AI has made significant progress and is being used in many commercial applications today. The bulk of AI adoption so far has been in B2C consumer applications, but B2B applications offer an equally exciting opportunity for AI. Developing AI for B2B applications, however, comes with its own constraints and challenges. In particular, the availability of high-quality data and a solid understanding of the business process are crucial for success. This talk will give an overview about developing B2B AI applications and how to avoid pitfalls on the way of productizing AI models. As an example, we will have a look at natural language processing, an active sub-field of AI research which can be applied to many B2B use cases.
Daniel Dahlmeier is Chief Data Scientist at the SAP Artificial Intelligence team. In his role, he is responsible for the data science strategy and orchestrating data science teams and initiatives across SAP. During his professional career, he has been involved in building AI products, from research and early-stage innovation to productization and operating AI at scale. Daniel’s academic research background lies in natural language processing. He holds a PhD from the National University of Singapore, an executive MBA from Mannheim Business School and a diploma in computer science from the Karlsruhe Institute of Technology (KIT). SAP’s technologies for machine learning, the Internet of Things, and advanced analysis methods help our customers on their way of becoming intelligent companies. Artificial intelligence is already included in the core of SAP's corporate software. SAP's strategy is to bring AI into applications and business processes for the benefit of its customers and partners.