Leveraging LLMs for Real-Time CPQ Optimization and Enterprise Decision Insights

Large Language Models (LLMs) are increasingly enhancing enterprise decision systems through semantic reasoning, adaptive configuration, and contextualized automation. This review examines the integration of LLMs into real-time Configure–Price–Quote (CPQ) optimization systems to improve enterprise decision intelligence. Although current CPQ systems can be effective, they often lack the analytical depth needed to generate insights that inform configuration and pricing policies. The designed hybrid architecture will utilize retrieval-augmented generation and constraint-based pricing optimization and validation. Conceptual evaluation suggests that the proposed hybrid architecture may improve the assessment of existing configurations and pricing schemes, using both past and current products or service utilization in suggesting dynamic and usage based schemes that provide more value to the customer. This paper outlines the major challenges, future research directions, and potential contributions of hybrid reasoning systems to more effective real-time enterprise CPQ decision-making.