David Yong
Senior Principal, Head of Board and Executive Remuneration Advisory Services, Pacific
AI is the future of total rewards
The future of total rewards is brighter than ever as artificial intelligence (AI) evolves in the workplace.
Managing the total rewards function in Australian companies has become increasingly complex. Rising costs, evolving employee preferences for diverse rewards, and the need for sustainable strategies to attract and motivate top talent pose significant challenges, particularly in today's demanding economic landscape.
While the full extent of AI's impact on rewards management is still being explored, rewards teams can already leverage AI to augment rewards workflows, amplify intelligence, optimise investments, and improve employee experience.
In this article, we explore ways in which AI can empower the reward function to maximise their efforts, improve decision-making and impact in the business.
Augmenting rewards workflows with AI
AI’s ability to learn, analyse, predict and create can streamline numerous HR tasks to boost efficiency, improve outcomes and drive greater productivity.
Much of the work in total rewards involves transactional tasks that are suited for human-machine teaming. A recent Mercer study of 10 markets globally found that AI and automation could replace more than half (52%) of a rewards team’s workload. This is especially critical as rewards teams are being asked to do more with less. For example, tasks related to routine employee inquiries and benefits administration can be handled by AI and free up total rewards professionals to focus on more strategic and value-added activities. Mercer’s Global Talent Trends 2024 study found that approximately 44% of HR leaders in the Pacific region now use AI for benefits administration, skills insights and talent management — with an additional 39% planning to follow suit in 2024.
Organisations are already using AI to support the rewards function more broadly, particularly in these five areas:
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Policies and proceduresAI can analyse data across a company’s remuneration and benefits programs to streamline total rewards policies, ensuring greater fairness and consistency thereby enhancing the effectiveness of the remuneration and benefits programs. Another situation where AI data analysis can be particularly impactful is in merger and acquisition activities when large volumes of HR materials need to be reviewed within a short timeframe. In multinational organisations where the wide variety of local programs and supporting documents has become unmanageable AI can provide much needed help.
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Job descriptionsIt is not unusual to see companies struggling to keep their job descriptions up-to-date. Employers can leverage AI to identify areas where job descriptions are out of date, including flagging when they need to be updated, if reporting lines are incorrect. AI can also review, enhance and standardise job descriptions, incorporating more contemporary and inclusive language, and translating them into multiple languages, as required. Additionally, AI can enhance job descriptions by accelerating the process of adding key skills to jobs, and by helping to ensure that they align with corporate values and legal obligations.
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Job architectureAI can provide support in aligning job levels and job families, sorting these into job and skills structures, and recommending career paths for specific functions. Some companies have implemented talent marketplaces which have this functionality already built in and are using AI to map individual employees to the overall job structure.
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Goal setting and alignmentSome organisations have started adopting AI tools to effectively cascade business goals based on organisational objectives and performance data. Some can also define SMART (Specific, Measurable, Achievable, Relevant, Time-bound) goals to improve firm-wide consistency and alignment across diverse employee groups and teams.
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Performance management systemsChatbots and other AI-powered tools can automate performance tracking, monitor workflows, provide real-time feedback, send task reminders based on business priorities, and generate performance reports with recommendations for improvement. These systems have the potential to become an essential part of employee experience (EX) design, reducing administrative work for employees and allowing for more informed in-person discussions to support a culture of growth.
Clearly, AI’s ability to integrate large datasets and automate certain tasks will change the nature of work. The opportunity for total rewards teams to leverage generative AI will help streamline and simplify the function, improving EX while also producing higher-quality outputs and driving more objective decision-making.
Amplified intelligence in total rewards
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 remuneration and benefits. But amid volatile market conditions, fluid business goals and the needs of a diverse workforce, the challenge is for reward professionals to glean more insights from the data specific to their organisations so as to offer fair and competitive packages that strike an optimal balance of pay and benefits within their capacity to pay.
AI can play a crucial role in achieving this objective by effectively aligning disparate datasets, revealing hidden patterns and insights, and proposing innovative reward strategies. This empowers employers to customise their total rewards offerings to meet the specific needs of employee segments that are most in demand.
Leading firms are actively exploring various avenues to leverage amplified intelligence, including:
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Predictive performance analyticsAI can analyse performance data for trends, performance drivers and areas of opportunity. Predictive models could take these insights one step further, integrating dispersed datasets to identify high-potential talent and propose the optimal set of rewards programs and working conditions to maximise future performance. For example, Mercer’s Global Talent Trends 2024 has also shown that 43% of companies in the Pacific region are already using AI to predict absences.
