In our last post, we wrapped up talking about how artificial intelligence can help agents perform better, and how that benefits your business. In this post, we’ll explore how AI can help create more effective supervisors and enhance the performance of agents through that enhanced supervision. And of course, that can translate to clear benefits for your business. As we’ll explore in this post and the rest of the series, next-gen AI has benefits that compound and amplify each other across your business units and functions. That’s part of why AI is such a transformative technology.
So, why should we care about supervisor effectiveness? The answer is simple: supervisors who can better understand what their agents do and say can be better coaches to those agents. And better coaching can lead to better performance. Now, let’s look at a few statistics to help frame our discussion more.
First, approximately 60% of enterprises saw an increase in interaction volumes across voice and email channels. That increases the pressure on supervisors, as this increase in customer interactions leads to busier agents. It may also mean increased staffing, with new agents requiring more training and intense supervision. As workloads increase, agent frustration can increase, and as we’ve explored in prior posts, agent attrition is both costly and difficult for you to manage. So we know that supervisors are under increased pressure to manage agents and help them deal with higher interaction volumes.
To add to that, 43% of IT decision makers indicate that improving employee development is a top priority. Now, why might this be the case? Employee development might not seem immediately like a solution to higher workloads, but it absolutely can be. How? Let’s see. First, employees who feel like they have fresh skills and the right tools in front of them may be less frustrated and better able to cope with higher interaction volumes. Next, agents who feel their supervisors are helpful and look out for them may be less likely to churn and may offer better service to customers. Finally, supervisors who feel like they can coach and support their agents without adding to their own workloads may be able to offer more empathetic, engaged coaching to their agents.
So, how does this all connect to generative AI? Next-gen artificial intelligence has numerous benefits that can help create more effective supervisors. First, with 100% transcription done in real time, supervisors have immediate insight into multiple calls at once. Rather than listening to audio, once we generate text we’re able to apply powerful tools to analyze these interactions at scale. What does that look like in practice? After all, a supervisor can’t read through call logs and identify problems without it taking up all their time. Well, the first solution we can explore is real-time scorecarding and alarms. For example, if an agent misses a required disclosure in a call, an AI powered scorecard can alert the agent that they missed something crucial. That can free up supervisors from having to worry about the consequences of those missed disclosures. And if an agent misses a disclosure a second time, a supervisor could get a real-time alarm and jump into the call, whisper into the agent’s ear, or send them a chat reminding them of their need to issue the disclosure. All of this, of course, requires the right CX platform to pull off seamlessly. So we know that AI can power better real-time insights and help supervisors be more effective by knowing what actions they need to take in the moment to ensure that the agent pool stays on track.
But what about after the call? How does this connect to better supervision and coaching overall? Well, AI can help identify high and low performing agents based on transcriptions and subsequent scorecarding. This of course paves the way for better quality monitoring. In fact, one Thrio customer went from being able to do QM on a small fraction of calls that took an FTE to accomplish to being able to analyze 100% of calls with far less work. So that quality monitoring can happen at scale, without requiring more resources. That’s a win.
Next, supervisors can use these same AI tools to capture insights from high performing agents and incorporate those into future training, in order to create more of the most successful agents. Improving cohort performance is another win that can result from deploying AI tools.
Finally, one speculative area that we’re eager to explore involves using AI tools to simulate interactions for trainees and existing agents. This might look like using AI to generate hard, complex, or previously-difficult interactions and letting agents get exposure to those via AI. That’s a future state that we’d be excited to see happen and help develop the technology to achieve. We mention this as an example of future areas where AI can transform all elements of the agent and supervisor experience.
In our next post, we’ll look at how AI can transform the work of business-unit leaders.