In the ever-evolving landscape of customer experiences, AI has become a beacon guiding businesses toward seamless interactions. While AI has been transforming businesses long before the latest wave of viral chatbots, the emergence of generative AI and large language models represents a paradigm shift in how enterprises engage with customers and manage internal workflows.

“We know that consumers and employees today want to have more tools to get the answers that they need, get things done more effectively, more efficiently on their own terms,” says Elizabeth Tobey, head of marketing, digital & AI at NICE.

Breaking down silos and reducing friction for both customers and employees is key to facilitating more seamless experiences. Just as much as customers loathe an unhelpful automated chatbot directing them to the same links or FAQ page, employees similarly want their digital solutions to direct them to the best knowledge bases without excessive alt-tabbing or listless searching.

“We’re seeing AI being able to help uplift that to make all of those struggles and hurdles that we are seeing in this more complex landscape to be more effective, to be more oriented towards actually serving those needs and wants of both employees and customers,” says Tobey.

The capacity for AI tools to understand sentiment and create personalized answers is where most automated chatbots today fail. Enter conversational AI. Its recent progression holds the potential to deliver human-readable and context-aware responses that surpass traditional chatbots, says Tobey.

“We’re seeing even more gains that no matter how I ask a question or you ask a question, the answer coming back from self-service or from that bot is going to understand not just what we said but the intent behind what we said and it’s going to be able to draw on the data behind us,” she says.

Creating the most optimized customer experiences takes walking the fine line between the automation that enables convenience and the human touch that builds relationships. Tobey stresses the importance of identifying gaps and optimal outcomes and using that knowledge to create purpose-built AI tools that can help smooth processes and break down barriers.

Looking to the future, Tobey points to knowledge management—the process of storing and disseminating information within an enterprise—as the secret behind what will push AI in customer experience from novel to new wave.

“I think that for me, one of the exciting things and the challenging things is to explain how all of this is connected,” says Tobey.

This episode of Business Lab is produced in partnership with NICE.

Full Transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab. The show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is creating great customer experiences with AI, from the call center to online, to in-person. Building relationships with customers and creating data-driven but people-based support teams is critical for enterprises. And although the technology landscape is ever-changing, embracing what comes next doesn’t have to be a struggle.

Two words for you: foundational AI.

My guest is Elizabeth Tobey, head of marketing, digital and AI at NICE.

This podcast is produced in partnership with NICE.

Welcome Elizabeth.

Elizabeth Tobey: Happy to be here. Really excited to talk about this today.

Laurel: Great. Well, let’s go ahead and start. To set some context for our conversation, what is the customer experience landscape like now? And how has it and will it continue to change with AI?

Elizabeth: Well, to start, I think it’s important to note that AI isn’t a new technology, especially not in the customer experience (CX) era. One of the things that is quite new though is generative AI and the way we are using and able to use large language models in the CX paradigm. So we know that consumers and employees today want to have more tools to get the answers that they need, get things done more effectively, more efficiently on their own terms. So for consumers, we often hear that they want to use digital solutions or channels of their choice to help find answers and solve problems on their own time, on their own terms.

I think the same applies when we talk about either agents or employees or supervisors. They don’t necessarily want to be alt-tabbing or searching multiple different solutions, knowledge bases, different pieces of technology to get their work done or answering the same questions over and over again. They want to be doing meaningful work that really engages them, that helps them feel like they’re making an impact. And in this way we are seeing the contact center and customer experience in general evolve to be able to meet those changing needs of both the [employee experience] EX and the CX of everything within a contact center and customer experience.

And we’re also seeing AI being able to help uplift that to make all of those struggles and hurdles that we are seeing in this more complex landscape to be more effective, to be more oriented towards actually serving those needs and wants of both employees and customers.

Laurel: A critical element of great customer experience is building that relationship with your customer base. So then how can technologies, like you’ve been saying, AI in general, help with this relationship building? And then what are some of the best practices that you’ve discovered?

Elizabeth: That’s a really complicated one, and I think again, it goes back to the idea of being able to use technology to facilitate those effective solutions or those impactful resolutions. And what that means depends on the use case.

