The High-Luminosity LHC (HL-LHC) will open an unprecedented window on the weak-scale nature of the universe, providing high-precision measurements of the standard model as well as searches for new physics beyond the standard model. Such precision measurements and searches require information-rich datasets with statistical power that matches the high luminosity provided by the Phase-2 upgrade of the LHC. Efficiently collecting those datasets will be a challenging task, given the harsh environment of 200 simultaneous proton-proton interactions per HL-LHC bunch crossing. For this purpose, CMS is designing an efficient data-processing hardware trigger (Level-1) that will include tracking information and high-granularity calorimeter information. Trigger data analysis will be performed through sophisticated algorithms such as particle flow reconstruction, including the widespread use of Machine Learning. The current conceptual system design is expected to take full benefit of advances in FPGA and link technologies over the coming years, providing a high-performance, low-latency computing platform for large throughput and sophisticated data correlation across diverse sources. The expected impact on the physics reach of the experiment will be summarized in these proceedings and illustrated with selected benchmark channels.