From Data to Dollars: What AI Really Means for Your Comp Strategy

As we near the midpoint of 2025, artificial intelligence (AI) is no longer just a buzzword — it’s a driving force behind more efficient, accurate, and automated compensation management programs. From workflow automation, insight capturing, and communication coaching, AI is transforming how organizations manage, motivate, and, ultimately, pay their employees on incentive compensation plans. 

Incentive compensation is one area that's especially poised to benefit from AI-driven advancements. “AI is evolving from these broad reaching use cases into specialization. One of the areas that is particularly interesting for specialization is the sales performance management space and the incentive compensation space,” shares Lindsey Bly, Senior Director, Product & Corporate Marketing at CaptivateIQ. “Both of these areas have high volumes of data, they have a lot of repeat processes, and they also have the need for precision. So when I think about where AI is primed to disrupt, this space is a really interesting one.”

Given the potential benefits of AI, how have compensation leaders adopted new intelligent technologies, and where are they seeing the most value? In this post, we’ll explore the latest AI trends in compensation management and what they mean for leaders striving to stay competitive in an increasingly data-driven landscape.

AI Adoption Trends

In the past year, AI adoption has increased rapidly. According to CaptivateIQ’s 2025 State of Incentive Compensation Management (ICM) report, 64% of companies are using AI today. And, another 12% have concrete plans to explore AI solutions soon, totaling 76% of companies are either using or planning to use AI. 

This maps to a recent survey by The Alexander Group with 79% of companies reported currently using or planning to use AI. Adoption has more than doubled since early 2024 when 35% of companies were either using or planning to use AI.

That leaves nearly one-fourth (24%) of compensation teams still waiting to explore and adopt AI solutions. This is likely, in part, due to the importance of accuracy and precision in compensation management. 

“In a world where you're dealing with compensating people and with very sensitive data, the importance of getting it right is paramount. That's really putting some of these use cases a little bit further out in terms of when I can see the technology being ready to tackle 'em,” explains  Nahi Ojeil, SVP of EPD and Technology at CaptivatIQ. “That balance and tension will continue to exist until either some new innovation comes along that's going to allow us to bridge that gap in precision or find other ways to still make it work within the constraints that are very real in the ICM space.”

That said, technology and AI is a Top 3 investment priority for Go-to-Market (GTM) executives in 2025. In fact, 57% of executives are investing in technology and/or AI this year. Though AI’s full potential for improving commission accuracy and forecasting is still unfolding, companies are already experiencing gains in automation, insight discovery, and team communication.

How are Successful Compensation Teams Using AI?

Automation 

Automation is critical in improving accuracy and efficiency. In fact, 61% of compensation leaders believe automation would significantly improve their reporting and analytics capabilities. This aligns with the fact that 58% of companies using AI leverage it to automate manual tasks—the No. 1 most-common AI use case. 

Top-performing teams are implementing automation not just to eliminate manual work, but to improve the overall return on their compensation investment — through better insights, faster payouts, and reduced risk.

“AI can be seen as a copilot for getting all the work on a day-to-day basis done more efficiently and more effectively. You see that continuing to evolve from being focused on assisting humans to being able to take on some of the work, as well. Not just be that copilot and assistant, but overall that help and support with the day-to-day tasks of managing the administrative work, managing the inquiries and the disputes, managing some of the plan building and management aspects,” shares Nahi. 

Reporting and Insights

Unfortunately, 36% of comp leaders do not have enough access to data and insights about their incentive compensation program performance. Many are turning to AI to garner greater, more actionable insights. The second most-popular AI use case is summarizing insights from reports, with 56% of AI users employing this strategy. 

Nahi explains, “AI is driving more insights. AI being integrated with your data is helping you make better decisions along the way and drive a bigger strategic impact beyond managing the day-to-day of the work.”

Communication

Nearly half (48%) of AI users rely on it to summarize and communicate compensation plans to payees. This may be because communication is a proven, popular use case for AI across industries. 

“On the planned communication front, it's relatively easy to enter into the world of Generative AI with a chatbot or ChatGPT explaining how you can communicate this plan to different kinds of seller personas and you can get something spit out that doesn't require a lot of engineering,” says Deima Tankus, AI Research Leader at The Alexander Group.

