图书简介
Artificial Intelligence in its various forms – machine learning, chat bots, robots, agents, etc. – is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets.Increasing business process and workflow maturity coupled with recent trends in cloud computing, datafication, IoT, cyber security, and advanced analytics, there is appreciation that the challenges of tomorrow cannot be solely addressed with today’s people, process, and products. A recent Gartner article supports our contention that AI is essential because it "promises to solve problems organizations could not before because it delivers benefits that no humans could legitimately perform."There is still considerable mystery, hype and fear about AI. A considerable amount of current discourse focus on a dystopian future – with adversity impacted individuals/employees/society. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, current state of business/technology, or the primarily augmentative nature of tomorrow’s AI.Our book demystifies AI for the enterprise. Our journey takes the reader from the basics (definitions, state of the art, etc.) to a multi-industry journey, and concludes with validated expert advice on everything an organization and its people must do to succeed. Along the way, we also debunk myths, provide practical pointers, and include best practices with appropriate vignettes. In summary, AI brings to enterprises capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficient, effectively, and colla
Chapter 1: AI Strategy for the Executive Chapter 2: Learning Algorithms, Machine/Deep Learning, and Applied AI - A Conceptual Framework Chapter 3: AI for Supply Chain Management Chapter 4: HR and Talent Management Chapter 5: Customer Experience Management Chapter 6: Financial Services Chapter 7: Artificial Intelligence in Retail Chapter 8: Visualization Chapter 9: Solution Architectures Chapter 10: AI and Corporate Social Responsibility Chapter 11: Future of Enterprise AI Appendix: Banking Case Study #1: Get More Value from Your Banking Data - How to Turn Your Analytics Team into a Profit Centre Banking Case Study #2: AI in Financial Services - WeBank Practices Retail Case Study: 7-Eleven and Cashierless Stores Supply Chain Case Study: How Orchestrated Intelligence is Utilising Artificial Intelligence to model a Transformation in Supply Chain Performance FMCG Case Study: Paper Quality at Georgia-Pacific Healthcare Case Study: GE Healthcare: 1st FDA Clearance for an AI-enabled X-ray Devices -
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