Companies everywhere are investing significant resources into building AI projects and services that they hope will transform their business. In order to deliver real results, AI leaders 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 enterprise AI program. It provides leaders with guidance on how to operate an effective AI program that delivers clear and effective results. Covering how to find and prioritize the right use cases for the business, enable a community of practice in the business and setup the best operating model for an organization's goals and targets, this book explains how AI can drive business success through focussing on ethical, productive and responsible projects.
This book provides practical frameworks to help leaders setup 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 a core enterprise AI team for lasting business success.
Chapter - 00: Introduction
Section - ONE: The optimal enterprise AI structure
Chapter - 01: Setting up the right operational model
Chapter - 02: Building a community of practice to enable 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 enterprise AI projects into the value stream
Chapter - 01: Establishing a project charter and implementing design thinking
Chapter - 02: Project management and agile scrum
Chapter - 03: Ensuring positive end-user experiences and interfaces
Chapter - 04: Approaches to change management and adoption
Chapter - 05: Managing costs and creating rewards for the business
Section - THREE: Dependencies on other teams, companies and society
Chapter - 01: Conducing AI responsibly and ethically
Chapter - 02: Establish high quality data governance
Chapter - 03: Maintaining and governing AI models and application over the long-term
Chapter - 04: Managing vendors and encouraging open innovation
Chapter - 05: Lifelong learning for the team and the company
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