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Accounting Automation

AI Can Reconcile a Bank Account End to End Without Instructions

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The accounting profession sees AI companies building tools that weren’t meant for accountants but are increasingly doing accountants’ work. On Episode 482 of The Accounting Podcast, hosts Blake Oliver and David Leary discussed Perplexity and Palantir’s moves into core accounting territory while most firms struggle to see any productivity gains from their AI investments.

Trump Accounts Hit 90% Adoption in First Year

Before getting into AI disruption, the hosts discussed a surprising government success story. Around four million children have been signed up for Trump Accounts in the program’s first year. That’s an 85-90% adoption rate among eligible births since January 2025.

“Think about 401(k) plans,” David pointed out. “It’s been decades and they’re only at 35-40% participation. With college savings plans, it’s been 25 years and 25% participation.” The difference is Trump Accounts combine simplicity (a one-page form filed with your tax return) with immediate value (a $1,000 government contribution, plus additional funds from donors like Michael Dell for low-income families).

Blake ran through the potential impact. If families contribute the maximum $5,000 annually, a child could have $271,000 by age 18 based on historical S&P 500 returns. “That’d be pretty nice,” he said. “Turn 18 and you get $271,000. Maybe that would be a down payment on a house someday.”

Perplexity and Palantir Target Core Accounting Work

The real disruption story started with a simple Instagram ad that caught David’s attention. It read “Find every duplicate entry hiding in your QuickBooks.” The advertiser wasn’t a QuickBooks app developer or accounting software company. It was Perplexity, an AI company better known as a search engine competitor.

Clicking through revealed Perplexity’s new QuickBooks “health check” offering. It includes P&L analysis, expense categorization, AR/AP aging reviews, and reconciliation audits. Perplexity’s homepage also features tax-specific prompts, and they’ve officially launched “Computer for Taxes,” an AI agent that drafts full federal tax returns.

“Perplexity is doing what a whole company just launched to do,” David observed, referring to startups like TaxGPT that built entire businesses around AI tax prep.

Meanwhile, the IRS is testing a $1.8 million pilot with Palantir to build an AI system called SNAP for selecting audit targets. The system will analyze both structured and unstructured data to identify potential fraud and noncompliance.

“People have called Palantir the most dangerous company on earth,” David noted. “So that’s who’s going to pick audits now for the IRS. It’s a little bit scary.” The system could potentially cross-reference tax returns with e-commerce storefronts, social media activity, and data from other government agencies where Palantir operates.

Blake’s Test Whether AI Actually Does the Books

Blake decided to test whether these AI tools could handle real accounting work. He asked Claude Cowork to reconcile a brokerage account in Xero that had no bank feed connection.

Without detailed instructions, Cowork created its own task list, asked clarifying questions, then got to work. It parsed the PDF bank statement, converted it to a CSV formatted for Xero’s import requirements, imported it as a bank feed, matched transactions through the Chrome browser, ran the reconciliation report, and exported it as a PDF.

“End to end, no hand-holding,” Blake said. Even better, he had Cowork save the entire process as a reusable “skill” that improves with feedback. “Now I can upload a PDF and say ‘reconcile this account’ and it will do everything as I like it.”

This raises questions for accounting software companies. “What is the point of Jax in Xero then?” David asked. If external AI agents can perform full reconciliation by interacting through a browser, why build internal AI at all?

Blake says Xero built Jax by copying chatbot functionality from ChatGPT. It’s limited to conversation windows with no ability to handle multi-step workflows. “The AI platform companies have simply raced ahead,” he said.

Firms Are Automating the Wrong Things

Despite all this technological capability, 80% of firms report no measurable AI impact on productivity after three years and billions in spending. The problem, according to a CFO.com opinion piece Blake highlighted, is that firms use the wrong measurement framework.

Most organizations use cost accounting, which rewards local efficiency gains, including hours saved here, a task automated there. But they should use throughput accounting, which asks whether AI actually removes the main bottleneck limiting revenue generation.

“You may have theoretically saved all these hours on tax prep, but if all the returns pile up at review, it’s not getting to the client any faster,” Blake explained. “You’re not delivering any additional value.”

Both hosts agreed the real bottleneck in most accounting workflows isn’t doing the work; it’s getting the information needed to do it. Client document collection causes the biggest delays.

Blake sketched a solution: an AI agent that monitors client folders, compares submissions against a requirements list, and automatically follows up on missing or incomplete documents. “AI won’t be tired on day two or day three,” David said. “It can review these things and send the email again tomorrow. And it’s never going to complain.”

