Proceedings of the International scientific and practical conference ―Synergy of Modern Science and Education‖ (February 2-4, 2026) / Publisher website: www.naukainfo.com. – New York, USA, 2026. - 324 p.

44 Introduction. Crisis conditions have become a persistent operating mode for enterprises, reducing the validity period of strategic assumptions and increasing the cost of slow reaction. When disruptions are frequent, the value of diagnostics shifts from retrospective performance reporting to near-real-time detection and explanation of deviations, while the value of foresight shifts from long-horizon ―prediction‖ to structured preparedness through scenarios and decision triggers. Digital technologies matter because they reduce latency between events and managerial interpretation, expand the feasible scope of monitoring (internal and external), and enable repeatable learning cycles; however, these benefits materialize only if analytics and governance are integrated into a coherent operating model rather than deployed as disconnected dashboards and one-off studies. Literature review. In work [1], Fraunhofer INT positions data-driven foresight as a research and implementation agenda that leverages large-scale datasets (e.g., publications, patents) and continuously evolving analytical capabilities (KATI) to strengthen technology foresight processes; the gap is that platform-level foresight capability is often discussed without a sufficiently explicit mechanism for converting foresight outputs into enterprise development governance (decision rights, thresholds, cadence, and portfolio rules) that ensures timely action under crises. In work [2], Marinković et al. systematize corporate foresight research and clarify its antecedents, tools, moderators, and outcomes; yet foresight remains frequently conceptualized as a parallel function, and the key gap is the translation layer that converts scenarios and weak signals into disciplined strategic choices such as option portfolios, trigger-based escalation, and measurable renewal routines under volatility. In work [3], Akhramovich et al. review how process mining is applied in Industry 4.0 and highlight benefits across multiple aspects of industrial transformation; the limitation for crisis contexts is that diagnostics tends to remain internally scoped (process behavior, conformance, performance), while the integration of exogenous weak