Yet underneath the hype, most firms aren’t racing ahead. They’re parked in the same place — curious about AI, but not confident enough to let it near work they sign, let alone build a new operating model around it.

Your caution is not the problem to be “fixed.” It’s the professional instinct you need to design around so AI becomes an advantage, not a liability.

Accountability, not technology, is the real barrier

From the outside, it’s easy for tech folks to shrug and say, “The tools are ready; firms just need to get on board.” But inside firms, the questions are different:

• “If an AI system drafts this work and something is wrong, am I still signing?”

• “If the agent misclassifies, miscalculates or misses an edge case, who is actually on the hook?”

• “If AI touches the books, can I still stand behind the engagement the same way?”

In accounting, you don’t get to blame the system. You sign the work, so you own the result regardless of which platform prepared it. That simple fact changes the entire AI conversation.

AI can’t be treated like a trendy gadget or a lab toy. It has to show up as an extension of your professional judgment. As something you’ll monitor, test and govern with the same rigor you apply to any other part of the engagement.

The real risk is AI without oversight

Uncontrolled AI is at the heart of concerns. The issue isn’t that a system can draft a reconciliation, propose a journal entry or categorize transactions. The danger is when those actions happen quietly in the background with no boundaries, no audit trail and no clear review step.

When automations post directly to the GL or push information to clients without human oversight, you quietly drift from “AI‑assisted work” to “AI‑substituted work.” And that’s exactly the scenario that triggers every concern about accountability.

The safer — and smarter — way to think about AI is simple:

• AI prepares the work

• Accountants review, challenge and approve

• Nothing posts or goes out the door without human sign‑off

Puzzle is a good example of an AI-native accounting platform intentionally leaning into this “AI prepares, humans approve” model. Instead of hiding AI in the background, Puzzle brings AI agents to the front of the experience so firms can see exactly what each agent is doing, where it stopped and what requires human review.

When you design your AI for accountants, you get the speed without sacrificing control.

Shifting the mindset from autonomous to AI-led

A lot of AI marketing is built around speed, not accountability. You’ve seen the claims of a fully automated close or hands‑free reconciliations. But for a profession that signs its work, that’s a red flag, not a selling point.

Many tools fall short for firms because they treat governance and controls as an afterthought instead of a core requirement. By contrast, tools that resonate with accountants lead with governed automation accountant-approved workflows, offering clear checkpoints for review and approval.

Considering all of this, it’s truly a mindset shift that needs to occur first — one from autonomous automation to accountant-approved AI. The first takes humans out of the loop, while the latter keeps accountants not only in the loop, but in control.

The AI Adoption Ladder

Adopting AI is not an overnight decision. Successful firms progress at a steady, intentional pace. Consider each rung of the Adoption Ladder:

Intentional adoption is key

AI is real and it’s here. However, firms should not adopt it without an intentional strategy in place.

That strategy starts with setting an important goal: AI does not remove accountants from the process, but rather it changes what they spend their time on. When AI prepares work faster and more consistently, accountants can stay focused on what only they can do, like review, interpret, advise and sign.

Okay, fine, not every email. But enough of them to notice? Do you feel like you’re constantly reviewing responses, tweaking wording, double-checking tone and stepping in before something goes out the door?

If you’re leading a growing firm, I have a feeling you know exactly what I’m talking about.

It’s not a bad thing! At first.

The Problem Isn't the CC

A lot of founders built their firms on strong relationships and high-touch communication. They care deeply about client experience. They know how they want the firm represented. They know how to handle difficult conversations, explain complicated issues clearly and make clients feel taken care of.

So naturally, people keep coming to them. They don’t want to get it wrong and screw up an important client relationship.

At first, it probably even feels good. It means your team trusts you. It means people care about getting things right.

But when your firm starts growing… More clients come in and more employees are hired… And they’re ALL. SENDING. EMAILS.

Suddenly, you’re an editor, a coach, a relationship manager and an emotional support system for half the organization. As if keeping the firm’s lights on wasn’t enough.

It’s exhausting!

Founder Burnout Often Looks Like Responsibility

We don’t talk enough about the emotional labor of that kind of leadership dependency.

From the outside, growth looks exciting. There’s more revenue, more momentum. You have a bigger team and bigger opportunities.

But internally, growth can start to feel like being emotionally responsible for more and more people until it gets to a point where you can barely step away for a bathroom break.

It might seem harmless, but think about it. You’re reviewing client communication late at night. You’re mentally carrying difficult conversations. You’re desperately trying to maintain consistency across a team that’s getting larger and more complex every year.

You can improve workflows and hire managers, but that dependency doesn’t always disappear.

I think this is where a lot of founder burnout quietly starts. It’s not easy to be the person everyone relies on for reassurance, judgment and communication support.

Especially in accounting firms, where so much of the business depends on trust.

We all know that clients don’t just remember whether the work was technically correct. They remember responsiveness, clarity, tone, confidence and how interactions made them feel.

For founders, that creates an invisible pressure to personally protect the quality of every client relationship.

At Some Point, Trust Has to Scale

But eventually, growth forces you to realize that you can’t personally hold every relationship, review every interaction and coach every employee forever.

At some point, scaling a firm becomes less about workflows and more about trust.

• Can your managers lead without constant oversight?

• Can employees communicate confidently on their own?

• Can other people carry the culture and client experience consistently?

And if they can’t, then the question becomes: What do they actually need in order to get there?

• More coaching?

• Clearer communication standards?

• More opportunities to practice difficult conversations?

• More confidence?

• More support from managers who already feel overwhelmed themselves?

Eventually, founder dependency stops being a “people keep CC’ing me” problem and starts becoming a signal that something deeper inside the organization needs attention.

At a certain point, leaders have to stop asking: “How do I keep up with all of this?”

And start asking: “What would need to change for my team to succeed without needing me involved in every step?”

Many founders spend years proving they’re the smartest person in the room. Scaling requires something much harder. Creating a room full of people who no longer need to ask for your approval.

That’s not losing control. That’s leadership working!

It happens every busy season. And for a lot of tax professionals, that cleanup process takes up far more time and mental energy than it should. The return itself is often fairly straightforward. It’s the reconstruction that drains you.

The firms gaining leverage right now are finding a faster path from those bank statements to usable numbers. That means less time buried in cleanup and more time for the work that actually grows the practice. Here’s how they’re doing it.

Why Cleanup Has Gotten Harder

The challenge today goes beyond bookkeeping habits. Small business finances are simply more fragmented than they used to be. A decade ago, most clients had a checking account and a credit card. Today, revenue might flow through Stripe, PayPal, Shopify, Square and Venmo. Expenses run through several cards, subscriptions, apps and payment platforms. Even a client with a few hundred thousand dollars in revenue can generate a surprising amount of transaction activity across a surprising number of sources.

That means you’re not just getting one bank statement anymore. You’re getting a stack of them, plus credit card exports, plus whatever else they managed to pull together before the deadline. Someone still has to work through all of it, figure out what’s income, what’s an owner’s draw, what’s a transfer and what belongs in each expense category. And all of that happens before the return even starts.

This is the bottleneck. And it’s exactly where there’s an opportunity to work differently.

What a Better Workflow Looks Like

The firms handling this best are not eliminating the cleanup process entirely. They’re just getting through it faster.

Instead of starting from zero and manually coding everything line by line, they’re finding ways to organize the activity quickly and generate a first-pass P&L so they can get into the actual accounting and tax work sooner.

That changes how busy season feels. Instead of spending hours building the first draft, your team can spend more time reviewing the numbers, correcting exceptions and advising clients.

That’s a much better use of talent than manually sorting transactions all day.

Where AI Can Help

This is exactly where AI can help. A lot of cleanup work is repetitive. The same transaction patterns tend to show up over and over again. That’s exactly the kind of pattern-recognition work AI is built for.

That becomes especially useful when you’re dealing with:

• Clients who only provide bank statements

• Catch-up bookkeeping tied to a return deadline

• Quarterly estimate work requiring current numbers

• Smaller clients who don’t have monthly bookkeeping support

• Last-minute engagements where there’s no time to start from zero

So, what does a faster cleanup process actually look like?

Platforms like Adam by Tyms are designed to help firms shorten the path from bank statements to a usable starting point for tax prep. You upload bank statements or transaction exports and it generates a draft P&L in minutes. Firms can also manage multiple clients and team workflows from a centralized dashboard, making it easier to scale this process across engagements.

Instead of manually building the books from scratch, you can start with organized numbers and focus your time on the actual accounting and tax work. There will always be transactions that need your eyes for a second look. But even reducing part of the reconstruction process can create meaningful time savings during busy season.

Curious? See Adam in action here.

