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AI Models Now Outperform Human Bookkeepers and One Controller Proves a Finance Team of One Actually Works

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A controller at a SaaS company that processes $50 million a month through its marketplace went on a two-week vacation. When he returned, his AI agents had already coded, categorized, approved, and synced 2,000 transactions. He reviewed just 67 (about 3%) by hand, and the entire cleanup took 30 minutes.

James Agius, Financial Controller at Skool, described his actual workflow on a recent episode of The Accounting Podcast. And it landed alongside benchmark data proving that, for the first time, off-the-shelf AI models from OpenAI, Anthropic, and Google are outperforming human accountants at basic bookkeeping tasks.

Hosts Blake Oliver and David Leary unpacked a series of developments that signal a genuine turning point for accounting. New studies from Digits and Ramp put hard numbers on AI’s bookkeeping abilities. A venture-backed startup led by a former PCAOB board member is building an AI-first audit firm. And KPMG’s entire US management committee flies to Silicon Valley every five to six weeks to meet with startups it views as potential threats.

But AI isn’t arriving to replace a surplus of accountants. It’s showing up amid a talent crisis that has more than tripled the number of unfilled accounting roles in a single year.

The Numbers Don’t Lie: AI Now Matches Human Bookkeepers

For years, the accounting profession has heard promises about AI. Now there’s data to back them up.

Digits just released the fourth version of its benchmark study, and CEO Jeff Seibert shared the results in an interview with David, which is featured on the episode. The test included categorizing over 2,000 transactions across multiple businesses into the correct chart of accounts. They tested all the major AI models (OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini) against outsourced human accountants.

“All of the major model providers have, for the first time, beaten real, outsourced human accountants at bookkeeping tasks,” Jeff told David. The humans scored about 79% accuracy. The AI models came in between 79.4% and 80.7%. The margin is small (about 1.6%), but the direction is clear.

Before anyone dismisses 79% as a low bar, Jeff offered important context. That’s actually typical for outsourced accountants who understand general accounting principles but don’t know the specific business. “They don’t know anything about that business or its industry, supply chain, geography, or customer base,” he explained. That missing context accounts for the 20% error rate.

What’s striking is how similar all the models performed. They’re all within three percentage points of each other. As David put it, basic transaction categorization “is kind of a commodity now.” It’s something everyone will essentially get for free from these models right out of the box.

But purpose-built systems go much further. Digits’ own AI, which learns from each business’s transaction history and can’t hallucinate by design, hits 97.8% accuracy. “Digits mimics the knowledge of a dedicated accountant who you’ve worked with for a number of years,” Jeff said.

The picture changes when you look at more complex work. Ramp tested its new Stack platform on 237 accounting tasks across eight synthetic businesses for categorization and financial close work. Its system scored 65.8%, beating the raw models but well short of perfect. This matches what most accountants experience. AI is great at pattern recognition but still struggles with judgment-heavy tasks.

AI still falls short in complex accruals, according to Jeff. Journal entries, fixed asset schedules, and prepaid expenses are the remaining frontier. Digits responded by launching automated accrual schedules where the AI identifies potential prepaids or fixed assets, drafts the schedule, and the accountant approves it.

Jeff drew an interesting parallel. At his tech company, engineers went from zero AI use to 100% in a single quarter. Jeff himself hasn’t written code since December, despite coding being his passion since age 12. “We have not fired our software engineers,” he said. “They are still critical, but the day to day has changed completely. Instead of them writing the code, they’re guiding the agents.”

One Controller, Zero Staff, $50 Million in Monthly Transactions

James Agius proves what these benchmarks mean in practice. He’s the financial controller at Skool, a SaaS company running online educational communities. The company handles over $5 million in monthly spend with nearly $50 million flowing through its marketplace each month.

James is also the company’s entire finance department. The company doesn’t have any staff accountants, AP clerks, or analysts. It’s just him and seven specialized AI agents, plus an eighth admin agent that checks the others’ work and enforces controls.

When Agius took two weeks off, those 2,000 transactions piled up. His automations handled almost everything, from coding, categorizing and approving to syncing to the ERP. When he returned, just 67 transactions needed human judgment. The cleanup took 30 minutes.

“His job changed from doing the work to reviewing the work,” Blake explained on the podcast. That shift freed Agius for forecasting, cash management, and strategy. It’s the work finance leaders always say they want to do but rarely have time for.

