Paper Type
Short
Paper Number
PACIS2026-1313
Description
Organizations increasingly deploy AI-generated reporting to automate project status summaries, yet the consequences for blocked work coordination remain understudied. Using data from a software-as-a-service firm, we find that AI-generated reporting reduces team collaboration on blocked project work and lowers blockage resolution. Drawing on the Attention-Based View and integrating research on generative AI, we identify two sequential mechanisms underlying these effects. First, AI-generated reports systematically reduce the distress cues embedded in blocked work descriptions, causing blockages to appear less urgent and less help-seeking in tone, which weakens the signals that ordinarily attract collaborative intervention. Second, AI-generated reporting reduces direct communication between team members, suppressing the shared diagnosis and path-clearing through which collaboration translates into resolution, such that collaboration under AI-generated reporting is less effective than under human-generated reporting. These findings reveal an unintended consequence of AI adoption for team coordination and contribute to research on AI in organizations.
Recommended Citation
Duan, Menghan; Zhang, Yueyue; Wang, Yu; Lu, Xianghua; Fang, Yulin; and Zhang, Cheng, "Lost in Smoothing: How AI-Generated Report Suppresses Distress Cues and Undermines Blockage Resolution" (2026). PACIS 2026 Proceedings. 5.
https://aisel.aisnet.org/pacis2026/general_topic/general_topic/5
Lost in Smoothing: How AI-Generated Report Suppresses Distress Cues and Undermines Blockage Resolution
Organizations increasingly deploy AI-generated reporting to automate project status summaries, yet the consequences for blocked work coordination remain understudied. Using data from a software-as-a-service firm, we find that AI-generated reporting reduces team collaboration on blocked project work and lowers blockage resolution. Drawing on the Attention-Based View and integrating research on generative AI, we identify two sequential mechanisms underlying these effects. First, AI-generated reports systematically reduce the distress cues embedded in blocked work descriptions, causing blockages to appear less urgent and less help-seeking in tone, which weakens the signals that ordinarily attract collaborative intervention. Second, AI-generated reporting reduces direct communication between team members, suppressing the shared diagnosis and path-clearing through which collaboration translates into resolution, such that collaboration under AI-generated reporting is less effective than under human-generated reporting. These findings reveal an unintended consequence of AI adoption for team coordination and contribute to research on AI in organizations.
Comments
17-General