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Process Mining Pioneer Wil van der Aalst on the Future of BPM: Object-Centric Models, AI Synergy, and the Evolution of Business Processes
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In this episode of BPM360: Covering Every Angle, Caspar, Russell, and Prof. Wil van der Aalst engage in a deep dive into the evolution of business process management (BPM), process mining, and the intersection of data and processes. From Wil’s early academic journey to his groundbreaking work in object-centric process mining, this conversation explores the challenges, misconceptions, and future trends in BPM and AI-driven process intelligence.
🔑 Key Discussion Topics & Takeaways
1️⃣ From Academia to Industry: Wil’s Journey to Process Mining
2️⃣ The Data vs. Process Debate: Breaking the Silos
3️⃣ The Flaws of BPMN & Traditional Workflow Modeling
- BPMN and Process Modeling Are Oversimplifications
- Why Workflow Management Systems Failed
4️⃣ Object-Centric Process Mining: The Next Evolution
- The Problem with Case-Centric BPM
- Object-Centric Models Solve This Limitation
5️⃣ Process Mining’s Future: AI, Dynamic Process Views & Business Intelligence
- Static Models vs. Dynamic Views
- AI + Process Mining = The Next Leap
- Hybrid Intelligence – The Future of Workflows
6️⃣ The Real Roadblocks to Process Automation & AI-Driven BPM
- The Over-Reliance on Standardization
- The 80/20 Rule in BPM
Why Self-Driving Organizations Are Still a Dream:
- Unlike AI in self-driving cars, organizations lack extensive, structured training data, making full automation difficult.
- AI can’t replace human-driven process adaptation—at least not yet.
🎯 Final Thought: The Paradigm Shift in BPM
Wil van der Aalst challenges long-standing assumptions in BPM, advocating for a move beyond static process models to dynamic, data-driven process intelligence. His insights pave the way for object-centric, AI-augmented process mining as the next frontier in BPM.
🚀 Key Takeaway:
The future of BPM isn’t about defining processes first—it’s about uncovering how work truly happens and adapting processes dynamically based on real-world data.
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