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

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

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

“We’re seeing AI having the ability to help uplift that to make all of those struggles and hurdles that we’re seeing on this more complex landscape to be more practical, to be more oriented towards actually serving those needs and desires of each employees and customers,” says Tobey.

The capability for AI tools to grasp 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 much more gains that regardless of how I ask an issue otherwise you ask an issue, the reply getting back from self-service or from that bot goes to grasp not only what we said however the intent behind what we said and it’ll have the option to attract on the info behind us,” she says.

Creating probably the most optimized customer experiences takes walking the advantageous line between the automation that permits 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 might help smooth processes and break down barriers.

Seeking to the long run, Tobey points to knowledge management—the strategy of storing and disseminating information inside an enterprise—as the key behind what is going to push AI in customer experience from novel to recent wave.

“I feel that for me, one among the exciting things and the difficult things is to elucidate how all of that is connected,” says Tobey.

Full Transcript

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

Our topic is creating great customer experiences with AI, from the decision center to online, to in-person. Constructing 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 must be a struggle.

Two words for you: foundational AI.

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

This podcast is produced in partnership with NICE.

Welcome Elizabeth.

Pleased to be here. Really excited to discuss this today.

Great. Well, let’s go ahead and begin. To set some context for our conversation, what’s the shopper experience landscape like now? And the way has it and can it proceed to alter with AI?

Well, to begin, I feel it is vital to notice that AI is not a brand new technology, especially not in the shopper experience (CX) era. One in every of the things that is sort of recent though is generative AI and the best way we’re using and in a position to use large language models within the CX paradigm. So we all know that customers and employees today wish to have more tools to get the answers that they need, get things done more effectively, more efficiently on their very own terms. So for consumers, we frequently hear that they wish to use digital solutions or channels of their alternative to assist find answers and solve problems on their very own time, on their very own terms.

I feel the identical applies after we discuss either agents or employees or supervisors. They do not necessarily wish to be alt-tabbing or searching multiple different solutions, knowledge bases, different pieces of technology to get their work done or answering the identical questions over and all over again. They wish to be doing meaningful work that actually engages them, that helps them feel like they’re making an impact. And in this manner we’re seeing the contact center and customer experience typically evolve to have the option to satisfy those changing needs of each the [employee experience] EX and the CX of all the things inside a contact center and customer experience.

And we’re also seeing AI having the ability to help uplift that to make all of those struggles and hurdles that we’re seeing on this more complex landscape to be more practical, to be more oriented towards actually serving those needs and desires of each employees and customers.

A critical element of great customer experience is constructing that relationship together with your customer base. So then how can technologies, like you have been saying, AI typically, help with this relationship constructing? After which what are a few of one of the best practices that you’ve got discovered?

That is a very complicated one, and I re-evaluate, it goes back to the concept of having the ability to use technology to facilitate those effective solutions or those impactful resolutions. And what which means is determined by the use case.

So I feel that is where generative AI and AI typically might help us break down silos between the several technologies that we’re using in a company to facilitate CX, which also can result in a Franken-stack of nature that may silo and fracture and create friction inside that have.

One other is to essentially be flexible and personalize to create an experience that is sensible for the one that’s in search of a solution or an answer. I feel all of us have been consumers where we have asked an issue of a chatbot or on a web site and received a solution that either says they do not understand what we’re asking or a listing of links that perhaps are generally related to at least one keyword now we have typed into the bot. And people are, I’d say, the infant notions of what we’re trying to attain now. And now with generative AI and with this technology, we’re in a position to say something like, “Can I get a direct flight from X to Y at the moment with these parameters?” And the self-service in query can respond back in a human-readable, fully formed answer that is targeting only what I’ve asked and nothing else without having me to click into numerous different links, sort for myself and really make me feel just like the interface that I have been using is not actually meeting my need. So I feel that is what we’re driving for.

And although I gave a use case there as a consumer, you may see how that applies in the worker experience as well. Because the worker is coping with multiple interactions, perhaps voice, perhaps text, perhaps each. They’re attempting to do more with less. They’ve many technologies at their fingertips that will or will not be making things more complicated while they’re imagined to make things simpler. And so having the ability to interface with AI on this approach to help them get answers, get solutions, get troubleshooting to support their work and make their customer’s lives easier is a big game changer for the worker experience. And so I feel that is really what we wish to have a look at. And at its core that’s how artificial intelligence is interfacing with our data to truly facilitate these higher and more optimal and effective outcomes.

And also you mentioned how individuals are aware of chatbots and virtual assistants, but are you able to explain the recent progression of conversational AI and its emerging use cases for customer experience in the decision centers?

Yes, and I feel it is vital to notice that so often within the Venn diagram of conversational AI and generative AI, we see an overlap because we’re generally talking about text-based interactions. And conversational AI is that, and I’m being kind of high level here as I make our definitions for this purpose of the conversation, is about that human-readable output that is tailored to the query being asked. Generative AI is creating that recent and novel content. It isn’t just limited to text, it will probably be video, it will probably be music, it will probably be a picture. For our purposes, it is mostly all text.

