Artificial intelligence in strategic foresight: Evidence from a longitudinal case at Siemens Professional Education

Artificial intelligence (AI), and particularly generative AI, is increasingly discussed as a transformative force in strategic foresight. Yet empirical evidence on how AI is integrated into organizational foresight practices, and with what epistemic and governance implications, remains limited. This paper examines the longitudinal evolution of strategic foresight practices at Siemens Professional Education (SPE), a central unit of Siemens AG responsible for anticipating future skill requirements and translating them into education and reskilling initiatives for more than 290,000 employees worldwide. The study compares a fully human-led foresight process (2023) with an AI-augmented process (2025). Drawing on process documentation, foresight outputs, time and resource estimates, interviews, and focus groups, we analyze how AI integration reshapes task allocation, resource use, and multiple dimensions of analytical quality across the foresight process. The findings show that AI integration does not lead to uniform acceleration or automation. Instead, it produces task-level efficiency gains, a redistribution of human effort, and enhanced analytical breadth, structure, and completeness, particularly in scanning, consolidation, and drafting activities, while framing, prioritization, contextual interpretation, legitimacy building, and accountability remain firmly human-led. Empirically observed trade-offs reveal persistent tensions between speed and legitimacy, analytical breadth and strategic focus, and standardization and organizational contextualization. Building on these findings, the paper develops a phase-specific model of human–AI collaboration in strategic foresight. The study contributes empirical grounding to debates on AI-enabled foresight and advances a socio-technical understanding of how anticipatory practices can evolve responsibly in the presence of increasingly capable AI systems.