The timing couldn’t be more ironic. Just as AI enables one person to run an entire finance function, the profession can’t find enough people to fill open roles.

A Personiv study cited in Accounting Today found that the number of unfilled accounting and finance positions per company jumped from 5 to 17 in a single year, more than tripling. Eighty-four percent of finance and accounting leaders say there’s a talent shortage. The hardest role to fill is the senior accountant role, cited by 43% of respondents.

The drivers aren’t mysterious. The profession has talked for years about how 75% of CPAs were approaching retirement. “Well, now they’re doing it,” Blake said. And the pipeline is thin because staff accountants have been leaving after just a few years.

As David pointed out, senior accountants are exactly the people who would manage AI agents, so the talent shortage and the AI transition are colliding at the worst possible moment.

Firms are responding by racing to adopt AI. Sixty-three percent of leaders use AI to ease hiring pressure, up from 23% last year. For example, Bennett Thrasher moved talent acquisition from HR to the growth function, treating recruiting as strategically as business development. “The human labor becomes more valuable because it’s augmented,” Blake noted.

The Race to Reinvent

The competitive landscape is shifting as fast as technology. New entrants and incumbents alike are making moves that suggest they see this transformation as irreversible.

Christina Ho, former PCAOB board member and past podcast guest, joined Oath, a venture-backed firm building an AI-native audit practice from scratch. No legacy systems or technical debt. It’s AI-first from day one. They raised $6.6 million in seed funding and aim to automate 80% of audit work by 2030.

Oath plans to connect directly to clients’ accounting systems for continuous verification rather than year-end evidence gathering. CEO Lucas Ward emphasized audit remains “a human accountability function” even as machines handle verification. They’re recruiting “accounting engineers,” hybrid roles combining accounting expertise with computer science skills.

The Big Four are taking notice. KPMG’s US CEO now takes the entire management committee to Silicon Valley every five to six weeks, meeting with venture firms like Andreessen Horowitz and Bessemer to identify potential disruptors. They’re open to partnerships or investments, anything to avoid being blindsided.

On the platform side, Ramp’s new Stack product shows where AI agents might actually live in the workflow. Stack connects to existing tools like QuickBooks and accepts plain-language instructions, like “This client allocates revenue by location, not department. Split it across six cost centers.”

As Blake observed, “The GL is not the best place for agents to live. You want the agents at the point of the transaction.” Ramp already sits at the point of spend, giving its agents rich context about each business. The market agrees. Ramp just raised $750 million at a $44 billion valuation.

Not every AI adoption strategy works, though. KPMG rolled out a dashboard requiring employees to use AI for roughly 75% of their working time. Predictably, employees immediately gamed it. They had AI summarize emails they’d already read or generate random drawings — anything to hit targets. Blake called it “token maxxing,” comparing it to padding billable hours. Amazon shut down a similar program after seeing the same behavior.

What Humans Still Own

Where does human value go when AI handles the routine work? Jeff identified three things AI can’t replace.

  1. Judgment. “AI goes off in weird directions,” he said. Experienced professionals must guide it through ambiguous calls.
  2. Trust. “The AI will tell you anything you want. You can never trust AI.”
  3. Accountability. “It’s never going to be liable for the numbers it gives you. What are you going to do, sue your AI?”

These are the differentiators for accountants who want to stay relevant as machines take over the rest.

All of the evidence from this episode points to AI crossing the competence threshold for basic bookkeeping and advancing toward complex tasks. One controller already runs a $50 million operation solo. Yet unfilled roles have tripled. Senior accountants are impossible to find. The retirement wave is here, and the pipeline is thin.

To thrive, you need to bring what AI can’t: judgment, trust, and accountability. The transition is here.

Listen to the full episode for the rest of Jeff’s interview, details on KPMG Australia’s whistleblower scandal fallout, and a discussion of the IRS leadership vacuum.

Accountants Rush to Adopt AI While Ignoring the Security Risks That Come With It

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Nearly nine out of ten accountants using AI report positive returns. But another statistic is more troubling. Over half of accounting firms have experienced data breaches recently, yet fewer than half have guidelines for how AI handles sensitive financial data. The productivity gains are real, but so are the risks we’re ignoring.

Blake Oliver, host of the Earmark Podcast, recently sat down with David Jani, Senior Content Analyst at Capterra, to unpack Capterra’s 2026 Accounting Software Trends report. The survey of 500 U.S. accounting managers shows the profession has moved beyond testing AI and into territory where the gap between adoption speed and security readiness is becoming dangerous.

