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To accelerate measurable impact while upholding the integrity, reliability and trust that are fundamental to the finance function, CFOs can adopt six practices:
1. Lead with AI sponsorship, stakeholder engagement and access to tools.
AI adoption begins with people. The first step is to educate and empower employees with core AI tools such as copilots and identify change champions within each group who can accelerate peer-to-peer learning. While many leaders have already mandated AI use and set clear expectations, visible sponsorship by CFOs remains critical. At the same time, CFOs need to articulate the vision for how AI adoption supports finance priorities and implement a clear change management plan.
CFOs can also support boards of directors in overseeing the broader organization’s AI activities. Because CFOs play a central role in determining strategic fit, capital allocation, third-party dependencies, cyber and privacy risks, model governance and workforce implications, they can facilitate the board’s much-needed engagement in these critical areas. While the National Association of Corporate Directors (NACD) reports that more than 62% of directors now allocate agenda time for AI, only 23% of boards have assessed its strategic impact, and just over 11% have approved an annual budget for AI projects.3
2. Treat AI as a transformational technology, not a solution for a specific use case.
Although organizations often begin with POCs, AI is not simply a tool to address isolated problems. CFOs need to understand the transformational benefits that the enterprise and finance function can achieve. They should sponsor the program, define the vision, develop the plan and execute it like any large-scale transformation initiative. The case for change should identify expected outcomes and business priorities, such as improving forecasting accuracy, accelerating the close, decreasing cost to serve and reducing working capital.
An effective transformation will require changes to major elements of the operating model, not just to technology. This means that CFOs must engage the right stakeholders and collaborate across functions, including business, IT and human resources. CFOs also should anticipate how AI-driven changes across other parts of the business will impact finance processes. For example, an AI-driven procurement process where an AI agent places replenishment orders without any human interventions will require redesigned controls.
3. Reimagine end-to-end processes with humans in the loop.
The strongest results come when organizations take an AI-first approach to reimagining end-to-end processes rather than layering AI onto existing workflows. For example, redesigning billing and billing support may require ERP configuration changes, improved integration with contract management systems and AI-driven customer portals. Stakeholders such as tax and internal audit should be engaged early and in parallel. In most cases, the new design will impact existing controls and require additional controls to oversee AI activities. Equally important is designing processes that keep humans in the loop at the right decision and control points. For instance, in a billing dispute scenario, a billing manager should review and approve AI-generated responses for nonstandard inquiries.
4. Prioritize data readiness and governance as the most important enabler for scale.
While pilots can proceed without robust data layers, scaling AI requires harmonized, well-governed data. In fact, 77% of finance teams who struggle to scale AI lack effective data governance.4 Reinforcing its importance, the NACD advises boards to prioritize scalable data architecture, data lineage, metadata, interoperability and cross-functional governance for reliable AI.
Most large firms operate multiple ERPs and applications without unified data governance. However, organizations do not need to wait for every entity or business unit to be on a single ERP. EY teams have supported multiple Fortune 500 companies by deploying finance data appliances that integrate data from over 50 systems, enabling AI-driven reconciliations and insights seamlessly across business units.