We surveyed over 200 of your peers to find out how they'd use an AI magic wand if they had one. Here's what they told us.

When we asked compensation professionals north of the border to imagine they had an AI magic wand, we wanted to understand where they see the biggest opportunities. More than 200 of you participated in our poll, and the responses paint a clear picture of where AI fits into today's compensation work and where it doesn't. At least not yet.

Market pricing is the clear leader

Thirty-six percent of respondents identified market pricing as the top task they'd hand off to AI. That makes sense. Keeping up with market data is labour-intensive work that demands a lot from your team. You're constantly pulling benchmarks, tracking how your numbers stack up against the market, and managing the data that keeps your pay structures current.

Where AI shines here is obvious: this type of work is repetitive, data-heavy, and time-consuming. AI tools are getting better every month at pulling market comparators, spotting outliers, catching data that's gone stale, and running sensitivity tests. These are exactly the kinds of tasks where you can get a real productivity boost from automation.

Pay equity analysis sits in second place

Twenty percent of respondents picked pay equity analysis as their top AI priority. This one's worth paying attention to because it tells you something important about where your peers are feeling the strain.

Pay equity work is complex and demanding. You're weighing legitimate factors that should influence pay, hunting for gaps that can't be explained by those factors, and running that analysis across different roles, different regions, and demographic groups. The computational side is genuinely heavy lifting, and it's easy to see why AI would appeal as a way to handle that burden.

Here's the catch: pay equity analysis is an area where your involvement matters. AI can find a gap for you. It can't tell you why the gap exists, whether it's defensible in your context, or what your next steps should be. The professionals who'll get the most value from AI in pay equity are those who understand the analysis well enough to look critically at what the model is showing them and ask the right follow-up questions.

Pay recommendations: a growing edge

Fifteen percent of respondents flagged pay recommendations as their top choice for AI input. This is an area where AI vendors are channeling real development efforts right now. Platforms like Workday, SAP Joule, and Mercer's PayAI are embedding recommendation engines directly into compensation workflows to generate pay suggestions based on candidate or employee profiles.

The tasks further back in the line

Only 7% chose reporting and insights as important AI tasks. Just 1% of respondents said they'd want AI to help with employee communications— a low response that is telling. Here's something interesting: AI is considered relatively effective at helping comp professionals draft clear explanations of why someone's paid what they're paid. It's one of AI's more developed capabilities in HR right now. The low response probably reflects a different concern. A lot of comp professionals see the real challenge as more of a strategy question than a writing question.

The skeptics are in the room

About 20% of respondents opted out. They were curious how others want to use an AI magic wand but didn’t have a response at the ready. Some of you are watching from the sidelines, and that's fair—there's real skepticism in the compensation world about whether AI is going to solve the genuinely hard problems you face.

But here's what's happening: AI tools are already built into compensation platforms and they are rapidly changing the way work gets done. The real conversation isn't whether AI will change your work. It's whether you're going to be active in shaping how it does while learning the ever-changing skills to harness the power.

Looking ahead

Your peers have spoken. The appetite for AI in compensation work is real, and it's concentrated in the most data-intensive parts of your job. That tells us something important about where the pain points are today, and where the biggest gains might be waiting for you. The comp professionals who will navigate this best aren't the ones waiting to see what AI can do. They're the ones feeding it the right data—and that starts with having reliable, current market data to begin with.

Have thoughts on what you'd want AI to tackle next? We'd love to hear from you. Reach out at surveys@mercer.com or call us at 855-286-5302.