 

The Productivity Gains Are Real (With a Catch)

AI in accounting has crossed from experiment to standard practice. More than half of accountants now use AI in their accounting software, and it appears across all company sizes, not just enterprises with big tech budgets. As David noted, “We’ve gone beyond the point of it being companies testing the water with this stuff.”

The most common uses for AI are chatbots and AI assistants, followed by data entry automation and fraud detection. AI is also making headway in predictive analytics, cash flow forecasting, smart invoicing, and bank reconciliation. David described it as “a coalescence around analytics and process-driven tasks.”

The 89% positive ROI figure comes from two main benefits. Half of respondents cite productivity gains, and nearly as many report reduced errors. So firms see real time savings and quality improvements.

But 48% of accountants manually check every single AI output. Not spot-checking, but checking everything. And about a third catch errors in their AI outputs more than half the time.

How do you square 89% positive ROI with error rates that high? David’s practical take is AI is “creating some gains in some areas, creating some extra work in others,” but the net result stays positive. Even when you add review time, firms come out ahead. But he cautioned, “It’s important that businesses still keep a close eye on the ROI of these situations and confirm it is delivering those gains.”

Meanwhile, plenty of work remains manual. More than half of respondents still handle financial reporting through spreadsheets or manual processes. Accounts payable and receivable, billing, invoicing, and payroll are all heavily manual. And yes, 51% of accountants still use Excel or Google Sheets for financial data. As Blake observed, spreadsheets have survived 40 years and aren’t going anywhere soon.

The Security Gap No One’s Taking Seriously

While firms celebrate productivity wins, the security picture is alarming, and almost nobody seems concerned enough to act.

Consider 52% of accounting managers surveyed have experienced a data breach in the last two years. That’s more than half. While David doesn’t have data linking these directly to AI, what he found about AI and sensitive data should worry every firm leader.

“Most companies don’t have clear guidelines on how they use AI tools with sensitive data,” David revealed. Fewer than half (49%) have guidelines for employee and payroll information. Coverage of bank reconciliation and customer billing data is even lower.

The perception gap is striking. Nearly half view AI cybersecurity risk as “minor,” another 12% as “insignificant,” and only 3% as “critical.” This might be “why so many people don’t have guidelines. Unfortunately, they just don’t perceive the risks at play,” David said.

Blake painted a scenario that’s probably happening now. Someone uploads payroll reports into free ChatGPT, where the terms of service may allow the vendor to train on that data. “We really need to step up,” he said.

The risks go deeper. Blake raised the issue of prompt injection, which involves hidden text in documents that manipulates AI agents into leaking data or changing payment information. It’s sophisticated and hard to defend against. As David acknowledged, “It’s a very new and rather sophisticated way of extracting information from a company. We still don’t really know enough about it.”

David didn’t sugarcoat his advice. “Guidelines around this don’t seem like much, and obviously, everyone is rushing to get AI tools. But it’s a huge risk factor we need to address.”

AI Is Raising the Bar

If AI makes accountants more productive, you’d expect fewer jobs. But the data tells a different story, and it came as a surprise to David.

“Despite a lot of reports predicting the end of accountants, it’s not really what we found,” he said. Companies are adopting AI, but “it’s not necessarily affecting hiring decisions in the same way. A lot of companies are actually more focused on upskilling.”

Blake offered a historical perspective. The same panic hit when VisiCalc and Excel arrived 40 years ago, yet accounting jobs grew. When cloud computing transformed the industry, client accounting services didn’t shrink. Instead, it’s grown year over year for a decade.

The talent shortage persists, with 73% of firms reporting trouble with retention and hiring. The hardest roles to fill are mid-career positions. About a third struggle to find financial analysts, with specialized accountants (tax and cost accounting) close behind.

The paradox is AI actually increases the need for experienced professionals. Someone must review those AI outputs that are wrong half the time. Someone must understand the AI well enough to catch mistakes. Someone must manage the security implications. All that requires judgment and experience, and that’s exactly what’s hardest to hire right now.

The data backs this up. Upskilling existing staff is the dominant strategy at 40%, double the 21% using AI to fill staffing gaps. Traditional hiring sits at 31%, with graduate programs at 23%. The profession is betting on people, not automation, to solve its workforce problem.

Looking Ahead: Challenges and Choices

What keeps accountants up at night? Budgeting and forecasting in an uncertain economy tops the list, followed by figuring out how to use AI effectively. As David put it, firms are trying to understand AI “in a way that makes sense.”

