Applied Artificial Intelligence for Business Development

Applied Artificial Intelligence for Business Development

Matthias Lederer (ISM International School of Management, Germany) and Werner Schmidt (Ingolstadt Technical University of Applied Science, Germany)
Projected Release Date: November, 2019|Copyright: © 2020 |Pages: 300
ISBN13: 9781799811206|ISBN10: 1799811204|EISBN13: 9781799811220|DOI: 10.4018/978-1-7998-1120-6


The use of artificial intelligence (AI) is basically possible at two levels in business development: for the automation of activities and for innovative business models. Approaches at the first level aim to increase efficiency in existing processes. For example, neural networks in customer dialogue increase the productivity in existing procedures. Applications on the second level that are not incremental but potentially radical or disruptive, such as intelligent systems, are being used in devices (e.g., vehicles) to foster new demand or to further develop a company’s portfolio. These techniques and tools based on AI help to foster smarter market and portfolio decisions and optimize entire value chains.

Applied Artificial Intelligence for Business Development is a collection of innovative research on applying AI techniques, methods, and tools within businesses and includes examples of how AI is embedded in products and services. While featuring topics including organizational learning, consumer behavior, and business intelligence, this book is ideally designed for IT specialists, IT consultants, managers, executives, business administrators, consultants, industry leaders, entrepreneurs, start-ups, academicians, researchers, and students interested in current research on advanced business development through smart technologies.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Business Intelligence
  • Consumer Behavior
  • Consumer Experience
  • Data Collection
  • Deep Learning
  • Digital Banking
  • Expert Systems
  • Immature Technology
  • Knowledge Management
  • Organizational Learning

Table of Contents and List of Contributors

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