Mid-year has a way of making firm owners look up. The extension rush is behind you, the inbox has finally thinned out, and for the first time since January you’ve got six months of real data to work with instead of a hunch. The patterns that were invisible back then are obvious now: which clients are slow to pay, which payables keep slipping through the cracks, where your team’s time actually goes versus where you wish it went.

When that moment prompts the harder question: is our infrastructure actually working for us, or have we just gotten used to it? AP is usually where the answer gets uncomfortable. Because the honest answer, in July just like it was in January, is usually: it’s fine.

“Fine” is quietly killing your margins.

It’s the revenue you’re leaving on the table because your services aren’t packaged the way you’d like them to be. It’s the advisory work you can see clearly but that never actually gets off the ground.

It’s also a time thief. It has a way of following you home and setting up shop in the hours you never planned to be working.

When it comes to AP specifically, “fine” can be a genuinely chronic pain. It’s manual, it’s messy, and it’s usually the last thing on the list firms get around to fixing.

Classic “if it ain’t broke, don’t fix it.”

Except not broken and actually serving you are two very different things. Six months into the year is exactly when that gap starts to show.

Teams put up with fragmented apps, or work around a platform that’s outgrown its usefulness, because the pain never quite crosses the line that forces a decision. That tolerance still has a cost, though. It just doesn’t always show up somewhere obvious. By mid-year, you’re staring at half a year of numbers, and it gets a lot easier to see how much it’s actually added up to.

A clunky AP process isn’t only an operational headache. It’s a CAS opportunity sitting on the table, untapped.

For a lot of firms, the right platform is the difference between that opportunity staying theoretical and actually turning into something the firm can deliver in the second half of the year.

A better AP platform opens doors that wouldn’t otherwise exist:

• Packaging AP oversight as its own offering, with real pricing attached

• Advising clients on cash flow and payment timing to manage vendor relationships and head off cash crunches

• Helping clients put business credit cards to strategic use, earning rewards, stretching liquidity, and turning ordinary expenses into a financing tool

• Moving away from hourly billing toward value-based pricing that reflects what’s actually being delivered

So if the mid-year view makes the problem obvious, why aren’t more firm owners switching?

Because “functional” feels like the safe choice. Researching, evaluating, and migrating sounds like a lot of effort when the current setup is getting the job done, even if imperfectly.

There’s the sunk cost of a platform you’ve already paid for, the comfort of sticking with the industry standard, and the pull to accommodate clients who use different platforms because it feels more service-minded. Nobody wants to become the cautionary tale of a rollout that dragged on too long and burned through too much goodwill, especially with half the year already gone.

But when your infrastructure bends to individual client preferences instead of running on firm-wide standards, consistency takes the hit. Service that feels personal because it’s flexible ends up being much harder to deliver well once you try to scale it.

Most of the time, the barrier is perception, not reality.

A few things are worth checking for specifically when you’re evaluating platforms:

• Can you see every client’s payables from one screen, without bouncing between accounts?

• Are approval workflows and role-based permissions configurable per client?

• Does it sync cleanly with the accounting software you already run, without creating a second reconciliation job?

• Does the fee structure get better as your client base grows, instead of working against you?

None of that is an unreasonable ask. Platforms purpose-built for accounting firms, Melio included, treat it as table stakes. Centralized oversight across clients, AI-powered bill capture, direct sync with QuickBooks and Xero, and onboarding support that walks firms through setup rather than leaving them to figure it out solo.

The economics often look different than firms expect, too. Melio doesn’t charge the firm a subscription fee regardless of client volume, and Accountant Partner Program discounts start at 30% and climb to 45% as the client base grows. The cost structure gets better with scale instead of tighter.

CPA Business Advisors switched for exactly this reason. They now manage payments for up to 300 clients and estimate $40,000 in annual savings versus their old bill pay setup. In Bruce Robertson’s words, the firm’s Head of Growth and Development: “The fee structure of Melio is far more beneficial for our needs and client needs than that of the competitors on the market.”

Done right, the switch pays for itself, and the second half of the year is the cheapest time to find out.

Bringing all your AP systems under one roof does more than solve a headache.

