Paper Type
Complete
Abstract
Generative AI is increasingly used in collaborative work but introduces generative uncertainty, as outputs are probabilistic and difficult to evaluate. Prior research emphasizes individual judgment, overlooking how teams collectively assess AI outputs. This study examines team-based evaluation through a sensemaking lens. We find that teams manage uncertainty through iterative experimentation, heuristic cue, plausibility judgments, and retrospective refinement, with evaluation emerging as a socially negotiated. This study extends sensemaking theory to human-AI collaboration, highlighting collective evaluation, and positions generative uncertainty as a central challenge in AI-supported teamwork.
Paper Number
1380
Recommended Citation
Hussain, Ghazal Manzoor and Sveen, Julia, "Collective Sensemaking in Generative AI Contexts: Evaluation Practices Under Uncertainty" (2026). AMCIS 2026 Proceedings. 13.
https://aisel.aisnet.org/amcis2026/conftheme/conftheme/13
Collective Sensemaking in Generative AI Contexts: Evaluation Practices Under Uncertainty
Generative AI is increasingly used in collaborative work but introduces generative uncertainty, as outputs are probabilistic and difficult to evaluate. Prior research emphasizes individual judgment, overlooking how teams collectively assess AI outputs. This study examines team-based evaluation through a sensemaking lens. We find that teams manage uncertainty through iterative experimentation, heuristic cue, plausibility judgments, and retrospective refinement, with evaluation emerging as a socially negotiated. This study extends sensemaking theory to human-AI collaboration, highlighting collective evaluation, and positions generative uncertainty as a central challenge in AI-supported teamwork.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
Comments
NEXTTRANS