# A Qualitative Study of User Perception of M365 AI Copilot

arXiv: https://arxiv.org/abs/2503.17661 · HTML: https://mlsanity.com/paper/2503.17661/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 42 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 3 of 3)

- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Intelligent Tutor: Leveraging ChatGPT and Microsoft Copilot Studio to Deliver a Generative AI Student Support and Feedback System within Teams](https://mlsanity.com/paper/2405.13024/) (2024)
- [Survey Insights on M365 Copilot Adoption](https://mlsanity.com/paper/2412.16162/) (2024)

## Built on by (most cited first, 2 of 2)

- [From Overload to Insights: How AI Agents Can Support Scientists in Analyzing Complex Data](https://mlsanity.com/paper/2607.16845/) (2026)
- [The Vibe Shift in Software Engineering: Evaluating AI-Led Conversational Programming for Performance, Cognition, and Responsible Adoption](https://mlsanity.com/paper/2609.09560/) (2026)

## Abstract (arXiv)

Adopting AI copilots in professional workflows presents opportunities for enhanced productivity, efficiency, and decision making. In this paper, we present results from a six month trial of M365 Copilot conducted at our organisation in 2024. A qualitative interview study was carried out with 27 participants. The study explored user perceptions of M365 Copilot's effectiveness, productivity impact, evolving expectations, ethical concerns, and overall satisfaction. Initial enthusiasm for the tool was met with mixed post trial experiences. While some users found M365 Copilot beneficial for tasks such as email coaching, meeting summaries, and content retrieval, others reported unmet expectations in areas requiring deeper contextual understanding, reasoning, and integration with existing workflows. Ethical concerns were a recurring theme, with users highlighting issues related to data privacy, transparency, and AI bias. While M365 Copilot demonstrated value in specific operational areas, its broader impact remained constrained by usability limitations and the need for human oversight to validate AI generated outputs.