When every client sits on one platform with shared visibility and consistent workflows, the work that once felt too scattered to package into a CAS offering finally has real structure behind it.

And as a bonus, your team gets capacity back for advisory work, because senior staff aren’t spending their day bouncing between systems they didn’t choose, doing manual work that resists standardizing or delegating.

That effect ripples across the whole firm. Efficiency, scalability, fee structure, team cohesion: they’re not separate problems with separate fixes. They’re all symptoms of one underlying question. Is the infrastructure you’re running on actually built for the firm you’re trying to become?

Most firm owners already know the answer, especially with six months of evidence behind them now. The hesitation is rarely about diagnosing the problem. It’s about whether fixing it feels worth the cost compared to just living with it for another six months.

For most firms, a switch that pays for itself within 12 to 18 months is worth making. Firms dealing with real limitations tend to hit break-even much sooner than that, often well before year-end.

“Fine” is rarely a neutral state. It’s a default choice to keep absorbing the cost of infrastructure that almost works. The platform you build on shapes what you can charge, how efficiently your team operates, how confidently you can scale, and ultimately the kind of firm you end up building.

Mid-year is as good a time as any to stop treating that decision as background noise.

We imagine leaders discovering a new tool, getting excited about the possibilities, and deciding to bring it into the business.

But really, most technology purchases don’t begin with excitement. They start with a realization like, “I can’t keep doing this forever. There has to be a better way.”

By the time a leader starts seriously evaluating a new tool, they’ve often been living with the underlying problem for months or even years.

Most Technology Decisions Start with Frustration

Good technology simply gives language to define a problem that already exists. A lot of times, leaders don’t fully understand or articulate a problem until they see something that helps them name it.

Maybe you’ve been frustrated because everyone still comes to you for approval, or you’ve noticed that newer employees need more coaching than your managers have time to provide. Maybe you’re growing quickly, but you’re worried about maintaining the same client experience that helped your firm become successful in the first place.

On the surface, those seem like separate challenges, but sometimes they’re all symptoms of the same underlying issue.

• Suddenly, “everyone keeps copying me on emails,” becomes: “we have too much knowledge concentrated in a few people.”

• “Managers are overwhelmed,” turns into: “our development model isn’t scaling.”

• “We’re growing too fast and something feels off,” becomes: “We’re struggling to create consistency as the firm expands.”

Once you can really articulate the problem, a new conversation about solutions begins.

The Right Solution Starts with the Right Problem

The solution itself almost doesn’t matter. The important thing is understanding what problem you’re actually trying to solve.

The firms that get the most value from new tools are the ones that have done the work to become deeply aware of the challenges holding them back.

Technology just gives leaders a new way to see them.

The good news is that you don’t need a sweeping transformation. You just need to start small. Think of it less like flipping a switch and more like onboarding a new client or system.

Running the experiment in 5 steps

A small experiment solves three problems at once:

1. It keeps risk low because you are not changing everything at once

2. It makes learning real because you are working with actual data, actual deadlines and real client expectations

3. It builds trust because your team can see where AI helps and where it requires tighter rules

Starting with one to five clients is the sweet spot for most firms. Even with a small group, you can start to see repeating issues, recurring wins and the beginnings of a playbook you can standardize.

Running this as a proper experiment also means keeping your existing processes intact.

Most firms feel safer running parallel systems for a while, meaning that your legacy GL and workflows stay live while you spin up an AI‑enabled version of the process for the same five clients and compare results side‑by‑side.

Step 1: Choose 1-5 simple clients on purpose

The experiment begins with picking the right clients. You’re not looking for your most complex, dramatic or high‑risk engagements. You want stable, predictable clients where a new workflow can be tested calmly.

Look for clients who are:

• Operationally stable: Those with a consistent business model

• Data‑reliable: Those with reasonably clean books

• Relationship‑strong: Those who trust you and will be comfortable as part of the experiment

Your mindset: You’re not rolling out AI to the whole firm; you’re running a controlled experiment with clients we know well.

Time commitment: Partners/managers: 1–2 hours to select clients, set guardrails and brief the team. Team: 1–2 hours per week in the first month for workflow changes, vendor sessions and debriefs.

