The AI ecosystem is flooded with demos that fail in production due to cost, scalability, or reliability gaps. This challenge is amplified in FMCG, where millions of interconnected outlets, SKUs, and supply nodes offer opportunities for AI enablement at scale. At Bizom, India’s top Supply chain automation provider, we’ve engineered a hybrid AI framework combining generative and predictive methods. Generative models create contextual training data, while lightweight predictive models—like XGBoost—enable scalable, low-cost deployment. This approach transforms fragile prototypes into production-grade intelligence for the FMCG sector. In this session, I will share field-tested use cases and technical insights on designing hybrid AI systems that turn the promise of AI into measurable business performance at scale.