Gerhard Kress is responsible for the digital portfolio and technology in Siemens Xcelerator, responsible for growing the digital business across all of Siemens. Before that he was for 8 years building up Digital Services for Rail in Siemens Mobility, developing and bringing the IoT platform “Railigent” to market, building up AI enabled offerings for rail customers across the globe. Prior he held roles with regards to digitalization in Siemens Corporate Research and in Corporate Strategy. His first role in Siemens was in IT solutions and services, managing systems and technologies for the global service desks and in for major IT outsourcing projects. Gerhard Kress started his career in McKinsey & Company. He holds a German diploma in Theoretical Physics and a Master of Arts in International Relations and European Studies. During his studies, Gerhard Kress worked for the student NGO “AEGEE-Europe” where he was President and Member of the European board.
Industrial environments have started undergoing digital transformation during the last decade. Now, many industrial organizations are implementing AI to further improve their businesses. However, many of them are struggling and realize that AI in the industrial environment is much more difficult. Identifying appropriate use cases with a sufficiently positive business cases as well as the operationalization of the use cases proves to be much more complex. Therefore, today we see only few real implementations of AI in a core industrial process and have not lived up to the expectations expressed in the last years. In this presentation we will learn what industrial AI is and which typologies of use cases it can address. We will discuss the special characteristics of industrial AI applications and will describe what specifically makes it so difficult to implement successfully. However, at the end we will discuss ways to resolve these problems and look at real-world examples of AI use for factory optimization and predictive maintenance. In these examples we will also see how use cases driven by industrial AI can be leveraged to create real tangible value and what role industrial AI can play in the near term future.