You introduce a new AI tool inside your firm to transcribe meetings and capture action items. And then, in one of your first meetings using it, a client says: “I’m not comfortable being recorded.”

It’s usually said politely. Sometimes even apologetically. But the reaction inside the firm can be immediate and intense.

Partners start wondering whether they’ve moved too fast. Someone asks if recording is even allowed. Another person worries that clients will think the firm is monitoring them or collecting data in ways they didn’t expect. The technology that felt like progress suddenly feels like risk.

Pause … Right… There.

This is not about recording. It’s about communication!

Let’s Start with the Rules

In the United States, call and meeting recording laws generally fall into two categories: one-party consent and all-party consent. In one-party consent states, only one participant in the conversation needs to agree to the recording. In all-party consent states, everyone on the call must consent before the conversation can legally be recorded.

Accounting firms rarely operate inside a single state boundary. A partner may be sitting in Ohio while the client is in California. Another team member may be dialing in from Texas. When conversations cross state lines, you have to default to the stricter standard. That means ensuring that everyone on the call knows the conversation is being recorded and has the opportunity to consent.

The good news is that transparent consent isn’t a new concept. Tools like Zoom and Microsoft Teams both display an on-screen notification the moment recording or transcription begins. Participants can see it, and they can talk about it with you if they have concerns. The notification protocol on these platforms helps ensure the recording process is designed to be transparent from the moment the meeting begins.

Why Firms Are Beginning to Record Client Conversations

Client conversations typically disappear the moment they end. Often, someone leaves a meeting with a few handwritten notes and maybe sends a follow-up email with whatever details they happened to remember later. If another team member joins the engagement later, they have to reconstruct the context from fragmented notes or quick hallway explanations. Details get missed. Follow-ups get delayed. Institutional knowledge walks out the door when a team member changes roles.

Recording and transcription tools allow firms to preserve the substance of important discussions in a way that handwritten notes never could. Instead of trying to type while a client is explaining a complex situation, an advisor can focus fully on the conversation. Decisions, commitments and next steps are captured automatically and can be referenced later.

The First “No” Often Comes from Inside

More often than not, the biggest resistance to recording comes from you and your team and not from the clients.

There is concern that asking to record will feel intrusive and prompt negative reactions. As a result, someone in your firm is choosing not to start the recording because they don’t want to create an awkward moment. But clients are highly attuned to tone and confidence. If you apologize for using AI to help, it will be treated as something risky. If you speak about it confidently as a service enhancement, it will be viewed as professional evolution.

This is why firms that successfully adopt conversation intelligence tools focus first on internal alignment.

Introducing Recording to Clients

Don’t wait until the meeting has started to introduce recording. Start before the meeting ever begins. Send a short note explaining that your firm has implemented a transcription tool to improve follow-up and reduce note-taking distractions. Make it clear that its purpose is accuracy and service quality, and that clients can opt out if they prefer.

This does two important things. It removes surprise, and it removes pressure. If resistance still surfaces during the call, stay steady. You don’t need to defend the technology. You simply need to clarify intent.

Explain that the recording allows you to stay fully present, ensures commitments are captured correctly, and it helps your team deliver better continuity between meetings. If a client remains uncomfortable, offer flexibility. Ask if it would help if you paused recording during sensitive segments or provided transcription (no video) only. And if they still say no? Respect it immediately.

A calm response, such as “That’s no problem, I’ll just take notes manually today,” reassures the client that their preferences matter. Consider it a trust-building move and effective communication.

And one “no” is not a “no” forever. Once clients experience clearer summaries and stronger follow-through, their resistance softens naturally. Proof builds comfort.

Increase Client Comfort with Recording

In many cases, a client’s hesitation has less to do with the recording itself and more to do with uncertainty. Easing those uncertainties will help obtain approval. Here are a few considerations:

1. Clarify how the information is used and protected. Recordings should be treated in the same way as other client information, with strict access controls and clear retention policies. Simply explaining that the recording is used internally to document discussions and improve follow-up can alleviate many concerns.

2. Language matters. Some clients may react strongly to the word recording, while they are comfortable with the idea of transcription or meeting notes generated automatically. Emphasizing purpose rather than technology helps to alleviate uncertainty.

3. Connect the practice to the client’s own experience. Comparing the practice to an experience the client has already had, such as a Teams Meeting recordings or Fireflies notetaker, can introduce familiarity and reduce pushback.

The key is to demonstrate that recording is about improving communication and client satisfaction.

The Bigger Shift Happening

Recording client conversations is really about creating value. As AI takes over more routine technical work, the differentiator shifts. Clients aren’t paying for data entry, calculations or compliance alone. They’re paying for judgment, insight and the quality of the conversations that help them make better decisions.

That’s why accountants are paying more attention to conversation intelligence tools. These tools do more than create transcripts. They create visibility into moments that have historically been difficult to capture and even harder to scale.

Where your opportunity lies is in what you do differently now that you use them. You can stay present in a conversation instead of taking notes. You and your teams can follow through more consistently. New team members can understand client history faster. And you can learn from your best client conversations and share those practices more broadly.

Recording is not a radical departure from professional norms. It’s simply another form of documentation. The difference is that today’s technology makes that documentation more complete, more accessible and more useful.

At its core, recordings are about clarity. And as you shift toward advisory services, clarity becomes one of the most valuable assets you can create.

• You have to tell a client their fees are going up

• You need to explain why their bill was higher than expected

• You have to push back on their investment strategy

• You have to remind someone, once again, that you really do need their documents by the deadline

You know you need to make the call. You probably even know what you need to say. But that doesn’t mean you’re excited to pick up the phone.

So what if you could practice it first?

Using an AI Coach

Imagine telling an AI coach: “I need to tell Fred our fees are going up 12% next year, and I need to hold the line if he pushes back on the value.”

Then you tell it who Fred is. Maybe he’s skeptical or price sensitive. Maybe he’s warm but distracted. You can even tell the AI what you’re trying to get better at, like handling objections, asking better questions, explaining your value or actually setting next steps before the call ends.

Then you have the conversation. No, not with the real Fred. (Not yet, at least.) You practice with the AI playing Fred.

And maybe AI-Fred pushes back: “Twelve percent? What exactly am I getting for that?”

Now you have to answer the question instead of just thinking about how you might answer it. You have to explain the value you delivered this year. You have to listen instead of immediately getting defensive. You have to get comfortable sitting in that uncomfortable moment.

Practice, Get Feedback & Try Again.

And when the practice call is over, AI can tell you what actually happened.

Maybe it’s something like:

• You talked 71% of the time

• You never clearly explained the value

• You handled the price objection really well

• You completely forgot to establish the next step

So you practice again and it goes a little better this time.

It may still be a bit uncomfortable when you call Fred, but you’re more prepared, more confident and a lot less likely to panic when he pushes back.

