AI

AI Can Reconcile a Bank Account End to End Without Instructions

Earmark Team · May 15, 2026 ·

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

Trump Accounts Hit 90% Adoption in First Year

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

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

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

Perplexity and Palantir Target Core Accounting Work

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

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

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

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

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

Blake’s Test Whether AI Actually Does the Books

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

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

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

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

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

Firms Are Automating the Wrong Things

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

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

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

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

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

The Three-Person Firm of Tomorrow

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

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

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

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

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

The AI Trust Problem Accounting Can’t Afford to Ignore

Earmark Team · May 15, 2026 ·

Here’s a thought experiment. You ask ChatGPT to write you a research summary. It comes back 70% accurate. You tweak a few paragraphs, fix some facts, and you’re good to go. Now imagine that same 70% accuracy rate applied to your general ledger, audit workpapers, or financial statements.

As Mark Hickman, Sage’s Managing Director for North America, puts it bluntly, “You go to jail for that.”

That line from Episode 34 of The Unofficial Sage Intacct Podcast cuts straight to a tension every CFO, controller, and accounting professional grappling with AI needs to understand. The technology promising the biggest efficiency gains in a generation operates in a profession where approximate answers are a liability.

Hosts Doug Lewis, Matt Lescault, and Emily Madere sat down with Mark for their second annual Sage Future conference preview episode. Mark oversees Sage’s largest and fastest-growing region. He’s watched the Intacct acquisition grow more than 10x over the past 11 years. He’s in front of customers and partners constantly.

When he talks about what finance leaders say about AI, it’s field intelligence from someone who’s been in tech for nearly 25 years and has seen every major shift from dial-up internet to cloud computing.

He argues that while every corner of technology races to bolt on AI capabilities, accounting demands a fundamentally different approach built on trust, traceability, and human control. And the companies best positioned to deliver that are the established platforms sitting on vast reservoirs of trusted data.

Accounting AI Can’t Be a Black Box

Strip away all the marketing noise around artificial intelligence and you land on a simple question: where did that number come from?

In most industries, that question is nice-to-have. In accounting, it’s everything. And it’s why Mark frames Sage’s entire AI strategy around three pillars: trust, control, and accountability.

“When you type into ChatGPT, write me an essay on this or write me a paper on this, it can be 70% right and you can tweak it,” Mark explains. “You can’t be 70% right in accounting.”

Trust means AI outputs can’t disappear into a black box. Sage built what Mark calls a “trust label.” It’s a mechanism that lets users click into any AI-generated output and trace exactly where the data came from and how AI reached the conclusion. Think of it as an audit trail for the AI itself.
Control means humans stay in charge. “Accounting is different,” Mark emphasizes. “We can’t just have AI running everything behind the scenes. We need humans to control that AI and deliver what they need from those outputs.” The AI changes day-to-day workflows, but it assists rather than drives.
Accountability means everything is traceable. “We don’t want some large language model that somebody’s just pumping stuff into. It’s coming back. You have no idea where it came from, how that data was trained. Is it hallucinating? Is it not hallucinating? You need to be able to trust that that AI is credible, and you’re going to be able to use it in your accounting when you produce that to the auditors.”

Emily pushed Mark on a question many Intacct users ask: what about these new solutions flooding the market that claim to be “AI first”?

Mark’s response was diplomatic but pointed. New organizations saying “we’re AI native” and driving innovation are “good things for our industry.” But “how do you train AI? You train AI with data. We have a lot of data.”

Beyond raw data, Sage just kicked off what Mark calls its “Agentic AI marketplace.” This is a framework where partners build specialized AI agents that work across the broader Sage ecosystem. “We’re building hundreds and hundreds of agents that will be available to our customers,” he notes. The company is taking a platform approach where domain expertise gets layered onto trusted financial infrastructure.

The Adoption Paradox: Faster Than Cloud, Slower Than the Hype

Mark brings perspective from his 25 years in tech. He remembers when the internet arrived on dial-up connections that took five minutes to load. He watched the cloud evolve from radical concept to default infrastructure. Now he’s seeing AI reshape everything again.

“People thought it was going to move much quicker than it actually is,” he observes. “Adoption in these things is way more complicated than actually delivering the tech for it.”

