Most organisations in talent-driven economies are experiencing “Great Resignation.” The pandemic, increased inflation, and robust economy have created extreme labour shortages. Though great resignation is a newer phenomenon, the underlying reasons for why people leave are mostly the same for many organisations. Compensation, culture, management, work-life balance, and innovation are the primary reasons people quit, and compensation tops the list.
Having a proper compensation function is critical for an organisation to retain the top talent and prevent them from being poached by their competitors. Yet, organisations struggle significantly to understand the salary needs of their employees.
There are several reasons why organisations face challenges when understanding employees’ compensation needs. HR must get several things right – incorporating the correct elements in the overall compensation, increasing the salary by the right amounts at the right time, reflecting the short-term needs of employees while balancing the organisation’s long-term vision, to name a few.
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The traditional ways of determining the salary increment involve acquiring the data from an external vendor that generates the salary ranges from various proprietary data. They often augment this data with government surveys to get a comprehensive picture. While this approach looks foolproof on paper, its capability to reflect the needs of modern enterprise employees is not up to the mark.
The current compensation system assumes that everything falls at the level of education, experience, job category, and location. This archaic approach toward generating compensation doesn’t factor in the emerging paradigms. Take the impact of Covid-19 on employees, for example; it fundamentally changed how employees work. Yet, organisations are expecting the existing compensation systems to do justice. Here is precisely where advanced data-driven decision-making can help.
“The current compensation system assumes that everything falls at the level of education, experience, job category, and location. This archaic approach toward generating compensation doesn’t factor in the emerging paradigms.”
Artificial Intelligence (AI), a field of computer science, leverages advanced statistical and machine learning algorithms and big data to generate latent insights and recommendations. Advancements in data storage and computational methods have encouraged most enterprise business functions to embed AI-based products and solutions into their strategy, and HR is no different. When done right, AI can generate personalised pay recommendations that align with the organisation’s goals. Below are a few ways in which AI can help you with establishing a sophisticated compensation function:
Skills-based compensation aims to recognise the talent gaps in an organisation and reward the candidates with niche skills differently. Consider an example where your company is trying to build a state-of-the-art customer experience by embedding a chatbot feature into the current website. If your existing compensation structure categorises this role as a typical software engineering role, the compensation recommendations will also reflect the pay that’s normally paid to a software engineer. But the skills required by a chatbot developer are a mix of software engineering, machine learning, and natural language processing. An AI-based system can build a profile of skills and assess them against the organisational success measures. The insights generated by the AI systems can then be used to decide the salary based on skills.
A Homegrown Compensation System That Meets Your Talent Needs:
The compensation discussions in many companies are based on the market survey data they purchase from third-party vendors. The pay scale often depends on multiple dimensions of the job that are standardised such as education, experience, and level. The fundamental problem with this approach is that it relies on stale data, data from a government survey from previous years (in some cases, five years ago).
In other words, it would probably take a couple of years for these standardised models to factor in life-changing events such as recession or Covid. AI-assisted homegrown recommendation systems can detect the strengths and weaknesses of your existing salary band system, and help you create new ones that are more in line with the market requirements.
“It would probably take a couple of years for these standardised models to factor in life-changing events such as recession or Covid. AI-assisted homegrown recommendation systems can detect strengths and weaknesses of your existing salary band system, and help you create new ones that are more in line with the market requirements.”
This means that you can change from a role-based to a skill-based approach, valuing the skills your organisation needs. It’s important to note that the immediate value is in generating insights for the compensation team to help them understand current market conditions rather than generating personalised salary recommendations at an individual level.
We often hear a significant complaint from associates that the compensation discussions are not always transparent. Total compensation at most major companies is a three-legged stool. Base salary, bonus, and employee stocks form a significant part of an employee’s overall compensation. The compensation function often determines the yearly appraisal recommendations, and managers typically have a substantial say in the percent bonus awarded. These complex interdependencies between different enterprise organisations make this process opaque.
Furthermore, organisations are vulnerable to the unconscious biases that can arise while awarding compensation packages. AI can bring transparency into this process. It can do so by aggregating the performance, skills, and training data together and providing unbiased recommendations to managers solely based on their tangible contributions. This will help managers to evaluate candidates objectively.
Also read: We Expect Even Better Offer-To-Joining Ratio with the New Boundaryless Workplace Model: Ashish Kumar Singh, Meesho
Improve Retention Using Compensation:
Compensation plays a crucial role in retaining employees. Most companies design their compensation programs to satisfy employees’ short-term needs and keep them meet the organisation’s long-term objectives. Though having a balanced compensation program that incentivises employees to stay sounds interesting, it often fails to see this in context. For instance, a mix of compensation that incentivises a customer service associate to stay can be very different from that of a technology associate. AI can help you understand the latent compensation-related attributes that motivate employees to stay. This knowledge can then be used to design a tailor-made compensation package that’s personalised for different skills.
HR needs to take an intentional approach to keep employees’ needs front and centre when it comes to compensation. Organisations need to use technology to enhance the human experience, not detract from it, always remembering to keep the “human” in human resource management.
Though AI-enabled systems are great at generating recommendations, their performance is only as good as the data that feeds them. It is critical for organisations to carefully evaluate the gaps in their data before fully operationalising their advanced analytics-based systems.
Registered name and location: Bose Corporation was founded in 1964 by Dr. Amar G. Bose, Framingham (United States)
Business line: Audio products manufacturer & technology developers
Market presence: Bose operates in multiple sectors such as Online Travel, Fitness & Wellness Tech, Enterprise Collaboration, and more
Number of employees: 7000 globally