Despite Artificial Intelligence revolutionizing various industries, its integration into Project Management remains fragmented, particularly across structured (product-focused) and adaptive (service-oriented) environments. Through a comparative analysis of two empirical case studies, this study examines AI’s role in optimizing decision-making, resource allocation, and risk mitigation within these distinct project domains. In product-focused projects, AI enhances cost prediction, timeline estimation, and quality control by leveraging structured data analysis and predictive modelling for process automation. Conversely, in more service-oriented projects, it facilitates dynamic resource allocation, real-time decision support, and customer interaction, emphasizing adaptive learning and contextual responsiveness. Results highlight AI’s differential applications and distinct challenges associated with its adoption, including high implementation costs, dependence on high-quality data, and organizational resistance. Empirical findings underscore the need for tailored adoption strategies that align with industry-specific constraints, emphasizing explainability, trust calibration, and the balance between automation and human oversight. This study contributes to the broader Information Systems (IS) literature by providing actionable recommendations and a structured framework for effective AI adoption, ensuring its potential is fully realized in both structured and adaptive settings.