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Merging Artificial Intelligence (AI) with Business Process Management Systems (BPMSs) has given rise to AI-Augmented Business Process Management Systems (ABPMSs). These modern setups deliver flexible adaptability, instantaneous process refinement, and superior knowledge management workflows. Yet, notable hurdles persist, especially regarding contextual clarity, user buy-in, and the incorporation of behavioral dynamics. This research introduces a novel conceptual model that unifies the Socialization, Externalization, Combination, and Internalization (SECI) knowledge paradigm with Agile workflows (specifically Scrum) and advanced AI capabilities like explainable AI (XAI), process mining, and Robotic Process Automation (RPA). This setup streamlines the formalization, validation, and dissemination of insights through structured, intuitive software and collaborative frameworks, particularly Communities of Practice (CoPs). The system highlights context-sensitive transparency, step-by-step deployment, and iterative refinement to foster productive human–AI partnerships. It delivers both conceptual and practical blueprints for linking AI strengths with corporate knowledge assets. The introduced model eases the upgrade path from legacy BPMSs to advanced ABPMSs by utilizing systematic practices and technologies. This strategy accelerates knowledge distribution and operational refinement, mapped out via exhaustive BPMN 2.0 diagrams.

The critical nature of explainable artificial intelligence and machine learning (XAI/XML) is gaining widespread recognition. This field focuses on uncovering how input variables influence automated choices, identifying systemic algorithmic bias, and detecting vulnerability to anomalies in data. Much of the existing literature centers on creating post hoc explanations tailored for opaque, black box architectures. These secondary techniques introduce novel vulnerabilities to error without correcting the fundamental flaws of the core model. Conversely, the development of inherently transparent systems receives far less attention. This paper introduces an inherently interpretable strategy rooted in the principles of ensemble learning, designated as the League of Experts (LoE) framework. We construct the initial theoretical foundations and subsequently derive a flexible, modular meta-algorithm. Although our presentation focuses primarily on classification tasks, the LoE framework adapts identically to regression scenarios. For classification specifically, we deploy traditional decision trees to form classifier ensembles as a concrete implementation. This paradigm simplifies the extraction of human-comprehensible decision rules for the underlying classification task, yielding an independent rule-induction system called RuleLoE. Beyond evaluating 12 KEEL classification datasets, we validate the LoE framework using two benchmarks from highly critical domains—healthcare and finance—to demonstrate its utility. The predictive capability of LoE matches contemporary state-of-the-art classification algorithms regarding both accuracy and rule coverage. Furthermore, LoE outputs a highly transparent group of decision rules featuring customizable complexity to map out the target classification problem. LoE provides a dependable approach for handling classification and regression problems. Its predictive precision is highly suited for environments where discovering root causal mechanisms is the primary objective, rather than merely maximizing predictive or classification performance.