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After 50 Years in Internal Audit, Richard Chambers Sees the Profession’s Greatest Risk Yet

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“Who’s going to provide the skepticism, the intellectual curiosity, and the institutional knowledge to our audit teams in ten years? Because the rest of us are going to be gone.”

Richard Chambers drops this stark warning after 50 years in internal audit. His concern isn’t about losing jobs to technology. It’s about the growing gap between how we’ve always trained auditors and what the profession now demands.

On this episode of the Earmark Podcast, host Blake Oliver sat down with Richard, Senior Advisor for Risk and Audit at AuditBoard. He brings a unique view of internal audit’s transformation. When he started in 1974, fresh out of college and working in a bank’s internal audit department, the job was all about checking financial records and looking backward. Today? Financial risks make up only 25% of audit plans. The rest involves cyber threats, AI governance, supply chain chaos, and what Richard calls “perma-crisis”—our new normal where tariff rates can change three times in a single day.

Most companies use AI, but only a quarter have set up proper governance over it, according to AuditBoard research. That gap presents massive risk and opportunity for internal auditors who can bridge it.

From Bean Counting to Risk Navigation

Internal audit has changed dramatically since Richard joined that bank in 1974. Back then, it was all ledgers and reconciliations—purely financial work focused on last year’s numbers. Today, financial risks are just a quarter of what internal auditors examine.

“The profession has matured,” Richard explains. “While we still do some work in the financial space, that’s really a small percentage of internal audit’s focus.”

The real game-changer has been what Richard calls “perma-crisis.” It started with the COVID-19 pandemic and hasn’t stopped. “We’ve been lurching from one risk-induced disruption to another,” he says, listing the cascade: pandemic, forty-year-high inflation, supply chain breakdowns, wars in Europe and the Middle East. “We’re in our sixth year of it, and I would submit this is the new normal.”

This constant chaos makes traditional planning almost useless. Richard found that nearly 60% of internal audit departments had already changed their 2025 plans by May. When tariff rates can swing wildly in a single day—Richard recalls hearing three different numbers from Washington in one day—annual planning is dangerous.

“You can no longer have any confidence that one scenario is the only one you have to worry about,” Richard emphasizes. Organizations need what he calls “scenario risk management,” or planning for multiple possible futures at once.

This need for flexibility shifts how internal audit works with other departments. The old model was called “three lines of defense”: management controlled risks (first line), oversight functions monitored them (second line), and internal audit was the last barrier before disaster (third line).

But pure defense isn’t enough anymore. In 2019, the Institute of Internal Auditors dropped “defense” from the name. The new message? “Independence does not mean isolation.”

Richard uses a ship analogy that really hits home. Organizations are like vessels at sea that need lookouts watching in all directions and talking to each other. “If your internal auditors are looking in one direction and your risk managers are looking in another,” he warns, “but they aren’t sharing what they’re seeing, then you don’t know whether there are gaps.”

AI: The Top Risk and Best Opportunity

Three years ago, AI wasn’t even on internal audit’s risk list. Today, it’s number one, pushing even the talent crisis to second place.

“Pre-2022, before ChatGPT came out, we weren’t asking about it,” Richard admits. Once he started surveying the profession, AI rocketed up the list: middle of the pack the first year, third place the next, then straight to number one.

This isn’t just another tech disruption. After watching five decades of change, Richard doesn’t mince words: “In the five decades I’ve been in internal audit, there’s never been a greater risk to this profession in terms of becoming irrelevant.”

The scariest part? When Richard asks why audit teams aren’t using AI more, the top answer is, “We don’t really understand it enough.” That hesitation could be fatal.

Yet Richard himself uses AI daily as his “research assistant.” He asks it to identify industry risks, outline articles, analyze data. “It takes me longer to write the prompts than it takes to give me the answer,” he notes.

The use cases are obvious and powerful. Risk assessments that used to happen annually can now be continuous. AI can scan for threats humans would never spot. Data analysis that took weeks happens in minutes. Even audit reports can be AI-generated.

But the trap is that AI excels at exactly the work that trains new auditors. Entry-level graduates traditionally learned by doing routine tasks. Now AI does those tasks better and faster.

“College graduates have traditionally been able to ease into professions by doing some of the more rudimentary tasks,” Richard explains. “But AI is prime for rudimentary tasks.”

This creates a vicious cycle. Companies hire fewer entry-level auditors. Without that pipeline, who develops the judgment for complex work? Richard’ solution: “We shouldn’t refrain from hiring them. We should be willing to bring them in and help them leap the learning curve.”

“AI won’t replace internal auditors,” Richard predicts, “but it will replace internal auditors who don’t use it.”

