Enhancing Strategy Planning Using AI

This article presents the development of the Generalized Strategic Foresight Model embedding machine-learning operations (MLOps) (GSF(M)²), a unified governance architecture that integrates the long-term interpretive depth of scenario-based strategic foresight with the real-time adaptivity of machine-learning operational pipelines. Its development as the aim of our research responds to a structural misalignment in current decision systems. Specifically, foresight methods generate rich anticipatory insights but lack mechanisms for continuous operationalization. Meanwhile, MLOps frameworks offer robust automation and model lifecycle management, but remain weakly connected to strategic reasoning, participatory intelligence, and socio-organizational context. To this end, systematic literature reviews guided by Preferred Reporting Items for Systemic reviews and Meta-Analyses (PRISMA) were conducted, encompassing sixteen scenario-planning studies and sixteen MLOps lifecycle studies. Each corpus was meticulously mapped onto its respective reference architecture, a process that was undertaken to identify methodological gaps and complementary capabilities. The analysis demonstrates that foresight approaches have reached a conceptual maturity in the diagnostic, systemic, and scenario construction phases. However, these approaches remain underdeveloped in the domains of decision translation, implementation, and feedback. Conversely, machine learning operations (MLOps) architectures demonstrate proficiency in design, experimentation, deployment, and pipeline orchestration. Nevertheless, these architectures exhibit deficiencies in strategic framing and continuous governance. The proposed model GSF(M)² synthesizes these strengths by embedding foresight logic into adaptive ML workflows and integrating automated feedback loops into the foresight cycle, thereby creating a continuously learning decision ecosystem that recalibrates scenarios, model parameters, and strategic options in real time. The model enhances policymaking by supporting anticipatory analysis, evidence-based prioritization, and ongoing horizon scanning, while improving institutional responsiveness under volatile geopolitical and socio-technical conditions. Although conceptually validated and technically operationalized, the model requires empirical testing across diverse governance settings. Overall, GSF(M)² provides an original contribution by offering the first dual-core framework in which human-centered strategic anticipation and machine-driven adaptivity co-evolve within a unified, reflexive, and governance-ready architecture.