AI Should Make You Better, Not Replace You

That’s a different way of thinking about AI. The goal isn’t to have AI call Fred for you. It isn’t even necessarily to have AI write a perfect script that you read to him. The goal is to use AI to make you better at having the conversation.

We don’t practice this stuff in accounting. We’re really into technical skills and CPE. Most of us learn communication skills on real clients, in real time, with real money and relationships on the line. No wonder we’re nervous!

A Safe Place to Be Bad at Something

AI can give us a safe space to be bad at something before we really need to be good at it. And unlike outsourcing the conversation altogether, practicing it actually builds your skill. You learn what an objection sounds like. You get more comfortable answering difficult questions. You hear yourself overexplaining. You learn to stop talking and listen.

Eventually, you don’t need the nudge telling you what to say next because you’ve developed the muscle yourself.

Practice Before the Stakes Are Real

AI gives us something accountants have never really had before: a place to practice before the stakes are real. So use it.

Before the next uncomfortable client conversation, don’t just rehearse it in your head. Don’t ask AI to write you a script. Have the conversation, let yourself stumble and get the feedback. Then do it again.

You don’t have to focus solely on getting better at using AI, but you do to need to get better at the part of the job your clients will still need you to do.

The traditional timesheet assumes that work happens in clean blocks, but the actual workday does not roll out that way. In fact, according to Laurel’s State of Work Report:

Professionals have, at most, 15-minute periods of uninterrupted focus during business hours before communication requires attention again.

Additionally, a typical day includes more than 110 email interactions, each adding another break in attention and another data fragment.

That fragmentation isn’t just annoying; it literally rewires how work gets done. Deep work, that high-value analysis and problem-solving clients actually pay for, does not thrive in the spaces between pings, chats, meetings and hallway-level requests. It gets pushed out of the core business day and into the edges.​

The report shows exactly that as:

• Nearly half of weekly deep work happens outside standard business hours

• 28.2% of all work takes place outside of 9 to 5

This is all a blaring signal that the official workday is dominated by communication and coordination, while focused value creation gets displaced to early mornings, evenings and weekends.

And to be clear, the big issue is not that professionals suddenly became bad at timekeeping. It’s that firms have spent decades asking people to produce precise data from an imprecise environment, then acting like the output was good enough to run the business.

That’s the elephant in the room. Many leaders don’t want to admit that they’ve managed on incomplete data for a very long time. But now we know better because we have the data to back the claim.

So, the real question then becomes whether firms are willing to first admit the traditional workflow is flawed and then lead differently to correct it.

Interruption Becomes the Default Operating System

From an operational perspective, firms are still managing by timesheets instead of actual work being done, and by the time the reports are assembled, the information is often already stale.

This describes a current broken system where leaders are often relying on lagging indicators and manually pulled reports rather than trusted, timely visibility into what is happening across projects and people.​

That delay matters because the modern workday is dynamic. Fire drills happen. Senior people jump into tasks they should not be doing. Staff get blocked waiting on client information. Quick consults, out-of-scope asks and scattered billable moments happen in between other activities. And none of this fits neatly into the tidy narrative structure the old timekeeping model expects.​

So, when firms ask people to keep accurate timesheets, they aren’t asking for discipline but, rather, for a retroactive reconstruction of a day built on interruption.

That’s why the data is consistently incomplete. And this is not the result of professionals being careless. The problem is that the operating environment makes reliable manual capture structurally unrealistic.

This also explains why so many firms misread the symptoms. They see low realization, uneven utilization or margin pressure, and they assume the root cause is productivity. But if the workday itself is broken, then the numbers flowing out of it will be broken, too.

You can’t build sound economics on top of fractured attention and incomplete reporting.​

What This All Says About Value

There’s a second layer here that firms should not ignore, and that’s what the pattern says about value. If the work requiring the most concentration naturally migrates outside business hours, then the issue is not merely time capture. it’s operating design.

Firms have organized the day around responsiveness while leaving concentrated expertise to fend for itself.​ And while this model may have worked when work was easier to bucket and easier to remember, it doesn’t work so well today.

In a world of constant communication, AI-enabled acceleration and rising pressure on margins, firms need a better way to see how work actually flows. They need data that reflects reality as it happens, not as someone tries to summarize it later.

Until that happens, firms will keep diagnosing the wrong problem. They will keep treating missing time as a compliance issue instead of a design issue. And they will keep asking professionals to report work in a way that no longer matches how professional work is actually performed. The simple fact is that you can’t fix time tracking without fixing how work happens.

Firms can no longer treat missing time like a people problem. When the day is built around interruptions, meetings and reactive communication, of course the data that comes out of it is skewed.

So, no more playing the “blame game.” You can’t pin inaccurate reporting on your team when your current structure makes accurate reporting almost impossible.

There was a small discrepancy in the numbers. Mistakes happen. A human could’ve made the exact same error. But it made me think about the processes that we have to rework when we introduce AI into our firms.

You can’t just add AI to the process you already have and expect everything to work the same way. And I think a lot of firms are going to start learning that the hard way.

When a Human Makes a Mistake, We Ask Why

Think about what happens when a person on your team makes a mistake. You don’t just fix it and move on. You ask what happened. Was something missed during the close? Were the accounts reconciled? What was reviewed before the work went out the door? Most importantly, what needs to change so you don’t have the same problem next month?

That’s basic accounting. But something weird happens when AI enters the equation. We start thinking that because the agent did the work, the work is done. It’s not!

If you’re signing your name to something, you still need to know it’s right. That means someone needs to understand what the AI did, what it didn’t do and how its work was checked before it got to you. And that’s bigger than adding “review AI output” to your existing checklist.

AI Changes the Assumptions Behind the Workflow

Our accounting processes were built around humans. Someone prepares the work. Someone reviews it. We know what each person is responsible for, what they should check and when something needs to be escalated. Over time, we train people to develop enough judgment to recognize when something just doesn’t look right.

Now we’re putting AI agents into those workflows, and that changes the assumptions the workflows were built on. So the process has to change, too.

Firms need to start asking:

• Where does the AI belong?

• What should it be responsible for?

• Where does a human need to step in?

• What does that person need to review?

• How do they know when the AI is wrong?

• If something goes wrong, how do we figure out whether the problem came from the AI, the human or the process itself?

Those aren’t technology questions. They’re workflow and control questions.

Using AI Is Not the Same as Reviewing AI

There’s also a training issue here that I don’t think firms are talking about enough. You can’t teach a young accountant how to use an AI agent and assume you’ve trained them to review its work. Those are completely different skills.

If AI is doing more of the preparation, we have to be even more intentional about teaching people what good work looks like. They need to understand why something should tie out, what should be reconciled and what questions to ask the AI agent when the numbers don’t make sense.

Otherwise, how are they ever going to know when the AI is wrong?

Don’t Drop AI Into Step Four

This is why I think AI implementation is going to require firms to rethink a lot more than their technology stack. We need to rethink our workflows, our review procedures, our training and even what we consider a finished work product.

