Glossary
AI-Powered Process Optimization: Method and Business Value
AI-powered process optimization combines classic BPM (capture as-is, analyze, define to-be) with generative AI for analysis, measure derivation, and modeling. Key principle: AI produces drafts; humans retain approval and accountability.
Typical four-phase workflow
1) Capture the process (dialog, documents, workshops). 2) Model the as-is process and identify weaknesses—enhanced by process mining where event data exists. 3) Derive measures (eliminate, automate, parallelize) with effort/benefit assessment. 4) Model the to-be process, simulate, and roll out incrementally.
State of play in 2026: from assistance to orchestration
The BearingPoint BPM Pulse Survey 2026 reports process management as business-critical for 83% of organizations; 42% use generative AI, 16% already deploy AI agents that prepare decisions or steer processes. The bottleneck is less technology than data quality, clear goals, and scalable governance.
In practice: CT Flow
CT Flow guides you from the Process Agent (capture) through BPMN visualization and as-is/to-be comparison to process simulation and a measures backlog—in one end-to-end application.
FAQ
- Does AI replace process consultants?
- No. AI accelerates capture, analysis, and drafting. Strategy, change management, and approvals remain human core tasks.
- What data do I need?
- At minimum a understandable process description. Ideally supplemented with cycle times, media breaks, and—where possible—event logs for mining.
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Updated: 2026-06-29