For decades, the Metro Nashville schools in Tennessee created district-level jobs organically as the system started programs, responded to evolving student needs, or secured new funding. The result has been an organizational chart that features many employees with similar job titles but markedly different responsibilities and salaries.
This year, as district leaders set out to assess how the system鈥檚 jobs and wages stacked up against other systems鈥� and ensure employees were receiving fair compensation, they quickly learned the project was too large for any one person鈥攐r small group鈥攖o realistically tackle.
Even if staffers took the project in bite-sized pieces, the 80,000-student, 12,000-employee district鈥檚 organizational chart had lost cohesion over time and presented a monumental task to sort through. One prime example discovered early on: 435 employees shared the title 鈥渟pecialist鈥� but were spread among 24 pay grades and often had different job descriptions.
Manually reading each job description, analyzing whether the title and description make sense together, and recategorizing jobs that don鈥檛 align with their titles would take years of work鈥攖ime the district didn鈥檛 feel it had to lose, said Wayne Birch, the district鈥檚 strategic compensation and people analytics professional.
That鈥檚 where artificial intelligence came in.
With the help of staff at Vanderbilt University鈥檚 Data Science Institute, Birch and his colleagues developed a custom AI system, called PRISM (Progressive Refinement and Intelligence Synthesis Model). The model has five 鈥渢eam members,鈥� as Birch called them鈥攅ssentially, five branches of the model that work together to assess, analyze, and flag both patterns and inconsistencies in job descriptions for roles with the same title. The AI tool assessed 992 titles that covered more than 1,000 non-instructional jobs, Birch said.
The models were expected to perform as accurately as a human resources professional with about two years of job-classification experience, Birch said.
The AI 鈥渢eam members鈥� each analyzed the job titles and descriptions and decided whether they needed reclassification and, if so, to what. The models were designed to have 鈥渁rchitected friction,鈥� Birch said, meaning they were instructed to disagree with one another if needed rather than simply agree with and affirm the others鈥� work.
The friction is key, Birch said, because it creates 鈥渁nalytical checkpoints鈥� that force each role to be examined through several lenses before it ever lands on a human鈥檚 desk for review. It鈥檚 the equivalent of a group of employees with differing expertise and viewpoints offering their takes鈥攁nd disagreeing along the way to finding the best solution.
Metro Nashville schools鈥� use of AI offers a look at how a district is using the technology to solve a problem outside the classroom, where much of AI-related research and attention have focused.
And the problem the Nashville district is tackling isn鈥檛 unique, Birch said.
Most districts鈥� HR systems manage the same elements鈥攑rimarily job classifications, compensation, pay equity, and pay-grade structures鈥攁nd many districts have a job architecture that evolved organically over time and hasn鈥檛 been audited in years, if ever, he said. (Most Metro Nashville employees aren鈥檛 unionized, so titles and pay grades aren鈥檛 spelled out in contracts.)
鈥淭he focus on AI has been on student learning, and that鈥檚 understandable, but my argument is that in order to meet those goals, you have to have quality people to both educate the students and support the 糖心动漫vlog,鈥� Birch said. 鈥淣ow, this is also a good thing to point to and say, 鈥榃e鈥檙e trying to take good care of the funds that we鈥檙e given in the district.鈥�
The AI tool reclassified jobs that didn鈥檛 have descriptions that aligned with their titles correctly about 80% of the time, Birch said, which is about the same accuracy he would expect from a human doing the same work. For example, if someone is classified as a director but does nonmanagement work, that employee may be overpaid, Birch said.
When the AI tool reviewed higher-profile and higher-paid administrative positions, it flagged those cases for human review.
Now, all the tool鈥檚 work鈥攔eviewed by Birch鈥檚 team鈥攈as been handed over to senior leadership to review and decide what happens next.
鈥淭he goal of this was to provide the decisionmakers with the quality information they need to make decisions,鈥� Birch said, 鈥渁nd that part has been very successful.鈥�
