Organizational Practices and Socio-Technical Design of Human-Centered AI

2026-01-29

AI Summary

This study addresses how artificial intelligence can be effectively integrated into organizational practices in alignment with the principles of Human-Centered AI (HCAI), rather than being treated merely as a technical tool. Drawing on sociotechnical systems theory and HCAI principles, the research develops a multidimensional integration framework through an analysis of ten predictive maintenance cases, revealing how AI becomes embedded within organizational communication, collaboration, and decision-making processes. The findings demonstrate that AI implementation fosters cross-disciplinary interpretation of outputs, enables dynamic workflow adjustments, and supports iterative refinement of operational rules. This, in turn, drives organizational learning, enhances quality assurance, and facilitates continuous improvement, ultimately cultivating a novel mode of human-AI collaboration that reconfigures work practices around shared intelligence and mutual adaptation.

Technology Category

• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Multiagent Systems: Agent/AI Theories and Architectures
• Intelligent Robots: Embodied AI

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

This contribution explores how the integration of Artificial Intelligence (AI) into organizational practices can be effectively framed through a socio-technical perspective to comply with the requirements of Human-centered AI (HCAI). Instead of viewing AI merely as a technical tool, the analysis emphasizes the importance of embedding AI into communication, collaboration, and decision-making processes within organizations from a human-centered perspective. Ten case-based patterns illustrate how AI support of predictive maintenance can be organized to address quality assurance and continuous improvement and to provide different types of sup-port for HCAI. The analysis shows that AI adoption often requires and enables new forms of organizational learning, where specialists jointly interpret AI output, adapt workflows, and refine rules for system improve-ment. Different dimensions and levels of socio-technical integration of AI are considered to reflect the effort and benefits of keeping the organization in the loop.

Problem

• Human-centered AI
• organizational practices
• socio-technical design
• AI integration
• predictive maintenance

Innovation

• Human-centered AI
• socio-technical design
• organizational learning
• predictive maintenance
• AI integration