Sarath M

Traditional revenue management and marketing science models are increasingly throttled by Human-in-the-Loop bottlenecks and search space explosion where the exponential growth of variables (cross-elasticity, competitor tactics, and channels etc.) and the number of models outpaces manual oversight. Static ML workflows simply cannot scale when every scenario requires human-led iterative corrections. This session explores the agentic shift where classical ML models are wrapped within a reasoning loop. By treating models as "Tools" rather than final outputs, LLMs act as a central "brain" to decompose high-level goals into executable plans. These systems utilize long-term memory to retain historical context and self-correcting loops to autonomously refine model parameters or flag data anomalies. Unlike isolated model outputs that require human intervention to stay relevant, the autonomous system can perform diagnostic sub-routines, run multiple what-if and optimization scenarios, and automate modeling activities at scale.