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Blake Oliver

How Accountants Can Separate Real AI Gains From Financial Hype

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A French property company added €20 million of expected AI savings to earnings before those savings happened. Meanwhile, a new audit firm uses technology to test client data throughout the year. Both stories fall under “AI in accounting,” but one is questionable and the other changes how the work gets done.

On Episode 506 of The Accounting Podcast, hosts Blake Oliver and David Leary examine that divide. They discuss the risks of AI investment, questionable financial metrics, practical steps for adopting AI, and how continuous auditing affects fees and competition.

For accountants, the central question is whether a particular use creates lasting value or simply makes the numbers look better.

An AI Pullback Would Spread Beyond Technology

David opened with a Sona Asset Management report about the connections among hyperscalers, model developers, chip suppliers, and neocloud providers. The report identified 255 public companies in the AI ecosystem with a combined value of about $50 trillion and approximately $6 trillion in debt.

Revenue is also concentrated. David noted that CoreWeave gets 67% of its revenue from Microsoft, while Applied Digital gets 56% from Oracle and CoreWeave. He said roughly $3.6 trillion of the sector’s debt is interconnected.

That led the hosts to compare today’s AI market with the mortgage crisis. Before the Great Recession, subprime mortgages accounted for about $1 trillion of a $12 trillion U.S. mortgage market. The comparison was a warning rather than a prediction, but the scale caught Blake’s attention. “This could be worse than I thought,” he noted.

It wouldn’t require a full collapse to cause damage. If large technology companies reduce data-center construction or fail to pay contractors, the effects could reach construction firms, electricians, HVAC suppliers, truckers, and manufacturers.

David used Boot Barn to show how that chain might work. The retailer adjusts inventory near data-center projects and receives bulk workwear orders from construction employers. David also found that many of the construction boots shown on its website were made in the United States. Fewer data centers could therefore mean fewer construction jobs, fewer boot sales, and less manufacturing work.

This risk creates an opening for accountants. Firms can help clients review cash reserves, receivables, costs, and customer concentration before a slowdown arrives. Blake suggested packaging that work as a “recession-proof your business” advisory service.

When Expected Savings Become Financial Engineering

That wider financial risk makes honest reporting even more important.

Blake highlighted Emeria SASU, a French property services company that added €20 million to EBITDA for expected benefits from an agentic AI rollout. It projects the savings to occur over the next two years, yet the company included them in its last 12 months of earnings.

The incentive to make the adjustment stems from Emeria’s roughly €3.5 billion in debt with maturities approaching. Blake said its reported leverage was about 8.44 times. Removing the AI adjustment and several other items worth about €40 million moves leverage closer to 9.3 times.

The hosts compared the measure with “EBITDAC,” a pandemic-era metric that added back earnings supposedly lost because of COVID-19. Accountants should question whether claimed AI savings have already occurred, whether it’s possible to measure them, and whether a company excludes AI costs while adding projected benefits.

Efficiency gains deserve recognition, but companies shouldn’t book them before they exist.

Document the Process Before Applying AI

The discussion then moved from financial hype to practical adoption inside firms.

David’s recommended order is to document the process, put that documentation into the firm’s systems, automate appropriate steps, and then apply AI. “You can’t just start applying AI,” he said. Without a documented process, the technology may invent its own method.

One firm featured by AccountingWEB held an SOPathon. It paused normal work for half a day while each department documented its processes using a structured format. David suggested following that exercise with a hackathon where employees build small automations from the new documentation.

Blake offered an easier starting point. Bring everyone involved in a process into a recorded Zoom, Teams, or Google Meet call. Ask them to explain each step while the meeting software creates a transcript. An AI tool can turn that transcript into a standard operating procedure and suggest improvements.

The Accounting Podcast used this approach for its short-form video workflow. The old checklist largely said, “create the clips.” Blake connected Claude with Process Street, and the tool built detailed instructions for the human steps.

The lesson is to start small. Firms don’t need to automate an entire tax return at once. As Blake said, begin with “one checkbox, one task in the whole list.”