The Three-Person Firm of Tomorrow

The hosts painted a picture of accounting’s near future: firms where a few people do the work of ten, supported by AI agents handling staff-level functions.

“Instead of having 30 clients at your firm, you now can have 90,” David said. “That makes a huge difference.”

Blake envisions a new role structure that includes a CPA or tax expert, a dedicated technologist, and an apprentice, plus “half a dozen AI agents doing the staff functions.” He pointed to conversations with Peter McCarroll of Fuel Accountants, who predicts every firm will need a technologist role.

But Blake offered a reality check. “Cowork crashes. Agents are slow. Building a reliable tech stack in a period of this much change is genuinely hard.” Firms don’t need to adopt every new tool. But they do need to understand their workflows well enough to know exactly where to point AI.

General-purpose AI platforms are building accounting capabilities faster than the profession anticipated. They’re not waiting for permission or partnerships. Listen to the full episode for more insight into figuring out where your real bottlenecks are and pointing AI at them, or watch as others (maybe even your clients) use these tools to work around you.

The Month-End Close Is Accounting’s Biggest Bottleneck. Here’s How AI Is Dismantling It

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The day before a tax deadline, and accountants from Miami to Vancouver, Portland to New York, logged into a CPE-eligible webinar to learn something that could fundamentally change how they work. The webinar showed how these professionals can shrink the most time-consuming part of their month-end close, like reconciliations, transaction coding, and bank statement chasing, from days to minutes.

Megan Reid, product specialist at Digits, led the session, and she brings a unique perspective. She’s an accountant with 15 years in the trenches, starting at a Big Four firm, moving through banking and construction, and now helping firms build what she calls an “AI-native” practice. As she put it to the audience, “As accountants, we want to be able to serve more clients, provide better service, and do so quickly and efficiently.”

The traditional month-end close is accounting’s biggest bottleneck. It’s that manual grind through booking transactions, reconciling statements, updating schedules, reviewing anomalies, and (if there’s time left) analyzing the numbers and creating the reports clients care about. “It’s a manual, tedious, time-consuming process that honestly leaves a lot to be desired for both the business owners and the accountants,” Megan said bluntly. 

But what if you could flip that entire workflow? What if instead of reviewing every transaction, you only touched the ones AI couldn’t confidently handle? That’s exactly what Megan demonstrated live, showing how AI-native platforms transform the close from a compliance chore into an opportunity for real advisory work.

The bottlenecks killing your efficiency

Before diving into solutions, Megan mapped out where the traditional close breaks down. You start in QuickBooks or your ledger of choice, but quickly find yourself bouncing between Excel, browser tabs for vendor research, your close management tool, and who knows what else. “Not only are you managing the work across all these multiple platforms,” she explained, “you’re also spending time validating sync accuracy, troubleshooting issues, and making sure the data moves seamlessly throughout the various systems.”

Each phase has its own special frustrations:

  • Manual data entry and rule management introduce human error
  • Fighting with bank access and chasing clients for statements
  • Disconnected tools for AP, credit cards, and close management
  • Team members use different processes, causing rework and confusion
  • Manual journal entries pile up at period-end

As a result, most of your time goes to necessary but low-value tasks, leaving little room for the analysis and insights your clients actually hired you to provide.

How AI learns your way of doing things

The shift to AI-native platforms involves intelligence that learns and adapts. When Megan pulled up the demo client in Digits, she showed hundreds of transactions the AI automatically categorized. Only eight were flagged for review.

“How does it know how to categorize transactions?” she asked, anticipating the obvious question. The answer lies in three layers of learning.

First, there’s client-level learning. When you correct a categorization for a specific client, the system learns instantly. “If you review something for a brand new client and you say, ‘nope, you categorized this to software, but I actually want it to be cost of revenue,’ Digits learns from that instantly,” Megan explained.

Second, there’s firm-level learning. The system recognizes patterns across your entire client base. If the system does not have the client-level layer of knowledge, it falls to the firm-level. “How has my firm done this across all of my clients? It automatically applies your firm’s unique value to your client base.”

Third, when a transaction is entirely new, proprietary models trained on billions of dollars’ worth of transactions make the call.

During the live demo, Megan reviewed a U.S. Patent and Trademark Office transaction the AI thought might be taxes. She looked at the suggestions (taxes, legal, or a new intangibles account), selected “Legal,” and clicked save. The system immediately found two similar transactions and updated them automatically. The review queue dropped from eight to five in seconds.

