If we think the language models are these huge, vast libraries, these companies are going into certain shelves, certain colour books. They're far more interested in the outcomes and the technology, the labelling software and the less likely to want to be involved with the manpower. So we can see a natural relationship between companies that need vast amounts of subject matter expertise in a burst of activity as well and needs them across the world in different professions. So freelancing is the best way of tapping into that type of resource. Call centres are at the front line of generative AI. Salesforce just announced thousands of roles being cut after implementing new AI tools and every brand is asking what comes next. Few people are in a better place to answer that question than Chris Dumbleton, executive at Limitless Technology, who spent 20 years in telecom and contact centres and is now bringing innovation using the gig economy and AI approaches to global brands like Microsoft, Sony, eBay and more. Now the same gig experts aren't just supporting customers, they're helping train the next wave of AI itself. Chris, welcome to Expert Intelligence. Thanks, Paul. It's good to be here. So you can't miss a headline that says AI is replacing call centre jobs. It just seems to be the kind of one place where AI driven chat bots are going to replace humans and it's all going to make it okay. You've been in this space. What's real? What's really happening? Do you know what I heard the other day, which really landed, was first of all, I think everyone's probably a bit tired of hearing this phrase, which is AI is coming for the simple stuff and then all the complex stuff is what's left and trying to figure that out. So I tested that theory on a consultant I know and he said, do you know what that's what I hear all the time? But what's real in the contact centres is the team that's being asked to use AI to remove some of the headcount. I'm like, well, I don't want to lose my team and I've got to still pay my wages and they don't want to do it. So there's almost like a paternal control problem with the people that are being asked to go and implement AI by removing costs from their front line to go and pay for it. And then the cost benefit payoff and how long it takes. So there's no doubt that what it's capable of, but the actual applicability, and this is someone that I know really well and he's one of these dudes that is in front of loads of contact centres, assessing what they want to do, implementing people, process technology changes and all that stuff. And this is the overriding thing he's hearing. It's widespread. It's across every vertical. It's now it's about people actually struggling to implement it because of that challenge. One of the things I experienced in working with gig economy models in general of bringing external people in kind of changing to your point, like trying to change organizations at a fundamental level was people's desire to try something new. You have a concept gig CX and one of the things that you've been working on for quite a while is bringing gig economy strategies to the call centre, which is in many ways equally as disruptive as AI and sort of faces the same thing. So when you talk about the Uber business model for contact centres, help explain to the audience what that is and how it works. So that's my classic elevator pitch and I've even tested out in an elevator and it worked. So the simplest way I describe them, what we do is taking the Uber business model to the contact centre industry and everyone kind of gets that straight away. More often now we're talking more about sort of crowdsourcing CX rather than gig, but it's the same principle, which is no one's employed. We're using freelancers. So we go to big organizations and we say that we've got this idea for you, which is rather than using in-sourced or outsourced contact centre staff with mega-hiatrician minimum wages, you're constantly dealing with the seesaw of cost and see-sat, where to apply automation, where to keep people and trying to keep the lights on, is we're going to route contacts to your own customers that we're going to source and we're going to invite to become your experts and we qualify them, verify them, train them in exactly the same ways in how it's agents are, but they are a freelancer. And then the magic happens and this was my question. So when I first joined Limit List, I asked the same question as our clients ask now when we talk about this model. I was like, so we're not going to employ anybody, but they're going to turn up for work and they're going to do just as good a job if not a better job than someone that's in a contact centre. The answer was yes. And I was like, okay, I'm in. And then six years later, this is me now convincing everyone that that's the way it works and that's how it works at a high level. One of the case studies that I read was about eBay. And being an eBay seller is a very specific thing. You have to have knowledge on how to do it, how to use the platform best practices. When you talk about a platform that has customers or people that are engaging in the platform, making money on the platform, being able to talk to other people who do the same thing, how is that different than me just hiring a contact center? The eBay story was one of our favourite case studies. So I think we can all attest to this, right? Which is that if you need to contact a company because you've got a support issue or a council issue or whatever it might be, right? Unless it's a trivial thing like moving house or administration or something like that. If you want to get some actual expertise, you want to talk to someone that knows more than you do, then I think it makes sense to talk to someone that's already got the product or service because they're