Scientific and technical monitoring - Leveraging generative artificial intelligence

Scientific and technical monitoring faces a paradox today. Despite an overabundance of information, making use of it is becoming increasingly complex. In fact, each year, more than 3 million scientific articles, an equal number of preprints, and nearly 3 million patent applications are published. Furthermore, the time between academic discoveries and their industrial application has shrunk from several years in some fields to less than two years in others.

Faced with this situation, traditional methods are reaching their limits. Automated alerts generate significant noise, and the analysis phase remains largely manual.

Generative AI, driven by large language models, is revolutionizing this landscape by automating low-value-added tasks and expanding analytical capabilities.

This article provides an overview of how these technologies are used at each stage of the intelligence cycle. Through various use cases, it highlights what is currently working as well as the limitations. It also explores the challenges posed by their deployment, particularly regarding reliability, costs, and environmental impact.