Proceedings of the International scientific and practical conference ―Modern Science: Challenges and Perspectives‖ (February 9-11, 2026) / Publisher website: www.naukainfo.com. - London, United Kingdom, 2026. - 121 p.

9 but a prerequisite for trustworthy diagnostics and foresight. Explainability further becomes a governance requirement when diagnostic and predictive systems inform high-stakes resource reallocations; consequently, explainable analytics and process- mining-based interpretation are increasingly necessary to ensure that the organization can justify decisions, validate causality assumptions, and learn systematically rather than merely reacting to opaque model outputs. Innovative contribution: ForesightOps as an operational integration model To address the identified integration gap, this paper proposes ForesightOps as a conceptual operating model that unifies diagnostics and foresight into a measurable enterprise development loop. ForesightOps specifies five tightly coupled components: continuous sensing and diagnostic explanation (integrating performance, process, risk, and external signals); structured foresight (scenario generation and weak-signal qualification); scenario-to-trigger translation (converting narratives into indicators, thresholds, and monitoring rules); portfolio decision gates (pre-defined go/pivot/stop logic linked to staged commitments and decision rights); and learning and renewal (post-decision evaluation, assumption updates, and indicator/model recalibration). The novelty is not the individual elements, which exist across fragmented literature, but the explicit ―interface design‖ between them: diagnostics produces explainable signals that feed foresight; foresight outputs are constrained into triggers that can be operationally monitored; and governance gates ensure disciplined action and organizational learning. In this view, the strategic value of digital infrastructures is defined by their ability to sustain this loop at scale with transparency and maintainability, rather than by isolated analytics success. REFERENCES: 1. Fraunhofer INT. Data Driven Foresight / KATI. Fraunhofer Institute for Technological Trend Analysis, 2024. URL: https://www.fkie.fraunhofer.de/en/departments/int.html (дата звернення: 31.01.2026).