Transforming Call Centers with AI with Terence Chesire and Glenn Nethercutt at ServiceNow Knowledge 2024

This is Textron tv. Hello and welcome back to Knowledge 2024, the ServiceNow Conference. We're here with Terrance Chester, Ray and Glen Nethercutt.

Terrance is with ServiceNow and Glen is with Genesis, and they have a new partnership with the these two companies. So we're gonna dive into what's going on with that. And let's start with Glen, welcome to show.

Thank you. Nice to be Here. Good to see you as all.

Thank you. Mike. I don't think lot of folks know exactly who Genesis is, so maybe you might want to brief us a little bit.

I know you're an integration consulting firm, but what makes you guys different from everybody else that's out there? And, and, and, sure. Yep.

How'd you get here besides taking a plane? I did take a plane. You're right.

Uh, this, I would say the biggest thing is for the longest time, Genesis has been synonymous with the customer experience voice market, right? That's where we've played. And really over the last seven years, we've seen an expansion in that.

For us, we're now thinking of ourselves as an experience as a service company. We've expanded it to the digital taxonomies. Uh, we've got a good overlay into AI and workforce engagement.

All of those industries are now represented by what we do, uh, mission wise. Uh, we say that we're going to bring the power of empathy to every customer experience, both within and beyond the contact center Center. ServiceNow has a lot of partners.

What did you see in these folks? What did they add to the ecosystem? Yeah, so obviously I'll recap a little bit of how we view the market at ServiceNow.

We think that there's two critical halves of serving customers. One is delivering best in class engagement and with a leading provider, uh, like Genesys. And the second half is orchestrating the workflows so you can actually deliver on what the customer does.

And we got both customer interest and a technology evolution that brought us together that this was the right moment to build, to take our integration to the next level. What are you hearing that customers need help with? Because on the face of it, hey, it's all turnkey.

There's ai, it's all magical. It'll just happen. Sprinkle the pixie dust and off you go.

Yeah, I wish it was that simple. Uh, we have seen customers increasingly demand not only good integrations, like we believe ourselves to be a platform. We've sort of repositioned ourselves that way over the last seven years as well.

So we like being very open to the builder mindset. That part still resonates, but we're seeing a lot of customer service, uh, and a lot of just industry in general, I think also want turnkey. They want that out of the box experience.

They want deep integration, not just a pile of Lego on the table, right? They wanna start with a solution and, and iterate in a way that differentiates their business, but they wanna start with something more meaningful. Terry, where do we work that line between what is the out of the box experience and then the extensions and the customizations?

Because sometimes people get afraid that they're gonna buy something and then they're gonna spend more money on integrating than they will on the actual outcome. Yeah, certainly. And so that challenge is potentially present when you have two platforms.

And these are, were both platforms and we both do the key things that we do extremely well. And we had integrations previously, but what brought us to this juncture is it turns out at the largest customers like Siemens Healthineers, who operate in over 60 countries, need to be mission critical. That the work it takes to both, uh, deploy and maintain our two stacks was too much.

And so we reframed it with a view of, now that we're in the cloud, what can we free integrate? And it led us to three elements. One is how do we deliver a single unified desktop so that the agent at that point of connection of interacting with a customer has everything they need with all the integrations and the data and the context necessary to be successful.

The second thing is, which is a little behind the covers. How did you get to that most appropriate person? You needed intelligent routing and understanding of what the context is, and that's what Genesis brings to the table.

And then third, how do you drive that the right person is available, uh, to perform the work? And that's workforce engagement. And that's another key element.

And then we both apply AI to all these things. So once you have the systems working together, the customer conversation being understood the context of the need, being understood, how do you apply AI to either serve the customer directly or assist the agent in delivering better service? And so for us, this is the next inning of turnkey, which is we've thought through how the experience should behave.

We've thought through all the different data layers, uh, we think deeply about how these processes work, but we've actually sweated those details. And better yet, we've built it an expansive way so that the futures truly scalable. Ah, well we got to the four minute mark without mentioning ai, but here we are.