David has specific advice for where firms should invest their AI dollars. Map investments to your particular needs rather than chasing trends. For general guidance, he pointed to data entry automation and predictive modeling tools, especially cash flow forecasting and analysis dashboards, as areas delivering the most value.

When asked to predict what might change by the 2027 survey, David hopes to see more firms with updated security guidelines. “I think as these tools become more mature, more people will update their guidelines, especially for handling sensitive data like payroll and cash flow,” he said.

A Gap Between Speed and Safety

The Capterra data shows the profession is getting AI both right and dangerously wrong. The 89% positive ROI is genuine. Firms are saving time and reducing errors, even after factoring in review burdens. But that headline obscures the fact that over half have experienced breaches, fewer than half have AI data guidelines, and most dismiss the cybersecurity risk as minor, even with threats like prompt injection that the profession barely understands.

AI isn’t solving the talent crisis either. It’s raising the bar for what accountants need to know, making experienced reviewers more critical while the mid-career talent shortage intensifies.

Firms must build guardrails, write guidelines, and invest in upskilling their people to successfully work alongside technology that’s powerful but imperfect.

Want to dig deeper into these findings? Listen to Blake’s full conversation with David on the Earmark Podcast, and earn free NASBA CPE while you’re at it. 

Why the Most Profitable Accounting Firms of the Future Might Have No Employees at All

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One guy. Zero employees. He spends 70% of his budget on technology.

Sam Leon runs The Millennial CPA in Richmond, Virginia, where AI does most of the tax prep work while he reviews and signs off. He just landed on Accounting Today’s 2026 Best Firms for Technology list, not by building a bigger team, but by proving you don’t need one at all.

Meanwhile, KPMG is shutting down its entire federal government audit practice after losing a $60 million Pentagon contract. They’re reassigning 450 employees and cutting another 400 from advisory. The old work is shrinking. The new AI, cyber, and forensics work is growing fast.

On this week’s episode of The Accounting Podcast, hosts Blake Oliver and David Leary discussed what these stories mean for the profession. They explored how AI is making the “firm of one” model possible, tested the new QuickBooks and Xero connections to Claude, and wrestled with a big question: If AI can replace so much labor, what happens to the people and the economy that depend on them?

 

The Solo Practitioner Who Turned AI Into His Staff

Sam Leon took a simple but radical approach to building his firm. AI handles the grunt work of tax return preparation, including creating workpapers, doing year-over-year comparisons, and mapping QuickBooks data to tax forms. He reviews everything and signs the returns. That’s it.

“I see AI as coming together to be a total tax preparer, and whoever signs the returns is the reviewer,” Sam told Accounting Today. He thinks of the AI as his junior preparer while he’s the senior reviewer.

The time savings are wild. Work that would take a human three to five hours, such as creating detailed tax workpapers from QuickBooks exports, takes AI five minutes. And Sam has no plans to hire. “I won’t hire until I hit a wall with my AI preparers and AI workflow managers,” he said.

Blake validated this approach based on his own daily use of Claude Cowork. “To do it as an individual is totally possible,” Blake said. “And so I expect we’ll see more of these firms of one, and you’ll be able to scale up and make a lot of money, because you don’t have to hire employees.”

David connected this to a broader trend he calls the “minimum viable-sized company.” The old playbook was simple: raise money, hire people, grow. “You don’t need that anymore,” David said. “The future winners are going to be small, highly efficient teams with strong strategic clarity. Not large organizations.”

Of course, there are questions. How much revenue does Sam actually make? How does he handle client communication and invoicing? Is he a software engineer or just really good at prompting AI? Blake and David want to get him on the show to find out.

The Tools Are Getting Easier, But Still Have Limits

Right now, Sam’s model works because he’s willing to configure AI tools himself. But that’s changing fast as AI gets built directly into the software firms already use.

Canopy just launched an AI “Coworker” feature across its practice management platform. David was initially skeptical when he saw the sample prompts, which included things like “list all my clients,” that you could see with one click anyway. But Blake highlighted the real value: scope-creep detection that analyzes your billing and emails to spot when you’re doing more work than you’re charging for, automatic workflow updates when disaster declarations change filing deadlines, and meeting notes that automatically create tasks with assignees and due dates.

“These AI agents in practice management are going to be hugely important,” Blake said. “They’re going to make practice management ten times more valuable.”

