Are you prepared for the future of payroll?
Payroll is perhaps the most ubiquitous business function — everyone needs to be paid! It is also one of the largest business expenses, with wages equating to an average of more than 60 percent of total business costs. In theory, the function of a payroll system is simple: You pay your people and distribute their contributions accurately and on time.
The reality, of course, is more complex. Payroll is subject to a myriad of ever-changing complexities, from tax codes and distributions to bonuses and personnel changes. Fortunately, payroll was one of the first HR functions to fully embrace automation. Thanks in large part to technologically advanced payroll systems, most payroll departments are operating more efficiently, accurately, and economically than ever before.
Like automation, and the internet before it, artificial intelligence (AI) is set to disrupt the future of payroll once again. Unlike automation, which relies upon cause-and-effect, true AI systems can interpret data and leverage it to make decisions, learn from previous mistakes, and strategically solve problems.
Demand in this space is exploding: in 2018, the cloud-based payroll software market was valued at roughly $7.88 billion, a figure that’s expected to balloon to $13.37 billion by 2026. And, considering payroll’s significant impact on the bottom line, it’s no surprise that business leaders are increasingly looking to invest in sophisticated AI-supported payroll systems.
With new technology comes new opportunities. Here are four ways AI and machine learning are primed to reinvent the future of payroll…again.
Reskilling Opportunities for Payroll Professionals
As AI continues to improve payroll system functionalities, payroll professionals can seek valuable upskilling opportunities. Considering their data protection and compliance skills, payrollers may be prime candidates to assist with other data and compliance challenges. Less time spent manually inputting figures could lead to strategic sessions with other business functions, such as finance, employment law, or human resources. By proactively seeking growth opportunities and stretch assignments, payroll professionals can reimagine how they interact with — and influence — other departments.
Error Correction and Prevention
Payroll processing relies on coordinating and managing massive data sets, all of which are subject to complicated and constantly shifting compliance rules. Fortunately, this is exactly where AI and machine learning tools shine. By managing procedural data and automatically applying compliance regulations, AI significantly reduces both the likelihood and severity of human errors while improving compliance performance.
AI tools can also flag systematic issues by monitoring how data is collected and processed and offer strategies to improve efficiency.
Predictive and Prescriptive Analytics
Despite payroll’s significant financial impact on the organization, in the past, it was often seen as more as an administrative function than as a source of strategic insights. But most successful organizations now realize that analyzing payroll data is crucial to making meaningful, forward-thinking decisions.
Predictive and prescriptive analytics are prime examples of how AI technology can help organizations maximize efficiency and growth. Predictive analytics forecast what might happen in the future based on current and historical data. Predictive models are often used to assist with finances, such as helping to shape budgets, generate inventory, and cut down on unnecessary expenses. These tools could predict a retail establishment going over its payroll budget during the holiday season, for example, causing business leaders to seek creative solutions ahead of time.
Using the above example, prescriptive analytics take this one step further and actually suggest various ways to solve the problem — and show the potential implications of each option. Payroll professionals can manipulate their data to model countless payroll scenarios in a macro-, enterprise-level scenario modeling engine that produces different outcomes, learns from them, re-runs them, and ultimately helps guide informed decision-making.