The Human Superpowers AI Can’t Touch

“To assess culture, you also have to be able to rely on your sense of smell.”

A chairman of the board of a large Indian company shared this wisdom with Richard years ago, and it perfectly captures what separates humans from AI. Technology can analyze documents and data. But it takes human instinct to sense what happens when nobody’s watching.

Richard identifies three “human superpowers” that AI cannot replicate: professional skepticism, intellectual curiosity, and relationship skills. These aren’t soft skills; they’re the core value of internal audit.

Take culture assessment. Richard has done two major research projects showing how toxic culture can destroy organizations. But judging culture requires reading between lines, sensing unspoken tensions, and understanding human motivations. As Blake pointed out during the conversation, “The body language, the way people talk to each other, all of that is context that AI just cannot have access to.”

The audit committee relationship shows this even more clearly. Richard chairs an audit committee and knows these relationships need more than data transfer. They require courage to “grab them by the face” and focus them on hidden risks.

“If we’re content to just answer the questions they ask,” Richard warns, “then we’re not really serving our organizations well. We have to help them understand the questions they need to be asking.”

This shift, from giving answers to finding the right questions, represents a huge evolution. While AI can list potential questions, there’s something fundamentally human about knowing which questions matter.

Most critically, Richard identifies one role that must stay human: assessing AI’s own governance. “I shudder to think that there may be a day where we ask AI to assess its own governance,” he says. “We would never do that with anyone else.”

The challenge is developing these human skills when the traditional path is disappearing. Without routine work to learn on, how do new auditors develop judgment?

We need to help new auditors develop skepticism, intellectual curiosity, and institutional knowledge from day one. Teach them to ask “why” before teaching them “how.”

As Richard reflects after 50 years, “What a difference from the bean counter view of internal audit. You get to be so curious as an internal auditor these days.”

The Next 50 Years Start Now

Richard’s journey from a bank to internal audit’s leading voice shows a profession that has transformed before and must do so again.

The collision of perma-crisis and AI doesn’t doom internal audit. It clarifies its purpose. When tariffs change three times daily, cyber threats evolve by the hour, and AI makes decisions we don’t fully understand, organizations desperately need professionals who ask the hard questions.

Not “What does the data say?” but “What isn’t the data telling us?” Not “How do we implement AI?” but “How do we govern what we can’t fully understand?”

The saying “independence does not mean isolation” applies to both organizational relationships and the human-AI partnership. Tomorrow’s successful auditors won’t resist AI or surrender to it. They’ll orchestrate a sophisticated dance between computational power and human intuition.

The fact that entry-level work is vanishing while judgment becomes more critical demands new thinking about professional development. Organizations can’t wait for fully-formed auditors. They must cultivate intellectual curiosity from day one.

For accounting and tax professionals watching internal audit’s future, Richard warns those who avoid or fear AI will become irrelevant. But he also extends an invitation: those who combine technology with human capabilities will find themselves at the center of organizational decision-making.

Listen to the complete conversation to understand why this moment represents internal audit’s greatest challenge and its most exciting opportunity. After five decades in the profession, Richard reminds us the question isn’t whether internal audit will survive the age of AI. It’s whether individual auditors will choose to evolve with it.

Deloitte’s $440,000 AI Fabrication Scandal Exposes the Accounting Profession’s Deepest Fears

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A startup founder discovered $2.1 million in embezzlement by his co-founder in just 18 minutes using Claude AI. The company’s internal auditors, external auditors, and even the CFO had completely missed it. Meanwhile, Deloitte was forced to refund the Australian government hundreds of thousands of dollars after delivering a report filled with AI-generated fabrications.

In this episode of The Accounting Podcast, hosts Blake Oliver and David Leary dig into these stories. They explore how AI is both exposing massive frauds and creating embarrassing failures, examine the chaos from the government shutdown, and question whether traditional accounting services still matter when 86% of major companies use broken charts that nobody even notices.

When AI Catches What Humans Miss (And Creates What Shouldn’t Exist)

The accounting profession is experiencing an AI identity crisis. On one hand, artificial intelligence can spot complex fraud that teams of professionals completely miss. On the other hand, professionals are using it to generate work that looks legitimate but is actually riddled with fabrications.

Let’s start with Deloitte’s spectacular failure. The Big Four firm charged the Australian government $440,000 AUD (about $290,000 USD) for a 237-page report on welfare compliance systems. The problem? It contained over 20 AI-generated errors, including completely made-up quotes from federal court judgments and references to non-existent academic papers.

Chris Rudge, a Sydney University researcher, spotted the errors immediately. One fabrication attributed a non-existent book to constitutional law professor Lisa Burton Crawford on a topic completely outside her field. “I instantaneously knew it was either hallucinated by AI or the world’s best kept secret because I’d never heard of the book, and it sounded preposterous,” Rudge said.

