Every finance software vendor now has an AI story, and a growing number of them use the phrase “AI CFO.” For a founder watching payroll and wondering whether the next senior hire can wait another two quarters, the pitch lands. If software can close the books, build the forecast, and flag the anomalies, what is left for a financial leader to do?
The question deserves a straight answer. Over the past two years AI has taken over a large amount of work inside the finance function and barely touched the rest. Knowing where that line falls is more useful than any vendor’s claim about where it should be.
What the New AI Finance Tools Promise
The pitch is roughly the same across the category. Connect your accounting platform and your bank feeds, and the software will categorize transactions, reconcile accounts, draft the monthly reporting package, answer questions about your numbers in plain language, build a forecast, and tell you when something looks wrong. To a Series A company in Durham or a manufacturer in Raleigh, it sounds like CFO-level output without a CFO-level salary.
Much of this works, and finance teams have adopted it quickly. The narrower question is which parts of the CFO role these tools reach.
Where AI Helps the Finance Function Today
The clearest wins are in work that is high-volume, rule-bound, and repetitive. Transaction categorization that used to consume a bookkeeper’s week now takes an afternoon of review. Bank and credit card reconciliations run continuously instead of monthly. Reporting packages that someone used to assemble by hand come out as a first draft. Anomaly detection catches the duplicate invoice and the vendor charge that doubled, often before a person would have noticed.
Gartner’s research puts numbers to this. In a survey of 160 senior finance leaders conducted between January and April 2026, data extraction, accounts payable and receivable automation, and report creation delivered their expected value within nine to 10 months. More complex work, including data management, insight generation, and forecasting, took considerably longer to pay off.
The pattern holds across the category. Mechanical processing pays back quickly. Work that depends on judgment takes far longer, and for a simple reason: AI does not replace judgment. It collects data, manipulates it (in a good way), and analyzes it. The judgment itself, including the soft knowledge of people, history, and context that never makes it into a system, still comes from a person.
Side by Side: What AI Does and What a CFO Does
This division runs through the whole finance function.
| Finance work | What AI does today | What a CFO does |
|---|---|---|
| Transaction coding and reconciliation | Codes and matches at volume, continuously, and can draft a starting chart of accounts | Shapes the chart of accounts around how the business runs and the detail it needs to track |
| Monthly reporting package | Assembles a first draft on schedule | Reviews the draft, catches the errors, adds the analysis, and decides what the board needs to see |
| Forecasting | Projects forward from historical patterns | Sets the assumptions, adds what is known about the future, such as company or economic changes, and judges when the pattern has broken |
| Anomaly detection | Flags the easy-to-find problems: duplicates, spikes, and outliers | Stays out of the weeds, looks at the bigger picture, and finds the errors that are not obvious |
| Decisions on incomplete data | Produces an answer from the inputs available | Weighs what is missing and carries the risk of being wrong |
| Lender, board, and investor relationships | Prepares the supporting materials | Negotiates covenants and credit lines, and frames a difficult quarter |
| Accountability for the numbers | None; responsibility stays with the company | Signs, answers to the board, and owns the outcome |
| Building the finance team | Reduces the headcount needed for processing work | Hires, coaches, and decides who owns what |
| Directing the AI itself | Works with whatever it is given | Determines what information is needed, how it should be presented, and the best resources for it, then confirms the final presentation is accurate |
The right-hand column is the job that remains.
Creating the chart of accounts is a good example. “I recently did this for a new client whose existing chart of accounts didn’t fit the business,” says Rankin McKenzie Partner Christine Osborne. “AI gave me a strong starting point, with parent accounts, sub-accounts, and numbering. What it can’t do is understand the structure and level of detail that make sense for this particular business. I then refined it so we will capture the information they need to make operational, along with financial, decisions.”
Review matters for the same reason. AI does an impressive job on most routine finance work, often getting something like 90 to 95 percent of it right, depending on the task and how well the question is asked. The remaining 5 to 10 percent is the problem. A reviewer will find those errors, and a reporting package, lender submission, or filing that is even 5 percent wrong is an embarrassment at best and a legal exposure at worst. Every output has to be reviewed by someone who knows what correct looks like.
What AI Cannot Do in the CFO Seat
Behind everything in this section is a simple point: AI needs a supervisor. Someone with financial knowledge has to set it up correctly, know what to ask it for, refine what it produces, and review the final product so nothing is wrong or missing. In a finance function, that supervisor is the CFO.
Five parts of the job sit outside what these tools do.
Judgment on incomplete information. Most consequential financial decisions get made before the data is complete. Should you take the bridge round at a lower valuation or cut burn and wait two quarters? A model will produce an answer. It will not tell you that the answer rests on a revenue assumption your head of sales has missed three quarters running.
Accountability for the numbers. When the board asks why the forecast missed by 18 percent, a person answers. When the auditors have questions about revenue recognition, a person signs. Software cannot carry responsibility, and the company carries it regardless of which tool produced the output.
Relationships with lenders, boards, and investors. A covenant waiver is negotiated, not calculated. So is an extension on a credit line and the framing of a difficult quarter to an investor who has other portfolio companies to worry about. These conversations turn on credibility built over time.
Leading a finance team. Deciding whether you need a controller or a staff accountant, coaching the person doing the close, and setting who owns what are management problems.
Understanding the business. A fractional CFO who has sat in your leadership meetings knows that one customer is 30 percent of revenue and has grown quiet, and that your operations lead’s cost estimates always run optimistic. None of that is in the general ledger, and it changes what the numbers mean.
