Bookkeeping

Not All AI Is Created Equal and Your Next Software Decision Depends on Knowing the Difference

Earmark Team · April 17, 2026 ·

When Jeff Seibert ran consumer product at Twitter, he asked the finance team for his budget to throw a team event. They said they’d get back to him in 45 days. So he just ran the event without them.

That gap between real-time data and 30-to-90-day delayed financial reports was frustrating, and it eventually led Jeff to build Digits, a new general ledger designed from scratch for the machine learning age. After raising $100 million pre-launch, testing 2,000 monthly closes, and getting 80% of clients closed in under an hour, Digits launched in March 2025. Now, just over a year later, hundreds of accounting firms are onboarding thousands of clients onto the platform.

Jeff launched Twitter’s algorithmic timeline in 2016, and it was one of the first global deployments of machine learning. Now, the AI revolution Jeff helped launch is flooding the accounting profession with claims that are hard to verify. Every accounting software company seems to include AI in its marketing copy, promising everything from “fully automated bookkeeping” to capabilities that don’t add up under scrutiny.

In a recent Earmark webinar, host Blake Oliver and Rob Hamilton, Head of GTM at Digits, pulled back the curtain on how AI in accounting actually works. He was joined by Megan Reid, Product Specialist & Firm Enablement at Digits, who fielded questions throughout the session.

Every AI claim in accounting software isn’t real. But accountants who understand the four core model types (plus one common lie) will make smarter investments, automate the right parts of their workflow, and position their firms for a shift Rob sees coming by the end of 2026.

The AI hype problem (and one question to cut through it)

Before making any technology decision, you need a filter for separating real capabilities from marketing fluff. Rob offered a simple one that cuts through the noise.

He showed screenshots from multiple accounting software companies making bold AI claims. One promised “fully automated bookkeeping.” Another asked, “Do you do AI bookkeeping or do you use a dedicated team of experts?” The positioning has gotten so confusing that firms can’t tell what’s real anymore.

The confusion isn’t new. About five years ago, tech investor Naval Ravikant tweeted, “In most pitch decks, AI stands for Anonymous Indians.” For a long time, that was literally true. Services like Botkeeper rose and fell using offshore labor dressed up as automation. Today, “AI actually means we just bolted on and sent all of your data to ChatGPT,” Rob explained.

Here’s your filter: “AI is the same thing as machine learning,” Rob stated. “If someone is talking to you about AI and they’re not referring to machine learning as the underlying premise, it’s just BS.”

But this filter only works if you understand what machine learning actually is.

Traditional software is straightforward. You write code that tells the computer exactly what to do. It’s tedious to build, but rock solid once it works. Machine learning flips this completely. You feed the system thousands or millions of examples, and the model learns the patterns itself. As Jeff explained in a clip Rob played, “You give the computer the goal state—I want this outcome—and then the computer itself is learning how to do it.”

These models are neural networks. Thousands of hidden layers mimic how neurons connect, based on Google’s 2017 “transformer” research paper (the “T” in GPT). It’s a massive matrix multiplication problem where the system figures out how variables relate to each other.

But machine learning isn’t one thing. Different model types have different strengths and uses in accounting. Understanding these distinctions helps you avoid buying the wrong software and shows you exactly where AI can save time and where vendors are overselling.

The model types that matter (and one that doesn’t)

Rob walked through five categories that get lumped under “AI,” but understanding the differences is what separates informed decisions from expensive mistakes.

Generative models

Large language models (LLMs) are the ones you hear about most, ChatGPT being the prime example. GPT stands for “Generative Pre-Trained Transformer,” and these models generate the most likely continuation of whatever prompt you give them. Rob showed a useful application: turning bullet-point close notes into polished client emails. His advice is to write a “job description” for the AI once. Tell it who it is, give context, specify output format, add examples. Then just paste in different client notes as needed.

But generative models have serious limits. They’re “super eager” and always want to complete prompts, making them prone to hallucinations, or making things up that sound real. They’re bad at math because they generate text rather than calculate numbers. And they’re trained on the internet, not your specific clients. “The ways that it is hallucinating is stuff that maybe even is hard for humans to catch sometimes,” Rob warned. Always review the output.

Agents

These are LLMs with help. You give them a job description, a task, and tools, like computer programs they can use to generate reports, list accounts, or run calculations. The agent makes a plan, uses its tools, checks if the task is done, and loops until complete. Rob showed Digits’ agent answering “I want to hire 20 software engineers next year. Can I afford to?” with a data-backed response.

Guardrails are critical. Microsoft’s early agent “started asking people on dates in the chat,” Rob noted. “You don’t want your accounting agent dispensing dating advice.” Agents work well for updating schedules, running quality checks, and answering analytical questions, but they’re slow and need careful boundaries.

