Integrating SKM and STPA for Human-Centred Optimization of Sustainable AI-Driven Enterprise Systems

Abstract: Integrating artificial intelligence (AI) into companies is transforming practices : decision-making and strategic management, innovation and research, design and methodology, supply chain and production. While these technologies offer the promise of performance gains, they also introduce challenges in terms of knowledge governance, the reliability of automated decisions, security, and coordination between human actors and algorithmic devices. The human factor remains insufficiently formalized, even though operators, engineers, and decision-makers remain central to the supervision and adjustment of systems.This contribution proposes a conceptual and methodological framework combining Systemic Knowledge Management (SKM), System-Theoretic Process Analysis (STPA), and a human-centered approach to sustainably support AI-assisted systems. The objective is to move beyond a technology-centric approach by placing AI in a systemic perspective where human, organizational, and technological dimensions are jointly modeled, analyzed, and managed. SKM is thus used to structure the acquisition, formalization, organization, and exploitation of knowledge relating to the overall functioning of the company. It aims to achieve a shared representation (human/AI) of the processes, constraints, performance objectives, and behaviors of the technical and human components. Applied to systems incorporating AI, SKM makes explicit the model assumptions, decision rules, dependencies between subsystems, and feedback from operation. The human factor is integrated as a component of the system: a source of tacit knowledge, situational judgment, adaptive regulation, and decision-making in contexts of uncertainty. This approach promotes consistency of representations, traceability of decisions, and ownership of AI tools by field actors.In addition, STPA, which stems from complex systems engineering, is used to analyze interactions between humans, machines, software, and algorithms within control loops. Unlike risk management methods that focus on isolated technical failures, STPA identifies dangerous situations resulting from inappropriate decisions, coordination failures, organizational constraints, or loss of control. Applied to AI-driven decision-making and operational systems, STPA highlights the effects of cognitive biases, mental load, overconfidence in automation, interface ambiguities, or discrepancies between algorithmic prescriptions and actual practices on performance and safety.Our proposal integrates three levels: (1) systemic modeling of business processes involving AI, performance objectives, and human roles using SKM; (2) analysis of interactions, control constraints, and human factors via STPA; and (3) a continuous improvement loop, in which the results of STPA analyses and feedback from stakeholders are integrated into the system's knowledge bases and AI models. This integration creates a synergy where SKM supports the capitalization and structuring of critical knowledge, while STPA provides a rigorous framework for analyzing risks and performance deviations related to socio-technical interactions involving AI.The application of our method offers a path to improving operational performance and better integrating the human factor into corporate organizational processes. This contribution is based on an approach focused on human-system interactions and uses.

Keywords: System-Theoretic Process Analysis, Systemic Knowledge Management, IA, Sustainable Optimization, Human Factors

DOI: 10.54941/ahfe1007196

Cite this paper:

​