Even after getting caught, Deloitte insisted its findings and recommendations were still valid. This prompted Australian Labor Senator Deborah O’Neill to observe that Deloitte has “a human intelligence problem.”

But here’s where it gets interesting. While Deloitte was using AI to create fake references, a startup founder used it to uncover real fraud. He exported his company’s QuickBooks data into Claude AI and asked one simple question: “What’s wrong with this picture?”

In just 18 minutes, the AI found what everyone else had missed: 17 fake companies routing $2.1 million to his co-founder’s personal accounts through shell companies. The AI spotted patterns humans overlooked, including fake vendors paid on 23-day cycles while real vendors were paid on 28-day cycles, and payment amounts that followed Fibonacci sequences, which humans subconsciously create when making up numbers.

The founder has since turned this into a business, selling AI-powered fraud detection prompts for $10,000 each to 47 clients. He’s probably making more money from his fraud-detection business than from his original startup.

As Leary points out, this creates both an opportunity and a threat for accounting firms. “The real risk of AI taking accounting jobs isn’t that AI will take the job away. Clients are just going to say, ‘I can do that myself. I don’t need to pay somebody $400,000 to do a half-assed ChatGPT thing.’”

Government Shutdown: When Critical Systems Break Down

The conversation then turned to the government shutdown’s impact on air travel and tax services. The situation has become genuinely dangerous, with cascading failures that reveal how fragile our systems really are.

Air traffic controller-related delays jumped from a typical 5% to 53% as workers called in sick rather than work without pay. Oliver experienced this firsthand when his flight was delayed for hours with no official explanation, though flight attendants privately blamed air traffic control shortages.

The scariest incident happened at Burbank Airport in Los Angeles, where the tower went completely unmanned. “When that happens, there is a backup procedure, which is that the pilots have to do their own air traffic control,” Oliver explains. “They get on a shared frequency and have to communicate with each other. There’s no intermediary. So that not only slows things down. It also creates risk. There’s a huge risk of these planes crashing into each other because they miscommunicate.”

The economic impact is staggering. The US Travel Association estimates $1 billion in weekly losses to the travel economy. Over 750,000 federal workers have been furloughed, while more than a million work without pay. For TSA screeners earning an average of $51,000, the situation is untenable. “If they don’t get paid, they are not paying their bills,” Oliver notes. “They’re going to go drive for Uber to pay the bills.”

The IRS shutdown creates serious problems for accountants. Nearly half of IRS staff have been furloughed. While electronic returns continue processing and automated refunds still flow, human support has collapsed. Phone support is essentially gone, paper returns sit unprocessed, and audits have stopped. Yet interest and penalties continue to accrue, and all deadlines remain in effect.

Adding to the chaos, Trump fired over 4,100 federal workers instead of furloughing them. The Treasury alone lost 1,446 employees, including about 1,300 IRS workers. “It’s the first time in modern history that mass firings have happened during a funding lapse,” Oliver observes.

The administration also created a new “CEO of the IRS” position to bypass Senate confirmation, appointing Frank Bisignano, former CEO of Fiserv, who still owns about $300 million in company stock. This creates obvious conflicts of interest, especially since Fiserv is involved in launching digital stablecoin initiatives. “This is why you have to have hearings. You can’t just appoint somebody to a position,” Leary emphasizes.

When Independence Becomes a Joke

Next, Oliver and Leary discussed how financial entanglements are destroying audit independence while regulators focus on trivial violations.

Take BDO’s current crisis as an example. The firm took a $1.3 billion loan at approximately 9% interest from Apollo Global Management to finance its employee stock ownership plan. The debt forced the company to lay off employees, freeze travel, and conduct emergency cost reviews across all divisions.

But while BDO was giving First Brands a clean audit opinion, Apollo was actively shorting the company. First Brands collapsed months after BDO’s clean audit. “If I’m BDO and I audit a company that is being shorted by a company I took a $1 billion loan from, where’s the independence?” Leary asks. “What is the fraud triangle? Opportunity, rationalization, and financial pressure. All the parts of the fraud triangle are here.”

Meanwhile, EY is celebrating a “dramatic audit quality turnaround,” with its deficiency rate dropping from 46% in 2022 to below 10% in 2025. They achieved this miracle by firing 132 public company audit clients. In other words, the problematic audits didn’t disappear. They just moved to Deloitte and KPMG. “Have we actually achieved anything here? Or have we just shifted the bad audits somewhere else?” Oliver wonders.