Steps 2 and 3: Map the workflow, then give agents specific jobs

Before AI enters the picture, you have to be crystal clear on how the work gets done today. For each of the five clients, map the end‑to‑end flow start to finish. A monthly close might look like this:

• Pull data from bank feeds and accounting system

• Classify transactions

• Reconcile accounts

• Prepare schedules and workpapers

• Review, adjust and finalize reports

As you map, circle the steps that are highly repeatable, rules‑based and time‑consuming. Those are the pieces you want AI agents to draft under strict governance.

From there, you define agents as “digital staff with job descriptions,” not magic bots. Here are a few examples:

• An agent that classifies routine transactions using firm‑approved rules and history

• An agent that drafts reconciliations for specific accounts

• An agent that assembles standard workpapers from multiple data sources

• An agent that flags anomalies or exceptions that do not fit the usual pattern

Tools like Puzzle make this easier by allowing firms to build agents in minutes with plain English. You decide what the agent can do, what it can propose and where it needs human approval.

Mindset to remember: AI should plug into a process you understand. It cannot fix a process you never documented. Agents are here to handle repeatable steps and not to own the engagement.

Time commitment: 1–2 minutes per agent build (often reusable across similar clients) and 30–60 minutes with your tech partner to validate where agents make the most impact. Add 1–2 hours in the first month to refine rules as you see real behavior.

Steps 4 and 5: Review AI like staff work, then scale only what you trust

Once agents are drafting pieces of the work, your experiment is well underway. One rule, however, is a non‑negotiable. AI can prepare the work, but it cannot post, file or be delivered to a client without human review.

Your team should review AI output the same way they would a junior team member’s work:

• Check accuracy and completeness

• Look for patterns in recurring errors

• Compare time spent reviewing now to the time previously spent preparing from scratch

This review step is where trust is built. Teams see exactly where AI is strong, where it needs clearer instructions and where it should not be used at all.

As you gather experience across the five clients, start writing down what you learn: which tasks agents handle well; where predictable mistakes occur; and which prompts, rules or configurations deliver the best results. From there, tighten your setup: clarify boundaries; add rules for common edge cases; and decide which tasks are ideal for agents, which are shared and which stay fully human for now.

By the end of the experiment, you should be able to identify how much time was saved, if quality improved and if your team is ready to expand to more clients.

Mindset to remember: This isn’t about proving AI is perfect. It’s about finding where AI reliably helps and baking that into how you work.

Time commitment: 1–2 hours per month to review metrics and feedback across the five clients, plus a short internal debrief at the end to decide what to standardize and what to try next.

How this moves you up the AI Adoption Ladder

Decisions around AI adoption take time as firms begin with awareness and move toward being an AI-enabled firm, as shown on the AI Adoption Ladder below. The 5‑Client AI experiment sits on the Experimentation rung of the AI Adoption Ladder but nudges you toward Adoption and Integration.

You’re not talking about AI in theory anymore. You’re using it in live work, with clear guardrails, and learning what’s worth scaling.

AI is getting more capable every day. It can summarize a meeting, flag a risk, analyze a pattern and draft a follow-up before you have finished your coffee. Some accountants look at that and feel nervous. If AI can do all of that, what exactly is left for the professional?

Quite a lot, actually. And it is worth getting specific about what that is.

The firms and professionals who will pull ahead are not the ones who learn to use AI the fastest. They are the ones who understand what clients still need most from a person and use AI to amplify that. When you look honestly at what makes a great accountant, three things come up every time. Judgment. Context. Relationship. AI does not have any of them. But it can make all three stronger.

Judgment: Connecting Facts to Decisions

AI is good at gathering facts. It can pull patterns out of a transcript, flag where a client raised a concern or kept circling back to the same issue and line up options and scenarios. What it cannot do is decide what any of that means for this client, at this moment.

Judgment is the bridge between what we know and what we recommend. Two advisors can look at the same data, the same notes, even the same AI summary, and hold two completely different conversations. The difference is not in the output. It is in how each person reads it, weighs the trade-offs and decides what makes sense for that client.