best place to be able to help you. That's what we got and that's what eBay did. So we tested a delivery model with those guys where there's a lot of buyers sellers as a free-sided marketplace. So with sellers, they have an extraordinarily high drop-off rate with sellers who register to become a seller who then go on to become hooked on the eBay drug and carry on and become prolific. So that drop-off rate is probably won't like me sharing the number. So I'm not going to share the number, but let me just say it's really high. It was really high. So we got involved with them and said, well, you have millions of sellers and this was in COVID that kicked it all off. Everybody then registered to become a seller on eBay. They were having millions of new registrations a day. But then when they run the numbers in six months time, how many of those sellers went on to list five products, sell five products, stay for six months? The number was single digits from all of that. So what we enabled is for very experienced sellers to help the new sellers. They're literally coaching them. So when you log to become a seller on eBay platform, you go through a seller on boarding flow. And during that flow, we got a little widget which said, do you want to talk to an expert seller? And the expert seller would coach them on their listing, on their photos, on where to list, pricing, everything like that. And they saw some massive uptick in some of the metrics. And it was A-B tested. So it was like, yeah, eBay is, whilst it looks like an online marketplace, is a data science business. So behind the scenes is some very clever people that are working out how best to optimize the platform and to gain the data analytics to show what people are buying, where they're selling, how much is being run. And it was the data that led the results. So they had a 24% increase in the sales revenue from those that went through that process, 20% more new or reactivated sellers. And the cost of them handling those chats was just under 20% less than they were in the actual contact centers themselves. So it was a great metric. It won one of those prestigious gold awards at the European Contact Center. And it was the only year. So I've been going to that award ceremony. I used to sponsor it in my previous organization, used to come out of my marketing budget. And so I've been going to that for years and years and years. And this was the first time I was kind of like shoulder to shoulder with a client who was up for an award. And it was the year of COVID when it was remote. So I'm there in a tux in my kitchen or this on my own thing. And then it got announced and we were able to jump in around stuff. So we didn't get the on stage moment and the Oscar speech and all that sort of stuff. But it won one best customer engagement award. It was great. It was a wonderful time. It's good to get that recognition for the model. One of the interesting things as we start to talk about AI is outcome based pricing. Yeah. The SASS is you pay your $10 a month or enterprise level thousands of dollars a month for a SASS platform. You use it. If you don't, you don't. Yeah. In a lot of the generative AI products, you pay for the token and it's really outcome based pricing. Tell me a little bit about the gig CX model and outcome based pricing versus, hey, I just hire a group of people to do support. To your point, I pay those people to do that work. This is kind of the real essence of the gig economy, which is the difference between getting in a black cabin London. I don't mean any Dutch and more so black cab drivers, but the difference between getting in a black cabin London or through a taxi firm versus an Uber driver is the driver is he wants a really high rating because the rating is what drives additional work, preferential rates, further opportunities, continuation to work, better earning opportunities, that sort of stuff. So that's the same sort of principle as we have when it's all to do with outcome based pricing. We don't exist as a business unless a question from a customer is an answer to correctly by a freelance expert that we've onboarded. If we can't make that happen, then there is no business model. There is no limitless. There is nothing. The difference between me rocking up to a company and going, I'm going to sell you 1,000 contacts into licensed software seats or tokens or whatever it might be, something that you pay, whether you use it or not, doesn't matter. I've sold you a license. It's down to you. Obviously, there's a big desire to make sure that customers do because of retention rates and stuff like that. In principle, I can sell you what I think it's going to do. People go, yeah, I want to buy that. And then whether you use it or not is after the event. Outcome based pricing is where you have to trust that there is no business unless there are positive outcomes. So it's the most optimized way of commercializing a delivery. It means that we have to put all of the skin in the game in terms of the risk, if you like, because we're not asking people to pay for platform licenses and things like that. We're literally just giving people a price for outcomes and it's those outcomes that drive the continuation of the program. I've built a couple of these systems, expert based systems. And one of the things that I always got from people was the biggest pushback. So they're not employed. You're not training them. No one's telling them they must be here. But why would they show up? There's this idea that the only way to create that value, hey, here's a customer with a problem or somebody who needs something and here's an expert that can help them. The only way to do that is a traditional model where you go to a staffing firm or some big company that hires them, trains them and on boards them versus passionate experts