DESIGN AFFORDANCES OF GENERATIVE AI IN ROUTINES: AN EXPLORATORY STUDY

This study investigates how organizations develop an understanding of the designed affordances of generative AI to intentionally redesign their routines. Generative AI introduces new action potentials that developers embed into device-enabled routines to enhance organizational capabilities. Employing a mixed-method approach, including focus groups, participant observation, document studies, and interviews, we analyze four case studies across diverse industries. The findings reveal a set of designed affordances as well as four development paths through which developers build their understanding of generative AI’s capabilities. These paths shape how affordances are framed and embedded in routine design. This study contributes to affordance theory by highlighting the design phase as a site of affordance construction, and by introducing development paths as an analytical tool for understanding how organizations make sense of new technological potentials. Future research should explore how these designed affordances are enacted and adapted through routine execution and diffusion.