At a small start-up, the entire development team is often able to fit around a large desk. Here, they are able to come up with ideas together, quickly source an expert for a specific topic or pick up on the insights of their colleagues. This is all very simple when employees know each other and no one is far away. However, the situation is totally different at larger companies. Employees work at different locations, in different teams and on different issues. This makes it difficult for each individual to keep up-to-date and maintain an overview of developments at the company. This can result in employees working on similar topics at the same time without each other knowing, time-intensive research processes or unsuitable cooperation partners.

AI applications perform their best in complex situations in which information is scattered across an entire company, different data sources are present, and structured and unstructured data exists side by side. The system first searches through different sources such as document collections, article archives, websites, publications and chats. Processes such as text analysis, information extraction and topic identification ensure that the system automatically identifies the most important topics.

This means that overlaps are discovered and similarities between topics, or even teams that are working on similar topics, are identified.

This type of AI solution creates a common knowledge database that experts can rely on. This reduces time spent doing research and leads to better results, approaches for new topics or cooperation can be identified more quickly and parallel work on similar issues is prevented.