The Opportunity for Firms

What would you do with more capacity during busy season?

For smaller firms and lean teams, reducing even part of the cleanup process can make a real difference. Less time spent organizing transactions means more room to take on additional work and fewer situations where a relatively simple return turns into hours of unpaid cleanup.

It also changes how experienced professionals spend their time. Instead of sorting transactions and cleaning up books all day, they can spend more time reviewing issues, answering client questions and helping businesses think ahead before filing deadlines hit.

And honestly, that’s where a lot of firms want to go. More planning conversations. More advisory work. More opportunities to strengthen the client relationship instead of simply organizing records.

Try It for Yourself

If cleanup work is eating into too much of your busy season, Adam by Tyms is worth a look. It’s a new solution that is still evolving, and it’s designed for exactly the kind of reconstruction work tax professionals already deal with every year. And generating a quick P&L is just one part of what the platform can do.

Schedule a demo to see how firms are using Adam to reduce cleanup work and move from bank statements to usable numbers faster.

Thanks to my XcelLabs sponsor

Adam by Tyms is a complete practice management software and AI bookkeeping platform for accounting firms — white-labeled under your brand, with your pricing, your clients and your margin. Learn more about how you can get smarter numbers with effortless control.

People are constantly saying things like:

• I asked ChatGPT…

• My AI assistant reminded me…

• Claude helped me think through…

• It’s like having a tutor…

• I talk to it when I’m anxious…

• It’s my brainstorming partner…

Culturally, people are treating AI differently from other technologies that have come before it.

I’ve never heard anyone say, “Excel helped me process my feelings,” or “QuickBooks gave me confidence before a difficult meeting.”

When people evaluate an AI tool, they’re starting to compare it to coaches, mentors and administrative assistants. No one’s comparing the robot that answers all their questions to tax software or traditional productivity tools.

That’s a completely new kind of conversation.

From Automation to Development

For years, most accounting tech conversations focused on operations. But lately, some of the most interesting use cases seem to be moving away from pure efficiency and toward something much more human.

Firm leaders are talking more about communication quality, employee development and consistency across teams.

Some firms are starting to think about AI as something that can support human development inside the firm itself.

The firms most seriously exploring these tools often aren’t just asking: “What can this automate?”

They’re asking:

• Can this help our people learn faster?

• Can this help newer staff communicate more confidently?

• Can this support managers who are already overwhelmed?

• Can this create more consistent coaching experiences across the firm?

AI Is Revealing Human Challenges

Industry leaders are starting to think more intentionally about how people learn, communicate and build confidence inside modern accounting firms.

Once firms start thinking about AI through a more human lens, it naturally leads to bigger questions about how teams learn, communicate and develop. It makes you think more critically about where your team is struggling and what they need help with.

And honestly, that’s valuable whether the answer to these problems ends up being AI or not.

Once you identify the gaps, the solution might be AI, or it might not be. But viewing technology through that lens forces firms to think more intentionally about what their people actually need to succeed.

The “ugly” stuff is all that repetitive, manual, error-prone work that no one wants to do but everyone ends up doing anyway: keying bills, chasing down backup in email, fixing client mistakes in the GL and re-entering the same data across multiple systems. But what if you could automate this layer and wrap it in smart approvals and permissions?

You can, and when you do, you’ll experience the Holy Grail of efficiency and control.​

What we’re talking about here is the “efficiency + trust = higher margins” play. You’re not just speeding things up; you’re also building an AP/AR environment that flows on its own, protects the GL and gives both your team and your clients confidence that nothing is slipping through the cracks.

And it all starts with automating the front end…

Automate the front end: OCR, inbox-forwarding and sync

The first job of automation is to stop humans from being data entry bots. Instead of staff or clients keying every vendor bill, you route documents into a single AP hub, like Melio’s, using tools like email forwarding, drag-and-drop uploads and direct connections to vendor portals. OCR reads the invoices, extracts the key fields and pre-populates bills so your team is reviewing, not re-keying. That alone eliminates a big chunk of the “we’re too busy to do advisory” bottleneck.​

Next, let integrations do the rest. Two-way syncing with systems like QuickBooks or Xero keeps payables and receivables flowing automagically between your AP platform and the GL. No more double work, copy-paste or hoping someone remembered to update both systems. When a client creates an invoice or pays a bill in the AP tool, those changes appear in the accounting software in real time, keeping the GL current without inviting the client into the GL itself.​

The net effect:

• Fewer manual touches and less time on low-value tasks

• Fewer client-created errors, because they’re pressing buttons in a simplified AP view, not experimenting in the GL

• Faster month-end, because you’re not hunting for missing transactions or reconciling between out-of-sync systems

The goal (and ultimate win) is turning AP/AR into a clean, digital pipeline that feeds the GL instead of constantly clogging it.

Add control: Approvals, roles and audit trails

Automation without control is just faster chaos. Modern AP platforms let you define approval workflows that mirror how decisions really get made in your clients’ businesses. You can set thresholds (who approves under $5k vs. over $5k), route certain vendors for extra scrutiny and build multi-step approvals for sensitive payments. Instead of random “Does this look okay?” emails, everything moves through a consistent, trackable path.​

Role-based permissions then keep the right people in the right lanes. Owners can approve, managers can review, staff can prepare bills, and your firm can oversee the entire workflow without giving anyone a free pass to override the GL. Clients still “press the button” where appropriate, but only inside the guardrails you design.​

Audit trails tie it all together. Every approval, change, rejection and payment is logged. So when a vendor calls about a payment, or a client questions a bill three months later, you’re not digging through inboxes. You can see exactly who did what and when.​

That structure does three things at once:

• Protects against fraud and out-of-policy spending

• Builds confidence for owners and controllers who need oversight but don’t want to micromanage

• Gives your firm cover when something goes sideways because the history is transparent and documented.

This is how you earn trust while still staying out of day-to-day chaos.

Turn efficiency + control into margin

Once the ugly stuff is automated and controlled, the economics of AP/AR change.

Your team isn’t stuck in the weeds cleaning up client errors in the GL or chasing down missing bills. They’re monitoring flows, handling exceptions and using the time they get back for higher-value conversations around cash flow, payment strategy, credit card usage and pay-over-time options. And that’s where advisory lives.​

On top of that, predictable workflows make it easier to package and price AP/AR as a standardized service instead of one-off, “it depends” clean-up work. You can build recurring offerings where:

• OCR, inbox-forwarding and syncing are baked into your baseline AP/AR process

• Approval workflows and permissions are part of onboarding, not an afterthought

• Oversight, not data entry, becomes the core of what clients are paying for.

You’re no longer undercharging for messy, manual work that kills morale and margins. You’re charging appropriately for a system that keeps money moving, protects the books and gives clients peace of mind.

Stop doing, start owning

The radical move here isn’t just adopting tech. It’s deciding you’re done being the human safety net for broken AP/AR.

Automating the ugly stuff means you stop burning margins on manual entry, rework and chasing client errors and start owning the AP/AR environment as a controlled, automated high-trust channel. You become the architect of how money and data move, not the person stuck cleaning up what happened after the fact.​

This is where efficiency and control finally meet. And once you’ve got both, AP/AR stops being a chore you tolerate and becomes an engine you can scale.

A lot of firms spent years untangling messy systems, moving to the cloud, implementing new technology and trying to modernize the way work actually gets done.

That’s really important work, and it’s definitely where I’d recommend any firm start first. (You’ve heard me say it time and time again, tech doesn’t fix broken processes.)

But what if you’ve done all of that? (If you’re reading this, I’m guessing that you’ve at least tried.)

Maybe you took it seriously, and you feel pretty good about where your firm is at now. It took a while to notice real results, I’m sure. But maybe, you’re finally starting to look around and realize there’s less manual work to do, your processes move along pretty quickly, and everyone seems a little less frazzled.

You’re not alone! I’ve noticed a lot of firms that seem to be entering a different stage of growth, and I think they’re some of the most interesting case studies in the industry right now.

I’m talking about firms that already invested heavily in technology and improved a lot of their workflows. The operational side of the business is in a much better place than it was five or 10 years ago.

Because of that, they’re starting to notice a different kind of problem. (There’s always something, isn’t there?)

The New Bottleneck Isn't Technology

The new problem has to do with people.

Some of the most forward-thinking firm leaders aren’t spending all their time talking about automation anymore. Instead, common topics include things like: communication quality, younger staff development, coaching, client relationships and consistency across teams.

I think that means some firms have gotten far enough along in their technology journey to realize that smoother systems don’t automatically solve human challenges.

In a weird way, better technology almost makes those challenges more noticeable.

Once the day-to-day operations get less chaotic, leadership gaps become harder to ignore.

