Radical Change in Industry
Artificial Intelligence and self-learning systems process data into knowledge, thus making it possible to utilize the burgeoning volumes of data in products and services. This will enable the development of entirely new business models, for example for the digital platforms on which a large number of businesses of various sizes and industries will cooperate in future.
Whether it’s a motor vehicle, fitness bracelet or wind turbine, nearly every object nowadays can be connected and generate a constant stream of data when they are in operation or use. An analysis of these data makes it possible to transform these objects into “smart” products and services. Data about the condition of systems and their operations can be implemented directly to enhance existing products and services. Interfaces enable these systems to make data available to other products and applications or vice versa.
Self-learning systems and the methods of Artificial Intelligence can be used to extract knowledge from vast amounts of complex data and to generate new knowledge which provides the basis for adjusted or new, innovative business models. This will allow existing products and services to be further developed and entirely new data-driven and AI-based business models to emerge.
New business models will also generate new forms of networked cooperation among businesses. These businesses will be the suppliers of products and services for end users but may also be the consumers of data and knowledge from other businesses. Yet another possibility is for these businesses to offer their own AI-enhanced data for value creation in other businesses.
The focus of these new business models is no longer on the businesses with their products and services but increasingly on users and their personal needs and preferences. Users can receive – on demand – products and (Internet-based) services which adapt to their individual requirements. One example of this are mobility apps which can optimize travel routes taking into account the user’s preference for a certain mode of transport or route, identify the quickest and least expensive transport options among various mobility providers, and sell the appropriate ticket from any of the providers. Self-learning systems also enable the production of entirely new products such as the automatic configuration and production of tailor-made goods in real time for individual customers.
The great volumes of many different kinds of data on which these business models are based come from different sources. They are compiled, evaluated and made utilizable on digital platforms. Methods of machine learning can be used to identify patterns in the data and make predictions on their basis, for example in the predictive maintenance of machinery. In many cases a single business is not in a position to implement these data-driven business models, which is why digital platforms are being established which bring together businesses from various industries and of varying sizes (digital ecosystems).
These issues are the focus of working group 4 headed by Ms Susanne Boll (University Oldenburg/OFFIS) and Mr Wolfgang Faisst (SAP) of the Plattform Lernende Systeme.