The big platforms are also opening up to AI. Intuit just released connectors linking Claude to QuickBooks, TurboTax, Mailchimp, and Credit Karma. Xero has one too. But Blake tested both and found them pretty limited. You can pull basic reports and import transactions, but you can’t actually analyze transaction-level data yet.

“If they don’t make connectors more robust, they’re kind of useless,” Blake said. Still, the direction is clear. As David put it, “Claude becomes like your central gear that’s spinning data out to these other spots.”

KPMG’s Federal Exit Shows Where the Profession Is Heading

While solo practitioners are using AI to do more with less, KPMG is learning what happens when you can’t adapt fast enough.

The firm just lost its contract to audit the U.S. Army. It was a $60 million annual deal they’d had for over a decade. The Army has never passed an audit, and now the Pentagon wants to restructure the whole approach. KPMG responded by shutting down the entire federal audit practice and reassigning 450 people.

But that’s not all. They’re also cutting 4% of U.S. advisory staff, or about 400 people, mostly in regulatory risk and financial services consulting. These cuts continue a pattern that started in 2023.

Instead, KPMG is investing in AI, cyber, forensic services, and managed services. Traditional audit work is shrinking, while tech-enabled services are growing.

The Big Risk 

If companies use AI mainly to eliminate jobs, who’s going to buy their products?

Christine Kuglin and Bright Ikwetie wrote about this in Accounting Today, calling it the “AI efficiency paradox.” Businesses get more efficient by replacing workers with AI, but they’re also eliminating the incomes that drive consumer spending. It’s a potential death spiral. Less spending means less revenue, more layoffs, and more AI. Rinse and repeat.

The economic data is confusing. Weekly jobless claims just hit 189,000, the lowest in more than five decades. Yet manufacturing employment is down 88,000 jobs year over year. How can unemployment be so low when we keep hearing about layoffs?

“Is this just lagging?” Blake wondered. “Are these workers just finding jobs in other parts of the economy or maybe working for themselves?”

For accounting specifically, the demand for talent remains strong. Intuit analyzed LinkedIn data and found that both tax and accounting roles are “very hard to hire” nationally. They’re actively recruiting with flexible, remote-first benefits, which is exactly what the Big Four firms are cutting.

What This Means for Your Firm

The lesson from Sam is that one person can now deliver what used to require a team. The same principle scales up. A small firm can compete with a large one, and a mid-size firm can offer enterprise-level services.

But don’t use AI just to do the same work with fewer people. Use it to do work you couldn’t do before. As Blake put it, “The growth opportunity in accounting is advisory-type services. And AI paired with expert humans is just so incredibly powerful for doing advisory work like fractional CFO services, M&A advisory, and cost segregation studies.”

David sees another opportunity in helping clients “vibe code” custom apps instead of stacking expensive SaaS subscriptions. “I am confident that accountants could vibe code,” he said. “The old stack of app stacking is going to go away. You’re just going to help your client build the app they need.”

The tools are here. The demand is there. The question is whether firms will use AI to shrink or to grow. Firms that use AI to expand what’s possible rather than just cut costs will set the terms for everyone else.

Want to hear Blake test the QuickBooks-Claude connector live? Curious about how much Sam actually makes? Listen to the full episode of The Accounting Podcast for all the details, plus discussions on new IRS whistleblower rules, tariff refund lawsuits, and why procrastinating on AI adoption might actually pay off.

Stop Losing Money on Cleanup Work by Automating the Parts That Don’t Need You

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Cleanup and catch-up work is among the most in-demand services accounting firms can sell, and among the hardest to deliver profitably. That was the starting point for a recent webinar led by Megan Reid, a 15-year accounting veteran who started in Big Four, moved through private industry, and now works on the firm enablement team at Digits.

In the webinar, Megan demonstrated how AI-native accounting tools can transform cleanup engagements from time-intensive projects into scalable service offerings. She built a client file from scratch, imported raw PDF bank statements, and walked through an entire cleanup workflow in real time.

Why cleanup work kills profit margins

“Cleanup is obviously valuable work and it’s hard to scale,” Megan said, framing the core challenge clearly. New clients almost always arrive with some sort of mess to clean up. Maybe you have 18 months of uncategorized transactions or transactions that haven’t been posted from the bank feed. You want to take the engagement, but you know it’s going to be hard to make it profitable.

