AI-Driven Integration Framework for Enterprise Logistics and Financial Systems: A Technical Review

Abstract

Enterprise environments today wrestle with a fundamental problem. Legacy financial planning systems need to work alongside AI-driven automation platforms. This isn't just about connecting systems. Warehouse management, collaborative robotics, and enterprise resource planning create a web of technical dependencies. Traditional batch-oriented financial tools operate on different timescales than real-time operational systems. Manual processes and outdated middleware make seamless interoperability difficult. Organizations need integration layers that translate between different system architectures. Data consistency matters. Operational reliability cannot be compromised. Exception handling protocols must be robust. The validity of human oversight is a critical factor in processing and decision-making. The need for an audit trail to meet regulatory standards is also a major factor. Getting this integration right delivers substantial operational improvements. The framework addresses workforce adaptation concerns. Safety protocols protect workers and equipment. Cross-platform orchestration enables enterprise-wide visibility. Standardized exception handling can be replicated across facilities. Forecasting logic becomes reusable across domains. Scalability drives architectural choices from day one. Horizontal expansion and future integrations must be possible without major rework.