Artificial intelligence – From Strategy to Practice
Summary
This means that complex systems and processes no longer follow a limited set of deterministic rules but, among other things, “self-learn” to adapt to different situations. This article builds on the previous in terms of content and is intended to clarify the question, particularly for IT, digital and data managers, of how ambitious AI projects can be implemented in practice beyond the strategy. Nevertheless, defined expert teams should also be able to make decisions independently within the scope of their capabilities and authority, which are in the best interests of the project and do not require separate clarification: A brief overview of relevant AI use cases in the industry The majority of companies today rely on AI functionalities aimed at classic process optimization, for example by networking equipment in production. For this purpose, the view of users and customers should be considered in addition to a project position in order to be able to formulate requirements of machine learning applications in a practical way. In addition, AI projects are running behind expectations that are too high – continuing with a lack of specialists, silo thinking, and even rejection within internal structures, which also lead to possible failure.