Authors: Balraj Dhillon
Abstract: Salesforce CRM has become a critical platform for enterprises aiming to enhance customer engagement, streamline business processes, and generate data-driven insights. However, system performance often depends on the efficiency of SOQL (Salesforce Object Query Language) queries, which directly affect data retrieval, reporting, and analytics. This review examines the role of SOQL query tuning as a core strategy for improving Salesforce CRM performance, with a particular emphasis on hybrid Unix infrastructures that support enterprise-grade workloads. It highlights optimization techniques, AI-assisted monitoring, and automation frameworks that improve execution efficiency while reducing latency. The review further explores the integration of AI-driven solutions that provide predictive insights, autonomous query optimization, and adaptive workload management. Case studies across finance, healthcare, retail, and government sectors illustrate practical applications, benefits, and limitations. Key challenges, including security, compliance, integration complexity, and cost considerations, are analyzed in depth. Future research opportunities include AI-driven autonomous optimization, security-aware models, edge-based query processing, and unified monitoring systems. The findings suggest that combining SOQL query tuning with AI-powered assistance in hybrid Unix environments creates a scalable, secure, and resilient framework for modern CRM performance optimization.
DOI: http://doi.org/10.5281/zenodo.17366907