Don’t take a 20-year-old workflow, drop an AI agent into step four and call yourself an AI-enabled firm. Start over. Look at what the technology can do, where it needs human judgment and what controls need to exist between the two. Then build the process around that reality.

Because whether a mistake comes from a human or an AI almost doesn’t matter. If bad work can make it all the way through your workflow without being caught, you don’t have an AI problem. You have a process problem.

AI didn’t create it. It just made it impossible to ignore.

We’ve spent the last few years teaching accountants how to use AI. That matters. People need to understand the technology, know how to use it responsibly and get comfortable incorporating it into their work. But that cannot be the end goal.

The real question is what accountants need to become because AI exists.

Where Does the Value Move?

As AI takes on more of the work accountants have historically performed, the value doesn’t disappear. It moves. If AI can:

• Summarize the data, the accountant needs to explain what it means

• Generate possible answers, the accountant needs the judgment to know which one makes sense

• Automate more compliance work, the accountant needs to have better conversations about what the client should do next

That means skills we have sometimes treated as “soft” become incredibly important: curiosity, communication, empathy, critical thinking, professional judgment and the ability to challenge someone constructively. Those aren’t nice extras in the Firm of 2030. They’re core accounting skills.

We Have to Rethink How People Learn

Historically, accountants developed judgment by doing the work. They had thousands of repetitions, saw different situations and learned from more experienced people along the way. If AI removes some of those repetitions, we need to be intentional about replacing the learning that came with them.

We can’t automate the work and then wonder why younger professionals aren’t developing experience fast enough. That means rethinking training, giving people client exposure earlier and creating more opportunities to practice judgment, communication and problem solving. AI changes the work and our talent model has to change with it.

That’s the Conversation I Want to Have

That’s why I was excited to help ADP shape its AI Forum. I’ll start the afternoon with Heather Sperduto talking about what the Firm of 2030 needs to become, then join Laci Buzzelli and Tal Goldhamer for a conversation about how we prepare our people for an AI-powered future. Because figuring out which AI tool to buy next is probably the easy part.

Figuring out what kind of firm — and what kind of accountant — we’re building is much harder and much more important.

Register now for the ADP Accountant Connect AI Forum on September 29 from 1–3 p.m. ET, with up to two free CPE credits available.

Join us and be part of the conversation.

Lately, I’ve been trying to be a little more discerning. I think the better question is: What should I give it?

There are things AI can do for you that save time, eliminate work you hate and make you better at your job. Great. Give it those things! But there are also things you could hand over to AI that you probably shouldn’t, especially if they’re the skills that make you valuable.

Think about it like outsourcing. If I’m really good at talking to clients, but terrible at organizing my notes afterward, why would I outsource the client conversation? Let AI organize the notes! Let it summarize the meeting, create the follow-up list and remind me what I promised to send.

I don’t need to become the world’s greatest meeting-note taker, but I absolutely want to keep getting better at the conversation.

Efficiency Isn’t the Only Thing That Matters

That distinction is important. We’ve spent the last few years talking about AI primarily in terms of efficiency. What can it automate? How many hours can it save? How much more work can we get done?

Those are accounting questions, right? Of course, we want to measure the efficiency. But I think there’s another side of the equation we’re missing: What skills are we losing when we automate them?

If you use AI to write every email, eventually you might become a worse writer. If you ask it to analyze every complicated situation before you’ve thought about it yourself, maybe your critical thinking gets a little rusty. If you let it handle every difficult client interaction, how are you going to get better at having difficult conversations?

Don’t Automate Away Your Advantage

AI makes it incredibly easy to avoid practicing things that are hard. And sometimes that’s exactly what we want! I have no desire to practice a skill I don’t need, don’t enjoy and don’t believe is important to my future value. But if it’s something that differentiates me? I want to be very careful about giving that away.

This is especially important for accountants because we tend to start from the technical side. Maybe you’re incredible technically. You can spot something in a tax return nobody else sees. You understand the numbers at a level that makes you invaluable to your clients.

If that’s your superpower, don’t hand the entire thinking process to AI just because you can. Use AI to challenge your thinking. Ask it what you might have missed. Let it be your second set of eyes. Use it as a coach instead of a replacement.

On the other hand, maybe the technical stuff comes naturally, but you struggle to connect with clients.

That’s probably not the skill you can actually outsource, but it’s a skill that AI can help you practice. Role-play the conversation. Ask it to challenge your explanation. Have it tell you where you’re using too much jargon. Then go have the actual conversation yourself.

Decide What Belongs to You

That’s why I don’t think there’s one “right” AI stack for every accountant or every firm. Before you choose the tool, you need to understand what you’re trying to protect.

Ask yourself:

• What am I uniquely good at?

• What do I want to become better at?

• What makes clients want me involved?

• What work do I genuinely not care about mastering?

Then decide what belongs to you and what belongs to AI.

The accountants who win with AI won’t necessarily be the ones who automate the most. They’ll be the ones who know what not to automate.

AI should make you better at your job, not slowly make you less capable of doing it. Use it everywhere you can. Just don’t outsource the parts of you that your clients are actually paying for.

You’ve found $500,000 in untapped opportunities within your client base. Maybe this was through data mining, client whitespace in CRM or even using a new AI tool. Regardless, it’s great to see that potential growth right in front of you.

Before you get too excited, though, how are you going to sell these services? Because finding opportunities and creating revenue are not the same thing.

AI Makes Opportunities Easier to Find

AI tools today have advanced enough to help uncover opportunities buried in tax returns, financial statements and client conversations. They can analyze vast amounts of client data, identify patterns and surface opportunities that might have otherwise gone unnoticed. AI is helping you see where value can be created.

You now have access to more insights about clients than ever before. That visibility matters. You cannot address needs you do not recognize. AI helps you see patterns, but it can also help you ask better questions and approach client relationships with a broader perspective.

This makes cross-selling more proactive, too. You no longer have to wait for clients to raise concerns. You can identify potential issues and opportunities before they become urgent.

However, uncovering an opportunity is only the first step. A recommendation sitting in a dashboard does not create revenue. An alert does not strengthen a client relationship. A report does not create a new engagement. Those outcomes only occur when an advisor successfully helps a client understand the issue, evaluate possible solutions and decide to take action.

The opportunity may begin with technology, but it ultimately succeeds or fails through a conversation.

The Next Challenge is Action

If finding opportunities were the only challenge, firms would already be capturing significantly more revenue from their existing client base. The reality is that many opportunities never progress beyond recognition. Even when you know the client would benefit, the conversation never happens.

The reason is often discomfort. You likely don’t see yourself as a salesperson. The stereotype of the aggressive salesperson pushing unnecessary services feels fundamentally at odds with your role as a trusted advisor. As a result, conversations that feel too much like selling are avoided, regardless of how a service might help a client.

Other times, accountants like you make decisions for clients, assuming the client can’t afford it or the timing is wrong. Rather than allowing the client to evaluate the opportunity and decide for themselves, the conversation is quietly removed from consideration.

