Can AI Keep Its Time-Saving Promise to CFOs?

Finance teams could use a refund on some of that promised free time. 

Years of technology spending have left finance teams with plenty of manual work, and AI is arriving with another promise to clear their desks for the highly coveted operational duties. Whether it delivers, finance leaders say, depends partly on how much work remains after the agent finishes.

The monthly close is one process where promised relief has been slow to arrive, according to Alyona Mysko, founder and CEO of Fuelfinance and a former CFO. “[Many] CFOs paid for an ERP to solve it, and the manual work is still there,” she told CFO Upside. “Having the system in place doesn’t mean people stopped doing the controlling by hand.”

At Prophix, a provider of financial planning, budgeting and reporting software, CFO Aaron Levine sees fragmentation persisting after companies moved finance onto cloud platforms. “You can’t throw an LLM on top of disconnected data and expect the kind of consistent, reliable answers finance requires,” he told CFO Upside.

Data, Duh

Mysko said AI rollouts can stall when data needs attention and responsibility for that work is unclear. “The data isn’t ready, and nobody owns fixing it,” she said.

When systems produce conflicting numbers, she also added that teams need to decide which one to use. “When you have two revenue numbers in two systems and no rule for which one wins, AI gets confused exactly where a person would,” she said. “You have to write down the rules and decide where the final number lives.”

Before an agent can take over a task, she said the process also needs to be documented. “Is the process written anywhere, or is it in someone’s head?” Mysko asked. Implementing agents often means standardizing processes first, she said, sometimes changing them entirely if required.

Within his own finance team, Levine said AI’s payoff comes from reducing the time spent preparing information for analysis. “The biggest gain has been in the manual, repeatable work around the forecast, not the forecast logic itself,” he said.

To help explain variances and update forecasts, the team uses agents that also gather input from budget owners. That means less time spent chasing people for updates, while calculations stay within the model. “Every action still goes through human approval, which keeps the process auditable,” he said.

Buying Time

If checking AI’s work takes as long as doing it, the promised time savings become hard to find. “There’s no return if you switch from doing every line to checking every line,” Mysko said. “That’s the trap.”

To reduce that burden, Mysko recommends audit logs and clear rules about which exceptions need human attention. “If you used to check 3,000 rows doing the same task on repeat, you now check 10,” she said.

“A process that used to eat a chunk of someone’s day now gets reviews in minutes,” Levine said of automated bank reconciliations. His team still reviews the results, but, like in his forecasting, some of the work leading up to the review takes way less time.

When explaining why human review still matters, Levine compares AI to a familiar hire on the finance team. As he put it, “think of AI as a junior staff accountant. It still needs review, but we need junior staff accountants to handle some of the mundane work to free us up to focus on more strategic areas.”

Even after an agent starts handling tasks, Mysko pointed out, someone has to keep it running. “When you build agents yourself, in Claude or similar, you’re building software,” she said. “Someone on the finance team has to support it, update the sessions and maintain the context.”

Earning Its Keep. For Levine, the return on an AI investment depends on how much work it takes off the team’s plate, including the upkeep it requires. “My first question is always whether it will actually give finance teams more capacity,” he said. “Any AI tool worth its salt needs to prove that it’s not just another system that finance teams have to actively maintain.”