“If the data's wrong, it doesn't matter how advanced your model is" - Tom Hinkle on AI and data strategy in FP&A

Data governance serves as the essential foundation for successful analytics and AI implementation in finance. Without clean, verified data, advanced models fail, making rigorous data quality controls—such as volume threshold alerts and consistent metric definitions—critical for organizational trust. Effective AI adoption in finance requires more than just tool deployment; it demands a clear change management strategy that provides employees with tangible productivity incentives and ethical usage policies. While generative AI significantly accelerates tasks like SQL coding and email drafting, human domain expertise remains indispensable for validating outputs and ensuring accuracy. Finance teams should evolve into active data stewards, bridging the gap between raw data and actionable business insights by embedding themselves within operational units to ensure consistent, reliable reporting across the enterprise.