The hosts also discussed a new scheme where crypto promoters target CPA firm clients. The Truevestment Bitcoin Legacy Fund wants CPAs to help raise $150 million from their clients, which institutional investors will then match before merging into a Nasdaq entity—essentially a SPAC wrapped in Bitcoin speculation.

The marketing compares buying Bitcoin today to “buying the Dow at 900.” But as Leary points out, when the Dow was at 900 in the mid-1960s, it consisted of companies like AT&T and General Electric—”companies that made things” and created real value, not speculation.

Why Nobody Cares About Financial Reports Anymore

Perhaps the most damning revelation from the podcast’s recent news roundup is that 86% of major companies are using broken charts in their financial reports. A CPA Journal study found bar charts with misleading axes, pie slices that don’t match percentages, and deliberate distortions to exaggerate performance. Of 1,584 charts reviewed, 12% had fatal flaws that completely misrepresented the data.

“The fact that so many of them have errors and nobody’s pointing them out indicates to me that nobody’s reading them,” Oliver observes. Indeed, 10-K filings get downloaded an average of just a few dozen times.

The hosts even shared a bizarre example where social media bots criticizing Cracker Barrel’s new logo caused the stock price to tank. According to Wall Street Journal data, 44.5% of posts about the logo change were from bots. “Maybe nobody cares about your charts because nobody even cares about the financial statements,” Leary suggests.

What This Means for Your Firm

The key insight from Hector Garcia stuck with David: “AI is never going to do perfect accounting, but it’s going to do it good enough.” For most clients, “good enough” financials that they can generate themselves might be perfectly adequate.

Accounting professionals can embrace AI for meaningful fraud detection and insights, or watch clients realize they can generate “good enough” work themselves. As this episode of The Accounting Podcast makes clear, the traditional value proposition of professional accounting services is crumbling. The firms that survive will be those that identify and deliver human value that transcends what AI can do: strategic insight, ethical judgment, and genuine expertise that no algorithm can replicate.

Listen to this episode to understand not just the challenges facing accounting, but what you need to do differently starting today.

The Accounting Platform That Achieves 96.5% Automation Reveals How They Did It

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“No one’s going to be outcompeted by the AI itself. You are going to be outcompeted by firms that really adopt this aggressively,” warns Jeff Seibert, whose company just hit 96.5% accuracy in automated bookkeeping—something that seemed impossible just a few years ago.

In this milestone 100th episode of the Earmark Podcast, Blake Oliver sits down with Jeff Seibert, co-founder and CEO of Digits, to explore how AI is fundamentally changing the architecture of accounting software. Seibert brings fresh eyes to accounting—he previously led consumer product at Twitter and built Crashlytics (now running on six billion smartphones). His frustration was simple: Why could product teams access real-time analytics while business owners waited weeks for black-and-white spreadsheets?

Founded in 2018, Digits set out to reimagine accounting in the age of machine learning. While traditional software treats transactions as meaningless text in rigid databases, Digits achieves near-perfect automation by treating financial data as interconnected objects that learn from patterns across millions of transactions.

The 30-Year-Old Problem Holding Back Accounting

As Seibert sees it, the fundamental issue facing bookkeeping automation is that every major accounting platform—QuickBooks, Xero, and even NetSuite—runs on relational databases designed 20-30 years ago. These systems treat transactions as simple text entries with no understanding of what they mean.

“QuickBooks is just going to see an Uber transaction as “U-b-e-r”. It just sees text,” Seibert explains. “It doesn’t understand the data, it doesn’t know what Uber actually is.”

This limitation explains why Intuit, with all its resources, has yet to deliver meaningful automation. The answer is architectural. Each QuickBooks company exists in its own isolated database, preventing the software from learning patterns across businesses. The constraints run so deep that QuickBooks still can’t handle having a vendor and customer with the same name—it appears they chose “name” as the primary database key decades ago.

Digits takes a completely different approach using what’s called a vector graph data model. Everything becomes an object—Uber is an object, your expense categories are objects, your bank accounts are objects. Transactions become connections between these objects, creating a web of financial relationships the AI can understand.

This mirrors how large language models (LLMs) work, converting transactions into vector embeddings, essentially plotting them in multi-dimensional space where similar items cluster together. When trained on 170 million transactions representing nearly $1 trillion in business activity, patterns emerge that would be obvious to humans but invisible to traditional software.

“When you have that scale of data and you see how everyone has booked Uber before, you start to see patterns,” Seibert notes. “The model starts learning. If it sees Lyft in your accounting for this client, it then knows how to book Uber.”

How AI Agents Actually Work (Hint: Like Clever Interns)

The accounting world is buzzing about “AI agents,” but what are they really? Seibert explains, “An agent is simply an LLM that you run in a loop. You give it a task, it attempts the task, you ask if it completed it. If not, it continues until it’s done.”