Continuous Auditing Could Put Pressure on Fees

Once you document processes, AI can do more than speed up internal tasks. It may change the audit business model.

Former PCAOB board member Christina Ho joined Oath, which operates as both a software platform and a licensed CPA firm. Blake said Oath connects directly to client systems, tests 100% of the data, and verifies records alongside the monthly close.

That model spreads audit work across the year instead of forcing it into a short busy season. It can also identify problems sooner and reduce the labor needed for routine testing.

Fee pressure is already underway. Blake cited Financial Times reporting that audit fees, which had typically risen 4% to 5% each year, increased by less than 2% last year. Fees paid by the 100 largest London-listed companies rose 75% over the five years through 2023.

Labor consumes much of an accounting firm’s margin, so technology-enabled firms may be able to charge less while remaining profitable. Blake expects this to help regional firms compete with the Big Four and small firms compete with regional firms. He also imagines a future in which solo CPAs supervise teams of specialized AI agents. But human oversight is still essential. Higher-quality results depend on a qualified professional “watching it and signing off.”

Judgment Is Still the Differentiator

All four stories show that AI can change the economics of the work, or it can change only how results look on paper.

Accountants should trace how an AI slowdown could affect clients, challenge savings booked before they happen, document workflows before automating them, and prepare for technology to increase competition in audit.

Judgment matters more than technical skill. Accountants who test assumptions and keep humans accountable will be better prepared to capture AI’s real benefits without overlooking its risks.

Listen to Blake and David’s full discussion in Episode 506 of The Accounting Podcast.

AI Can Prepare the Work, but Humans Still Own the Risk

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An off-the-shelf AI agent opened two general ledgers, compared the balances with prior-year returns, built book-to-tax workpapers in Excel, and entered two business returns into TaxAct. It even found and corrected errors in the tax software.

The process took a few hours. Yet the most important step was still human. Blake Oliver downloaded the final returns, checked them against the general ledger, and transmitted them himself.

The tension between machines doing the work and people remaining accountable runs through Episode 504 of The Accounting Podcast. Blake and co-host David Leary discuss agentic tax preparation, AI-native ledgers, flawed do-it-yourself cost segregation studies, accounting salaries, staffing, and failures of judgment at the Big Four.

 

AI Is Moving From Research Assistant to Tax Preparer

Last year, Blake mainly used AI as a research partner. This year, he gave Claude Cowork a much larger role in preparing two returns: his S corporation, which uses Xero, and Earmark Media’s partnership, which uses QuickBooks.

Claude pulled the profit and loss statement, balance sheet, and trial balance for each entity. It then compared those reports with the prior-year returns. The S corporation tied out, but the QuickBooks file didn’t. Claude traced the problem to a dropdown issue that caused reports to run on the accrual basis even though they appeared to be set to cash basis.

After resolving the mismatch, Claude asked Blake about book-to-tax adjustments and built Excel workpapers. It then opened TaxAct Business, completed the interview, entered the data, and worked through the software’s error checks.

The agent also found several problems. TaxAct omitted fully nondeductible entertainment expenses from its calculation, so Claude created a custom Schedule M-1 add-back. It corrected distributions that had defaulted to zero, fixed ending retained earnings so Schedule L balanced, removed an incorrect name-change selection, and addressed an Arizona filing checkbox that kept resetting.

David described the workflow as using AI to “bridge the gap between the GL and the tax return.” Blake estimated active agent time at about an hour per return.

That experience raises the question: if AI can connect the books and tax return, will those systems eventually become one platform?

AI-Native Ledgers Could Change the Technology Stack

David pointed to Accrual, an AI tax platform, agreeing to acquire Puzzle’s accounting-firm business and technology. General Catalyst backed both companies, so David viewed the deal as a possible combination of related investments.

Blake saw practical value in the pairing. Puzzle’s AI works within Puzzle, but accounting firms can’t move every client away from QuickBooks, Xero, and other ledgers. Accrual can let agents work across existing systems while providing a tighter experience with its own ledger.

