  In this article we describe the implementation of Artificial Intelligence
models in track reconstruction software for the CLAS12 detector at Jefferson
Lab. The Artificial Intelligence based approach resulted in improved track
reconstruction efficiency in high luminosity experimental conditions. The track
reconstruction efficiency increased by $10-12\%$ for single particle, and
statistics in multi-particle physics reactions increased by $15\%-35\%$
depending on the number of particles in the reaction. The implementation of
artificial intelligence in the workflow also resulted in a speedup of the
tracking by $35\%$.
