Ep. 79: "Defining Context in BPM: Six Dimensions to Enable AI and Intelligence"

In this introductory episode to a new mini-series, Caspar and Russell tackle a concept everyone discusses but few truly understand: context and context models. They reveal that while organizations constantly invoke the need for "context," most stakeholders make vague assumptions about what context actually means without rigorous definition. The hosts outline their research-driven framework identifying six distinct dimensions of context essential to modern BPM: the "why layer" of business logic and decision drivers; state and conditions representing the current process status; temporal and seasonal patterns that vary across business cycles; stakeholder and role perspectives that differ by geography and function; performance baselines and anomaly contexts that define normal versus exceptional; and explainability and audit trails that prevent AI black boxes. The discussion explores implementation challenges beyond the technical, including organizational "ego"—the resistance from vendors and systems to share data in neutral platforms. Russell introduces the concept of a "context orchestrator" that governs how data across multiple enterprise systems relates to each other, without requiring centralized data warehouses. They emphasize that proper context architecture enables AI and intelligent decision-making but requires thinking beyond traditional approaches. The episode sets the stage for a 7-9 episode deep dive into each dimension and how organizations can practically build comprehensive context capabilities.

5 Key Takeaways:

1. Context Has Six Distinct Dimensions: Proper context goes far beyond process models and includes the "why layer" (business logic and compliance), current state and conditions, temporal patterns, stakeholder perspectives, performance baselines, and explainability—each requires separate consideration and governance.

2. Process Management Alone Doesn't Provide Complete Context: The assumption that process management automatically provides sufficient context is incomplete; it's merely one component. Organizations need deliberate, multi-dimensional context architecture that spans business logic, performance data, and organizational perspectives.

3. Organizational "Ego" Is a Major Implementation Barrier: Consolidating context requires extracting data from multiple vendor platforms and systems, which triggers resistance from vendors protective of their data and organizations comfortable in siloed systems—overcoming this ego-driven resistance is as critical as solving technical integration challenges.

4. Context Orchestration Without Data Centralization Is Possible: Rather than copying everything into a new data warehouse like old BI approaches, modern architecture should establish a context governance layer that knows where data lives, how different systems' data relates, and enables referencing across platforms through APIs and connections.

5. Context Enables AI Without Restrictive Performance Requirements: For most organizations (outside high-frequency dealing rooms), context doesn't need to be instantaneously centralized; slightly slower reference-based access to distributed data sources is acceptable—this enables practical architecture that respects existing systems while enabling intelligent orchestration.

If you have suggestions or questions, please reach out to us via questions@bpm360podcast.com

If you enjoy our content, please like, rate, subscribe… we do appreciate that…