A livestream discussion also raised the possibility of pairing AI with an open-source ledger. For basic write-up work, an accountant might give an AI tool a year of bank statements, create an import file, and avoid paying for features the client doesn’t need. Blake and David treated this as an idea worth testing rather than a proven replacement for established systems.

But the limits of general-purpose AI are clear when the work demands specialized evidence.

Polished Output Isn’t Defensible Work

Blake cited an Accounting Today article by Heidi Henderson of Engineered Tax Services. Over six weeks, three prospective clients brought her firm cost segregation studies created with ChatGPT or Google Gemini and asked the licensed engineering firm to validate them.

One Gemini spreadsheet contained six rows and assigned $100,000 of bonus-eligible basis to a $400,000 property. A ChatGPT workbook reclassified $1.24 million of a $4.7 million athletic facility to shorter-life assets.

Measured against 13 principal elements in the IRS audit technique guidelines, the ChatGPT study satisfied one, and the Gemini study satisfied none. They lacked items like a stated methodology, an engineer of record, engineering takeoffs, reconciliations, and required statutory analysis.

Blake argued that a specialized model trained on a firm’s methods and IRS guidance might prepare parts of a report. It still couldn’t perform the site visit, take photographs, or provide an expert’s sign-off. General-purpose tools, he warned, can “BS their way through it” and produce weak work that looks convincing.

That makes experienced review more important, but the profession may not be investing enough in the people who provide it.

Firms Need People Who Can Challenge the Machine

EY plans to distribute $100 million in bonuses tied to “human skills,” including adaptability, innovation, and judgment. Awards can reach $25,000 for material contributions, including team awards.

David viewed the program as paying employees to remain the human in the loop. Blake called it a smart way to align staff incentives with firm risk. Both hosts noted that all four Big Four firms have faced problems involving fabricated AI citations in published reports.

Yet median entry-level accounting pay fell from $75,000 to $73,000 even as compensation rose overall. David warned that firms especially need seniors and managers. These are the people who “watch the agents.” Lower starting salaries could weaken the future supply of those experienced reviewers.

Accountability Still Reaches the Top

KPMG Australia cut 360 employees and 27 partners after consulting revenue fell nearly 17%. The cuts follow allegations involving client data leaks, confidential client information used to win new work, and poor whistleblower investigations.

“Accountants will lose their jobs if people at the top-end management level are not doing ethical things, or if the firm’s culture is not ethical,” David said, pointing out the consequences.

Deloitte, meanwhile, agreed to pay $21.5 million to settle Justice Department allegations involving race- and sex-based employment practices on federal contracts. Deloitte denied the allegations and admitted no liability. A Hong Kong court also refused to dismiss PwC International from the Evergrande liquidators’ multibillion-dollar lawsuit, although the ruling didn’t decide the cases’ merits.

Technology changes how quickly work gets done, but it doesn’t change who must answer for the result. To benefit from AI, firms need to know how to direct it, test it, and take responsibility for its output.

Listen to the full discussion in Episode 504 of The Accounting Podcast.

IFRS 18 changes the story your income statement tells

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Two companies operate in the same industry, under similar conditions, in the same year. One includes share-based payment expense in operating profit; the other excludes it. One includes a goodwill impairment; the other presents it below that line. Both comply with IFRS, yet investors end up “comparing apples to oranges,” Blake Oliver, CPA, said on an episode of Standard Practice.

IFRS 18 was created to address that problem. Host Meredith Mednick, CPA, CA, walked through the new standard with Blake. His first reaction to replacing IAS 1 fell “somewhere between mild panic and deep curiosity.”

Issued in April 2024, IFRS 18 applies to annual periods beginning on or after January 1, 2027. It doesn’t change when companies recognize transactions or how they measure them. Instead, it changes how they present and disclose financial performance. That may sound limited, but the implementation work isn’t.

 

Operating profit gets a standard foundation

Under IAS 1, companies had broad discretion over how they classified income and expenses and the subtotals shown above profit or loss. Most presented operating profit, but there was no standard definition of what belonged in it.