But what really eliminates busywork is the AI agents run 24/7 in the background, researching vendors and populating details. “None of this has been populated manually,” Megan showed, clicking through a vendor profile complete with name, logo, description, and related websites. “We’re essentially researching them and populating all of the data for you.”

Bank reconciliation without the chase

If transaction categorization is tedious, reconciliation might be even worse. You know the drill: fighting for bank access, emailing clients for statements, then manually comparing the ledger to the statement line by line.

Megan demonstrated the “happy path” first. Digits pulled a Mercury bank statement via an API, automatically kicked off reconciliation, matched every transaction with pixel-level precision on the PDF, confirmed the ending balance, and finalized everything. Zero human touches required.

“Some firms we work with actually say, ‘I uploaded six months of bank statements and just watched them finalize one by one. And I didn’t do anything,'” Megan shared.

When auto-reconciliation can’t finalize completely, it doesn’t leave you guessing. The system flags specific issues, such as:

  • Missing transactions that exist on the statement but not in the ledger (one click to create)
  • Date mismatches where something cleared May 31 but hit the ledger June 1 (one click to adjust)
  • Unsettled items like checks that haven’t cleared yet

For banks without API access, such as small credit unions, you simply drag and drop a PDF statement. During the demo, Megan dragged a statement into the system and watched it extract data and start reconciling in seconds.

She took it further with a cleanup scenario. Starting with a brand-new bank account, she imported a PDF statement. Within moments, 14 transactions appeared as uncategorized. Seconds later, the AI had populated every vendor name and category without a single manual input.

Turning saved time into client value

Speed alone isn’t the point. As Megan emphasized, “the compliance and the month-end close is really just a means to an end,” the end being insights and value for clients.

The dashboards in Digits default to the current month because, as Megan noted, “knowing something two months late doesn’t usually help.” Every metric is live and drillable. Click into gross income, and you see the definition, calculation, and every underlying transaction. Your clients finally understand how you arrived at the numbers.

Each client gets customized dashboards. “Maybe you have a client that’s like, ‘we’re spending so much money on travel,'” Megan explained, showing how to add customized metrics that are specific to each client. A profitable client with ten years of runway might swap that widget for gross profit or vendor analysis.

Collaboration happens right on the platform. On any transaction, category, or report, you can leave a question. The client receives a notification and can respond directly from email without logging in. “One of the biggest pain points is transfer of knowledge,” Megan said, “making sure that you have everything that you need from your clients and vice versa.”

Custom reports become interactive stories rather than black-and-white PDFs. The AI generates insights like “You earned 33% more in March compared to the prior month” with drill-down capability to see exactly why. Important insights can be pinned to the executive summary so they’re the first thing clients see.

What this means for your firm

During the Q&A, attendees asked practical questions. One wondered if this integrates with QuickBooks or replaces it entirely. “Digits is a complete ledger system. So it’s a complete replacement,” Megan answered. They can migrate QuickBooks data in about two minutes, but this is a ground-up rebuild, not a bolt-on tool.

Another attendee asked about company scale. The focus is on small and medium-sized businesses, which is the client base most firms serve.

The shift from reviewing everything to reviewing only exceptions makes the close faster and more consistent across your team, less error-prone, and it frees up capacity to serve more clients without hiring proportionally.

“It’s a very exciting time to be an accountant while also a little bit scary,” Megan acknowledged near the session’s end. “I think it’s a time to really lean in and be excited.”

She’s right. The firms embracing AI-native tools now will deliver premium advisory services while their competitors are reconciling bank statements at midnight.

To see these workflows in action, watch the full webinar. Every accountant who signs up gets access to a sandbox demo environment where you can test these workflows with real data. And if you attended live or watch the recording, you can earn CPE credit through the Earmark app. Just search for the course and complete the quiz.

The close is changing. Will you lead that change or follow it?

When Tax Day Was Party Night at the Post Office — And Why AI Is About to Upend Everything Else About Accounting

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Before tax e-filing took over, April 15th was a public spectacle at American post offices. As Blake Oliver and David Leary discussed on their Tax Day episode of The Accounting Podcast, crowds would gather until midnight, with live entertainment, giveaways, and even Playboy offering “stress relief massages” in pink booths. In Philadelphia, there was a “dunk the IRS agent” booth for charity. Radio stations broadcast live. Fast food chains handed out samples. It was America’s weirdest annual party.

