From back office to strategic insights: How AI is transforming payroll

Artificial intelligence has quietly moved into payroll — and in many organizations, it’s already doing far more than people realize. Beyond basic automation, AI is now helping with timesheets and approvals, flagging anomalies before a pay run, and tracking legislative changes across multiple jurisdictions.

At the same time, AI is helping to shift payroll from a back-office function to a more strategic role in spotting trends and providing real-time insight into one of an organization’s largest costs: compensation.

There are risks, of course, with AI in payroll touching highly sensitive personal and financial data, raising the bar on privacy, cybersecurity and algorithmic bias.

But overall, it’s an exciting time for the payroll industry amid AI transformation, says Steven Van Alstine, vice-president of professional standards and education at the National Payroll Institute.

“It isn't replacing the professionals. It's augmenting, it's leveraging... it's intelligent assistance that's layered on top of payroll systems,” he says.

“It's able to then improve accuracy, it cuts some of that transactional work, it really boosts the employee experience and it provides the ability for organizations to gain some insight into that data.”

Repetitive tasks lean on AI

When it comes to using AI in payroll, we’ve gone beyond the question of whether to use it because the answer is obvious: yes, given the repetitive nature of many tasks, says Edward Rajaratnam, partner at EY Canada.

As an example, one of the biggest “wins” that makes sense is timesheets, whether for regular hours or overtime.

“So, just to really automate that process to compare timesheets to ‘What is that individual scheduled to do? How was the individual attendance? And does that align to what is done on the timesheet?’” he says, adding that extends into off-cycle runs, as AI can automatically push out requests to supervisors for approvals.

And while many payroll professionals may be concerned about losing their jobs to AI, Rajaratnam disagrees.

“Payroll practitioners should look at this as going to free up their time to really focus on helping strategic decision-making — and they have the skill sets,” he says. “I think there’s no limit to what payroll can do… it’s really elevating their game within the organization."

Watching for payroll anomalies

Another advantage is for anomalies, when it comes to variance reports for reconciliation, says Rajaratnam.

“Whether it's talking about reconciling tax balances or anything that was feeding in, and comparing it, over the years. There's been a lot of time spent on that by humans — not to say that that shouldn't take place now, but to a lower degree,” he says.

“There are so many anomalies, so many things that can be built into an AI tool that will automatically give something. And the beauty of that is it’s going to give that before you actually post something.”

Given the need for high accuracy in payroll, AI is a natural addition, says KJ Lee, president of Employment Hero Canada.

“It is super helpful in terms of finding inconsistency between different sources, so if there's a difference between a time schedule and employee submitted hours,” he says.

“These discrepancies [can] happen by simple mistakes. And there's usually a reconciliation process with any sort of payroll to catch misaligned data sources.”

AI is great at surfacing that quickly across many different sources before payroll gets submitted or run, ensuring greater accuracy, says Lee.

Compliance advantages with AI

A third key area for using AI in payroll is tax compliance, says Rajaratnam.

“You can link to whatever is happening from a legislation [point of view] whether it’s provincial health taxes or anything.”

The same is true when it comes to compliance monitoring, he says.

“There's lots of changes — shifting provincial rules, federal rules — so AI can really help track all these changes and surface areas that become a risk and let these HR and payroll personnel review things early on.

“So, it supports that… rather than ‘I’m just going to read it from our CRA guidelines and call it a day.’”

Van Alstine agrees that AI tools integrated into payroll systems can help validate employee data and deductions and legislative components automatically.

“It can look for continuous compliance monitoring across different provincial jurisdictions.”

For example, with a minimum wage change in a particular province, artificial intelligence can be monitoring those kinds of legislative changes to make sure that anything within the system that is tied to a legislative change is flagged.

“You can see where this can really be leveraged in large global organizations when there's tax laws that are changing [and] government reporting requirements that are changing.”

Predictive analytics with AI

When it comes to emerging areas where AI is being increasingly used in payroll, the underlying theme is about being more predictive, according to Rajaratnam.

“The more information you feed into that, the more intelligent it's going to be. So, rather than it being a reactive tool, it can be like a predictive, a proactive tool [and] you can prompt it to do a lot more.”

That can even help with C-suite decision-making with the realization that payroll has a lot of vital information, he says.