AI can make that part of your work stronger. It can show you where you spent most of the conversation, where a concern kept resurfacing or where your explanation may not have landed as clearly as you thought. That gives you a better starting point. Then your judgment takes over. You decide what needs to be clarified, what needs to change and what you are willing to stand behind.

That is the part no tool can do for you. It can give you more to work with. It cannot make the call.

Context: Knowing the Story Behind the Numbers

AI sees the conversation in front of it. You see everything that came before and everything at stake after.

You know why the numbers looked the way they did last year and what the client isn’t saying out loud. You know that certain topics are sensitive and which decisions have history behind them. None of that shows up in a summary. But all of it changes what good advice looks like.

AI can tell you what was said. You can tell your client what it means, for them, right now, given everything you know about where they have been and where they are trying to go. And when AI handles the recap efficiently, you have more time and more presence for the part that actually requires you. That is the difference between information-delivery and true advisory.

Relationship: Trust You Have Earned Over Time

You have spent years building client trust. They call you when something feels off. They ask what you would do if this were your business. They lean on you in the hard years and want to celebrate with you in the good ones.

AI cannot recreate that. It has not seen the client through tax seasons, audits, growth and setbacks. It does not remember what you recommended two years ago or how it played out. It cannot say, “We have been here before, and here is what we learned.” It has no skin in the game.

What it can do is help you take better care of the trust that is already there. Tools that capture and analyze conversations can show you where you rushed past something the client cared about or where a call never quite got to a decision. They can help you keep track of promises and follow-ups so fewer things slip through the cracks.

When you bring AI into the trusted advisor relationship, you are still the person they trust. You are simply using better information to show up for them.

AI amplifies the value you already bring to the table.

Your Human Advantage

AI will keep getting better. The real opportunity is to use it in ways that make you stronger and more present for the parts of the work that only you can do.

That is where accounting advisory technology like Navi can help. It makes conversations easier to see after the fact. It shows you where something mattered more than you realized, where a client hesitated or where a decision never quite got nailed down.

Use what you find to show up sharper next time. That is not AI replacing your value. That is AI amplifying it.

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.

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.

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.

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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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.

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.

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?”

The work is getting faster. The tools are getting smarter. And yet, somehow, the pressure isn’t going down. If anything, it’s increasing. Because the expectation is no longer just to get the numbers right. It’s to help clients understand what those numbers mean and what to do next. That’s a very different job.

And it’s exactly why I wanted to record a full demo of Navi. It’s not a pitch, but a real, behind-the-scenes look at how we, at XcelLabs, are thinking about the future of this profession and what we’re building to support it.

If you have 20 minutes, I’d encourage you to watch the full walkthrough. But if you don’t, here’s what I want you to take away.

We’ve Been Measuring the Wrong Things

For decades, firms have been built around metrics like billable hours, utilization and realization. Those numbers made sense when accounting was primarily about production like getting work done, getting it out the door and getting paid for it. But that’s not the world we’re operating in anymore.

Today, the real drivers of growth look very different. They show up in the quality of your client conversations. In how clearly you communicate. In the level of trust you build. In your ability to guide decisions, not just report results. The problem is, we’ve never had a way to measure any of that. So firms keep optimizing what they can see, even if it’s no longer what matters most.

Advisory is a Skillset Not a Service Line

We’ve been talking about advisory for years. At this point, it almost feels like table stakes. But most firms are still trying to figure out how to actually deliver it consistently. That’s because advisory requires a different set of skills than most accountants were trained for.

Technical expertise if the foundation, but how you handle a conversation when a client is uncertain is what really matters. It’s how you guide someone hesitant to make a decision. It’s how you simplify something complex without losing its meaning. It’s how you lead.

For a long time, the only way to learn those skills was by proximity. You sat outside a partner’s office. You listened in on calls. You picked it up over time. That doesn’t work anymore is a remote world where things are changing faster than ever.

AI Isn’t Just About Efficiency

Most of the conversation around AI in accounting has been about doing things faster. And yes, that’s part of it, but it’s also the least interesting part. The real opportunity with AI is using it to change how we think.