How will AI change the way both the customer experience and the experience of the agent that serves them? 'cause both parties will benefit, but how, I mean, we're already seeing massive transformation, uh, at Genesis today in that regard. Thankfully, it's not a relatively new phenomenon for us to invest in ai.

We've been doing it actually on and off for about 20 years. Uh, everything from traditional a SR type systems now complete with more language based models, uh, on the a SR side, things like conformer models, for example, that give us some pretty powerful, uh, things to deal with. That means we can drive not just transcription use cases, which is the traditional way, but we can take that same set of technology and now empower things like agent Copilots, uh, take technologies like summarization to radically change the amount of say, after interaction work that's done.

And we've already found that's made a massive impact on the customer experience, not just in the reduction of handle time, which is very interesting and, and still a, a, a, a powerful way to get more leverage from a cost standpoint. But we see now that agents don't have to spend as much time preparing for the summarization activity. They're actually focused more on the experience in the moment.

So we're seeing our customers that are embracing AI are already benefiting both on the backend from a time standpoint, but also in the quality of the conversations that their agents are able to have. Let me ask you a follow up to that. There's a lot of turnover in the agent space, right?

A lot of those people get per out. Do you think we'll reduce the stress level and for that job? I would like to believe that's very much the case.

My, my, uh, outlook on AI is pretty rosy. Uh, I don't think everything is a panacea by any means. We're trying to be very mindful with our concepts around, uh, governance and constitutional AI policies and ways that we can protect businesses not just from hallucinations, but toxicity and bias.

But we're also thinking a lot about the agent experience there mentioned, uh, workforce engagement is a massive part of this. And training and coaching have always been a fundamental aspect of what Genesys has been investing in. Now we can leverage AI in ways that let you not just learn from metrics, right?

Not just quantitative decisions, but create guided flows, for example, that take the conversations your best agents have had, use those to upskill say the newest members of your staff to your point of turnover, and actually uplevel everyone in that regard. Reduce some of the stress on the coaching and get opportunities to have a much higher bar of service than you've ever had before. So I'm really hopeful that that's gonna reduce stress and actually make it faster for, for people to onboard.

Where is that line between AI and automation? I think a lot of people today are like, well, AI does that, but there's a difference between what the LLM does and what the automation framework does. And how do these two things come together to make something, you know, bigger than the whole?

Certainly. So I, for me, and for us, we think outcomes first. So we think of the end customer and their goal, and they usually reach out to an organization because they needed something done.

You have a credit card dispute, you want that resolved, you've got an insurance claim, you want that put through, you've ordered a service, you want that fulfilled. And then when you break it down, we find that there is a number of menial steps still today that happened. And what I, the way I think through is machines are really good at eliminating the low scale, low empathy type work.

And so I do not distinguish between AI and automation. It is, I I come back to that north star of the agent at that moment of need and interaction, understanding why you reached out, understanding your emotion at that point, understanding your potential frustration and being ready through integrations and context to systems of record that you don't have to repeat why you called. You don't have to repeat your account number.

You don't have to restate the issue. And that's what builds empathy. And the agents get to be, feel empowered to actually help people get the job done or accomplish what they needed.

And that's usually why people enter customer service. But if I could take a, a bit of a divergence, Glenn, I heard you say something really interesting about your evolution and thinking from, you know, calls and touch points to, to experiences to actually orchestrating journeys. Could you, could you Absolutely.

Yeah, that's, that's a great one. Uh, for a long time we've been articulating this new value of experience as a service. And ultimately what that means is, if, if you look back as Terrance Witch is, uh, we used to talk about calls and chats, right?

We talked about the interactions, we talked about very finite moments where people are interacting, which was still very powerful. It's great for us to intercede there. Then we started talking about conversations, something a bit longer lived, something more contextual.

Try to get, uh, a 360 degree view of it. I think that's empowering for agents. It, it's, uh, a good way to build a relationship in the customer side.

Uh, but now we think about journeys, right? It's a much longer term story. And this, this starts well before the traditional CX based.

