2013 – How AI is Transforming Customer Interactions and Contact Centers with Waterfield Tech’s Kerry Robinson

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1:05With us right now, it's Kerry Robinson. Kerry, you are the Vice President of Conversational AI with Waterfield Technologies. Your website is waterfieldtech.com. Kerry, it's a pleasure to have you. Thanks, Josh. Pleasure to be here. Well, I would love to hear more about the work that you are doing, the work that Waterfield is doing in the space of AI, because there's a lot of cool innovations happening right now. Yeah, a lot has changed. I like to say I got into AI when I was nine or 10 years old. That was quite a long time ago now, a few decades, and been through quite a few cycles and big hype cycles in AI.

1:46And yeah, the last two years has been the biggest of those. But the most meaningful, finally, after 20, 30 years of being involved in this tech, we have AI that really has a form of intelligence that we can really leverage. It's not human intelligence exactly, but it's real intelligence and it's exciting putting it to work. For someone that, you know, obviously this is what you do day in, day out. I think obviously we see the main tech headlines. We see, you know, obviously the tools that are very close to us, you know, ChatGPT and Gemini and so forth. But what are you most excited about that's been taking place over the past six to 12 months that maybe we don't understand the true impact of this bubbling under this groundswell that's been happening?

2:38Yeah, I think I was at Amazon Web Services Leadership Summit in London. And I think it really crystallized for me because people talking about this as just another tool. and an important tool and very powerful. But when I talk to some of the keynote presenters there about how are they discussing it? How are they managing up? They're like, I'm just telling them it's another tool that we can add to the repertoire. And I think that totally misses it. I think this is really, really something quite different. Like I said earlier on, before now, I mean, I've been working in AI or what we used to call AI for decades.

3:13And it was a slog. You know, we had to really work through and write down rules and scripts for everything that we wanted the system to do. And we had limited machine learning capabilities like speech recognition and vision and those things that will say people like you bought something like this and recommender engines. But they were hard yards. Whereas nowadays, we've got AI models that can hold a natural conversation in chat and now in voice with the new streaming models. it can coat it can summarize it can translate and and critically it can reason when we give it really quite tough math and science problems with the right training these models can can solve for them and that that isn't just a new tool but if you look around if you look around everything that we've created as human beings we created using intelligence that's what means that we cater the tiger rather than the tiger eats us and and having that on tap Having that with almost unlimited scalability and abundance is going to change, of course, our working lives, but also our personal lives and our society beyond recognition, I think, over the next 10 years or so.

4:28Yeah. So Waterfield Tech works with contact center solutions or call centers, which, you know, we think about overall operational requirements that some of these larger companies have. These are just huge operations and staffing. And there's obviously a lot of issues that go along with that. There is huge opportunity here because I think, you know, a lot of right now what we outsource to people likely could be better resourced to AI technologies. And so I would imagine we're going to probably see some pretty big movements in this space over the next year or two. Do you mind maybe giving us a peek of, you know, kind of what Waterfield is doing right now?

5:25Oh, yeah. And, you know, there was a lot of hesitance because, as we talked about earlier, there's been lots of hype, lots of false dawns in terms of the capability of AI. But I think a real watershed moment was when Klarna, a financial services company that handles many, many millions of interactions, very, very large customer services team dealing with financial services with sometimes, you know, quite individuals with financial difficulties. You know, it's almost like the hardest use case you could imagine in customer services to automate. And they work very, very closely with OpenAI, one of their lead partners.

6:07And they released a case study a few months back where they said they had automated 70 % of the interactions that previously were handled by customer service agents. And critically, the customers were happier with the interactions they had with the AI than they were with the interactions they had with humans and less likely to call back afterwards. So that was a real, I think, watershed moment for the capability of Gen.AI in the contact center. And not a surprise to me. I think a lot of people were surprised when that came out. But when you really work closely with the tech and you realize what it's capable of, I think you also realize that contact centers is the perfect place for it because it is intelligent.

