EY needed to respond to rapid generative AI disruption across its own business, but scattered proofs of concept, skill gaps, decentralized operations and fast-changing regulatory expectations made it difficult to turn experimentation into a coordinated, trustworthy enterprise transformation.
Reported impact
EY moved from fragmented experimentation toward a governed enterprise AI operating model that let teams test internal deployments, close skills gaps, centralize technology decisions and scale LLM-enabled work more confidently across the firm.
EY treated itself as "Client Zero" and reorganized its own transformation around enterprise AI. The program built on strategic acquisitions and a prior shift to cloud and consolidated data systems, then mapped AI use cases directly to business goals and the firm-wide value chain so investment would not be wasted on isolated pilots. EY centralized AI technology consumption, data and systems integration, and developer tooling to create a common foundation for large language model adoption across business functions. Advanced tools and platforms accelerated the AI development lifecycle, while the EY responsible AI framework, firm-wide AI definitions and updated risk processes provided governance for ethical use, information security and data privacy. In parallel, targeted upskilling and change management helped employees integrate LLM-enabled workflows into day-to-day work at global scale.
EY mapped priority AI use cases to business goals, routed data and systems into a centralized cloud-based foundation, and used advanced tooling to integrate LLM capabilities into employee workflows. Governance definitions, responsible AI controls and updated risk processes then governed how those AI-enabled workflows were developed, deployed and scaled across the organization.
"By acting as our own 'Client Zero' and testing AI deployments internally, EY is determining answers to these questions now."
"Developers were empowered with advanced tools and platforms to accelerate the AI development lifecycle."
"EY responded to the AI disruption swiftly and accordingly, allocating an initial US$1.4 billion investment in an extensive transformation program with humans at the center."
"The transformation team recognized the need to bridge skill and knowledge gaps, particularly in integrating large language models (LLMs) into daily workflows."
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