In each major tech shift, people overestimate adoption speed. “The cloud is still being adopted in some places,” Mark points out.

The hosts brought up a perfect example of the hype-reality disconnect. Allbirds, a shoe company, rebranded itself as an AI organization and watched its stock rocket 600% in a single trading session before promptly crashing. “It’s reminiscent a little bit of the dot-com boom,” Mark says, “where people had a website and therefore their business was worth billions. And then everybody figured out they didn’t actually have a business case.”

But he distinguishes this moment from that bubble. “If you look at AI, it’s being driven predominantly by very large companies that are established with lots of customers and lots of money.” The recent tech stock dip “is just a reset on the adoption.”

So what are finance leaders actually saying when Mark sits across from them?

“We’re hearing this is game changing,” he reports. But it’s game changing in specific, practical ways:

Efficiency that enables strategy. “They look at AI to help them be more efficient so they can be more strategic,” Mark explains. The role of the CFO is becoming “significantly more strategic.”
Faster closes. “We’ve been talking for years about reducing time to close and eliminating the month-end close. AI is really going to speed that up.”
Immediate productivity gains. “When we let customers use our agents, they’re like, ‘I’m three times more productive. I cannot believe how much faster we’re getting things done.'”

But adoption takes time. “You can’t just inject things like that into your business overnight,” Mark cautions. “It’s got to be done in a way that makes sense to your workflows and your teams and how your processes roll.”

The conservative pace of AI adoption in finance is an essential feature in a profession where errors carry legal consequences.

Beyond the AI Headlines: What Else Is Changing

Matt asked for more insight into some other things happening at Sage that might not get as much attention because of how much focus is on AI.

Mark shared several initiatives that may have immediate impact:

Speed to market and faster implementations. “How are we going to implement faster? How are we going to get customers’ time to value reduced?” Mark asks. Matt reinforced why this matters. “If we have implementations that take three, six, eight months, we’re going to lose on that side of things.”
Vertical and micro-vertical specialization. “Our solution addresses the needs of those businesses within those verticals, which is something we’ve always done, but we’re doubling down on that,” Mark explains.
Strategic acquisitions filling gaps.
- Expense management. “We’re about 12 months into it and it’s done incredibly well. Overachieved all targets. Customers love it.”
- Sage HCM (payroll and HR). “It’s a great technology that’s really going to help us grow our business and deliver for our customers in a complete solution.”

Emily confirmed these acquisitions address real needs. “That was a big gap. In the past, a lot of clients asked for an expense management solution and wanted it all in one.”

What to Expect at Sage Future 2026

The Sage Future conference runs April 28-30 in San Francisco. Based on last year’s feedback, Sage restructured the entire event.

The new three-tier structure includes:

Keynotes now feature primarily external voices. Mark will interview Scott Krug, CFO of the New York Yankees. “He closes the books and does audits just like everybody else,” Mark says. Kara Swisher will discuss AI trends.
Super Sessions are new, product-specific deep dives.
Breakout sessions provide the next level of detail for features that caught your attention.

Matt made an important observation: “I’ve seen a lot of clients implement Intacct and then don’t go back to reconfigure over their lifetime. They’re not getting the full feature set out of the product.” The Super Sessions directly address this education gap.

Other highlights include:

Three embargoed announcements that Mark calls “very exciting and game changing for customers and partners”
CPA.com as titanium sponsor, validating Sage’s positioning as “accountants who serve accountants”
Monday night at Oracle Park (Giants stadium) and Thursday’s Sage Fest at an undisclosed “top” San Francisco venue
Post-event content distribution through webinars for those who can’t attend

“We want people to leave understanding where we’re going as a business and how we’re supporting them,” Mark says.

The Question Every Finance Leader Should Ask

The thread running through Mark’s conversation reframes how accounting professionals should evaluate every AI pitch landing on their desk.

A 70% accuracy threshold works for drafting marketing copy, but it’s a non-starter when the output feeds financial statements and regulatory filings. That constraint reshapes everything about how we build and deploy AI in accounting.

Whether you’re a CFO weighing technology investments, a controller separating AI substance from hype, or a partner building around the Sage ecosystem, listen to the full discussion. And if you’re heading to Sage Future 2026 in San Francisco, you now know exactly what to look for.