That’s where this idea of AIQ (Artificial Intelligence Quotient) comes in. It’s the ability to work with AI as a thinking partner. To bring context into it. To challenge it. To use it to sharpen your thinking instead of replacing it.

Two people can use the exact same AI and get completely different results. The difference isn’t the technology. It’s how they show up.

What Navi Is Actually Trying to Solve

When we built Navi, we were trying to solve for the moments where you leave a client call and think, “That could have gone better.” For the times when you hesitate before saying what you really mean. And for the situations where you get pulled into a mental loop, replaying a conversation, second-guessing a decision, wondering what you missed.

Those moments are everywhere in firms. They just haven’t been visible. Navi brings visibility to them.

It takes a real conversation with an actual client and helps you see how you showed up. It’s not judgmental. Rather, it’s grounded in improvement. It highlights where you were clear and where you weren’t. Where you led and where you gave up control. Where you created impact and where there was an opportunity to go further.

And then it lets you do something we’ve never really been able to do before. It lets you ask better questions about your own performance… Why did that conversation stall? Where did I lose alignment? What could I have done differently in that moment?

And because it’s grounded in your actual interaction, the answers are relevant. They’re actionable. They stick.

The Real Value Isn’t the Score

Navi scores how you perform in different areas. It’s useful because it gives you a baseline and shows you progress over time. But that’s not where the real value is. The real value is in what happens after. It’s in the awareness you start to build. The patterns you begin to recognize. The confidence that comes from understanding not just what happened, but why.

Because once you see it, you can change it. And when you change how you show up in conversations, everything else follows. Your client relationships improve. Your team becomes more capable. Your firm starts to operate differently.

This Is About Relevance

At the end of the day, this isn’t really about technology. It’s about relevance.

Compliance work is already being automated. That trend continues to accelerate. So the question becomes: What makes someone choose you?

It’s not just your ability to produce the work. It’s your ability to lead, interpret and guide. It’s how you make someone feel in a conversation. It’s the clarity you bring when things are uncertain. That’s the value. And right now, in most firms, that value lives in a few individuals. A handful of partners. A few strong advisors.

What happens when you can scale that across an entire team?

That’s the opportunity. And it’s also the urgency. Because the future of this profession isn’t 10 years away. It’s the next few.

We don’t need to become something completely different. We already have the foundation. We just need to learn how to use it intentionally, consistently and at scale.

See Navi in Action

If you want to see what all this actually looks like in practice, the full demo above walks through it step by step. But more than anything, I hope it changes how you think about where we’re headed. Because it’s something you can actively shape.

AI-powered tax research is having a moment. It seems like every day there’s another headline about what AI can do for tax workflows, how fast it’s evolving, and why your firm needs to act now.

These headlines aren’t wrong. AI-powered tax research just keeps getting more sophisticated and firms are adopting it at a rapid pace.

Research from Blue J’s own Outlook Report had similar findings:

• 54% of respondents reported using some form of AI to do tax research

• 86% of respondents agreed that AI-powered tax research creates an advantage for firms

• 735% YoY increase in tax questions asked on Blue J from July 2024 and July 2025

Despite all this positive momentum, plenty of tax professionals still feel stuck. With multiple vendors all making the same promises of speed, accuracy, and security, it’s a wonder any of us ever move past the evaluation stage. After subjecting yourself to a blur of vendor demos and seemingly interchangeable feature lists, sticking with your current tech stack can start to feel like the easiest option.

Why Information Overload Leads to Inaction

No matter how much we might like to think of ourselves as rational, human beings are prone to cognitive biases. When you’re overwhelmed—possibly from all those demos and feature lists—defaulting to one of these biases is an easy way to cut thinking costs. Most of the time, the reason you hold off on choosing a new tool has nothing to do with the tool itself. And everything to do with the process of choosing one.

Status Quo Bias

When faced with uncertainty, we tend to stick with what we already know—even if it’s slower or less efficient. In psychology, this phenomenon is called the status quo bias. While leaning on this cognitive bias can free up space for other mental tasks, it can also lead to missed opportunities.

Accounting has a well-known acronym related to this bias: SALY, or Same As Last Year. It may be rooted in a desire to drive consistency, but defaulting to SALY can also drive stagnancy. When you don’t take the time to reevaluate your tech stack, all the issues caused by that tech can compound year over year.