There is also the fear of damaging the relationship. Maybe you worry that discussing additional services will make you appear self-serving. Or you fear rejection and interpret a client’s “no” as a threat to the relationship itself. So those opportunities that could genuinely help the client are never discussed.

The challenge becomes even greater when multiple opportunities exist. Maybe you hesitate to bring several ideas to a client at once because it will seem overwhelming or imply the client has been managing their business poorly. Instead of helping clients prioritize opportunities and make informed decisions, many accountants remain silent.

This hesitation isn’t fear. It’s uncertainty. You have spent years learning technical skills, but very little time learning how to lead an advisory conversation. Even if you recognize an opportunity, you may struggle with how to introduce it, how to explain the value or how to respond when a client pushes back. When people don’t know how to navigate a conversation, avoiding it often feels safer than risking getting it wrong.

Ironically, these concerns often prevent firms like yours from delivering the value you are uniquely positioned to provide. Clients engage you because they want your expertise, perspective and guidance. They expect you to identify risks, surface opportunities and recommend solutions. When those conversations do not occur, clients are left to solve important problems on their own.

Expertise Doesn’t Always Scale

As AI continues to identify more opportunities, firms must help more professionals develop the confidence and capability to have those conversations. The challenge is that these skills do not always scale naturally across an organization.

Your firm likely has a few individuals who excel at turning expertise into client action. You know the ones. Those people who simply go meet with a client and come back with more work. They know how to ask the right questions, connect technical issues to business outcomes and help clients make decisions with confidence. What makes them valuable is their ability to apply technical expertise in conversations that create trust, clarity and action.

These skills are difficult to scale. Firms have successfully standardized workflows and strengthened quality control procedures, but what they have not figured out is how to scale the instincts of their best advisors…

• How do you help a newer manager learn how a partner navigated a difficult client discussion?

• How do you capture the questions that uncover deeper business issues?

• How do you teach someone to recognize the moment a client is ready for a larger strategic conversation?

Historically, these skills have been transferred through observation, mentorship and experience. While those methods remain valuable, they are difficult to replicate consistently across an entire organization.

As firms expand advisory services and AI surfaces more opportunities, the ability to scale relationship-building and advisory skills becomes increasingly important. Future growth cannot depend on a small group of rainmakers. It requires more professionals across the firm to develop the confidence and capability to lead meaningful client conversations.

The Missing Layer in the AI Conversation

Much of today’s AI innovation focuses on helping firms identify opportunities. That is important work, and the profession is benefiting from better visibility than ever before. However, there is still a challenge around helping professionals navigate the conversations that follow.

Finding an opportunity is only the beginning. You still need to understand how clients respond to recommendations, which questions create engagement, where conversations lose momentum and what differentiates a productive advisory discussion from one that goes nowhere.

This is where a new category of AI is beginning to emerge. Rather than focusing exclusively on identifying opportunities, these tools help you understand and improve the human side of advisory. They help you learn from experienced advisors, identify coaching opportunities, strengthen communication skills and create greater consistency in how client conversations are approached across the firm. In other words, they focus on the conversation itself.

Many of the factors that drive growth, trust and client loyalty happen inside conversations. Some AI is helping you understand those interactions, develop stronger advisors and scale the relationship-building skills that have traditionally lived in the heads of a few top performers.

Translate Insight Into Action

AI is helping identify client cross-selling opportunities. But the next challenge is helping professionals confidently act on them.

Firms that solve this challenge will generate more revenue, create stronger client relationships, develop better advisors, and build organizations that can scale expertise beyond a handful of rainmakers.

Finding $500,000 in new opportunity is impressive. Helping your people turn it into action is where the real value lies.

Accountants understand something that accounting startup culture often doesn’t: relationships that look unproductive today can become adoption a year from now.

I know, I spend a lot of time talking about how slowly accountants adopt technology. They want to see what everyone else is doing before they’re willing to try something new. Sometimes they’ll complain about a problem for three years, build an absolutely unhinged Excel workaround for it, and still need six more months before they’re ready to buy the thing that solves it.

I think we’ve gotten so used to blaming accountants for moving slowly that we’ve stopped asking whether the startups trying to change accounting are actually giving the profession enough time to change.

Accounting Runs on a Different Clock

It takes roughly two years to sell into accounting firms. That’s an eternity in startup land.

Someone hears about your company today. Six months later, they see you at a conference. Then someone they trust mentions your product. They start seeing your name more often. Maybe they have a conversation with you. Maybe they don’t. Then busy season happens. Then budget becomes an issue. Then another implementation takes priority.

Eventually, a year or two after they first heard your name, the problem, timing and trust all line up and they buy.

Trust takes time and I don’t think most accounting technology companies are built to wait for it.

Maybe Accountants Know Something Startups Don’t

Maybe that’s part of why I’m so interested in seeing accountants build technology for accountants. They already understand things about this market that outsiders can spend years trying to learn.

They know January is a terrible time to ask a firm to implement something. They know a conversation that doesn’t end in a demo isn’t necessarily a failure. The person who has been answering questions without trying to sell you something matters. The accountant who tells five other accountants, “I actually use this and it works,” matters even more.

The ROI Might Show Up Later

I recently saw the long-tail results of work I did with an accounting technology company. A year later, that influence is on track to produce the equivalent of roughly a 3.4% increase in active users.

At 30 days, there probably wasn’t much to show. At 90 days, probably still not enough. Six months in, someone could have looked at the numbers and reasonably decided it wasn’t working.

Except it was working. We just couldn’t see it yet.

What If You Just Stopped Too Soon?

That’s where I think startup culture and accounting culture collide.

A company can spend months showing up, creating useful content, building relationships and becoming trusted in the profession without having enough immediate revenue to prove that any of it is working.

And startups aren’t famous for waiting around.

So the strategy changes. Leadership changes. Messaging changes. Partnerships change. The people accountants were starting to recognize disappear. A year of relationship-building gets filed under “we tried that,” and everyone moves on.

But did you try it? Or did you stop before the accountants you were trying to reach had enough time to respond?

It Takes Patience

That’s what I think we miss when we talk about accounting being slow to innovate. We assume the timeline itself is evidence of a problem. Sometimes it is. There are absolutely firms using “we’ve always done it this way” as an excuse to avoid making decisions they should have made five years ago.

But taking time to trust a technology company isn’t inherently anti-innovation. Especially when accountants have watched plenty of companies enter the profession loudly, promise to change everything, and disappear a few years later.

If you’re asking a firm to move important parts of its business onto your technology, sticking around long enough to become trusted seems like a reasonable part of the deal.

Maybe the accountants aren’t moving too slowly. Maybe startups are moving on before they’re ready to move with you.

Without visibility into how work actually gets done, every AI investment is a bet, not a decision.

That was the thread running through our recent webinar with Eric Zaarour, Co-founder at Laurel, and Tom Barry, Managing Partner at GHJ and North America Chair at HLB International. The conversation kept returning to the same place: you cannot improve what you cannot see.