IPA Survey Data Reveals What Best of the Best Firms Actually Do Differently Than the Rest

Earmark Team · May 15, 2026 ·

“I can’t wrap my brain around how we’re going to utilize technology and make our work more efficient. How do we bill that if we’re billing by the hour? Are we going to start having reduced fees on their invoices? No. So what does that look like?”

That question from Chelsea Summers, Executive Director of Inside Public Accounting, captures the paradox facing the profession right now. Two-thirds of accounting firm revenue still comes from hourly billing, even as AI promises to slash the time it takes to complete work. Something has to give.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Chelsea to dig into firm performance data heading into 2026. Inside Public Accounting has been benchmarking accounting firms since 1987. Its latest survey includes over 600 firms, from Deloitte all the way down to firms around $6.5 million in revenue. The numbers tell a story that’s both reassuring and challenging for firm leaders.

The reassuring part is the playbook for outperformance isn’t complicated. Top firms charge what they’re worth, leverage their staff better, and embrace offshore teams. The challenging part is the profession’s attachment to hourly billing might be the single biggest barrier to capturing value from technology investments.

The Best Firms Don’t Work Harder; They Work Smarter

Every year, IPA identifies its “Best of the Best” firms based on 30 different operational metrics. These firms are profitable, but they also have low turnover, succession plans, marketing strategies, and overall organizational health. “Operationally, you’re a high performing firm that’s going to succeed,” Chelsea explained.

The performance gaps between these top firms and everyone else are striking:

Revenue per employee: The best firms generate $272,000 per full-time equivalent versus $220,000 for all firms
Leverage ratios: Top performers maintain 17.7 professionals per partner compared to 11.8 for average firms
Partner billing rates: Best firms charge $588 per hour while others charge $448

That last number deserves emphasis. Top firms are charging $140 more per partner hour, a 30% premium.

But these high-performers don’t necessarily burn out their people to get these results. “The big myth is that high performing firms push people harder, and that’s why they’re making more money,” Chelsea said. “But in reality, those high performing firms often have healthier capacities because they’re using that leverage and they’re using more specialized roles.”

When IPA compared utilization rates and chargeable hours between Best of the Best firms and everyone else, the numbers were nearly identical. Same hours worked, dramatically different outcomes.

The secret is putting the right people in the right roles. Top firms use more client service staff for production work and keep partners focused on partner-level activities like training, business development, and client relationships. When partners step back into production work and start micromanaging, it hurts morale and growth.

Offshoring Has Reached a Tipping Point

Over half of IPA’s survey participants now use some form of offshoring or outsourcing, and less than 5% plan to decrease it. Nearly everyone else plans to grow or maintain their offshore headcount. This is the new normal.

The performance data backs up the strategy. Firms with offshore teams reported 8.1% organic growth versus 7.5% for firms without them. They also saw a 9% improvement in margins.

“Nine percent is a lot,” Blake noted during the conversation. And he’s right. That kind of margin improvement can transform a firm’s economics.

What’s changed is how firms use these teams. The old model treated offshore staff like a processing center for data entry. Today’s successful firms fully integrate offshore team members. They have branded offices, firm email addresses, training opportunities, and direct client communication.

“Really making that individual feel a part of the team is very helpful in correctly utilizing them and making sure they feel the value of working at the firm,” Chelsea explained.

As technology automates the basic data entry tasks that initially justified offshoring, these team members are moving up to manager-level work, supporting advisory services, and contributing to internal operations. The offshore strategy and the technology strategy work together.

Firms Have More Pricing Power Than They Think

One pattern emerged repeatedly in Chelsea’s conversations with firm leaders: they consistently underestimate what clients will pay. “We have all these D and F clients, we want to cull them so we raise their prices 40%. But they all stay,” she shared.

A 40% price increase, and the clients don’t leave. That should make every managing partner pause and reconsider their pricing strategy.

In today’s inflationary environment, not raising prices can actually send the wrong signal. “When your CPA firm doesn’t increase their prices, then you almost say, are they not very good? Do they not believe in their work?” Chelsea observed.