You start noticing that managers are overloaded and younger staff need more coaching than anyone has time to provide. You see how communication can vary wildly from person to person. Even with better systems, the firm still relies heavily on a few experienced people to keep things running smoothly.

Technology Scales Faster Than Mentorship

Growth is happening faster. Managers are stretched thinner. Professionals often get fewer opportunities to absorb communication skills and advisory instincts just by being around experienced people all day.

At the exact same time, clients are expecting more human connection than ever.

They want responsiveness. They want strategic thinking. They want confidence. They want advisors who can communicate clearly and build trust.

That creates an interesting tension inside modern firms. Scaling technology is one thing, but scaling people is way messier.

ESPECIALLY when growth starts accelerating.

The Real Challenge is Scaling Humans

The firms furthest along in their technology journey aren’t asking which software to buy next. They’re asking questions like:

• How do we help people grow faster?

• How do we maintain quality as the firm scales?

• How do we support managers who are overwhelmed?

• How do we create more confident communicators and advisors?

Maybe this is the next phase of growth for the accounting profession. The first era was about digitizing work. The second era was about automating work. The next era may be about accelerating human development. Because once technology stops being the bottleneck, people become the competitive advantage.

Welcome to the Post-Tech Firm

The future is not about having the most technology. It’s about using that technology to develop better leaders, better advisors and better humans.

That’s not a technology strategy. That’s a people strategy. And I think that’s where the real future of accounting begins.

AI is moving into more parts of the accounting profession. Research, summaries, drafting, workflow support, meeting follow-up. And increasingly, the relationship-driven advisory work that firms are trying to grow.

That’s exciting. For a lot of accountants, it also sets off alarm bells. Honestly, both of those reactions make sense.

The excitement comes from seeing what is possible. The caution comes from caring deeply about doing the work right. Those are not opposites. They are the same professional instinct, looking at a new tool from two different angles. So, how do we do this right? How do we use AI responsibly?

Trust Your Instincts

You already know what is at stake. When a client sits down with you, they are sharing more than numbers. They are sharing decisions that affect their business, their livelihood, sometimes their family. You’ve probably felt the weight of that.

All of a sudden, AI enters the relationship, and your instinct is to ask hard questions. To see risk before opportunity. Where is the data going? Who can access it? Can the output be reviewed? What happens when context is missing? Who is responsible if something goes wrong?

Those questions come from the values that drive the profession. Confidentiality is the reason a client tells you things they would not tell anyone else. Objectivity is what makes your judgment worth trusting in the first place. Due diligence is the discipline of getting it right even when no one is watching. And those same values tell you exactly how to approach a new tool.

Three Questions Worth Asking

Responsible AI use in accounting tends to come down to three questions. Let’s unpack them.

1. Is client information protected? This is about security. Firms need to understand where data goes, how it is stored and who can access it. If you cannot answer those questions, the tool probably doesn’t belong in client work yet.

2. Can we trust the output? AI does not need to be perfect to be useful. But it does need to be treated as a draft, not a conclusion. That means bringing the same professional skepticism to AI-generated content that you would bring to any other source. Checking it, questioning it and filling in what it may have missed.

3. Who is responsible for the final result? This is the most important question. AI can assist with the work, but it cannot carry the professional responsibility that comes with it. Someone still decides what the tool is used for, what requires human review and what gets shared with a client.

This might look like a manager reviewing AI-generated meeting notes before a client follow-up goes out, catching a missed nuance, adjusting the tone, adding context the tool could not have known. AI got you 80% of the way there. But human judgment made it ready to send.

Judgment Still Belongs to You

The division of labor between humans and AI deserves a closer look. We know that AI is not an oracle. It does not know your client the way you do. It has not sat across the table from them, heard what they did not say or carried the responsibility of getting it right. What it can do is act as a second set of eyes, a tool that helps you see something you might have missed.

That is genuinely helpful. But it doesn’t get the final word, even when its output sounds convincing. AI can sharpen your thinking. It cannot replace your judgment.

Think of it the way you already think about tax software. It does the math, but you decide whether the position is supportable. AI belongs on that same side of the line. It can inform the work. You still decide what matters and what happens next.

Where to Start

If you want to use AI responsibly, start small.

Pick one meeting. Upload the transcript to a tool like Navi, built specifically for accounting advisory work. It analyzes the conversation, finds what got left on the table and flags where a follow-up might add value. Use what you find to shape the next conversation.

That’s it. One meeting. One insight. That’s how responsible AI use develops across a firm, by learning what acceptable use looks like in your own work. We have always figured out new tools that way. This one is no different.

That’s only kind of true.

After listening to more conversations with managing partners and firm leaders, I’m starting to think the firms most ready for AI actually have something else in common.

The Problem Isn’t Technology Anymore

These firms have figured out some of the biggest issues with their workflow, and they’re ready to think more about people.

Recently, I listened to a conversation with a managing partner at a fast-growing firm. They were already heavily invested in technology, automation and modern systems. This wasn’t a firm stuck in the past.

At one point, the partner casually said, “We’re kind of a technology firm at this point.”

But almost none of his biggest concerns were actually about technology.

He kept coming back to things like communication quality, coaching younger staff, consistency across teams, client relationships and how hard it is to maintain a culture while growing quickly.

The firms most interested in AI are trying to figure out how to grow without losing what made the firm good in the first place.

For years, accounting firms focused heavily on efficiency. We were trying to automate tasks, move to the cloud, create better systems and workflows. I was right there with you. We’re at a point now where a lot of firms have gotten pretty good at that part.

The problem is that technology scales faster than leadership does.

Growth Creates a Different Kind of Bottleneck

For me, the most telling part of the conversation was when the managing partner explained that staff members still CC him on emails so he can review responses and coach them before they meet with clients. Naturally, he said that he doesn’t want to keep doing all that.

I think a lot of firm leaders feel the same way.

Sometimes, growth creates a new kind of pressure. It’s not just that there’s more work. More people are depending on a small group of leaders for guidance, approval and coaching.

That becomes really hard to scale.

Firms Used to Learn Through Proximity

For a long time, accounting firms grew through proximity. Younger staff learned by sitting near managers, overhearing client conversations and watching how experienced professionals handled difficult situations.

Firms don’t really work like that anymore.

Teams are more spread out. Managers are overloaded. Growth is happening faster. Younger professionals often get fewer opportunities to build confidence in client communication and advisory conversations.

That’s part of why so many firm leaders are suddenly focused on “human skills.” Obviously, the tech still matters, but tech alone doesn’t create strong client relationships.

Why Human Skills Suddenly Matter More

I think many of the firms leaning into AI right now aren’t actually trying to replace people. They’re trying to support people better.

They want to help younger staff grow faster, maintain communication quality, reduce leadership bottlenecks and protect the client experience while the firm scales.

The firms most ready for AI aren’t the ones chasing the newest tools. AI works best for the firms that already spent years improving systems and workflows and are now realizing their biggest challenge is helping people grow alongside the business.

Firms spend a lot of time building the front end of CAS, focusing on all the service delivery processes like reporting, workflows and the tech stack. Very little time is spent thinking about the foundation on which everything depends, protecting the underlying financial data.

CAS teams live inside their clients’ accounting systems. You reconcile transactions, integrate apps, sync payroll platforms and generate reports. That means you’re responsible for the integrity of the data itself. And when something goes wrong, like when a transaction gets deleted, overwritten or corrupted by an integration, you’re usually the one fixing it. That often means spending hours rebuilding data manually. No one wants to do that.

For accounting firms, this creates an opportunity most firms haven’t recognized yet. You can use data protection as both a differentiator and a profit lever inside CAS packages.

The Hidden Risk in “Cloud Accounting”

Just because your accounting software is in the cloud does not mean it’s protected. Your clients assume their data is automatically backed up forever. In reality, most SaaS platforms focus on availability, not comprehensive recovery.

Platforms like QuickBooks Online and Xero keep systems running, but they typically don’t protect against:

• accidental deletions

• overwritten transactions

• bad data flowing from integrations

• user errors

If one of those things happens, the software reflects the new data state. You cannot easily roll things back, and sometimes it’s impossible to fix without rebuilding records manually. That leaves your CAS teams exposed.

Backup solutions fill that gap by keeping an independent copy of accounting data so records can be restored quickly if something goes wrong. For CAS teams, that’s operational protection.

Why Backup Belongs in Every CAS Package

Hopefully, your firm has become very intentional about packaging services, bundling software, workflows, reporting and advisory into clear monthly offerings. It’s great because clients know what they’re getting, and your firm can price services consistently.

Backup fits naturally into that model. When you deliver CAS, you’re responsible for the integrity of the client’s financial system. The reports you generate and the advice you give depend entirely on reliable data. Data protection should be part of the service.