Those days are gone — 94% of returns are now filed electronically. But as the hosts explored in this wide-ranging episode, the accounting profession faces disruptions far more profound than the shift from paper to pixels. Within three years, KPMG expects routine audit testing to have “next to no human beings” doing the work. Hobbyist developers are cloning QuickBooks with AI over a weekend. And a third of workers aren’t even checking AI outputs before they submit them.

The IRS Can’t Keep Up — With Rules or Technology

The profession’s struggles with rapid change start at the top. Just five days before the filing deadline, the IRS finalized which jobs qualify for the new no-tax-on-tips deduction. Podcasters made the cut (Oliver and Leary were pleased), along with tattoo artists, ice sculptors, and golf caddies. Accountants didn’t.

“Five days after they finalized these rules to implement them for our clients,” Oliver noted with frustration. The deduction allows eligible workers to exclude up to $25,000 in tips from taxable income, but mandatory service charges don’t count. “This could be the death of the automatic gratuity,” Leary speculated, since those forced tips won’t qualify.

Meanwhile, Americans are spending 11.6 billion hours completing federal compliance forms — mostly tax returns. The value of that labor? Over half a trillion dollars. “That’s material,” Oliver said, noting it represents a significant chunk of the economy devoted to paperwork.

The IRS’s own modernization efforts tell a cautionary tale. The agency had 126 AI projects running as of last summer, up from just 10 in 2022. But after losing 25% of its workforce, 61% of those projects remain unfinished with no plan to close the skills gap. Even more puzzling: the IRS killed its Direct File program despite it costing only $16 million instead of the estimated $61 million and growing 78% year-over-year. “The program was gaining traction and was less expensive than they thought it was going to be, and yet it got canceled anyway,” Oliver observed.

The Big Four’s Radical Restructuring

While the IRS struggles with basic modernization, the Big Four are racing ahead with AI automation that could eliminate thousands of jobs and upend the billable hour model that has defined the profession for decades.

KPMG is moving fastest. They’re piloting AI systems this summer and deploying them next year for routine testing of transactions like payroll, receivables, and cost of goods sold. “Within 2 or 3 years, routine testing could become the first major audit area with effectively no human audit team directly doing the work,” Oliver quoted from KPMG’s audit chief digital officer. “Next to no human beings.”

The other firms aren’t far behind. PwC’s evidence-matching tool now processes 30 client document types, up from six months ago. EY is testing something even more futuristic: AI audit agents that talk directly to client AI agents to gather documents and prepare workpapers. Only Deloitte is publicly pumping the brakes, emphasizing AI should “augment not replace” human auditors.

The numbers are stark: Big Four leaders expect 20-30% of a typical audit to be fully automated by 2029. KPMG UK is already cutting 440 audit jobs. “I don’t see any other outcome than the Big Four just cutting massive numbers of staff jobs,” Oliver said. “If they do this right… that’s 20 to 30% of their billable hours. What are they going to do? Just raise their rates 20 to 30% to compensate?”

Leary had the line of the episode: “Agents are the perfect accounting firm employees. The partners are going to love them.”

The traditional career path is crumbling too. EY’s talent chief told Business Insider that linear career models are becoming “less relevant” as AI values skills over tenure. Oliver speculated firms might shift from hiring masses of new graduates to recruiting experienced professionals from industry, or moving to an apprenticeship model with smaller, more intensively trained classes.

Everyone’s Building Their Own QuickBooks Now

The disruption isn’t just coming from the top. A Reddit user built a full accounting system that runs inside Claude Desktop — no interface, just chat. You tell Claude what happened, and it updates your books. Another developer cloned QuickBooks Desktop using AI, creating a free open-source alternative. The motivation? “I didn’t want to pay for QBO.”

“You as an accounting firm had control over your tech stack and your clients’ tech stack,” Leary explained. “We’re a Xero shop or a QuickBooks shop… Now your clients are just building their own stuff. How do you as a firm manage this now?”

Oliver’s prediction, based on every past tech revolution: “We will end up with more work rather than less, because it will enable our clients to do way more accounting stuff that we’ll have to clean up.”

On the funded startup side, Juno raised $12 million to build AI tax prep that automates 90% of data entry while keeping CPAs in the loop. The key: transparency over autonomy, with source-to-return traceability and visual validation tools. Artifact launched Omni, which Leary called “a Zapier for accounting firms” — it trains AI agents to use your existing tech stack rather than replacing it.

Meanwhile, legacy players are scrambling. Xero published a blog post claiming to be an “AI native operating system.” Leary counted over 20 buzzwords and read them aloud in a devastating list: “AI native, intelligent SaaS, autonomous finance, system of action…” His verdict: “I don’t think this is written for customers. I think this article is written for the street in an attempt to move the stock price.”

