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Leading Enterprise AI Programs

Optimize AI Teams for Value Creation

Medium: Buch
ISBN: 978-1-3986-2321-7
Verlag: Kogan Page Ltd
Erscheinungstermin: 03.02.2026
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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.

Produkteigenschaften

  • Artikelnummer: 9781398623217
  • Medium: Buch
  • ISBN: 978-1-3986-2321-7
  • Verlag: Kogan Page Ltd
  • Erscheinungstermin: 03.02.2026
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2026
  • Produktform: Gebunden
  • Seiten: 296
  • Format (B x H): 156 x 234 mm
  • Ausgabetyp: Kein, Unbekannt

Themen

Autoren/Hrsg.

Autoren

Patrick Bangert is an experienced technology executive who has led AI and machine learning teams and programs for over two decades. He was named a Top 10 Influential Leader in AI and Big Data by CIOLook Magazine and is currently VP and Chief of AI at Occidental Petroleum. He was previously the SVP of AI for Samsung SDS, the founder of algorithmica technologies and the SVP of Data, Analytics, AI at Searce. He lives in San Francisco, CA.

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