I feel that is where we’re seeing those gains in conversational AI having the ability to be much more flexible and adaptable to create that recent content that’s endlessly adaptable to the situation at hand. And which means in some ways, we’re seeing much more gains that regardless of how I ask an issue otherwise you ask an issue, the reply getting back from self-service or from that bot goes to grasp not only what we said however the intent behind what we said and it’ll have the option to attract on the info behind us.

That is where the AI solutions are, again, greater than only one piece of technology, but the entire pieces working in tandem behind the scenes to make them really effective. That data may even drive understanding my sentiment, my history with the corporate, if I’ve had positive or negative or similar interactions previously. Knowing someone’s a brand new customer versus a returning customer, knowing someone is coming in because they’ve had various different issues or questions or concerns versus just coming in for upsell or additive opportunities.

That is going to alter the tone and the trajectory of the interaction. And that is where I feel conversational AI with all of those other CX purpose-built AI models really do work in tandem to make a greater experience since it is greater than just a really elegant and personalized answer. It’s one which also gets me to the resolution or the final result that I’m in search of to start with. That is where I feel like conversational AI has fallen down previously because without understanding that intent and that intended and best final result, it’s totally hard to construct towards that optimal trajectory.

And speaking of that form of optimal balance between all the things, attempting to balance AI and the human touch that many shoppers actually wish to get out of their experiences with corporations like retail shopping or customer support interactions, after they lodge complaints, refunds, returns, all of those reasons. That is a advantageous line to walk. So how do you strike the balance to be sure that customers enjoy the advantages of AI, automation, convenience, and availability, but without losing that human aspect to it?

I feel there’s many various ways to go about this, but I feel it’s again about connecting quite a lot of those touch points that historically corporations have kept siloed or separate. The notion of an internet presence and a marketing presence and a sales presence and a support presence and even an operations’ presence feels outdated to me. Those areas of experience and even those organizations and the people working there do should be connected. I feel in some ways we have gone down this rabbit hole where technology has advanced and we have added it on top of our old processes that sometimes date years or a long time back which are not applicable.

And until we get to the foundation of rethinking all of those, and in some cases this implies adding empathy into our processes, in some it means breaking down those partitions between those silos and rethinking how we do the work at large. I feel all of these items are needed to essentially construct up a brand new paradigm and a brand new way of approaching customer experience to essentially suit the needs of where we’re straight away in 2024. And I feel that is one among the massive blockers and one among the things that AI might help us with.

Because among the solutions and advantages we have been seeing are really about identifying gaps, identifying optimal flows or outcomes or employees who’re generating great outcomes, after which finding a approach to utilize that information to take motion to higher the business and higher the flow. And I feel that that is something that we really need to hone in on because in so some ways we’re still talking about this technology and AI typically, in a really high level. And we have gotten most folk bought in saying, “I do know I would like this, I need to implement it.”

But they do have to take a step back and take into consideration what are they in search of as a hit metric after they do implement it, and the way are they going to vet all of the several technologies and vendors and use cases to decide on which one to go after first and the way to implement it and the way even to decide on a partner. Because even when we are saying all solutions and technologies are created equal, which is a really generous statement to begin with, that does not imply they’re all equally applicable to each business in each use case. In order that they really have to grasp what they’re in search of as a goal first before they will be sure that whatever they purchase or construct or partner with is a hit.

So how can corporations benefit from AI to tailor customer experiences on that individual level? After which what form of major challenges are you advising that they might come across while creating these holistic experiences?

I do think that change management inside a company, understanding that we’ll must change those muscles and people workflows is one among the largest belongings you’ll see organizations grapple with. And that is going to occur regardless of what partner or vendor you select. That is something you will just must embrace and run with and understand it’ll occur. And I feel also having the ability to take a step back and never assume one of the best use case, but let AI almost guide you in what can be probably the most impactful use case.

A number of the technologies and solutions now we have can go in and find areas which are best for automation. Again, once I say best, I’m very vague there because for various corporations that can mean various things. It really is determined by how things are arrange, what the info says and what they’re doing in the actual world in real time straight away, what our solutions will find yourself finding and recommending. But having the ability to actually use this information to actually have a more solid base of what to do next and to have the option to fundamentally and structurally change how human beings can interface, access, analyze, after which take motion on data. That is I feel one among the large aha moments we’re seeing with CX AI straight away, that has been previously not available. And the one way you may truly utilize that’s to have AI that’s fully connected inside your whole CX workflows, tools, applications and data, which suggests having that unified platform that is connecting all of those pieces across all interactions across all the customer journey.

And I feel that is one among the massive areas that’s possibly going to be the largest hurdle to get your head wrapped around since it sounds enormous. Nevertheless it’s actually a really fundamental and base level change that can then cascade out to make every motion you’re taking next far simpler and faster and can begin to speed up the pace of the innovation and the change management inside the organization.

