More than 40% of all employees were thinking about leaving their jobs at the beginning of 2021, and as the year went on, workers quit in unprecedented numbers.1 Between April and September 2021, more than 24 million American employees left their jobs, an all-time record.2 As the Great Resignation rolls on, business leaders are struggling to make sense of the factors driving the mass exodus. More importantly, they are looking for ways to hold on to valued employees. To better understand the sources of the Great Resignation and help leaders respond effectively, we analyzed 34 million online employee profiles to identify U.S. workers who left their employer for any reason (including quitting, retiring, or being laid off) between April and September 2021.3 The data, from Revelio Labs, where one of us (Ben) is the CEO, enabled us to estimate company-level attrition rates for the Culture 500, a sample of large, mainly for-profit companies that together employ nearly one-quarter of the private-sector workforce in the United States.4 Monthly research-based updates on what the future of work means for your workplace, teams, and culture. While resignation rates are high on average, they are not uniform across companies.

Some of the largest employers in the world are increasingly, and in some cases controversially, relying on AI-based technologies to hire new workers. Companies like Tesla, Accenture and LinkedIn are using technology from Pymetrics to better vet qualified candidates and reduce the time and resources required for what has traditionally been a labor-intensive hiring process. The company, which boasts more than 80 global clients, uses a blend of data science and I/O psychology to create its "people recommendation engine." The Pymetrics platform is designed to improve employee retention while also increasing efficiency and diversity throughout the recruiting process. The results are parsed by AI to generate measurements related to candidates' problem-solving skills, ability to multitask and even their levels of altruism.

If you work at IBM or a company equipped with their artificially intelligent employee retention software, your planned two-week's notice may already be old news. According to a recent panel discussion with IBM's CEO, Ginni Rometty, at a Work Talent and Human Resources Summit in New York, the company's'predictive attrition' software is now 95 percent accurate in determining when an employee is ready to quit. Using their predicative software, Rometty says that IBM has been able to preempt employees on the cusp of quitting, bolstering their retention rates, which has reportedly saved them $300 million. Human resources may need a new name as a host of new software has begun to disrupt jobs and efficiency. Among the many AI-powered tools being developed by IBM for human resources is'predictive attrition.'

To solve the challenge of employee turnover, one senior living provider turned to artificial intelligence--and improved its 90-day retention rate by 27%.

Jay Kiew is a management consultant and MBA 2018 from the Ivey Business School. One of the predominant workplace communication tools is Slack, whose userbase has grown to 70,000 paying organizations and eight million daily active users. Canada, we have a problem. Our companies are struggling to retain their employees. According to a 2017 study by LinkedIn, Canada has the fourth-highest employee-turnover rate globally at 16 per cent (compared with the global average turnover rate of 11 per cent).

Over the years Artificial Intelligence (AI) tools have been gradually getting smarter and reaching new levels of sophistication and precision which are beginning to address complex business issues. Artificial Intelligence is being deployed in multiple ways depending upon the business needs. AI manifests itself as assisted intelligence replacing the mundane and repetitive tasks being performed and provides directions or guidance. Augmented intelligence underscores the importance of man and machine working together and enabling superior decision making. Emotional intelligence, creativity and innovation that humans possess when combined with the ability of the machines to crunch enormous datasets and provide predictive analytics to define probability of occurrence of certain events lead to higher order outcomes.

Machine learning algorithms are often showcased in customer churn study. Applications in fields such as telecommunication or product marketing (gaming, insurance etc..)(see [1],[2] for a recent review) are multiple. The implementation of these methods in Customer Relationship Management is becoming the new norm, as improving customer retention yields superior business results. We argue that this type of techniques can easily be applied to employee turnover. Note that the employee turnover can actually be subdivided in 3 buckets: involuntary turnover (induced by the company), voluntary turnover (employee resignation) and retirements. Retirement and an involuntary turnover are out of the scope of this paper.