📄 Abstract
Business Continuity Management (BCM) has long been concerned with keeping critical activities running when unexpected events interrupt normal operations. Traditionally, organizations have relied on business impact analysis, continuity strategies, documented response procedures, and recovery plans. These practices remain essential, but the disruption environment has changed. Cyber incidents, technology failures, supply-chain interruptions, extreme weather, geopolitical uncertainty, and workforce shortages can develop quickly and interact in ways that are difficult to anticipate through periodic reviews alone. Organizations increasingly need BCM arrangements that can identify warning signs early and support decisions before a disruption becomes a major operational problem. This article develops an integrated framework that connects AI-enabled BCM capability with employee engagement, human–AI collaboration, organizational resilience, and sustainable organizational performance. AI governance is treated as an important condition that can influence whether these technological capabilities produce useful and trustworthy outcomes. The framework draws mainly on Dynamic Capability Theory and Socio-Technical Systems Theory. Recent research on AI and organizational resilience provides encouraging evidence, while emerging work on human–AI collaboration highlights the importance of employee acceptance, skills, and judgment. The article translates these ideas into research objectives, questions, hypotheses, measurement dimensions, and a PLS-SEM research design that can be tested in a subsequent empirical study.
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📚 How to Cite:
Mr. A. Mohammed Asif, Dr. L. Rathakrishnan , ARTIFICIAL INTELLIGENCE-ENABLED BUSINESS CONTINUITY MANAGEMENT: AI GOVERNANCE, EMPLOYEE ENGAGEMENT, AND SUSTAINABLE ORGANIZATIONAL PERFORMANCE , Volume 12 , Issue 9, September 2026, EPRA International Journal of Multidisciplinary Research (IJMR) , Pages: 244 - 253 , DOI: https://doi.org/10.36713/epra28994