Think of them as clever interns who never get tired. Digits has been running these agents in production since January 2024, primarily for researching unfamiliar transactions.

The system uses three layers of intelligence. First, it checks if this specific client has seen this transaction before. If yes, it books the transaction exactly the same way. Second, if the transaction is new to this client but familiar to the platform, it uses its global model trained across all users. Third, for completely novel transactions, the agent literally Googles them.

“What would you do as an accountant? You would probably Google it,” Seibert explains. “What do our agents do? They literally Google it, research the transaction, build a dossier about it.”

As a result, only 4-5% of transactions now require human review, compared to the 20% that typically slip through even well-maintained rule-based systems. Notably, the system maintains strict confidence thresholds. Any transaction it is unsure about gets flagged for human review. It never guesses when uncertain.

The upcoming reconciliation feature shows how sophisticated these agents have become. The system pulls statements directly from banks or extracts them from PDFs, then matches transactions with pixel-level precision. “You can literally click on the transaction and see it on the statement and vice versa,” Seibert says. This builds trust with accountants who need to see exactly where the numbers come from.

What This Means for Your Firm’s Future

As of August, Digits hit 96.5% accuracy, up from 93.5% in spring. Each percentage point represents thousands of transactions that no longer need human touch. But it begs the question: how do you price services when the work happens automatically?

“If you’re charging purely per hour right now, then automation may make that challenging,” Seibert acknowledges. But forward-thinking firms are already adapting. They’re moving to fixed-fee models for routine work like monthly closes, which become increasingly profitable as automation reduces time investment. Many use a hybrid approach, charging fixed fees for the close, and hourly rates for advisory work.

At a flat $100 per month (with volume discounts for accounting partners), Digits offers predictable pricing that contrasts sharply with QuickBooks’ constant increases. The platform even offers specialized SKUs for ledger-only or reporting-only clients, accommodating diverse practice needs.

The staffing implications are real but not apocalyptic. Junior bookkeeping roles focused on data entry will diminish. But Seibert points out this could make the profession more attractive: “You don’t want to just sit there doing data entry all day long. You want to learn how to advise businesses.”

Seibert recommends firms start small when implementing automated bookkeeping. “Pick one client in your firm and see what you can achieve,” Seibert challenges. Choose a simple, digital-native business like consultants, SaaS companies, or agencies with predictable electronic expenses. Build confidence, then expand to complex cases.

Building Trust Through Transparency

With financial data flowing through AI systems, security is crucial. Digits addresses this with architecture developed at Seibert’s previous companies, where they handled crash data from billions of smartphones.

Everything stays within Digits’ systems; they don’t send raw data to OpenAI or other third parties. All data is encrypted at rest using per-object envelope encryption, where each object has its own encryption key. Even if breached, stealing one key wouldn’t compromise the system.

The platform is SOC 2 Type 2 certified, with complete audit trails showing who changed what and when. You can even grant granular access, like giving your marketing manager visibility into only marketing expenses. “They can see marketing, all the transactions booked to marketing, and nothing else,” Seibert explains.

Importantly, when AI does the work, you can trace exactly what happened. Click on any transaction to see the activity log. This solves the common problem of clients making changes in QuickBooks without anyone knowing.

The Competitive Reality Check

Seibert’s warning deserves repeating: “No one’s going to be outcompeted by the AI itself. You are going to be outcompeted by firms that really adopt this aggressively.”

This isn’t hypothetical. Firms using advanced automation already serve more clients with similar-size teams, offer competitive pricing while maintaining margins, and provide real-time insights that clients increasingly expect.

You don’t have to become a tech expert. Set aside time each month after the close to try new tools. Watch YouTube videos about AI agents (though Oliver warns to avoid the hype channels). Most importantly, maintain healthy skepticism. As Seibert notes about AI doing math, “If it’s not 100% correct, what’s the point?”

Remember, AI agents are like clever interns. They’re eager, overconfident, and need supervision. They excel at tedious, repetitive tasks but need human judgment for nuanced decisions. The goal isn’t to replace accountants but to eliminate the work accountants wish they didn’t have to do.

Taking the First Step

Thoughtfully evaluate how these innovations can augment your practice. Start with one simple client. See what 96.5% automation actually feels like. Build confidence, then expand gradually.

Listen to the full episode to hear Seibert’s complete vision and practical guidance on everything from selecting pilot clients to restructuring pricing models. The tools to eliminate tedium while amplifying expertise aren’t coming; they’re here, proven, and improving rapidly. How quickly and thoughtfully can you integrate it?

From Vanishing Jobs to Work Slop: Inside Accounting’s AI Reality Check

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The accounting profession faces a stark reality-check as entry-level auditor positions have declined by 43% since January, and a third of accountants admit they cannot identify AI-generated fake receipts. 

