S3 Ep. 10 The next AI frontier: EX superpowers

Jane Hendricks (01:56): 

I’m the product marketing manager for conversational AI. What I do is make this awesome technology simpler and explain how it’s being applied.

David Wasserman (02:19): 

I’m involved with our workforce engagement products. We leverage some of the wonderful AI innovations that Jane and team are creating to engage agents in their jobs.

Jane Hendricks (02:44): 

It’s not necessarily about definitions, because AI is a technology. If you search the term AI, you’ll get a lot of articles about algorithms, data, neural, all this technical stuff. When we at Genesys talk about AI, we keep all of that in mind because we want to make sure it’s good. But we think of how to apply it, what role it plays. Is it allowing our customers to effortlessly innovate? That means they can do something using AI they couldn’t do manually.

What we do with this technology is a couple things.

One, we want to make sure we’re accelerating time to value. We make it easy to use, so it’s turnkey. You don’t have to build algorithms yourself. You don’t need to hire an army of developers or data scientists. We put all that complexity behind easy-to-use tools.

Also, we use it to improve the employee experience. That’s important, because it’s not just about automation, but smart automation. AI enables data-driven decisions.

And last but not least is optimization. Our AI is constantly learning — and not just by itself all the time. There is a human-in-the-loop approach.

David Wasserman (05:06): 

If we think about workforce engagement management as an example, it’s really three groups of products. One is around the quality management process. The second is around resource management and scheduling agents. And the third is around performance management. A lot of those processes were previously done manually.

If we look at something as simple as the quality management process, it was typically done in the past with side-by-sides between supervisors and agents. Well, what does that mean? If a contact center is taking thousands of calls in a typical day, the opportunity for a supervisor to sit with an agent and evaluate those calls was limited. So maybe you’d get 1% or 2% of the calls in a given day evaluated.

The problem with that approach is it’s not a big sample and you’ve involved a human. The great thing about AI is it’s unbiased. It’s using data to make the decision. So, using quality as an example, we can now automate that entire process. It’s not just about a supervisor sitting next to an agent; it’s about a machine listening to an agent and evaluating every interaction. And then you bring in the human element. You’ve scaled up the process completely. You’ve eliminated the human element as it relates to evaluating. You’ve increased the sample size, you’ve eliminated bias, and you’ve allowed the supervisor to spend time with the agent, versus struggling to do the evaluations to begin with. Now it’s about coaching and improvement, not about evaluating.

David Wasserman (07:15): 

Yes. It’s a basic example of: If one of the cues for the agent is to say, “Good morning,” “Good afternoon,” or “Good evening” to a customer, now we can understand whether it was said on every call and said as appropriately as it should be — not through a few examples where it didn’t happen, and it’s assumed that it never happened.

David Wasserman (08:10): 

With AI and workforce engagement management, you’ve taken some of that real-time evaluation and extended it to the agent in real time. And to your point about nudging, now it’s not the supervisor saying, “Remember, you have to say, ‘Good morning.’” It’s also about the agent being given a prompt during the interaction itself, where they can maybe recover and to try to deliver what’s expected of the agent on that call. So, it’s not just post-call coaching, it’s real-time coaching.

Jane Hendricks (08:40): 

Customer expectations have evolved. Agents have to deal with customers who expect a higher level of service, even instant service. And technology has evolved. We’re capturing more data than we ever had available to us in an electronic, structured way. We have a lot of data from our agents, from our customers, from third-party systems. This data’s coming at us quickly and we have computing power that’s evolved. The AI algorithms have become more sophisticated and with that better computing power.

When we look at how AI is used within Genesys, 95% of our use cases are real time. It’s not just looking at a report and using sentiment analysis to create monthly reports and then tracking how customer sentiment has changed. That’s valuable, but if you can do that in real time — while the agent is on the phone — and change the behavior in real time, that’s a lot more powerful.

David Wasserman (12:51): 

I look at it as AI is reducing friction. If you think about our personal lives and using GPS, in the past it was all about reading a map, writing down directions and asking someone on the road when you got lost. That’s friction. That’s not a bad thing. It’s just something that’s in our way and distracts us from maybe doing other things that are more critical. AI-enabled capabilities are reducing friction in the role of an agent.

As a result of that, their frustration levels are lower, and they can do a better job handling the customer. Eliminating manual processes and handing those over to AI and computing allows the level of friction in a conversation to be significantly lower and therefore enables a better interaction and experience for both parties.

Jane Hendricks (14:11): 

When we talk about these friction points, what we sometimes neglect is how AI is able to make sense of data quickly and in innovative ways. Without AI, the agent desktop just shows you who the customer is, their name, age, etc. The agent doesn’t know that their likelihood to purchase a certain product is 80%. That’s a piece of insight. There’s no way they’re going to know without AI.

So, I think some of the friction comes from having to do things that stand in the way, and some if it comes from not knowing things.

Jane Hendricks (18:35): 

They’re all part of the same thing. Experience orchestration is the full journey. This is what Genesys does. We orchestrate experiences on behalf of our customers and enable them to orchestrate better experience. Agents, customers, everything is part of that world. And AI helps make experience orchestration easier, smarter, and more fluid. It removes the friction.

David Wasserman (19:26): 

And as it relates to employee superpowers, eliminating friction — eliminating these mundane tasks, helping people do their job and in some ways helping them do their job where they have weak points — allows their strengths to shine through in their interactions with customers.

Jane Hendricks (19:53): 

One misconception is that AI will replace humans. That’s just not true. AI supports humans and helps makes the human experience a better one. It’s not about, “Let’s automate everything and we don’t need anybody.”

Another misconception is that AI is really hard and is going to take forever; that you’ll need an army of data scientists. That’s one of the reasons we built AI into our platform as opposed to making it like a separate thing. If AI is built into your platform, if all the tools are there, you’re not necessarily incurring those additional expenses. You’re turning on smarter capabilities that are already there.

The last one is that AI is a black box: I’m going to turn it on, it’s going to do stuff and I won’t have control over it. What we’ve done at Genesys is built in the analytics that show what AI is doing and give you tools to control how AI is implemented and control the experience so it’s not a black box.

David Wasserman (22:07): 

We’ve all gone through a series of technology changes. Some of it has been around computing, some has been around how we communicate with customers or communicate with each other. And in all those cases, the customer experience and the agent experience has been improved.

I think the risk of not embracing this next evolution in technology is you’ll fall behind and eventually go out of business. Dipping your toe in the water and getting an understanding of what’s happening is important, so you can harness this next evolution of technology within the contact center.