Abstract : Enterprise resource planning (ERP) transformation programmes generate exponential scenario complexity that conventional testing approaches are structurally unable to handle. As organisations migrate to cloud-native ERP platforms and connect diverse upstream source systems, the number of valid transaction combinations routinely exceeds the capacity of manual, rule-based, and module-focused automation tools. This article presents AutoGenius, an AI-driven, event-driven framework for end-to-end ERP transaction validation and observability at enterprise scale. The framework adopts a plug-and-play, ERP-agnostic architecture built on asynchronous event streaming, loose coupling, and horizontal scalability, enabling transaction throughput to grow without degradation. AutoGenius provides complete lifecycle tracking from order creation through fulfilment and billing, with context-rich failure diagnostics anchored by system-level correlation identifiers. An RAG layer for defect correlation against existing issue trackers and a hybrid conversational interface for failure diagnosis and order tracking implements 'automated business validation testing (BVT)'‚ making production-ready status available within any 24 hour runtime period between maintenance windows or scheduled outages. The framework design is evaluated against established software quality criteria and compared with existing testing paradigms, demonstrating superior coverage breadth, failure traceability, and scalability. AutoGenius offers a replicable architectural pattern for organisations undertaking large-scale ERP system transformation