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Process models in AI projects

Not just by collecting data, but only through a value-adding configuration, new knowledge can be extracted to help you achieve your business goals.

Digitization affects us all. Professionally and privately. The value of data is undisputed and can be considerably increased through the use of artificial intelligence (AI) and here especially through machine learning. But it takes much more than this insight to evaluate the AI potential for your company and to use it profitably. Not only by collecting data, but only by a value-adding configuration new knowledge can be extracted, which supports you in achieving your business goals. Nevertheless, it often turns out during project implementation that the hoped-for results cannot be achieved due to the special features and the multi-layered complexity of AI projects. Critical success factors are, for example, a clear customer and value orientation, the choice of suitable data science methods, an iterative approach, cross-domain collaboration and the establishment of AI-specific skills in the company. We provide you with advice on procedures and activities and work with you to create an individual needs analysis.

Author: Tobias Fahse

Date: 28. July 2020