The last few years have been marked by major advances in hardware and algorithms for AI. They could pave the way for new application scenarios in economy, politics and society.
A better understanding of the fundamentals of AI seems to be urgently needed. What is needed are practicable procedures, sensible methods and simple instruments to manage the use of AI. Even a quick fact-check shows that the relationship between humans and intelligent machines has been discussed in depth academically for decades, but has received little practical attention. And this may be one reason why the conceptual world of AI today is quite confused. A look at AI from the perspective of companies and organisations could help to provide more clarity.
The use of AI in a business context serves to create value: it enhances the ability of an organization to make adequate decisions for any situation in a short period of time ("cognitive enterprise"). This goal has been pursued by every type of information technology - including AI. Based on this assumption, two results are developed in this guide: Firstly, a taxonomy of the automation of decision making and secondly, a stage model of the automation of decision making. Both perspectives are intended to help channel the bumpy discourse about AI and to complement it with a view that focuses on the actual purpose of AI: The focus is on the interaction of people with tools that simulate cognitive abilities and support decision making or that act autonomously.