Predictive models

These got Rob visibly excited, and for good reason. These models take an input and predict an output from known options. When the model sees a $5 Starbucks charge, it considers the client’s location, history, and chart of accounts. For a local client, it’s meals and entertainment. For a New York client in California, it’s travel. A $157 Starbucks charge is probably an event, regardless.

What makes predictive models perfect for transaction categorization is they can’t hallucinate; they only choose from existing options. They’re deterministic (same input, same output), include confidence scores, and run fast and cheap once trained.

Digits built a “layer cake” of predictive models:

Client-level (learns each business)
Firm-level (encodes your best practices)
Global (trained on 180 million transactions worth nearly $1 trillion)
An LLM fallback for completely new transactions

The result was over 97% accuracy, compared to standalone LLMs that plateau below 80%, which is about the same as outsourced bookkeepers.

Document extraction models

These combine OCR with layout-aware language models that understand document structure. Previous tools used Amazon’s Mechanical Turk, which relied on humans manually extracting data and took hours. Modern extraction models work in seconds. Digits’ bank reconciliation automatically pulls PDF statements, matches transactions to the exact spot in the PDF, and generates audit reports.

Data analysis

This model is where Rob pulled the rug out. Financial reporting and analysis is “actually just math. It’s not ML.” Computers have done statistical analysis for decades. Could you build an agent to do it? Sure, but it would be slow, expensive, and probably wrong. “If anyone says their AI does reporting and statistical analysis, please ask them what they’re talking about.”

Here’s how the right models map to your month-end close:

Book transactions: Predictive models (with LLM fallback)
Reconcile statements: Extraction models plus matching algorithms
Update schedules: Agents
Review and correct: Agents with quality checklists
Analyze and report: Statistical analysis plus agents for questions

“Shoehorning an LLM in to solve a problem and just sending a bunch of information is fundamentally incorrect,” Rob emphasized. Each step needs the right model. No single AI approach handles everything.

The 2026 prediction

Understanding model types is just the foundation. The urgency comes from how fast everything is converging.

“Across a large client set in different industry types, it’s highly likely that the month-end close process is looking to be completely automated by the end of 2026,” Rob predicts. Even his “95% automated” hedge probably sounds aggressive. But his logic follows directly from the technology.

If predictive models hit 97% accuracy on transactions, extraction models automate reconciliation in seconds, agents handle schedules and quality control, and statistical analysis covers reporting, then manual work drops to a fraction. Rob’s goal is to see accountants doing “1/20th of the work you’re doing today.”

He acknowledged limits. Construction firms with complex job costing might not hit that threshold. But for firms serving professional services, cash-basis businesses, and straightforward accrual clients, the automation curve is steep.

An AI-native firm will focus on value instead of tedium. Deeper industry expertise. Stronger client relationships. Higher margins. You’re not reviewing every transaction, you’re supervising the system and handling exceptions. Those hours saved on reconciliation become advisory time clients actually value.

But this is also a competitive necessity. “AI won’t replace you. Someone who’s good at using AI is going to,” Rob said, quoting a common warning. And he was direct about the stakes. Firms that don’t adapt will face “a cascading effect on business models” as early adopters pull ahead.

For overwhelmed firms, which Rob acknowledged includes most firms, he offered practical starting points:

Map your processes first. If you use workflow tools like Karbon or Keeper, you’ve probably documented your steps. If not, start there. You can’t identify where AI fits until you know what you’re actually doing.
Start small and low-stakes. Don’t tackle your biggest challenge first. Try drafting emails, testing categorization, or visualizing data. Build your intuition gradually.
Get hands-on with new tools. Rob mentioned being impressed by Claude Opus, which could build HTML dashboards from his data (something he couldn’t do as a non-engineer). The specific tool doesn’t matter; hands-on experience builds judgment.
Know your business before choosing where to start. As Rob put it, “You need to know the details of your business to know where you can start and where the right places to poke and prod are.”

The “wait and see” window is closing. Firms that develop AI literacy now by asking questions about models, data handling, and use cases, will be ready for the rest of 2026 and beyond.

Your next move: Better questions, smaller steps, faster action

Let’s turn Rob and Megan’s insights into actionable takeaways:

Not all AI is equal. Four real model types plus one fake (data analysis) get lumped together. When vendors pitch “AI-powered reporting,” you now know to dig deeper.
Each close step needs a different model. Predictive for transactions. Extraction for reconciliation. Agents for schedules. Statistics for reporting. Anyone claiming one solution does everything deserves scrutiny.
Predictive models beat LLMs for categorization. Layered architectures that learn your clients and firm patterns dramatically outperform chatbots. Bigger isn’t always better.
Ask vendors the hard questions. What model type? Where does data go? Are you training your own models or sending financial data to third parties? This is due diligence.
The tipping point is closer than you think. Whether Rob’s 2026 prediction proves exactly right or directionally right, the trajectory is clear. Understanding these distinctions now positions you to take action.

