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

TL;DR

This paper examines how integrating AI into organizational practices from a socio-technical perspective supports human-centered AI by emphasizing communication, collaboration, and continuous learning within organizations.

Contribution

It introduces ten case-based patterns demonstrating socio-technical integration of AI in organizational processes for human-centered AI support.

Findings

AI adoption fosters new organizational learning practices

Socio-technical integration levels vary with effort and benefits

AI supports quality assurance and continuous improvement

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.