So I think this is where generative AI and AI in general can help us break down silos between the different technologies that we are using in an organization to facilitate CX, which can also lead to a Franken-stack of nature that can silo and fracture and create friction within that experience.

Another is to really be flexible and personalize to create an experience that makes sense for the person who’s seeking an answer or a solution. I think all of us have been consumers where we’ve asked a question of a chatbot or on a website and received an answer that either says they don’t understand what we’re asking or a list of links that maybe are generally related to one keyword we have typed into the bot. And those are, I would say, the infant notions of what we’re trying to achieve now. And now with generative AI and with this technology, we’re able to say something like, “Can I get a direct flight from X to Y at this time with these parameters?” And the self-service in question can respond back in a human-readable, fully formed answer that’s targeting only what I’ve asked and nothing else without having me to click into lots of different links, sort for myself and really make me feel like the interface that I’ve been using isn’t actually meeting my need. So I think that’s what we’re driving for.

And even though I gave a use case there as a consumer, you can see how that applies in the employee experience as well. Because the employee is dealing with multiple interactions, maybe voice, maybe text, maybe both. They’re trying to do more with less. They have many technologies at their fingertips that may or may not be making things more complicated while they’re supposed to make things simpler. And so being able to interface with AI in this way to help them get answers, get solutions, get troubleshooting to support their work and make their customer’s lives easier is a huge game changer for the employee experience. And so I think that’s really what we want to look at. And at its core that is how artificial intelligence is interfacing with our data to actually facilitate these better and more optimal and effective outcomes.

Laurel: And you mentioned how people are familiar with chatbots and virtual assistants, but can you explain the recent progression of conversational AI and its emerging use cases for customer experience in the call centers?

Elizabeth: Yes, and I think it’s important to note that so often in the Venn diagram of conversational AI and generative AI, we see an overlap because we are generally talking about text-based interactions. And conversational AI is that, and I’m being sort of high level here as I make our definitions for this purpose of the conversation, is about that human-readable output that’s tailored to the question being asked. Generative AI is creating that new and novel content. It’s not just limited to text, it can be video, it can be music, it can be an image. For our purposes, it is generally all text.

I think that’s where we’re seeing those gains in conversational AI being able to be even more flexible and adaptable to create that new content that is endlessly adaptable to the situation at hand. And that means in many ways, we’re seeing even more gains that no matter how I ask a question or you ask a question, the answer coming back from self-service or from that bot is going to understand not just what we said but the intent behind what we said and it’s going to be able to draw on the data behind us.

This is where the AI solutions are, again, more than just one piece of technology, but all of the pieces working in tandem behind the scenes to make them really effective. That data will also drive understanding my sentiment, my history with the company, if I’ve had positive or negative or similar interactions in the past. Knowing someone’s a new customer versus a returning customer, knowing someone is coming in because they’ve had a number of different issues or questions or concerns versus just coming in for upsell or additive opportunities.

That’s going to change the tone and the trajectory of the interaction. And that’s where I think conversational AI with all of these other CX purpose-built AI models really do work in tandem to make a better experience because it is more than just a very elegant and personalized answer. It’s one that also gets me to the resolution or the outcome that I’m looking for to begin with. That’s where I feel like conversational AI has fallen down in the past because without understanding that intent and that intended and best outcome, it’s very hard to build towards that optimal trajectory.

Laurel: And speaking of that kind of optimal balance between everything, trying to balance AI and the human touch that many customers actually want to get out of their experiences with companies like retail shopping or customer service interactions, when they lodge complaints, refunds, returns, all of these reasons. That’s a fine line to walk. So how do you strike the balance to ensure that customers enjoy the benefits of AI, automation, convenience, and availability, but without losing that human aspect to it?

Elizabeth: I think there’s many different ways to go about this, but I think it is again about connecting a lot of those touch points that historically companies have kept siloed or separate. The notion of a web presence and a marketing presence and a sales presence and a support presence or even an operations’ presence feels outdated to me. Those areas of expertise and even those organizations and the people working there do need to be connected. I feel in many ways we’ve gone down this rabbit hole where technology has advanced and we’ve added it on top of our old processes that sometimes date years or decades back that are no longer applicable.

