- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Companies everywhere are investing significant resources into building AI products and services that they hope will transform their business. To deliver real results, AI and data leaders need to build a strong business-oriented enterprise AI program.
Leading Enterprise AI Programs is an essential guide to establishing and directing an agile, ethical and business-focussed AI strategy and program for the whole enterprise. It provides leaders with guidance on operating a portfolio of use cases delivering effective and lasting business value. You will learn how to set up the best operating model for an organization's goals and targets, find and prioritize the right use cases for the business, and build a community of citizen data scientists. This book explains how AI can drive business success through focusing on users and interfaces, with clarity on the challenges to be solved as the primary drivers of value.
This book provides practical frameworks and actionable advice to help leaders set up a program and project portfolio, assess costs and benefits and embed AI into an organization's value generation ecosystem. With real-world examples, Leading Enterprise AI Programs helps leaders steer an enterprise AI team to lasting success.
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Information
Table of contents
- About the author
- Acknowledgements
- Introduction
- PART ONE The Optimal Structure of the Team for Success
- 1 Setting Up the Right Operating Model
- 2 Building a Community of Citizen Data Scientists
- 3 Identifying and Prioritizing Use Cases
- 4 Creating Common Product Platforms and Organizational Programs
- 5 Managing Risk and a Portfolio of Projects
- PART TWO Embedding the Enterprise AI Program into the Value Stream
- 6 Establishing a Project Charter and Implementing Design Thinking
- 7 Project Management and Agile Scrum
- 8 User Experience and Interfaces
- 9 Change Management and Adoption
- 10 Managing Costs and Rewards
- PART THREE Dependencies on Other Teams, Companies, and Society
- 11 Ensuring High-Quality Data
- 12 Conducting AI Responsibly and Ethically
- 13 Governing and Maintaining AI Models and Applications Over the Long Term
- 14 Managing Vendors and Encouraging Open Innovation
- 15 Lifelong Learning for the Team and Company
- Further Reading
- Index
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