The ability of machines to execute tasks and solve problems in ways normally attributed to humans through advanced algorithms – or in other words, Artificial Intelligence, will revamp the way HR manages employee reward programs in the future. According to Ericsson’s research, Adopting AI in Organizations, “as many as 69 percent of AI leaders foresee a constant ﬂow of new AI/advanced analytics applications in their companies – new applications that, in turn, drive more process change and reorganization.”
Companies are already leveraging smart technologies to source talent, screen applicants and manage candidate databases. The HR Learning & Development function has been using AI to better inform and equip employees with tailored learning programs to fit requirements of the job. But can artificial intelligence determine the exact level of reward – merit increases and bonus payouts - for employees? The short answer is, yes.
After the COVID-19 pandemic, companies had to revisit their total reward strategy. After employees were asked to work from home, they quickly grasped the potential to accomplish daily work responsibilities along with the occasional trip to the gym or supermarket. Employees understood work-life-balance and loved it! Now, more and more employees are pushing back against the idea of returning to the desk for an eight-hour shift. In reaction to this hesitation, companies have been forced to relax the work-from-office requirement. The flexibility to work-from-home is now an intangible benefit within the total reward framework.
While the new way of working is great for an employer’s Employee Value Proposition (EVP), this benefit comes with a few drawbacks for a reward strategy that typically places a high emphasis on recognizing consistent performance in the workplace. Working from home has limited the manager’s visibility into daily employee behaviors and has partially restricted the ability to assess performance consistently across individual employees. Given these limitations, companies should revisit the reward philosophy and consider reliance on artificial intelligence to measure performance more objectively by using machine-learning and data science to guide compensation decisions
Employers take pride in the “pay-for-performance” reward philosophy. If an employee meets the behavioral and technical competencies for the grade level and achieves all the goal metrics set in the year, the company rewards the employee with a bonus and a favorable merit increase. In the most traditional sense, this concept makes perfect sense and the philosophy will continue to be integral to the reward strategy. But can employers reward employees in an equal and non-biased manner, especially when work patterns are not easily observed?
Enter artificial intelligence to define a new standard of performance to determine the required work-level output and quality expected in a job. By applying performance insights, repetitive patterns of leadership and correlating predictive computer patterns aligned to business outcomes, employers can now establish parameters to redefine the meaning of “performance” on the job. Allowing artificial intelligence to drive and interpret appropriate, high quality standards of work removes any potential biases and need for a manager to observe daily work habits. Companies can be more practical by using artificial intelligence to learn, interpret levels of productivity, adjust to new work input, and replace human-like management to qualify performance.
How it works
With more than 80% of companies investing or planning to invest in emerging technologies, (KPMG) employers can use this investment to derive the exact merit or bonus that recognizes performance through calculated work deliverables, relevant skills learned, or characteristics deemed necessary for their business. Artificial intelligence would, in essence, re-define “pay-for-performance”. It would serve as a catalyst for the organization to manage reward spend more strategically by only paying for the specific contribution, knowledge and experience required to operate the business effectively. As employees demand more flexibility in their work environment, employers need to get smarter in managing total rewards. Maintaining competitive compensation levels for recruitment and retention will continue to be paramount in a total rewards framework. However, getting creative with how to reward employees in the absence of evaluating work progress or employee development in real-time will force employers to rely more heavily on scientific methodologies to assess employee performance. Artificial intelligence will facilitate the appropriate level of performance by collecting data from multiple sources, analyzing behavioral patterns, and assessing quality work output to ultimately personalize the right level of reward to be invested in each employee.
The days of traditional employee appraisals – manager providing feedback, calibrating performance, providing a performance rating, and using that rating to determine pay increases or bonuses – may be nearing the end. Furthermore, providing employees with subjective assessments may no longer be consistent with company culture or social norms. In the era of diversity, equity and inclusion, companies will begin to move away from rewarding employees based on the manager’s subjectivity and gravitate to machine-based intelligence that more objectively measures, and subsequently, rewards the employee experience.
HR will need to define the standards for how and what patterns AI must learn to ensure consistency in measuring performance. While AI is a computer science, HR will have an integral role in providing the necessary insights to establish the machine-learning pattern requirements that qualify inputs and outputs, as well as assessing the efficiency, effectiveness, and value of the employee’s contribution to the business. HR will need to ensure the job-specific key performance indicators defined by the business are objective, measurable and fair so that the patterns learned by AI support the reward philosophy.
The use of artificial intelligence in reward management will establish a standard for non-discriminatory compensation where employee pay is differentiated using tech-science and statistical classified outcomes, rather than the subjective performance assessment tools used for compensation decision-making. In time, the total rewards framework will evolve along with artificial intelligence. For now, the challenge for HR is to embrace artificial intelligence and to recalibrate the concept of “pay-for-performance” in an era where smart-machine learning can extract the precise level of rewards to recognize the employee’s value to the organization.