And until we get to the root of rethinking all of those, and in some cases this means adding empathy into our processes, in some it means breaking down those walls between those silos and rethinking how we do the work at large. I think all of these things are necessary to really build up a new paradigm and a new way of approaching customer experience to really suit the needs of where we are right now in 2024. And I think that’s one of the big blockers and one of the things that AI can help us with.

Because some of the solutions and benefits we’ve been seeing are really about identifying gaps, identifying optimal flows or outcomes or employees who are generating great outcomes, and then finding a way to utilize that information to take action to better the business and better the flow. And I think that that’s something that we really want to hone in on because in so many ways we’re still talking about this technology and AI in general, in a very high level. And we’ve gotten most folks bought in saying, “I know I need this, I want to implement it.”

But they do need to take a step back and think about what are they looking for as a success metric when they do implement it, and how are they going to vet all of the different technologies and vendors and use cases to choose which one to go after first and how to implement it and how even to choose a partner. Because even if we say all solutions and technologies are created equal, which is a very generous statement to start with, that doesn’t mean they’re all equally applicable to every single business in every single use case. So they really have to understand what they’re looking for as a goal first before they can make sure whatever they purchase or build or partner with is a success.

Laurel: So how can companies take advantage of AI to tailor customer experiences on that individual level? And then what kind of major challenges are you advising that they may come across while creating these holistic experiences?

Elizabeth: I do think that change management within an organization, understanding that we’re going to have to change those muscles and those workflows is one of the biggest things you’ll see organizations grapple with. And that’s going to happen no matter what partner or vendor you choose. That’s something you’ll just have to embrace and run with and understand it’s going to happen. And I think also being able to take a step back and not assume you know the best use case, but let AI almost guide you in what will be the most impactful use case.

Some of the technologies and solutions we have can go in and find areas that are best for automation. Again, when I say best, I’m very vague there because for different companies that will mean different things. It really depends on how things are set up, what the data says and what they are doing in the real world in real time right now, what our solutions will end up finding and recommending. But being able to actually use this information to even have a more solid base of what to do next and to be able to fundamentally and structurally change how human beings can interface, access, analyze, and then take action on data. That’s I think one of the huge aha moments we are seeing with CX AI right now, that has been previously not available. And the only way you can truly utilize that is to have AI that is fully connected within all of your CX workflows, tools, applications and data, which means having that unified platform that’s connecting all of these pieces across all interactions across the entire customer journey.

And I think that’s one of the big areas that is possibly going to be the biggest hurdle to get your head wrapped around because it sounds enormous. But it’s actually a very fundamental and base level change that will then cascade out to make every action you take next far simpler and faster and will start to speed up the pace of the innovation and the change management within the organization.

Laurel: Since AI has become this critical tool across industries for customer interactions and experiences, how does generative AI now factor into a customer experience strategy? What are the opportunities here?

Elizabeth: We always go immediately to those chatbots and that self-service. And I think the applications there are wide and broad and probably fairly easy for us to conjure up. That idea of being able to on your own time in the channel of your choice, have a conversation in the future state, not know and not care if you are speaking to an artificial intelligence or a human led interaction because both are just as quick and just as flexible and just as effective for you. I think the ways that are more interesting to talk about now that maybe aren’t top of mind to everyone right now are around how we help agents and supervisors.

We hear a lot about AI co-pilots helping out agents, that by your side assistant that is prompting you with the next best action, that is helping you with answers. I think those are really great applications for generative AI, and I really want to highlight how that can take a lot of cognitive load off those employees that right now, as I said, are overworked. So that they can focus on the next step that is more complex, that needs a human mind and a human touch.

And they are more the orchestrator and the conductor of the conversation where a lot of those lower level and rote tasks are being offloaded to their co-pilot, which is a collaborator in this instance. And so they’re still in control of editing and deciding what happens next. But the co-pilot can even in a moment explain where a very operational task can happen and take the lead or something more empathetic needs to be said in the moment. And again, all of this information if you have this connected system on a unified platform can then be fed into a supervisor.