“Salary cost is one of the biggest costs of any company, so they have a lot of data that can be instantly brought forward in real time to be making informed decisions.

“That is where I think the shift is. And there's a lot more investments being put into that.”

Rajaratnam is a big believer that AI can be used in payroll as a strategic tool for decision-making, especially when it comes to predicting compensation.

That can mean looking at the FTE count for this year compared to last year, for example, and feeding market data into GenAI to make comparisons and predictions.

“It really is augmenting the strategic decision because it has a lot of data [that] a lot of the other functions don’t have.”

Strategic partnership with payroll

These predictive analytics can be used to forecast and detect trends, to assist organizations in making beneficial changes, says Van Alstine.

“When you're deploying artificial intelligence effectively, it can be used to reduce errors, to save time, to reduce costs, to enable payroll professionals really to focus on that higher value work,” he says, which is then about strategically partnering with business.

“[It’s about] being seen less as just that transactional role within the organization and more a part of the organization that can really provide insight and drive efficiency and effectiveness within the organization.”

Labour forecasting capability

Lee agrees, saying that payroll has often been treated as a retroactive function — paying people based on their hours worked — but now is turning to a more proactive approach.

“What we've seen and what we're really excited about are things like labour forecasting, so we can marry sales data with payroll data because we believe that, ultimately, good service should result in better business.”

A restaurant, for example, can look at sales per labour hour (SPLH) to forecast staffing schedules, in realizing, “Hey, based on six months of data sales and labour, you actually are overstaffed on these hours,” says Lee.

“You can find that optimal balance between great customer service and cost… reducing some of that noise and also taking that administrative burden away.”

Before, a human would have to evaluate three different sources to understand those insights, he says: “That's what's really powerful about marrying that AI into payroll.”

Chatbots and self-service options

Another huge area of growth for AI is in the self-service experience with employees, says Van Alstine. For example, if someone moves from a part-time to a full-time role, their whole experience with their pay statement changes, with certain deductions and benefits.

“So, it's tools that are being utilized to transform payroll into something that employees can engage more directly with.”

Chatbots can provide 24-7 employee support, at least for tier-one type questions, he says.

“The ones that are more close-ended, the ones that are easier to respond in a chatbot environment are where we're seeing the real impact.”

Potential risks to using AI in payroll

Despite the many advantages, there are, of course, risks.

With payroll handling sensitive data, it’s a very high-value target from a bad actor or hacker’s perspective, says Lee, which can lead to costly issues such as data manipulation or data extraction.

That’s why it’s so important to use software that is fully SOC2 compliant, to use security features such as multi-factor authentication — and to do periodic audits, he says.

“There's so many of these AI companies popping up left, right and center right now — you have very little idea sometimes as to how that data gets shared across different actors.”

The more information that’s fed in, the better, says Rajaratnam, but it’s important for payroll that it doesn’t compromise the data privacy and confidentiality: “That’s the balance that we are trying to figure out.”

Payroll may also be entering sensitive information around gender or race, for example, for comparison analysis, which is why caution is needed, he says.

“Just be aware — set boundaries and safeguards. Really using it within that safe environment, I think that is very critical.”

Keeping humans in the loop

As part of that, independent audits are key, looking at whether things are working as planned and what source material is being used, says Rajaratnam. That means asking if things are turning out as predicted or if some kind of virus skewed the results.

“This is not eliminating the human element in this but having a human as a more strategic oversight… you need to keep them in the loop.”

AI should be used as leverage or an augmentation as opposed to something that's going to replace that human element, says Van Alstine.

“It's making sure that you're doing the due diligence and not opening the organization up… to reputational risk by using something untested, untried.”

Payroll has to be prepared to understand risk mitigation in using AI, he says.

“We have the whole cybersecurity, ensuring that we're keeping the data private. And then there's the artificial intelligence piece, which this technology is there and available — but we have to make sure that the two are intersecting each other effectively or else it will create risk.”

At the end of the day, AI is like a black box in taking up the data, ingesting it and using it to feed other people's insights, says Lee.

“You’ve got to be very, very careful there. And that's why you’ve got to make sure when you're using an AI, you have a clear understanding of ‘Where does that data live? Where does that data go? And, ultimately, who is accountable if something happens?’”