LLM-Driven Predictive Data Mesh Architecture for Unified Workforce and CRM Intelligence across SAP Success Factors, Oracle HCM, and Salesforce Platforms

Large enterprises increasingly depend on multiple cloud applications to manage workforce operations and customer engagement, yet these systems frequently operate as isolated data silos that restrict organizational visibility and delay strategic decision making. Human capital management platforms such as SAP SuccessFactors and Oracle HCM maintain detailed employee and organizational data, while customer relationship ecosystems such as Salesforce capture behavioral, transactional, and revenue signals. Traditional centralized data warehouses and extract transform load pipelines struggle to integrate these heterogeneous domains at scale and often fail to support real time intelligence or adaptive analytics. This paper proposes an LLM driven predictive data mesh architecture that combines domain oriented data ownership, distributed data products, large language model powered semantic harmonization, and embedded machine learning services to create a unified intelligence layer across workforce and customer systems. The framework introduces semantic metadata discovery, automated schema alignment, cross domain entity resolution, and predictive modeling pipelines to enable continuous forecasting of staffing demand, attrition risk, productivity trends, and customer impact metrics. The design emphasizes governance, privacy, lineage, and accountability to ensure responsible and auditable analytics. Experimental evaluation demonstrates improved query agility, reduced integration complexity, and enhanced predictive accuracy compared with centralized approaches. The proposed architecture establishes a scalable foundation for converged workforce and CRM intelligence and contributes a practical blueprint for next generation enterprise analytics platforms.