IFRS 18 introduces five required categories for income and expenses:

  1. Operating
  2. Investing
  3. Financing
  4. Income taxes
  5. Discontinued operations

Operating is the residual category. You first ask whether an item belongs in one of the other four categories. If it doesn’t, it is operating. As Blake explained, “If it doesn’t meet the criteria for another category, it’s operating, full stop.”

That means a company can’t remove a cost from standardized operating profit simply because it is large, volatile, or unusual. For example, restructuring costs remain in operating even if management considers them one-time charges. A gain or loss on production machinery also goes to operating, although the related cash proceeds may appear in investing activities under IAS 7.

That distinction is important because the investing and financing categories under IFRS 18 don’t match the categories with the same names in the cash flow statement.

Classification follows the economics

Although IFRS 18 reduces presentation choices, it doesn’t eliminate judgment. Instead, accountants must determine how an asset or liability produces its return or cost.

The investing category includes specified income and expenses from associates, joint ventures, unconsolidated subsidiaries, cash and cash equivalents, and other assets that generate returns individually and largely independently of the company’s other resources. Dividends or fair value gains from listed shares may qualify. Depreciation on a delivery fleet doesn’t qualify because those vehicles work with employees, warehouses, and other operating resources.

The financing category focuses on liabilities. Its treatment depends on whether a liability arises only from raising finance:

  • Bonds, bank loans, and similar financing arrangements can place several related gains and expenses in financing
  • Trade payables, leases, pension liabilities, and provisions generally place only separately identified interest and interest-rate effects in financing

For example, current service cost on a defined benefit plan is operating, while separately identified net interest is financing. Foreign exchange differences generally follow the underlying item: FX on a supplier payable is operating, while FX on a bond payable is financing. Limited relief allows operating classification when allocation would require undue cost or effort, but inconvenience alone is not enough.

Once IFRS 18 establishes those general rules, it adds an important exception for certain business models.

Core financial activities may stay in operating

Investing in assets or providing financing to customers may be a company’s main business activity. This can apply to investment entities, investment property companies, insurers, banks, other lenders, and manufacturers that finance customer purchases.

These entities may have to include items that would normally fall under investing or financing in operating. Otherwise, a lender’s operating profit could exclude the interest spread at the center of its business.

This is a fact-based assessment rather than an accounting policy choice. Evidence may include gross-profit-style measures such as net interest income and information reported under IFRS 8. The assessment also occurs at each reporting level. A subsidiary and its consolidated parent can reach different conclusions, creating a need for consolidation adjustments.

Once you settle those classifications, the new statement structure begins to take shape.

New subtotals and disclosures improve comparability

IFRS 18 introduces two key subtotals: operating profit and profit or loss before financing and income taxes. The second combines operating profit with the investing category, allowing users to compare performance before financing costs and taxes.

An exception applies when an entity provides financing to customers as a main business activity and elects to classify in operating the income and expenses from all its finance-raising liabilities, including those unrelated to customer financing. It can’t present a subtotal labeled “before financing” because that description would be misleading.

Companies may still communicate adjusted measures, such as operating profit before restructuring costs. However, qualifying management-defined performance measures used in public communications will require disclosures in the financial statements. Blake described this as bringing non-GAAP measures into the statements “in a controlled and transparent way.”

IFRS 18 also requires the analysis of operating expenses to appear in the statement of profit or loss by nature, function, or a mix. If the financial statements present expenses by function, the footnotes must also disclose key nature amounts, including employee benefits (including share-based payments), depreciation, amortization, impairments, and inventory write-downs, by function. Companies should use labels like “other” only when they don’t have a more informative description available.

These changes lead directly to the most urgent issue of timing.

The practical deadline arrives before year-end 2027

IFRS 18 applies retrospectively. For a company with a December year-end, that means:

  • They must apply IFRS 18 to their 2027 annual statements
  • They must restate their 2026 comparative statement
  • The financial statements must present a reconciliation from the previous IAS 1 presentation
  • Its first 2027 interim report must already use IFRS 18 headings and subtotals and include the required comparative reconciliation

As Meredith warned, implementation must be “substantially completed before the first interim financial reporting date of the adoption year, not just before the annual year-end.”