What are the Benefits of AI in Compensation Management?

Increased Efficiency 

The biggest benefit of AI on compensation programs is its efficiency gains, with 61% of users saying AI reduced time spent on administrative tasks. 

This is critical since teams without automation are losing an average of 89 hours each month on calculating payouts, triaging approvals, and fixing errors — that’s more than two full weeks of the team’s time, every month.

More Accuracy

The second most-popular benefit is Increased accuracy in commission calculations, with 58% of users experiencing this. 

With a whopping 66% of companies having overpaid or underpaid commissions before, improving commission accuracy is critical for success. Manual errors aren’t just frustrating, they’re expensive, risky, and entirely avoidable. Investing in automation not only protects your bottom line, it also builds the transparency and trust your sales teams need to stay focused and motivated.

Improved Real-time Visibility 

AI is transforming how companies manage incentive compensation — especially when it comes to improving real-time visibility and insight delivery. Over half (55%) of respondents say AI has helped surface key performance data for reps, allowing them to better understand how they’re tracking against goals, forecast earnings, and identify course-correction opportunities earlier.

What are the Challenges of AI in Compensation Management?

Lack of Internal Expertise, Resources, and ROI Understanding 

For those compensation leaders that have not yet adopted AI, 40% are struggling with lack of internal expertise and resources and 28% are uncertain about the ROI. 

Typically, a company’s executive leadership team has a big impact on AI use, either fueling or preventing adoption. “The first thing that I've seen being a blocker is the point of view of that organization around AI. Is leadership forward thinking and willing to adapt to these new technologies that are going to require change in behavior?” shares Nahi. “Overall culture and broader strategic positioning of a company matters a lot, because, otherwise, it becomes very hard to go against the grain if there isn't already an established acceptance that AI is valuable.”

“Change in an organization is generally hard and it requires a lot of overhead to realign how the organization does things once you introduce a new tool. So that's usually the next area where we see friction: are there tools already available that you can start adopting or are you going to have to go and make a case for one? If there is a tool, how integrated is it into your existing workflows? If it's not, are you going to have to go and implement new workflows and now you have to train up a lot of different people to start doing their job a little bit differently?” says Nahi. 

“Assuming you have a problem that is very clearly understood and important to address, is this AI solution guaranteed to solve the problem? If not, it starts getting challenging to convince people to take a leap of faith,” comments Nahi. “There are a few other things that we've seen help prove ROI, like having communities where you're able to talk to other professionals in the same space where maybe they have adopted these tools to solve similar problems and now that gives you a lot more validation and credibility that how you're trying to solve this problem is truly going to work and has a higher chance of success.”

He continues, “The other areas is if there's an ability to deploy AI in a way that's a little bit more incremental versus all-in, where the upfront investment is a little bit more limited and we're able to test things out, see whether they're working or not, and, through that, build more conviction and buy-in within the organization to go and invest even more.”

Integration Challenges and Accuracy Concerns 

One-third (33%) of comp leaders struggle to adopt AI due to integration challenges. Further, 26% worry about accuracy — which is often an extension of integration challenges and data reliability.  

“Our point of view throughout [our research] is not really going to be AI first. It's really going to be more process first with a large focus on how AI can improve that process. The companies that have seen significant uplift from AI have been the ones that have peeled back the layers, answering: What is wrong with my process? To do this thing that I want to do, what am I going to need to change? They looked at how they acquire and use data, turning more to machine learning solutions to first get their sales forecasting, opportunity modeling, and customer segmentation to be accurate, and then using these inputs to feed a number of strategic decisions. Only after they do this do they really get good use of their AI tools,” explains Deima. 

Conclusion

As AI continues to evolve, its impact on compensation management is becoming more tangible — driving improvements in efficiency, accuracy, communication, and strategic insight. While challenges like integration, expertise gaps, and ROI uncertainty remain, the momentum is clear: forward-thinking compensation teams are leveraging AI not just to streamline workflows, but to elevate the role of compensation as a driver of business performance. As we look ahead, the organizations that thoughtfully embrace AI — aligning it with well-defined processes and clear goals — will be the ones best positioned to turn data into dollars in 2025 and beyond.