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Product Manager Practices for Delegating Work to Generative AI: "Accountability must not be delegated to non-human actors"

Mara Ulloa, Jenna L. Butler, Sankeerti Haniyur, Courtney Miller, Barrett Amos, Advait Sarkar, Margaret-Anne Storey

TL;DR

This paper examines how Generative AI reshapes Product Manager work at a large technology company, addressing gaps in PM-focused GenAI research. Using a mixed-methods design with an 885-PM survey, telemetry from 731 PMs, and 15 interviews, it characterizes adoption, uses, benefits, and barriers, and introduces the Selective Delegation Framework to explain how PMs decide which tasks to delegate to GenAI across individual, team, and organizational levels. The study shows high adoption, time-saving benefits in writing and planning, and persistent concerns about output quality and ethics, alongside a shift toward more hands-on, SWE-like tasks and blurred PM-SWE boundaries. It argues for value-aligned, sociotechnical approaches to AI workflow adoption, highlighting the need for guardrails, continuous upskilling, and organizational strategies to support responsible delegation and evolving software development roles.

Abstract

Generative AI (GenAI) is changing the nature of knowledge work, particularly for Product Managers (PMs) in software development teams. While much software engineering research has focused on developers' interactions with GenAI, there is less understanding of how the work of PMs is evolving due to GenAI. To address this gap, we conducted a mixed-methods study at Microsoft, a large, multinational software company: surveying 885 PMs, analyzing telemetry data for a subset of PMs (N=731), and interviewing a subset of 15 PMs. We contribute: (1) PMs' current GenAI adoption rates, uses cases, and perceived benefits and barriers and; (2) a framework capturing how PMs assess which tasks to delegate to GenAI; (3) PMs adaptation practices for integrating GenAI into their roles and perceptions of how their role is evolving. We end by discussing implications on the broader GenAI workflow adoption process and software development roles.

Product Manager Practices for Delegating Work to Generative AI: "Accountability must not be delegated to non-human actors"

TL;DR

This paper examines how Generative AI reshapes Product Manager work at a large technology company, addressing gaps in PM-focused GenAI research. Using a mixed-methods design with an 885-PM survey, telemetry from 731 PMs, and 15 interviews, it characterizes adoption, uses, benefits, and barriers, and introduces the Selective Delegation Framework to explain how PMs decide which tasks to delegate to GenAI across individual, team, and organizational levels. The study shows high adoption, time-saving benefits in writing and planning, and persistent concerns about output quality and ethics, alongside a shift toward more hands-on, SWE-like tasks and blurred PM-SWE boundaries. It argues for value-aligned, sociotechnical approaches to AI workflow adoption, highlighting the need for guardrails, continuous upskilling, and organizational strategies to support responsible delegation and evolving software development roles.

Abstract

Generative AI (GenAI) is changing the nature of knowledge work, particularly for Product Managers (PMs) in software development teams. While much software engineering research has focused on developers' interactions with GenAI, there is less understanding of how the work of PMs is evolving due to GenAI. To address this gap, we conducted a mixed-methods study at Microsoft, a large, multinational software company: surveying 885 PMs, analyzing telemetry data for a subset of PMs (N=731), and interviewing a subset of 15 PMs. We contribute: (1) PMs' current GenAI adoption rates, uses cases, and perceived benefits and barriers and; (2) a framework capturing how PMs assess which tasks to delegate to GenAI; (3) PMs adaptation practices for integrating GenAI into their roles and perceptions of how their role is evolving. We end by discussing implications on the broader GenAI workflow adoption process and software development roles.

Paper Structure

This paper contains 36 sections, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and Related Work
  3. The Software Product Management Role
  4. GenAI Adoption and Delegation Practices
  5. GenAI Adoption in knowledge work
  6. GenAI Delegation vs Usage
  7. Methods
  8. GenAI-Early Adopter PM Environment
  9. Instrument Design and Data Collection
  10. Survey Design, Recruitment, and Data Collection
  11. Interview Design, Recruitment, and Data Collection
  12. Telemetry Data Collection
  13. Data Analysis
  14. Qualitative Data:
  15. Quantitative Data: Statistical Analysis of Survey and Telemetry Data
  16. ...and 21 more sections

Figures (4)

  • Figure 1: Product managers (PMs) identified values at the individual, team, and organizational levels influencing task delegation to GenAI-driven tools. These are discussed further in Findings, \ref{['sec:framework']}.
  • Figure 2: ICs and Managers shared how concerned they each were about GenAI job displacement. Additionally, high encouragement to use GenAI was reported by both groups.
  • Figure 3: ICs' coded responses to the open-text question, Is there anything else you'd like to tell us about barriers to using GenAI more effectively in your role?
  • Figure 4: A selection of responses to Likert questions posed to IC PMs about their thoughts and beliefs on GenAI.