Process Mining with AI: Discovering and Optimizing Hidden Workflows

Process mining — the technique of discovering actual business processes from system event logs — has been transformed by AI. In 2026, AI-enhanced process mining platforms can automatically discover process variants, identify bottlenecks and deviations, predict process outcomes, and recommend specific optimizations — capabilities that have moved process mining from a specialized analytical technique to a mainstream component of continuous process improvement. According to a June 2026 Gartner Market Guide for Process Mining, 45% of large enterprises now use process mining, up from 22% in 2023.

Traditional process improvement relied on workshops, interviews, and manual observation to understand how processes worked — approaches that captured how people thought processes worked (or how they were documented) rather than how they actually executed. Process mining closes this gap by reconstructing actual process flows from the digital footprints that every process execution leaves in enterprise systems — ERP transactions, CRM updates, workflow logs, and application events.

How AI-Enhanced Process Mining Works

Automated Process Discovery

AI analyzes event logs to automatically discover: the actual process flows (not just the documented ones), all process variants (including rare paths that manual analysis would miss), frequency and duration of each path, and compliance with defined process standards. The result is an objective, data-driven picture of how processes actually work — typically revealing significant divergence from documented processes.

Conformance Checking

AI compares actual process execution against the designed process model, identifying: where processes deviate from the intended flow, which deviations are harmless and which create risk, and the root causes of non-conformance (system limitations, unclear procedures, deliberate workarounds).

Performance Analysis

AI identifies process performance issues: bottlenecks where work accumulates, steps with high variability in duration, rework loops indicating quality issues, and resource utilization patterns showing over- or under-loaded teams.

Predictive Process Analytics

Going beyond analyzing the past to predicting the future: which running process instances are likely to miss SLAs, which cases will require escalation, and what the expected completion time is for in-flight processes.

Prescriptive Recommendations

The most advanced AI capability: recommending specific process changes — "adding a validation step here would reduce rework by 25%" or "reassigning this task to the accounts payable team instead of procurement would reduce cycle time by 40%" — with predicted impact quantified.

Why Informat Incorporates Process Mining

Informat's platform integrates process mining with process automation, enabling organizations to discover, analyze, improve, and automate processes in a continuous improvement cycle — all within a single platform that business analysts can use without specialized data science skills.

Conclusion

Process mining has evolved from an academic technique to an essential component of process management. Organizations that systematically mine their processes gain objective visibility into how work actually gets done — visibility that is the necessary foundation for effective process improvement. The combination of AI-enhanced process mining for discovery and no-code platforms for implementation creates a powerful continuous improvement engine: discover what's happening, identify what to improve, implement the improvement, and measure the impact — all faster and more objectively than traditional process improvement methods ever could.