6:55It is capable of doing some really quite amazing things, but it's not magic. It has its limitations. And the thing is contact centers are literally built to hire junior intelligences. There's quite a transient workforce, a young workforce or part-time workforce. And they hire these people in. they give them a few weeks training and then they get them on the phones and they have managers listening in, they have technology listening in, there's lots of support around them. If there's any problems, they can escalate to a more senior member of the team. And so it's the perfect environment to deploy Genitive AI because you've got lots of humans watching what's going on and picking up the pieces of the junior agents who we used to use or now the Genitive AI chatbots and voice bots that we can use make a mistake.

7:41So you've got the perfect environment to harness intelligence, but have all the safety net around it in case something goes wrong. So who would be examples of who Waterfield is working with right now? And I'm just curious that, you know, if there's someone listening right now that, you know, they're in that space, that they're involved in that customer experience, where Waterfield fits in here and exactly what that ends up looking like? Yeah, a really interesting question because there's going to be quite a lot of shaking up and thinking change in this area because you've got the likes of Waterfield Tech, which is a contact center customer service focused integrator.

8:27So we resell contact center systems and we integrate them with the business systems and then we integrate AI into that and then we can manage the whole thing. So you've got integrators like us, you've got the contact center vendors like Genesis and VIA and Cisco and the newer ones like Five Nines and Twilio and Amazon have their own. Microsoft just launched their own contact center. And then you've got the BPOs, the business process outsourcers who essentially take all of that and then just sell it to businesses by the minute. So sell customer services by the minute. And the reality is that all three of these these partners have have have for many years co-existed quite happily but now with AI it's kind of changed it all because the contact center vendors are trying to bring AI in the AI vendors are trying to bring contact centers in the BPOs realized that that they don't really need to hire humans anymore they can do this with AI so so the chase is the space is really changing very rapidly And there's lots of technological changes, but also lots of economic changes as a result of that.

9:33So it's really kind of tough to navigate at the moment. And that's kind of the way we try to describe the situation to people is that this is a journey that we're all going on. Literally, like most of us only really learned about this technology in the last 18 months or so. We were tracking it for two or three years. But I think even the most, those deepest into the research have been quite surprised at how capable these models have become as we scaled up their sizes. So it's a journey. And I think the key thing is not to wait. because the impact of scalable, cheap intelligence that never goes on holiday, never goes sick, sticks to the rules if you do it the right way and program it the right way, is just so powerful.

10:24And obviously, we can see the power of that already in the contact center with the impact of the likes of Klarna. We're deploying chatbots on people's websites. We're deploying voicebots to replace humans in the contact center and we're creating co-pilots so so so ai that can can ride shotgun with human agents and and help them make sure they've got the right documentation right information to help customers um there's lots of different ways of deploying the technology the technology is changing very rapidly and so the key thing is is get started get started somewhere at the minimum you and your team need to be using chat gpt daily um or you might choose one of the other models because only by using this tech do you start to get a real feel for what it's capable of.

11:10And my big tip is don't just ask it questions about stuff that you don't really know very much about. Actually use it to have a discussion in areas that you do understand well. So I'm very into AI. So I talk to AI about AI a lot. And I'm also quite into speaker building and audio. And so I'll talk to you about that a lot. And that's a really great way to learn what the models are good at and where they fall down. So that's kind of my number one tip is make sure you're using AI daily. And the second is use it to do analysis. So one of the great things in customer services is there's lots of data.

11:48There's lots of conversations happening all the time. And some of our biggest wins have come from using AI to analyze that data and look for opportunities to improve customer service, improve the way agents are interacting with customers, but also look for opportunities to deploy automation. We had one example where we analyzed 1 ,600 conversations, every word in every conversation. We analyzed it all, we categorized it all, and we identified 10 % of the interactions with a particular type that could be automated. And now we're just beginning the process to deploy that use case. But we would have never found that before, because we could have never looked at 1 ,600 conversations.

12:32But now you can do that. It cost a few hundred dollars of open AI usage. Yeah. I'm certain that you have likely seen, Kerry, some instances where AI was implemented poorly. And there are consequences to that. I don't know if there's any that comes to mind. You don't have to call anyone out personally. Oh, no, there's some quite famous ones, actually. So there's one, DPD, which is a delivery firm that operate in the UK and Europe. They deployed a chatbot quite early and they didn't do the work required to put what's called guardrails in. And so a disgruntled consumer deliberately led it astray and got it to make up poems and songs about how bad DPD was.