“We always uncover more skeletons in the closet than we think,” Megan noted during the demonstration. If you’re billing fixed fees, you get squeezed by unpredictable hours. Clients want fast turnarounds. Your teams are leaner. “You’re asked to do more with less,” she said.

“Business owners need that work to be done,” Megan pointed out. But the question is “whether or not your workflow lets you take them profitably.”

Breaking down a traditional cleanup shows where the hours go:

  • Gathering data
  • Importing it or connecting feeds
  • Categorizing tons of transactions
  • Reconciling accounts
  • Resolving exceptions
  • Making adjusting entries
  • Reviewing everything with your client
  • Delivering the final report

“In a typical 12-month cleanup or catch-up, you spend the majority of your time categorizing and reconciling transactions,” Megan explained. These tasks are also “the most repetitive, pattern-based parts of the job, which is exactly what AI is good at.”

From blank file to categorized transactions

Megan started her demonstration with a completely blank client file, essentially just an empty ledger. She then showed how to handle a common scenario in which a new client hands over a stack of PDF bank statements with no bank login credentials.

She dragged and dropped the first PDF bank statement directly into Digits. “It is extracting all that data from the bank statement, booking it and categorizing it as well,” Megan explained as the system processed the document.

The AI extracted transactions, identified vendors and customers (called “parties” in Digits), populated company logos and descriptions, attached website links, and categorized each transaction into the appropriate account. Megan noted the system pulls from models trained on “more than 800 trillion dollars’ worth of transactions.”

After uploading statements for June through October, hundreds of transactions flowed in. When processing finished, only 12 were flagged for review. “Instead of manually clearing bank feeds,” Megan said, “come here and look at the exceptions.”

These were transactions that required confirmation. Megan clicked into one from Swift Courier Services. The AI suggested “contractors and consultants.” She confirmed it with one click.

From there, the system natively learned from that categorization. It immediately found two similar transactions and offered to update them together. The exception list dropped from 12 to 8 in seconds.

Bank reconciliation without the manual work

Megan demonstrated three ways to get bank statements into the system for reconciliation. You can connect directly to banks like Mercury, Wells Fargo, Chase, and US Bank, which pull statements automatically via API. You can drag and drop PDF statements anywhere in the product. Or you can use email ingestion, where each client gets a unique email address to forward statements.

She uploaded the June statement by dragging it onto the reconciliation screen. The system read the PDF, extracted every line item, and verified each against the ledger. Megan explained that the system uses “pixel bounding boxes” to match statement entries to ledger entries.

June needed one manual step: adding a beginning balance entry that the system couldn’t infer without a connected bank account. Megan created the entry directly in the reconciliation screen. “Unlike legacy systems, where you may have to have three different tabs open and make changes and then come back and refresh, everything can be done directly in here.”

Then she uploaded July’s statement and navigated away. When she returned, it was done. “The statement was uploaded by me. The auto reconciliation was kicked off by Digits and even finalized by Digits,” she showed in the timeline view.

For larger cleanups, Megan recommended uploading multiple statements at one time. Handle any beginning balances in the first month, then subsequent months often complete automatically.

Review tools that surface what matters

Even with AI handling categorization, accountants still need to review and sign off. “It doesn’t replace the accountant. It just removes that tedious work so that you can focus on those judgment calls,” Megan emphasized.

She demonstrated several review approaches. The general ledger view shows all transactions organized like a trial balance, including assets, liabilities, equity, revenue, and expenses. You can filter by status, amount, source, department, or location. Bulk updates work on hundreds of transactions at once.

Megan said the vendors and customers views are her favorite. They each flag two critical items:

  • New vendors or customers: Any vendor (or customer) the AI sees for the first time in your selected period
  • Split categorizations: Vendors (or customers) whose transactions appear in multiple categories

“I just need to have eyes on things it has not seen before,” Megan explained. Even if the AI categorized with high confidence, you have final review and say on how it was categorized..

For transactions needing client input, the collaboration happens in one place. Megan showed how to comment on any transaction: “Hey client, what is this for?” The client receives an email with a link, can respond directly in Digits or reply to the email, and the response appears on the platform. “All the collaboration is centralized in one location,” she said, “instead of you having to manage a ton of emails and download Excel files.”

Delivering professional reports, not data dumps

The final step Megan demonstrated was creating custom reports. While the financials inside Digits update live as transactions flow in, cleanup engagements need a formal deliverable, a static document that locks the numbers in place.

