Ep. 69: "BPM AI Orchestration: Building the Next Generation of Process Management"

In this guest episode, Russell and Caspar welcome Ahmad Daliri, a process management specialist working at NN in the Netherlands and author of multiple BPM books, for a conversation about the intersection of artificial intelligence and business process management. Ahmad shares his unconventional journey from mechanical engineering to falling in love with BPM after discovering the missing link between operational work and strategic objectives. The discussion explores Ahmad's current work on "BPM AI orchestration"—a concept focused on how AI can make process management more effective and accessible rather than just automating existing processes. The hosts examine the shift from traditional process modeling to AI-assisted approaches, including the emerging capability of converting voice conversations into process models. Ahmad introduces his framework of five layers for BPM AI orchestration: voice-to-BPM conversion, context understanding, rules and responsibility interpretation, intelligence and decision-making, and user interface design. The conversation highlights the critical importance of context and quality data in training enterprise AI agents to understand organizational boundaries and process standards. They debate the current maturity level of AI in BPM, acknowledging that while the technology shows promise, we're not yet ready for fully autonomous AI-driven process management. The episode concludes with insights on preparing for the paradigm shift in how process work will be conducted in the coming years.

5 Key Takeaways:

1. BPM AI Orchestration Is About Making BPM Easier: The goal isn't just automating processes with AI, but using AI to make process management itself more effective and accessible for process specialists—reducing manual work in modeling, analysis, and documentation.

2. Voice-to-Process Modeling Is Emerging: AI is enabling the conversion of natural language conversations with subject matter experts directly into process models, potentially transforming how process knowledge is captured from interviews and workshops into structured BPMN or other notations.

3. Context and Quality Data Are Critical: For enterprise AI to work effectively in BPM, it needs high-quality contextual data including documented processes, compliance frameworks, and operational standards—this organizational knowledge becomes the guardrails that keep AI aligned with requirements.

4. Five Layers of BPM AI Orchestration: Ahmad's framework includes voice-to-BPM conversion, context and knowledge understanding, rules and responsibility interpretation, intelligence and decision-making capabilities, and user interface design—all necessary for comprehensive AI integration in process management.

5. We're in Transition, Not There Yet: While AI shows significant promise for transforming BPM work, the technology isn't mature enough for 100% autonomous process management—the industry is currently in a paradigm shift that requires preparation and gradual adoption rather than immediate wholesale replacement.

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