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

• URL: http://arxiv.org/abs/2401.08960v1
• Date: Wed, 17 Jan 2024 04:20:10 GMT
• Title: From User Surveys to Telemetry-Driven 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: We first conducted a survey with 363 participants, exploring various aspects of productivity, communication style, agent approach, personality traits, personalization, and privacy. We developed a GPT-4 powered personalized productivity agent that utilizes telemetry data from information workers to provide tailored assistance. Our findings highlight the importance of user-centric design, adaptability, and the balance between personalization and privacy in AI-assisted productivity tools.
• Score: 22.443000599725313
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present a comprehensive, user-centric approach to understand preferences in AI-based productivity agents and develop personalized solutions 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 the insights distilled from our study, we believe that our work can enable and guide future research to further enhance productivity solutions, ultimately leading to optimized efficiency and user experiences for information workers.

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