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Source note3 min read

The impact of artificial intelligence on enterprise software user roles

Source

arXiv

arxiv.org/abs/2606.25525v1

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Source

Plain-Language Brief

This research study explores how Artificial Intelligence (AI) is impacting the roles of users in enterprise software development, specifically focusing on SAP's Business Technology Platform (BTP). The study involves interviews with 20 experts and a participatory workshop with 24 individuals. The findings suggest that AI is leading to significant changes in daily tasks and roles within the development domain, including increased automation, human-AI collaboration, and reliance on agentic AI systems. The research also indicates that existing user-role frameworks like the BTP User Type Matrix may need to be adapted due to these role changes.

What the Document Says

  • The study investigates the impact of AI on user roles in enterprise software development, focusing on SAP's Business Technology Platform (BTP).
  • The research involves expert interviews (n=20) and a participatory workshop (n=24).
  • The findings suggest substantial shifts in day-to-day tasks and roles in the development domain due to AI, characterized by increasing automation, expanding human-AI collaboration, and growing reliance on agentic AI systems.
  • The study identifies significant implications for existing user-role frameworks like the BTP User Type Matrix, which may need adaptation due to role changes.

Key Concepts Mentioned

  • Artificial Intelligence
  • enterprise software development
  • SAP's Business Technology Platform (BTP)
  • user roles
  • day-to-day tasks
  • automation
  • human-AI collaboration
  • agentic AI systems

Claims Made by the Document

  • AI is reshaping work in software development.
  • The study reveals substantial shifts in day-to-day tasks and roles in the development domain due to AI.
  • Existing user-role frameworks like the BTP User Type Matrix may need adaptation due to role changes.

Evidence Used

  • Participatory workshop with 24 individuals
  • Expert interviews with 20 individuals

Methods / Approach

  • Qualitative study
  • Expert interviews
  • Participatory workshop

Limitations Stated by the Authors

The sample size for the expert interviews and participatory workshop might be considered small for some readers.

Limitations Noted by the Agent

Agent interpretation — not from the source.

As an assistant, I don't have access to additional context or data beyond what is provided in the source. It's unclear if the authors have addressed other potential limitations.

Open Questions

  • What specific adaptations are needed for the BTP User Type Matrix?
  • How widespread are these role changes across different enterprise platforms?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The text does not provide detailed information about the specific adaptations needed for the BTP User Type Matrix or the extent of role changes across different enterprise platforms.

Raw Abstract / Excerpt

Artificial Intelligence (AI) is rapidly reshaping the nature of work in software development, transforming user roles, workflows, and collaboration patterns across enterprise platforms. This qualitative study investigates how AI alters professional responsibilities within the context of SAP's Business Technology Platform (BTP), combining expert interviews (n=20) and a participatory workshop (n=24). The results reveal substantial shifts in day-to-day tasks and roles in the development domain, characterized by increasing automation of operational tasks, expanding human-AI collaboration, and growing reliance on agentic AI systems. The study further identifies significant implications for existing user-role frameworks, such as the BTP User Type Matrix, which requires adaptation as the workforce is undergoing significant role specific changes. Collectively, these findings highlight a workforce landscape in transition and underscore the need for revised role taxonomies, new governance and oversight functions, and updated design approaches for AI-native enterprise software systems.