Blake connected this to a broader pattern he’s seen across firms of all sizes. “We talk a lot when we talk about small firms about how they’re underpricing. It’s the same tendency in the midsize and the larger firms where some firms just don’t charge enough. They have pricing power and they’re not using it.”

The Advisory Pivot Is Slower Than Expected

Despite years of conference presentations about the shift to advisory, most firms still generate less than one-third of their revenue from advisory services. Tax and assurance continue to dominate, accounting for about two-thirds of revenue at the average firm.

“That’s really contrary to all the talk that we’re hearing on advisory,” Chelsea said. “I think it is [the future], but the data just isn’t showing that that is yet the predominant model inside most firms.”

Client accounting services, once positioned as the gateway to advisory, are growing but not explosively. Larger firms have shifted their thinking about CAS. “It seems like a lot of firms, especially the larger firms, have shifted away from feeling like that’s a foot in the door to like, that might be a strategy, but that’s not our only strategy going forward,” Chelsea explained.

For firms succeeding with advisory, a few patterns stand out. They have a dedicated internal champion who isn’t juggling 15 other responsibilities. They invest upfront and accept that returns take time. And they recognize that advisory service lines need different processes than tax and assurance work.

AI Faces Cultural Barriers More Than Technical Ones

When Chelsea asks firms about their return on technology investments, the responses are telling. “They’re like, how do we even do that? What does that look like? What does an ROI even mean?”

That said, firms are finding value in specific areas. Tax research stands out as a clear win. Being able to have AI synthesize complex tax code information saves significant time. Workflow automation, document processing, data extraction, and AI-assisted drafting also deliver results.

But adoption is slower than expected, and the blockers are mostly cultural. Partner skepticism leads the list, followed by change management resistance. “The accounting profession is certainly not known for being early adopters,” Chelsea noted.

There’s also a timing problem. Many firms shelved their AI discussions in December for tax season. When they picked them back up in May, there was different software, different models, different capabilities. “You’ve missed all of that research time and possible adoption time just because you’re too busy doing tax season,” Chelsea explained.

We Can’t Ignore the Billing Model Problem Much Longer

Throughout the conversation, Chelsea kept returning to the incompatibility between hourly billing and efficiency gains from technology.

She actually expected the 2025 data to show movement away from hourly billing. Instead, it went slightly in the other direction. Two-thirds of revenue still comes from billable-hour models, and much of what firms call “fixed fee” pricing is just hourly billing in disguise: time estimates multiplied by rates, presented as a flat fee.

Blake shared his own experience to illustrate the problem. When his CAS firm adopted cloud technology early, efficiency gains were 80%. “We couldn’t bill hourly or we’d lose all our revenue,” he said. “We were forced to switch to fixed fees.”

If AI delivers even half those efficiency gains for tax and audit work, firms clinging to hourly billing will face the same reckoning. Except unlike CAS, which was easier to start fresh with new pricing models, tax and audit are where hourly billing is most entrenched.

For firms evaluating technology investments, Chelsea recommends asking three questions:

Does this reduce manual work in a measurable way?
Does it integrate with existing workflows?
Will it free staff to do higher-value work?

If the answers are yes, the investment probably makes sense, even if you can’t calculate a ROI yet.

The Clock Is Ticking

The IPA data paints a clear picture of where the profession stands today. Top performers are executing on fundamentals. They charge appropriately, leverage staff effectively, and embrace offshore teams. Meanwhile, the broader profession remains tied to hourly billing, is moving slowly toward advisory services, and is largely waiting for clearer signals on AI.

For firm leaders, this creates opportunity and urgency. The playbook for better performance isn’t complicated, but the window to adapt might be narrowing. Firms that figure out how to decouple revenue from hours worked will be positioned to benefit from technology investments. Those that don’t may watch their revenue shrink as efficiency gains eat into billable hours.

“I’m crossing my fingers that 2026 we’re going to see some change,” Chelsea said about the billing model evolution. Given what’s at stake, the entire profession should be crossing their fingers with her.

For a deeper dive into these insights, including specific benchmarks on compensation trends, capacity planning, and technology adoption, listen to the full conversation between Blake and Chelsea on the Earmark Podcast. You can earn free NASBA-approved CPE credit for listening.

