Integrating Generative AI Tools into Software Development

Abstract

Generative AI (GenAI) and large language models (LLMs) have become popular in recent years for their versatility, usefulness, and ease of use. Now, these GenAI tools are being adopted for more professional use cases, such as software development. The case study presented in this thesis took place from March to August 2025 at a larger international IT company, while they tried to test and adopt Google’s GenAI tool Gemini into the organization, with a special focus on the software development department. This case study examines different aspects of the integration and adoption of GenAI tools in a larger organization, as well as how developers view and trust these tools in a software development context. The results showed that both the perceived usefulness and perceived ease of use of generative AI tools for software development are high, and that the vast majority of developers perceive their productivity, development velocity, and problem-solving speed to all increase over a four-month period when using GenAI tools. As a product of this study, a conceptual model was developed based on collected data and adoption theory to show how different factors influence each other in the selection and adoption of GenAI tools. Tool selection from an organizational perspective showed that cost was of secondary importance to compliance, productivity, and integration into existing workflows.