Just about every industry in America has been rocked by the Great Resignation, but as any CPA will tell you, staffing shortages are nothing new for most accounting firms. In a 2021 survey by the American Institute of Certified Public Accountants (AICPA), staffing topped the list of concerns for firms of all sizes and has been a top concern since at least 2015. Obviously, the pandemic and ensuing burnout have exacerbated matters, but there are industry-specific issues that make its impact on accounting a different case.

From stagnant wages to stressful tax seasons, the industry as a whole isn’t doing enough to entice younger generations to pursue a career in accounting or to convince seasoned professionals to stick around. While there is plenty of technology available to help firms begin to fill these gaps, accountants have historically moved slowly when it comes to adopting new methods, in part due to the various laws, regulations and tax codes that change nearly every year, making implementation of any new tech tricky at best. Another frequent barrier to would-be adopters is the specialized and ever-evolving jargon of these technologies.

As the founder of an artificial intelligence (AI) bookkeeping software company, I often find that clients’ hesitancy to adopt new technology is mostly due to a lack of understanding of how these developments work generally and how they can support their efforts specifically. Accountants need to understand the technology that’s being marketed to them and the language used to discuss these features in order to ask the right questions and make informed decisions on behalf of their firm—the future of the industry depends on it. Let’s dig in.

A Brief History of Software Development

In order to understand the technology behind AI and why it’s useful for accounting, it’s important to review how we developed AI by exploring the iterations of technology that came before. 

Generally speaking, software development began as an effort to make manual processes faster and easier to replicate. A lot of first-generation softwares were built to aid a human user in doing specific things, not to do those things on their own on behalf of the user. From a labor standpoint, this was still a huge improvement on the old model and provided a measurable jump in the efficiency of storage and accessibility. Once those early softwares came around, we were largely able to say goodbye to shuffling papers and rifling through file cabinets.

Although it was impressive at the time, challenges quickly arose because one software was great at one task and another software great at another task, but neither could be used in place of the other. Suddenly, we had a bunch of siloed applications and people had to utilize multiple apps at a time to accomplish a single task. In the accounting sector, that meant you had an app to pay bills, an app to invoice clients, an app for task management, and so on. 

“New technology can be incredibly intimidating, but don’t let that stop you from bringing your firm into the future.”

– Enrico Palmerino


To solve for this, developers built systems that allowed you to move data between unrelated parties, making the data more accessible. This allowed us to skip the step of manually duplicating information across systems, a process that took more time and was obviously susceptible to human error. This was the first stage of real automation, which we call robotic process automation (RPA).

Then developers started to think, “Can we program scripts and softwares to replicate what the human is doing in the software?” So, for example, let’s say a lead comes in and the human takes the email address, copies it, types a message and sends it. Developers asked, “Is it possible to build a script that tells the computer to grab this email address, put it in this other email, type a message and shoot it off in a more automated fashion?” Obviously, it was possible, which was great, but at the end of the day, these softwares were designed to work the way that humans work. The downside to this approach is that every time a tool or an interface changes, the technology breaks down.

Here’s an example I like to use: Let’s imagine we’ve tasked a robot to put dishes in the kitchen sink. With RPA, we can tell the robot, “Go forward, take a left, turn right and drop the dishes in the kitchen sink.” But what happens if the kitchen sink moves? You or I, as a human, would understand that the sink moved and adjust accordingly. But the robot would drop the dishes on the ground or fail and do nothing. That’s RPA. And in an ideal world, if one thing changes, the script breaks, and nothing happens. Unfortunately, what tends to happen is one thing changes and the script still executes, but now it’s executing a mistake. When that happens, you have to find all the mistakes, stop them from happening and undo the damage which can be a nightmare. 

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Artificial Intelligence: The Next Frontier

This is where machine learning (ML) and AI came in. Once again, developers aimed to mimic the human user, but decided, this time, they weren’t going to mimic specific actions, but rather mimic the human user’s logic. So, going back to our example, how do we mimic the way a human understands that the sink moved? Well, it starts by understanding what the hell a sink is.

The way we typically learn as humans—and I’m oversimplifying things here—is by seeing lots of examples of different sinks. Even if they’re made from different materials, of various shapes and sizes, in all sorts of contexts, you and I would still recognize the core attributes of a sink. So instead of telling the program, “Drop the dishes at X location,” developers taught the software how to identify a sink so that even if it comes across a sink in an unfamiliar context, the software will still execute properly. Not only that, but it will continue to learn on its own, evolving and continuing to improve over time. 