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Pay equity and transparencyMercer’s Global Talent Trends 2024 study also found that, globally, thriving employees are two times more likely to report that their firms provide pay transparency for all internal jobs. Pay clarity now required in at least 20 countries in addition to pay equity requirements in many jurisdictions. In Australia, amendments to the Workplace Gender Equality Act were passed in March 2023 which requires greater transparency on gender pay equity, amongst others, as well as legislation banning pay secrecy being in place as of 7 June 2023. These changes mean that AI can become an essential tool to identify gaps, narrow down the causes and enable companies to adjust their approaches to better ensure compliance with local obligations.
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Rewards and recognitionGenerative AI can suggest rewards and recognition values based on performance metrics, company guidelines, market benchmarking data, employee listening and benefits take-up. In fact, some companies are loading their pay equity analyses, competitive market data and individual performance data into AI systems that can generate pay recommendations for new hires, promotions and annual adjustments for individuals across the organisation. While still leaving room for manager input that reflects a strong business rationale, the addition of AI will drive an increase in fair and competitive pay decisions and provide a robust foundation for full pay transparency.
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Executive compensationAI can be used to collect information on peer group strategy and pay policies and practices — including incentive plan metrics, performance targets and payouts — to recommend adjustments to a company’s executive reward programs.
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 including market benchmarks. 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.
AI for impactful and improved EX
What do employees have to say about their rewards? When asked how their compensation could improve, workers’ top response in the 2024 Mercer Global Talent Trends was more types of rewards and personalisation. Many would even give up a 10% pay raise for other incentives, from more well-being benefits (45%), to paid training (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 personalise total reward packages and optimise 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:
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Optimised ex- and repatriationAI could align multiple data sources with employee information, enabling it to suggest appropriate mobility support and remuneration for expatriates. It could also facilitate repatriation by predicting opportunities based on skills, experience, and employee preferences.
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Workforce and remuneration planningRewards experts can use AI to synthesize market pay data, demographic and country information, risk predictions, and supply and demand of key skills for workforce and pay planning purposes. AI could also assess potential talent shortages and the need for real-time pay adjustments in order to meet future workforce needs.
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AI/Employee performance reviewsAI-driven platforms could empower employees to conduct self-assessment and peer reviews. With the assistance of Generative AI, these platforms can provide guidance, ensuring that the evaluations are objective, constructive and aligned with organisational goals. Keeping managers in the loop to validate and discuss feedback from AI can ensure this doesn’t damage the Employee experience (EX).
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Sentiment analyses and neuroscientific assessmentsGenerative AI could analyse digital communication patterns, facial expressions, verbal cues and other data to gauge team dynamics, workforce sentiment and emotional well-being. Organisations can then apply the findings to improve collaboration, communication, and other issues that impact team performance and the overall EX.
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.
Welcome to the future of total rewards — where do you begin?
Total rewards leaders that are considering embarking on their AI journey should consider these three key areas:
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Data: Your foundation for AI capabilitiesMost organisations think that their data is in good shape, only to find that both the quality and quantity of employee-level data is not where it needs to be. Is your data related to pay, performance and benefits easily accessible and in systems that are integrated? Is job-level data correct and up-to-date? Are your jobs aligned into a consistent job architecture that incorporates key skills? Have you analysed your pay data to identify any unwanted bias, address inequitable pay gaps and understand the drivers of these? All of this work forms the foundation upon which future AI capabilities can be built.
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Address the risks head-onThe use of personal, employee-level data within AI models presents tremendous opportunity, along with significant risk. Now is the time to bring your IT, legal and HR teams together to develop internal governance, policies and practices to ensure AI models are used for maximum benefit while aiming to minimise and mitigate risk. Key areas in Australia that HR teams need to be cognisant about include emerging changes to the Privacy Act, where does the AI system store employee data that has been used to generate outcomes, ensuring the use of AI does not result in potential discriminatory outcomes, potential infringement of copyright, amongst others.
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Get smart and have a planThis year, high-growth companies are two times more likely than low-growth ones to create a dedicated HR team or role that’s focused on new technology. Understanding how AI works, the potential use cases within your organisation, and having a roadmap that outlines the key steps and priorities for implementation are all critical for long-term success.
Still not sure how to start? Download our point of view The Future of Human Resources to learn what HR leaders can do to evolve the HR function for the age of AI or contact a consultant to set off on the journey.
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Read the original version of this article: https://www.mercer.com/insights/total-rewards/employee-benefits-strategy/ai-is-the-future-of-total-rewards/
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