In episode 455 of The Accounting Podcast, hosts Blake Oliver and David Leary address the growing evidence that AI is disrupting accounting more rapidly than most firms can keep up with. From vanishing entry-level jobs to the rise of “work slop” (low-quality AI output that wastes time and money), the profession is struggling with changes that are both promising and perilous.

The Tech Stack Problem Nobody Wants to Talk About

Before diving into AI’s disruption, Oliver shared a surprising statistic: only 37% of accounting firms require their clients to use their technology stack. That means 63% let clients choose their own tools, creating a mess of incompatible systems and inefficient workflows.

“It’s one of the things we did in my firm that was a differentiator and allowed us to scale quickly,” Oliver explained. “It reduced training time. It increased the speed at which we worked.”

The reluctance to standardize reveals a deeper problem in the profession: firms are so afraid of losing clients that they sacrifice efficiency and scalability. Yet Oliver found the opposite: “The ones that were willing to make that shift ended up listening to us about other things, too. So you might want to consider requiring clients to switch as, like a testing mechanism to see if they’re actually going to be a good fit.”

This standardization challenge becomes even more critical as firms try to implement AI. Without consistent data inputs and workflows, automation becomes nearly impossible.

The Vanishing Entry Level: A 43% Wake-Up Call

The most alarming news Oliver shared was the 43% drop in entry-level auditor job postings since January, based on a study of 126 million job postings. Meanwhile, senior positions requiring ten or more years of experience increased by 6%.

“These firms are extremely shortsighted,” Oliver argued. “They are just trying to juice profits and revenue in the short term. And the easiest way to do that is to replace your entry-level people with AI.”

The vulnerability of these positions is clear. As Oliver explained, “The stuff an entry level auditor does is so basic, like cash confirmations. You can have an AI agent doing cash confirmations all day long. It’s not complicated.”

The fear extends beyond auditing. Nearly half (45%) of accounts payable professionals now fear layoffs in 2025, up from 27% last year. These workers see AI agents matching invoices, approving bills, and processing expense reports—tasks that once required human oversight.

Leary raised an important question: Are firms actually succeeding with AI, or are they cutting staff first and hoping to automate later? In Oliver’s view, the automation is working well enough to justify the cuts. But this creates a long-term problem. Without entry-level positions to train tomorrow’s senior accountants, where will future leaders come from?

Work Slop: The $200 Hidden Cost of Bad AI

A new Harvard Business Review study coined a term for low-quality AI output: “work slop.” And work slop is expensive. Each incident wastes nearly two hours and costs about $186 per worker per month.

Forty percent of workers report receiving work slop in the past month. More than half feel annoyed when they get it, and 42% view the senders as less trustworthy.

“Every time one coworker gives another coworker slop, it costs your company 200 bucks,” Leary emphasized. But, “Employees who turn out work slop probably already did work slop before. They just did it at a much slower volume.”

The hosts shared their own experiences with work slop. Job applicants submit unedited ChatGPT responses. Guest pitches reference the wrong podcast. Some candidates even feed interview questions into AI during live video calls.

“It looks good,” Oliver said about typical work slop. “Like if you look at the email, it’s nicely formatted and it looks good and then you actually read it and you realize that it’s garbage.”

The paradox is striking: 97% of firms admit they’re not using technology efficiently, yet 86% believe AI-using firms have a competitive advantage. The gap between aspiration and execution means firms produce more low-quality work faster rather than better work more efficiently.

The Fraud Detection Crisis

Perhaps most concerning is accountants’ declining ability to spot fraud. Thirty-two percent admit they can’t recognize AI-generated fake receipts. Another 30% are seeing more fraudulent receipts than last year, and 42% suspect colleagues have submitted fake or altered receipts.

“If you want to see just how difficult it is or how easy it is to make one, just go and ask ChatGPT to make you a receipt,” Oliver challenged listeners.

Leary noted that expense fraud isn’t new. After all, people used to pick a receipt up off the ground at McDonald’s. But AI changed the game. Now anyone can generate perfect forgeries on demand.

Oliver explained that current AI models don’t understand physics, so shadows and lighting in fake images often don’t match reality. But detecting these requires expertise most accountants don’t have.

“When nothing is physical anymore, how do you, as an auditor or an accountant, rely on a scanned document?” Oliver asked, highlighting a fundamental challenge for the profession.

Solutions Emerging from the Chaos

Despite the challenges, practical solutions are emerging. Zapier announced a “human in the loop” feature that pauses automated workflows for human review at critical points. “Don’t try to automate the whole workflow,” Oliver advised. “Try to automate one task in the workflow.”

