The recent confluence of Industry 5.0 aspirations, generative artificial intelligence (AI), advanced data analytics, and heightened cybersecurity demands has created a complex landscape for organizations seeking sustainable digital transformation. This article advances a comprehensive framework for integrating generative AI, business intelligence, and robust security practices to support ethical, efficient, and resilient Industry 5.0 ecosystems. Drawing on extant literature on AI-driven manufacturing, supply chain optimization, healthcare and cybersecurity, human–AI collaboration, and large language model (LLM) operations (LLMOps), this study synthesizes cross-domain insights to articulate key enablers, systemic risks, and mitigation strategies. Through a structured qualitative methodology combining integrative literature review and theoretical synthesis, the paper identifies core dimensions—technological capability, data governance & privacy, cybersecurity, human–AI collaboration, continuous learning & observability—and illustrates their interplay. The resulting framework offers a roadmap for stakeholders to implement AI-enabled transformations that balance innovation, sustainability, and security. Limitations of current knowledge, such as inadequate empirical grounding on long-term human–AI interplay and privacy-preserving generative systems, are discussed, along with future research directions and practical recommendations for governance, design, and deployment of Industry 5.0 systems. The proposed framework aims to inform academics, industry practitioners, and policymakers seeking to operationalize secure, sustainable, and ethical AI-enhanced operations.

Industry 5.0, Generative AI, Business Intelligence, Cybersecurity

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