WORKFORCE ENGAGEMENT

Back in the 2015-2016 timeframe , when contact center-as-a-service ( CCaaS ) providers were migrating a lot of contact center customers to the cloud , there was no AI present yet . Predictive routing was the only use case that we all thought would be feasible at that time .

Now there are so many different AI use cases that providers are now considering that revolve around improving the contact center workforce .

HOW COVID KICKSTARTED AI

The COVID-19 pandemic accelerated the movement of applications to the cloud and revved up the incorporation of AI in the software . It forced a lot of these businesses into the cloud faster than they would have migrated on their own . And likewise for developers to utilize AI in their products : like workforce engagement .

During the pandemic , organizations became strapped for human resources . Maintaining a workforce was difficult as contact center workers went home , and some made new life and work choices .

The contact center industry was growing exponentially but desperate for enough agents to function . These factors combined to make operators consider how to make their workforces more efficient .

To drive efficiency , operators had to break activities down to a set of tasks performed by their agents . They then determined which tasks are easier to automate : those ones that return the best bang for the buck to show instant results .

Leading companies began offering rules-based tools like topic detection , followed by sentiment analysis , disposition prediction , and theme detection . They became bolder and tackled harder problems like summarizing calls or creating cases in Salesforce or automatically generating action items that the agent should take after the call .

As these companies became more experienced and more technologically advanced , they solved more problems with a more holistic approach to AI - without the constraints of rules - with impact on agent efficiency , automation , and business outcomes . What was really exciting : it was all real-time .

Has AI in contact center applications finally passed the famous Turing Test where machines showed the same intelligent behavior as people ? It appears that way .

Predictive routing was the first revelation of AI . Suddenly , for a large telecom carrier , suppliers like us could improve their call handling time by 2 % or more .

Imagine a 20,000 or 40,000 agent contact center improving their individual call times that much : it equates to millions of dollars in savings .

Then suppliers expanded that AI concept to different use cases such as topic detection and sentiment analysis .

When I first saw a summary of a call produced by a machine , it just blew my mind ! A machine took a 10-minute conversation and wrote such a crisp summary in three to five sentences .

I listened to 10 different calls from the five to 10-minute range . I checked the summary and listened to the conversation across multiple customers to validate it . Right away I could see the machine could be trusted .

I went to my customers and showed them , call by call , row by row , how much more effective and efficient the machines were than the humans . They realized the business value immediately .

It takes an average of around two minutes for a human to write call summaries . One agent typically handles 40 calls a day . This saves 80 minutes of time per agent per day , not to mention achieving greater accuracy .

At the same time , the shift to remote work created its own issues . Some agents didn ' t have a way to work from home . Some may have worked in a closet , while dogs were barking , or kids were screaming outside the door .

The need for better workforce management ( WFM ), specifically agent care , increased dramatically . How do you give them the right resources when they can ' t tap someone ' s shoulder and ask a question , especially if they ’ re new and inexperienced ?

Contact centers began adopting tools that provide a really good forecast of the strengths and weaknesses of the workforce . With an accurate prediction of how many calls your contact center will likely receive next week , you can staff accordingly .

If you tend to understaff , you could know the workload that is coming and can adjust for the anticipated 50 % increase in calls per day . This saves everyone from a strained , stressed workforce .

After getting the right forecasting and scheduling tools , other tools arrived that detected when improvements are needed . Like when agents are demonstrating a lack of knowledge around a product or not handling customer objections around cancellation effectively . If you know exactly where and why they ' re struggling , you can properly coach them .

AI ASCENT AT THE AGENT LEVEL

Any change in agent experience is carefully looked at by contact centers . For good reason : because it would impact hundreds of thousands of people who need to be retrained .

Top contact center vendors were not just worried about the customer experience ( CX ), but also understood the importance of the agent experience and the need to make their jobs easier . So , they created AIbased tools that monitor agent-customer conversations in real-time and give agents guidance on next steps .

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