AI-driven transformation in a medium-sized IT firm : impacts on productivity and code quality

Abstract

AI adoption strategies for medium-sized businesses need proper identification to prevent major problems and incorrect AI capability assessments. This research investigates how different AI strategies affect Smart Land Solution (SLS) as a case study of the medium-sized IT companies. Our study includes three controlled teams, one avoided using AI, one used AI selectively, and the other used AI extensively. The evaluation of team performance depends on two essential factors which are productivity and code quality. The evaluation process used both quantitative and qualitative data as its information source. The task management system Jira and quality monitoring system QualCode provided quantitative data for evaluation purposes, and qualitative data came from interviews from the product manager and questioners. The combination of human judgment with AI adoption produced the best results in terms of code quality and productivity maintenance. The excessive use of AI technology produced duplicated code and technical issues and made systems harder to understand yet the complete avoidance of AI created slow operations and limited scalability.