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

Earmark Team · May 7, 2026 ·

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

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

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

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

The bottlenecks killing your efficiency

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

Each phase has its own special frustrations:

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

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

How AI learns your way of doing things

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

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

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

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

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

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

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

Bank reconciliation without the chase

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

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

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

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

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

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

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

Turning saved time into client value

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

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

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

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

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

What this means for your firm

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

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

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

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

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

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

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

The Billable Hour Is Broken and Every Firm Leader Knows It. So Why Won’t Anyone Kill It?

Earmark Team · April 25, 2026 ·

Richard Lynch can list every reason the billable hour is broken. It undervalues experienced professionals, creates perverse incentives, burns people out, and reduces human beings to time-tracked units. But without a hint of irony, he admits that Sikich, one of the largest CPA firms in the country, still tracks hours “on a religious basis.”

That contradiction tells you everything about where the accounting profession stands right now.

On a recent episode of the Earmark Podcast, host Blake Oliver sat down with Richard, a managing principal at Sikich with over 25 years in public accounting. They had an honest conversation about where things actually stand. Not where the conference keynotes say they stand or where vendor demos suggest they stand, but where they actually stand inside accounting and advisory firms, at the level where someone still has to fill out a timesheet at 9 p.m. on a Tuesday.

The takeaway is that the profession’s transformation is stalling because firms can’t let go of the operational scaffolding they’ve built around the billable hour.

The Super Accountant Vision

Richard has a term for what’s coming: the “super accountant.” It sounds like marketing language, but his definition is specific. A super accountant has AI fluency, strong judgment, and understands compliance without needing to physically perform it. “They’re not a tech person doing accounting work,” Richard explains. “They are a technical person, maybe a CPA, that specifically knows how to leverage technology.”

The structural change everyone talks about is the pyramid becoming a diamond with fewer people at the entry level and more in the middle. But Richard makes an important distinction. The bottom rung is moving up in capability, not disappearing. Future CPAs will “reach a higher level of intellect, capability, and advisory skills at a much earlier age without decreasing the standards.”

Richard points to how this has happened before. Thirty or forty years ago, interns got coffee and made copies. Today, interns do actual client work. “The capability of interns moved up,” he says. The same shift is about to happen again, just bigger.

But the education system isn’t ready. Accounting programs are mostly theoretical. They “lay a foundation,” Richard says, but “certainly don’t give you anything that is pretty or accessible to a client.” Firms will have to bridge the gap with intensive training, it may look like six to eight months where new hires don’t touch billable work, just learn the craft.

The Review Problem

Blake raises the concern many accountants have voiced. If AI does all the basic work, how do people learn to review? The whole system depends on doing the work first, making mistakes, getting feedback, and building judgment.

Richard doesn’t dismiss this. He calls it “a real concern” and says you “can’t underestimate or understate the value of experience.” But then he reframes it with an analogy.

Try explaining to kids today why they need to know how to use an encyclopedia. It seems absurd. The skill became irrelevant because the tool changed. What replaced it was arguably harder: filtering reliable information from unreliable information online.

The same thing is happening with review. “Technology will take care of putting it in the proper box,” Richard says. “Your objective is to have the filter of understanding how to interpret the outcome.” And he goes further: “There may be a benefit to actually not having that anchor of how we used to do business.”

This isn’t theoretical. Tax GPT claims it fully automated 1040 preparation. Basis says it’s done the same for partnership returns. Richard has talked to both vendors. The pace of accuracy improvement is “impressive.” AI is rapidly getting to where it’s “right more than it’s wrong.”

But Richard draws an important distinction. Completing a tax return is just compliance. The real product is what happens after: the advice on paying less tax, structuring a business sale, or planning succession. “When you engage with your clients beyond delivering compliance services,” Richard notes, “fees don’t really come up.”

Why the Billable Hour Won’t Die

“Our people hate entering their time,” Richard says plainly. “There’s no value to the time they spend entering their time and it undervalues us.” Experienced professionals solve complex problems in an hour because they have 30 years of experience. Bill that as one hour, and you’re “undervaluing the 30 years of experience that allowed you to answer that question.”

