What building an AI-native finance function taught me
2026-08-20 · OpenAI
Five Lessons from Building an AI-Native Finance Function
OpenAI CFO Sarah Friar has distilled five critical lessons from her experience building an AI-native finance function. These lessons provide a practical roadmap for finance organizations seeking to deeply integrate artificial intelligence into their operations and evolve their strategic role.
Friar stresses that becoming AI-native is not merely about adopting new tools but requires systematic transformation across technology, risk management, value measurement, collaboration, and organizational learning.
Lesson 1: Introduce Automated Forecasting Tools to Improve Prediction Speed and Accuracy
The first lesson Friar highlights is the introduction of automated forecasting tools. Traditional financial forecasting often relies on manual models and spreadsheets, which are time-consuming and prone to human bias.
Automated AI-powered tools enable finance teams to process vast amounts of data rapidly, dramatically reducing forecasting cycle times while improving both accuracy and consistency. This shift allows the finance organization to move from backward-looking reporting to forward-looking insights that better support business decisions.
According to Friar, the dual improvement in speed and accuracy forms the foundation of an AI-native finance function.
Lesson 2: Strengthen Internal Controls to Ensure AI Compliance and Security
While embracing AI, Friar emphasizes the simultaneous need to strengthen internal controls. The introduction of AI systems creates new risk vectors including data privacy, model bias, and explainability challenges.
Robust internal control frameworks must be established to govern model training, data inputs, output validation, and ongoing monitoring. These controls ensure AI applications remain compliant with regulations and internal policies.
Only by embedding compliance and security into the DNA of the AI-native finance function can organizations innovate responsibly while maintaining long-term stability.
Lesson 3: Precisely Measure AI Investment Returns to Quantify Technical Value
A key responsibility for finance in the AI era, Friar notes, is the precise measurement of returns on AI investments. This goes beyond traditional ROI calculations to encompass efficiency gains, cost savings, risk reduction, and new value creation.
By developing rigorous methodologies to quantify the business impact of AI technologies, finance teams can provide leadership with clear evidence to inform future investment decisions.
This capability to accurately measure value not only justifies continued investment but also builds organizational confidence in AI initiatives.
Lesson 4: Drive Collaboration Between Finance and Technology Teams for Data Sharing and Process Reengineering
Siloed operations limit AI's potential. Friar identifies cross-functional collaboration between finance and technology teams as essential.
Effective collaboration involves two key elements: breaking down data barriers to enable seamless sharing, and jointly reengineering business processes to create truly AI-driven end-to-end workflows.
When finance and technology professionals work closely together, domain knowledge is fused, resulting in more intelligent, efficient, and continuously improvable processes. This represents a fundamental difference between AI-native finance and traditional digitization efforts.
Lesson 5: Build Continuous Learning Mechanisms to Keep Pace with AI Development
Given the rapid evolution of AI technologies, Friar stresses the importance of establishing continuous learning mechanisms. Finance professionals must maintain technical curiosity and regularly update their skills.
Organizations should implement systematic learning programs including training sessions, knowledge-sharing forums, technology scouting, and hands-on pilot projects. This ensures the finance team remains relevant as AI capabilities advance.
Without sustained learning, even well-designed AI initiatives risk becoming obsolete.
Conclusion
Sarah Friar's five lessons form an interconnected framework for transforming finance into an AI-native function. When implemented together, these practices enable finance departments to evolve from traditional support roles into true strategic partners that actively drive enterprise digital transformation and business growth.
Her experience at OpenAI demonstrates that a thoughtful, holistic approach to AI integration can fundamentally elevate the value finance delivers to the organization. (Word count: 578)