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

arXiv: https://arxiv.org/abs/2401.08960 · HTML: https://mlsanity.com/paper/2401.08960/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 8 of its 83 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 8 of 8)

- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop](https://mlsanity.com/paper/1703.08428/) (2017)
- [Understanding Chatbot-mediated Task Management](https://mlsanity.com/paper/1802.03109/) (2018)
- [Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination](https://mlsanity.com/paper/2312.13581/) (2023)
- [Is the Most Accurate AI the Best Teammate? Optimizing AI for Teamwork](https://mlsanity.com/paper/2004.13102/) (2020)
- [A Survey of Passive Sensing for Workplace Wellbeing and Productivity](https://mlsanity.com/paper/2201.03074/) (2022)
- [Towards effective AI-powered agile project management](https://mlsanity.com/paper/1812.10578/) (2018)
- [Imagining Future Digital Assistants at Work: A Study of Task Management Needs](https://mlsanity.com/paper/2208.03443/) (2022)

## Built on by (most cited first, 1 of 1)

- [Faithful Where It Can Be Checked: Auditing a Reflection Agent Against Its System Prompt in a Randomized Trial](https://mlsanity.com/paper/2609.19635/) (2026)

## Abstract (arXiv)

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.