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arxiv: 2503.17661 · v2 · pith:7IMS2Y7K · submitted 2025-03-22 · cs.CY · cs.AI

A Qualitative Study of User Perception of M365 AI Copilot

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classification cs.CY cs.AI
keywords copilotm365areasconcernsethicalexpectationsimpactproductivity
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. VizCopilot: Fostering Appropriate Reliance on Enterprise Chatbots with Context Visualization

    cs.HC 2025-10 unverdicted novelty 5.0

    VizCopilot integrates topic modeling with document visualization to support user oversight of retrieved context in enterprise chatbots, enabling detection of misalignments and adaptation of prompting strategies.