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Beyond Training: How Workers Discover Value in Enterprise AI

Riya Sahni, Lydia B. Chilton

TL;DR

This study investigates how employees identify valuable use cases for enterprise AI, focusing on M365 Copilot, by conducting exploratory interviews with 10 experienced U.S. professionals and analyzing responses through Rogers’ Diffusion of Innovations. It finds a strong preference for informal, experiential, and social learning over formal training, with discovery occurring mainly through peer demonstrations and trial-and-error in real workflows; participants report efficiency gains but low confidence in mastering advanced features, revealing an efficiency–mastery gap. The paper discusses social learning strategies and proposes design implications—such as enhancing observability, lowering trial costs, and embedding progressive in-product guidance—to support rapid discovery of high-value AI use cases. Collectively, these findings offer practical guidance for organizations seeking to accelerate the diffusion and effective utilization of enterprise AI tools beyond traditional training programs.

Abstract

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.

Beyond Training: How Workers Discover Value in Enterprise AI

TL;DR

This study investigates how employees identify valuable use cases for enterprise AI, focusing on M365 Copilot, by conducting exploratory interviews with 10 experienced U.S. professionals and analyzing responses through Rogers’ Diffusion of Innovations. It finds a strong preference for informal, experiential, and social learning over formal training, with discovery occurring mainly through peer demonstrations and trial-and-error in real workflows; participants report efficiency gains but low confidence in mastering advanced features, revealing an efficiency–mastery gap. The paper discusses social learning strategies and proposes design implications—such as enhancing observability, lowering trial costs, and embedding progressive in-product guidance—to support rapid discovery of high-value AI use cases. Collectively, these findings offer practical guidance for organizations seeking to accelerate the diffusion and effective utilization of enterprise AI tools beyond traditional training programs.

Abstract

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.

Paper Structure

This paper contains 27 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Theory Approaches for Workplace Technology
  4. Informal and Social Learning in the Workplace
  5. AI Adoption At the Individual Level
  6. Methodology
  7. Study Procedure
  8. Participant Criteria and Selection
  9. Participant Demographics
  10. Qualitative Coding & Analysis
  11. Findings
  12. General Use Cases, Sentiments, and Usability Barriers in M365 Copilot Adoption
  13. What Participants Enjoyed About M365 Copilot
  14. Issues That Participants Faced With M365 Copilot
  15. High Perceived Efficiency using M365 Copilot at Work but Low Confidence in its Mastery
  16. ...and 12 more sections

Figures (3)

  • Figure 1: Example interfaces of M365 Copilot embedded into Microsoft Outlook (left) and Teams (right), as seen by study participants (from https://support.microsoft.com/).
  • Figure 2: Distribution of positive and negative sentiments in Copilot use cases
  • Figure 3: Perceived Efficiency and Confidence ratings across participants. Red dots indicate confidence exceeds efficiency; green dots indicate confidence is lower or equal to efficiency.