A dedicated backup solution like SysCloud allows firms to restore deleted data, recover overwritten records or roll systems back to a clean version when errors occur. Instead of rebuilding records for hours, your team can restore the correct data and move on.

That’s a huge operational win, and it aligns perfectly with how CAS is evolving. Yes, you are producing financial statements, but more importantly, you are managing the financial systems that power a business.

The Profit Opportunity Most Firms Miss

Most firms look at backup tools and see another technology expense. But in a CAS model, backup can actually improve profitability.

Clients pay for a reliable financial system, and that includes the tools needed to support it. Data protection can easily be incorporated into your monthly CAS packages.

For example, if you charge $1500 a month for your service, you can easily add on another $50 (or more) for financial data protection. Because backup software is inexpensive at the firm level, the math often works in your favor. In many cases, the addition becomes revenue neutral — or even budget positive — because the value to the client exceeds the cost of the tool. More importantly, it increases the perceived value of your CAS packages without significantly increasing delivery costs.

It also eliminates unplanned cleanup work. Without backup, data errors can lead to hours of reconstruction that firms rarely bill for. With backup, you restore the correct data quickly and keep the engagement moving. That alone can protect margins across your CAS portfolio.

Backup Builds a Different Kind of Client Trust

If you are positioning your CAS team as partners in a client’s financial operations, backup reinforces that positioning. When you recommend data protection, you’re showing clients that you’re thinking ahead. Instead of reacting when something breaks, you’re protecting their financial history before problems occur.

Clients understand this immediately. Backup is simply insurance for financial data. It ensures their books can be recovered if something goes wrong and creates a traceable record of changes and restores that can simplify audits and compliance reviews.

Those moments matter more than you realize. Clients rarely remember routine work. But they always remember when their advisor prevented a crisis or helped them recover from one.

How to Turn Backup Into a CAS Offering

Don’t think of backup technology as software. Think of it as protection for the client’s financial system. This simple shift in language turns a technical feature into a marketable service. Here are three ways you can bring that to life.

1. Package It. Include data protection directly in your service tiers. For example:

• CAS Essentials

  • Bookkeeping and reconciliations

  • Monthly reporting

  • Technology stack management

  • Financial data protection

• CAS Advisory

  • Everything in Essentials

  • Forecasting and planning

  • KPI dashboards

  • Advisory meetings

  • Enhanced data protection

Clients don’t see a tool; they see a more sophisticated service. And because the cost is wrapped into your package, no one is nitpicking the price. Add on more than you pay for the solution and improve the profitability of the packages.

2. Add It. If you prefer add-ons, create a simple upgrade for something like financial data protection. It could include:

• automated daily backups

• recovery of deleted or overwritten transactions

• protection against integration errors

• audit-ready recovery logs

Position it the same way you would cybersecurity or document storage. It’s optional, but highly recommended. Once again, the add-on price can be many times higher than the cost your firm would pay to provide it to them.

You could also clearly set the expectation that if a client declines protection and data needs to be rebuilt, those reconstruction hours become billable work. Most clients will quickly understand the value.

3. Stack It. If your firm already offers a “managed tech stack” for CAS, backup fits naturally into that narrative. The positioning becomes something like:

“Our CAS services include a managed finance stack. We handle the accounting platform, integrations, reporting tools and offer data protection that ensures your financial history is always recoverable.”

This reinforces that your firm manages the entire financial system, not just the bookkeeping. Add it to the stack and factor it into your next price increase. Don’t focus on recouping the cost you pay. This is a value-add and should be priced as such.

The Cost of Ignoring Data Protection

Without backup, a small mistake can turn into a major disruption. You’re left spending hours rebuilding the data or explaining to the client that information may be lost. No one wants to have that conversation.

Backup changes that outcome. Instead of reconstructing records manually, your team restores clean data and moves forward quickly. For CAS practices managing dozens or hundreds of clients, that protection safeguards your time, your reputation and your ability to deliver consistent service.

Think About CAS as a System, Not Just Services

CAS has become a platform for advisory. Firms build systems that combine technology, workflows, reporting and insight to help businesses operate smarter. But every system needs a foundation.

Dashboards and forecasts are only as reliable as the data behind them. When that data is lost or corrupted, the entire structure becomes less trustworthy. That’s why forward-thinking firms are treating data protection as part of the CAS model itself.

Backup doesn’t only prevent problems. It strengthens your services, protects your margins and shows clients that you’re managing their financial infrastructure and not just their books.

Start by reviewing your CAS packages and asking one simple question: “Where does data protection fit in?” Because if your firm is responsible for the books, protecting the data behind them shouldn’t be optional. It should be part of how you position your CAS services in the market.

Being expected to do something you were never taught is becoming a theme, isn’t it?

At some point in your career, usually after you’ve become technically competent enough to stop freaking out every time a client emails a question, someone suddenly tells you to “focus more on relationships.”

So, now you’re going to conferences. You’re scheduling coffee chats. You’re introducing yourself to referral partners, attorneys, bankers, consultants, your hairdresser and her friends, fractional CFOs, and random people someone swears you “absolutely need to know.”

Most of those conversations feel… Fine.

You talk about what you do. They talk about what they do. You exchange stories about busy season, staffing problems, difficult clients and maybe complain about the IRS for a little while.

Then everyone says, “We should definitely stay in touch.”

And nobody does.

A lot of these conversations are pleasant without actually being productive. They feel socially successful while creating very little momentum underneath the surface.

Business Development Conversations Are Weird

Part of the problem is that business development conversations live in a weird middle ground. They’re not sales calls exactly, because nobody wants to feel pitched. But they’re also not casual conversations, because there is an underlying purpose, even if nobody says it out loud. You’re trying to figure out whether there’s alignment, overlap, trust, opportunity or some reason this relationship should continue beyond this interaction.

That requires a very specific kind of communication, and most professionals default to one of two extremes. Either they stay so surface-level that the conversation becomes forgettable, or they force the conversation into business too quickly and kill the natural connection before it has time to develop.

The strongest business development conversations sit somewhere in the middle. They feel relaxed, but there’s still direction underneath them. By the end of the conversation, both people have a clearer understanding of not just what the other person does, but how they think, who they work best with, what kinds of problems they solve and where there might actually be overlap.

What People Actually Remember

Most referrals happen because someone remembers a specific conversation where you explained something in a way that made your expertise feel tangible and relevant.

Successful business development conversations create trust, familiarity and enough clarity that when the right opportunity appears six months later, someone remembers you. They remember the way you framed a problem, the kinds of clients you work best with or the fact that talking to you felt useful instead of transactional.

What Navi Reveals About Your Networking Style

That kind of relationship-building is hard to measure on your own, which is part of why so many professionals struggle to improve at it intentionally. Most people leave these conversations with a vague feeling that they “went well,” but they don’t really know why or whether they created enough momentum for the relationship to continue.

This is where Navi becomes surprisingly useful. Even though business development conversations can feel very specific and situational, Navi is equipped to help you understand how you’re actually showing up in them. It can surface whether you stayed too surface-level, whether you talked too much about yourself, whether the conversation created enough clarity and engagement or whether there was any real direction by the end.

Because these conversations do have structure, even if they feel casual in the moment. The strongest ones create trust, establish relevance and leave both people with a reason to continue the relationship beyond a polite LinkedIn connection request.

Over time, that kind of feedback makes it easier to recognize the patterns that actually lead to stronger professional relationships, instead of just more conversations.

The firms that will look back on their early AI investments and wonder what went wrong won't have chosen the wrong tools. They'll have deployed them without knowing which work deserved to be automated and which deserved to be protected.

Here’s an uncomfortable truth about the AI investments being made across professional services right now: most of them are solving the wrong problem.

Not because the tools aren’t capable. Not because the strategy is misguided. But because the work being accelerated is only a fraction of the work actually being done. And in many cases, it’s the less valuable fraction.

Speed is only useful if you’re going the right direction

When a firm deploys AI to draft documents, scan contracts, or summarize research, something real happens: that work gets done faster. That’s a genuine win. But it’s a win that only touches the work that was already visible, the activities that made it into time entries, project logs, and system records.

The work that didn’t get captured, the research that happened before the formal engagement started, the judgment call a partner made in a hallway conversation, the hours absorbed managing a client crisis, that work still doesn’t exist, as far as your systems are concerned. AI can’t accelerate what it can’t see. And the activities it can’t see are often the ones your clients are actually paying for.

The result is a firm that’s getting faster at the visible work while the invisible work stays unmeasured, unprotected, and unpriced.

The cost of optimizing in the dark

Consider what happens when a firm decides to automate a category of work without knowing how that work connects to profitability.