Keeper launched a new AI product that converts payroll reports and settlement statements into journal entries—a task that previously required complex spreadsheets and manual work. At $50 per client per month, it represents the kind of targeted automation that actually works.

Even Drake Software, long criticized for being behind the times, launched cloud-based tax software. While limited to certain forms, it signals that even legacy providers recognize the need to modernize.

These tools show that successful AI implementation isn’t replacing humans entirely. Instead, it augments specific tasks while maintaining human oversight for quality and judgment.

Looking Ahead: A Profession at a Crossroads

The accounting profession faces interconnected challenges that require more than technological solutions. The 43% drop in entry-level positions poses a threat to the talent pipeline. Work slop erodes trust and efficiency. Fraud detection capabilities are falling behind those of fraudsters.

Yet there are opportunities within these challenges. Firms that thoughtfully integrate AI, maintain human oversight, and invest in training the next generation will have an advantage over those who chase short-term profits by cutting entry-level positions and blindly implementing AI.

As Oliver noted about his own firm’s success, standardizing technology, requiring client buy-in, and focusing on quality over quantity created real competitive advantages. The same principles apply to AI adoption. Success requires strategy, not just software.

To hear Oliver and Leary’s complete analysis of these shifts in accounting, including their discussion of H-1B visa changes, Trump’s latest tariff threats, and more practical insights for navigating AI’s impact, listen to the full episode of The Accounting Podcast. Their unfiltered weekly discussions provide essential perspective for anyone trying to understand where the profession is heading and how to thrive despite the uncertainty.

Why Accountants Are Both Thrilled and Terrified by QuickBooks’ Latest AI Push

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How much should we trust AI with our critical financial processes?

In a recent episode of The Unofficial QuickBooks Accountants Podcast, hosts Alicia Katz Pollock and Matthew “Spot” Fulton break down the August 2025 “In the Know” webinar from Intuit, where AI agents take center stage alongside major Enterprise Suite enhancements and ProAdvisor Academy improvements.

From payment collection to payroll processing, QuickBooks is pushing automation further than ever before. But as Fulton and Katz Pollock discuss, the technology that saves you hours today needs careful oversight to avoid compliance nightmares tomorrow.

ProAdvisor Academy Gets Smarter

Before diving into the AI updates, the hosts highlighted some welcome improvements to ProAdvisor Academy. You can now filter courses by length and CPE credit amount—perfect for those moments when you think, “I have an hour, what can I learn right now?”

Even better, the system finally saves your CPE certificates in the “My History” section. As Katz Pollock notes, “They used to email them to you and you had to save them, and that was it. So the fact that you can actually now track your CPE is pretty darn awesome.”

Intuit is also launching a new quarterly series called Solution Spotlight, where support experts will tackle complex challenges and deep-dive into underutilized tools. The first topic? Bank transactions and reconciliation—the community’s most requested subject.

Enterprise Suite: The Multi-Entity Game Changer

Fulton and Katz Pollock spent considerable time discussing Enterprise Suite’s powerful consolidation features, and for good reason. These updates address long-standing issues that have plagued multi-entity businesses for years.

The Shared Chart of Accounts feature uses AI to standardize accounting across all your entities. As Fulton explains it, “You choose which chart of accounts you want to be your primary one, and then you can use the AI to say, okay, we think these accounts are going to match up with those accounts. You still have the ability to review and say, yep, you got this right.”

The time savings are massive. Fulton speaks from experience, “As an accountant, the time and energy it takes to try to normalize a chart of accounts is extensive. There’s a lot of thought and knowledge and wisdom that goes into it.”

Multi-entity transactions are even more impressive. When you invoice another entity in your organization, the system automatically creates the corresponding bill in that entity, complete with a PDF attachment. Fulton recalls the old way: “You would pull up two browsers, you’d have both companies up, and you look at the intercompany exchanges between one company and the other, and you go line by line to make sure both sides are there.”

But Katz Pollock raises an important point about accessibility. She has clients with multiple small entities—”literally QuickBooks Ledger or Simple Start”—who desperately need these consolidation features but can’t justify Enterprise Suite’s price tag. Her suggestion? “I think they should make an Enterprise Lite version focused solely on multi-company functions.

The Payments Agent: Getting You Paid Faster (and Smarter)

The Payments agent analyzes customer behavior to optimize your collection strategy. When you create an invoice, it shows you how long they’ve been a customer, their payment history, open invoices, and average payment time.

But here’s where it gets interesting. The agent suggests payment methods based on what will get you paid fastest. It even calculates total time to receive funds, including your customer’s typical delay. When Katz Pollock saw “ACH 14 days” in the demo, she clarified, “It wasn’t that ACH takes 14 days to clear. It’s that the customer takes on average nine days to pay, and then you have the three to five days it takes to clear.”

