Finance teams are adopting AI faster than they are training their employees to use it. According to research from CrossCountry Consulting and the Financial Education and Research Foundation (FERF), 87% of finance teams report that 25% or fewer of their members have formal training in data analytics or AI, and 51% have no formal preparation for AI and digital transformation.
The constraint on AI value is human judgment, not technology. An AI-native finance function is defined by the measurable capability of its people, not by the tools it deploys. The AI competency framework from CrossCountry and QuantumRise’s AI for the Office of the CFO workshop gives CFOs a practical way to build that capability: five knowledge domains, four proficiency levels, and a clear place to start.
What does an AI-native finance function look like?
In an AI-native finance function, professionals at every level apply AI, evaluate its output critically, and exercise governance judgment as a matter of course. Leaders measure that capability instead of assuming it.
| Domain | L1 Literacy | L2 Application | L3 Build | L4 Transform |
| AI | What AI can and cannot do | Use AI tools in daily work | Design and build AI solutions | Set AI vision and direction |
| Data | Data shapes AI output | Evaluate AI outputs critically | Build AI data architecture | Define data and AI strategy |
| Human skills | What the skills are and why they matter | Apply human skills when working with AI tools | Lead AI change at team level | Lead org-wide transformation |
| Workflow design | Spot AI’s task fit | Redesign personal workflows | Redesign team workflows | Redesign the operating model |
| Governance and ethics | AI risks and governance needed | Apply governance daily | Build governance frameworks | Lead initiatives to establish and implement governance |
The grid lets a CFO place every team, in every domain, at a specific level. A tool inventory measures adoption; the grid measures capability. A vague mandate for “more AI training” becomes a plan with a starting point and a sequence.
Where should finance teams start?
Build “Literacy” and “Application” across all five domains, before advancing to “Build” or “Transform.” Advanced tools in the hands of people who cannot yet evaluate output or apply governance judgment do not create capability. They create exposure, especially when informal use outpaces the foundation.
Application-level proficiency is specific and observable:
- Data: Evaluate AI output before it reaches a workpaper or reconciliation.
- Governance and ethics: Apply review, documentation, and accountability standards daily.
- Workflow design: Redesign personal workflows around what AI makes possible.
Uneven profiles show where to invest next. A team at “Application” in AI but still at “Literacy” in governance, has a clear gap. The most reliable assessment is to watch how a team reviews AI-assisted work, such as a reconciliation, and judge whether it can defend the result.
Where do human skills fit in the framework?
“Human skills” is the domain finance teams overlook most often. It has no tool to procure and no output to inspect, so it rarely appears in capability reviews. Yet evaluating AI output and applying governance daily both depend on critical thinking. At higher levels, human skills shift toward leading change across teams and the organization, and a function that skips the foundation has no one ready to lead when it matters.
This domain also addresses the concerns finance professionals raise about AI: accuracy liability in audit-sensitive work, reputational risk, and uncertainty about changing roles. Critical thinking equips people to defend an output before relying on it. Continuous learning and emotional intelligence help teams and managers navigate how their work evolves.
These concerns should shape how training is designed, so change management belongs inside the learning experience, not after it. That reflects CrossCountry’s approach to human-centered transformation inside finance organizations. QuantumRise, an AI transformation firm, examines how to embed that preparation directly into an AI rollout and prepare teams for AI through change management.
How training mirrors the framework
The framework maps to three stages, and finance teams should begin at stage one:
- Applied AI Fluency: moves people from ad hoc assistant use to systematic, safe use in their own workflows.
- Building AI: covers designing AI solutions, building AI data architecture, redesigning team workflows, and building governance frameworks.
- AI Transformation: sets AI vision, data and AI strategy, and the redesigned operating model.
CrossCountry and QuantumRise built the Applied AI for the Office of the CFO workshop for stage one. The one-day program covers AI foundations, a function-specific AI lab, and agentic workflow redesign, with governance built in from the first module. Participants earn six continuing professional education (CPE) credits and leave with a workflow they redesigned themselves. For depth on governance where AI intersects with SOX controls, see CrossCountry’s analysis of generative AI governance in finance.
Finance functions do not become AI-native on a tool rollout schedule. They get there one domain and one level at a time, and CFOs who measure where their teams stand make better decisions about what to build, buy, and train next. Explore how CrossCountry’s AI Strategy and Transformation team supports the Office of the CFO.
About this series: This article is part of a joint thought leadership series developed by CrossCountry Consulting and QuantumRise. Together, the two firms offer AI for the Office of the CFO, a practical AI workshop designed specifically for professionals in Finance, Accounting, and Risk Management. To learn more, visit https://crosscountry-consulting.com/ai.