A practice group that looks efficient on paper, high utilization, fast turnaround, may be quietly absorbing hours of invisible work that never surface in any report. When AI speeds up the visible portion, the firm celebrates a productivity gain while the invisible hours continue to erode margins in the background. The problem doesn’t get solved. It gets obscured.

This plays out in pricing too. Fixed-fee arrangements are only defensible if you understand your true cost of delivery. But if a meaningful share of the work that went into an engagement was never recorded, your cost model is based on a partial picture. You’re not setting a price. You’re making a guess and hoping it holds.

The firms that will look back on their early AI investments and wonder what went wrong won’t have failed because they chose the wrong tools. They’ll have failed because they deployed those tools without knowing which work deserved to be automated and which deserved to be protected.

What it means to actually know your work

Distinguishing between work that should be automated and work that should be protected requires a complete picture of what your professionals are actually doing, not what they report, not what their time entries suggest, but the discrete activities that make up the working day.

That means capturing work as it happens across every surface: calls, laptops, virtual environments, collaboration tools. It means classifying those activities against a definition of work that reflects how your firm operates. And it means connecting that data to business outcomes so you can see, with precision, where human judgment drives the most value and where automation is genuinely the right call.

Without that foundation, every AI investment is a bet. The technology may deliver exactly what it promises and still point the firm in the wrong direction.

The question driving better decisions

The firms getting this right aren’t asking “how do we deploy AI faster?” They’re asking a harder question first: which of our work should exist at all, which should be automated, and which is too valuable to hand off?

That question can’t be answered with submissions and reported time. It requires intelligence about work itself, the kind that most firms are only beginning to build.

The good news is that building it creates compounding returns. Every activity captured is a data point. Every data point classified is a clearer picture of where your firm’s value actually lives. And every decision made with that picture is more defensible than the one made without it.

Faster is only better when you know what you’re accelerating.

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There’s a tendency to walk into these calls and default to what feels familiar. We explain numbers, share observations and offer recommendations without fully recognizing that advisory is really more about helping someone make sense of information so they can move forward.

Most Accountants Were Never Trained for This Part

Advisory conversations ask something different of you than internal meetings, one-on-ones or even status updates with clients. The goal isn’t just to inform, and it’s definitely not to impress.

The goal is to take something complex, often overwhelming, and turn it into something usable that fits into the client’s actual life and decision-making process. That requires a level of intentional communication that most people haven’t been trained to practice.

What makes this even harder is that we usually don’t have a clear way to evaluate how we’re doing. Most professionals leave a call with a general feeling about how it went, but those reactions don’t tell you much about whether the conversation actually worked.

Strong advisory conversations have a certain rhythm to them, and once you start to notice it, it’s hard to unsee.

Strong Advisory Conversations Create Clarity Early

They usually begin with a clear sense of purpose that makes it obvious why the conversation matters and where it’s going. Instead of jumping straight into numbers or analysis, there’s a moment where everything is grounded and framed so the client can actually follow what’s about to happen.

From there, the conversation builds in a way that feels structured but not rigid. Complex ideas are broken down into something manageable, and there’s enough space for the client to process what’s being said instead of just absorbing a stream of information. You can tell when this is working because the client starts to respond, not just react.

Trust Comes From Sounding Human

There’s also a noticeable difference in tone. The conversation feels human. It sounds like someone who understands the situation and is speaking directly to it, rather than delivering a polished explanation that could apply to anyone.

As the conversation unfolds, it doesn’t stay one-sided for long. There’s a back-and-forth that develops, where the client is brought into the process instead of sitting on the outside of it. Questions come up, clarifications happen naturally and the direction of the conversation adjusts based on what the client is actually thinking and feeling in the moment.

And by the end, there’s very little ambiguity about what happens next. The client understands what’s changing, what they need to do and how it connects to the bigger picture you’ve been talking through together.

What If Advisory Communication Could Be Measured?

For years, accountants have tried to improve advisory through training, templates and good intentions. But communication is hard to improve when you can’t really see it.

That’s where a tool like Navi by XcelLabs changes the game. Instead of guessing how a conversation went, you can start seeing where clarity was lost, where trust was built, where engagement increased and where momentum disappeared. Because once communication becomes visible, improvement gets much faster. And when communication improves, advisory usually follows.

Most accountants already know enough to be great advisors. The real gap isn’t expertise. It’s learning how to communicate expertise in a way clients can hear, trust and act on.

Global work is just work now. Your clients are not only hiring talent across borders; they’re transacting with vendors and contractors in multiple currencies as a routine part of how their businesses operate.

As a result, many accounting leaders are asking the same question right now - both for their own firms and for the clients they advise: What’s actually the best way to handle international payments and foreign exchange (FX)?

Part of the issue is that most firms haven’t landed on a clean solution for their own international payments. So you’re figuring it out for yourself at the same time as you’re managing it for clients, and the complexity compounds.

Business owners have normalized the friction. They’re not asking for a better setup because they don’t know one exists. But you’re in a position to see it clearly for both of you — and that’s where the opportunity sits.

First, a distinction:

The terms “international payment” and “foreign exchange (FX) payment” are often used interchangeably, but there’s an important distinction worth making.

International payment is the umbrella term for any money movement across borders, regardless of currency.

Within that, there are two approaches: you can send USD as-is, so the recipient gets dollars; or you can make an FX payment, where the currency is converted at the point of transaction. Your client’s account is debited in USD, but the vendor receives payment in their local currency.

Contractors and vendors almost always prefer local currency, and for good reason. When payments are sent in USD internationally, vendors can be hit with surprise fees or unfavorable exchange rates on their end, meaning they receive less than expected. That creates friction, erodes trust and puts your client in the awkward position of explaining a shortfall they didn’t cause.

FX payments, by converting the currency upfront, remove that uncertainty.

The vendor knows exactly what they’ll receive, and your client has full visibility into the true cost. Most firms, however, aren’t making a deliberate choice between the two - they’re managing whatever setup they’ve accumulated.

Most firms fall into a messy process without meaning to.

A client brings on an overseas contractor, so you find a way to pay them. A supplier wants to be paid locally, so you add a platform for that. Another client is already using something else and wants to stay on it, so you work with that too.

Each decision makes sense in the moment. Over time, they accumulate, and you end up managing international payments across multiple systems, with inconsistent data and no simple way to see the total cost without recalculating it.

The most visible problem with the lack of strategy is fees. When you’re running payments across multiple platforms, you’re absorbing some combination of:

But the harder problem is that you can’t compare across platforms, which means you can’t tell clients what things actually cost, and you can’t budget for it accurately yourself. Here’s an example to demonstrate:

In this example, platform A may look cheapest until you do the math. And nobody’s doing that math every time, which means clients are making payment decisions based on habit, not cost.

The financial hit is only part of it. When payment data lives across multiple platforms, each with different formatting, settlement timing, and exchange rate disclosure, consistency erodes.

That inconsistency doesn’t just create reconciliation work. It:

• Weakens reporting clarity

• Introduces variability into forecasting

• Increases the likelihood of misclassification or incomplete documentation

Over time, fragmented payment processes don’t just cost money. They compromise system integrity, which is the very foundation that advisory confidence depends on.

Evaluate what your setup is really costing you

Whether you’re evaluating your own setup or a client’s, these four metrics tell you if international payments are working or costing more than they should:

1. Total cost per payment: Add up transaction fees, intermediary bank fees, and FX spread markups. If you can’t easily calculate this across platforms, that’s already a problem.

2. Reconciliation hours per month: How much time is spent matching international payments to accounting records? You know manual matching eats hours and takes focus away from higher value work.

3. Payment timing predictability. How much advance notice exists between initiating a payment and when funds actually leave the account? Inconsistency here creates forecasting problems that ripple forward.

4. Cash flow flexibility. Does the current setup allow any control over when funds leave, or does every international payment require immediate outlay? Domestic payments have long offered breathing room between initiation and settlement. FX payments are catching up, and the ability to manage that timing is a lever most clients aren’t using yet.

If any of these are hard to answer, that’s the signal to dig deeper.

Here’s how your international payments could be running

Once firms take a step back and look at the real costs, the next question is inevitable: well, what does a well-structured setup actually look like? The good news is you don’t need to rebuild anything. The capabilities already exist.

Here’s what to look for in the tech:

Everything runs through one platform. Domestic and international, together. That means a consistent audit trail, unified reporting, and no more tab-switching to figure out what a payment actually costs. If you can’t see all your payments in one place, you don’t have a clear picture of your payables - full stop.

Currency conversion happens upfront, not on the vendor’s end. When your client’s vendor is the one absorbing the exchange rate uncertainty, that’s a relationship risk your client may not even know they’re carrying. A better setup converts at the point of transaction, so both sides know exactly what’s moving.