Richard calls abandoning timesheets “the Mount Everest” of firm transformation. The billable hour is the operating system. Everything runs on it, including utilization, productivity, margin, capacity planning, performance evaluation, even work-life balance monitoring. “You can’t really erase billable hours without erasing all of it,” he says.

Then Richard makes an argument Blake hadn’t considered before. Timesheets might actually help prevent burnout. Sikich monitors employees running over their expected hours and treats it as a capacity problem. Without those guardrails, Richard argues, ambitious people “will work so hard, they’ll burn themselves out really quickly.”

But Blake zeros in on the real issue. AI has destroyed the link between time and value. If AI makes your team twice as fast, the client pays half as much under hourly billing. That math doesn’t work anymore.

So what replaces hours? “We haven’t necessarily identified a better alternative,” Richard admits. Accountants like data and hours provide lots of data. Any replacement becomes more subjective. Client satisfaction? Value delivered? Team engagement? These are harder to measure, and for a profession built on measurement, that’s a problem.

The Basketball Team Problem

Richard draws on his sports background to explain what might work better. Think about the 1990s Chicago Bulls. Michael Jordan and Scottie Pippen scored the points. But Dennis Rodman, the defensive specialist who didn’t score much, was essential. His contribution didn’t show up in the headline stats, but the team needed him.

“We’re not even looking at points. We are looking at time on the court.” Blake points out. The profession measures the wrong thing entirely.

But Richard warns that team models only work if everyone performs. If Rodman doesn’t hustle for rebounds while Pippen is scoring, or if Pippen takes a game off while Rodman is sacrificing his body, the whole thing falls apart. “You have to have a culture where the team performs within kind of a standard deviation of each other.”

The deeper problem is cultural. “The connotation of the employee becomes, I am an hours-based person. All I am is hours,” Richard says. When every review, promotion, or conversation starts with “how many hours did you work,” people internalize that their value is their time. Not their judgment or ideas.

And the system treats every hour as equal, which Richard calls “baseline, categorically false.” Some people think faster. That doesn’t make them more valuable, but under an hours system, it makes them look more productive.

The Implementation Gap

Richard says people actually don’t burn out from long hours. “I don’t hear complaints about the hours when it’s engaging work,” he says. He says his team gets excited working a long weekend for a complex client issue. The burnout comes from being stuck at 9 p.m. “dealing with software issues and plugging numbers into spreadsheets.”

AI can eliminate that burnout-causing work. But only if firms actually let it.

“We’re playing with it, but we’re not really implementing it,” Richard says. “We’re purchasing it, but we’re not really relying on it.” Firms pour billions into AI tools, but their training, career paths, and daily operations haven’t changed. The technology is there but the willingness to break old processes isn’t.

“There will be progression and there will be extinction. The question is at what pace,” Richard says, framing the stakes clearly.

Working harder won’t compensate for failure to adopt anymore. Buying AI products doesn’t mean you’re adopting AI. And trying to fit AI into existing processes instead of letting it break them is a choice with consequences.

“If you consistently try to find a place of complacency and comfort, you will not adopt at the pace necessary,” Richard warns.

The Choice Firms Are Making Right Now

What makes this conversation valuable is Richard’s willingness to acknowledge he doesn’t have all the answers. “I still have a lot to learn,” he says.

He can see the billable hour is broken and the pyramid is unsustainable. He can see buying AI tools without changing operations is theater. And Sikich is still tracking hours religiously.

That honesty tells you where the real work is. The super accountant future requires dismantling training models, educational assumptions, and measurement systems that have existed for decades. Not just purchasing new software.

For accounting professionals at every level, including partners making decisions, managers caught between old metrics and new realities, or someone early in their career wondering what’s ahead, the question is whether the firm will let AI change your work.

Richard has a message for other firm leaders: “Don’t let fear rule the day.” The firms that use AI as permission to break outdated processes will thrive. The firms that bolt AI onto unchanged operations will struggle. And that divergence is accelerating.

“I have every desire to be on the side of progression,” Richard says. Which side is your firm choosing?

Listen to the full conversation between Blake and Richard on the Earmark Podcast for deeper discussion on replacement metrics for the billable hour, building the super accountant pipeline, and why letting go of the past might be the profession’s biggest challenge. Then visit earmark.app to earn free NASBA-approved CPE credit.