The accelerating digitization of organizational systems, combined with the proliferation of artificial intelligence across industrial, environmental, and governance domains, has profoundly reshaped how contemporary enterprises conceptualize, manage, and mitigate risk. Change management, once grounded primarily in procedural controls and human judgment, has increasingly become a data driven, algorithmically mediated domain in which predictive systems continuously evaluate the potential consequences of organizational transformation. This article develops a comprehensive theoretical and empirical synthesis of predictive artificial intelligence as a core infrastructure for risk governed change management and organizational resilience. Drawing on a wide range of interdisciplinary scholarship, including disaster resilience theory, intelligent process automation, predictive maintenance, and digital supply chain surveillance, the study situates predictive risk scoring within broader socio technical systems of governance and control. Central to this analysis is the concept of Change Advisory Boards as epistemic and regulatory institutions that must now operate within environments of algorithmic foresight and automated decision support, as demonstrated by recent developments in predictive risk scoring for change management (Varanasi, 2025).

The article advances three interconnected arguments. First, predictive artificial intelligence fundamentally redefines organizational risk from a retrospective assessment of failure to a prospective calculus of probabilistic futures, thereby transforming how change initiatives are authorized, sequenced, and monitored. Second, the integration of predictive risk scoring into governance structures such as Change Advisory Boards generates new forms of institutional rationality that blend human expertise with machine based inference, producing both enhanced resilience and novel forms of opacity and ethical risk. Third, these systems must be understood within a larger ecology of digital infrastructures, including Internet of Things enabled environments, intelligent manufacturing, smart cities, and climate adaptive systems, all of which contribute streams of data that feed algorithmic risk engines.

Methodologically, the study employs a qualitative integrative research design grounded in interpretive synthesis of the provided scholarly corpus. Rather than treating predictive models as purely technical artifacts, the article analyzes them as socio technical constructs embedded in regulatory regimes, organizational cultures, and epistemological assumptions about risk and control. The results demonstrate that predictive artificial intelligence enhances the anticipatory capacity of organizations, allowing them to simulate the cascading effects of change across complex systems, but also introduces challenges related to transparency, accountability, and institutional trust.

The discussion situates these findings within broader debates on the governance of artificial intelligence, the limitations of existing risk management standards, and the future of human centric decision making in algorithmically mediated organizations. By articulating a theoretically grounded framework for predictive AI enabled change management, this article contributes to the emerging field of digital risk governance and provides a foundation for future empirical and normative research on resilient, ethically governed intelligent enterprises.

Predictive artificial intelligence, Change Advisory Boards, risk governance, organizational resilience

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