Financing flexibility is built in. This is a real needle mover. At Melio, it’s one of the capabilities we’re most proud of, and consistently one of the most talked about with our clients. International payments can be funded by credit card rather than immediate bank transfer. That means the payment goes out right away, but the cash doesn’t leave your client’s account for up to 45 days. Imagine telling a client: “I can pay your overseas vendor today, but you don’t have to part with the cash for over a month.” For clients managing tight cash flow, that timing can change everything, and it’s the kind of insight that makes clients realize their accountant is thinking about their business, not just their books. Win win.

Clean data, clean books. Every international payment should generate the same clear record: amounts paid, currencies used, exchange rates applied, and settlement dates, and integrate cleanly with your general ledger. If you’re spending hours each month on manual workarounds, that’s a signal the setup isn’t working.

A solution built to scale. The right setup handles five international payments a month the same way it handles fifty, without adding complexity or requiring a new process every time volume grows.

Next time you’re reviewing a client’s payables, ask the question.

How much are they really paying for that overseas wire?

The answer might surprise both of you. The conversations that follow about true cost, cash flow timing and vendor relationships present an advisory opportunity many accountants haven’t tapped yet, because international payments have never been this visible or this manageable before.

The good news is that improving your setup doesn’t mean adding another app to your stack. You get to cut tools rather than accumulate more.

If you’re ready to consolidate your workflow, check out how Melio can help you handle international payments more seamlessly, with you in control.

But if the conversation ends with, “Let me think about it,” or “I’ll get back to you,” and then… nothing happens, it’s worth thinking about if the conversation was actually effective.

A strong sales conversation creates enough clarity, trust and direction for someone to make a decision. It requires a different kind of communication than most people realize.

Sales Conversations Aren’t Presentations

There’s a tendency in accounting to treat sales conversations like mini presentations. You explain your services, walk through your process, maybe highlight what makes you different, and hope the other person connects the dots.

But that’s not really how most people make decisions.

A strong sales conversation starts with the other person. You have to make a clear effort to understand what’s actually going on in their world, what they’re dealing with and why they’re even considering a change.

That context shapes everything that comes next. It’s what makes a conversation relevant to your audience.

By the time you start talking about your services, the other person should feel like their problem has been articulated more clearly than they’ve been able to explain it themselves. There’s a sense that you understand not just the surface issue, but what’s underneath it, and that creates a different level of attention.

From there, when you do talk about how you can help, it feels connected. Not like a list of offerings, but like a response to something specific that’s already been established.

How You Speak Can Build Trust

Sales conversations often go sideways when they start to feel scripted or overly polished. The more someone sounds like they’re following a process instead of responding to the moment, the harder it is to build real trust.

The strongest conversations feel grounded. There’s room for nuance, for uncertainty where it exists, and for language that actually matches the situation instead of defaulting to generic explanations.

People can feel the difference immediately.

Engagement Isn’t Optional

The accountant talks. The prospect listens. There are a few polite questions, maybe some nodding, and then the conversation ends without much resistance… or commitment.

A strong sales conversation has more movement than that. There’s a back-and-forth that builds over time, where the other person is actively participating, asking questions and reacting to what’s being said.

You have to pull your listener into the process of thinking through their own situation. If you don’t, the conversation stays theoretical. Theoretical conversations rarely lead to decisions.

Decisions Need Direction

One of the biggest gaps in sales conversations is what happens at the end.

There’s often a soft close, something like “Let me know if you have any questions,” or “I’ll follow up,” which leaves the next step undefined and easy to avoid.

In a strong conversation, the ending feels much more intentional. There’s a shared understanding of what happens next, whether that’s moving forward, gathering more information or deciding that it’s not the right fit.

It doesn’t feel forced, but it also doesn’t leave things open-ended. Because without direction, even interested prospects tend to drift.

Did the Sales Conversation Actually Work?

The most effective sales conversations feel like someone understood the situation, helped make sense of it and offered a path forward that felt relevant and doable.

Most people don’t have a way to see whether their sales conversations actually have this shape. They rely on whether someone said yes or no without understanding what happened inside the conversation itself.

Navi, by XcelLabs, changes that by looking at how the conversation unfolded in real time. It can highlight where you stayed too high-level, where you missed an opportunity to understand the prospect more deeply or where the conversation lost momentum before a clear next step was established.

With an understanding of how you communicated across clarity, authenticity, engagement, action and impact, you know whether the conversation “worked.” But also, how and why it did.

Over time, that makes it much easier to recognize the difference between a conversation that feels good and one that actually leads somewhere.

Many firms are approaching AI as a technology decision when it is really a business model decision. Adding another tool to an outdated operating model rarely creates transformation. It often just adds noise. The firms that will benefit most from AI will be the ones willing to rethink how work gets done, how people are developed, how value is priced and how client relationships grow.

That makes this ADP whitepaper, The Rise of the AI-Enabled Accounting Firm, especially timely. It focuses less on hype and more on the broader shift happening inside the profession right now.

This Shift Is Bigger Than Technology

The whitepaper opens by talking about how AI is accelerating forces that have already been building for years. Those pressures are familiar to every firm leader:

• Rising client expectations

• Persistent capacity constraints

• Consolidation pressure

• Erosion of traditional time-based pricing models

AI did not create these realities. It simply makes them harder to ignore.

That is why this conversation cannot be limited to software demos and prompt tips. Firms are navigating structural change.

The Real Opportunity Isn’t Automation Alone

For years, firms have used technology to improve efficiency. That matters. But efficiency alone has limits. If all AI does is help you complete the same work faster, you may create some short-term capacity. But if you do nothing different with that capacity, the long-term impact is limited.

ADP’s research found that accountants see the greatest revenue opportunities from AI come from improving efficiency and expanding advisory offerings. Those pairings matter. Capacity creation and growth strategy must work together.

The smarter question is not: How much time did AI save us? It is: What higher-value work did that time allow us to do?

Most Firms Are Early — And That’s Okay

One of the more revealing findings in the report is that most firms are still in the early stages of adoption. ADP notes that 88% of firms remain in exploration or early experimentation phases.

That should not create panic. It should create perspective. Many leaders feel behind because the AI conversation moves so quickly. But being early in the journey is normal. What matters now is moving with intention.

The firms that win this phase will not chase every new tool. They will build practical habits, clear policies and real use cases.

Leadership Matters More Than Software

The whitepaper outlines a simple but important truth: firms do not need everyone to be an AI expert. They do need:

• Internal champions

• Managers who know how to integrate AI into workflows

• Staff trained in responsible use

• A culture that encourages learning and experimentation

That is leadership work.

Technology without leadership creates scattered experimentation. Technology with leadership creates progress.

The Human Advantage Gets Bigger, Not Smaller

One of the biggest misconceptions in the market is that AI reduces the importance of people. In reality, it often increases it.

As routine work becomes easier, the differentiators become more visible. Things like judgment, communication, trust, strategic thinking and empathy are capabilities clients remember and reward.

Our skills in analyzing data and helping clients make sense of it are not going away. This is the moment to use AI to become better professionals and better humans.

Start With the Model, Not the Tool

If your firm is evaluating AI right now, begin with bigger questions:

• What work should humans spend less time doing?

• Where do clients need more guidance from us?

• How should we develop our people differently?

• What outcomes are clients truly paying for?

• What kind of firm are we trying to become?

Those answers should shape your technology decisions, not the other way around.

What This Means for Firm Leaders

AI is important. But tools alone will not transform firms. A better operating model will.

The firms that thrive in this AI era will be the ones with the clearest leadership, the strongest curiosity and the most thoughtful blend of technology and human expertise.

AI won’t save outdated firms. But it will accelerate the ones brave enough to change.

Read The Rise of the AI-Enabled Accounting Firm by ADP. It’s a strategic framework for understanding why this moment matters, what forces are driving change and practical steps firms can take to use AI confidently without overextending themselves.

Then, they go right back to work and communicate the same way they always have, regardless of who’s in the room or what the goal is.

It’s not that you’re a bad communicator; it’s that you’re using the same communication style in a bunch of different situations.

Same Person. Wrong Room.

Think about a typical week...

• You’re in a one-on-one with a team member where your job is to listen, support and create space.

• You jump into an internal team meeting where the goal is alignment, decisions and clear ownership.

• You’re on a mixed call with leadership and junior staff, where you need to bridge gaps in context and understanding.

• And then you switch into an advisory conversation with a client, where your role is to translate complexity into something usable and guide them toward action.

Those are not the same conversations. If you show up the same way in all of them, something is going to break.

