Artificial intelligence (AI) in its various forms –– machine learning, chatbots, 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.
With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products.
There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI.
This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes.
AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
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
- 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 –