that want to show up and help people. How do you overcome that when you go into a client who's like, hey, I'm interested in learning more? We have to basically start with a pilot somewhere, but we'll say, look, firstly, we'll go back to that original point, which is that there is no business unless we can prove this works. We're not trying to get you to remove everything that you're doing today. But if you give this a shot, give it three months and you'll see the outcomes, what you're going to get, we're pretty confident you want to double down on that. It is a proof based whole delivery model. For me, the answer is like, so my first ever job was in a contact center. I think I might have just turned 17. I've definitely 17. I remember I got promoted when I was 18. So I was definitely there when I was 17. And what I can say is it was a job. I was 17. I can't say I had glorious career ambitions at that stage. I was interested in acquiring some beer tokens and that was about it. It was a job that allowed me to do that. In the end, I absolutely loved it. Here I am only a couple of years later, still very much. I didn't have a passion for the brand. It was a job and I enjoyed my workmates and the banter and all that sort of stuff. So I was working for an electronics company in the UK, massive like department store one. And if you bought some stuff and then you needed to buy some additional stuff, things went wrong, warranties and all that sort of stuff. And you called in, you might have been lucky enough to speak to me. And my job was to sell you some accessories or figure out warranties or even deal with returns and stuff like that. That was my promotion, by the way. I was then in charge of people returning stuff. That was my big gig, no pun intended. The point I'm trying to make is I didn't shop in that brand. I shopped in one of their competitors because that was my preference. And my question to everyone that leaves contact centres today, even those outsources, if you were to get everyone to go, right, stop what you're doing. I'm going to ask you a question. Who here has actually got our products or services or uses what we do? How many hands do you think would go up? And obviously, nobody knows the real answer, but it's not as many as people think they are. Because people are doing it as a job that wants to do a good job, but they don't have the relationship with the brand first. We go the other way around, which is we'll go to a company and go, right, we'll source these experts from your customer list. They've already chosen your products on their own volition, on their own free will. We will now invite them to work as a freelancer to support your other customers. And only those that qualify to do it will be able to do it. And they fit it around their lives. We surveyed the thousands of experts we've got working on the platform. They are stay at home parents, their retirees, their students, their commuters going in between jobs. They're working for two hours a day. So they're on an hour on the train going in, an hour going home, and they're working on behalf of the brands that they've already got a relationship with. So it's not like you're asking them to do some outside job where they're not having a real sort of feeling for it. And the feedback we get is sensational because it unlocks people's earning opportunity, allows them to fit it around their schedules. They've already got a relationship with the brand and they want to help people. It's kind of like if you had a BPO and community forums and they had a love child, that's what you get with GIG. Because with forums and community forums, you've got people that are really knowledgeable because they're the ones that are going on there. They work on their own free will and they want to help people. They're working digitally. They're working digitally, they want to help people and they've got product knowledge. But it's not a channel that you can scale or rely on because it has no SLAs, has no KPIs and it has no operation control. Whereas a BPO, you might have, you have all the SLAs and management insights, reporting, technology and that sort of stuff. But you may have a bunch of people that are just trained through the sheep dip system that are going to get their head turned for 50 cents a dollar more at another organization and nutrition is 50, 70, 80% in contact centers. So crowdsourcing, GIG economy, what we do fits in the middle of all that. It takes all of the best bits around community forums but professionalizes it so it does become a dependable channel that you can then scale. One of the big secrets about generative AI, we hear about transformers and all the technology. Scraping the entire internet, that's a whole different conversation. But what really created generative AI was the humans that trained it. Especially you go back and you look at Anthropic and how they got so good at coding. Most of the vibe coding platforms are run, the underpinning is the foundation model from Anthropic and it was trained by expert coders, people that write code for a living. Limitless has gotten into recently the AI training business. Can you help me understand what was the insight that said, hey, this is a good product to invest in and what you're seeing from customers who are interested in exploring that with you? So it came naturally evolution, I guess. We could see the huge uptake in it. And we're like, well, the difference between generative AI is that with contact centers, you're doing the customer service, you don't need things like knowledge in mass or post-grad qualifications and that sort of stuff. What generative AI required was that level of expertise, a huge subject capability. So we evolved our thinking around what we do. What we've done really is rather than the question coming inbound from a customer