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should proceed or require human review. This is the direction we have been pursuing with our AI capabilities. Rather than limiting AI to a chatbot sitting on top of payroll reports, the objective is to embed intelligence into the payroll lifecycle itself. Mock payroll comparisons, YTD audits, reporting, anomaly identification, and compliance monitoring can increasingly operate against the same underlying payroll data and calculation environment. There is an important difference between AI sitting on top of payroll and AI operating within payroll. The first can summarize what happened. The second can potentially help prevent a problem from happening. That distinction represents the transition from payroll processing to payroll intelligence. Historically, payroll technology has primarily been designed to calculate what employees should be paid. The next generation of technology will be expected to explain payroll, predict risk, identify anomalies, monitor regulatory changes, reconcile results, validate inputs, and recommend actions. Instead of a payroll professional discovering after calculation that an employee’s net pay increased
unexpectedly, technology should be able to identify the variance before final approval, isolate the components responsible for the movement, and explain whether it resulted from compensation, tax, benefits, time, statutory changes, or incorrect data. This does not mean removing payroll professionals from the process. Payroll is too regulated, financially significant, and employee-sensitive to eliminate human governance. Instead, technology should change where humans spend their time. Payroll professionals should not spend hours manually comparing spreadsheets simply to determine why an employee’s net pay has changed. Technology should identify the variance, determine the components responsible for it, evaluate whether those components are consistent with the employee’s data and applicable rules, and present the payroll professional with
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GLOBAL PAYROLL MAGAZINE ISSUE 28
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