But what does this mean for accounting? It means that tasks like bookkeeping, forecasting, payroll and many more can be largely automated, freeing up your time to focus on other tasks, like communicating with your clients, that robots will never be able to do.   

The Future of Accounting

New technology can be incredibly intimidating, but don’t let that stop you from bringing your firm into the future. Now that you have a base understanding of how these tools work, you can keep up with the jargon and ask the right questions of potential providers before you commit to implementing their software. Seek out technology that will help stop the bleed now and make workflows easier in the future, whatever that means for your firm. 

It’s time for a reality check on your data-gathering efforts. You’re probably monitoring basic workforce demographics, and you should feel good about the weekly “pulse” surveys you’ve implemented to broadly track employee satisfaction.

But what about specific departments? Do you see data that would reveal team-wide disengagement before it becomes a retention problem? Do you act on it?

Or even a specific worker, one of thousands of employees… working in a remote country? Is he happy? Does he have any lingering questions or concerns about employee benefits or company policies?

“The biggest handicap I see for HR individuals and HR executives is [not] having real-time data and metrics,” says Patricia Sharkey, senior director of human resources at IMI, a company that provides resources and software to automate distribution facilities across the globe. “That’s the way we’re going to be taken seriously.”

The company uses a homegrown, AI-driven HR platform called Rhonda to engage employees regularly, execute key HR functions such as performance reviews, and collect and analyze data to identify HR hotspots for leaders to act on. “What Rhonda does for HR, and what it brings to this company, is that our CEO has real-time metrics and data that he can make decisions based on,” Sharkey says. 

Senior Executive Media recently interviewed Sharkey about the company’s approach to employee communication and engagement. Read on for edited excerpts from our conversation.

“The biggest handicap I see for HR individuals and HR executives is [not] having real-time data and metrics. That’s the way we’re going to be taken seriously.”

– Patricia Sharkey


Senior Executive Media: How is Rhonda regularly engaging employees and improving employee retention?

Patricia Sharkey: We have weekly surveys that go out, and employees rate, on a scale of one to five, how their week has gone… It’s a simple weekly, like, “Hey, please let us know how you’re doing.” And this goes out to the entire company. If an employee scores a three or lower, they’re going to get contacted by their manager, or by HR, and in some cases by the CEO directly, which is awesome.

I have employees that, every once in a while, I think they want to put a two because they want to talk to [CEO] Rudi [Asseer] because it turns out things are pretty good.

We run weekly reports that measure how many people are responding to us… I receive weekly reports of who’s engaging… If they’re not responding, or we’re getting a low response, we ask the manager, “Hey, how come your team isn’t responding?”

Senior Executive Media: What else does Rhonda do?

Patricia Sharkey: It’s a big part of the safety culture. We send out safety messages every Thursday. People can talk to us about any safety concerns.

We also have a weekly “hustle” that we send out via Rhonda, which is a newsletter, which reminds employees to respond to Rhonda and lists the employee of the week, by the way, too. So they’re engaged in the employee hustle because they may also see rewards.

Our employees can ask questions to Rhonda, like, “Hey, what’s up with my bank account?” … While we have an HR help desk and different areas where employees can contact us, the most successful is the AI application.

And with AI, it’s been much easier for us to get [performance] reviews back from the employees… This approach has increased accuracy, speed and employee satisfaction because it’s so easy for them to complete.

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Senior Executive Media: During the actual conversations within the performance review process, how does the data you’ve collected come into play?

Patricia Sharkey: Managers… talk to their employees about their [self-evaluations] and how, say, for example, the employee gave themselves a three [in a certain area], but the manager scored a four for them.

Then I’m given all that information as well. Not only am I able to see the scores of each employee and what the managers are giving them, the AI also does the average of what the department’s overall score is, which is pretty interesting—great data for the CEO. Because it takes some of the subjectivity out and goes, “Alright, you’ve got your divisional lead, maybe saying he has the greatest department in the company. But look at these overall scores.”

Senior Executive Media: You’re gathering so much data. What are the most important or most interesting metrics that you specifically look out for?

Patricia Sharkey: What I’m looking for, as I’m doing a temperature gauge on my employees: Are they happy? Are they going to stay with us? Through data, you can see patterns of behavior, right? I can tell if someone’s not happy if I see that I’m getting a lot of twos, right? I have to not only do a one-time check in, but now I have to go and say what’s going on? What systemically is happening in this department if I see, you know, in one department, I’m getting lower scores, or people not responding? (People not responding is almost the same thing as giving a low score, in my opinion.) And the question is not about the employee, it becomes about the company and systemic practices. What are we doing well, and what aren’t we doing well?

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