From the perspective of an employee, hearing that their company is embracing AI-based systems and automation might sound like the equivalent of bringing in “efficiency experts” from the 1990s. It can signal that the company is looking to replace them.

While early implementations of automation displaced riveters, assemblers and many other manual labor jobs, those losses typically were offset by the creation of new roles that hadn’t existed before. In the contact center, automation doesn’t immediately translate into job loss, but it could change the way you work — in a positive way.

AI-based systems and automation have an effect on at least these three types of work.

  1. Repetitive tasks.

These tasks typically involve verifying a customer’s address or order number, or addressing issues that occur repeatedly, such as a password reset.

  1. Dull or boring activities that require a lot of focus.

This work might involve the review of document for keywords or pouring over hours’ worth of transcripts. It could mean a supervisor who’s listening in on hundreds of calls in an attempt to ascertain what agents did right or wrong.

  1. Complex activities that require looking up and analyzing a lot of data very quickly.

Machines can look up information from many data sources within milliseconds; they also can look for patterns that would difficult for people to spot. For these tasks, a paycheck can only serve as so much motivation.

Some studies have shown that, for complex tasks, using money as an incentive can actually lead to performance reductions. The best use of human capital is to remove people from doing this type of work. However, moving toward AI-based systems and automation mean training machines to mimic the best-performing employees. To do this, companies must pair humans with the systems that will learn from them. AI-based systems can learn with either active or passive participation from employees.

For systems that learn without intervention — in the context of a contact center — this typically involves observing interactions between agents and customers, reading over call and chat transcripts and recordings for sentiment and emotion, and organizing different parts of an interaction into topics. Systems can then analyze how different responses from agents can affect customer sentiment or even build a list of common questions and answers. This knowledge can be used to provide tips to agents in future interactions with customers.

Where Humans Help AI-Based Systems
Automation and AI-based systems also could benefit from manual input and training from agents. This input typically takes the form of tagging, flagging or providing samples.

With tagging, an agent highlights sections of a transcript and identifies it as a particular topic or as critical information, such as personal identifiable information for a future redaction.

In flagging, the agent alerts a system that there is an issue with the. Flagging might also allow for some follow-up choices on why the item was flagged. In a transcript, the agent might flag an interaction as particularly difficult or that it contains profanity, so a supervisor must review it. Flagging might also take the form of agreeing or disagreeing with a service assessment, such as a thumbs up or thumbs down. A parallel to flagging is rating an assessment.

By providing samples, an agent can supply the system with the data for it to continue training. For example, a system might want to know synonyms of a word or equivalent phrases. This could involve the agent typing in the text or providing a voice sample. This manual input makes it possible for systems to learn from many agents, so that they can then provide insights for individual agents. Agents answer how they would respond in certain situations — and the system provides suggestions to agents in the future if they encounter a similar situation.

By training these AI-based systems, agents can benefit from their insights and speed — and then can focus on providing deeper value in interactions. Instead of replacing their jobs, these systems will augment the agents’ capabilities. Their work will be spent less on answering the same questions repeatedly, and instead they can be left to deal with more complex issues. This, ultimately, means agent will feel more empowered, valuable and satisfied in their work.

Read this report to learn how to engage your team for the best customer experience using technology like AI. And check out why Gartner named Genesys as a Visionary in Workforce Engagement Management.