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

We all the time go immediately to those chatbots and that self-service. And I feel the applications there are wide and broad and possibly fairly easy for us to conjure up. That concept of having the ability to on your individual time within the channel of your alternative, have a conversation in the long run state, not know and never care in the event you are chatting with a synthetic intelligence or a human led interaction because each are only as quick and just as flexible and just as effective for you. I feel the ways which are more interesting to discuss now that perhaps aren’t top of mind to everyone straight away are around how we help agents and supervisors.

We hear lots about AI co-pilots helping out agents, that by your side assistant that’s prompting you with the following best motion, that helps you with answers. I feel those are really great applications for generative AI, and I really need to spotlight how that may take quite a lot of cognitive load off those employees that straight away, as I said, are overworked. In order that they will deal with the following step that’s more complex, that needs a human mind and a human touch.

They usually are more the orchestrator and the conductor of the conversation where quite a lot of those lower level and rote tasks are being offloaded to their co-pilot, which is a collaborator on this instance. And so that they’re still answerable for editing and deciding what happens next. However the co-pilot may even in a moment explain where a really operational task can occur and take the lead or something more empathetic must be said within the moment. And again, all of this information if you’ve this connected system on a unified platform can then be fed right 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 would like to do today? Who do I would like to deal with?” Answer that query for the supervisors so that they can change into much more strategic and impactful into not diverting crises as they seem. But understanding the total context of what is happening inside their organization and with their teams to have the option to construct them up and higher them and be much more strategic, proactive, and personalized in giving guidance or coaching and even determining the way to raise information to leadership on what’s going well.

In order that again, they’re helping improve the pace of business, improve the standard of their employees’ lives and their consumers’ lives. As a substitute of feeling like they’re almost triaging and attempting to work out even where to spend their energy. Their co-pilot can actually offload quite a lot of that for themselves. And that is all the time happening through generative AI since it is that conversational interface that you’ve, whether you are pulling up data or actions of any sort that you need to automate or personalized dashboards.

All of this might be done with no need to know the way to code, to have to put in writing a SQL query, anything like that, that was once a barrier to entry previously.

So that is kind of a follow-on to that, which is how can corporations put money into generative AI as a approach to support employees internally? There is a learning curve there, in addition to customers externally. And I comprehend it’s early days, but what other advantages are possible?

I feel one among the “a-ha” moments for among the technology we’re working on is de facto around, as I said, that conversational interface to tap into unstructured data. With the fitting knowledge management and with the fitting purpose-built AI, you are going to have the option to take an individual like me. It has been a long time since I’ve written any code or done anything that complex, and you are going to have the option to have me have the option to interface with everything of our CX data. Find a way to drag it, ask questions of it through a conversational interface that appears lots like a search engine we all know and love today, and get back personalized reports or dashboards that can help inform me.

And nevertheless, after seeing all of that information, I can proceed the conversation that very same approach to drill down into that information after which perhaps even take motion to automate. And again, this goes back to that concept of getting things integrated across the tech stack to be involved in all of the info and all of the several areas of customer interactions across that entire journey to make this possible. I feel that is a very huge moment for us. And I feel that that is where… Not less than I’m still attempting to help people understand how that applies in very tangible, impactful, immediate use cases to their business. Since it still looks like an enormous project that’ll take a protracted time and take quite a lot of money.

But actually that is just really recent technology that’s opening up a completely recent world of possibility for us about the way to interact with data. That we just have not had the flexibility to have previously before. And so again, I say this is not eliminating any data scientists or engineers or analysts on the market. We already know that regardless of what number of you contract or hire, they’re already fully utilized by the point they walk in on their first day. This is de facto taking their expertise and having the ability to tune it so that they’re more impactful, after which give this type of insight and outcome-focused work and interfacing with data to more people.

In order that they will all make higher use of this information that before was just not in a position to be accessed and analyzed.

So when you concentrate on the long run, Elizabeth, what innovations or developments in AI and customer experience are you most enthusiastic about and the way do you anticipate these trends emerging?

I feel you are going to hear from me and folk inside our organization talking lots about how knowledge management is on the core of artificial intelligence. Because your AI is simply nearly as good as the info that it’s trained on and the way your data is presented and accessible to AI is a big game changer in whether your AI projects are going to essentially be just right for you or falter and never meet your goals. And so I feel that for me, one among the exciting things and the difficult things is to elucidate how all of that is connected.

And that while in some ways we’re talking lots about large language models and artificial intelligence at large. That sometimes among the things that we have been discussing for a very long time in CX, knowledge management is the key behind all of this that is going to take us from novel and interesting and a fun thing to demo to something that is actually really impactful and revenue generating for what you are promoting.

Thanks a lot Elizabeth for joining us today on the Business Lab.

Thanks for having me. This was an incredible conversation.

That was Elizabeth Tobey, who’s the pinnacle of promoting, digital and AI at NICE, who I spoke with from Cambridge Massachusetts, the house of MIT and MIT Technology Review.

That is 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 on the Massachusetts Institute of Technology, and you will discover us in print on the net and at events every year around the globe. For more details about us and the show, please try our website at technologyreview.com.

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