One of the biggest challenges in preparing for pay transparency is the sheer volume of data needed for implementation. In a larger organisation, hundreds or even thousands of jobs, pay records, classifications and other factors need to be compared to identify differences and their possible causes. This is where AI can provide substantial support, as it can find connections more quickly in the sea of data and identify patterns that would be difficult or considerably slower to recognise manually.

At the same time, it is very important to define exactly what we use AI for during the process. It can be a very useful tool for showing us where to take a closer look, but it should not decide what we need to do on our behalf.

It can help bring order to the sea of data

Preparing for pay transparency often starts with a very practical task: putting the data in order. The same job may be recorded under several different names, classifications may differ, certain data may be missing, or information from different systems may need to be organised into a consistent format. For a few dozen jobs, this can still be managed manually, but with hundreds or thousands of positions, the task takes on an entirely different scale.

Using AI can speed up data cleansing, flag inconsistencies and help identify jobs that may involve similar responsibilities despite having different names. However, there is a reason why we use conditional language: two similar job titles do not necessarily mean the same work or work of equal value. AI suggestions must therefore always be checked, as professional assessment remains the responsibility of HR professionals.

It can be particularly effective at identifying differences

One of the most interesting applications of AI may be recognising patterns and unusual differences in pay data. For example, it can show that the difference between men’s and women’s pay in a particular job group is greater than the organisational average, or that different patterns consistently emerge at certain locations, in organisational units or at particular job levels.

Among other things, the EU Pay Transparency Directive requires employers subject to reporting obligations to examine average and median gender pay gaps, variable pay components, pay quartiles and differences by category of workers. Artificial intelligence can therefore also provide meaningful support in analysing these data.

However, it is important to emphasise that an outlier is not necessarily evidence that pay is unjustified or discriminatory. Rather, it indicates that the area in question deserves closer examination. At this stage, AI can therefore primarily serve as a radar.

Benchmarking and simulation can also become faster

When preparing for pay transparency, we need to understand not only how our processes have worked so far, but also what consequences a particular change to our system might have.

What happens, for example, if we modify a pay band? How much would it cost to correct certain unjustified differences, and how would this affect our company’s pay structure or gender pay gap?

AI-supported modelling can help compare several possible scenarios quickly, and the same applies to benchmarking. By structuring market data that are lawfully available from reliable sources, we can more easily understand how our company’s pay bands compare with the market.

At this point, however, the accuracy of the analysis is not the only thing that matters. We also need to know what data the system uses, how up to date and comparable they are, and what assumptions underpin the results. And this brings us to another problem.

Historical data can carry historical problems with them

AI does not automatically make the data it processes objective. If a company’s pay practices have been affected by a particular bias for years, traces of it may remain in the data, and a system built on those data can easily reproduce previous flawed practices.

This can be particularly important when AI is no longer simply identifying differences, but also recommending pay decisions. Past salaries, for example, do not in themselves establish that they provide an appropriate basis for determining future pay.

That is why, alongside the connections identified by AI, it is always worth examining how and why the situation in question developed.

What we use AI for matters

European AI regulation also distinguishes between different uses. Certain AI systems related to employment and worker management may fall into the high-risk category. In such cases, data quality, the ability to document processes, traceability and human oversight are particularly important.

This also represents an important boundary in the context of pay transparency. A tool that searches for duplicates in thousands of rows of data presents a different risk from one that supports or influences a decision about an individual employee’s pay. The closer AI gets to an individual decision, the more important it becomes to understand what data and reasoning led to its result.

Given the sensitivity of pay data, data protection must also be considered from the outset. It matters which employee data are entered into an AI system, for what purpose they are used, where they are processed, how long they are retained and who has access to them.

Human oversight is more than an approval button

And perhaps this is where the most important boundary lies between AI use and human oversight! AI can flag an unusual pay gap in a particular job group, help uncover its possible causes and calculate the expected cost of different corrections. However, HR professionals and the organisation itself must assess whether the difference can be justified by objective criteria, whether intervention is necessary and, if so, what change is appropriate. Human oversight is therefore essential because we need to understand the reasoning behind the results produced by AI, be able to challenge it and, where necessary, make a different decision.

This may also transform the role of HR. Less time may be spent manually organising and comparing data, leaving more attention for activities that truly require human judgement: interpretation, decision-making and communication with employees.

AI can therefore be truly useful in pay transparency when we use it as a radar rather than an arbiter. It can show us where to take a closer look, recognise patterns and help model possible solutions. However, we all need to understand that, while technology provides a better basis for decisions, responsibility for the chosen solution and its consequences continues to rest with the human oversight provided by HR professionals.