How AI will reshape finance work without replacing the people doing it
Let’s start with the thing most people in finance are quietly thinking: will AI take my job?
The question comes up in nearly every conversation about automation in finance, and it deserves a direct answer. Based on what’s actually happening inside finance teams that have started adopting AI, the answer is no. The work changes, often significantly, but the roles become more valuable rather than less. The professionals who understand both the numbers and the systems producing them are exactly the people businesses need more of, because the judgment, interpretation, and communication that make finance useful to leadership cannot be replicated by a model.
That said, the change is coming quickly enough that ignoring it is its own risk. In a recent survey of 166 finance professionals by ApprovalMax, Joiin, and Mayday, only 10% currently use AI in their finance processes, even though 63% said they’re interested. That gap between curiosity and adoption is where the next few years of competitive advantage will be decided.
• Only 10% of finance teams currently use AI, but 63% are interested. Most haven’t started because they’re unsure where to begin or worried about accuracy in a function where precision matters.
• A 15-entity firm asked their AI which software they were paying for across the group. It found five duplicate licenses instantly, leading to a renegotiation that saved thousands.
• The most valuable AI in finance will run in the background. Automated classification, matching, and anomaly detection, with the finance team reviewing exceptions rather than processing every transaction.
• AI-generated fake invoices are a growing risk. Structured approval workflows with segregation of duties become more important in an AI-enabled environment, because the fraudulent inputs are getting better.
• The three-to-five year window is short. The teams that start experimenting now will have built institutional knowledge by the time the tools mature further. The ones that wait will be starting from scratch.
What AI is already doing inside finance tools
A large portion of month-end work has very little to do with actual accounting. The bulk of the effort goes into identification, classification, and matching: figuring out which transactions belong where, reconciling entries across entities, and manually catching things that slipped through during the month. That kind of work is exactly what AI handles well, and in many cases it’s already doing it.
OCR tools that learn which supplier maps to which account code after a few repetitions. Bank feed matching that improves over time. Anomaly detection that flags duplicate invoices or unusual spending patterns before a human reviews them. Much of this is already embedded in the tools finance teams use daily, often running in the background without anyone thinking of it as “AI.”
The top use cases finance teams are most interested in pursuing further, according to the same survey, are fraud detection, predictive cash flow, and invoice categorization. All three are areas where the rules are well-defined, the data is structured, and the output is easy for a qualified person to verify. That’s the sweet spot: AI handling the volume and the patterns, with a human reviewing the results and making the calls that require context.
What this looks like when the foundation is right
The most compelling real-world example from recent sessions came from a VC-backed recruitment firm running 15 entities across Xero and QuickBooks. They pulled all their financial data into a single AI-powered model. When their CFO asked which software tools the company was paying for across all entities, the AI surfaced five separate licenses for the same platform, with no collective bargaining in place. That single query led to a renegotiation that saved thousands of dollars.
Producing that same insight manually would have taken a finance team days: exporting expense data from 15 separate accounting files, cross-referencing subscription lists, and reconciling naming inconsistencies. The AI produced it in seconds.
The critical detail is the foundation. The AI worked because the underlying data was clean and consistently structured across all 15 entities. Same chart of accounts, same coding conventions, same approval processes feeding standardized data into the GL. Without that foundation, the same model would have produced unreliable output, and the finance team would have spent more time verifying results than they saved.
Where the risks are real
AI-generated fake invoices are becoming increasingly sophisticated, with formatting, supplier details, and payment terms that closely mirror legitimate bills. If those invoices enter an AP workflow without structured checks, they can pass through approval chains that weren’t built to catch them.
Structured approval workflows become even more important in this environment. Tools like ApprovalMax flag changes to bank details, enforce segregation of duties, and require multiple approvers for high-value transactions. Those controls were valuable before AI. In a world where fraudulent invoices can be generated at scale, they become a baseline requirement.
There’s also the automation bias problem: people tend to accept outputs from automated systems without enough scrutiny, especially when the output looks polished and confident. Finance teams building AI into their processes need to treat the output as a starting point for review, with clear checkpoints where a qualified person verifies the results before anything reaches a payment or a board deck.
The most successful AI implementations in finance constrain the model to a specific dataset and a specific task, built on a solid data foundation connected directly to the accounting platform. A hallucinating model during a board presentation where the balance sheet doesn’t balance is something nobody wants to experience. Start constrained, verify rigorously, and expand from there.
How the day-to-day actually changes
When invoice categorization, transaction matching, and variance flagging are handled by automated systems, the finance team’s week starts to look different. The time that used to go into processing and compilation shifts toward interpretation and communication: explaining to the board why revenue grew in one segment and contracted in another, advising department heads on budget allocation based on real-time spend data, and identifying risks that the numbers suggest but cannot fully explain on their own.
That shift mirrors what happened when cloud accounting automated bank reconciliation and data entry a decade ago. The profession didn’t shrink. The work changed, and the professionals who embraced the change became more valuable to their clients and employers. AI is the next iteration of that same pattern, moving faster and touching more parts of the workflow, but following the same underlying logic: automate the repetitive, elevate the human.
The window for this transition is relatively short. Multiple practitioners across recent sessions converged on a similar estimate: AI will meaningfully reshape finance work within three to five years. The teams that start experimenting now, even in small and contained ways, will have built the institutional knowledge and confidence to scale effectively when the tools mature further. The teams that wait will be learning the basics while their peers are already operating at a different level.
This article draws on insights from recent To the Max webinars, including sessions covering finance team priorities for 2025, multi-entity visibility, and the future of financial controls.