The Paradox Hiding in Plain Sight

Firms have never been more profitable, and they have also never been more confused about why.

Over the last five years, the traditional KPIs used to measure performance, charge hours, utilization rates, hours worked per person, have been trending down. At the same time, revenue is growing and clients are being served. The metrics that were supposed to explain firm health are telling a different story than the P&L.

A professional still works eight hours a day, but with better tooling, more gets done within those same hours, and the input, measured in hours, no longer reflects the output being delivered. Capacity increases without any corresponding increase in recorded time, and the traditional metrics have no way of capturing that shift.

The instinct many firm leaders have is to push for more hours. If we get five more billable hours out of each person, profits go up, and mathematically that is true. But it is also the wrong question. The right question is what is actually happening inside those hours, and whether leadership has the visibility to know.

The Gap Between Assumption and Reality

Most firms are operating on estimates about what their work costs to deliver, and the gap between those estimates and reality tends to be larger than anyone expects when they finally see it.

The pattern is consistent across firms: what leadership assumes takes six hours routinely takes ten, and that gap is where margin compression lives. It is almost always more significant than firms predict when they finally have the data to see it clearly.

Part of what makes this hard to diagnose is that the drift in efficiency is not uniform. High performers complete work at a different pace than junior associates, and firms that build margin models assuming average performance across a service line are quietly underestimating their costs. The data, when it finally surfaces, tends to show that those assumptions were optimistic across the board.

When timekeeping is inaccurate, everything built on top of it is inaccurate too. Without objective visibility into what is actually happening at the task level, recovery rates are guesses, realization is guesswork, and any conversation about AI-driven efficiency has no baseline to measure against.

Why AI Makes This More Urgent, Not Less

AI accelerates the work that was already captured, but the work that was never recorded stays invisible regardless of what tools a firm deploys.

When a firm uses AI to speed up drafting, research, or document review, it is making captured work faster. It is not surfacing the work that was never recorded in the first place. And if that unrecorded work contains the highest-leverage activities, the partner judgment calls, the nuanced client conversations, the reasoning clients are actually paying for, then the AI strategy has no way of knowing where to focus.

Firms need to be intentional about understanding what they are doing, because time is the largest asset the firm holds, and timekeeping, done right, is the foundation for every downstream decision that matters: pricing, staffing, AI investment.

Think about how you treat your health. When a doctor gives you data showing where something is wrong, you act on it because the data makes the path forward clear. The problem in professional services has never been a lack of willingness to act. It has been a lack of ability to observe, and the firms that close that gap will be the ones making decisions grounded in evidence while everyone else is making educated guesses.

The Question Worth Taking Back to Your Firm

AI is not arriving. It is already inside your firm, changing how work gets done faster than most leadership teams can track, and the firms that navigate this well will not be the ones that deploy the most tools.

They will be the ones that understand their own work clearly enough to know which tools to deploy, which activities to protect, and which decisions actually require the kind of human judgment that cannot be automated. That clarity requires visibility, and visibility requires capturing work as it actually happens rather than as people remember it.

If you don’t know what your work actually costs to deliver today, you don’t have a foundation to price it, staff it, or improve it tomorrow.

It’s a fair concern, but it always makes me wonder if we’re trying to preserve the wrong part of the profession.

Doing the Work Isn’t the Same as Learning the Work

I spent five years in tax compliance before going back for my master’s in taxation. Looking back, I don’t think I learned tax by preparing hundreds of returns. I learned tax when I started understanding why the rules worked and when I could see how changing one fact changed the answer. The thing that helped me the most was listening to experienced professionals explain how they were thinking, not just what they were doing. Those are the things that made everything click.

On the other hand, I’ve also met people who came into the profession with advanced tax degrees, but very little real-world experience. They understood tax code, but they hadn’t learned how to apply that knowledge when a client couldn’t answer every question, when records were incomplete, when two good options existed or when the “right” answer on paper wasn’t necessarily the best business decision.

My path had the opposite problem. I had seen hundreds of returns before I truly understood the theory behind them. I could complete the work, but it took much longer to develop the deeper understanding of why one approach was better than another.

Neither path was complete because one emphasized doing, while the other emphasized knowing. Great advisors need both.

Compliance Was the Exercise, Not the Lesson

That’s why I wonder why we keep framing AI as a threat to learning because it reduces manual compliance work. Was compliance ever the thing we were actually trying to teach? Or was it simply the way we got exposure?

The value wasn’t typing numbers into tax software. It was:

• Seeing hundreds of different client situations

• Recognizing patterns across those situations

• Watching experienced advisors work through ambiguity

• Hearing the questions they asked

• Understanding why one answer made more sense than another

If the goal has always been to develop professionals who can think critically, advise confidently and solve complex client problems, then perhaps this is our opportunity to redesign how we teach those skills instead of assuming they’ll emerge after enough years of repetitive work.

Instead of asking, “How do we make sure junior accountants still prepare enough tax returns?” maybe we should be asking, “How do we help them develop judgment faster?”

AI Can Be More Than an Autopilot

What if AI didn’t just prepare the return, but it also:

• Explained why it classified something a certain way?

• Highlighted the assumptions it made and alternatives it considered?

• Showed how changing one fact changes the outcome?

• Explained why certain language in a client email builds trust?

• Surfaced patterns across hundreds of engagements so junior staff can learn to recognize them, too?

What if it became less like an autopilot and more like a mentor sitting beside you?

Protect the Skills Clients Actually Pay For

The skills that create good advisors include critical thinking, pattern recognition, strategic decision-making, client communication and professional judgment.

Compliance has always been one way to develop those skills. AI gives us the opportunity to separate the lesson from the exercise.

The real goal has never been to create accountants who are great at compliance. It’s to create accountants who can use what they know to help clients make better decisions. If AI can take care of more of the mechanics, our responsibility isn’t to recreate the old apprenticeship model just so the next generation learns the way we did. It’s to build a better one.

That means intentionally designing tools and training that develop the skills clients actually pay for: judgment, critical thinking, communication and the ability to turn knowledge into advice.

We don’t need to protect the old work. We need to protect what the old work was supposed to teach us.

If you’ve ever subscribed to a meal kit service, you’ve probably had this problem. As you’re browsing websites for the most appetizing option, all of them start to look the same after a while. Everyone is claiming that their particular kit “solves dinner”. Everyone is promising fresh ingredients delivered to your door. Everyone is boasting about step-by-step recipes that are quick to prepare. Nobody will ever want to order takeout again.

In reality, there are differences between kits. But these differences usually aren’t obvious until after you’ve redeemed that 20% off discount code on your first order. It’s only then you’ll discover bruised produce and missing ingredients in your kit—provided it even shows up in the first place. At that point, you might find yourself back on their website wondering what you could’ve possibly missed.

