Artificial Intelligence

Shaping the Future of Innovation
1. Auflage 2025
ISBN: 978-3-11-158238-2
Verlag: De Gruyter

Buch, Englisch, 200 Seiten, Format (B × H): 155 mm x 230 mm

Reihe: AI Essentials for Leaders

Artificial Intelligence: Shaping the Future of Innovation provides a concise non-technical introduction to artificial intelligence as the key driver of innovation in the new economy. The book positions AI as a General Purpose Technology, vital for driving advancements across all sectors of business, government, and society.

Written for leaders, innovators, and decision-makers, this book bridges the gap between technical complexity and practical application, demystifying AI's core concepts while illuminating its catalytic impact across industries and organizations. Through a careful examination of innovation dynamics, computational foundations, and core AI principles, readers will gain the essential knowledge needed to both understand and effectively navigate the AI landscape.

This succinct guide makes technical concepts accessible—from algorithms and machine learning to deep learning and generative AI—giving leaders the clarity they need to engage confidently with AI technology.

Zielgruppe

Business and technical professionals in leadership positions; C-s

Autoren/Hrsg.

Essa, Alfred

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Management Strategisches Management
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein Soziale und ethische Aspekte der EDV

Über Autor(innen)

Alfred Essa has led pioneering AI and advanced analytics teams across academia and industry. As Simon Fellow at Carnegie Mellon University, VP of Analytics and R&D at McGraw Hill Education, and CIO at MIT's Sloan School of Management, he has consistently bridged the gap between theoretical innovation and practical implementation. His most recent book is Practical AI for Business Leaders, Product Managers, and Entrepreneurs. Essa has published more than 25 research papers spanning adaptive learning systems, risk analytics, and innovative approaches to data visualization using predictive model ensembles. He holds degrees from Haverford College and Yale University.