國立虎尾科技大學 |

Enterprise AI

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Enterprise AI/ edited by Shazia Sadiq.
其他作者:	Sadiq, Shazia.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	x, 331 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Business applications. -
電子資源:	https://doi.org/10.1007/978-3-032-01940-0
ISBN:	9783032019400

Enterprise AI
Enterprise AI[electronic resource] /edited by Shazia Sadiq. - Cham :Springer Nature Switzerland :2025. - x, 331 p. :ill. (some col.), digital ;24 cm.

PART 1: Scalable and Sustainable Practices for Enterprise AI -- 1. Resource-efficient Model Deployment for Enterprise AI -- 2. Dataset Distillation for Enterprise Applications -- 3. Federated Learning for Enterprise AI -- PART 2: Safe and Responsible Enterprise AI -- 4. Data Quality for Enterprise AI -- 5. Data Privacy in Enterprise AI -- 6. MAGIX: A Unified Framework for the Use of XAI in Enterprises -- 7. The Enterprising and Elusive Prospects of Human-AI Collaboration -- PART 3: Value Creation with Enterprise AI -- 8. Creating Value from Enterprise AI -- 9. The Rise of Enterprise Autonomization -- 10. Trust in AI: Evidence of Trust-supporting Mechanisms from 17 Countries -- 11. Insights into AI's Influence on Enterprise Software and Systems: Lessons from Varied Contexts.

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.

ISBN: 9783032019400

Standard No.: 10.1007/978-3-032-01940-0doiSubjects--Topical Terms:

1252499
Artificial intelligence
--Business applications.

LC Class. No.: HD45

Dewey Class. No.: 650.028563

Enterprise AI
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520 $a 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.
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