Foresight can be viewed as an approach to managing uncertainty—an instrument that enables foreseeing while actively shaping the future under conditions of unpredictability. The rapid development of artificial intelligence (AI) has introduced new opportunities for foresight research. Although AI methods have not traditionally been part of the foresight canon, they offer significant potential for future applications. Integrating machine learning (ML) techniques into foresight research appears to be a natural progression. AI provides transformative capabilities by analysing complex datasets, uncovering hidden relationships, and generating data-driven recommendations. This work investigates the integration of AI tools into technology foresight projects by reviewing existing literature on their combined application. The analysis identifies the most frequently used AI and foresight methods, along with their primary objectives, providing a structured overview of current practices. Empirical analysis, based on data from a technology foresight project, demonstrates how AI can be utilised to enhance data analysis, thereby supporting theoretical considerations and complementing the traditional expert panel approach for technology clustering. The AI-assisted process provides a scalable alternative to traditional methods, with code tools, enhancing perspectives on identifying technology clusters, selecting key attributes, and incorporating expert self-assessment. However, the value of the proposed approaches lies more in a posteriori analysis, which can be utilised in future foresight projects regarding the attributes used for evaluation or the selection of expert panels. The diversity of proposed analyses demonstrates various interpretation possibilities but does not fundamentally influence the achievement of the main goal, which is the identification of key technologies.