Megan built one on screen. She added a cover page, used AI to draft an executive summary, embedded links to the client’s checklist, and configured the financial statements with period comparisons and trend lines. The system includes “hover to discover” insights that show period-over-period changes and what drove them.

When you need to make adjustments after sending a draft, you create a new version. “Any adjustments you’ve made in Digits will then update directly to this report,” Megan explained. Publishing the final version removes the draft watermark and notifies the client.

The platform tracks everything, including when you created the report, when you published it, when the client viewed it, and all comments from either party. You have a complete record of the deliverable and the conversation around it.

“We’ve done 12 months of cleanup in an hour and a half instead of days,” Megan concluded.

What this means for your firm

The key takeaways from Megan’s demonstration show how cleanup engagements can become profitable:

  • AI categorizes the vast majority of transactions automatically, flagging only true exceptions
  • Bank reconciliations can run automatically when you upload statements
  • The system learns instantly from every correction without rules to build or maintain
  • Your time shifts to reviewing anomalies, making judgment calls, and delivering polished reports

One practical consideration came up during Q&A. When asked about importing messy QuickBooks Online data, Megan confirmed that direct QBO migration exists but cautioned, “You maybe don’t want the AI to learn off of really messy data. You maybe just want to start fresh.” The system uses imported data for baseline training, so starting clean might make more sense for particularly messy files.

For firms trying to grow, this changes the economics of client acquisition. Every prospect with messy books becomes an opportunity rather than a capacity problem. When you can handle cleanup work profitably, predictably, and consistently, you can say yes to more engagements while maintaining margins.

Watch the full on-demand webinar to see Megan’s complete demonstration from blank file to published financials. If you have cleanup engagements in your pipeline right now, consider what your workflow could look like when the repetitive work is automated.

The Accounting Profession’s Favorite Performance Metrics Are Now Dangerously Misleading

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PwC Australia cut partners by 35% and staff by nearly 40% since 2023, yet partner income went up 6%. Meanwhile, the IRS says it just had its “most successful filing season in history” with 25% fewer employees. Fewer people are doing more work than ever. But the accounting profession’s core systems for measuring performance, deciding who to hire, and tracking technology investments were built for a different world.

In a recent episode of The Accounting Podcast, hosts Blake Oliver and David Leary talk about a profession transforming from the inside out. From IRS staffing cuts and Big Four workforce reductions to outdated metrics and licensing bottlenecks, we’re seeing technology race ahead while the infrastructure lags.

Tax Season Success Story (with a Catch)

IRS CEO Frank Bisignano told the Senate Finance Committee that the 2025 filing season was remarkably successful despite the agency losing about a quarter of its staff. The IRS received more than 134 million individual returns, 98% of which were filed electronically. Over 90% of filers got refunds in under 21 days, and the average refund jumped 11% to over $3,400.

The agency credited technology upgrades and AI for the performance boost. Using AI and data analytics to identify underreporting, the IRS sent 500,000 letters that prompted corrections, generating $250 million in additional collections. Enforcement revenue was up 12%, and amended return processing improved from six weeks to just three days. Five noncompliance cases alone brought in $2 billion.

“Just five cases and $2 billion,” Blake noted. “That shows there are some real whales out there when it comes to not paying your taxes.”

But David pointed out an interesting wrinkle. There’s still no confirmed IRS commissioner. Bisignano is serving as CEO without congressional approval, yet Congress seems to have accepted this arrangement with little pushback.

Managing by an Outdated Scorecard

For decades, accounting firms have relied on metrics known as LUMBAR: Leverage, Utilization, Margin, Billing rate, and Realization. These metrics made sense when firms billed by the hour and success meant maximizing billable hours. But as AI compresses work time and firms shift to fixed fees and advisory services, these metrics become misleading.

Douglas Slaybaugh argued in Accounting Today that firms need to track different categories entirely. Instead of hours and billing rates, he suggests measuring:

  • Value creation, like advisory revenue as a share of total revenue
  • Automation rates
  • Redefined leverage, like revenue per employee rather than staff-to-partner ratios
  • Organizational health, including “regrettable turnover,” or losing people you wanted to keep
  • Client relationships

Blake was blunt about why traditional metrics fail. “If you go over or under on a job based on a job profitability calculation, which is based on hours, it doesn’t actually change anything in the firm because your staff costs are fixed.” When staff are salaried and clients pay fixed fees, being “over budget” on hours is meaningless. “We get so in the weeds,” he added. “We lose the forest for the trees.”

