Intuit AI expert says future of finance is real-time, not monthly
For many finance teams, month-end close remains a recurring scramble to reconcile accounts, assemble reports and piece together what happened across the business. Intuit executive Ashley Still sees artificial intelligence changing that model.
“The manual work that consumes half a finance team’s week today — reconciliation, exports, error-fixing and report stitching — should be largely invisible” within the next five years, Still said.
Still — executive vice president and general manager of Intuit’s mid-market business — envisions AI agents handling that work continuously rather than in a monthly scramble.
Earlier this month, Intuit announced new AI capabilities designed to help mid-market businesses move from manually assembling financial information to continuously maintaining and analyzing it.
In an email responding to questions from CFO Dive, Still detailed the problems Intuit is trying to solve with the rollout and how CFOs should measure returns. She also weighed in on how AI could change the day-to-day work of finance teams, the challenges of putting AI into critical workflows and the emerging debate over AI software pricing.
The following Q&A with Still has been edited for clarity and brevity.
CFO Dive: For a mid-market CFO reading your recent announcement, what is actually different about what Intuit is offering today compared with what was available to them before?
Ashley Still: There are two new capabilities that we’ve announced that accelerate value for mid-market CFOs.
First, we introduced Intuit Intelligence Chat, the conversational interface across Intuit Enterprise Suite. You can simply chat with Intuit Intelligence to query your numbers and why the numbers moved, like why your margin dropped or spending spiked, and get answers grounded on your books, traceable back to specific customers, vendors, products or accounts driving the numbers.
Second, we delivered over 40 new AI skills powering the interface for mid-market businesses to automate complex financial processes conversationally. These span payroll, including running and correcting pay runs and preparing payroll tax; multi-entity intercompany transactions, such as sales, expense allocation, and intercompany journal entries; and custom objects, letting businesses create and manage data fields unique to how they operate.
CFO Dive: What’s a concrete example of how these new AI capabilities could change a finance team’s day-to-day work?
Ashley Still: Rather than asking their teams or digging through reports, business and finance leaders can now ask Intuit Intelligence plain-language questions and get answers grounded in their own books, and find out what’s behind an anomaly or a trend. They can also now build a live report on the spot with Intuit Intelligence.
For example, rather than waiting for month-end close or a manually assembled dashboard, a CFO using Intuit Enterprise Suite can ask in Intuit Intelligence Chat how projects are tracking against budget and receive an answer in seconds across multiple entities. Intuit Intelligence then recommends next steps and acts only after the user confirms an action, helping finance teams move faster while maintaining control, auditability and clarity.
Ashley Still, EVP/GM of Midmarket at Intuit
Permission granted by Intuit
CFO Dive: Finance leaders have to think about accuracy, controls, auditability and accountability when putting AI into critical workflows. How is Intuit addressing those challenges with this latest rollout?
Ashley Still: Absolutely, finance leaders cannot compromise speed and control — they need both! From day one, our system was designed with a differentiated approach that combines AI reasoning and deterministic accounting logics.
The numbers Intuit Intelligence cites come from deterministic calculations based on how an experienced accountant would do, not LLMs guessing the math. And, we embed human decision making and accountability throughout the process. This results in higher accuracy and more trust than generic LLMs.
The combination of AI and deterministic logics means that standard, high-confidence workflows can be handled autonomously, while any task requiring genuine judgment is flagged for human review. Nothing is posted, transferred, or finalized without direct approval, and, of course, there’s an audit trail.
CFO Dive: How is Intuit pricing these new AI capabilities?
Ashley Still: We’ve leaned into embedding AI value directly into the platform and the existing business model of Intuit Enterprise Suite and Quickbooks because it’s a simpler model that delivers the visibility and predictability that customers ask us for.
We have also introduced a consumption model with Intuit Intelligence queries through the new conversational interface and look forward to experimenting further with this approach.