Luckily, the behavioral science experts at The Decision Lab have some advice on how to disrupt this bias:

• Increase awareness: This requires weighing your options, giving equal consideration to each.

• Create space to consider: In a busy firm, it’s easy to put off making a decision until a block of free time magically appears in your calendar (spoiler: it will never happen). To get past this, book blocks of uninterrupted thinking time in your calendar and look to expert-vetted evaluation questions to make the best use of that time.

Loss Aversion

Humans don’t weigh gains and losses evenly. A potential loss feels more painful than the joy associated with an equivalent gain. This cognitive bias is called loss aversion, and makes us want to hang on tight to what’s known and familiar—including legacy tax research solutions.

When you’re evaluating AI-powered tax research tools, the promise of saving hours on research doesn’t feel nearly as impactful as the perceived risk of losing credibility or wasting spend.

But once again, the experts at The Decision Lab have a few strategies for fighting back against this bias:

• Frame wisely: Pay attention to the benefits you want to achieve, not just the risks you want to avoid. For a benefits-focused framework for evaluating AI-powered tax research, look through this buying guide.

• Use perspective: Take a step back and pressure-test potential decisions. Consider what would be the worst realistic outcome if you moved forward. This helps put the fear in perspective and makes it easier to decide whether the risk is real or just perceived.

Even after reading this blog, these cognitive biases will almost certainly creep in as you’re evaluating AI-powered tax research solutions. After all, you’re only human. But that doesn’t make decision paralysis any less dangerous.

With so many firms having made the move, staying where you are means getting left behind.

The good news is there’s still time to catch up. With the right comparison tools, you can finally move past the evaluation stage and find the AI-powered tax research solution that works for you.

Top 7 Factors to Evaluate in AI-Powered Tools

When you’re evaluating solutions that all sound similar, focus your questions on the things that actually change outcomes:

1. Answer quality: You need answers you can trust, validate, and stand behind—not just answers that show up fast.

2. Content: Answer quality depends on content quality. Look for deep, authoritative tax source coverage, curated by people who understand the subject.

3. User experience: If a tool isn’t easy to use, it’s just going to collect dust. Widespread adoption demands a workflow that feels intuitive from day one.

4. Price and value: Look past the per seat license price. What matters more is the value you get per active user—based on real adoption rates.

5. Security: Make sure the platform is built to handle sensitive tax work safely, with clear standards and safeguards.

6. Speed of execution: Choose a vendor with a proven track record of ongoing product improvement, so your solution stays current and gets more valuable over time.

7. Post-purchase experience: Implementation is only the start. Ongoing support and reliability are what keep your tax research tool useful long-term.

Want to deep-dive into each of these categories? Download your copy of the AI-Powered Tax Research Solutions Buying Guide.

So Long, SALY

You’ve read the headlines, you’ve watched the demos, and you’ve even memorized the feature lists. But all that information only made you feel more stuck.

Now, you finally have a tool that can get you past the evaluation stage. Armed with a buying guide, you’re ready to cut through the “claim clutter”, to focus on the features, capabilities, and support that really matter. So you can stop watching from the sidelines and see just what this headline-making technology can do for your firm.

We suck up a lot of air talking about creating better workflows and using new technology.

And that’s the problem!

(Okay, fine, I’ll give you points for doing better than the vast majority of professionals who still swear by doing it the same as last year, but you’re not off the hook.)

Technical excellence and efficiency are low-hanging fruit. Of course, customers expect it, but it’s not the part of your business that provides actual value to your clients.

Your clients want to work with someone who can help them understand the big picture of their finances. Anyone (even computers) can spit out an accurate tax return, but it takes someone special to take the time to strategize about their goals and answer the financial questions that keep them up at night.

How Do We Make Accountants Better Advisors?

It starts with self awareness.

Most professionals have no real way to understand how they communicate. It’s really hard to self-reflect and figure out how often you interrupt people, whether or not you use a lot of jargon, if you tend to guide clients or simply react.

This creates a massive blind spot because you can’t improve what you can’t see.

