TY - GEN
T1 - Requirements for AI-based Teammates
T2 - 55th Annual Hawaii International Conference on System Sciences, HICSS 2022
AU - Elshan, Edona
AU - Siemon, Dominik
AU - de Vreede, Triparna
AU - de Vreede, Gert Jan
AU - Oeste-Reiß, Sarah
AU - Ebel, Philipp
N1 - Publisher Copyright:
© 2022 IEEE Computer Society. All rights reserved.
PY - 2022
Y1 - 2022
N2 - Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams.
AB - Innovation requires organizations to tap into the knowledge and creativity of teams. However, teams are confronted with massive amounts of data and information, necessitating a broad set of knowledge, methodologies, and approaches to solve problems and innovate. Consequently, team composition has become a critical challenge. Recent advances in artificial intelligence (AI) may assist in addressing this challenge. As AI is permeating both business and private sectors, organizational teams may be augmented with AI team members. However, given the nascent nature of this phenomenon, little is known about the specific roles and requirements for such AI teammates. Within an interview study we discover common challenges in teams and identify recurring capability gaps of participants and behaviors that negatively impact the team's collective performance. Based on our findings, we propose requirements for AI-based teammates to address these gaps and support beneficial collaboration between humans and AI in teams.
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M3 - Conference contribution
AN - SCOPUS:85137313712
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 164
EP - 173
BT - Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022
A2 - Bui, Tung X.
Y2 - 3 January 2022 through 7 January 2022
ER -