And we do now have a co-pilot in our ecosystem for supervisors who can then help them change from being more of a taskmaster of coming in and saying, “What do I need to do today? Who do I need to focus on?” Answer that question for the supervisors so they can become far more strategic and impactful into not diverting crises as they appear. But understanding the full context of what’s happening within their organization and with their teams to be able to build them up and better them and be far more strategic, proactive, and personalized in giving guidance or coaching or even figuring out how to raise information to leadership on what is going well.

So that again, they’re helping improve the pace of business, improve the quality of their employees’ lives and their consumers’ lives. Instead of feeling like they are almost triaging and trying to figure out even where to spend their energy. Their co-pilot can actually offload a lot of that for themselves. And this is always happening through generative AI because it is that conversational interface that you have, whether you’re pulling up data or actions of any sort that you want to automate or personalized dashboards.

All of this can be done without needing to know how to code, to have to write a SQL query, anything like that, that used to be a barrier to entry in the past.

Laurel: So this is sort of a follow-on to that, which is how can companies invest in generative AI as a way to support employees internally? There’s a learning curve there, as well as customers externally. And I know it’s early days, but what other benefits are possible?

Elizabeth: I think one of the “a-ha” moments for some of the technology we’re working on is really around, as I said, that conversational interface to tap into unstructured data. With the right knowledge management and with the right purpose-built AI, you’re going to be able to take a person like me. It’s been decades since I’ve written any code or done anything that complex, and you’re going to be able to have me be able to interface with the entirety of our CX data. Be able to pull it, ask questions of it through a conversational interface that looks a lot like a search engine we know and love today, and get back personalized reports or dashboards that will help inform me.

And then again, after seeing all of that information, I can continue the conversation that same way to drill down into that information and then maybe even take action to automate. And again, this goes back to that idea of having things integrated across the tech stack to be involved in all of the data and all of the different areas of customer interactions across that entire journey to make this possible. I think that’s a really huge moment for us. And I think that that’s where… At least I am still trying to help people understand how that applies in very tangible, impactful, immediate use cases to their business. Because it still feels like a big project that’ll take a long time and take a lot of money.

But actually this is just really new technology that is opening up an entirely new world of possibility for us about how to interact with data. That we just haven’t had the ability to have in the past before. And so again, I say this isn’t eliminating any data scientists or engineers or analysts out there. We already know that no matter how many you contract or hire, they’re already fully utilized by the time they walk in on their first day. This is really taking their expertise and being able to tune it so that they are more impactful, and then give this kind of insight and outcome-focused work and interfacing with data to more people.

So that they can all make better use of this information that before was just not able to be accessed and analyzed.

Laurel: So when you think about the future, Elizabeth, what innovations or developments in AI and customer experience are you most excited about and how do you anticipate these trends emerging?

Elizabeth: I think you’re going to hear from me and folks within our organization talking a lot about how knowledge management is at the core of artificial intelligence. Because your AI is only as good as the data that it is trained on and how your data is presented and accessible to AI is a huge game changer in whether your AI projects are going to really work for you or falter and not meet your goals. And so I think that for me, one of the exciting things and the challenging things is to explain how all of this is connected.

And that while in many ways we’re talking a lot about large language models and artificial intelligence at large. That sometimes some of the things that we’ve been discussing for a long time in CX, knowledge management is the secret behind all of this that’s going to take us from novel and interesting and a fun thing to demo to something that’s actually really impactful and revenue generating for your business.

Laurel: Thank you so much Elizabeth for joining us today on the Business Lab.

Elizabeth: Thank you for having me. This was a great conversation.

Laurel: That was Elizabeth Tobey, who is the head of marketing, digital and AI at NICE, who I spoke with from Cambridge Massachusetts, the home of MIT and MIT Technology Review.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the Global Director of Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

This show is available wherever you get your podcasts. If you enjoyed this episode, we hope you’ll take a moment to rate and review us. Business Lab is a production of MIT Technology Review. This episode was produced by Giro Studios. Thanks for listening.

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.