I think that's the important part in our, our strategy here as well. Uh, Genesys is not focused just on what is now traditionally the front office, like where customer experience has typically been. Uh, this partnership is actually allowing us to go across that spectrum, starting well before a contact ever happens.

Being able to take in data and event streams and do journey analytics well before an intercession point needs to happen to be able to do the right routing based on all of that context when it does happen. And now connect that with the back office and the middle office. That's a radically different story at this point.

Do you think we'll surface more opportunities to do customer service better? And I'm asking this question because if I think back in time, a lot of people, they might call in for some level of support, but more don't, they just go away kind of grumpy and angry and they don't do anything because the customer service experience is too cumbersome. So are we gonna get to the point now where maybe more people will be willing to engage to having a, an issue addressed versus today?

I think more people go away than actually bother to engage. I, I certainly think so. Uh, I'd say the transition that we've already seen on a move from more synchronous channels, like voice to more asynchronous ones, like, like chat, email, interacting with bots on websites, uh, we wanna make that one a more joyful experience and context.

I think there helps, uh, there's some generational divides on how we all like to communicate. So that's something we're mindful of. Uh, but I don't think people's propensity to need help is going to change all that much.

It's just the format in which I think they wanna solicit it has changed. And their expectation when they get it, has changed. Uh, to your point, we don't, we don't wanna keep asking the same questions every time it escalates to a new level of the company or a new part of the company.

Uh, and you shouldn't have to, right? The idea of having all of this data at our fingertips is to build that context and to make that, uh, just sort of a very engaging experience despite handoffs throughout, uh, a customer's full lifetime. And if I can add on to that, we start to do some interesting things where, for example, we look at what was the reason why the customer called in the first place.

And when you're exposed to the middle and the back office, maybe there was a promotion that is not getting fulfilled at your local retailer. And after the first few phone calls, we can close the loop and say, it appears that at the point of delivery or fulfillment, this is causing an issue. So we start to take this as a signal and we, we reduce the friction to, to get to service, which is what he was describing.

But the, the power of the individual interaction connecting to the middle and back office processes is asking, why did this need to become an issue? Is there a policy change? Is there a process?

Was there a miss on the rollout of something that can obviate the need to communicate? And so combining our data, combining the understanding of what occurred, only leads to better insights of why did the reach out happen in the first place. Do you think the whole process will become more condensed?

And I'm asking the question 'cause it was not uncommon even a few years ago where I might call in and the person on the other end of the line would be like, oh, I understand. And then they, then they put me on hold while they go sort something out and there'd be all this like, awkward four or five minutes, and then I wonder if they're coming back and then eventually they come back. Can the whole thing become more condensed and more processes may be running in parallel in an asynchronous way so that we all have a better experience?

Uh, Absolutely. Uh, I think another major aspect there that's gonna be driven by the sort of agent copilot experience that we see already coming to fruition now is, uh, raising knowledge. Bubbling it up during the conversation.

So the need for you to go out to another system to swivel over and ask a question of, say, a fulfillment system or order management system, uh, that starts to drop away, right? We can present that by the AI reaching out to do those double clicks onto other systems. Maybe it's just a knowledge basis.

Maybe it's for information surfacing. Uh, but I think that has led to a lot of that kind of bifurcated experience of, great, I'm focused on you, I'm listening to you now I need to go do something else before I can answer you. I think we actually blend those two together.

And So I'll, I'll, I'll add to that. And that brings me back to the opening, which is we've always at ServiceNow been focused on why does that gap happen? And when we examined it, we found agents had over a dozen applications they needed to swivel across.

They had to understand context, they had to switch context. And when you asked me earlier about the difference between AI and automation, the reason I didn't, um, differentiate it is the, the reason we're so excited about a single agent workspace, which is where we will deliver all this information, is the automations are the integration to those systems of record. The fulfillment system is already pre-integrated.

The knowledge article is assisted with the ai and then clicking the action, the automation performs that track and trace. So your package didn't get there on time. We now need to find out what actually occurred.

