Managing Artificial Intelligence
Description
Artificial intelligence (AI) is reshaping the way organizations operate, make decisions, and create value. As AI systems become increasingly embedded in business processes, the challenge lies not only in understanding the technology but in managing it effectively. This book provides a comprehensive and structured overview of the principles, strategies, and practices required to integrate AI into modern organizations.
It spans the full AI lifecycle, from foundational concepts and learning methods to the identification of use cases, the implementation of AI strategies and governance mechanisms, as well as the design and development of AI applications. It examines how to design meaningful human-AI interactions, navigate workforce transformation, and operate AI systems at scale. Ethical, legal, and social dimensions are addressed to ensure that AI adoption aligns with values such as transparency, fairness, and accountability.
The book is written for decision-makers, professionals, and students who are not only curious about AI - but who want to actively shape its role in organizations. Whether you're leading AI initiatives or preparing for the future of work, it provides essential guidance for leveraging AI in a strategic and impactful way. After all, AI hasn't (yet) figured out how to manage itself.
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Persons
Nils Urbach is Professor of Information Systems and Digital Business and Director of the Research Lab for Digital Innovation & Transformation at the Frankfurt University of Applied Sciences, Germany. He is Director at the FIM Research Center for Information Management, and the Branch Business & Information Systems Engineering of Fraunhofer FIT. Nils Urbach has been working in the fields of digital innovation and transformation for several years. His work has been published in several academic journals such as Information Systems Research (ISR), the Journal of Strategic Information Systems (JSIS), the Journal of Information Technology (JIT), MIS Quarterly Executive (MISQE), IEEE Transactions on Engineering Management (IEEE TEM), Information and Management (I&M), Business & Information Systems Engineering (BISE), and Electronic Markets (EM). He advises several companies on digitalization issues and regularly appears as a speaker on this topic.
Daniel Feulner received his M.Sc. in information system from the Otto-Friedrich University of Bamberg, Germany in 2023. He is an affiliated researcher at the Branch Business & Information Systems Engineering of the Fraunhofer FIT and FIM Research Center for Information Management at the University of Bayreuth. In his research, he aims to understand how Business Analytics and Artificial Intelligence can be effectively and responsibly utilized in organizations. His goal is to enable companies to drive innovation and achieve sustainable growth in a rapidly evolving technological landscape.
Content
Introduction to Managing Artificial Intelligence.- Technological Foundations of AI.- Foundations of Neural Networks.- Introduction to Generative AI.- Evaluating and Optimizing AI Models.- Application Potentials of AI Technologies.- Identifying, Designing and Evaluating AI Use Cases.- AI Strategizing and Readiness.- Governance and Management of AI.- Techno-Economic Decisions of AI.- Designing Human-AI Interactions.- AI Monitoring and Change Management.- Ethical, Legal and Social Implications of AI.