What building an AI-native finance function taught me

Finance has become a real-time function. To me, the opportunity is much bigger than closing the books faster or refreshing a forecast more often. It is about seeing the business as it changes, helping leaders act sooner, and giving finance teams more time to shape what happens next.

When I joined OpenAI two years ago, there was only a small finance team supporting a company growing at extraordinary speed. We needed to build the function from the ground up and make AI fundamental to how we work, make decisions, and support the business.

I also saw a familiar starting point. Closing the books and updating forecasts still involved manual, recurring work: finding information, explaining what changed, and assembling the inputs for a decision. We had access to the most advanced AI tools in the world, and we were still learning how to redesign finance around them.

Getting there requires more than adopting new technology. It requires redesigning work around the decisions that matter, giving people room to experiment, building clear accountability into every workflow, and measuring the dependable work AI completes to provide a clear ROI.

The hackathon turned AI from an abstract capability into a working tool. In a single day, people could identify a recurring task, build a solution, test it with colleagues, and improve it. The use cases came from the people closest to the work, while technical experts helped them move faster.

An AI-native finance function is defined by faster cycles, stronger controls, better decisions, and more time for judgment. The path begins with a meaningful workflow and expands through evidence: give people the tools, help them rebuild the work, keep accountability clear, and measure the outcome.

The broader lesson is to begin with a consequential decision and work backward. Map the data, tools, approvals, and handoffs required to support it. Then determine which parts AI can analyze, coordinate, or complete. This improves the speed and quality of the entire decision cycle.

So we set two bold ambitions: a zero-day close and automated, continuously updated forecasting.

The idea behind a zero-day close is to give leaders a real-time, reconciled, and traceable view of the company’s financial position. Continuous forecasting builds on that foundation, showing how the business is changing, what could happen next, and which decisions could alter the outcome.

We are still building toward both ambitions. The work has already changed how our team operates. It has pushed us beyond the limitations of static spreadsheets, manual searches for supporting records, and presentations toward live tools built on the full context and data of the business. It has also given finance professionals the ability to build the tools their work requires and carry their expertise further.