Building a Data-First Finance Function

• You cannot separate the AI conversation from the Data conversation.
• "Data as a Product" transforms finance into a manufacturing plant for insight.
• The Single Source of Truth is the holy grail for enterprise alignment.
• Fixing data quality is the first step to solving the talent crisis.

Use whatever advanced AI model you want—if you feed it bad data, you will get bad answers. In the Agent CFO podcast, Ashok Manthena emphasizes that the journey to AI adoption starts with data hygiene. A "Data-First" finance function prioritizes the cleanliness and structure of its information assets above all else.

This means moving away from ad-hoc spreadsheets where data goes to die. It means establishing rigorous standards for how data enters the system. It means treating every transaction as a valuable record that needs to be preserved and protected.

Without this foundation, AI is just a toy. With it, AI becomes a superpower.

Every organization suffers from "dueling spreadsheets." Sales has one revenue number; Finance has another. This creates friction and mistrust. The goal of the Data-First function is to establish a Single Source of Truth (SSOT).

The SSOT is the definitive record of the business's performance. When an AI agent is asked a question, it queries the SSOT. When the CEO looks at a dashboard, it pulls from the SSOT.

Creating this requires political will as much as technical skill. It requires the CFO to assert ownership over the definitions of key metrics across the enterprise.

Think of the finance department not as a back office, but as a factory. The raw material is data (invoices, orders, payroll). The finished product is insight (profitability analysis, forecasts). The machinery is the tech stack.

In this model, the finance team are the factory operators. Their job is to ensure the line keeps moving and the quality remains high. They are constantly tweaking the machinery (the agents and APIs) to improve yield.

This industrial approach to data processing brings discipline and scale to what was once a craft-based profession.

Caitlin Haberberger makes a crucial point: bad data burns out good people. Nothing is more demoralizing to a high-potential analyst than spending 40 hours cleaning up a messy CSV export.

By solving the data problem, you solve the retention problem. You remove the friction from the employee experience. You allow your team to do the work they were hired to do—thinking, analyzing, and advising.

A Data-First finance team is also a People-First finance team.

Building a Data-First function is an incremental process. You don't boil the ocean. You pick one domain—say, customer data—and you fix it. Then you move to vendor data.

With each step, the organization gets smarter, faster, and more aligned. The finance function evolves from a scorekeeper of the past into the architect of the future.