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.