Authors: Kamlesh Jangra
Abstract: The convergence of legacy Unix systems with AI-powered Customer Relationship Management (CRM) platforms, such as Salesforce Einstein Copilot, represents a critical strategy for modern enterprises seeking operational continuity and enhanced customer engagement. Legacy Unix servers, including AIX, Solaris, and older Linux distributions, continue to support mission-critical CRM workloads, storing historical data and managing transactional processes with high reliability. At the same time, AI-driven CRM introduces predictive analytics, automated workflows, and intelligent insights that enable personalized customer interactions and strategic decision-making. This review examines methodologies, architectures, and best practices for bridging legacy Unix infrastructure with AI-enhanced CRM, highlighting middleware solutions, API frameworks, hybrid deployment models, and automated data pipelines. It explores challenges related to data compatibility, infrastructure limitations, security, compliance, and organizational change management. Case studies from financial and healthcare sectors illustrate practical implementations and lessons learned, emphasizing phased migration, continuous monitoring, and performance optimization. By synthesizing technical strategies and industry examples, this review provides actionable guidance for IT architects, administrators, and decision-makers to modernize CRM operations, maintain data integrity, and ensure seamless integration of AI capabilities while leveraging the reliability of existing Unix environments. The discussion concludes with insights on emerging trends, including predictive analytics enhancements, cloud-first strategies, edge computing, and automation-driven Unix modernization, framing a future-ready approach to enterprise AI CRM adoption.
DOI: https://doi.org/10.5281/zenodo.17520771