The Quality Crisis Nobody’s Talking About

Here’s what should terrify every firm leader: 35% of workers rarely or only occasionally review AI output before submitting it, according to a Resume Now survey. Eighteen percent trust it straight out of the box. Only 40% review AI output every single time. And 15% use AI at work secretly without telling their manager.

“That should scare you as an accounting firm owner,” Leary said.

Oliver argued firms need systems with built-in controls: “If an employee is just generating something with AI… and they didn’t change anything or they didn’t spend any time looking at it, then flag that.”

The stakes are real. The episode covered two fraud cases that show what happens with weak oversight. A New Jersey preparer filed over 100 false returns seeking $170 million in pandemic credits, getting $55 million before being caught. A Pennsylvania preparer started a new $5.5 million fraud scheme while still on supervised release from a previous conviction.

What Separates Winners from Losers

A Hinge Marketing study of 133 firms revealed a massive performance gap emerging. High-growth firms are growing at 33% annually versus 9.6% for average firms. The difference? High-growth firms spend 9% of revenue on marketing (versus 5% for others), and over 90% use AI for content creation, automation, and research.

“If you have a firm that’s growing at 10% and you want it to grow at 30%, spend 10% of your revenue on marketing,” Leary summarized, though Oliver questioned whether it’s causation or correlation: “Is it just that the firms that are growing really fast have money to burn on marketing?”

The Reckoning Is Here

The accounting profession has always adapted slowly. As Leary noted, “Just ask Xero how it takes decades for them to barely make a scratch into the QuickBooks world.” But this time feels different. The changes are coming from every direction at once — Big Four automation, bedroom coders, funded startups, and clients building their own systems.

The irony is thick. Even as AI promises to make location irrelevant, EY is requiring US tax staff to work in-office 12 days a month. The IRS has 126 AI projects but can’t finish them. Firms are adopting AI while a third of workers don’t even review its output.

For firms willing to invest, experiment, and build proper controls, the opportunity is massive. For those hoping to wait it out, the message from this episode is clear: the profession that gathered at post offices until midnight to file paper returns is gone. The question isn’t whether AI will transform accounting — it’s whether the profession can maintain its core promise of trustworthiness while everything else changes around it.

To hear the full discussion — including the story of a disgruntled worker who burned down a $500 million Kimberly-Clark warehouse over pay disputes — listen to the complete episode of The Accounting Podcast.

The Manager Paradox: Why AI Agents Need Just as Much Oversight as Human Employees

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David Leary had something to confess at the start of The Accounting Podcast episode 471. He needed an employee health insurance survey for his company, and the whole thing, from blank page to finished Google Form, took him three and a half minutes.

“I started with nothing, and I needed a result, and end to end it did everything for me,” David told co-host Blake Oliver. ChatGPT created the survey questions first. When its implementation got clunky, Google Forms with built-in Gemini AI took over and built the entire form. No tedious field creation or manual option adding. Work that would have taken an hour vanished in the time it takes to brew coffee.

It’s the kind of AI success story that’s becoming common: technology wiping out drudgery and freeing humans for better work. But as the hosts dug deeper in this episode, they uncovered a reality check for accounting firm leaders.

AI’s Hidden Cost: Same Management Time, Different Headaches

The tools keep getting better at connecting dots. Blake pointed to Google’s new “Personal Intelligence” feature that links Gmail, photos, YouTube, and search into Gemini with one click. ChatGPT has similar workspace integrations that search your email history for client and project information.

“Once your firm gets big enough, you don’t realize three other people also have relationships with that client,” David noted. AI that surfaces that context before you act is a real leap forward.

But the success story gets complicated when you deploy AI agents across an organization. Jason Lemkin, who runs SaaStr (a community for software startup founders), has been tracking the results of such deployments. At SaaStr, about 60% of the team is now made up of AI agents. They deliver huge productivity gains, but also need about the same weekly management time as humans.

“The big mistake,” David explained, summarizing Jason’s findings, “is that you can’t treat AI as set-it-and-forget-it. You have to have daily management of AI.”

The reason you need that oversight is the accuracy rates. For five- to ten-minute tasks, AI hits near-perfect accuracy: 99.9%. But stretch those tasks to an hour or two, and accuracy drops to 80% or even 50%. And AI mistakes don’t announce themselves.