The investment data suggests the market already senses this boundary. A Gartner survey of 204 finance leaders in March 2026 found that 45 percent of finance AI investment leans toward productivity, while only 20 percent leans toward decision quality. Companies are buying what the tools are good at.
The Risks of Relying on AI Alone
Five risks deserve attention before you decide the software is enough.
Data quality. AI works with the data it has or retrieves. It can test for potential errors, such as duplicates or anomalies, but it does not know about changes in the business that should be reflected in the data. That is why it asks for human review and approval before making a change. If no one updates the technology when account mapping, business structure, tax position, or HR policy changes, erroneous information from sources like a credit card or payroll provider can flow straight into the books.
Imperfect. The companies that build AI know its limitations. Users are warned that giving blanket approval for changes, without reviewing each one, is high risk, and AI search results caution that any critical information should be confirmed before use. The right person is needed for these reviews.
Misplaced confidence. AI does not know where to be conservative. A forecast built from historical results with contract or CPI updates looks sound until it is compared with one that was rigorously thought through with the management team, using a strategic and economic lens. An experienced finance professional builds in a financial buffer when the ground is uncertain.
No accountability. Regulators, lenders, and auditors hold the company responsible for its numbers. A vendor’s terms of service will not absorb a restatement.
Data security. Uploading your general ledger, payroll, and customer contracts to a third-party tool is a decision about your most sensitive data. NIST’s AI Risk Management Framework organizes this work around four functions, govern, map, measure, and manage, and it is a reasonable starting point for deciding what belongs in a vendor review.
The Best Combination Is a Fractional CFO Who Puts AI to Work
The choice that matters is how well your finance function uses these tools. Some companies ignore them. Others trust them further than the output warrants. The ones that do well put an experienced person between the tool and the decision.
An experienced financial leader knows which outputs to check and which processes to automate first. They push the categorization and reconciliation work to the tools, then spend the reclaimed hours on pricing, capital planning, and the decisions that move the business. The modern CFO role has been heading in this direction for years, and AI accelerates it.
Rankin McKenzie treats that evaluation as firm-level work rather than leaving it to each engagement. An internal AI committee reviews the tools, procedures, and implementations our Partners recommend, which means a tool that reaches your business has already been examined by people with no stake in selling it. When a Partner brings you a recommendation, the committee’s review comes with it.
This matters in the Research Triangle, where tech and life sciences companies in Raleigh and Durham adopt new tools early. Moving first is an advantage only when someone with judgment is reading the output. Rankin McKenzie’s CFO Partners each bring at least 15 years of experience, which is what it takes to know when a confident-looking number deserves a second look.
The Honest Answer
AI is taking over a large share of the work that used to sit underneath your CFO, and that is worth having. It is not the same as replacing the CFO. The companies getting the most from these tools are the ones that paired them with someone who knows what the numbers are for, and the judgment and leadership side of the role has become more valuable as the mechanical side has become cheaper.
Wondering which parts of your finance function should be automated and which need a person? Schedule a consultation with Rankin McKenzie to talk it through.
Frequently Asked Questions
Can AI replace a CFO?
No, though it can take on a good deal of the work that sits below the CFO, and some of the CFO’s own work too. AI handles categorization, reconciliation, reporting drafts, and anomaly detection well. That frees the CFO to spend more time on the parts of the role AI cannot touch: managing the team, the analytical overview, strategic thinking, and relationships with lenders, investors, and other partners. AI does not exercise judgment on incomplete information or carry accountability for the numbers. The mechanical part of the function gets cheaper while the judgment part becomes more valuable.
Which finance tasks should we automate first?
Start with the work that is high-volume and rule-bound: transaction categorization, bank and credit card reconciliation, accounts payable and receivable processing, and the first draft of your reporting package. Gartner’s research found these deliver value within roughly nine to 10 months. Forecasting and insight generation take longer to pay off, so they are better approached once your underlying data is clean and someone is reviewing the output.
How does Rankin McKenzie decide which AI tools to recommend?
Through an internal AI committee that reviews the tools, procedures, and implementations our Partners put forward. Evaluating a finance tool well takes more than one person’s time, and the questions that matter, including data handling, output reliability, and how a tool behaves on messy books, are the same across clients. Doing that review once at the firm level means a recommendation that reaches your business has been examined by people who are not selling it.
Is it safe to put our financial data into AI tools?
It depends on the tool and on what you check before connecting it. Your general ledger, payroll, and customer contracts are among the most sensitive data your company holds. Review where the data is stored, whether it is used to train models, who can access it, and what happens when you cancel. NIST’s AI Risk Management Framework is a sound structure for that review, and the questions it raises are worth answering before you connect anything to your accounting system.
We are an early-stage company with no CFO. Should we buy AI tools or hire a fractional CFO first?
The person comes first. You need someone with financial knowledge in place before you automate, because automating a process that is not set up well only increases the volume of things done wrong. AI tools amplify whatever structure already exists, so running them on a chart of accounts that was never designed for your business produces fast, confident, wrong answers. A fractional CFO or fractional controller is the right place to start: they get the foundation right, then select and implement tools that fit. The reverse order usually means paying twice.
This article was produced with the assistance of artificial intelligence and reviewed and edited by Christine Osborne, Partner. It is intended for general informational purposes only and does not constitute financial, tax, investment, or legal advice.
