From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions

• URL: http://arxiv.org/abs/2401.08960v2
• Date: Thu, 05 Jun 2025 05:00:36 GMT
• Title: From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
• Authors: Subigya Nepal, Javier Hernandez, Talie Massachi, Kael Rowan, Judith Amores, Jina Suh, Gonzalo Ramos, Brian Houck, Shamsi T. Iqbal, Mary Czerwinski,
• Abstract summary: Information workers increasingly struggle with productivity challenges in modern workplaces.<n>Despite availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights.<n>We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs.
• Score: 21.79433247723466
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utilizing a two-phase method, we first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. Drawing on the survey insights, we developed a GPT-4 powered personalized productivity agent that utilizes telemetry data gathered via Viva Insights from information workers to provide tailored assistance. We compared its performance with alternative productivity-assistive tools, such as dashboard and narrative, in a study involving 40 participants. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools. By building on these insights, our work provides important guidance for developing more effective productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

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