How GenAI is Changing Compensation Management: Lessons from the Field

AI is reshaping how we approach compensation in HR, but implementing it comes with its own challenges. Inspired by my recent visit to a Fortune 500 Pharma, where they are rolling GenAI in all areas of HR, I have gone around asking anyone that would talk to me how they are implementing GenAI to improve HR, and specially compensation. Here are the key lessons I’ve learned from how leading practitioners are integrating GenAI into comp processes:

1. Clean Data is Essential

GenAI is only as good as the data it uses. Before getting started, it’s important to  address issues like missing information, outdated benchmarks, differences in coding and formatting, and biases baked into past decisions. Special attention goes to obtaining data from outside the standard demographics in the HRIS system. This extends to other HR data such as how much various skills are worth in the market, or results of the engagement surveys to determine how much pay impacts turnover. However, it also extends to non-HR data that we need to assess the impact of our practices, such as financial sales, or customer satisfaction results. The latter proves to be difficult, as Finance, Sales and Marketing are as usually as willing to share their confidential data as we are about sharing individual pay numbers. Some negotiation is required. My view is that we should share pay data with supervisors, to help them manage their staff.

I have said before that bad data is still better than no data. Best is to have data that is accurate and representative. 

Then it is important to have the data in one place, so it can be used. More importantly, we need governance to make sure only people with access to the data can see it, and only those who can change it can do so. 

For example, one organization discovered discrepancies in pay data for similar roles across different regions. The job evaluations were inconsistent, and so were the assigned salary grades. Fixing these gaps was critical to ensuring fair and meaningful recommendations from the GenAI tool they built to carry out job evaluations.

2. Transparency Matters

When introducing GenAI-driven recommendations, HR staff, and employees, have many questions—and some concerns, derived from what they have read about biases, hallucinations, and the like. 

To build trust, it is important ot be upfront about how the system works, what data it uses, and how decisions are made, and specially that GenAI is not making the decisions by itself: It is helping some human to make those decisions by assisting in the analysis and processoing of the data.

In one organization, they gave employees access to their own pay and benefits data, including their compa-ratio and how they compare to the market data which was stored within the system, which helped demystify the process. Clear communication was a game-changer in getting people on board with the new approach.

3. Human Judgment Still Counts

As stated above, at least for now GenAI gives us recommendations, but it doesn’t make decisions. People do. In one of the best cases I encountered, HR staff was trained to prompt GenAI, integrate it with other data via APIs, and invited to explore how they could improve their work processes by deploying GenAI tools. An outcome of this exploration was a home-grown tool that could take the input from the TA team in terms of the characteristics of candidates vs. their job profiles, and would help to calculate a job offer, balancing with other factors, like an individual’s unique circumstances.

For example, one AI recommendation suggested a standard package for a mid-career joiner. However, the comp manager knew this person was being hired to take on a major project, so they adjusted the job offer to reflect that. AI helps guide decisions, but humans still make the call.

4. Regular Updates Keep the System Relevant

Compensation trends and employee needs change, so the GenAI system can’t be static. After every compensation cycle, and to keep the systems performing optimally, it is necessary to test the system frequently to ensure there are no hallucinations: For instance, if you give it the same job description several months later, will it still do the same job evaluation?

Every so often, and util we can be sure that it all is working as it should it is important to constantly review feedback, update the data, and refine the algorithms.

In one instance, the comp manager realized the GenAI tool that was generating job offers wasn’t providing competitive recommendations for highly specialized roles. By incorporating additional skill-based data sourced by a vendor that searches industry job portals for this information, they were able to make the system more accurate for the next iteration of job offers.

5. Fairness Requires Constant Attention

Bias can creep into any GenAI system, depending on how it is trained, but also on how it is monitored. As stated above, the ones that have GenAI tools in place regularly audit the GenAI-driven recommendations to ensure fairness.

One audit flagged that employees in certain departments were receiving lower bonus recommendations than their peers, even when performance metrics were similar. This led the comp manager to revise how the system weighted certain criteria so that the results were comparable. These audits are an ongoing process, not a one-time fix.

6. Personalization Boosts Engagement

One of the biggest benefits of using GenAI is the ability to create more tailored compensation packages. By analyzing employee preferences and career goals, organizations can offer options that better align with individual needs.

For instance, an employee who prioritized flexibility over pay increases was offered additional time off instead of a higher base salary. This approach has improved satisfaction by addressing what matters most to employees, and makes it easier to implement a personalized pay philosophy by standardizing the approach through the GenAI algorithm. 

7. Communication and Training Are Key

The hardest part of implementing GenAI is not the technology—although many are still not comfortable with the notion that we are doing our own “programming”. The biggest challenge is in managing the change. Employees and managers need to understand not just how the system works, but why it was being used.

Training sessions for HR teams and Q&A sessions for employees to address concerns are the minimum required. These efforts will allow for further exploration on how GenAI can help, while providing for a smoother adoption by making the process feel collaborative rather than imposed.

The Impact So Far

Since adopting GenAI for comp, some organizations are seeing measurable improvements:

• 30% reduction in pay disparities in one case, by identifying and addressing inequities.

• 25% increase in employee satisfaction with compensation, in another thanks to greater personalization and transparency in communication.

• 40% less time spent on compensation planning, including the calculation of merit and bonus, preparing job offers, answering employee questions and submitting data for consultants, freeing up HR teams’ time.

Final Thoughts

AI isn’t a magic solution, but used wisely, it can provide needed resources to compensation management. It certainly beats having to do so many things manually, with only the help of excel!

Have you tried using GenAI in comp? What’s worked—and what hasn’t? I’d love to hear your experiences.