In this special guest episode, Russell and Caspar welcome Michel Kirsch, a 22-year-old final-year international business student at the University of Paderborn, whose fresh perspective on AI and business process management challenges conventional thinking. Michel discovered BPM through the BPM winter school and is currently researching AI-driven business model innovation for his bachelor thesis. The conversation centers on Michel's provocative thesis: that while AI is a powerful strategic resource, it's also becoming a commodity, and the real competitive advantage lies in how companies leverage AI through business model innovation—yet traditional BPM thinking may actually constrain this radical innovation. The discussion explores the fundamental difference between how startups approach business models from a greenfield perspective versus how established incumbents struggle with legacy structures and capabilities. Russell and Caspar examine the tension between BPM's traditional operational excellence focus and the need for radical business model rethinking in the AI era. They debate whether BPM should expand beyond process optimization to encompass broader operating models, enterprise architecture, and digital twins of entire companies. Michel introduces the concept of "nudging" as a transformation technique and raises the challenge of measuring exploration and innovation—metrics that don't fit traditional BPM's operational KPI frameworks. The episode concludes by questioning whether future business model innovation will require rethinking traditional BPM practices entirely.

5 Key Takeaways:

1. AI Is Strategic but Increasingly Commoditized: While AI represents enormous strategic potential, nearly universal access means competitive advantage no longer comes from having AI—it comes from how companies translate AI into new business models and value propositions through thoughtful innovation.

2. Traditional BPM Can Actually Limit Radical Innovation: Process-focused optimization works well for operational excellence but can constrain the kind of radical business model rethinking needed in the AI era—incumbents stuck optimizing existing processes may miss transformative opportunities that startups embrace with greenfield thinking.

3. Greenfield vs. Brownfield Determines Innovation Capacity: Startups can design business models from scratch for AI and innovation, while incumbents must navigate existing structures, capabilities, and constraints—the key question is: which legacy capabilities enable future value, and which become anchors that must be shed?

4. Expand the Frame Beyond Processes to Operating Models: A complete "digital twin" of the company requires more than just process documentation—it needs operating models, enterprise architecture, capability mapping, and strategic positioning to give AI something meaningful to optimize and reimagine.

5. Innovation Metrics Don't Fit Operational KPI Frameworks: Traditional BPM excels at measuring efficiency and compliance, but measuring exploration, experimentation, and innovation requires different metrics entirely—organizations need new frameworks to balance operational excellence metrics with innovation and capability development indicators.

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