13:19And of course, they went all over Twitter and social media and was very embarrassing. Now, that's quite straightforward to fix. You can walk what's called guardrail the model, so you could give it very specific things it can and can't say. And doing that right will avoid issues like that. There's other issues. There was one with Air Canada, actually, where their chatbot got inadvertently led astray by a customer, poor, bereaved customer, needed to change their ticket. And the chatbot made up, and it's called hallucinating when a model make something up, it made up a false refund policy for bereaved passengers.

13:58And then Air Canada took that poor bereaved customer to court to claim that the chatbot didn't really represent them. I think absolutely correctly, the judge sided with the claimant and Air Canada had to honour the refund that its chatbot offered. I think just a couple of examples that I think tell us quite a lot. Firstly, your AI, your chatbot, your voicebot, it does represent your business. The courts will side with claimants who want to uphold what your bot said. And the second is, it's really quick and easy to build a prototype, to build a really cool looking demo. We do it all the time. But actually doing the work to really evaluate, does it work the right way all the time?

14:44And how do you make sure that it doesn't go off topic and make sure It's got all the information it needs to answer questions. So I think that's an intuition that you'll get as you start to work with modern genitive AI more, is that it's been trained to help. I mean, the way it works is it was trained on the whole of the internet. And so it learned a lot of concept about language, but also things that have been published on the internet, all sorts of science and languages and practical things too. but that wasn't useful until we did what's called fine-tuning it to answer questions it's called instruction tuning so we teach it that when we answer it ask it a question it should answer us but through that process you know we've literally whipped it into submissions that whenever we ask it a question it will use its knowledge to answer that but if we ask it a question that it doesn't have the knowledge to answer then it will do its best and it will end up making something up And so that's where the tools and techniques come in to make sure that the model always has the information it needs to answer the questions that your customers might have of it, but also you give it out.

15:52If it doesn't know the answer, it needs to say it doesn't know the answer. And these techniques and many others deployed appropriately can help us wrestle this quite amazing technology to actually do something useful. But I think that is one of the hardest things is really recognizing what it's capable of, understanding its limitations. And then in the process of going from a cool demo to a production system, really putting a lot of work into the evaluation. And some of that evaluation is manual, people manually interacting with the models. But actually, we found that a lot of benefit comes from automated evaluations.

16:31So we'll have one chatbot playing the customer and another chatbot playing the business and have them interact. And we'll have the third chatbot look at those conversations and say, like, how good was it? And did it stay on compliance? Did it get drawn off into conversations about things that it really shouldn't do? Yeah. So your website, waterfieldtech.com, to our friend that's been listening, and like, how do they begin this? So if they were to have a conversation with you, what usually happens in that conversation? So everyone wants to talk about Gen AI because everyone's bosses are asking, are we using AI?

17:11How are we going to leverage it? So we end up having lots of conversations, people just wondering what this technology is capable of. The thing that I think surprises a lot of people is really the quality of conversations they can have. So we end up doing a lot of demos, show people what's possible. And that's a benefit, but it's a trap too, because it shows what's possible. But as I said, the hard work is in getting from that demonstration and from that prototype into production safely. And so that's actually a lot of the work that me and my team do is helping people understand the whole journey from finding the opportunity, finding the concept, picking a use case that could work, that's going to give value, and then doing all the work it takes to get that deployed safely to what's called red team it to make sure nothing goes wrong in production and do all that automated and manual evaluation, as I said.

18:04So a lot of it is laying out that journey and then just being by the side of our customers as they go through that journey. Because it is another source of intelligence. It's part of the team. I think rather than think of AI as a particular tech that you might deploy, I think it's much, much more useful to think of it as a workforce. Absolutely. And they need leading and managing just like your regular workforce does. Yeah. Well, Kerry Robinson, again, the website, waterfieldtech.com. There's an orange button that says talk to us. Click there and grab some time together. but everything again, CX Consulting, Contact Center, Applied AI, Workforce Optimization, Managed Services, Product Development.

18:53Again, you'll learn a little bit more about that at waterfieldtech.com. Kerry Robinson, you are the Vice President of Conversational AI. Very, very exciting space to be in. Kerry, it's been a great conversation. Thank you so much for joining us. I'm enjoying it. Thanks, Josh.

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