Machines, both simple and advanced, have been used in past several decades to help workplaces determine outputs of work and, to replace work through automation using artificial intelligence (AI) tools and applications. What sorts of “insight” are expected from technologies? How do organizations use personal data accumulated by machines and make predictions of different types of intelligence? When data is sufficiently large, it is utilized to train algorithms that predict talents and capabilities; screen performance; set and survey work outputs; link workforce to various state of emotions; provide training and development; search for patterns across various teams; and more. How does AI become key to this dynamic? What risks do employees face in the modern workplace?
Workers are always subjected to performance monitoring where business profit dominates the employer-employee relationship, and workers need a nice and charming life, paid for by their work and commitment to their employer. Today, in any case, the business relationship is changing, and there is a novel “catalyst” in the workplace. Machines have been to help workplaces determine output of work and, without a doubt, to replace work through automation; today, using the integration of artificial intelligence (AI) tools and applications, a few machines have new responsibilities and even autonomy, just as being exected upon to show different types of human insight and settle on choices about workers themselves.
For HR, the so-called ‘Big Data’ can be used to collect large volume of data, to train algorithms that predict job candidates and workers’ abilities; monitor performance in the workplace; assess output; judge workers’ state of emotions’ provide training and development on the assembly line’ and look for patterns across the workforce, and significantly more.
Here, we outline how artificial intelligence is progressively essential for the cycle of decision-making. It can be used to identify the risks that workers face today, which should be acknowledged and recognized by both HR and management.
Artificial intelligence is considered today to be the most creative and promising field for workforce management. A little less than half of HR functions being applied across the world in organizations are using AI-based applications. These organizations are generally outside in the USA, yet some European and Asian companies are also adopting the trend. A PricewaterhouseCoopers study shows that an ever-increasing number of companies are starting to see the benefits of AI in supporting workforce management. Moreover, 32% of departments in tech companies and others are redesigning functions using AI to streamline for versatility and figuring out how to best integrate the experiences gathered from worker feedback and innovation. A new IBM report shows that half of CHROs identified for the study recognize the possibilities for innovation in HR operations and the acquisition and development of talent. A Deloitte report shows that 71% of worldwide organizations consider people analytics a high priority for their companies as it would allow them to not only provide good business insights but also deal with what has been known as a ‘people problem.’
“People problems” have several dimensions, as outlined here:
- talent acquisition and talent management;
- worker health and wellbeing;
- employee morale;
- diversity and inclusion;
- employee relations;
- compliance risk
People analytics is becoming an increasingly popular HR practice, where big data along with other tools is used to measure, report and understand workforce planning, employee performance, and talent management. Every sector and organization needs people analytics for everything from recruitment activity to managing the relationship with workers and employers.
There is some inconsistency in the role of HR, where some argue its capacity is just regulatory, while others guarantee that it should play a substantial role in business operations. People analytics practices are essential for the two levels of HR, where computerization, data accumulation and monitoring tools allow organizations to leverage real-time analytics for a deeper understanding of issues and actionable insights for the organization.
People analytics, also known as “talent analytics,” “human analytics,” and “human resource analytics,” is defined as the use of individualized data about people to help organizations make well-informed decisions about talent acquisition. For instance, who to hire; how to identify when an employee is likely to leave their job; and in appraisals and promotion considerations; and to create the future leadership pipeline. People analytics is additionally used to deal with worker problems.
While performance management is seen in most organizations, there are many strategies that have been tried and tested over the years. It got popular when organizations started using analytics to make decisions about workers’ performance in an increasingly industrialized world. The notable industrialists Taylor and the Gilbreths formulated plans to understand worker productivity. These industrialists looked for logical methods to distinguish and portray ideal bodily movements for ideal productive behaviors through work designs.
If people analytics does not involve ethical consideration then it could expose workers to increased risks and stress. How can workers be assured that certain choices are being made accurately, especially when they do not have access to the data that is being held by their employer? OSH risks of stress emerge if laborers feel that choices are being made dependent on numbers and data that they have no access to, nor control over. This is ethically wrong if people analytics leads to job replacement, job restructuring and job description changes, among other things.
People analytics is likely to increase workers’ stress and anxiety if the data is used in appraisals and performance management without proper due diligence during planning and implementation, leading to concerns about micromanagement and workplace monitoring. If workers know that their data is being used for talent spotting or for choosing potential cutbacks, they may feel forced to increase their productivity, and begin to overwork, posing several OSH risks.
In recent years, worker data collection for decision-making has led to the emergence of issues in the workplace. More OSH risks emerge, for example, worker stress and layoffs, when the usage of such technologies is done in a hurry and without appropriate consultation.