Artificial Intelligence

The use of artificial intelligence methods is the key to interpreting digital behaviors. In particular, we rely on the use of ideal machine learning algorithms to assess complex relationships between behaviors and their causes.

When we talk about AI (Artificial Intelligence) or AI (Artificial Intelligence), it is not the clever, omniscient consciousness known from film and literature that is meant, but methods and methods that imitate human abilities – supplemented by perfect memory and almost infinite computing capacity.

The use of artificial intelligence makes sense:

  • … when things need to be done that are recurrent and where people tend to be bored (and mistaken).
  • … when things need to be done where decisions and recommendations are based on amounts of data where ordinary people can no longer make objective decisions.
  • … when things need to be done or things need to be identified that require efficient and fast learning.

The main differences in the use of existing AI technologies can be roughly divided into two classes:

  • Cognitive Services: Use of services that have already undergone massive learning and focus on mimicing human skills. The aim is to convert language into text, to recognize the intentions of texts, to recognize emotions in texts or faces, or to recognize content in images.
  • Machine learning: Use of existing algorithms and models (including neural networks) that can be created and used as “artificial intelligence” for individual problems after experiencing individual “learning”.

We currently use AI technologies, among others:

  • For the forecast of ideal measures in our consulting mandates in the area of“Digital Adoption” and our success measurement tool, the “AdoptionBoard
  • For the chatbots that actively support employees in digitization (as part of Natural Language Processing)
  • For complex analysis of communication streams

Neural networks are similar in structure to the synapses of a brain – but are only one of many model variants from machine learning.
The basis for each AI is good and clean data, which sometimes results from complex analyses – as here a graph analysis.