Although it can make selection more complicated, seeing such similar product promises across providers isn’t necessarily a bad thing. In fact, it’s a sign of maturity. Well, category maturity. When a category reaches maturity, baseline features become table stakes and the real differences move below the surface. It’s this exact market dynamic that we’re now seeing in AI-powered tax research.

Mature categories, murky buying signals

A few years back, the conversation around AI-powered tax research was focused on whether or not the tech actually worked. Today, that’s no longer a question. Instead, firms are focused on figuring out which solution will work best for them. You’d think that getting past those early AI uncertainties would make the evaluation process easier. But here’s why it feels even more challenging now:

• When a technology is new, standout product features are easier to spot

• Buyers can rely on more obvious signals like speed, efficiency, and whether the tool has been purpose-built for tax

• But as more products enter the category, every vendor starts claiming to offer the same baseline benefits

• Eventually, feature lists converge and demos are polished to perfection across the board, making them almost impossible to tell apart

It’s then that we end up with the same indecision issue as choosing a meal kit—just somewhat less appetizing. Buyers see AI-powered tax research tools all claiming to do similar things, and they’re left wondering which tool can really deliver. To spot those key differences, they need to try the tools themselves.

Of course, it helps to know what you’re looking for when trying out a new tax research tool. With a solid evaluation framework in-hand, you’ll be able to separate meaningful features from marketing hype once you’re in the product. A good place to start is separating baseline expectations from what actually differentiates these tools.

Efficiency and speed are becoming table stakes

The central promise of AI is increased efficiency. Beyond all the marketing hype and ever-changing buzzwords, that’s what the value of AI often comes down to. AI-powered tax research solutions are no different. If an AI-powered tool can’t save you time, it shouldn’t even be in your consideration set.

With that out of the way, there are still plenty of factors that set one solution apart from another. The most effective solutions pair a smooth user experience with comprehensive, defensible answers. This combination is critical because it takes away the frustration of repeat prompting and gives practitioners a strong foundation for layering in their own expertise. Getting faster answers doesn’t mean much, unless you’re getting to the right answer and getting there easily.

But now, let’s imagine there are two distinct AI-powered tax research tools that are both promising this elusive combination of ease of use and accuracy. How can you tell which you should choose? Here’s what you might want to ask to see how well reality maps to marketing claims:

Answer quality as the defining differentiator

When it comes to whether or not AI improves the accuracy of tax research, the findings are split. In the AI Tax Research Solution Outlook Report, half of respondents (51%) agreed that it does, with the rest less sure. In many ways, this is unsurprising. With answer quality a key differentiator between research tools, getting answers you can trust isn’t a given. But that trust is essential.

Because it’s only when you’re consistently getting answers you can rely on that you can realize the true benefits of time saved using AI. More time for the strategic consultative work that’s billed at a premium. Anything less than consistently high-quality, defensible answers, and all those time savings go right back into correcting the AI’s work.

When boutique accounting firm R2 Advisors was re-evaluating its tax research solution, the team prioritized a particular kind of answer quality. They wanted a tool that was capable of delivering scenario-based analysis backed by reliable, citable sources. In other words, they wanted a tool that could support complex advisory work.

“Speed was important, of course, but we were never willing to sacrifice accuracy in the name of speed,” says Rose Araghchy, CPA, founder and president of R2 Advisors. “Trust is everything with our clients.”

According to Araghchy, the shortcomings of most tax research tools really show up when you start engaging in sophisticated scenarios with clients. “You start to realize that the tech you’re working with isn’t just slowing you down—it’s ultimately preventing you from doing your best work.”

But with the right tool, whole new opportunities can open up for your practice.

Bring clarity to your AI tax research vendor shortlist

Searching for the right AI-powered tax research tool isn’t easy. As this category continues to mature, it’s only going to get harder. But that doesn’t mean you’re bound to get stuck with bruised produce or missing ingredients. You just need the right tool for selecting a tax research tool.

Instead of sifting through another glossy feature list or sitting through another canned product demo, try the tool that can actually help you make a decision. Learn how to critically evaluate AI-powered tax research solutions side by side by downloading your Buying Guide for AI-Powered Tax Research Solutions.

They happen when…

• Someone stops by your office with a quick question.

• After a client meeting when a new staff accountant asks why you handled something a certain way.

• During a return review or in the five minutes between Zoom calls.

Most coaching happens in the moment.

The problem is those moments are easy to miss. Someone asks a question, and our instinct is to answer it as quickly as possible. We have deadlines, clients and a dozen other things competing for our attention.

But if every conversation ends with us providing the answer, we’re solving today’s problem without preparing someone for tomorrow’s.

As you move into leadership, your value isn’t measured only by what you know. It’s measured by how well you help other people learn what you know. If you’re the only person who can handle a difficult client conversation, explain a complex issue or make the tough decisions, you’ve become indispensable in the wrong way.

The best leaders aren’t the ones with all the answers. They’re the ones who develop more people who can find them.

Leadership Is Measured by What You Leave Behind

That feels especially important right now. As experienced professionals retire and AI takes over more routine work, the most valuable thing you have isn’t just technical knowledge. It’s judgment. It’s perspective. It’s the lessons that only come from years of working with clients.

Those skills don’t get passed down through a training manual. They get passed down through conversations.

The strongest coaching conversations start with curiosity. Instead of immediately explaining what they would do, great coaches ask questions. They help someone think through the problem, understand the “why,” and build confidence in their own judgment.

The goal isn’t to make someone dependent on your expertise. It’s to help them develop their own.

Coach Thinking, Not Just Answers

Of course, coaching is one of the hardest leadership skills to improve because it’s difficult to evaluate yourself. Did you listen enough? Did you ask thoughtful questions? Did the other person leave with greater confidence, or just a list of instructions?

Like every important conversation, coaching has a rhythm. The more intentional you become about how you show up in those everyday moments, the more capable your team becomes.

The conversations that shape someone’s career — and the future of your firm — rarely begin with a meeting invite. They usually begin with, “Do you have a minute?”

Timesheets are more of a reconstruction… a story told after the fact by busy professionals trying to remember what happened in a day already packed with interruptions, client demands, internal coordination and deep work squeezed into the margins.

And that’s the core problem. Firms are not making decisions on objective operational data; they’re making decisions on incomplete, self-reported data, and then acting as though it’s precise.

The Data Speaks Volumes…of Truth

Leaders are making decisions based on bad data, and the result is that pricing, utilization, staffing and performance conversations are all built on a shaky foundation.

In fact, in the Laurel State of Work Report, about 30% of work time goes unsubmitted, which equates to about three hours per day. On average, professionals work 55 hours but only submit 39.5.

In other words, the average timesheet does not document the full workday. It documents the part someone remembers, enters and decides is worth reporting.

That distinction matters more than most firms want to admit. Timesheets do not measure work; they measure memory, judgment, bias, and sometimes, self-protection. In some cases, timesheets are more about:

• What story do I want to tell today?

• I worked long hours…I’m awesome!

• I worked less hours and accomplished just as much…I’m awesome!

