Foresight in the age of artificial intelligence: from analytical tool to strategic partner

Purpose This study aims to address the key challenges of traditional foresight methods, including cognitive biases, high costs and the inability to process large-scale textual data under conditions of deep uncertainty. The primary objective is to comprehensively examine and critically analyze how artificial intelligence (AI) can overcome these limitations. Design/methodology/approach The research method is based on an integrative review approach, which uses inductive thematic analysis of the leading scientific literature to extract and categorize the applications of AI. Findings The findings reveal that AI functions as a “computational collaborator” in four complementary roles: a scanner and discoverer for automatically identifying weak signals; a structurer and evaluator for systematically classifying knowledge and validating human judgments; a predictor and opportunity scout for forecasting technological convergences and identifying innovation “white spaces”; and a simulator and creative partner for exploring complex dynamics and enhancing participatory processes. Originality/value The main conclusion is the establishment of a new “assistant-driven hybrid foresight” paradigm, wherein the role of the futurist evolves into that of an architect and orchestrator of intelligent systems, requiring new epistemic competencies such as data literacy and prompt engineering.