Fulton cuts to why this matters, “As business owners, all too often we rely on small margins to where we are super sensitive to cash flow. If it’s going to take somebody longer to pay, we need to know that.”

The system can also parse invoices from text, images, or PDFs, though Katz Pollock admits it “doesn’t do the line items yet. But you know, it’s just the infancy of the technology.”

One limitation bothers Katz Pollock: Reminder settings apply to all customers universally. “I have placeholder invoices or agreements with customers where it’s okay that they’re not going to pay for another 90 days,” she explains. Her workaround? Adjust due dates to match actual payment expectations.

The Payroll Agent: Convenience Meets Controversy

The Payroll agent’s text-message time collection generated the most heated discussion. Employees receive texts asking for hours, overtime, and tips. They respond with simple messages, and the system compiles everything for manager approval.

Sounds great, right? Not so fast.

“If they’re not keeping a time card, you know they’re going to overestimate how much they actually worked,” Katz Pollock warns. Fulton agrees, “How many employees are always completely honest with their hours and their overtime and their tips?”

The system is heavily restricted during beta. It’s only for US customers who don’t use auto payroll or QuickBooks Time, have one pay schedule, and use basic pay types. Fulton sees wisdom here, “Let’s make sure this is working before we give it to all the crazies out there.”

Still, there are safeguards. The system flags anomalies, requires manager approval, creates audit logs, and needs employee consent for each payroll period. Fulton even sees potential for construction companies where daily time certification is required. “They’re having to certify by responding back to this the amount of time they worked.”

Katz Pollock’s verdict? “The technology is going to be great. It’s the humans that you can’t trust in this particular issue.”

Customer Leads: Your Email Becomes Your CRM

Currently in Gmail-only beta (Outlook coming soon), the Customer Leads agent scans your email for customer interactions and organizes them into a sales pipeline: inquiry, negotiation, finalization, contracted, or lost.

Fulton’s excited about consolidation. “I’ve been using 17 Hats, but the challenge I’ve always had is the integration piece. I can handle all this stuff up to the estimate and invoice somebody, but it’s always been external.”

Katz Pollock uses Method CRM currently and sees the appeal, “This will be really nice to be able to just keep it right inside QBO and not have to go to another app.”

The hosts admit they’re still learning this feature, and Katz Pollock has a future episode planned to dive deeper.

More Updates Worth Your Attention

A few other updates the hosts are looking forward to include:

Scheduled Compensation Changes

This might be the sleeper hit of the updates. You can now pre-program raises and bonuses with effective dates. As Fulton exclaims, “This is sunlight shining down onto us so we can take a vacation at the end of the year, too!”

Katz Pollock shares a perfect use case: “I had a client whose employee broke their field service iPad and was reimbursing them out of their payroll, $150 per month for six months.” With scheduling, that deduction would automatically end on the right date.

Sales Tax Automation Expands

QuickBooks now handles sales tax filing for Iowa, Minnesota, North Carolina, Rhode Island, Vermont, and West Virginia at $40 per filing. While the hosts debated the price, Fulton notes it’s actually market rate compared to services like Avalara.

Looking Ahead

The hosts emphasized community feedback throughout the episode. As Fulton puts it: “Are you using Enterprise yet? If you are, what features are you loving? If you aren’t, what features are most enticing?”

They’ve even started a LinkedIn group for the podcast where listeners can discuss episodes and share experiences.

Katz Pollock is launching her “Great QBO Refresh” training series in September, completely rebuilding her curriculum to address all the interface changes. 

Don’t miss Intuit Connect (October 27-29 in Las Vegas) or Reframe Conference (November in Florida), which Fulton calls “by far, hands down, the best conference I’ve been to in years.”

The Bottom Line

These AI agents aren’t replacing accounting professionals; they’re redefining the role. The firms that thrive will leverage AI for efficiency while maintaining the human judgment that ensures accuracy, compliance, and client trust.

As Katz Pollock wisely advises about the payroll agent’s rollout, “Intuit, go slow on this one. We want to actually see use cases before it becomes universal.”

The future of accounting isn’t human versus machine. It’s human with machine, each doing what they do best. Ready to dive deeper? Listen to the full episode above and join the conversation in the Unofficial QuickBooks Accountants Podcast LinkedIn group.

Alicia Katz Pollock’s Royalwise OWLS (On-Demand Web-based Learning Solutions) is the industry’s premier portal for top-notch QuickBooks Online training with CPE for accounting firms, bookkeepers, and small business owners. Visit Royalwise OWLS, where learning QBO is a HOOT!