When Classification Fails, Compensation Follows: An AI-Assisted Job Classification Framework for Large Public Sector Employers

Abstract

This case study describes a classification reform at a large urban U.S. public school district of 11,480 employees and $724 million in payroll. An internal pay equity review found pay gaps between demographic groups that job content and tenure did not explain. The gaps came from how jobs had been assigned to titles and grades over the years, not from the pay decisions supervisors made within a role: single titles spanned 15 or more pay grades, and salary ranges within one role exceeded a 4.71-fold spread. The reform paired PRISM, an AI-assisted framework that classified 992 job descriptions through five validated passes, with PERCS, a model that placed each employee on the corrected grades. Estimated annual cost ran from $3 million in savings under initial placement to roughly break-even once every employee was protected against a pay cut. Human judgment governed every decision; AI informed it but never replaced it.