How Machine Learning Makes the Day-to-Day of the Work Easier

How Machine Learning Makes the Day-to-Day of the Work Easier

Repetitive tasks or stagnation in a daily routine in the office are some of the aspects that experts point to as the main reasons workers become demotivated in the work environment, causing company resignations.

According to data from the US Department of Labor, proof of this is the Great American Resignation, in which more than 50 million workers resigned from their jobs in 2022 after the emotional effects of the pandemic.

For this reason, companies are increasingly concerned about the well-being of their employees, especially considering that when an employee is happy, their productivity and creativity increase by 31% and 55%, respectively, while their errors are reduced by 19%, according to a Great Place To Work study. 

At the same time, companies have taken action on the matter for some time to prevent their workers from becoming demotivated by the daily routine. As a result, almost half of them (45%) are expected to automate repetitive tasks during 2022, according to IDC.

In this way, Enreach has pointed out 5 cases in which using machine learning or WFM (Workforce Management) improves the day-to-day of employees and makes their routine tasks more enjoyable, saving companies time and money: 

Assignment of tasks based on the worker’s skills: Modern WFM solutions often include a database of skills and abilities for each worker, allowing scheduling algorithms to assign tasks based on what each worker is best at. For example, if a worker is especially good at customer service, the WFM solution can assign tasks related to this type of service. 

At the same time, another who excels in sales management will be referred to the department in charge. These solutions also consider other factors such as availability, previous experience, and complexity. Therefore, the job assignment process is not based on skills and titles alone; a combination of factors is used to optimize productivity. 

Optimization and flexibility in schedules: Modern WFM (Workforce Management) solutions use algorithms to automatically create optimal schedules for staff, considering each worker’s preferences and availability. This is achieved by developing machine learning (ML) and artificial intelligence (AI) that more accurately predict staff needs than previously used systems. This type of solution is essential in departments such as customer service, which must have workers 24 hours a day.

Adaptation of remote and hybrid work models: After the pandemic, hybrid work models are becoming more common, and WFM solutions have had to evolve. WFMs allow teams to maximize time when they meet in the office. If the employees are only in the shared workspace for a few days, this moment should be used, for example, for training sessions and team meetings.

Guarantee of fairness in scheduling: One of the most effective systems to legally and equitably control the schedules of company employees is the so-called “intelligence of fairness.” This is a model that is fed by machine learning to verify that all schedules conform to the laws imposed by the Government in 2019. In addition, machine learning monitors the sequences of shifts. It ensures that the least desired ones, such as, For example, consecutive or night shifts, are distributed fairly so that all workers have the same conditions and opportunities.

Performance Monitoring: Modern WFM solutions can monitor staff performance and make adjustments in real-time to optimize schedules and improve productivity. For a contact center to function correctly, it must allow an equitable structuring of the programming, the evaluation of the use of skills, and efficiency if this harmony is achieved thanks to machine learning. 

The results will be translated into figures, as has been verified on other occasions, since, for example, in contact centers, it allows the reduction in programming to be reduced by 8% and administrative hours by 9% while at the same time increasing agent occupancy by 9%.



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