AI-Enhanced Event-Driven Payroll Processing: A Cost-Optimized Framework Using Managed Container Services and Intelligent Automation

Traditional payroll systems maintain dedicated infrastructure on rigid batch schedules, yielding idle capacity rates of 97–99% between execution cycles. This paper introduces an artificial intelligence (AI)-enhanced, event-driven framework integrating managed container services with machine learning agents for payroll processing. Empirical analysis of 94 enterprise implementations spanning 500 to 50,000 employees demonstrates 71–74% monthly cost reduction, improvement in payroll accuracy from 94.2% to 99.7%, and an 87% reduction in errors relative to traditional architectures. A quantitative decision framework incorporating payroll complexity, regulatory requirements, and AI model selection is presented, alongside comparative analysis of Amazon Web Services Elastic Container Service Fargate, Azure Container Instances, and Google Cloud Run implementations. Four pseudocode algorithms formalize the event dispatch, anomaly detection, tax form extraction, and pipeline orchestration logic. Real-world validation with an 8,500-employee healthcare organization confirms $906 monthly infrastructure cost with zero compliance violations over 120 days.