Faster than the Team, Faster than the Customer: Tool Integration, Collaboration, and Organisational Lag in AI-assisted RE

2026-06-01

AI Summary

This study addresses the unclear practical impact of generative AI in requirements engineering (RE) within current industrial practice, particularly regarding tool integration, team collaboration, and organizational adaptability. Drawing on a company-wide use case survey conducted in 2024 and two rounds of interviews with eight product owners during 2025–2026, the research systematically analyzes fifteen RE use cases across four categories, leveraging an in-house chatbot and seven commercial generative AI tools. Findings reveal that AI adoption has moved beyond individual productivity gains to influence complex scenarios such as cross-tool integration, customer governance responses, and role boundary reconfiguration. The degree of tool integration critically determines performance benefits, while single-user interaction modes may undermine collaborative dynamics. The study proposes a practitioner-oriented set of evaluation questions to guide effective industrial deployment of AI in RE.

Abstract

The impact of applying generative AI tools to requirements engineering (RE) in industrial practice remains poorly understood. This paper examines how AI-assisted RE tools are used in industrial practice at XITASO, a medium-sized enterprise for high-tech software engineering, and how they reshape workflows, tool integration, and PO--developer relationships. We combine a 2024 company-wide use-case survey with two rounds of semi-structured interviews with eight product owners (POs) in late 2025 and spring 2026, covering an in-house chatbot and seven commercial AI tools. We identify 15 distinct use cases across four categories: product backlog management, tender management, requirements and domain understanding, and document and artifact creation. Three findings emerge. First, the effect of AI on PO--developer interaction is mixed: the prevailing single-user interaction model can substitute for collaborative dialogue, and developers do not always welcome AI-generated artefacts. Second, tool integration -- not tool capability -- is the binding constraint: where integration is in place, time savings are dramatic; where it is missing, POs fall back on manual workarounds. Third, AI advances faster than the surrounding organisational systems, so its benefits accrue to individual POs while team processes and customer readiness remain the limiting factors. AI-assisted RE in practice is more advanced than the GenAI-RE literature reflects: practitioners are already assembling cross-tool integrations, navigating customer governance, and renegotiating role boundaries in ways that evaluations focused on isolated tasks and single-engineer scenarios do not capture. From these patterns we derive a set of questions practitioners considering AI-assisted RE may ask of their own situation.

Problem

• requirements engineering
• generative AI
• tool integration
• organisational lag
• collaboration

Innovation

• AI-assisted requirements engineering
• tool integration
• organisational lag
• collaboration dynamics
• generative AI in practice