AI won't replace insurance call center agents, it's here to replace burnout

The role of a call center agent or customer service representative (CSR) is often known for its rigid schedules, repetitive work, and sometimes challenging customer interactions, all of which create stress. The resulting burnout from this stress directly contributes to high agent attrition rates, which in turn raises operating expenses and undercuts consistent customer brand experiences.

Agent attrition is estimated at a staggering 40-70% annually with some organizations turning over their entire agent populations each year. This problem is especially costly in the insurance industry, which is highly complex and heavily regulated, requiring significant onboarding time and a great deal of continued education to ensure agents are up to speed on the latest policies and procedures.

When a policyholder is calling after a highly emotional incident, such as a car accident, a house fire, or the death of a relative, the last thing they want to do is interact with an agent who is not adequately trained to handle their needs or is too overloaded with calls to take the time to display empathy and understanding. Front line agents are the face (or voice) of the organization for the policyholder experience, and when burnout levels are high, the risk of damaging policyholder relationships also runs high.

The solution to burnout might be staring us in the face, but the narrative around it would suggest otherwise. AI-powered technologies have often been portrayed as a threat, coming to replace workers and do their jobs more efficiently. HR teams and businesses at large have pushed back, saying AI is designed to help employees reduce repetitive tasks and focus on complex activities that need human reasoning.

But AI, and more specifically machine learning capabilities, can also have a critical purpose for agents in roles that are both repetitive and complex: stopping burnout before it takes hold. Here’s how.

Monitoring the signs of burnout

In some instances, agent burnout may be clearly visible, such as when a specific employee continually receives poor post-call survey results, but it’s rarely that simple. More often, struggling agents fly under the radar, especially in larger or remote organizations. The signs can be as subtle as them logging on late for their shift or logging off early. They may be taking more time to complete calls or spending more time in between calls. These burnout signs often come before the more visible issues, at which point the customer experience is more likely to be impacted, and it may be too late to prevent attrition.

Technologies built on machine learning can detect these cases much quicker than any human. Using AI-enhanced data collection and cleaning, these technologies can look for patterns in millions of burnout cases to find commonalities, then build data-sound models of the path a typical employee follows to burnout. Once deployed within a network, machine learning technologies can monitor several key metrics throughout agents’ shifts to find burnout patterns in practice. Field testing has determined these solutions can correctly predict employee churn 80% percent of the time – and they’re becoming more accurate daily.

Machine learning tools can not only alert management that an employee is struggling, but also suggest an appropriate remedy, such as extra coaching, more training, schedule changes, or more frequent breaks. In the call center example, it may be something as simple as recommending a wellness break or the employee taking the rest of the day off, or it may be more involved, such as suggesting training for a specific action causing the employee’s struggles.

These technologies offer endless opportunities. In the short term, they can help agents have a better workday, feel more comfortable with their tasks and improve their employer perception. On the management side, they can not only offer deeper visibility into employee wellness but also automate the task of monitoring it – reducing the possibility of attrition well before it’s perceptible by humans. In the long term, the data gathered from burnout reduction efforts can strengthen operational processes throughout the employee lifecycle.

Changing the narrative around AI

Much like implementing any other new technology, convincing frontline employees of AI’s benefits can be an uphill battle – even if the results are clear on paper. The shifting perceptions from AI as a tool that adds burdens, to one that lifts them, requires full senior leadership buy-in.

Management teams looking to implement AI should:

Involve employees at every step of the process. Transparency is key to a successful AI implementation. Management should be open about how it plans to use the technology and invite feedback, both through 1:1 sessions and town halls. When employees feel included in the process, they’re more likely to embrace the technology.Take security seriously. In addition to dispelling the narrative of “big brother,” management should be prepared for concerns about how data is used and protected – after all, the AI vendor will have access to highly sensitive information. Vet all technologies to ensure the developer has the proper SOC certifications and can clearly explain the steps they take to keep data under lock and key. Be open with employees about how their data is being used, and stress the benefits of collecting it.Follow tried-and-true change management techniques. AI may be cutting-edge technology, but the process of leading organizational change remains the same. The Prosci ADKAR model will help bring employees on board: make them aware of the need for AI, kindle a desire to support the new technology, help them know how to master AI, give them the training that makes them able to work with AI, and reinforce the technology’s benefits during and after implementation.