Getting started with AI-enabled rewards? Start here

A mature CEO and her team of professionals having a briefing before the work.

Gordon Frost

Global Rewards Solution Leader

Six steps for using artificial intelligence in total rewards

For many in the rewards world, the past few years have been an exercise in triage. First, there is the chaos of crafting rewards in a rapidly evolving world of work, the necessity of responding to economic volatility, and, of course, addressing growing demands for pay transparency and pay equity. Add to that shrinking budgets, mounting workloads, and rising expectations from leadership, employees and regulators, and rewards teams are stretched thin, leaving a lot of uncertainty and little room for innovation.

Is artificial intelligence (AI) the “white knight” total rewards needs? The headlines are full of stories about AI becoming integrated into every area of work, so you may be wondering if it can be harnessed to help your team streamline administrative tasks, gain deeper insights into workforce dynamics and deliver a more personalized, engaging rewards experience.

The short answer is yes. But how? And where can you start?

In my previous article on this topic, I discussed the transformative potential of AI for total rewards, highlighting how it simplifies workflows, optimizes investments and creates personalized employee programs. But determining exactly where to start can feel daunting. This article is meant to offer a practical roadmap with six steps that can help you begin integrating AI into your rewards strategy.

Whether you’re cautiously curious about AI’s capabilities or excited to jump in immediately, these steps can help you move past that day-to-day rewards scramble and start building a strategy that’s future ready — and maybe even a little fun.

Before diving headfirst into AI, it’s important to take a good, hard look at where you’re starting. Think of this step as laying the groundwork. Without a solid foundation, even the best tools can only take you so far. Assessing your current state will help you spot gaps, find opportunities, and make sure your data infrastructure can actually support all that cool stuff AI can do.

Are your data ready for AI?

Data can be messy. That’s why your first move should be a thorough data audit. Take stock of your pay, performance and benefits data. Are they complete? Are they accurate? If they’re aren’t, AI won’t be a magic fix. Garbage in, garbage out, as the saying goes.

But don’t stop there. Even if your data check out, it’s worth asking: are they fair? Differences can hide in the details, even when the numbers seem “right.” For example, are certain colleagues consistently underpaid compared to their peers? Identifying and addressing these issues upfront means your AI initiatives won’t accidentally double down on existing pay differences.
Break down those silos

Another big hurdle for many organizations is fragmented data. If your systems don’t talk to each other, it’s going to be tough to get the full picture. AI thrives on connections — pulling together insights from all corners of your HR ecosystem. Make sure your systems are integrated and easily accessible so AI can work its magic without getting stuck in data silos.
Dust off those job descriptions and power up your job architecture

When was the last time you updated your job descriptions and levels? If it’s been a while, now’s the time. Outdated or inconsistent job-level data make it harder to ensure fair pay — and it can trip up AI tools too. Take a fresh look at your roles, and align them into a clear, consistent job architecture that includes the skills and competencies needed for success.

All this might not feel super exciting when you’re eager to get to the AI assist, but it’s absolutely where the magic starts. If your data are clean, fair and accessible, you’re setting yourself up for success. Skipping this step is like trying to bake a cake without measuring the ingredients — it might work, but the odds aren’t great. By investing time here, you’ll make sure AI has what it needs to deliver insights and efficiencies you can trust.

With your foundation set, you’ll be ready to explore the areas where AI can make the biggest impact.

Before you get too far in planning your AI roadmap, you’ll want to focus on one of the most important pieces of the puzzle: your team. AI is only as effective as the people using it, so building AI literacy among rewards professionals is nonnegotiable.

Make AI accessible

Start by demystifying AI for your team. It can be intimidating for people who are unfamiliar. Training sessions should cover the basics of how AI works, where it can add value and how it integrates with your existing processes. For example, your team might attend Mercer’s AI Essentials for HR to become more acclimated in the technology. The goal is to make AI feel less like a black box and more like a helpful teammate.
Foster a learning culture

AI is evolving rapidly, which means your team’s knowledge needs to keep up. Encourage ongoing learning through workshops, webinars and even certifications. Create an environment where team members can experiment, learn and become more confident in their own AI literacy. Consider designating a team member — or an entire role — to focus specifically on staying ahead of AI advancements.

Your team’s comfort and confidence with AI will make or break your integration efforts. The better they understand the tools and their potential, the more likely they are to use them effectively — and maybe even uncover new opportunities you hadn’t thought of yet.

With your data in great shape and your team excited and ready to dive in, it’s time to lay the groundwork for experimentation. This is your AI sandbox — a safe space to test and refine how AI can be integrated into your rewards strategy. Starting small and pragmatic is the key to building confidence, identifying gaps and understanding what works best for your organization.

A sandbox allows you to experiment with specific use cases in a low-risk environment — one that is protected within your company’s firewalls to ensure that sensitive data remain confidential. By focusing on manageable projects with clear objectives, you can learn from successes and setbacks while avoiding overcommitment to unproven tools or processes.

What belongs in your sandbox?

Focus on tasks and challenges that are time intensive, repetitive or prone to human error. These are ideal opportunities for AI to lend a hand. Start with areas that deliver immediate impact, and scale from there.

Not all AI tools are created equal, so take the time to do your research. What problems are you trying to solve? What outcomes are you hoping to achieve? Look for applications that align with your organization’s goals and that incorporate features like predictive analytics, skills insights or automated performance tracking.

