2024-03-01 16:22:08
On average, ML (Machine Learning – Machine Learning Engineering, in Portuguese) and AI (Artificial Intelligence) are expected to create 69 million jobs by 2027. The estimate is part of the “Future of Work 2023” report, from WEF (World Economic Forum), released at the end of the first half of 2023.
According to the study, shared by Data Centre Dynamics, several professions and vacancies will undergo transformations in the coming years. It is expected that 83 million jobs will disappear by 2027 and that 23% of jobs will undergo changes in this period, driven by the resources created through the use of AI.
According to the projection, solutions related to ML and other AI tools are among the trends in this new market. “Among other solutions, ML tools have already been successfully applied in generating work schedules all over the world”, says José Epifânio, Head of Product Innovation at SISQUAL WFM, a company that works with WFM tools ( Workforce Management – workforce management).
“The suitability of the model, or models chosen for each country, is very dependent on the labor legislation of each region and the sophistication and digital historical record that the company itself has”, he explains.
He highlights that, in order to feed ML models with specific data, promoting higher quality at the generated scale, it is necessary to ensure a careful analysis of the restrictions to be imposed. Furthermore, it is necessary to have a digital support for the scales that can be used as learning for the algorithms.
“It is exactly at this point that the difficulty for many companies lies, as there is no consolidated list of scales with clearly identified rules that allow the creation of scales that are possible to apply in practice – and with financial benefits and quality of life for employees. its workers”, adds Epifânio.
How can ML help create work schedules?
According to the Head of Product Innovation at SISQUAL WFM, ML is a technology made up of several types of models and algorithms that can, when combined in the right formula and with quality data, solve several complex problems of generating scales with greater agility.
“There are many restrictions linked to labor legislation and other ethical restrictions that make the task of generating viable scales a process in continuous improvement”, he points out. “It is recommended that a supervision process is always applied to the work generated by ML in order to ensure that, over time, the scales continue to be adapted to the needs of the client, as the market context changes”, he says.
To conclude, Epifânio highlights that the generation of schedules is an area that impacts the workforce and, therefore, must be faced with responsibility. This is because, depending on the problems and restrictions to be resolved at each scale, and depending on the sector or service in question, the needs and work dynamics are different. Therefore, what works in one sector cannot automatically work in another.
“It is recommended that these projects [de ML] are implemented and supervised by experienced teams, always starting with the implementation of tools that ensure the quality and reliability of historical data”, he concludes.
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