As we head into 2025, it’s clear that AI isn’t just hype anymore – it’s a game-changer for HR when it comes to compensation. Artificial intelligence makes it easier to find data, offers repeatable and explainable methodologies for market pricing jobs, and enables the strategic deployment of compensation funds.
AI is increasing efficiency and enabling comp pros to harness data to enhance consistency and fairness in compensation, all while driving your organization’s most critical strategic goals.
AI goes beyond traditional compensation data
Job benchmarking, or identifying the market range for a specific job, is typically accomplished by leveraging aggregated salary data from third parties. HR professionals often need to price a particular job in a location or industry without possessing the aggregated pay data that produces an exact match.
Historically, highly trained compensation professionals have performed this task. They find the best available data or job matches and then triangulate between available data points to determine the reasonable pay for a particular role.
However, recent advances in HR tech system integrations, AI techniques, and computational tools will lead to better benchmarking tools in 2025. These tools will enable the processing and aggregation of pay data in near real-time (rather than yearly) and make reasonable suggestions for matching jobs. They will use explainable models to fill in gaps transparently and defensibly.
These advancements will enable more efficient and consistent large-scale market pricing, freeing compensation professionals from the mundane chores of job matching and benchmarking and allowing them to spend more time on higher-level tasks.
Additionally, AI tools will enable organizations to make data-driven strategic compensation decisions. For example, artificial intelligence will empower organizations to engage in department-level budget planning in volatile labor markets with greater precision and accuracy.
Will artificial intelligence close the gender pay gap?
You likely know that women earn less money than men, even if they’re doing the same job.
One reason the gender pay gap has persisted is that organizations compensate employees for factors they shouldn’t. For example, a worker’s confidence during interviews is not a compensable factor, nor is their height – yet the typical CEO is three inches taller than the average male.
Human biases can reward employees for non-compensable factors favoring certain demographic groups, but machines don’t inherently possess these biases. Leveraging artificial intelligence to make compensation decisions can improve pay equity because it enables reducing bias at scale rather than remediating each individual case.
Algorithms and tools can measure, correct, and report on the biases in their underlying data. Compensation models that are explainable, transparent, and include only acceptable compensation factors (such as experience, industry, location, company size, and skills) will lead to fairer pay decisions.
While it’s unlikely that AI will completely solve pay equity issues, it can significantly reduce disparities by providing comp professionals with a powerful tool for making equitable and unbiased compensation decisions. Integrating artificial intelligence into compensation software platforms will catalyze change, pushing us ever closer to the goal of fair pay.
Should compensation professionals worry about the black-box problem?
AI ethicists worry about artificial intelligence’s black-box problem. Generally speaking, deep learning and generative AI systems don’t track how an algorithm arrived at its answer. When you enter a prompt into some AI systems, they spit out a response, but we lack clarity about why they generated this response – the model is opaque.
While these opaque models are powerful, sometimes using them introduces no or very low ethical risk. For instance, take the task of generating draft job descriptions or performing a Google-like search. Black box models can perform these low-risk tasks, offering significant value to HR professionals.
In these cases, we can simply inspect and correct the model’s output without understanding why the model generated a suggestion.
But in other instances, we need to “see the thinking” to evaluate and potentially correct an AI model’s output. With a broad set of data inputs, a black-box model might inadvertently learn a false or biased correlation that leads to an illegal or indefensible prediction.
For example, living in a certain zip code may incorrectly correlate with lower credit scores. In this case, using explainable, transparent models (like regression, k-nearest neighbors, etc.) is better. With transparent models, the impact of each input feature is directly observable.
Compensation professionals interacting with either transparent or opaque AI models will still need to rely on their expertise to correct outputs. Employers are legally responsible for their employment decisions, whether or not an AI tool was used. For this reason, AI in the HR space will be a mix of both black box and transparent models based on how much of the “thinking” human employees need to see to stand behind AI-assisted decisions.
Can AI advance a skills-based compensation strategy?
For the past several years, organizations invested in pay transparency and equity have been rethinking how they can compensate employees based on their skills rather than biased factors such as where they went to college.
A skills-based approach to compensation benefits organizations by promoting a more flexible and dynamic workforce and encouraging employees to obtain and refine their skill sets to achieve higher pay.
Some advocates have latched onto how artificial intelligence might advance a skills-based compensation strategy by more accurately assessing and tracking employee skills and competencies.
In the next year, there will be some movement in this space. HR professionals interested in the shift to a skills-based workforce will leverage job postings and emerging AI technology to derive meaningful insights into how posted pay ranges vary by skill requirements.
With the growth of publicly available salary information, employees are developing a clearer picture of how much their skills, education, and experience are worth. Organizations also need to leverage this information to attract and retain employees.
Payscale is at the forefront of compensation AI
Payscale has been in the AI space since 2008 – long before it was fashionable. This past year, we were among the first in our industry to integrate AI into our technology workflow. We possess a deep knowledge of compensation and artificial intelligence and will continue to offer our clients business-ready AI solutions.
Here are just a few of the tools our data scientists have been perfecting for our customers:
Suggested Model Matches – Leveraging machine learning and natural language processing, Payfactors’ Peer delivers highly accurate job matches by analyzing various factors, including job title, any existing matches to survey data, industry, and more.
AI Match Suggestions in MarketPay – MarketPay’s AI Match Suggestions streamline market pricing by instantly identifying strong survey matches using only your job title, purpose, and currency. Optional filters allow for further refinement, so users can spend less time sifting through rows of data and price more confidently.
AI Match Suggestions in Payfactors – Payfactors uses advanced large language models (LLMs) to suggest the top five most relevant job matches from new surveys when last year’s match is unavailable. It analyzes and compares job data (including job descriptions) between survey jobs year-over-year to identify matches. Users can quickly review and accept the AI-generated matches, saving significant time and reducing manual effort in updating pricings.
AI-generated Job Summaries in Payfactors – Job Description Manager generates job summaries using generative AI. Instead of starting from scratch, HR professionals can review and revise a solid draft and focus more on collaborating with managers to ensure the job description is accurate and the pay is correct.
As we look ahead, we will continue to deploy the latest AI capabilities to empower our customers to operate more efficiently. Want to know more about Payscale’s AI-powered compensation solutions? Discover our AI compensation solutions today.
