Manufacturing enterprise resource planning (ERP) systems are experiencing a paradigm shift, shifting to the more dynamic rule-less architecture that can make adaptive decisions using AI. This article presents a two-intelligence model that combines Generative AI to address creative problem-solving and knowledge combining with Genetic Algorithm to make an ideal decision-making in multifaceted engineering products. Utilizing generative models in the proposed architecture, the overall production planning, design variants and predictive maintenance solutions can be automated with genetic intelligence narrowing down scheduling, resource allocation and supply chain structures. An experimental validation of an in-simulated smart manufacturing environment reveals improvements of lead timer (17%), use of resources (12%) and accuracy of forecasting (21%) over conventional ERP systems. Nonetheless, the biggest issues pertain to computational scaling, explainability of AI, and compatibility with the legacy systems. The next major pursuant is going to be on the hybrid optimization systems, targeted domain generative models, and energy-efficient AI operations to broaden the industrial adoptions.