Start by assessing main business activities at every reporting level. Then map each income and expense item to a category, identify data gaps, review your expense presentation, inventory externally reported performance measures, and plan the required systems, training, and consolidation changes.

IFRS 18 doesn’t change what a company earns. It changes how clearly financial statement users can understand and compare that performance. For the full classification walkthrough and implementation discussion, listen to the Standard Practice episode.

AI Is Rewriting the Economics of Accounting Software

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Intuit just reported $21.4 billion in annual revenue, 14% growth, and about $4.5 billion in profit. Wall Street punished the stock anyway.

On Episode 503 of The Accounting Podcast, hosts Blake Oliver and David Leary examine why with Hector Garcia, a CPA, firm owner, and QuickBooks educator. They also speak with Britten Ratcliff, an accounting student and public member of the New Mexico Public Accountancy Board.

Their discussion points to a larger shift. AI is speeding up work inside accounting and tax software, but it’s also changing where pricing power, value, and competitive advantage come from.

 

TurboTax Faces a Pricing Squeeze

Intuit’s results don’t look like a crisis at first. TurboTax Live revenue rose 37% and now represents 53% of TurboTax revenue. But Intuit forecast only 9% to 10% companywide growth for fiscal 2027, with TurboTax revenue expected to grow just 2% to 3%.

Hector sees the stock decline as part of a broader market reaction to AI’s effect on software-as-a-service (SaaS) companies. Intuit’s price-to-earnings ratio fell sharply from its July 2025 peak, following a path similar to Adobe’s. “The market is reacting to the impact that AI has on SaaS,” he said.

There is also a direct pricing problem. Intuit acknowledged that it’s losing do-it-yourself filers to cheaper competitors. Price is now the leading reason customers leave TurboTax.

Blake estimated that an AI agent could complete a simple return using about 25 cents of tokens. That makes prices of $100 or more difficult to defend, especially when startups can use AI instead of rebuilding decades of rules-based software. David remained skeptical that millions of taxpayers will quickly abandon a familiar product for a chatbot, however. For many households, taxes are too important to make that switch casually.

Hector suggested a barbell strategy: offer more free filing at the low end while moving customers with greater needs toward premium help or Intuit’s small-business products. In his view, Schedule C filers could become customers for QuickBooks Payments and Payroll. He noted that QuickBooks generates about $12 billion in revenue, compared with roughly $5 billion from TurboTax.

That strategy connects with Intuit’s tests of QuickBooks Free and QuickBooks Lite. The free version allows a few invoices each month and encourages users to adopt payments. Both products can serve as stepping stones to the $38-per-month Simple Start plan.

Human-Assisted AI May Be Only a Bridge

Lower prices create another problem: human support is expensive.

Hector described a conversation with a TurboTax seasonal worker about her hours, pay, and time spent answering customer questions. His rough calculation suggested that a $100 return requiring 90 minutes of phone support leaves little or no margin.

A more sustainable model, he argued, could charge little or nothing for AI-based preparation. Customers would interact with a chatbot while tax professionals reviewed the conversation and return behind the scenes. Full access to a professional would cost much more.

“I honestly think it’s a bridge. I don’t think that’s a strategy,” Hector said of the current AI-plus-expert model. He expects a clearer split between low-cost automation and much more expensive professional service.

Thomson Reuters Is Building Around Trusted Data

While Intuit wrestles with pricing, Thomson Reuters is taking a different approach to AI. It purchased an open-weight model and trained it on 175 years of proprietary material from Westlaw, Practical Law, Checkpoint, and Reuters.

That paywalled content includes analysis created by subject-matter experts. Thomson Reuters also brought in partner-level practitioners to build grading standards. Lawyers spent thousands of hours comparing outputs, while 1,500 attorney editors helped identify errors.

The company reported a 0.914 score for following instructions. In deep research, the model scored 0.83 for factuality, compared with 0.65 and 0.68 for two leading models using the open web. This measure tested whether claims were supported by their cited sources.

