Don’t Train in Vain: Use Automation to Develop Agent Skills

For many years, we had been warned about the artificial intelligence (AI) revolution — the belief that computers will take our jobs and replace us completely. The reality is that technology advancements actually create new industries and more jobs for those with new and varying skills sets.

In the service industry, contact center employees have been seen as a sunk cost; many solutions that are implemented aim to reduce the number of employees needed to handle the same number of — or more — interactions. However, companies are adopting a new approach to empower their frontline employees by making them a point of differentiation for their service.

Enabling your workforce to create a unique and positive experience comes from giving employees the right tools. To accomplish this, think carefully and deploy tools to upskill your employees so they can create this new level of service. This also means giving employees the skills necessary to work alongside AI-based technologies.

Contact center AI-based technologies consist mainly of speech transcription, sentiment analysis, emotion detection, and acoustic analysis, among other recognition tools. The next level of these tools extracts more details about interactions and can infer performance. For example, managers can see if customers are responding well to an agent, or if an agent is having difficulty handling certain types of calls.

If a company takes an old-fashioned approach, it can use these technologies to make employees’ lives miserable through constant nagging and a focus on compliance and adherence to scripts — rather than on creativity. But employees generally respond better to being treated as adults. Supervisors who take a new approach to creating unique services can use AI-based technologies to elevate the conversations and find better ways to train and upskill their teams.

First, you can gamify performance that relates to easily measurable metrics. Second, focus conversations between supervisors and their reports on the employees’ needs. And then assign training to help improve employee performance.

At a higher level, supervisors and training managers can measure how training modules actually affect performance. They can derive this based on measuring the performance of either individual employees or cohorts who’ve had — or haven’t had — specific training. They can then make changes, if necessary. The impact of training can occur across several dimensions, such as how it affects a particular skill, quality management scores, or a more traditional metric like average handle time or first-call resolution.

Beyond using AI to measure performance and then suggest training, companies should also upskill their workforces to work alongside AI tools. This means understanding how information employees receive is produced, which data sources the technology uses to make its predictions, and any sources of potential error or bias in determining results. Employees also need to understand when they should apply “gut feelings,” and when these hunches might be less accurate than the deployed technologies.

By analyzing performance, suggesting training based on that performance and teaching employees how prepare for the future, companies can free up resources to focus more on employees’ well-being. This usually translates into happier employees who are more knowledgeable — and who can better serve customers.  For more information, check out the Frost & Sullivan white paper “how to engage your team for the best customer experience”.