Historically, the kind of feedback you need to improve has been limited. Coaching is expensive and sporadic. Feedback is inconsistent. Your colleagues are busy with their own assignments. Most professionals are left to figure it out on their own.

Navi changes that.

The AI Mirror

Tools like Navi aren’t just trying to automate tasks. They’re starting to act as something else entirely: a mirror.

When you upload client conversations and team meetings into Navi, it reflects your behavior back to you. It can pinpoint:

• How you spoke

• How you structured a conversation

• Where you created clarity or confusion

• Where you missed opportunities to lead

It shows your invisible habits! And once you see them, you can improve them.

With continuous, personalized feedback, accounting professionals can intentionally develop the advisory skills that make them irreplaceable.

If the last 20 years of accounting technology were about building more efficient firms, the next phase is about building more self-aware professionals. Not just faster technicians, but better communicators, stronger advisors and more intentional leaders.

That doesn’t happen with more software alone. It happens when professionals finally start to see themselves clearly and decide to grow.

But by the time the report is finished, the business problem has usually already happened. The payment is late, cash flow is tight or there’s an operational bottleneck. And explaining the past, while important, isn’t what clients actually want from us anymore. They want help staying ahead of problems.

That shift is why we’re entering the end of month-end thinking.

Modern Advisory is About Visibility

Your real opportunity comes from gaining visibility into what’s happening inside a business in real time. Because once you can see patterns as they emerge, your role changes completely. Instead of showing up to meetings saying, “Here’s what happened last month.” You can say, “Here’s what I’m seeing right now and here’s what we should do about it.”

That’s advisory. That’s leadership. And that’s where accountants become truly valuable business partners.

Getting to that level of insight has historically required a lot of manual work. You have to pull reports, dig through dashboards and even ask staff to investigate transactions. All so you and piece together what the numbers might mean. By the time the answer surfaced, the moment for proactive action had often passed.

Enter the Era of AI Agents

We’re beginning to see AI agents embedded directly inside accounting workflows. Right inside the tools you use, allowing you to ask questions about financial activity and instantly surface insights.

→ Instead of running reports, you simply ask the system a question.

→ Instead of digging through dashboards, the platform finds the signal for you.

→ Instead of reacting to problems weeks later, you can see the pattern as it starts to form.

This agent is not replacing you. Actually, your role is being elevated! Because the value you provide does not involve collecting data. It comes from your ability to interpret it and advise on what to do next. AI removes the friction between those two steps.

The Real Advisory Opportunity

When accountants gain faster access to information, something powerful happens. The conversations with clients change. You stop being the person who explains financial history and become the person who helps protect the future of the business.

You start asking better questions:

• Why are these bills starting to creep overdue?

• Why is vendor spend increasing in this category?

• Did growth outpace the systems the company put in place?

• Are there operational pressure points forming?

Those questions open the door to conversations that matter. This is where advisory really lives. And it’s also where you can build stronger client relationships and command higher-value engagements. All because you are thinking about their business alongside them.

From Historian to Strategist

For years, I’ve talked about accountants moving into advisory roles. Luckily, AI agents accelerate that shift. They allow accountants to move:

From processor → protector
From report generator → insight leader
From bookkeeper → business advisor

This is what’s possible when technology frees you from the friction of gathering information. When you can access insights instantly, you spend less time hunting for answers and more time having meaningful conversations with clients. And that’s the work that actually matters.

A Quick Example

In the video above, I walk through an example of this shift using Mel, a new AI agent inside Melio’s AR and AP workflow. Instead of running reports or digging through dashboards, I simply ask questions about what’s happening in the system, and the platform surfaces the answers instantly.

It’s a small glimpse of what accounting looks like when visibility replaces month-end reporting. Take a look. It’s where accounting is heading!

The Firms That Lead the AI Era

Success does not come with the more technology you have. Rather, you have to use technology to become a better advisor. Embrace the tools that give you faster insight, clearer visibility and the ability to ask better questions about what’s happening inside your clients’ businesses. This lets you deliver better intelligence, faster insight and more proactive leadership.

And that’s exactly the kind of work Radical CPAs are built for.