Recommendations for Efficient and Responsible LLM Adoption within Industrial Software Development

2026-04-29

AI Summary

This study addresses the lack of actionable guidance for efficiently and responsibly adopting large language models (LLMs) in industrial software development. Through a multi-case study, qualitative thematic analysis, and a large-scale online survey, it proposes the first LLM adoption framework tailored to industrial contexts and systematically derives seven practical recommendations. These span key dimensions including human–AI collaboration, applicability boundaries, evaluation criteria, process impact, oversight mechanisms, and developer competencies. The framework aligns with the compliance requirements of the EU AI Act, and its recommendations have garnered broad industry endorsement, offering both an empirical foundation and a practical pathway for the responsible deployment of LLMs in software engineering.

Abstract

Context: Large language models (LLMs) are observed to have a significant positive impact on various software engineering (SE) activities. With improved accessibility, the adoption of powerful LLMs in industry has surged recently. However, there is a lack of actionable best practices for the efficient and responsible adoption of LLMs within industrial software settings. Objectives: We developed seven actionable recommendations to address this research gap. Methods: We conducted a multi-case study with three organisations that use LLMs within their SE activities and synthesised seven recommendations through qualitative thematic analysis. We conducted a complementary online survey with software practitioners from various industries to evaluate the perceived relevance of our recommendations. Results: Our results and recommendations focus on (i) users' preference to use LLMs as AI assistants, (ii) the importance of relevant stakeholders' satisfaction in the LLM-output evaluation, (iii) scoping the applicability of LLMs within SE tasks, (iv) the effect of LLMs on SE workflows, (v) the necessity and directions for developing human oversight mechanisms, and (vi) the necessary skills for practitioners for leveraging LLMs within SE. The online survey indicates a high level of agreement from the participants regarding the perceived relevance of the recommendations. Conclusion: We outline future research directions, including mapping the seven recommendations to the principles of the EU AI Act (AIA) in order to examine how they relate to the current regulatory compliance frameworks.

Problem

• Large Language Models
• Software Engineering
• Industrial Adoption
• Responsible AI
• Best Practices

Innovation

• large language models
• software engineering
• responsible AI
• best practices
• human oversight