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.

45 signals and scenario uncertainty into a single enterprise-level ―diagnostic–foresight‖ system is still underdeveloped. In work [4], Nannini et al. review explainable AI in process mining and show that interpretability is becoming a practical adoption constraint for predictive monitoring and diagnostic analytics; the gap is that explainability is usually treated at the model level, while crisis governance requires explainability at the strategic level—how diagnostic explanations and foresight rationales jointly justify portfolio decisions, reallocations, and trade-offs in accountable ways. In work [5], Bucaioni et al. synthesize evidence on digital twins for essential services and conclude that digital twins can support operational efficiency, strategic planning, and crisis management, but real-world implementation remains limited due to cost, complexity, and immaturity; the gap for enterprise development research is the absence of standardized integration patterns linking digital twins to continuous diagnostic telemetry, scenario exploration, and decision triggers in a governed loop. In work [6], Zarour et al. consolidate MLOps best practices, challenges, and maturity models and emphasize that operationalizing ML requires lifecycle discipline (monitoring, governance, standardization); however, the gap is that MLOps is typically treated as a technical-operational domain, while strategy and foresight research still under-specifies how drift control, retraining cadence, and model governance should be embedded into enterprise diagnostic and foresight routines to preserve reliability under rapidly changing conditions. Overall, the literature indicates strong progress in data-driven foresight, diagnostic analytics, digital twin planning, and ML lifecycle operationalization, but it still lacks an integrated, governable model that specifies how enterprises can run diagnostics and foresight as one maintainable control loop that continuously converts evidence and uncertainty into decisions, resource reallocations, and capability reconfiguration. Digital technologies enabling crisis diagnostics and foresight. Methodologically, crisis diagnostics increasingly relies on (i) event-driven sensing across operations and external environments, (ii) process-level reconstruction and constraint identification, and (iii) predictive monitoring that estimates near-term