AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study
Ze Shi Li, Nowshin Nawar Arony, Ahmed Musa Awon, Daniela Damian, Bowen Xu
TL;DR
The paper addresses how individuals and organizations adopt AI tooling for software development by deploying socio-technical grounded theory (STGT) to synthesize insights from 26 practitioner interviews and 395 survey responses. It identifies 12 factors split across individual and organizational motives and challenges, organized into four categories, plus three push-pull relationships that link motives and barriers to adoption. The study emphasizes organizational culture, cost, training, and privacy as critical levers, offering practical recommendations and a validated theory for practitioners seeking to increase AI tool uptake. Its findings highlight the need for structured guidelines and privacy safeguards to enable broader, safer adoption of AI in software development with real-world implications for policy, governance, and workforce development.
Abstract
AI assistance tools such as ChatGPT, Copilot, and Gemini have dramatically impacted the nature of software development in recent years. Numerous studies have studied the positive benefits that practitioners have achieved from using these tools in their work. While there is a growing body of knowledge regarding the usability aspects of leveraging AI tools, we still lack concrete details on the issues that organizations and practitioners need to consider should they want to explore increasing adoption or use of AI tools. In this study, we conducted a mixed methods study involving interviews with 26 industry practitioners and 395 survey respondents. We found that there are several motives and challenges that impact individuals and organizations and developed a theory of AI Tool Adoption. For example, we found creating a culture of sharing of AI best practices and tips as a key motive for practitioners' adopting and using AI tools. In total, we identified 2 individual motives, 4 individual challenges, 3 organizational motives, and 3 organizational challenges, and 3 interleaved relationships. The 3 interleaved relationships act in a push-pull manner where motives pull practitioners to increase the use of AI tools and challenges push practitioners away from using AI tools.