This is not a character flaw, but a structural flaw in the system itself.​

Memory is the Worst Strategy

When a measurement system relies on people recalling fragmented work after the fact, the resulting data will always be distorted. Quick email responses between meetings, brief client calls, ad hoc problem solving, review comments and scattered coordination work are exactly the kinds of activities that get lost. Yet those moments are often what keep projects moving, clients reassured and teams functioning.

Utilization isn’t the problem, visibility is.

Firms have spent years debating billable hours, realization and efficiency as though the underlying data were complete. In actuality, manual approaches capture less than three quarters of real, true work activity. That means many firms are optimizing around a subset of reality and calling it management.

The consequences form a ripple effect:

• If hours are underreported, project profitability is skewed

• If invisible work is ignored, staffing models are wrong

• If relationship-building time is minimized because it’s harder to capture cleanly, firms underweight some of the very work clients value most

At the end of the day, when leaders evaluate people using incomplete records, they may end up rewarding the “neatest” submitter rather than the highest-value contributor.

Getting to the Truth

What makes this moment different is that firms no longer have to accept the fog. The future is one where leaders can move from instinct and lagging reports to visibility-backed decisions.

Instead of waiting weeks or months for manually assembled reports, they can begin to see patterns in real time, like hidden write-offs, mismatched staffing, unreleased time and work happening outside the assumptions built into the traditional firm model.

That shift changes the conversation.

Once firms can see actual work, they can ask better questions:

• Are people underreporting because they are trying to look efficient?

• Are senior people doing work that should be delegated?

• Are fixed-fee engagements quietly absorbing time that never gets surfaced?

• Are operational decisions being made from the wrong baseline altogether?

This is an argument for more honest accountability. Firms can’t improve what they can’t see, and they can’t price accurately, coach effectively or deploy talent well when the data source itself is compromised.

The real opportunity is more than replacing one tracking method with another. It’s to get to a place of managing the business based on reality instead of reconstruction. Once firms accept that timesheets are fiction dressed up as precision, they can start building pricing, performance and client service strategies on something far more valuable: Truth … with a capital T!

The other day, my daughter texted me what I thought was a simple question about retirement planning.

As a tax professional, I did what came naturally: I launched into an explanation. I answered the technical question she asked, explained how Roth IRAs work and felt pretty confident I’d covered it.

She replied with one emoji.

🫠

It’s the one that looks like a melting smiley face.

If you know, you know. And I did not. She said it basically translates to, “My brain has melted.”

It made me laugh, but it also made me stop and think.

I’d answered her question accurately, but I hadn’t answered the question she was really asking.

As accountants, we’re trained to explain how things work. We know the legislation, the thresholds, the rules, and the tax implications. So, when someone asks us a technical question, our instinct is usually to explain the mechanics. But our clients aren’t approaching those conversations the way we are.

To us, retirement planning is a financial product. To them, it’s wrapped up in questions like, Am I already too far behind? Am I doing enough? What if I get this wrong? Will I ever actually be able to require?

Those aren’t technical questions. They’re emotional ones.

And if we answer an emotional question with a technical explanation, we shouldn’t be surprised when the response is a melting smiley face emoji.

Instead of leading with “Here’s how pensions work,” try leading with “You’re not behind just because you’re thinking about this now.”

Instead of listing contribution allowances, start by reassuring them that retirement planning isn’t about making one perfect decision today. It’s about taking the next sensible step.

Instead of explaining every investment option available, ask them what they’re hoping retirement actually looks like. Do they want to stop working completely? Reduce their hours? Travel? Spend more time with family? That conversation tells you far more than jumping straight into the mechanics ever will.

Of course, the technical advice still matters. But people are much more likely to absorb it once you’ve addressed the worry sitting underneath their question.

If we jump straight into explaining the rules, we’ve skipped the most important part of the conversation.

The accounting profession is filled with opportunity. Clients are asking for more guidance. Firms want to grow advisory services. And AI is automating parts of the work that used to take hours, creating more room for higher-value conversations with clients. That’s exciting!

But inside many firms, it’s the same people who keep leading the hardest client conversations. The same managers who keep reviewing the work. The same partners who keep building the relationships that drive growth. Firms can operate like that for a while. Until one day, they can’t.

If this sounds familiar, it’s time to invest in the next generation of professionals who can turn everyday conversations into revenue. Automation is part of the equation, but continuously improving the conversations that create value is the other.

AI Changed the Learning Model

For a long time, accounting developed people through proximity and repetition. You sat near experienced professionals. You listened in on client calls. You absorbed how strong professionals thought, communicated and made decisions. You were also doing the work yourself. Drafting, researching and revising, over and over again. That combination built professional judgment.

It wasn’t a formal leadership program. It simply took time. Your professionals learned the technical work first and built confidence through repetition. Only later were they expected to lead advisory conversations. Unfortunately, we don’t have that runway anymore.

The analysis and research that once filled the early years of a career are increasingly automated. The repetitive tasks are disappearing, and those reps were how professionals learned to recognize patterns, ask better questions and understand what clients actually needed.

So, what do we do now?

We use the tools in front of us. AI can continue to summarize, draft, organize and analyze. In many ways, it can even help speed up foundational knowledge. It gives professionals access to more information and more technical insight in less time.

But faster access to information is not the same as judgment.

Judgment doesn’t improve because someone attends another webinar. It improves one conversation at a time through feedback, reflection and practice. In an AI-first profession, learning has to become continuous instead of occasional.

Because at some point, someone still has to sit across from a worried client and know exactly what to say. Someone still has to interpret the numbers and guide a decision. Someone still has to coach a junior professional through a hard conversation or lead a team through uncertainty.

Those capabilities require human judgment and skill. And as routine work becomes more automated, those human capabilities become more valuable.

That’s why firms are already rethinking how they develop their people. Because if they don’t, they may find themselves with efficient processes and not enough professionals ready to lead the conversations that actually grow the business.

Firms That Grow Will Develop People Differently

Most firms would say they invest in their people. And they do, through annual training events, webinars and the occasional offsite. But that kind of development is disconnected from the actual work. It happens once a year, it isn’t reinforced and there’s rarely a targeted way to measure whether real advisory capability is improving.

The result is a ceiling. Advisory conversations stay concentrated at the partner level. Team members complete the technical work but do not move the conversation forward. Opportunities go unaddressed because the team is not prepared to act on them. Growth becomes dependent on a small group of people. And that just doesn’t scale.

You need to break that pattern. It’s time to build development into the daily flow of work, to create earlier exposure to client conversations and to provide feedback on communication while it is happening. Don’t think of this as annual training. It’s continuous conversation improvement. Every client meeting, pricing discussion, coaching conversation and difficult phone call becomes an opportunity to help professionals get a little better than they were yesterday.

You will need to build this skillset intentionally to get to stronger client relationships and expanded advisory capacity across the organization. Make sure your professionals are ready to lead when the time comes.

