Managing AI in the Enterprise

Delivering AI projects and building an AI organization are two big challenges for enterprises. They determine whether companies succeed or fail in establishing AI and integrating AI into their digital transformation. This book addresses both challenges by bringing together organizational and service design concepts, project management, and testing and quality assurance. It covers crucial, often-overlooked topics such as MLOps, IT risk, security and compliance, and AI ethics. In particular, the book shows how to shape AI projects and the capabilities of an AI line organization in an enterprise. It elaborates critical deliverables and milestones, helping you turn your vision into a corporate reality by efficiently managing and setting goals for data scientists, data engineers, and other IT specialists.

For those new to AI or AI in an enterprise setting you will find this book a systematic introduction to the field. You will get the necessary know-how to collaborate with and lead AI specialists and guide them to success. Time-pressured readers will benefit from self-contained sections explaining key topics and providing illustrations for fostering discussions in their next team, project, or management meeting. Reading this book helps you to better sell the business benefits from your AI initiatives and build your skills around scoping and delivering AI projects. You will be better able to work through critical aspects such as quality assurance, security, and ethics when building AI solutions in your organization.What You Will Learn

• Clarify the benefits of your AI initiatives and sell them to senior managers
• Scope and manage AI projects in your organization
• Set up quality assurance and testing for AI models and their integration in complex software solutions
• Shape and manage an AI delivery organization, thereby mastering ML Ops
• Understand and formulate requirements for the underlying data management infrastructure
• Handle AI-related IT security, compliance, and risk topics and understand relevant AI ethics aspects

Table of Contents

Chapter 1: Why Organizations Invest in Al

Chapter 2: Structuring and Delivering Al Projects

Chapter 3: Quality Assurance in and for Al

Chapter 4: Ethics, Regulations, and Explainability

Chapter 5: Building an Al Delivery Organization

Chapter 6: Al and Data Management Architectures

Chapter 7: Securing and Protecting Al Environments

Chapter 8: Looking Forward

About the Author

Klaus Haller is a senior IT architect and IT project manager with more than 15 years of experience in the IT industry. Originally from Germany, he has called Zurich, Switzerland home for many years. He currently works as a senior security architect for a global insurance company, focusing on protecting public cloud infrastructures and data management and AI environments. Klaus is passionate about designing complex solutions that fit into corporate application landscapes. He understands the interplay between technology, operations, engineering, and the business from his previous experience in various roles such as software engineer, project and product manager, business analyst, process engineer, and solutions architect. His expertise includes core banking systems and credit applications, databases, data analytics and artificial intelligence, data migration, public cloud, IT security, and IT risk management. He loves the outdoors and enjoys writing for magazines and online blogs and speaking at conferences and seminars.