How EY's AI-Powered Global Tax Platform Cut Manual Workload by 90%

EY's tax professionals spent approximately 45% of their time on manual data gathering and wrangling — transcribing complex K-1 forms spanning hundreds of pages, reconciling data from 36+ disparate source systems, and manually calculating withholding tax rates for intercompany transactions across dozens of jurisdictions. With over 3 million tax compliance deliverables annually and 200,000+ pages of complex tax documents to process each year, the manual workload was unsustainable, error-prone, and left little capacity for the strategic advisory work clients valued most. The firm needed a scalable AI solution that could handle unstructured document complexity while meeting the rigorous accuracy and auditability standards required in professional tax practice.

EY deployed its EY.ai Global Tax Platform, integrating Microsoft Azure AI Document Intelligence and IBM watsonx to automate document ingestion, data extraction, and tax determinations at enterprise scale. The platform uses advanced machine learning models to extract structured and unstructured K-1 tax data from complex multi-format documents, an Intelligent Tax Data Lake to consolidate transaction data from 36 disparate source systems into a single governed repository, and domain-trained AI agents for automated withholding tax calculations on intercompany transactions. EY adopted a 'Client Zero' strategy — validating the technology against its own internal tax operations before deploying to clients — building trust in AI-generated outputs and accelerating adoption. The EY.ai platform was built with enterprise-grade governance, human-in-the-loop review processes, and rigorous evaluation frameworks to ensure auditability and defensibility in a high-scrutiny regulatory environment.

Key Takeaways