Paper Type
ERF
Abstract
As companies invest in Artificial Intelligence (AI), assembling a team that can succeed in several types of AI projects is a complex undertaking. Furthermore, the talent shortage and the new skills and expertise necessary in AI projects require a thoughtful approach to building an AI team that meets company needs and the structure they need to succeed. We propose a conceptual framework for structuring AI teams based on Qualitative Comparative Analysis (QCA) and digital agility theory. We suggest a configurational analytics framework that examines how AI teams are embedded in a configuration of individual and AI project elements. Our conceptual model suggests five important factors to consider when building AI teams: expertise, experience, project complexity, project life cycle, and project objective. We discuss implications for theory and practice as well as future research avenues.
Paper Number
1737
Recommended Citation
Mosconi, Elaine and Gagnon, Elisa, "Enabling AI Team Agility" (2024). AMCIS 2024 Proceedings. 1.
https://aisel.aisnet.org/amcis2024/it_pm/it_pm/1
Enabling AI Team Agility
As companies invest in Artificial Intelligence (AI), assembling a team that can succeed in several types of AI projects is a complex undertaking. Furthermore, the talent shortage and the new skills and expertise necessary in AI projects require a thoughtful approach to building an AI team that meets company needs and the structure they need to succeed. We propose a conceptual framework for structuring AI teams based on Qualitative Comparative Analysis (QCA) and digital agility theory. We suggest a configurational analytics framework that examines how AI teams are embedded in a configuration of individual and AI project elements. Our conceptual model suggests five important factors to consider when building AI teams: expertise, experience, project complexity, project life cycle, and project objective. We discuss implications for theory and practice as well as future research avenues.
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