Additionally, bill pay, payments, business intelligence, and AI-driven bookkeeping are now part of QuickBooks Online Advanced, so we have already established this hybrid model of a per seat cost plus consumption pricing for processing payments, payroll and AI queries.
CFO Dive: There’s a growing debate over whether AI-powered software should be priced by the seat, by usage or based on the value it delivers. Where do you come down on that debate?
Ashley Still: The industry is in the early innings on pricing for AI-powered software. What we expect in the near term is a lot of experimentation with different models.
We believe the industry will evolve to a combination of per-seat/platform pricing and consumption/outcomes pricing that gives businesses confidence that they are paying for the benefit or outcome they receive while maintaining visibility and control over their costs.
We do not believe that unconstrained token-based pricing will win, and we have already seen pushback from both businesses and accountants on companies that are relying on this model.
CFO Dive: What metrics should CFOs use to determine whether an investment in AI tools like these is actually delivering a return?
Ashley Still: Ultimately the best metric is faster, more profitable growth that is delivered through better insights and decision-making, cost savings and time savings.
In short, I would ask yourself if you have access to the best insights and are you shifting time from manual work to unlocking growth. Consider Rhodes Companies: by enabling rigorous revenue recognition and accrual-based accounting within their Branding entity, they achieved a 15% EBITDA margin, marking their inaugural year of profitability.
Concretely, how much of the finance week is still spent on reconciliation and report-stitching versus strategic work, how current is the data behind your last major decision, and are you catching time-sensitive opportunities or risks before the window closes?
And, ultimately, is that speed and visibility translating into a healthier bottom line? We see real signals in things like 78% of customers telling us that Intuit AI makes it easier to run their business, and 83% of QuickBooks Online Advanced customers saying the platform gives them the data they need to make better decisions.
Those are the kinds of outcomes CFOs should be tracking internally, too.
CFO Dive: What’s the biggest mistake you see CFOs making as they begin adopting AI in finance?
Ashley Still: The biggest mistake is layering AI on top of fragmented, messy data and expecting it to somehow fix the underlying problem, an issue that disproportionately affects many small- and middle-market businesses.
Many of these companies are running seven to 25 different apps without realizing that app sprawl is exactly what’s limiting their ability to gain real insight, not the AI itself.
If your data is scattered across systems and spreadsheets, AI will just help you get to the wrong answer faster. This strains finance teams and forces them to focus on manual workarounds rather than the strategic work AI should free them up to do.
The CFOs who are getting real value are the ones who’ve consolidated their data foundation first and are thinking of AI as core to their operating model, not a bolt-on tool for a specific task.
CFO Dive: What should CFOs consider before putting AI into their finance workflows?
Ashley Still: Three things, in order. First, is your data actually consolidated, or are you asking AI to make sense of five disconnected systems? If you are relying on five different apps, why? Are they truly best in class, or should you consider consolidating accounting, payments, billpay, and Human Capital Management on a single platform to save both on technology spend and also achieve unified data.
Second, what’s your governance model? Who reviews what, and where does human judgment remain firmly in the loop before anything executes?
Third, how fast can you actually get to value? Don’t accept complex, high-cost implementations. CFOs should look for solutions that collapse the time-to-value gap by automating setup tasks, so leaders can focus on growth rather than being stuck in a lengthy migration for months before they see any benefit.
CFO Dive: Looking three to five years ahead, in what ways do you think the finance function will look different because of AI?
Ashley Still: I think the finance function five years from now will look less like a department that produces reports and more like a real-time strategic center for the business.
The manual work that consumes half a finance team’s week today — reconciliation, exports, error-fixing, and report stitching — should be largely invisible by then, handled by AI agents working continuously in the background rather than in a monthly scramble.
The scope of CFOs and finance leaders only grows from here. The bigger shift is cultural as much as technological: the AI-powered CFO reigns supreme today, and their businesses are lapping the laggards.
Five years out, I don’t think that’s even framed as a differentiator anymore; it’s just table stakes for running a finance function credibly.