The, the sequence of automations and integration and AI makes that amount of time shorter. And like I said, if you go back and reinvestigate the root cause, why did we lose that package? Hopefully you drive down the error rate for how many times it didn't, you know, the package didn't get delivered as expected.

To his point about the knowledge article generation, can we capture the knowledge of the event itself? Because so often, if I look across some of these service desks this point, that one, that one, that one and that one are all solving the same issue and neither one of them knows what the other one did about it. And half the time they come up with different answers.

And so can we kinda make it more consistent? There's an awful lot around that, say clustering of different topics that are being spotted automatically by the ai genesis is already surfacing those kinds of technologies there. Uh, it certainly leads to places where you need to focus your training, where you need to focus your hiring.

Perhaps if we can find, uh, consistent points that are maybe failure points or opportunities for improvement. Uh, so absolutely, it's, it's working there. And I don't even think summarization is something that's necessarily, that's not the end state.

The summary is just an enabling piece of technology like the rest of this, right? Uh, there's a lot of opportunity about us being to consolidate those properties together. If I look at the average service desk, there are multiple layers of expertise, sometimes one, two, sometimes even three.

Is that gonna change or will that become more condensed? Will there be fewer threes, more ones, more entry level people, more experts, or how it will play Out? I I don't want to hazard how it will change, but I will say what the AI is showing us is the type of work will change.

And I like to think of it as, as ICO described it, the soul crushing work. I've gone into environments where someone's full-time job was to manually categorize emails. I think we can all agree that is not a fulfilling day's work.

And I, and so if tier one used to manually categorize emails, it probably will no longer do that. But it doesn't mean tier one goes away. It goes to back to what I keep coming back to solving that issue that came in.

And there is always a need for a higher level of escalation or a higher level of expertise, but you upskill everybody and they deliver higher value. And so that is what we, we look at, at improving is even the individuals doing some of that work today did not get into customer service to manually categorize emails. I think we can all agree that that's not a, that's not an as career aspiration.

They got in to help people resolve their issues. It's not a real job. I'm pulling out this invisible crystal ball that's here, man, A year from now.

What is that crystal ball telling you? Where are we gonna be? What's this, this, this time next year at this show?

What will the customer experience Be like? I think by then you will have seen the fruition of, of this concept of singular panes of glass that not just, uh, are omnichannel but are channel less, right? The idea of fluidly being able to promote across those, even across, uh, uh, the two different platforms that are serviced down and genesis, uh, that is a no-brainer.

I think that I can, I can see very clearly in the ball, uh, where AI will be in a year is a hard one for anyone to, to wager a guess. But my thoughts here are likely seeing more autonomous agent fulfillment. And by agent here, I mean AI agents.

So actors within AI subsystems, the idea of being able to execute workflows to take action proactively or to at least suggest the action to be taken, not just to surface knowledge and data anymore, but to suggest a thing to be done and to offer at your request to go and do that work. I think that's easily within a year's timeframe, Walking around the show floor, there are tons of examples of interesting workflows. This is one of many, which ones kinda leap out at you at the show?

So, uh, I'll, you know, I will be selfish and say the, the area of my passion is customer. But the evolution for us is each customer journey turns out to be industry specific. And so a lot of what you will see there is the evolution from just generically describing customer service to how do we serve the telco industry, how do we serve the technology industry, financial services, banking, healthcare, manufacturing, public sector and retail.

And the, the, the really energizing thing for me is how these technologies come to bear in a specific way. Calling about a health insurance claim is a fairly different experience than calling about, uh, logistics around a package. All are meaningfully important.

And I think you were talking about earlier, some of the, um, some of the non-commercial use cases. We've got public sector customers, I think you mentioned you've got some, some non-profit who literally save people's lives. And so there is a mission element to this, and we truly want to make the world better.

Folks, you heard it here, I'll hazard a guess for this time next year. Customer service and support might actually be fun. There's an idea.

Hey guys, thanks coming So much. Thanks Mike. All right.

And we'll be back in a minute.