Applying AI for dynamic salary determination and trend forecasting

AI for salary determination: from manual to data-driven

The way organizations determine salaries is about to change radically. Where HR professionals have relied for years on annual benchmarks, spreadsheets, and manual analyses, artificial intelligence now enables real-time insight into what employees are worth in the labor market. And more importantly: what they will be worth tomorrow.

By 2028, 86% of employers expect to use AI-related tools in their HR and salary processes. This is no longer future talk, but a development that is already impacting how organizations attract, retain, and reward talent.

Why static salary structures no longer work

The traditional approach to salary management has a fundamental problem: it’s too slow for today’s labor market. Organizations often work with salary scales that are adjusted annually based on benchmarks that are already outdated by the time they’re applied.

Meanwhile, the labor market is constantly changing. New technologies create demand for specific skills, economic developments shift the balance between sectors, and the war for talent causes salaries in some positions to rise faster than in others. A data scientist who was paid market-conform last year may suddenly be 15% below market this year without HR noticing.

This leads to concrete problems. Employees who discover they’re being paid below market are more likely to leave. It costs organizations an average of 6 to 9 months’ salary to replace an employee. And precisely the most valuable employees, those who are actively approached by recruiters, are the most vulnerable.

How AI fundamentally changes salary determination

Artificial intelligence brings two crucial capabilities to salary management: real-time market insight and predictive analysis. Where traditional methods look backward, AI looks forward.

AI systems continuously analyze thousands of data points from job postings, salary surveys, labor market data, and internal HR systems. They identify which positions are being paid below market average before this leads to departures. They predict which skills are becoming scarcer and therefore more expensive. And they signal which employees have elevated turnover risk based on their salary position relative to the market.

This doesn’t happen with simple comparisons, but with machine learning algorithms that recognize patterns that remain invisible to humans. For example, an AI system can see that data engineers with specific cloud certifications have risen 12% faster in salary over the past three months than data engineers without those certifications. Or that marketing managers in scale-ups earn 8% more than their colleagues in corporate organizations, but only in the Randstad.

From reactive to proactive compensation management

The real power of AI in salary determination lies not in automating existing processes, but in enabling new forms of compensation management. Organizations can switch from a reactive to a proactive approach.

Instead of waiting until an employee comes with a counteroffer or resigns, HR professionals can now stay ahead. AI systems identify which employees are in positions where market salaries are rising fastest. They predict which teams have the highest turnover risk based on salary positioning. And they calculate what it costs to proactively adjust versus reactively increase after a counteroffer.

This enables different conversations. An HR director can go to the CFO with concrete data: “If we now invest €45,000 in targeted salary increases for these twenty crucial positions, we expect to prevent six departures that would otherwise cost us €380,000 in replacement costs.” That’s a fundamentally different conversation than “we need to increase our salaries because people are leaving.”

Pay equity: from compliance to strategy

One of the most valuable applications of AI in salary determination is ensuring equal pay. Organizations have legal obligations to guarantee equal pay, but traditional analyses are time-consuming and often incomplete.

AI systems can make thousands of salary comparisons within seconds, corrected for relevant factors such as experience, education, location, and job level. They identify not only where unequal pay exists, but also why. Is there a pattern that women systematically start at lower levels? Are certain departments structurally compensated differently? Do promotion processes create unintended pay gaps?

More importantly: AI can predict where equal pay problems threaten to emerge. If an organization, for example, promotes more men than women to senior positions, and those promotions come with larger salary jumps than lateral moves, then the system predicts that the pay gap between men and women will increase, even before this is measurable in the numbers.

Which professions are most affected?

The impact of AI on salary determination varies greatly by position and sector. Professions with transparent labor markets and clear competencies see the biggest changes. Think of IT positions, where supply and demand fluctuate rapidly and specific technical skills have directly measurable market value.

Positions in sectors with shortages, such as healthcare, education, and technology, are also strongly affected. AI systems can precisely predict here which specializations will become scarcest and where therefore the largest salary increases are to be expected. A nurse with ICU experience has a different market value than a nurse without that specialization, and that value changes depending on societal developments.

But HR professionals themselves are also affected. Salary administration is transforming from an executive to a strategic function. Where HR employees previously mainly entered salaries and consulted benchmarks, they now become data analysts who translate AI insights into strategic decisions. The question shifts from “what do we pay now?” to “what should we pay to achieve our strategic goals?”

Implementation: where do you start?

The transition to AI-driven salary determination doesn’t have to be overwhelming. Successful organizations start small and scale based on results.

Start with one specific problem. Perhaps it’s high turnover in a critical job group, or uncertainty about market-conform salaries for new positions. Choose a problem where you currently spend a lot of time manually or where you have little reliable data.

Then ensure your data is in order. AI systems are only as good as the data they analyze. That means: consistent job titles, current job profiles, and complete salary data including variable components and secondary employment conditions. Many organizations underestimate this step, but it’s crucial for reliable analyses.

Then start with external benchmarking. Compare your salary structure with real-time market data for comparable positions. This immediately provides insight into where you’re competitive and where you’re not. AI tools can automate this process and continuously update it, instead of the annual exercise it often is now.

Beyond the hype: realistic expectations

AI for salary determination is powerful, but not a miracle cure. The technology predicts trends based on historical data and current patterns. Unexpected developments, such as sudden economic shocks or disruptive technologies, can make predictions less reliable.

Moreover, human judgment remains essential. AI can predict that an employee has elevated turnover risk, but only a manager knows whether that employee is actually dissatisfied or highly engaged. AI can calculate what is market-conform, but strategic choices about where you want to be above or below the market remain human decisions.

The best results emerge when organizations see AI as support for decision-making, not as decision-making itself. HR professionals use AI insights to ask better questions and make informed choices, but don’t replace their own expertise and contextual knowledge with algorithms.

The strategic value for your organization

Organizations that deploy AI for salary determination achieve measurable results. They lower their recruitment costs because they need to recruit less often for positions where people leave due to salary. They increase retention in critical positions by proactively adjusting. And they make better budget decisions because they know where investments in salary have the most impact.

But the greatest value is strategic. With AI-driven salary determination, compensation becomes an instrument for talent strategy instead of a cost item that must be controlled. You can consciously choose where you want to lead the market and where you can follow. You can predict what your talent strategy will cost before you make commitments. And you can align compensation policy with business objectives.

For Deepler clients, this means that integrating salary data with other HR data, such as engagement scores and performance indicators, becomes even more valuable. An employee who scores high on engagement and performance, but is paid below market, is a clear retention risk that requires proactive action. That combination of insights makes the difference between reactive HR policy and strategic talent management.

Your first step toward data-driven compensation

The labor market doesn’t wait until you’re ready. Every month that you determine salaries based on outdated benchmarks, you risk valuable employees discovering they can earn more elsewhere. Every quarter that you don’t look ahead to which skills are becoming scarcer, you miss the opportunity to proactively adjust.

Start this month with one concrete action: analyze for your three most critical positions what the current market value is and how it has developed over the past six months. That analysis immediately gives you insight into where you’re vulnerable and forms the basis for a data-driven approach to compensation that goes beyond annual inflation corrections.