Why Accountants Get Trapped Here

The problem is that most of us don’t actually evaluate our communication in a structured way. We leave a call and think, “That went fine,” or “That felt a little off,” without ever breaking it down into what actually happened.

We rarely take the time to ask ourselves questions like:

• Did I clearly state the purpose of the conversation?

• Did I organize my thoughts in a way that made sense?

• Did I ask questions that helped me understand the other person, or did I just move forward with my own agenda?

• Did I define next steps clearly, with ownership and timelines?

• Did the other person leave feeling more confident, more clear and more able to act?

Those are the things that determine whether a conversation is actually effective.

Why This Matters Even More in the AI Era

AI is rapidly making technical answers easier to access. That means the human side of business becomes more valuable, not less.

When you better understand your communication style — your clarity, engagement, impact, authenticity and action — you can understand whether your conversations are actually productive, regardless of the situation. And recognizing of how these traits vary depending on the context you’re in provides a deeper understanding.

Clarity in an advisory conversation might mean breaking down a complex tax strategy into simple parts and checking that your client actually understands it. Clarity in an internal meeting might mean clearly assigning roles, responsibilities and deadlines so nothing falls through the cracks.

Engagement in a one-on-one might mean asking open-ended questions and letting the other person drive most of the conversation, while engagement in a leadership discussion might mean actively pulling in different perspectives and managing tension without shutting anyone down.

It’s the same skill, but with different behavior.

That’s why context matters so much, and why most communication feedback falls short. Generic advice like “be clearer” or “ask better questions” doesn’t actually help if you don’t know where, when and how to apply it.

What works in a client advisory call might not work in a team meeting, and what works in a low-stakes conversation might completely fall apart in a high-tension one.

You Can Address These Pitfalls

Navi, by ExelLabs, grades whether you’re clear and engaging, but it also evaluates you based on how you showed up in a specific conversation. It looks at whether the audience was internal, external or mixed, whether the relationship is new or established, and even whether there’s tension in the room. All of those factors change what “good communication” looks like.

You can become a better communicator, especially when you have a copilot that makes you more aware of the room you’re in and coaches you on how to adjust your approach to match it. You’ll see how you need to deliver information in a way that works for the person sitting across from you within a specific context.

Stop asking: “Am I a good communicator?” and start asking: “Did I communicate the right way for this moment?”

That question changes leaders. It changes client relationships. It changes firms.

Everyone is talking about AI agents, and accounting is no exception. But what is an AI agent in accounting, and what does it mean for the way your firm works? Here’s a plain-language breakdown.

An AI agent in accounting is a software program that executes financial workflows using rules, data inputs, and conditional logic. Unlike general-purpose AI models that generate responses to prompts, AI agents are designed to perform tasks consistently and repeatedly within structured accounting processes.

AI agents are typically used to automate routine activities such as transaction categorization, reconciliations, and exception detection, while operating within constraints defined by accounting professionals.

What an AI Agent is Not

An AI agent is not a replacement for an accountant. It extends the accountant’s capacity, executing what is defined, consistently and at scale. The accountant’s judgment goes into building the agent. What repeats is the execution.

Think of it like a racecar. The car is fast, precise, and built for performance. But you still need an experienced driver behind the wheel. You would not send someone without a license to a racetrack. The same logic applies here. You would not run an AI agent without someone who understands the accounting behind it.

The accountant manages the agent. The agent handles the repetitive work.
The accountant remains responsible for the outcome.

AI Agents vs. ChatGPT and Claude

General AI systems, such as chatbots or large language models like ChatGPT and Claude, are probabilistic. They generate outputs based on patterns in data and may produce different results when given the same input multiple times.

AI agents, by contrast, are deterministic in execution. They follow explicit rules and workflows defined by the accountant. That said, not all AI agents are built the same way. A well-designed accounting agent locks the logic so it runs identically every time. A poorly designed one can still drift. For accounting firms evaluating AI tools, that distinction matters more than almost anything else.

In accounting contexts:

General AI: Answers questions or generates explanations
AI agents: Execute accounting workflows and return results for review

How AI Agents Work in Accounting

Here is an example of how an AI agent can work in accounting. AI agents operate by combining structured data such as the general ledger and bank feeds with predefined instructions. Those instructions determine how the agent processes transactions, identifies discrepancies, and handles exceptions.

The typical workflow looks like this:

1. The accountant defines the workflow and sets the rules.

2. The agent pulls in transaction data and applies those predefined rules.

3. Anything that falls outside those rules is flagged for review.

4. The output is a set of completed tasks and a short list of exceptions.

5. The accountant reviews the results before anything is finalized.

Common Use Cases

A few examples include:

Bank Reconciliation: Match Transactions Without the Manual Work

An AI agent compares bank transactions with ledger entries. If records match, they are marked as reconciled. If discrepancies exist, the agent flags them and may generate suggested adjustments.

Transaction Categorization: Sort at Scale, Review Only What Matters

The agent reviews uncategorized transactions and assigns categories based on historical patterns or predefined rules. Transactions that do not meet criteria are flagged for review.

Exception Detection: Catch Errors Before They Become Problems

Agents identify anomalies such as duplicate transactions, missing entries, or unusual variances. These items are isolated for further investigation.

Role of Human Oversight

In purpose-built accounting agents, human oversight remains central. Accountants define the rules, thresholds, and workflows that guide the agent’s behavior.

Well-designed accounting agents do not make independent judgments or interpret accounting policy on their own. Final decisions, approvals, and adjustments remain the responsibility of the accountant.

The accountant manages the system. The accountant reviews the output. The accountant approves the final result.

This is the model firms should look for when evaluating AI in accounting: execution handled by the system, judgment retained by the accountant.

Impact on Accounting Workflows

The use of AI agents shifts accounting work from manual execution to review and analysis. Routine tasks are automated, allowing accounting professionals to focus on higher-value work such as financial interpretation, advisory services, and client communication.

This approach can improve efficiency, reduce processing time, and help firms handle more work without adding headcount.

For accounting firms, that shift can be significant. Firms using purpose-built AI agents for month-end close report managing 25 to 30 clients with the same team that previously handled 5 to 10.

The shift is not about speed for its own sake. It is about removing the repetitive work so the accountant’s time goes toward review, judgment, and client advisory.

What This Looks Like in Practice: AI Close

One example of this in accounting is AI Close by Puzzle. It is the first agent builder integrated directly with the general ledger, which means the agent and the books live in the same system. No export. No sync. No reconstructed audit trail. The work happens inside the ledger itself, with the audit trail built in.

Step 1: The accountant defines the task

An accountant opens the close checklist and adds a new step, like bank reconciliation. Instead of writing code or configuring a system, they describe the task in plain language, the same way they would explain it to a colleague: pull last month’s bank transactions, match them against the ledger, mark what reconciles, and flag what does not.

Step 2: The system turns that description into an agent

That description becomes the agent. The system translates it into explicit, repeatable steps that can run the same way every time.

Step 3: The agent executes the work

When the close runs, the agent does the work. It pulls the data, applies the logic, and returns a result: matched transactions and a short list of exceptions that need attention.

Step 4: The accountant reviews and approves

The accountant reviews the output, makes any adjustments, and approves it. Nothing posts to the ledger without that sign-off.

Step 5: The same agent runs again next period

The next month, the same agent runs again with the same logic, rules, and output format. The accountant does not need to rebuild it or re-explain it. They review what comes back.

Step 6: Execution repeats, judgment stays with the accountant

This is what makes agents different from a one-time AI query. The work is defined once and runs repeatedly. The accountant’s judgment is built into the setup. What repeats is the execution, not the thinking.

The accountant manages the workflow. The AI executes the task.

That is the real promise of AI agents in accounting: not replacing the accountant, but extending the accountant’s ability to execute with speed, consistency, and control.

AI agents do not change the role of the accountant. They change the shape of the work. Execution becomes faster and more consistent. Judgment, review, and accountability stay where they belong: with the accountant.

Totally normal question from an almost-26-year-old.

So, I told her to send me her bank statements. Her response?

“Like… from my jobs?”

No, babe… from your BANK. HELLO? CHASE?!

My entire accounting career flashed before my eyes, and NOT just because I was terrified and confused she’s made it this far living like this. (I mean, come on. How do you not know what a bank statement is? How is that even possible?)

But the longer I sat with it, the more I realized this wasn’t just about her — it was about how differently this generation experiences money, and what that means for how we show up as advisors.

A Completely Different Financial World

My daughter lives in a world where money moves instantly. Why would she know?

She’s never had to balance a checkbook, never had to wait for a check to clear, never had to sit down at the end of the month to reconcile what came in and went out. Her entire financial life happens instantly, invisibly, without friction.

She uses Apple Pay. She Venmos her friends. Money moves in real time, and then it disappears into the background of her life without requiring any deliberate interaction or reflection.

