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Full Description
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.
Contents
Chapter - 00: Introduction
Section - ONE: The Optimal Structure of the Team for Success
Chapter - 01: Setting up the right operational model
Chapter - 02: Building a Community of Citizen Data Scientists
Chapter - 03: Identifying and prioritizing uses cases
Chapter - 04: Creating common project platforms and organizational programs
Chapter - 05: Managing risk and a portfolio of projects
Section - TWO: Embedding the enterprise AI program into the value stream
Chapter - 06: Establishing a project charter and implementing design thinking
Chapter - 07: Project management and agile scrum
Chapter - 08: User experiences and interfaces
Chapter - 09: Change management and adoption
Chapter - 10: Managing costs and rewards
Section - THREE: Dependencies on other teams, companies and society
Chapter - 11: Ensuring high quality data
Chapter - 12: Conducting AI responsibly and ethically
Chapter - 13: Governing and maintaining AI models and applications over the long-term
Chapter - 14: Managing vendors and encouraging open innovation
Chapter - 15: Lifelong learning for the team and company
Chapter - 16: Further Reading
