Digital Innovation through Knowledge Processes

0026-06-05 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

This paper introduces a universal definition and categorization for knowledge-intensive processes (KPs) to enhance digital transformation. It addresses the ambiguity in defining KPs by analyzing the relationship between process resources like data, objects, artefacts, and humans. The research proposes a framework that categorizes process models into 6 types based on their knowledge intensity, ranging from low (e.g., Knowledge-based Artefact Generation, Ki → Ao) to high (e.g., Artefact Processing with Knowledge Extraction, Ki, Ai → Ko, Ao). This categorization relies on specific inputs (Input Knowledge Ki, Input Artefacts Ai) and outputs (Knowledge Output Ko, Output Artefacts Ao). Furthermore, the study identifies 31 common knowledge process patterns, grouped into 6 categories, derived from a real-world ideation process involving 13 human and 11 machine tasks. This holistic view aims to improve modeling, execution, monitoring, and assessment of KPs, especially relevant with the rise of Agentic BPM.

Key takeaway

For Process Architects or Directors of AI/ML designing future-proof business processes, this research offers a critical framework for understanding knowledge intensity. You should apply the 6-category knowledge process categorization to precisely identify automation opportunities and design agent-driven workflows. This shifts focus from human-centric views to asset-driven analysis, ensuring more effective digital transformation projects and successful integration of autonomous agents into your organization's processes.

Key insights

Knowledge-intensive processes are redefined and categorized into six types based on asset inputs and outputs, independent of resource type.

Principles

• All business processes inherently involve knowledge.
• Knowledge intensity depends on asset roles, not resource types.
• Agentic BPM necessitates resource-agnostic knowledge intensity analysis.

Method

Define knowledge process assets (Ki, Ai, Ko, Ao) and their dependencies, then categorize processes into 6 types based on these inputs/outputs to determine knowledge intensity. Patterns are derived from a real-world ideation process.

In practice

• Assess process knowledge intensity using the 6-category framework.
• Apply identified patterns to design or refine knowledge workflows.
• Evaluate replacing human tasks with agent tasks in high-intensity KPs.

Topics

• Knowledge Management
• Business Process Management
• Knowledge-Intensive Processes
• Process Categorization
• Process Patterns
• Agentic BPM

Best for: Research Scientist, AI Scientist, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.