The development of generative AI has made great strides. More and more organizations are looking into implementing this new technology to increase productivity and efficiency. One of these new AI system- development tools is GitHub Copilot. The tool has shown great promise by offering functions such as automatic code generation, but this does not come without faults, as the generated code may be lacking in quality. How system developers within organizations experience this new technology is unknown, nor is it a worthwhile investment for the organizations in question. A qualitative study with semi-structured interviews has been carried out to capture the experiences of system developers concerning GitHub Copilot. The study was based on the theoretical framework Technology Acceptance Model 2 (TAM 2), in which some selected factors were used to describe the intention to use the system. A study was conducted to identify factors that cause an increase and/or a decrease in user acceptance.  We believe this would provide insights into what context GitHub Copilot would lead to increased productivity and efficiency. Based on the four factors studied, perceived usefulness, perceived ease of use, job relevance, and output quality, the study concludes with factors that affect a user’s intention to use GitHub Copilot. The study reveals that system developers perceive the usage of GitHub Copilot as positive. They believe that it has the potential to increase both productivity and efficiency. They perceive the tool as easy to get started with and easy to use. The quality of the generated code is perceived as somewhat lacking, but this did not affect their acceptance of the system.