Where Navi Fits

This is exactly what Navi was built for. And it’s worth being clear about what makes it different from every other tool in the stack.

Navi isn’t a workflow tool. It doesn’t automate tasks or speed up compliance work. It grows the professional. Specifically, Navi creates a continuous conversation improvement loop. It analyzes real client conversations, identifies coaching opportunities, highlights strengths, surfaces missed opportunities and helps professionals improve before the next conversation, not six months later during an annual review. That is the kind of skill development that most technology doesn’t touch at all.

With Navi, you and your team members can get specific, timely feedback on real conversations rather than waiting for an annual review. Coaching becomes something the whole organization can access. Skill progression becomes visible, measurable and actionable over time.

None of that replaces the experienced professionals in your firm. Their judgment still matters enormously. Navi just helps you scale what they bring to the table so more people can develop faster.

The Business Decision

Every firm is making a choice right now, whether they realize it or not. They are choosing what kind of organization they want to build in the AI era.

AI can help firms identify opportunities, but opportunity only creates value when someone knows how to have the conversation. That’s why firms need to build systems that continuously improve the conversations that drive revenue, relationships and the people who create both.

In the AI era, people aren’t just part of the strategy. They are the strategy.

We’ll write the email. We’ll summarize the meeting. We’ll spot the opportunity. We’ll draft the proposal. We’ll tell you what to say next.

There’s nothing inherently wrong with that. In fact, lots of firms’ day-to-day tasks can and should be automated. AI tools can save time, reduce repetitive work, and make it easier to spot things that might otherwise be missed.

But we can’t be afraid to pause and ask an important question:

Are these tools actually making accountants better?

AI Should Make Us Better, Not More Dependent

There’s a fundamental difference between helping someone complete a task and helping them build a skill. One creates efficiency in the moment, but the other creates confidence that lasts long after the software is closed.

Take client conversations. Today, AI can analyze meeting transcripts and surface opportunities that were missed during the call.

While we’re focused on other aspects of the conversations, our AI notetaker might flag that a client offhandedly mentioned cashflow issues or that a business owner said they’re thinking about selling in the next few years.

That’s valuable because it helps us identify opportunities for advisory work. But by the end of it, the accountant still can’t recognize those signals in an in-person conversation.

The software has found the opportunity and the person hasn’t learned anything. Next week, they’ll probably miss it again.

Efficiency Isn’t the Same as Capability

Even worse, AI allows people to stop practicing the very skills that created the opportunities AI flagged in the first place.

An accountant might intuitively ask thoughtful follow-up questions that encourage a client to open up, without ever realizing that’s what made the conversation so valuable. If they start relying on AI to spot opportunities instead of staying curious themselves, those conversations may become shallower, and eventually, there may be fewer opportunities for AI to find at all.

The challenge with much of today’s AI landscape is that it focuses on replacing judgment instead of developing it.

What If Every Recommendation Became a Lesson?

Imagine if every recommendation became a lesson instead of simply an answer. Instead of saying, “This client may need bookkeeping support,” the software could explain why that sentence was significant. What words indicated uncertainty? Why is that often a buying signal? What follow-up questions could’ve uncovered more? How could the conversation have naturally progressed from that moment?

Over time, the accountant wouldn’t just have a growing list of opportunities. They’d have a growing understanding of how to identify those opportunities themselves. That’s a very different outcome.

Accounting has never been about perfectly completing a checklist. The value has always come from interpreting what clients are really saying, asking thoughtful questions and knowing when to explore something further. Those are learned skills and they don’t develop when AI just gives you the right answer.

The same principle applies across the profession:

• If AI writers every difficult email, when do we become better communicators?

• If AI decides every next step in a client relationship, when do we become better advisors?

• If AI identifies every commercial opportunity after the meeting has ended, when do we learn to recognize those moments while we’re actually sitting across the table?

Are We Automating Away the Skills We Need?

The most exciting future for AI in accounting isn’t one where software quietly handles more and more of the profession while we become passive operators. It’s one where AI acts like a coach sitting beside us, helping us understand why something matters instead of simply telling us that it matters.

The best technology doesn’t just make today’s work easier. It leaves you more capable tomorrow than you were yesterday.

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.

I mean the actual experience of being a client, not their opinion of your tax expertise or if they get along with the members of your team that they communicate with the most.

Would they all describe the same communication style? The same responsiveness? The same level of proactive guidance?

Or would it sound like they were working with five different firms?

As firms grow, that’s a question leaders have to consider more and more.

Growth Creates More Versions of Your Firm

When a firm is small, consistency happens naturally. Everyone learns from the same leaders, works alongside the same team members and has a pretty clear understanding of how client relationships should be handled.

Growth changes that.

New managers bring different communication styles. New hires arrive with different experiences and expectations. Acquisitions introduce entirely new ways of serving clients. Before long, people across the organization may be working toward the same goals while approaching them in very different ways.

Some variation is healthy. Different personalities, perspectives and strengths are part of what makes a team effective.

The challenge is figuring out where variation adds value and where it creates confusion.

Clients should be able to trust that they’ll receive the same level of care regardless of who they’re working with.

That kind of consistency comes from creating more shared habits within your team.

Consistency Isn’t Created by a Process Document

A lot of firms assume consistency comes from documenting expectations. “We told people how to communicate.” “We covered that during onboarding.” “We have a written process for that.”

Actual consistency is built through repetition, coaching, observation and reinforcement over time.

That’s why growing firms should regularly ask a few important questions:

• Does every employee know when to escalate a difficult client situation?

• Would every manager coach a team member through the same type of challenge in a similar way?

• Does every client receive the same level of communication?

• Do all of your employees know what great client service actually looks like inside your firm?

If the answer to any of those questions is no, it shows you where growth is creating variation.

Find What Works & Make It Repeatable

You can’t force everyone into the same mold, but you can get more intentional about what matters most in your firm.

What are the behaviors, communication habits and client service standards that define your firm? What do your best managers do that others could learn from? What does a great client interaction actually look like in practice?

Those answers can’t live exclusively inside the heads of your leadership team. They have to be talked about. Modeled. Reinforced. Repeated.

That might mean reviewing client communication together and discussing what worked well. It might mean giving managers more opportunities to share how they handle difficult situations. It might mean creating clearer expectations around responsiveness, escalation or client service.

Most importantly, it means paying attention to the places where your firm is already getting it right.

Every organization has people who consistently build trust, communicate clearly and create great client experiences. The goal isn’t to clone those individuals. It’s to understand what they’re doing and make those behaviors easier for everyone else to learn.

Your Clients Shouldn’t Have to Get Lucky

As your firm grows, you’re going to have more people, more clients and more ways of getting the work done. But growth shouldn’t mean creating more versions of your firm.

The firms that scale successfully will be the ones that figure out what makes their best client relationships work and intentionally teach, coach and reinforce those behaviors across the organization.

Because your clients shouldn’t have to get lucky and work with the right person to have a great experience.

They should know what it means to work with your firm.