AI is the future of total rewards 

AI’s ability to learn, analyze, predict and create can streamline numerous HR tasks to boost efficiency and improve outcomes. Unlocking AI’s full potential takes an investment not just in new tools, but in work design: the process of deconstructing jobs into tasks, redeploying those tasks to the optimal mix of talent and technology, and creating new ways of working that account for this fresh division of labor.

Much of the work in total rewards involves transactional tasks that are ideally suited for human-machine teaming. A recent Mercer study found that AI and automation could replace more than half (52%) of a rewards team’s workload, including tasks related to routine employee inquiries and benefits administration. Mercer’s Global Talent Trends 2024 study found that approximately 40% of HR leaders now use AI for benefits administration, skills insights and talent management — with an additional 40% planning to follow suit in 2024.

Organizations are already using AI to support the rewards function more broadly, particularly in these five areas:

There’s a more exciting upside to AI in total rewards, beyond the promise of improved productivity. Amplified intelligence is what happens when AI bridges gaps in our knowledge to stimulate new standards of work quality, decision-making and value creation.

Total rewards teams already work with huge volumes of data to make informed decisions around compensation and benefits. But amid volatile market conditions, fluid business goals and the needs of a diverse workforce, it’s now harder than ever to offer a fair and competitive package that strikes the perfect balance.

AI can help align disjointed datasets, uncover hidden insights and even suggest new reward strategies so employers can tailor their total rewards offering to the employee segments that are most in demand. As AI advances, here are some ways that leading firms are experimenting with amplified intelligence:

The rewards function has always been data-driven, but using AI effectively will require large volumes of high-quality data — potentially from a variety of sources. If there is bias in historical company data, such as pay gaps between workforce segments or individuals, AI models that train on this data could make recommendations that reflect and perpetuate this bias. It’s essential to keep humans in the loop who can identify and address these concerns, in addition to the critical issues of data privacy and overall data governance — both on a proactive basis and in real time.

These use cases show how AI continues to shift the nature of work in total rewards. Today, people in the function are manually sourcing, managing and integrating different datasets; tomorrow, they’ll need to work more strategically to advance the priorities of their organization. Success in rewards will take more sophistication in fitting these insights together, flagging and fixing the disconnects, and ensuring that AI-powered rewards outcomes align with compensation philosophies and pay practices.

AI will enhance the delivery of total rewards programs, which are essential for a compelling employee experience. Today, leading organizations are evolving their pay programs to attract key talent while responsibly managing costs. HR leaders say rising labor costs and skills shortages will be top pain points in 2024, and more than a third (36%) of executives don’t think their current talent models can meet demand.

What do employees have to say about their rewards? When asked how their compensation could improve, workers’ top response this year was more types of rewards and personalization. Many would even give up a 10% pay raise for other incentives, from more well-being benefits (46%), to paid trainings (26%), to work-from-anywhere setups (21%). These findings suggest that the best total rewards programs span a broad range of needs for a highly diverse workforce.

AI has tremendous potential to personalize total reward packages and optimize program spend and delivery — enabling top companies to win the talent wars, boost total well-being, improve benefits take-up and enhance the overall employee experience. Consider these potential use cases:

Some of these capabilities may be controversial. Efforts to collect biometric data or monitor employee conversations can be seen as intrusive and, given data protection laws in many geographies, even risky without informed consent and robust governance. Given the other challenges around AI, such as hallucinations and data security, it’s clear that relying on AI too heavily in these areas carries significant risk.

While advances in generative AI will make these applications technologically feasible, the best HR teams will use caution and diligence to understand the risks and develop clear governance policies around data privacy and ethical AI usage. The rules and regulations are sure to vary by jurisdiction and geography, especially as governments take their own positions on these issues.