Future Trends of AI, Big Data and Digital Business Transformation

Abstract

artficial intelligence (AI) and big data analytics have moved from specialised technical functions to the core of how organizations compete, create value and organise work. This concluding chapter looks ahead. It examines the technological, organizational, regulatory and societal trends that are likely to shape the next phase of AI- and data-driven business, and it considers what these trends imply for managers, employees, policymakers and researchers. The chapter begins by situating current developments within the historical evolution of business computing and analytics. It then analyses major technology trends, including generative AI and foundation models, agentic AI systems, edge and real-time analytics, modern data architectures such as the lakehouse and data mesh, privacy-enhancing technologies, digital twins, and longer-horizon developments such as quantum computing. The chapter next develops a framework for digital business transformation grounded in dynamic capabilities theory, and it examines the changing nature of work, including the balance between automation and augmentation and the emerging evidence on generative AI and productivity. Governance and responsibility are treated as central rather than peripheral themes, with discussion of explainability, fairness, sustainability and the evolving regulatory landscape, including the European Union Artificial Intelligence Act, the NIST AI Risk Management Framework and India's Digital Personal Data Protection Act, 2023. Special attention is given to India's digital public infrastructure and national AI initiatives as a distinctive context for transformation. The chapter proposes a phased roadmap for organizations, identifies the capabilities and skills that future managers will need, and outlines a research agenda. It closes with a synthesis of the themes developed throughout the book.