The good news is you might not need to start from scratch. Many HR platforms already have built-in AI functionalities. Start there — it’s a great way to dip your toes into AI without committing to a massive tech overhaul. If this feels overwhelming, an expert from Mercer’s Data Analytics team can help you prioritize, select and get up to speed on solutions.
Personalize with purpose

One thing AI excels at is helping you connect with employees in more meaningful ways. For instance, AI-driven tools can be used to simplify HR policies and respond to frontline employee questions, making the employee experience less one-size-fits-all and more tailored to employees’ unique needs.
Start where the impact is immediate

The trick here is to prioritize areas where AI can add immediate value. If your team is constantly swamped by manual processes or struggling to make sense of mountains of data, those are good places to start. This will help you get some quick wins under your belt before tackling the bigger stuff. You can always reach out to your Mercer team to identify the best starting point and strategy for your organization.
Learn, improve, scale
As you experiment within your sandbox, document learnings, identify gaps in your data, iterate based on feedback and gradually expand your initiatives. This approach not only minimizes risk but also builds a strong foundation for scaling AI’s impact across your organization. By starting small and methodically, you will create a practical and agile strategy that evolves with your team’s needs and capabilities.

Once you have your sandbox established and have started to build a track record of success, you can expand it to focus on broader AI use cases. Below is a list of ways AI can lend a helping hand in rewards.
- Benefits administration: Streamline enrollment processes, manage routine inquiries and ensure compliance with changing regulations.
- Compensation analysis: Identify pay gaps, optimize salary structures and predict future compensation trends.
- Manage compensation adjustments: Leverage AI to drive more consistency in salary levels for new hires and streamline decision-making across your employee population for the annual salary adjustment process. For example, Mercer offers Pay AI and Merit Co-Pilot solutions to help teams leverage AI for better compensation decisions.
- Pay equity audits: Detect and address disparities in pay to ensure fairness and compliance with transparency regulations.
- Performance management: Automate performance tracking, identify high performers and align rewards with contributions.
- Job description optimization: Generate or refine job descriptions to ensure alignment with organizational needs and skills frameworks.
- Personalized employee rewards: Tailor benefits and recognition programs to individual preferences based on data-driven insights.
- Retention and turnover analysis: Use predictive analytics to identify at-risk employees and proactively address their concerns.
- Workforce planning: Forecast future talent needs, and align rewards strategies with long-term business goals.
- Employee sentiment analysis: Analyze feedback to gauge employee satisfaction with total rewards programs and identify areas for improvement.

AI can be an incredibly powerful tool but only if you know how to control it. That’s where a solid governance framework comes in. Establishing clear policies and processes ensures that your AI initiatives are not only effective but also ethical and compliant.

Bring in the right people

AI governance isn’t something rewards teams can tackle alone. Bring together a cross-functional team that includes HR, IT and legal experts to create a balanced approach. Each group brings a unique perspective, from ensuring compliance with data privacy laws to managing technical risks.
Monitor for fairness

Even the best AI tools can reflect or amplify unfairness if left unchecked. That’s why continuous monitoring is essential. Build in mechanisms to ensure objectivity in your AI models, whether through regular audits or by leveraging external experts.

A governance framework can feel like red tape, but it’s really about protecting your employees, your organization and the integrity of your rewards program. With clear guardrails in place, you will be able to innovate confidently without worrying about unintended consequences.

Now it’s time to put everything into action. But before rolling out AI across your entire rewards function, start small. A thoughtful pilot program allows you to test the waters, learn what works and refine your approach before scaling up.

Start small, learn big

Choose a specific area or project for your pilot — something manageable but impactful. For example, you might start with streamlining your job descriptions or by using AI to analyze pay trends and identify pay gaps.
Gather feedback and iterate

During the pilot, collect feedback from both your team and employees. Are there gaps in your data? Is your team missing key knowledge or skill? Are the tools easy to use and delivering the expected insights or efficiencies? Use this feedback to fine-tune your approach before expanding.

Pilots give you the chance to experiment without committing to a full-scale rollout. By learning and iterating on a smaller scale, you’ll set yourself up for a smoother and more successful implementation when it’s time to go big.

By now, you should be well on your way to integrating AI into total rewards. But the journey isn’t over. AI isn’t a “set it and forget it” tool. To get the most out of it, you’ll need to continually evaluate its performance and adjust based on feedback and outcomes.

Keep an eye on metrics

Regularly review the impact of AI on your rewards processes. Are you saving time? Improving accuracy? Enhancing employee satisfaction? The more you measure, the better you can optimize.
Stay agile

AI and workforce dynamics are constantly evolving. Be prepared to adapt to new technologies, methodologies and employee needs. The organizations that succeed with AI are the ones that stay flexible and open to change.

Continuous improvement isn’t just about keeping your AI tools sharp — it’s about ensuring they continue to align with your overall goals. By staying proactive, you’ll keep your rewards strategy future ready and impactful.

Building a future-ready rewards strategy

The potential of AI in total rewards is immense, and deciding where to begin can feel overwhelming. However, by following these six steps, you can create a solid foundation for an AI-enabled rewards strategy that is effective, ethical and tailored to your organization’s needs.

As we explored in my last article on this topic , Mercer has deep expertise in helping organizations navigate the complexities of rewards transformation. Whether you need support with data audits, workshopping potential use cases, developing your AI roadmap or building governance frameworks, Mercer’s team can provide the guidance and insights needed to succeed.

So, whether you’re cautiously curious or ready to jump in, now is the time to take that first step. With AI on your side, you can move beyond the day-to-day scramble and build a rewards strategy that can lead the way into the future of total rewards.

About the author(s)

Gordon Frost

Global Rewards Leader, Mercer

Contact a consultant

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Getting started with AI-enabled rewards? Start here

Six steps for using artificial intelligence in total rewards

Step 1. Assess your current state

Step 2. Train your team

Step 3. Build your AI sandbox

Step 4. Develop a governance framework

Step 5. Pilot and scale

Step 6. Continuously evaluate and improve

Building a future-ready rewards strategy

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