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Enhancing Strategy Planning Using AI
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DOI：10.17323/fstig.2026.29810.png)
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Abstract
En 中文
Aproductive approach to integrating strategic Foresight and machine learning is the Generalized Strategic Foresight Model embedding MLOps (GSF(M)2), a unified governance architecture that combines the interpretive depth of long-term scenario-based Foresight with the adaptivity of real-time machine learning pipelines. The model addresses structural deficiencies in existing decision-making systems, where Foresight methods generate anticipatory insights but lack operationalization mechanisms, while machine learning algorithms automate processes but ignore strategic and participatory context as well as socioorganizational specificity. A systematic literature review following PRISMA methodology (16 publications in each block-Foresight and machine learning lifecycle) identified methodological gaps in both fields when compared against reference architectures. GSF(M)2 synthesizes the strengths of both approaches by embedding Foresight logic into adaptive machine learning processes and integrating automated feedback loops into scenario planning. The result is a continuously learning ecosystem that recalibrates scenarios, model parameters, and strategic options in real time. The synthesis of anticipatory analytics, continuous horizon scanning, and data-driven prioritization enhances policymaking effectiveness and institutional agility under conditions of international and technological uncertainty. GSF(M)2 represents the first dual-core framework for the co-evolution of strategic Foresight and adaptive algorithms within a unified reflexive governance architecture.
Keywords:
strategic foresight
scenario planning
MLOps
governance models
anticipatory systems
continuous learning
adaptive decision-making
automation pipelines
uncertainty analysis
policy intelligence
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