Ep. 78: "Process Modeling Is Dead—Long Live the Process Model: Context and Orchestration in AI Era"

In this episode, Caspar and Russell explore the fundamental transformation of process modeling's role in business and technology. They trace the evolution from the 1990s-2000s when process models were primarily project deliverables to today's paradigm where process models serve as essential working assets providing context for AI implementations. The discussion reveals how process mining rediscovered the critical importance of process models—not as decorative outputs, but as context providers that enable proper interpretation of data and intelligent decision-making. Russell emphasizes the shift from "working towards" a process model to "working with" process models throughout transformation journeys. They examine the philosophical evolution in vendor strategy, highlighting that the real competitive differentiation won't come from individual tools (modeling, mining, automation, workflow) but from orchestration philosophy—how vendors integrate these tools into a cohesive ecosystem. The conversation explores how AI systems require richer, more complete models than humans need, challenging the traditional approach of "simplifying for readability." They debate the necessity of agnostic AI layers that coordinate across multiple specialized AI tools rather than siloed point solutions. The hosts conclude that vendors demonstrating true understanding of orchestration, context modeling, and holistic ecosystem philosophy will define the next era of BPM and process intelligence, while others will fade in relevance.

5 Key Takeaways:

1. Models Are Working Assets, Not Project Deliverables: The fundamental shift is from treating process models as the end goal of a BPM project to understanding them as continuously evolving working assets that provide context throughout transformation, mining, automation, and AI initiatives.

2. AI Requires Richer Models Than Humans Need: Process models must now serve machine interpretation for AI and mining, not just human understanding—this means models need to capture complete context, handle edge cases, and answer technical questions that traditional simplified business models could ignore.

3. Process Mining Revalidated the Importance of Models: Process mining initiatives forced organizations to recognize that accurate interpretation of process data requires quality process models as context—mining alone produces data, but models transform that data into actionable business insights and transformation guidance.

4. Orchestration Philosophy Determines Vendor Relevance: The competitive differentiation won't come from individual point solutions (modeling tools, mining tools, workflow automation) but from vendors that architect holistic ecosystems with agnostic AI coordination layers that orchestrate across specialized tools and maintain consistent context.

5. Central Context Models Enable Optimal Decisions Across the Value Chain: Organizations need a single unified context model with an agnostic orchestration layer on top, rather than multiple disconnected AI systems—this architecture enables coordinated optimization across the entire process ecosystem rather than local optimization within individual silos.

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