Unlocking Real-Time Finance with AI-Native Data Analysis Tools
Finance has undergone a significant transformation in recent years, shifting from a static function to a real-time one. This change is not just about closing the books faster or refreshing forecasts more often; it’s about seeing the business as it changes and helping leaders act sooner. A finance team that can provide this level of insight can give the company more time to shape what happens next.
When I joined OpenAI two years ago, we had a small finance team supporting a rapidly growing company. We needed to build our function from scratch and make AI fundamental to how we work, make decisions, and support the business. This meant redesigning our processes around artificial intelligence tools that could help us analyze data more efficiently.
One of the key challenges we faced was manual, recurring work: finding information, explaining what changed, and assembling inputs for a decision. We had access to advanced AI tools but were still learning how to use them effectively. So, we set two ambitious goals: achieving a zero-day close and automated, continuously updated forecasting.
A zero-day close means giving leaders a real-time view of the company’s financial position, reconciled and traceable. Continuous forecasting builds on this foundation by showing how the business is changing, what could happen next, and which decisions can alter the outcome. We’re still working toward these goals, but our efforts have already changed how we operate.
Our team has moved beyond static spreadsheets, manual searches for supporting records, and presentations to live tools built on the full context and data of the business. Finance professionals now have the ability to build their own decision-making infrastructure using AI tools like ChatGPT Work and Codex.
The real promise of an AI-native finance function is a team that understands what’s happening as it happens, helps leaders see choices ahead, and gives the company more time to act while outcomes can still change. To get there, we need to redesign work around decisions that matter, give people room to experiment, build clear accountability into every workflow, and measure AI’s dependable work.
One of our key takeaways is the importance of broad access to AI tools. People need freedom to explore these capabilities in their own work context. We paired this with structured experimentation around real problems, which led to some innovative solutions like IR-GPT, a custom GPT for investor relations.
Our hackathon turned AI from an abstract capability into a working tool. In one day, people could identify recurring tasks, build solutions, test them with colleagues, and improve them. The use cases came from those closest to the work while technical experts helped move faster.
For CFOs, our lesson is simple: you need bottom-up experimentation combined with top-down strategy. Put secure AI in people’s hands and let those close to the work identify better ways of getting things done. At the same time, focus leadership attention on changes that will matter most to the business.