How EY’s Finance Transformation Team Leads AI Adoption From Within

How EY’s Finance Transformation Team is Approaching AI Strategy

Finance leaders are under constant pressure to modernize and demonstrate clear returns on technology investments. EY’s finance transformation team, led by Deirdre Ryan, is tackling this challenge head-on by putting emerging AI technologies to work both for clients and within EY’s own finance operations.

The team plays a dual role: guiding CFOs through AI adoption while piloting the same tools internally to refine processes and build expertise. This approach ensures that EY is “client zero,” using AI in real scenarios before advising others.

Success with Internal Technology

EY developed a platform called EYQ, a secure environment that allows employees to interact with large language models on laptops and mobile devices. This platform has been adopted by over 150,000 consultants worldwide, providing hands-on experience with AI tools.

In finance, EY is piloting an agentic AI solution for financial planning and analysis (FP&A). The system looks like a standard dashboard but automatically generates AI-driven insights as actual data comes in. Its key strength lies in scenario planning, where users can adjust key variables and instantly see forecast impacts.

The AI employs multiple agents acting like analysts, with a manager agent synthesizing the best answers. For example, you can ask, “What would a one percent drop in GDP do to our forecast?” The AI does the work but does not replace human oversight—finance professionals still make the final decisions. One client described the tool as having “an army of silent FP&A analysts,” highlighting how AI is changing the nature of finance work.

Psychological Safety During Transformation

Introducing AI tools can raise concerns among finance teams about job security and role changes. This is where leadership plays a vital role in maintaining psychological safety. Today’s workforce prefers strategic, insightful work over repetitive tasks like endless spreadsheet modeling.

Providing teams with AI tools that free them from manual work helps retain top talent. Finance must evolve or risk losing key people. EY not only advises clients on AI but also applies it internally, facing the same leadership and change challenges. Being “client zero” means leading by example.

CFOs as Role Models in AI Adoption

CFOs must move beyond traditional roles and become active participants in AI strategy. They need to understand AI capabilities firsthand to make informed decisions about capital allocation and innovation.

Starting small with proofs of concept helps CFOs and their teams learn the technology. This hands-on approach provides insights into practical applications and helps answer the critical question: How can AI meaningfully improve the finance function?

Structured vs. Ad-Hoc AI Learning

Adopting AI requires a balanced approach. CFOs should avoid fragmented pilots that lack a clear vision, which was a common pitfall with robotic process automation.

Setting a clear end state and helping teams climb the AI learning curve is essential. While informal sharing of use cases is valuable, it must be paired with a deliberate strategy aligned with how finance delivers value. This prevents scattered efforts and builds momentum toward impactful transformation.

Balancing Productivity and ROI

CFOs often focus on two outcomes: productivity gains and enhanced decision insight. Productivity improvements are easier to demonstrate, with many proven AI use cases automating routine work.

However, the real opportunity lies in decision insight—using AI to provide deeper analysis that supports faster, smarter decisions at the executive level. This requires imagining scenarios where perfect, timely data and AI tools enable new levels of analysis and competitive advantage.

The Role of Data Accuracy

The debate over a “single source of truth” in finance continues. While perfect data is rare, especially in large, complex organizations, prioritizing data quality is crucial.

Not every data point must be flawless. Instead, focus on the data that drives the most value and ensure it is consistently defined and reliable. This approach builds confidence in the insights finance presents and the decisions they inform.

By being intentional about data priorities, CFOs can support meaningful analysis without getting bogged down in endless data cleansing efforts.

For finance professionals interested in expanding their AI skills and strategies, exploring specialized AI courses tailored for finance roles can provide practical knowledge and tools to accelerate adoption.