matter most. Select high-impact processes, define success criteria and align stakeholders on expected outcomes before touching any data.
Extract & Prepare Event Data
Connect to source systems (ERP, CRM, ITSM), extract event logs, cleanse timestamps and case identifiers, and structure data into an analysis-ready event log.
Discover & Analyse
Run discovery algorithms to map actual process flows. Identify variants, bottlenecks and conformance gaps. Engage process owners to validate findings and prioritise opportunities.
Implement Improvements
Translate insights into action: redesign process steps, automate repetitive tasks, update system configurations and launch targeted interventions where the data shows the greatest impact.
Monitor & Scale
Establish continuous process monitoring dashboards. Track KPIs in real time, detect regression and emerging bottlenecks, and extend process mining to additional processes across the organisation.
Continuous Optimisation
Process mining is not a one-off project. Embed it as an ongoing capability: iterate on models, incorporate AI-driven predictions and evolve towards autonomous process improvement.
Everything about Process Mining
Process mining is a data-driven discipline that uses event logs from enterprise information systems to discover, monitor and optimise business processes. It reveals how processes actually execute, including all variations, bottlenecks and inefficiencies that manual analysis misses.
Traditional process analysis relies on interviews, workshops and assumptions about how work gets done. Process mining uses objective event data from IT systems to reconstruct the real process flow, revealing the gap between how processes are documented and how they actually run.
Process mining requires event logs containing at minimum a case ID (which process instance), an activity name (what happened) and a timestamp (when it happened). Additional fields like resource, cost and department enrich the analysis significantly.
The three types are process discovery (automatically creating a model from event data), conformance checking (comparing actual execution against a reference model) and process enhancement (enriching models with performance and cost data to guide improvement).
Any system that records transactional events can serve as a source: ERP systems like SAP and Oracle, CRM platforms, IT service management tools, healthcare information systems, financial platforms and custom applications with transaction logs.
With clean data and a focused scope, first actionable insights are typically available within four to six weeks. Data preparation is usually the most time-consuming step. Organisations with well-structured data can see results even faster.
Process mining analyses event logs from enterprise systems to map end-to-end process flows. Task mining captures user-level desktop interactions (clicks, keystrokes, screen changes) to reveal the detailed steps within individual activities. Together they provide complete process visibility.
AI adds predictive analytics (forecasting delays and outcomes), automated root cause analysis, intelligent automation identification and continuous real-time anomaly detection to traditional process mining capabilities. This transforms descriptive analysis into prescriptive action.
Organisations typically achieve a 20 to 40 percent efficiency gain through process mining by eliminating rework, reducing cycle times and automating repetitive steps. ROI is usually visible within the first quarter of deployment.
No. Any organisation with digitally supported processes can benefit from process mining. Smaller organisations can start with a single high-impact process and scale from there. Cloud-based tools have made process mining accessible and affordable at every scale.
Ready to discover what your processes really look like?
W69 AI Consultancy helps enterprises implement process mining and AI-driven process intelligence that reveals hidden opportunities and drives measurable operational improvement.