Can AI Help Identify Employees Who Want to Quit?

If so, can it help? Our expert explains how AI will facilitate the necessary transition to more people-centric workplace environments.

Written by Jennifer Lee
Published on Dec. 12, 2023
Can AI Help Identify Employees Who Want to Quit?
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Ive always believed that the greatest asset in any organization is its people. Unfortunately, businesses don’t always get the most from their people because they don’t prioritize their needs. That culture is beginning to change, with employers and HR teams taking extra care to understand their teams’ unique needs and deploying benefits, tech tools and resources to address them.  AI will make the transition smoother and more beneficial for both employees and their employers. 

Businesses have inherited a centuries-old tradition of prioritizing shareholders and customers, which makes obvious sense. But that tradition often implies an adversarial relationship between employees and management, which doesn’t. And though some company leaders will continue to view workers as inputs to be expanded, contracted, or otherwise manipulated to achieve business objectives, many others are coming to understand that employees who feel valued and respected are more likely to be loyal, reliable, productive, and able to satisfy customers and contribute to organizational successes.  

This rising awareness of the strategic value of employee well-being is coinciding with the maturation of artificial intelligence (AI). This development has been simmering for years but is now boiling with the promise to transform nearly every role in nearly every business. 

Much of the buzz around AI has been focused on its potential to wreak havoc — or at least put a lot of people out of work.  But I’m convinced that it will actually make a people-centric approach to doing business easier to achieve and more productive than many would have imagined. 

How Does AI Identify At-Risk Employees?

AI’s ability to extract insights from unprocessed raw data will help organizations solve persistent challenges like employee burnout and attrition. Many employees suffer in silence because of unrelieved pressure or insufficient training and support until, one day, they simply quit. With predictive AI, however, supervisors can identify troubled employees and address their problems before attrition occurs. 

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Why Does Employee Well-Being Matter?

Lets look at an example taken from the work of contact center agents. Consumers have more ways than ever before to interact with businesses, so a company’s ability to deliver consistent, strong customer service is critical to its brand reputation. That’s as true for large retailers as for healthcare, banking, insurance, government, and any other service providers. Contact center agents are often the main point of contact between these organizations and their customers. In this environment, one bad experience can make an outsized impact because today’s digital landscape allows customers to instantly share their unhappiness with millions of others. 

That’s why it’s so urgent for customer service leaders to address the high-stress environment in which their agents work. A certain amount of stress is inevitable in this line of work, of course. The intense pressure to deliver fast, satisfying solutions to an endless stream of incoming calls despite frequently inadequate support means that stress can accumulate quickly, however. This can have a negative effect on job performance and satisfaction. 

According to one study, 87 percent of agents surveyed reported high or very high stress levels at work. The resulting burnout often leads to attrition, which surpasses 70 percent in some centers. Replacing a single agent can cost up to $35,000, and constant turnover undermines the quality and consistency of service.  


How Can AI Make Work More People-Friendly?

I’ve spent more than two decades in the contact center space, and I’ve seen firsthand the frustration experienced by agents and supervisors alike. Bringing in AI to offload the tedious transactional work and assist agents with more complex aspects of the job will immediately improve conditions. Agents will be free to focus on the part of the job that most of us signed up for in the first place: helping people. And by increasing agent engagement and satisfaction, AI will also reduce attrition and the parallel burden on supervisors to spend so much of their time bringing new agents up to speed.

The contact center is a data-rich environment, ideally suited to AI’s data-centric approach to problem-solving. Multiple systems generate constant streams of data, which contain insights that could help solve problems if only they can be correlated. But the quantity of information is so vast that humans can’t process it quickly enough to make productive use of it. AI uses machine-learning models to ingest and process data, run various scenarios within a closed feedback loop, and predict the most desirable or accurate solutions to any problem under consideration. As indicated by the name, these models can learn, so their results get better over time. 

Intradiem’s Burnout and Attrition Indicator is an example of AI’s predictive capability in action. Drawing on and connecting insights from a broad range of contact center data, it identifies patterns that indicate approaching burnout. These patterns include more frequent absences or steadily rising call-handle time. Agents are assigned to a burnout risk category (low, moderate, high, or critical), which allows supervisors to prioritize remedial actions to support agents who are most at risk. Support may take many forms, including extra coaching, more training, schedule changes, or more frequent breaks. 


With AI, Data Analysis Has Caught Up With Output

AI’s ability to extract insights from unprocessed raw data will help organizations solve persistent challenges like employee burnout and attrition. Suffering in silence because of unrelieved pressure or insufficient training and support, many agents feel increasingly underappreciated until, one day, they simply quit. But with predictive AI, supervisors can identify troubled agents and address their problems before attrition occurs. Businesses need this capability more than ever today, as employees working remotely experience an added layer of isolation when things become overwhelming.

AI also improves conditions for supervisors by giving them critical insights they never had before. When team leaders are responsible for dozens or even hundreds of employees, it’s impossible to recognize which agents are most at risk of burning out. But all the red flags and warning lights are in the data. At long last, AI offers a data-driven solution to this chronic, elusive problem.

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Transparency Is the Key to Successful Implementation

Introducing AI into a company’s workflow is partly a technological problem, but it’s mostly a cultural issue. As such, organizations need to handle it transparently. If employees don’t know what their organizations plan to do with AI, they’ll probably fear the worst and their pessimism will spread like wildfire. Business leaders need to communicate to employees what they know about AI and its potential compatibility with their operations, what they don’t yet know, what they hope to achieve, and how they plan to go about implementing it. 

Leaders should also seek input from their teams and explain what they expect employees to do to help integrate the technology into their work environments. If employees feel they have a stake in the situation and that their input is valued, they’ll be less threatened by and more accepting of the changes. At Intradiem, for example, each functional team is charged with creating AI-driven projects that feed into larger company metrics to gauge the success of AI integration. 

Employees have every right to know the conditions under which this transformational new technology will be brought into their work lives. They shouldn’t hesitate to insist that company leaders define and share how they plan to use AI, and how they expect employees to contribute to its successful implementation.

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