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.

47 Research propositions (for empirical testing). P1: Under high crisis intensity, enterprises with mature event-driven sensing and process-mining diagnostics achieve shorter time-to-detect and time-to-explain deviations, which mediates higher recovery speed and cash-flow stability. P2: The effectiveness of corporate foresight under turbulence is increased when scenarios are translated into measurable triggers monitored through continuous data pipelines, compared to foresight practices that remain narrative and workshop-based. P3: The impact of AI-enabled diagnostics and forecasting on strategic decision quality is moderated by explainability and lifecycle governance (MLOps maturity); without these, the organization experiences lower trust, higher model drift risk, and weaker adoption under crisis pressure. P4: Digital twin adoption contributes to crisis-informed strategic planning primarily when integrated into the diagnostic–foresight loop (telemetry → simulation → trigger design → governance gates), rather than when implemented as a standalone simulation asset. Conclusions. Digital technologies are redefining enterprise diagnostics and foresight in crisis conditions by enabling continuous sensing, richer explanatory diagnostics, scalable scanning of weak signals, scenario-to-trigger operationalization, and maintainable predictive capabilities through lifecycle governance. The reviewed literature demonstrates substantial advances in the enabling components—data-driven foresight platforms, process mining, explainable AI, digital twins, and MLOps—yet remains insufficiently integrated at the level of enterprise development governance. The proposed Crisis SenseOps concept addresses this gap by framing diagnostics and foresight as one operational control loop with explicit interfaces between data, explanation, scenarios, triggers, decision gates, and learning. This provides a structured foundation for future empirical work on performance effects, governance designs, and robustness of diagnostic–foresight systems under global crises.