Competitive intelligence in the healthcare industry

Artificial intelligence (AI) can help executives and top managers to cope with strategic topics in nearly every company. One important strategic challenge is to monitor and analyze existing and new competitors from divergent business segments. Upcoming companies are entering markets dynamically and innovations diffuse rapidly. Competitive Intelligence collects data from external data sources to gain information about competitors. Structured, semi-structured, and unstructured data is exploited and transformed into knowledge by advanced network analysis.

The objective is to learn from descriptive and diagnostic results and build reliable predictive and prescriptive models. Besides the traditional machine learning (ML) concepts of supervised classification tasks, graph neural networks (GNN) are applied. Interactive simulation techniques are used to investigate what-if scenarios. In particular agent-based modeling (ABM) helps to go through scenarios that are probable to occur and to which there must follow a quick reaction. These simulations are merged with AI mechanisms to allow continuous improvement for the created predictive and prescriptive models. There are various opportunities to enhance ABM by AI: In all modeling phases (specification & formalization phase, modeling & verification phase, and calibration & validation phase) AI can be injected. This leads to more reliable, more realistic, and more specific outcomes.



  • Hauff, M. and Lurz, A. (2022): “Agent-Based Models Using Artificial Intelligence: A Literature Review”, PACIS 2022 Proceedings, 106.


  • Siemens Healthineers