AI for Requirements Engineering: Industry adoption and Practitioner perspectives

TL;DR

This study surveys software practitioners to understand AI adoption in Requirements Engineering, revealing a preference for human-AI collaboration and highlighting the need for tailored frameworks and governance as AI use expands.

Contribution

It provides empirical insights into current AI practices in RE, emphasizing the dominance of human-AI collaboration and identifying challenges and opportunities for future adoption.

Findings

58.2% of practitioners use AI in RE

HAIC is the most common approach at 54.4%

Full AI automation is minimal at 5.4%

Abstract

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.