“The AI makes these small mistakes that compound into big mistakes,” Blake said. “Humans do this, too. If you don’t have proper oversight of people, they’re just doing their own tasks, and small errors can compound into disasters.”

When There’s No One Watching the Store

The IRS is an excellent case study for what happens without human oversight of AI. The IRS just lost more than 25% of its workforce through various reduction programs, according to the IRS Advisory Council’s annual report. Over 2,000 IT workers have left since January 2025 alone. More than half of the $80 billion allocated under the 2022 Inflation Reduction Act has been rescinded, totaling about $42 billion since 2023, including nearly all enforcement funding.

Now the agency faces implementing the One Big Beautiful Bill Act (OBBBA), which includes more than 100 tax law changes. They need new guidance, technology updates, and process changes—all with fewer people and less money.

The consequences of this skeleton crew approach became clear in the case of Attallah Williams, a former SBA and IRS employee charged with stealing more than $3.5 million from federal COVID-19 relief programs. Williams used insider access at both agencies to approve fraudulent applications, recruiting accomplices through Instagram and collecting kickbacks. The scheme ran for three years and touched Paycheck Protection Program (PPP) loans, Economic Injury Disaster Loan (EIDL) grants, and employee retention credits.

“If one person can approve fraudulent pandemic applications, there are no controls at the federal level,” David said.

Tax Season Reality Check

Against this backdrop, tax season readiness varies wildly. CPA Trendlines’ busy season survey found that only 44% of firms feel about as ready as they were last year. Larger firms with 25 or more professionals report greater stability thanks to deeper staffing and refined processes. Smaller firms with 1-10 employees face the most volatility.

“Late documents, absences, compressed review cycles. When you have fewer people, you have less redundancy,” Blake noted. “When problems happen, it hurts more.”

Tax-heavy firms feel particularly exposed since their entire season depends on client behavior. Firms with recurring revenue from bookkeeping or advisory work report more stability because their work spreads throughout the year.

One bright spot came from Brenda Cannon of Cannon & Associates, who shared an innovation on the CPA Trendlines podcast. Instead of letting tax work pile up, she gives clients calendar links to schedule when they’ll submit documents. Eight slots per day, Monday through Thursday. Fridays for internal work. No slots three weeks before April 15th (reserved for extensions). Clients who don’t schedule by year-end get marked inactive.

“Clients no longer complain about extensions because they basically chose to miss their self-imposed deadline,” Blake explained. Only about 5% of clients left after implementing the system.

The Vanishing Entry Level

But even successful adaptations can’t solve a bigger problem: what happens when AI absorbs all the entry-level work that trains future professionals?

“The quality burden used to fall on the senior staff and managers,” David said. “But now the managers are going to have to bear that weight.”

Blake expanded the concern. Managers used to trust that trained seniors had learned to review work through years of practice. With AI handling those training tasks, that trust disappears. “I have a theory that life is going to get harder for managers in public accounting because they’re going to be the only thing between the AI doing the work and the partner.”

A viewer captured the problem in the live chat: “You can’t get experience to become a manager without an entry level. Bots and offshore have absorbed entry. So how do you get new managers?”

Blake’s answer was sobering. If firms can’t develop managers internally, they’ll have to recruit from industry. But industry professionals who’ve tasted work-life balance won’t return to the grind of public accounting. “The people won’t drink the Kool-Aid after they’ve had a break from drinking the Kool-Aid.”

Testing for Yesterday’s Skills

This transformation raises tough questions about the CPA exam itself. The 2024 pass rates were:

  • Audit and Attestation: 46%
  • Financial Accounting and Reporting: 4%
  • Tax Compliance and Planning: 73%
  • Regulation: 63%
  • Business Analysis and Reporting: 38%
  • Information Systems and Controls:58%

“The hardest part of the exam isn’t the material,” Blake argued. “We’re not doing advanced math. We’re doing algebra. It’s not complicated stuff; it’s just a lot of memorization, and it’s a real grind.”

Blake’s theory is the exam filters for grinders because that’s what firms needed. “The exam is a grind, and public accounting is a grind so they lined up.”

But that’s not the job anymore. “We don’t need accountants to come in and do a bunch of boring, manual, tedious work,” Blake said. AI does that now. The profession needs people who can analyze concepts and direct AI agents, not memorize rules they can look up in seconds.

“You have all these AI tools where they have all the knowledge. You don’t need to memorize things,” David added.

Yet change comes slowly. “Even if the powers that be agree with you, Blake, it’ll be a decade before they change that,” David said.