For those interested in going deeper, Rob mentioned resources like the AI Native Accounting Foundation and the AI-Native Accounting podcast hosted by Kacee Johnson, where industry leaders discuss the latest developments.

The accounting profession is at a real inflection point. Smart firm leaders will develop the literacy to ask smart questions, experiment in the right places, and redirect time from tedium to advisory work clients value.

Rob noted this might be one of the few professions with such clear AI use cases, putting accountants at the forefront of innovation. That’s an opportunity to shape how technology serves the profession, not the other way around.

Watch the on-demand webinar for complete details, including live demonstrations, security architecture specifics, and audience Q&A covering nonprofits, inventory clients, and platform migrations. The future of accounting is being written now. Make sure you’re part of the conversation.

The IRS Can Hit Your Clients With Criminal Charges for Bad Bookkeeping (And Most Tax Pros Don’t Know It)

Earmark Team · January 5, 2026 ·

If you’ve ever received a shoebox full of receipts from a client or struggled with QuickBooks files where half the expenses are labeled “miscellaneous,” you know the frustration. But according to Jeremy Wells, EA, CPA, in this episode of Tax in Action, poor recordkeeping isn’t just a workflow problem. It’s a legal violation that could cost your clients thousands in penalties.

Most tax professionals treat recordkeeping like a suggestion. But it’s actually a federal requirement with serious consequences, including a 20% penalty on underpaid taxes and even potential criminal charges. Understanding these requirements can transform your practice and create new revenue opportunities.

Your clients are breaking the law (and they don’t know it)

Wells starts with a section of the tax code that most practitioners overlook. IRC Section 6001 doesn’t suggest or recommend. It requires taxpayers to “keep such records, render such statements, make such returns, and comply with such rules and regulations as the Secretary may from time to time provide.”

The Treasury regulations spell it out even more clearly. Taxpayers must keep “permanent books of account or records, including inventories, as are sufficient to establish the amount of gross income, deductions, credits, or other matters required to be shown by such person in any return.”

“The way I read this,” Wells explains, “you as a taxpayer, in order to file a tax return, need to have permanent books and records you can rely on in order to justify and substantiate any amount of gross income, deductions, credits, or anything else that you’re putting into that return.”

Here’s what catches many people off guard: tax returns themselves don’t prove anything. In Wienke v. Commissioner (T.C. Memo 2020-143), the Tax Court established that returns are “merely statements of claims and are not considered evidence of the claims themselves.” The real evidence must come from the taxpayer’s books and records. So when your client thinks their signed tax return proves their income to a lender, they’re wrong. Without proper records backing it up, that return is just a piece of paper with numbers on it.

The penalties for inadequate recordkeeping can devastate a small business. Section 6662 imposes a 20% accuracy-related penalty on any underpayment due to negligence, which specifically includes “any failure by the taxpayer to keep adequate books and records, or to substantiate items properly.” That’s 20% on top of the taxes owed, plus interest.

But it gets worse. Section 7203 makes willful failure to keep records a criminal offense. The penalties are up to $25,000 for individuals or $100,000 for corporations, plus up to a year in prison. While Wells notes that your typical shoebox client probably won’t face jail time, the existence of criminal penalties shows how seriously the IRS takes recordkeeping requirements.

The three warning signs every practitioner must recognize

These requirements create ethical obligations for practitioners too. Circular 230, Section 10.34(d) allows you to rely on client information, but requires “reasonable inquiries if the information as furnished appears to be incorrect, inconsistent with an important fact or another factual assumption, or incomplete.”

Wells calls these the “three I’s” that should trigger immediate concern. He shares a common example: “When I ask them what their business mileage is, they’ll just tell me a flat number that has three or four zeros at the end of it. As soon as I see that information, I already know, just in my gut looking at that information, whether it appears to be incorrect, inconsistent, or incomplete.”

When you spot these red flags, you can’t just ignore them. Wells describes the uncomfortable conversation that follows when he asks for a mileage log. “Nine times out of ten, they’re going to tell me they didn’t actually keep up with one.” At that point, you face a tough choice. Do you push harder for documentation, accept questionable information, or potentially end the client relationship?

“It might be a tough decision to stop working with a taxpayer because they want to claim a certain amount of miles,” Wells acknowledges. But when clients repeatedly ignore recordkeeping requirements despite annual reminders, “at that point, we might have to reconsider the relationship.”

How good records flip the script on IRS audits

While penalties provide the stick, there’s also a powerful carrot for maintaining proper records. Wells reveals how good recordkeeping can completely change the dynamics of an IRS dispute.

