The field of people analytics is growing at a fast rate. With the introduction of new technology, more organizations are thriving to stay competitive. As a result, many businesses are making investments in new forms of technology.
Advancement of the recruitment process
Since 2016, according to statistics from LinkedIn, the demand for recruitment experts has increased by 63%, with the likelihood that this trend will continue going forward. According to the same data, 52% agree that one of the top priorities is keeping up with recruiting technology. More than 68% of recruiting professionals believe that the most effective way to boost recruiter performance in the future is through acquiring better-recruiting tools and technology. This means that more organizations would have to adapt to the advancement in the technology of the recruitment process to perform well in the future.
AI to transform People Analytics
According to the Gartner 2019 Artificial Intelligence Survey, 17 percent of firms now utilize AI-based technologies in their human resources function, with another 30 percent planning to do so by 2022. By 2027, the worldwide artificial intelligence industry is predicted to reach $267 billion in value, according to Fortune Business Insights. In other words, more human resource departments will begin to use artificial intelligence to evaluate their employee-related data for a variety of objectives, such as talent acquisition, retention, and development.
The majority of organizations investing in AI want to focus on the following goals:
predictive analytics for talent acquisition
advanced analytics for workforce planning and knowledge management
improving employee experience
increasing employee productivity
implementation of machine learning algorithms
augmenting new employees' onboarding and training
Why might AI be better at making hiring decisions than you?
AI Technology functions objectively and reduces human bias
In contrast, people may be prone to cognitive or motivational biases such as overconfidence, confirmation bias, and various other sorts of judgment errors.
AI Technology provides better efficiency
AI technology can automate repetitive tasks at a higher efficiency rate than humans. It can match or outperform experts at well-defined tasks, automate the categories of work that humans are more likely to make errors with, and has a better cost/benefit ratio.
AI technology provides a better structure for the recruitment and hiring process
It streamlines the searching for a candidate, monitors existing data, and may use that data in predicting future performance and understanding how well individuals fit with the organizational culture.
Should AI determine your salary?
According to a Gartner survey, 79 percent of respondents believed technological advancements assisted artificial intelligence in standardizing pay across organizations.
AI technology can make suggestions in determining your compensation. In fact, there are large corporations, one of which is IBM, that use artificial intelligence technology to handle their salary systems and procedures.
When it comes to the compensation process, IBM utilizes its artificial intelligence technology to determine if an employee is qualified for a raise or promoted based on performance. However, it is essential to note that artificial intelligence technology may be used as a reference in compensation negotiations; the ultimate decision may still be the discretion of the appropriate administrator.
Integrating AI into compensation decisions might garner mixed reactions from employees. Therefore, management must implement artificial intelligence technology in their organization with caution, fairness, and transparency and convey the benefits of artificial intelligence to employees.
According to Carolina Valencia, research director at Gartner, it is more efficient to modify algorithms than to teach managers new compensation procedures. When artificial intelligence is used, human resource managers can spend more time with people and developing employees, while AI will deal with other complicated tasks such as compensation decisions and analyze essential data to make faster, more accurate recommendations.