“I want dumb AI, like AI that only knows accounting,” David said, summarizing the appeal. Blake offered a better label: a specialist that performs well in one field and declines tasks outside it.

For Hector, adoption comes down to two questions: Is client data safe, and are answers grounded in authoritative information? Yet quality alone may not be enough. He argued that professional AI should be built directly into tools firms already trust, such as Microsoft 365, rather than forcing accountants to connect and manage separate agents.

AI-Native ERPs Still Must Overcome Switching Costs

The same tension appears in the ERP market. Rillet raised $100 million at a $1 billion valuation with about 600 customers. During the same period, private equity firm Silver Lake reportedly pursued Workday at a $51 billion valuation.

David contrasted the valuations to show that major investors still see value in established systems. Hector added that Intuit Enterprise Suite reached $145 million in revenue within two years without raising outside capital for the product. He argued that Intuit, like Thomson Reuters, benefits from years of customer and transaction data.

Blake countered that companies won’t replace an ERP merely to get a better general ledger. They may switch if automation lets them avoid major hiring costs. Rillet’s fundraising announcement claimed that some customers operate large finance functions with only a few people or close their books in three days. The hosts noted that those claims had not been independently verified.

Tokens Could Become a Direct Cost

This leads to a new accounting question: How should businesses classify AI spending?

Three years ago, few companies had separate budgets for ChatGPT, Claude, or AI tokens. Hector said executives now want that spending to grow when it can reduce labor costs. He predicted that ERPs with strong built-in AI could win by replacing separate chatbot subscriptions.

He also argued that tokens may shift from fixed software overhead to a variable cost tied to sales and production. “All of a sudden, we have a brand-new direct cost that never existed,” he said. Blake suggested that accountants may need new cost accounting methods to track it.

Efficiency Cannot Replace Professional Development

The cost of automation is not limited to software budgets. Britten Ratcliff said young professionals worry that AI and private equity-backed efficiency efforts are eliminating the entry-level work that once taught people how accounting operates.

Reviewing last year’s audit file or completing basic analyst tasks may be repetitive, but those assignments build context and judgment. If firms automate them, they’ll need new ways to teach junior employees.

Britten sees a similar gap in accounting education. He took cost accounting before gaining any exposure to manufacturing, and he criticized homework systems that look little like real financial statements. Students need technical skills, he argued, but they also need to understand how businesses work.

That may be the episode’s central message. AI can lower costs and reshape software, but accounting firms still compete through trust, judgment, and business understanding. As Hector put it, the claim that AI can do everything accountants do is still a narrative, and the profession doesn’t have to surrender to it.

Listen to the full discussion on Episode 503 of The Accounting Podcast.

Nvidia’s AI Funding Deal Has “Shades of Enron,” Even If It Follows the Rules

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The most unsettling part of a proposed $500 billion AI data-center fund isn’t that investor Michael Burry says it has “shades of Enron.” It is that, as Blake Oliver explains, nobody appears to be breaking a rule.

“There’s no fraud happening here,” Blake says in Episode 501 of The Accounting Podcast. “This is all happening in plain sight.”

That tension runs through Blake and David Leary’s discussion. AI makes financing structures more complex while helping firms complete audits and other accounting work faster. Yet many of the standards governing that work were written for a different era.

 

Nvidia’s financing shows how risk can grow within the rules

The proposal Burry criticized brings together Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR to finance AI data centers. The plan is to use special-purpose vehicles to own the centers, buy Nvidia chips, and lease computing power to companies such as OpenAI and Anthropic. The debt would be backed by the computing assets, with Nvidia reportedly guaranteeing about 25%.

Why would Nvidia support separate entities instead of building the centers itself? Blake explains that selling chips to those entities would let Nvidia record revenue upfront. If Nvidia built and operated the centers, it would absorb the construction costs and recognize revenue later when it sold computing services.

The proposal adds another layer to the circular financing problem discussed in Episode 488. Money moves in a circle, and both sides report revenue.

Burry called the proposal an effort to use “unnatural credits to prolong momentum late in the bull phase.” “Maybe the problem is that GAAP allows this,” Blake notes.