Normally, the IRS holds all the cards. The Supreme Court established in Welch v. Helvering (1933) that “the commissioner’s determinations have a presumption of correctness while the taxpayer bears the burden of proving the IRS position wrong.” Wells calls this “a tough hill to climb, especially for a taxpayer that has not kept good books and records.”

But IRC Section 7491 flips this burden. When taxpayers introduce credible evidence, comply with substantiation requirements, and maintain proper records, the burden shifts to the IRS to prove the taxpayer wrong.

“If a taxpayer shows up to an examination or an audit with good books and records,” Wells explains, “then the auditor knows that under Section 7491, now it’s on the IRS to prove the taxpayer is wrong.”

This creates “a more positive settlement climate,” according to a 2003 Tax Notes article Wells cites. Auditors become more willing to negotiate reasonable settlements rather than risk losing in court. He notes that even when a taxpayer takes a “technically incorrect position,” having good records to explain their reasoning can lead to much better outcomes.

Why the Cohan Rule won’t save your clients

Many practitioners rely on the Cohan Rule as a safety net, but Wells warns it’s been dangerously misunderstood. This 1930 court decision allows taxpayers to deduct “a reasonable estimate of the amount of a verifiable trade or business expense if the exact figure is unavailable.”

“I’ve heard, between bad tax advice on social media and some practitioners who haven’t really read the court case,” Wells says, people claiming “if the client doesn’t know how much, we’ll just fill in a number and appeal to the Cohan rule.” But that’s not how it works.

Courts take a harsh view of taxpayers trying to use Cohan without basis. In Barrios v. Commissioner (2023), the court stated it “bears heavily against the taxpayer who failed to more precisely substantiate the expense.” Translation: courts will slash your estimates, sometimes to zero.

Wells cites Williams v. US (1957), where the court refused to “guess” at expenses, calling relief without evidence “unguided largesse.” The message is clear: you need some reasonable basis for any estimate, not just a number that feels right.

Making matters worse, Section 274 completely blocks the Cohan Rule for certain expenses:

Travel
Entertainment
Business gifts
Listed property (especially vehicles)

For these categories, taxpayers must keep contemporaneous logs showing time, place, amount, and business purpose. Wells emphasizes how strict this is: “There have been tax court and federal court cases where the mileage log was simply thrown out and no deductions were allowed because the taxpayer attempted to recreate that log after the fact.”

Turn recordkeeping problems into profitable services

Instead of fighting poor recordkeeping every tax season, Wells outlines specific services that transform this challenge into recurring revenue.

His foundation is a “bookkeeping review service.” You’re not doing actual bookkeeping. Instead, you review the client’s records quarterly and flag issues. “We’re probably not going to look through a lot of five, ten, twenty dollar office expenses,” Wells explains. “But we might look through some expenses that are four or five, six figures.”

During these reviews, you might spot expenses that should be capitalized instead of deducted, deposits miscategorized as revenue when they’re actually loans, or aging receivables signaling cash flow problems. The key is efficiency. “They don’t take nearly as much time as actual bookkeeping does,” Wells points out.

He also strongly advocates for direct communication with clients’ bookkeepers, eliminating the game of telephone that wastes everyone’s time. Set up quarterly check-ins to discuss categorization questions, journal entries, and ownership changes before they become tax-time emergencies.

“This should not be free,” Wells stresses. “This should not be just included. You should not just start doing this out of the goodness of your heart.” Whether bundled into tax prep fees or structured as a monthly subscription, these services must generate revenue.

Some practitioners take this even further with preferred partner networks. Wells knows firm owners who refuse to prepare returns unless the books come from their vetted bookkeepers. While it sounds extreme, the benefits are clear. “They’re never going to have to worry about whether a deposit was really revenue or contribution of equity or new line of credit, because they trust the bookkeeper to have taken care of that already.”

For maximum scalability, Wells suggests creating educational resources. Use screen recording tools to solve common problems once, then share those videos with multiple clients. “Each time a client asks you a question, you know others have that same question,” he notes. This transforms repetitive education from a time drain into a reusable asset.

Listen to transform your practice

Recordkeeping isn’t optional; it’s legally required, with penalties ranging from 20% of underpaid taxes to potential criminal charges. But understanding this framework doesn’t just protect you and your clients from disasters. It opens doors to shift audit dynamics in your favor, negotiate better settlements, and create profitable advisory services.

Will you keep wrestling with shoeboxes every tax season, hoping estimates will pass muster? Or build systematic solutions that generate recurring revenue while protecting everyone involved?

Listen to the full episode to learn exactly how to implement these strategies in your practice. Because when you understand the legal framework—the requirements, the penalties, and most importantly, the opportunities—you stop just surviving busy season and start building a practice that thrives year-round.