Paper Type
Complete
Paper Number
PACIS2026-1885
Description
Workplace AI companions, systems with which workers form sustained relationships, are increasingly embedded in organizational life, yet their relational value remains underexplored. This paper develops a relational perspective on AI companionship through a systematic literature review of interdisciplinary research. We propose a relational competency framework organized around three domains: cognitive competency, which supports reasoning and task performance; emotional competency, which enables affective engagement and regulation; and developmental competency, which reflects how the human-AI relationship evolves through mutual learning and adaptation over time. We conceptualize these competencies not as fixed properties of AI systems but as relational achievements that emerge through ongoing worker-AI interaction in organizational settings. The framework contributes to sociotechnical research on workplace AI by shifting analytical focus from what AI systems can do to how workers and AI systems sustain meaningful relationships in work contexts, with implications for AI design, worker wellbeing, and the organization of work.
Recommended Citation
Ou, Min; Koch, Hope; and Weng, Qin, "Thinking, Feeling, Becoming: A Relational Competency Framework for Human–AI Companionship at Work" (2026). PACIS 2026 Proceedings. 18.
https://aisel.aisnet.org/pacis2026/ai_fow/ai_fow/18
Thinking, Feeling, Becoming: A Relational Competency Framework for Human–AI Companionship at Work
Workplace AI companions, systems with which workers form sustained relationships, are increasingly embedded in organizational life, yet their relational value remains underexplored. This paper develops a relational perspective on AI companionship through a systematic literature review of interdisciplinary research. We propose a relational competency framework organized around three domains: cognitive competency, which supports reasoning and task performance; emotional competency, which enables affective engagement and regulation; and developmental competency, which reflects how the human-AI relationship evolves through mutual learning and adaptation over time. We conceptualize these competencies not as fixed properties of AI systems but as relational achievements that emerge through ongoing worker-AI interaction in organizational settings. The framework contributes to sociotechnical research on workplace AI by shifting analytical focus from what AI systems can do to how workers and AI systems sustain meaningful relationships in work contexts, with implications for AI design, worker wellbeing, and the organization of work.
Comments
02-FutureofWork