New research shows the role of AI in retaining top talent

Employers, HR specialists, supervisors, and managers in organizations of all sizes and geographical locations place a high priority on their ability to retain excellent people. Artificial intelligence, or AI, may provide a remedy they may not have supposed of as they desperately search for trustworthy fixes that will assist them in minimizing talent loss. The capacity to develop is the main thing that research teaches us that is crucial for employees. If they are unable to achieve that within their respective company, they will search outside of it for these occasions. AI can assist you in making sure that you don’t pass over workers who are prepared to advance to new, higher positions. When there are prospects for career progression, people stay put longer, according to the director of advisory services and HR research at McLean & Company, Janet Clary.

Staffs who strongly believe or agree that they can grow in their career in their current business are 3.4 times more probable to be engaged, according to a study by McLean & Company, the speaker claimed. According to her, businesses may use artificial intelligence to “algorithmically equal individuals with internal prospects such as full-time employment, project and gig work, learning experiences, and mentorships based on that person’s specific abilities,  interests, and experiences.” Additionally, AI technology may assist businesses in allocating work in the most effective and efficient ways possible, ensuring that the right persons are working on the right projects and increasing the likelihood that they will be affianced. According to Lakshmi Raj, co-founder and Co-CEO of Redwood City, California-based Replicon, “automating regular processes such as filling up of  timesheets at scale offers several advantages beyond simply observing the in- and out-time of staffs.” “They can use data to locate the projects that match them best, enhancing the quality of the product by making the best use of their resources.” According to her, doing so can reduce the risk of burnout.

Preventing Burnout

Test Prep Insight’s HR director is Janelle Owens. She claimed that in an effort to lower attrition, her organization “uses behavioral analytics tools to identify major employee fatigue before it happens.” Turnover may be greatly influenced by burnout. Thankfully, she noted, “behavioral analytics can offer crucial perceptions into worker behavior and aid in preventing exhaustion before it gets to a breaking point.”

Since the beginning of the pandemic, Test Prep Insight has used technologies of AI-driven. She explained that “this software collects and analyses employee communications using already-existing channels such as email, Zoom, and Slack.” It uses its technology to identify at-risk employees after noticing trends and certain buzzwords in its texts. She claimed that in a remote work setting, it was particularly crucial. Finding resources that are being used too much or too little is one strategy for preventing staff burnout, according to Raj. Enterprises may evaluate real-time data to enable additional effective resource allocation, assuring balanced workloads, good employee morale, and decreased attrition. This is made possible by AI and machine learning-based professional services automation and cloud-first time-tracking technologies.

Identifying Employee Flight Risk

AI may be used to create prediction models of staff who might be a flight risk using both internal and external data, according to Clary. Job satisfaction, amount of positions held, engagement scores, and the number of years an employee has worked for their present management are a few examples of internal statistics. Additionally, external data may be used, such as benchmarking tenure-based pay rates. Other information that can be used to spot trends that might suggest an employee is in danger of quitting contains “how often staff is logging in, how engaged they seem in their work how much they are working, and how frequently they are interacting with co-workers,” according to Omer Usanmaz, co-founder and CEO of Qooper Mentoring and Learning Software.

In addition, Usanmaz added, “employee communication data can be analyzed using algorithms of natural language processing to uncover indicators that an employee may be considering quitting.” This could involve looking at chat logs, social media posts, and emails to see what people are saying. Clary, though, advised against using AI to anticipate what people may do. “There is a lot of uncertainty in forecasting whether a person will depart, but the accuracy will grow tremendously when you apply that prediction over thousands of people,” she added. So, for instance, these forecasts “should not be utilized to prepare to replace an individual as the algorithm suggests they’re a high flight risk, but rather to guide workforce planning on an organizational level.”