Paper Type
ERF
Abstract
Recent advances in AI enable systems to act as teammates in organizational processes, particularly relevant for resource-constrained enterprises in terms of AI shoring. However, research lacks a theory-grounded logic for configuring behavioral cues of such AI teammates based on human characteristics. We propose a KSAO-based framework that models AI teammate design as a configuration of behavioral cues, and we elicit user preferences using a choice-based conjoint analysis with clustering. The study is designed to provide exploratory insights into preferred AI teammate configurations and their potential alignment with complementarity principles. These preference-based design prototypes offer a foundation for future research on Human-AI team design and the development of configurable AI teammates.
Paper Number
1541
Recommended Citation
Schatz, Maximilian and Blaschke, Maximilian J., "Designing Complementary AI Teammates: A KSAO-Based Configuration Framework" (2026). AMCIS 2026 Proceedings. 4.
https://aisel.aisnet.org/amcis2026/sigcnow/sigcnow/4
Designing Complementary AI Teammates: A KSAO-Based Configuration Framework
Recent advances in AI enable systems to act as teammates in organizational processes, particularly relevant for resource-constrained enterprises in terms of AI shoring. However, research lacks a theory-grounded logic for configuring behavioral cues of such AI teammates based on human characteristics. We propose a KSAO-based framework that models AI teammate design as a configuration of behavioral cues, and we elicit user preferences using a choice-based conjoint analysis with clustering. The study is designed to provide exploratory insights into preferred AI teammate configurations and their potential alignment with complementarity principles. These preference-based design prototypes offer a foundation for future research on Human-AI team design and the development of configurable AI teammates.
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