Depreciation adds to his concern. AI chips are only useful for two or three years, but some companies use estimated lives of five or six years. Longer useful lives mean less annual depreciation and higher reported profit. Because large technology companies carry heavy weight in the S&P 500, a sharp correction could hurt ordinary investors holding index funds.

That same divide between reported results and underlying quality appears in audit.

Faster audits don’t automatically mean better audits

EY says AI improved audit speed or throughput by roughly 125% to 150%, while clients haven’t demanded lower fees. The firm also reported a 5% PCAOB deficiency rate, down from 28% the prior year, and pointed to its billion-dollar investment in people and technology.

David is skeptical that technology alone explains the improvement. Other large firms also posted better inspection results. He suggests the PCAOB’s changing focus on firmwide quality-control systems may affect the numbers.

Blake offers another theory. PCAOB inspections often focus on whether auditors followed required procedures, obtained approvals, and completed documentation. AI is well suited to checking those boxes. But it can also create work that looks “solid and sophisticated” while still being wrong. Complete documentation isn’t the same as sound professional judgment.

The productivity gains could still disrupt the market. Big Four firms may keep the savings as higher margins, but Blake argues that regional and smaller firms could eventually use the same tools to provide comparable services at lower prices.

Before that can happen safely, however, audit rules must catch up.

Audit standards weren’t built for AI agents

In a Gartner poll of 743 audit professionals, 93% reported using AI in some form. Yet only 30% used it for audit testing, 12% used it for quality reviews, and 38% of audit leaders had an AI strategy.

Hofstra University accounting professor and CPA Jack Castonguay argues that AI is audit’s biggest disruption since the corporate failures that led to the PCAOB’s creation. He says applying existing standards to a “fundamentally new operating model” won’t be enough.

The unanswered questions include:

  • Evidence reliability. What happens if AI invents evidence or changes data it believes is wrong?
  • Agent supervision. Who is responsible when auditors fail to review AI agents that gather and analyze evidence?
  • Independence. Could an AI-enabled accounting system and an audit platform trained on the same data reinforce the same errors?

AI can test every transaction instead of a sample. That is a major advance, but current standards don’t explain how much human review is needed when a machine examines the full population. Castonguay wants standards for acceptable use, oversight, evidence, supervision, and independence.

The mismatch is also visible in financial reporting.

Reporting and assurance are moving on different clocks

The SEC’s proposal to move public companies from quarterly to semiannual reporting drew about 225,000 comments. By comparison, the PCAOB received only 33 comments on its request for input about future priorities, including AI-related research.

David questions whether two reports or four reports is even the right debate. If automation leads to a continuous close, he asks, “Shouldn’t the discussion be moving to daily?”

Tether presents a related problem. Assurance has limited value if users can’t inspect it. KPMG US issued an unqualified 2025 audit opinion for the stablecoin issuer, but the report hadn’t been published at the time of the discussion. As David asks, “If they don’t publish the reports, did they really do it?”

While regulators debate these issues, small firms are already putting AI to work.

Small firms can gain leverage without removing human review

The hosts highlighted four firms with fewer than ten employees. One Stop CPA uses Blue J for source-backed tax research, applies professional judgment, and then uses ChatGPT Enterprise to create memos and presentations. Agate CPA built an automated client intake process that increased conversions by about 25%. Public Trust CPA created a nonprofit invoice-approval trail using Power Automate, Adobe Sign, and QuickBooks. High Rock Accounting built a client-feedback app in a few hours and now holds AI happy hours to identify repetitive work.

These examples show that small firms don’t have to wait for enterprise software. But client expectations are rising, and review is costly. Blake’s conclusion about QuickBooks Live applies across the profession: AI can do the work, “but it still needs a human to review it.”

AI exposes weak points in accounting’s rulebook while giving firms new ways to research, automate, and compete. The winners will be firms that define acceptable uses, review responsibilities, and evidence standards before regulators catch up.

Listen to Episode 501 of The Accounting Podcast for Blake and David’s full discussion.