The Bottom Line

David’s three-minute survey creation shows where we’re headed: routine tasks becoming instant. But efficiency isn’t freedom. AI needs as much management as humans, but a different kind of management. The cognitive burden shifts up while the entry-level work that trained judgment disappears.

Every knowledge profession will face the same questions. How do you develop talent when AI does the training work? How do you maintain quality when the middle layer of reviewers vanishes? How do you test for skills that matter when memorization becomes obsolete?

Listen to the full episode of The Accounting Podcast for all the details, including more on the IRS crisis, innovative tax season solutions, and a surprise supporter for millionaire taxes.

The Accounting Profession Has AI Completely Backwards

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When Accounting Today surveyed industry thought leaders about AI’s impact on the profession, every expert agreed that AI would automate the boring stuff like bank reconciliations, data entry, and transaction matching while humans would rise to strategic advisory work. Not one thought their own job was at risk.

On a recent episode of The Accounting Podcast, hosts Blake Oliver and David Leary did something clever. They fed the same questions to ChatGPT, asking it to respond as an accounting thought leader. The AI’s answers were just as good as the human experts’.

“None of the accounting thought leaders think their job could be replaced,” David said, “which is crazy because essentially AI can at least do the thought leader job.”

Blake and David argue that the profession has AI’s impact exactly backwards. While everyone confidently predicts automation will eliminate mundane bookkeeping tasks, the technology actually excels at synthesis, narrative-building, and strategic analysis—the very work that defines “thought leadership.”

What AI Actually Does Well

The standard story about AI in accounting is machines will handle the boring, repetitive tasks while humans ascend to strategic advisory work. It’s comforting and logical. But according to Blake and David, it’s completely wrong.

“AI can take financial statement information and turn it into a narrative better than I can, better than almost anyone can at this point,” Blake states. “That’s what we should be using it for.”

Consider Mike Salvatore, a Chicago business owner with two cafes, two bars, and a bike shop. He used to analyze his cost of goods once or twice a year, spending hours crunching numbers. Now he does it every three weeks by feeding data from QuickBooks and his point-of-sale system into Google’s NotebookLM, which creates a podcast-style summary of his business performance. He sends these AI-generated recordings to his managers.

“It’s essentially my CFO,” Salvatore told The Wall Street Journal.

This isn’t AI doing mundane bookkeeping; it’s performing executive-level analysis and communication.

Blake’s own experience drives the point home. He built an AI system that turns news articles into detailed research notes and social media posts. That work used to eat up hours each week. He also trained an AI ghost writer on hundreds of his past writings. Now he can dictate a voice memo and get back a polished article in his own style.

“Basically, it has made it so, as ‘thought leader,’ I don’t do any of that anymore,” he admits. “It’s like I have a team that does that for me. I started working out and I’m just enjoying life.”

Meanwhile, the supposedly “easy” transactional work is stubbornly resistant to automation. David, who spent years taking QuickBooks support calls before co-founding the podcast, gets fired up about this misconception.

“Matching bank feeds is not bookkeeping. That’s just matching,” he argues. “Accounting is sending an invoice to somebody so they’ll pay me.”

He describes his recent struggle trying to upload an invoice to a client portal. It’s a “mundane” task that should be simple but isn’t. The process requires navigating confusing interfaces, making contextual decisions, and handling exceptions that don’t fit predetermined patterns. AI can’t do this reliably because it lacks the real-world context that humans take for granted.

The disconnect is striking. Thought leaders keep repeating the same message they’ve preached about cloud accounting for a decade: technology will free you up for advisory work. But as David points out, “I don’t think AI is freeing up your time to do that work yourself.” Instead, AI is doing the advisory work directly.

Are You Willing to See the Opportunity?

Where things get interesting is the same AI capabilities that threaten thought leaders create a massive opportunity for regular practitioners if they’re willing to see it.

Mike Salvatore, the Chicago business owner interviewed by the Wall Street Journal, wasn’t working with an accountant before. His AI “CFO” didn’t displace a human. He simply started getting insights he’d never received.

“Very few accountants serving Main Street businesses will actually do that kind of work for a price these business owners want to pay,” Blake explains. “So they do it themselves, but they don’t do it often and they don’t do it well.”

AI is filling a vacuum, not replacing existing services. And that vacuum is huge.

If a business owner can get advisory insights that are even 50-80% accurate from AI, that’s better than the nothing they’re getting now. The question for accounting firms is whether to let clients figure this out themselves or to offer AI-powered advisory services with professional oversight.

