On a busy Thursday afternoon, your inbox goes PLOP and you start to perspire as you read the subject. URGENT support is requested by your board member. He has an upcoming conversation with his SVP about her compensation. The SVP wants to understand how the company arrived at her pay package. An explanation is required by tonight. Warm rivulets run down your neck.

There are few situations that can break the cool of Rewards professionals. This bunch is hardened by excruciating comp rounds, shoestring staffing, and caffeine-fuelled spreadsheet binges.

Fascinating how the easiest questions are often the most difficult to answer. Why am I being paid this amount? Answering this question is my gold standard in Rewards. Most of the Rewards work is about furnishing a response because answering it properly requires drawing on almost every discipline within Rewards. Let’s review the Reward topics (bold) addressed in responding to this inquiry.

Imagine providing the SVP with a comprehensive, personalised Pay Statement. Not merely a breakdown of her compensation, but an explanation of why she receives it.

It could look something like this:

Your total compensation is [X] and consists of [salary, STI, LTI, benefits in cash, benefits in kind - cash equivalent]. Over the past five years, your compensation has developed as follows [X], reflecting changes in [your responsibilities, performance and market positioning].

In line with the company’s Reward strategy, we emphasise collective performance. Accordingly, the STI is structured as follows [KPIs, target curve, etc.]. The LTI is structured as follows [performance period, payout modality, etc.].

Our benefits reflect our company values and ESG goals. Therefore, your individual benefits package entails [X].

We ensure competitive pay. In relation to comparable positions, your pay lies between the median and 75th percentile in the external market benchmark in line with our market positioning philosophy.

Internally, we ensure equitable pay. We structure our pay in line with the organisational set-up into job grades and pay bands. Your role is assigned to job grade [X] and your salary is positioned in the upper quartile of the pay band. Considering your direct reports, there are no pay compression issues.

Using a regression analysis to evaluate equal pay, the results show no statistically significant gender pay gap. Your pay is in line with our statistical predictive pay guideline.

Looking ahead, your compensation progression will be influenced by [performance, changes in responsibilities, market developments and relevant pay policies].

The above is an illustrative example. It takes the work in the likes of Dostoyevsky to answer in earnest why employees are paid their rates. In practice, however, the Pay Statement should be digestible by the modern standards of TikTok. Keep it brief. Make it understandable. Be able to substantiate every sentence.

Could you produce such a statement for any employee in your organisation today?

Firstly, do you even need AI to elucidate how an employee arrived at their individual pay package? No. Rewards professionals have been doing this for decades and will continue doing so. But it would be nice to prepare a Pay Statement without breaking a sweat; especially with Equal Pay regulations empowering all of your EU-employees with greater rights to information about their pay. AI can be the key in two ways.

Let’s return to our SVP. The Pay Statement is due in a few hours.

Most of the required information is available somewhere in the organisation, but it’s scattered across the workspace. So, you jump between your employee database for salary information, the equity platform for LTI awards, and the benefits suite just to collate the total pay package. This is then separately checked against an external benchmark file and run through the pay equity tool.

Toggling between systems takes time. Integrating MCP capabilities in your HRIT landscape allows AI tools to conduct the data-gathering for you.

But collating the information is only half the job. Now it’s time to evaluate and explain the pay package. As an experienced Rewards professional, you understand the intricacies of your company’s pay system. AI needs this context too. The Comp Context File by Range offers a practical example of how to provide AI with the context of your compensation environment. Continually train AI on your pay system and it will learn to provide a good initial Pay Statement for you.

Let's take a step back. Even if you had seamless access to every HR system, could you actually provide all the information in our illustrative Pay Statement?

If you're like many organisations, the answer is probably no. In my experience, this is most often due to a lack in capacity. The benefits data is not clean. The criteria for compensation progression are not defined. Pay equity analysis has been on your roadmap for months.

These tasks have been on your to-do list for some time now, but with shoestring staffing you didn’t come around to it yet.

Your business case for another Rewards specialist is still awaiting approval. At least your AI access has been approved. You can do a lot with standard AI tools. The possible use cases for AI are as large as the field of Rewards: review and clean your data, automate the job grading, build the pay dashboard for hiring managers, the list could go on.

This is where I see its greatest potential: not merely making existing Rewards processes faster, but enabling Rewards teams to build capabilities they previously lacked the capacity to deliver.

And every new capability brings us closer to answering our original question.

To explain an employee's compensation properly, you need defensible job grades, reliable market benchmarks, coherent pay structures, accurate compensation histories and credible pay equity assessments.

In other words, you need a mature Rewards function.

AI can help in building that infrastructure, from enhancing analytical capabilities to connecting information and translating complex Reward decisions into explanations that employees can understand.

That’s why the ultimate test for AI in Rewards is whether AI can produce an accurate, personalised Pay Statement for each employee from one single prompt.

Eduard Blum is a reward leader and founding member at Range. You can find him on LinkedIn and at eduard-blum.com.