There’s no moment she has to check her bank account in the way we were trained to, no system that requires her to engage with the mechanics of money just to function. The closest she comes is opening a banking app on her phone to see all her transactions in real-time.

So when I asked for a bank statement, I was asking her to step into a system she’s never actually needed to understand.

Money is No Longer Learned Through Friction

Most of us didn’t learn financial literacy because someone sat us down and explained it. We learned it because we had to.

We tracked balances so we didn’t overdraft. We paid attention to timing because transactions didn’t clear instantly. We built habits around checking and reconciling because there were consequences if we didn’t.

That friction created awareness, but that system doesn’t exist anymore.

When you remove friction, you also remove the moments where people naturally learn how money actually works.

If I’m being honest, I’m part of the problem too. I’ve always taken care of it for her. I’ve handled the details, answered the questions, made sure everything was fine without ever really pulling back the curtain and showing her how it all works.

If she was my client, it would’ve been different. I would’ve spent more time making sure she actually understood her financial world.

This Is A Shift The Profession Can’t Ignore

The next generation of clients doesn’t experience money the way we do, which means they’re not going to intuitively understand the frameworks we’ve relied on for decades.

If we keep expecting people to understand money the way we were taught, without adjusting how we teach, structure, and communicate it now, we’re going to keep running into the same confusion — not because clients aren’t capable, but because the system we’re asking them to use doesn’t match the way they actually live.

This Is Where Better Advisory Begins

It’s not about explaining what a bank statement is. It’s about recognizing that your clients need someone who can translate their real-life behavior into a system that actually makes sense to them.

That might mean walking them through how their money flows, simplifying what you ask for, or changing how you ask for it. It might mean building processes that don’t rely on them knowing things they were never taught.

We have to slow down enough to meet our clients where they are, instead of expecting them to catch up.

AP and AR have always been the unglamorous back-office chore — messy, manual and a total margin killer. Firms can’t ignore it, but they also can’t keep letting it chew up staff capacity, delay month-end and stall advisory work. That’s exactly where AI steps in as the radical sidekick, not a scary disruptor.​

Instead of team members chasing invoices, digging through email for client and vendor questions and re-keying the same data across systems, AI can sit on top of your AP workflows. It can answer questions, surface insights and keep things flowing so your team has time to think more profoundly about the numbers. In other words, AP becomes the first, practical place where AI is deeply helpful, not hype-driven.​

Where do you start? With busywork!

Give AI the busywork

Start by identifying the most time-draining, manual AP tasks in your firm and hand those off to AI first.​ Just think about all that AI can handle for your firm:

• Answer repetitive vendor and client questions like “Did this get paid?” or “What’s the status?” by pulling from synced AP and GL data instead of staff digging through systems.​

• Handle lookups like historical payment patterns, average days to pay or vendor totals so humans aren’t living in spreadsheets.​

• Flag anomalies in bills, possible duplicate invoices or out‑of‑policy payments before they hit the GL.​

• Summarize AP activity for the week or month so accounting professionals can walk into client meetings already armed with talking points.​

When AP is powered by a modern platform that syncs cleanly with the GL, you’re no longer asking team members to dig through and push paper. AI can now do the grunt work so your team can focus on judgment, relationships and strategy.​ AI can serve as your always-on AP assistant, there to answer questions in real time. All you have to do is ask.

It’s also important to note that AI is not something to be feared. There is no steep learning curve or deep knowledge of tech required. It really is just about having conversations. Talking to AI in plain language to get the information you need with speed and efficiency.

Predict cash crunches before they hit

The real magic happens when AI both answers questions and spots patterns.​ This is the level of power offered in advanced AI-driven AP/AR platforms. For example, Melio’s AI agent, MEL, allows you to ask deeper, analysis-based questions on demand to surface insights faster and with more accuracy.

This means that AI can quickly and reliably:

• Forecast upcoming cash crunches based on open bills, receivables and historical payment behavior.​

• Surface vendors that you can safely slow‑pay, and which ones you should fast‑pay to protect key relationships.​

• Highlight opportunities to use card-based payments or pay-over-time options to smooth out big cash outflows.​

• Turn raw transactions into plain‑language insights that open the door to deeper cash flow advisory.​

This is where AP stops being a cost center and becomes your on‑ramp into ongoing, high‑value cash flow conversations. It’s powered by AI, but delivered by you.​

AP as the gateway to GL modernization

The radical truth is that you don’t modernize the GL by starting in the GL. You start at the edges, like with AP and AR, then build inward.​

When clients live in a simple AP experience instead of inside the accounting software, you protect GL integrity and reduce the clean-up chaos that kills your margins. A modern AP platform that syncs with your accounting system in real time gives you:​

• Cleaner, more consistent data feeding the GL automagically.​

• Fewer client‑created errors, because they’re pressing buttons in a simplified AP environment, not experimenting in the GL.​

• Faster reconciliations and reporting, because AP and GL are working from one source of truth.​

Layer AI on top of that environment and you’ve quietly started your GL evolution without a massive, scary “system overhaul” project. AP becomes the low‑risk sandbox where your team learns to work with AI, and then those same patterns move deeper into GL review, month‑end, and eventually firm‑wide workflows.​

Adopt and leverage your first AI assistant: Start with AP

If AI feels overwhelming (and it likely does), just start small and strategic. Make AP the first place AI shows up as that “always-on” assistant, not an intruder to fear and avoid.

A practical rollout might look like:

• Phase 1: Use AI to answer AP status questions and support staff with simple data lookups. ​

• Phase 2: Turn on AI‑driven alerts for duplicate bills, policy exceptions and potential cash crunches.​

• Phase 3: Connect these insights to advisory, like regular cash flow check‑ins and payment strategy. This can evolve into GL-level pattern spotting, helping companies identify where clients can extend terms, smooth out cash swings or even leverage card-based payments to improve working capital. This turns foundational AP work into higher-value strategic offerings like cash flow advisory.

The goal is to get to a place of comfort with AI. But to do that, it’s time firms stop viewing it as scary and start envisioning what it can do. That includes leveraging a data-driven AP assistant that’s always ready and waiting to process questions and churn out answers. One that will never retire and represents a central knowledge base of all things AP.

It’s time to start building a modern AP environment that protects your GL and your margins. AP is the gateway to the next‑gen firm where AI handles the busywork so your team can finally focus fully on building for the future.

Start where it matters

AI doesn’t need to show up everywhere on day one to make an impact. It just needs to show up where it removes friction, protects margins and gives time back to your team.

AP is the most practical place to start.

When AI handles repetitive questions and manual lookups, your firm gains efficiency, but more importantly, it gains clarity. And from that clarity comes better conversations, stronger advisory, and a smarter path to modernizing the GL without disruption.

The future of accounting isn’t built in massive, risky transformations. It’s built one smart workflow at a time.

Start with AP. Give AI the busywork and let your firm focus on what’s next.

They’re adding new tools, testing automations and sitting through demos that promise to make everything faster and more efficient. And for a moment, it feels like progress.

But then… not much changes. The workflows are still messy. The team is still overwhelmed. The impact feels smaller than expected.

So what’s actually going wrong?

It’s not that AI doesn’t work. It’s that most firms aren’t set up for it to do anything helpful.

AI just amplifies broken systems. And accountants’ systems have been broken for a long time.

AI is only as good as your workflow

There’s a common misconception right now that adding AI will naturally make things more efficient. But AI doesn’t replace your systems, it runs on them.

It pulls from your data, your workflows and your processes. If those things are inconsistent or unclear, AI doesn’t clean them up — it scales them. That’s why some firms see real gains, while others feel like they’re spinning their wheels.

The difference isn’t the tool. It’s the system underneath it.

AI isn’t breaking your firm, it’s revealing it

Most of the time, when AI “fails,” it’s exposing problems that were already there.

Data is scattered across emails, spreadsheets and different tools, with no real source of truth. Workflows vary depending on who’s handling the task, making it hard to create consistency. Leaders don’t always have visibility into where work slows down or breaks, which makes it even harder to improve anything intentionally.

And underneath all of that, many processes were never actually designed. Things just piled on over time.

That kind of environment makes it nearly impossible for AI to do its job well.

Fix the system, not the tool

If firms want to see real impact from AI, the focus has to shift. Not toward more tools, but toward better systems.

That means getting data organized and accessible. Defining how work should flow, even if it’s not perfect yet. Creating visibility into what’s actually happening across the firm, instead of relying on assumptions.

Once that foundation exists, AI becomes powerful. Without it, it’s just another layer of complexity.

So the real question isn’t, “Which tool should we use?”

It’s, “What kind of system are we giving it to work with?”