While organizations continue to invest in enterprise AI, little is known about how individual employees find valuable use cases once these tools are deployed. We present an exploratory interview study of 10 experienced U.S. professionals using M365 Copilot and interpret accounts through Rogers' Diffusion of Innovations to examine where value appears and how use cases are found and shared. Findings reveal a strong preference for informal learning methods over structured training. No participants (0/10) reported formal training as their primary way of learning; most relied on trial-and-error (8/10) and on exchanging tips with colleagues (6/10). Participants most often used M365 Copilot for note-taking/summarization, information retrieval/explanation, and writing. They also reported perceived gains in efficiency but low confidence in mastering more advanced features. The paper discusses social learning strategies and outlines implementable steps for organizations to support the discovery of high-value use cases with available enterprise AI tools.

翻译：尽管组织持续投资于企业人工智能，但关于个体员工在这些工具部署后如何发现高价值用例，目前仍知之甚少。我们开展了一项探索性访谈研究，对象为10位使用M365 Copilot的美国资深专业人士，并运用罗杰斯的创新扩散理论对访谈内容进行解读，以探究价值显现之处以及用例如何被发现与共享。研究发现，相较于结构化培训，员工明显更倾向于非正式学习方法。没有参与者（0/10）将正式培训报告为其主要学习途径；大多数人依赖试错法（8/10）以及与同事交流技巧（6/10）。参与者最常将M365 Copilot用于记笔记/总结、信息检索/解释以及写作。他们还报告了感知到的效率提升，但对掌握更高级功能的信心较低。本文讨论了社会学习策略，并概述了组织可实施的步骤，以支持员工利用现有企业AI工具发现高价值用例。