“Firms can feed data from clients’ QuickBooks files and their point of sale systems into these tools to generate AI analysis,” Blake suggests. “You can charge for it, because you’re adding the oversight—checking the numbers, making sure it actually makes sense.”

David connects this to a decade-old challenge. He remembers when LivePlan tried to train bookkeepers to offer business planning services. “They really struggled with it because they’re good at bookkeeping. But it’s hard to teach somebody to tell a story and create the narrative around the numbers.”

Now, “all those bookkeepers can basically offer that with AI out of the box and charge for that additional service.”

When ChatGPT (playing the role of thought leader) was asked what would make it worry about being replaced, it gave a revealing answer: clients accepting “AI-generated advice as good enough, even in ambiguous scenarios.”

Blake’s interpretation is blunt. “That’s what AI will fill—the gap in the market where accountants aren’t providing the service. There’s a big gap and there aren’t enough of us.”

Why Billable Hours Kill Innovation

One survey question asked about the “AI premium.” How much more should an AI-savvy accountant earn compared to an identical colleague who doesn’t use AI? The thought leaders said these employees should obviously be paid more.

Blake laughed at this. “How can you pay them more if you’re looking at them in terms of billable hours? AI is going to actually reduce their billable hours, not add more.”

If an employee uses AI to finish work in half the time, they bill half the hours. Under the traditional model, they look less productive, not more. Under the traditional model, “you should pay the AI employees less because they’re working less,” Blake points out.

This creates a ridiculous situation where your most innovative, efficient employees appear to be your worst performers.

Ryan Lazanis, who built and sold an accounting firm and now coaches other firm owners, has a different approach. He focuses on just two numbers: bottom-line profit and monthly recurring revenue. Not billable hours, utilization rates, or time per client.

“He is not breaking it down by client. He’s not looking at individual job profitability,” Blake explains. The only thing that matters is whether the firm made money over the year.

This makes sense because staff costs are fixed. “The amount of hours they spend has no impact on your profitability,” Blake notes. You only need to worry if one client is so demanding they prevent you from taking on others.

“You don’t have to track hours for months to figure out which clients are eating up your profits,” David adds. “You just go to your team and say, ‘Who’s the biggest pain in the ass client?’ And they’re going to tell you.”

There’s also a technical angle to consider. Blake cites research showing AI is nearly 100% accurate on tasks that take humans 4-5 minutes. That accuracy drops for longer tasks, but the threshold is “doubling every seven months.” By the end of 2026, AI might handle 10- to 20-minute tasks reliably.

But this only matters if firms can capture the productivity gains. Under billable hours, faster work just means more hours to fill. Under outcome-based metrics, faster work means more capacity for growth.

Is the AI Accounting Influencer Coming?

As the episode wraps up, Blake and David float an idea that captures the absurdity of the current moment. They’re considering creating an AI accounting influencer—a completely artificial thought leader to see if it can build a following comparable to real industry voices.

“Let’s make an AI accounting influencer and see if we can build its following to eclipse that of those real influencers,” Blake suggests. They could have it write newsletters, create content, maybe even land sponsorship deals.

It’s partly a joke, but it makes a serious point. If an AI can answer thought leadership survey questions as well as humans, write articles, and provide strategic insights, what exactly makes human thought leaders irreplaceable?

The answer might be less comfortable than the profession wants to admit.

Looking Ahead

The Accounting Today survey offered some important insights, though probably not what it intended. The people most confident about AI’s limited impact are those whose work AI does best. When ChatGPT generated answers indistinguishable from human experts, it demonstrated the very vulnerability those experts deny.

The real story is that AI excels at synthesis and narrative, which are the heart of advisory work, but struggles with the contextual, exception-filled world of everyday bookkeeping.

Firm owners should rethink their services to capture the advisory opportunity AI makes possible, and abandon billable hours before they strangle your ability to innovate.

For individual practitioners doing transactional work, the news is actually good. Your skills remain valuable precisely because your work requires the messy, contextual judgment that AI lacks.

And for thought leaders? As David observed with obvious frustration, the elitist attitude that “I’m better than you” has been in accounting for 30 years. “The reality is completely opposite. People are completely missing what’s really going to be replaced by AI.”

The race isn’t between humans and machines. It’s between practitioners who recognize AI’s true capabilities and those who cling to comfortable narratives while missing the transformation happening around them.

To hear more about Blake’s AI-powered lifestyle, David’s thoughts on what bookkeeping really is, and their plan to create an AI influencer that might outperform the human ones, listen to episode 469 of The Accounting Podcast.