David pushed further, comparing it to Apple before Steve Jobs returned. The company had separate profit-and-loss statements for every product, optimizing each individually while missing the bigger picture. Jobs collapsed it all into one P&L, recognizing Apple as an ecosystem. “Why do you need all these metrics?” David asked. “Focus on the big picture of your firm.”

The shift is already happening at big firms. Client accounting services is the top growth driver for Top 100 firms for the third straight year, with 85% of firms reporting CAS growth. These services now include cash flow forecasting, budgeting, and strategic finance. That work doesn’t fit hourly billing models, yet many firms still try to manage these engagements with traditional utilization targets.

Licensing Rules as a Talent Bottleneck

Current CPA licensing creates what Jack Castonguay of Hofstra University calls a one-way street: firms can hire accountants and train them in AI, but they can’t easily bring in AI experts and train them in accounting.

“The US licensure model almost forces us to start with accountants and teach them AI skills,” Jack wrote in Bloomberg Tax. “It’s good to have accountants who are well versed in AI, but it would be better to also have AI experts trained in accounting. We should create space for both.”

Jack delivered a sharp observation about recent reforms. “We took away the 150-hour moat around the profession, but ultimately built a wall higher for non-accounting majors seeking to become CPAs.”

Blake agreed strongly. “If you can learn accounting theory on your own and pass the CPA exam, why do we require you to go take all these courses? The CPA exam is supposed to test the knowledge. And if you got the knowledge in another way, why do we care?”

The problem plays out in real life. A viewer shared that, despite having a business degree with an accounting minor, Arizona’s requirements and the need for CPA sign-offs create additional barriers for those with non-traditional backgrounds, such as military service.

There’s some progress. Maryland and Nevada joined roughly 30 states adopting alternative CPA pathways that require a bachelor’s degree, two years of experience, and passing the exam, without the 150-hour rule. But David expressed frustration. “We just got past the 150-hour rule, and we’re going to be on this debate and treadmill now for the next five years.”

Meanwhile, big firms aren’t waiting. Beyond PwC Australia’s dramatic cuts, Deloitte US slashed benefits for non-client-facing staff, halving parental leave from 16 to 8 weeks, cutting PTO by five days, and eliminating the $50,000 adoption and surrogacy benefit.

“What if this is just a way to get people to quit so you don’t have to lay them off from AI later on?” David wondered. The timing makes sense. While 51% of workers said they’d quit over return-to-office mandates in 2025, that number has crashed to just 7% in 2026. Workers are scared, and employers know it.

Betting on AI Without Measuring Results

A Thomson Reuters survey of 1,500 professional services respondents across 27 countries revealed only 18% track AI’s return on investment. Forty-two percent don’t measure at all, and 40% aren’t sure whether they do.

“Pretty much 80% aren’t tracking the return on their AI spend,” David said.

Those who do measure focus on the wrong things. Seventy-seven percent track cost savings, 64% track employee usage, but only 26% track client satisfaction, 23% track revenue growth, and just 17% track new business generation.

“They’re not tracking the correct metrics in their firms,” David noted. “This is not an accounting firm problem. This is professional services.”

The risks of poor AI implementation are real. Deloitte faces investigation in Newfoundland and Labrador after a resident discovered its $1.6 million healthcare report contained AI-generated fake citations. This is at least the third Big Four AI incident.

“They’re selling AI consulting services,” David said, “and then they prove they can’t do it themselves.”

The measurement problem extends beyond AI. Annual recurring revenue (ARR), the metric driving virtually every subscription company’s valuation, has no GAAP definition or standardized calculation. Companies define it however they want. A startup CEO recently made headlines for simply making up ARR numbers.

“If I were in charge of accounting standards, SaaS metrics is the first project I would have FASB do,” Blake said. “It’d be the best thing we could do for tech companies.”

The Path Forward

The accounting profession faces a challenge. The technology works, but the supporting infrastructure hasn’t caught up. Firms still manage by metrics that don’t reflect value creation. Licensing rules block the tech talent firms desperately need. And most organizations aren’t even measuring whether their AI investments pay off.

PwC Australia’s CEO, Kevin Burrowes, put it bluntly: “The future is fewer people doing the same amount or fewer people doing more.” Firms that don’t rebuild their internal systems to match this reality risk falling behind in a rapidly transforming profession.

For the full conversation, including discussions about Representative Ilhan Omar’s accounting disclosure error and more details on all these developments, listen to the complete episode.