How AI Enables Data-Backed Decisions That Show Employees They Matter

By: Sara Hillenmeyer, PhD/Senior Director, Data Science at Payscale

Properly rewarding a person’s work is the superpower of a successful business. When it comes to rewarding employes, compensation is one of the four components McKinsey points to.

Compensation isn’t just a line item in the organization’s budget. It determines who organizations attract, how organizations retain top talent, and how well organizations can deliver on their strategic goals. How an organization compensates employees is a competitive advantage for businesses.

Paying employees the right amount shouldn’t be a guessing game. Organizations need comprehensive data and benchmarks that keep pace with rapidly changing market conditions. To capitalize on the competitive advantage compensation provides, HR leaders need confidence in their salary data.

AI-infused data is a powerful tool in the comp pros’ arsenal

According to the 2025 Payscale Compensation Best Practices Report, almost half of organizations are experiencing an increased tension between fair pay and optimizing spend on compensation. At the heart of this issue is data. But data can be limited.

HR could spend hours and hours analyzing the data. But they have a secret weapon—AI.

AI is like a microscope, enabling users to see information that isn’t possible with the naked eye. Instead of relying on limited data from exact matches, AI can analyze patterns across vast datasets, covering various industries and company sizes. HR gets a comprehensive view of the labor market, ensuring compensation decisions are competitive and aligned with broader trends.

Organizations need confidence in the data they’re using. AI is able to constantly process vast amounts of data in real-time, significantly speeding up data analysis.

AI solves fundamental compensation challenges, whether it’s data blind spots or fresh data. Here are just a few ways AI can solve real problems.

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1. Price jobs in low/no data markets

One of the flaws of traditional compensation benchmarking is that it relies too heavily on exact job and data cut matching. This leaves HR professionals guessing or triangulating when data are sparse or unreliable. Let’s say you’re opening the first healthcare clinic in Flat, Alaska and hiring a nurse with a specific certification. There’s no available data that perfectly matches this scenario. You’re the first.

Compensation professionals equipped with AI can easily identify similar geographies, compare different data cuts, and supplement the data to make a confident decision.

2. Not limited by exact job matches

Compensation should reflect the broader labor market, not just the exact match of the job specifications. Focusing too narrowly can prevent you from offering competitive pay, as it overlooks the wider range of industries and company sizes that might be relevant.

Here’s an example. You’re pricing a culture experience specialist job in the hospitality industry. A few other hotels (your usual competition for talent) have similar roles, but most people who work as a culture experience specialist work in the travel and tourism industry, not for hotels. If you take the narrow view—and only look at market ranges for culture experience specialists at hotels—you limit the sample size of the data, resulting in a lower-confidence assessment of the market.

Plus, you may be missing important information about how much candidates for this role could expect to make if they took a job elsewhere. It’s tricky for humans to do this correctly, because picking which other industries may be relevant depends on the role itself. Accountants? You should basically look at pay across all industries. But project managers? If you’re hiring a project manager in the construction industry, then the only relevant market data to use also comes from other construction companies. Including data from a tech company or a hospital will result in a range that is quite a bit lower than market rate for construction project managers.

AI can do this work for us. AI-enabled tools can help HR understand how pay varies across related industries, roles, and locations.

3. Avoids data dominance

So often, surveys and data sets are dominated by large organizations because they have more jobs than others. This can skew the data. When a very small number of organizations are contributing most of the data, the aggregated statistics reflect those organizations rather than the whole market.

Big Dog Grooming Company posts lots of jobs all of the time because they have hundreds of locations. But you’re a smaller, regional shop with a few hundred employees. Data could be swayed by the number of jobs posted by Big Dog Grooming Company. AI can weight the data from big organizations, so salary predictions aren’t dominated by large organizations.

4. AI benefits employers and employees alike

Pay is a baseline in your employee value proposition, but it is more than a paycheck. Pay communicates how much the organization values an employee’s contributions. Employees need confidence that their pay is calculated accurately, fairly, and transparently.

Employees want to know that their compensation was determined through methodical analysis and market intelligence, rather than leaders simply throwing darts at a salary board to determine their pay. AI has the power to bring consistency to compensation practices that manual methods can’t match.

Externally, organizations need confidence that they’re offering competitive pay that matches market standards. AI empowers HR teams to make more informed and confident pay decisions, ensuring their compensation strategies are fair and competitive across diverse contexts.

Whether HR is pricing a cashier or a pharmacy technician position, AI keeps the strategy consistent, preventing issues like internal job hopping to chase a pay raise or problems attracting new talent.

5. Organizations need confident compensation

Compensation management is the foundation of strategic HR, directly impacting employee satisfaction, recruiting, and retention. Pay is too big of a risk to not have quality data sources combined with AI modeling to back up salary calculations.

Trusted and fresh data, transformed into insights with AI is the future. It’s how companies make compensation decisions that fuel lasting success, growth, and value.

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