Springer Enterprise AI

This book provides perspectives and deliberations on the barriers and opportunities for Enterprise AI, as well as a range of state-of-the-art approaches that can facilitate AI adoption more widely. It aims to provide a comprehensive and authoritative resource on Enterprise AI so that students, researchers and practitioners have the benefit of accessing the full scope of the problems and approaches in one place, relating to the critical aspects of Enterprise AI projects. The contributions by experts in multiple socio-technical disciplines have been accordingly structured in three parts: First, Scalable and Sustainable Practices for Enterprise AI explores emerging strategies that enable organizations to scale AI systems sustainably by maximizing performance while minimizing resource consumption. It offers a deep dive into three complementary approaches that address this challenge from different angles: data distillation, federated learning, and resource-efficient deployment. Next, Safe and Responsible Enterprise AI addresses the critical aspects of AI safety in the enterprise context. The four chapters provide a comprehensive set of resources for individuals and enterprises seeking to implement AI systems that are not only powerful but also principled. By addressing data quality, privacy, explainability, and human-AI collaboration, this part lays the groundwork for building AI systems that are safe, transparent, and aligned with human and organizational values. Eventually, Value Creation with Enterprise AI offers four chapters providing a multidimensional view of value creation with AI, that balances innovation with responsibility, and efficiency with trust. They provide a roadmap for enterprises seeking to harness AI not just as a tool for automation, but as a catalyst for meaningful, sustainable transformation.