Artificial Intelligence and Optimising Wage Structure

The Global Wage Structure 

The wage structure is an aspect of employment which is determined by several factors. Many of these factors are related to socio-economic conditions such as the establishment of unions, cost of living, government legislation and supply and demand. Wages are viewed differently by employers and employees. Because employers consider them as part of the cost of production, efforts are made by them to keep wages low. On the other hand, workers consider wages as remuneration for their work and desire to receive adequate payment for their work. Current statistics show that:

i. Minimum wages exist in about 90% of ILO member states. 

ii. Despite the existence of legislation, an estimated 19% of wage earners globally are paid below this minimum rate due to lack of enforcement of legislation. 

iii. This contributes to global pay inequality and rising levels of poverty

These defects in wage structure contribute to reduced job satisfaction and productivity among workers. Furthermore, the establishment of wage structure is affected by difficulty in calculating work hours with appropriate allowance for breaks and overtime. Thus, proper development of an adequate wage structure is necessary for maintaining the workforce as well as controlling the costs of production. Most employers establish a base structure which is then reviewed periodically and with respect to trends in the labour market. To ensure that these processes are effective, several methods are being explored to optimize wage structure.

The Role of AI in Optimising Wage Structure

There are various possible roles of AI in optimising wage structure. 

i. In order to better attract skilled workers, employers are using AI to match required skills to payment offers during the hiring process.

ii. Metrics to adequately compensate workers for the achievement of goals can be developed to aid employers in providing benefits to employees.

iii. In addition to these, AI can be used to analyze trends in the labour market and ensure that employers continue to make pay offers that are competitive with respect to the local environment. Thus, worker’s pay will reflect market provisions for the particular skill set provided by the employees and will also ensure that a review of pay structure focuses on regular feedback rather than annual appraisals.

iv. AI can be used to organise available data on labour according to location and also use past trends to predict future possibilities in the market. This will help employers to adequately prepare for possible shifts in demand and supply that can affect the cost of labour. 

v. Compensation bias and discrimination affecting pay have exposed employers to litigation. AI based compensation tools can be used to avoid these problems by building more objective systems for determining pay. These systems will help to ensure that employee pay becomes more personal and closely linked to job performance which will in turn create more incentive to increase productivity. 

vi. AI systems can better personalize recognition of job performance and help to ensure that benefits and raises are tailored to reward improved levels of performance. This will undoubtedly result in reduced turnover and increase talent and skill retention 

vii. The cost of managing the payroll is greatly reduced with AI as systems can manage far larger volumes of data than human managers and also do so with little or no errors. 

viii. These systems can also detect attempts at fraud and reduce losses due to workers claiming pay for time taken off work. 

The roles of AI in optimising wage structure can be applied at the various levels of hiring, compensating, promoting and rewarding employees. The use of AI systems will ensure that employers are better able to retain available skills and that employees attain higher levels of satisfaction and contribute to maintaining productivity in the workplace.


Resources

https://www.ilo.org/wcmsp5/groups/public/---dgreports/---dcomm/documents/publication/wcms_762302.pdf

https://www.shrm.org/resourcesandtools/hr-topics/compensation/pages/bringing-artificial-intelligence-into-pay-decisions.aspx

https://www.jdsupra.com/legalnews/ai-based-compensation-management-and-3369394/

https://www.innovyne.com/how-ai-can-help-compensation-managers-and-business/

https://www.beqom.com/artificial-intelligence-driven-compensation

https://sightsinplus.com/practices/rewards/the-impact-and-potential-of-ai-in-compensation-benefits/

https://www.hrmonline.com.au/innovation/how-ai-will-impact-the-future-of-payroll/

https://www.benefitnews.com/news/how-ai-can-eliminate-bias-in-compensation-practices



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