From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912413335420928 |
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| author | Nepal, Subigya Hernandez, Javier Massachi, Talie Rowan, Kael Amores, Judith Suh, Jina Ramos, Gonzalo Houck, Brian Iqbal, Shamsi T. Czerwinski, Mary |
| author_facet | Nepal, Subigya Hernandez, Javier Massachi, Talie Rowan, Kael Amores, Judith Suh, Jina Ramos, Gonzalo Houck, Brian Iqbal, Shamsi T. Czerwinski, Mary |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_08960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions Nepal, Subigya Hernandez, Javier Massachi, Talie Rowan, Kael Amores, Judith Suh, Jina Ramos, Gonzalo Houck, Brian Iqbal, Shamsi T. Czerwinski, Mary Human-Computer Interaction Artificial Intelligence Computers and Society H.5.0; H.5.3; H.5.m; J.0 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. |
| title | From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions |
| topic | Human-Computer Interaction Artificial Intelligence Computers and Society H.5.0; H.5.3; H.5.m; J.0 |
| url | https://arxiv.org/abs/2401.08960 |