Industrial companies are increasingly launching promising AI projects. But after the proof-of-concept, the process falters: important data is missing, the results are disappointing, or the concrete use case is not so clear after all. What you can do about it.
Tags Innovation
10 posts
Why the implementation of innovative processes and new platforms falters and how to solve the dilemma.
Microsoft Ignite conference will kick-off in a few days. Here‘s my personal list of expectations, narrowed down to productivity topics and business applications for operations.
Modern sensors and ML algorithms can provide valuable services to ensure that people in old age are adequately cared for in the future.
Blockchain technologies offer businesses tremendous potential. But many decision makers are faced with the problem of finding the right use case. In the field, a four-step approach has proven its effectiveness.
By involving Mixed Reality, an expert from a far distance quickly and easily explains to the employee on site what has to be done to bring production back on track.
Digital Twins of single rooms up to whole companies help to develop models for organizational processes and to increase their efficiency.
For applications in the areas of Artificial Intelligence or Machine Learning, the discussion quickly revolves around Chatbots. However, there are a few things that need to be taken into account during the introduction, such as the involvement of employees in the change process and the shift in their range of tasks.
Das IoT-Umfeld ist von einer hohen Entwicklungsgeschwindigkeit geprägt. Wie können Unternehmen auf dem Weg zum Fortschritt die richtigen Themen auswählen und in Projekte überführen? Ob Hackathon oder Design Thinking – es gibt verschiedene Möglichkeiten für das moderne Innovation Management.
Machine learning methods can be used to optimize production processes. A practical example shows how machine learning contributes to the detection of pseudo defects in quality assurance.
