AI Tool Use and Adoption in Software Development by Individuals and Organizations: A Grounded Theory Study
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2024.
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Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Reviewcited by 5
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Reviewcited by 3
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- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptationcited by 0
- 2026Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Studycited by 0
- 2026Adoption of Generative Artificial Intelligence in the German Software Engineering Industry: An Empirical Study23 Jan 2026
- 2026Coding in a Bubble? Evaluating LLMs in Resolving Context Adaptation Bugs During Code Adaptation10 Jan 2026
- 2025Walking the Tightrope of LLMs for Software Development: A Practitioners' Perspective9 Nov 2025
- 2025Novice Developers' Perspectives on Adopting LLMs for Software Development: A Systematic Literature Review10 Mar 2025
- 2025Human-AI Experience in Integrated Development Environments: A Systematic Literature Review8 Mar 2025