who needs customer support, it's not. It's a task. And that task is to collect data, to evaluate data, to label data, rewrite data, create data, to train a language model of some description. So 90% of what we needed to do was already there because we've got a platform that enables messaging, routing, payments. Really, really, something we've built from 2016 runs everything from a freelance perspective, all is on board in quality. And we've got thousands of thousands of experts. It's just that those thousands of experts are doing customer service work rather than doing AI training work. We can see the explosion in generative AI. We can see the need for people and need for more specialist skills. And these are the people that aren't going to come to work in a contact center or aren't going to go and sit down somewhere because they might be teaching, they might be in jobs, there's different type of access. So we always have this concept which we have in the customer service world, but it's never been more true in the AI training world, which is rather than ask the people to come to work, is we take the work to the people through digital means means that we can federate out the work to crowds of people that are on board, they're qualified, they're verified, their skills tested, and then they can choose to do the work and they can use the knowledge that they have built. It accelerates this brand ambassador concept, which holds loads of water. If you're a Sony PlayStation expert and you're helping another Sony PlayStation person, if I've got a PhD in astrophysics and I get asked to contribute more into that world or create stuff in that world, I'm probably going to be far more likely to want to get involved in it because I've spent the last however many decades sort of learning my profession in that space. So it opens up a whole new pool. And what we could see was that there is also a massive explosion in the types of companies in that space. So you've got the custodians of the language models, the ones we're all familiar with, the metas, open AI, etc. Then you've got this big subset of companies that are selling into those language models in a way that in my simple brain anyway, if we think the language models are these huge, vast libraries, these companies are going into certain shelves, certain color books, certain parts of the library and are going in a very direct way to create or improve the integrity of data that exists so that when someone types something, it goes to the right book on a shelf and presents it back. They're far more interested in the outcomes and the technology, the labeling software, the data evaluation, tagging and that sort of stuff. And they're less likely to want to be involved with the manpower, refining the people and dealing with them because it's probably not as sexy as what they're there for. So we can see a natural relationship between companies that need vast amounts of subject matter expertise, that needs them in bursts of activity as well. So it's not just a general hum of work and needs them across the world in different professions. So freelancing is the best way, whether limitless or alive or not, is the best way of tapping into that type of resource. And we've just got a mechanism that happened to already 90% of the way there. So it was a tweak in the top end to say, well, rather than this being someone just mapping in a problem about, I can't get my headphones to work. It was a task around, we need this piece of work done or this piece evaluated. Otherwise everything else is still the same. So that's where we are today. It's interesting because when you think of labeling, images would come in and people would, that's a cat or this is a dog. Yeah. It's like mechanical Turk, a couple of cents per task. And now when people talk about labeling and expertise, they talk about lawyers, doctors, scientists to build these models with all of the promise in medicine or in legal scenarios. That has to be done by people who understand the subject matter. Where are you seeing the most initial interest in the offering? We're seeing it in a couple of areas, definitely with certain skill sets. So a lot in STEM, a lot in maths, a couple of other subjects. And we are also seeing still, which is this enormous need for people to do, still do that labeling work, still doing the annotation and label work. So that part still seems to be just as prolific at the moment. And whether that's going to change or not, time will tell. I guess what seems to be happening is that the more advanced the language models become, so does the requirements around the skills that underpin the reinforcement learning. So I learned one of these many initialisms a long time ago, RLHF. And when I first heard it, I was like, what the hell is that? And it's reinforcement learning through human feedback. So basically humans in the loop helping train stuff. So the world of RLHF is absolutely vast, but it's also the wild west. So we regularly get requests to find people very, very quickly for huge spikes. So it's not like a contact center where it's like, you get 20% volume increase and that stuff. We are talking like, we need thousands of people to in this space, this level of qualification, or in this part of the country, or with that language skill set, to do this piece of work for a short period of time. And that's the change in all this. So the requirements are there and they're only going to get more. And they're probably almost definitely going to become more complex. So it's going to become harder to find the people. Like I said before, whether we're here or not, digital engagement is the way to bring people on to be able to do this. And freelancing is the best way for both to work. Because if you want to try and do this with a work from home, BPO arrangement, whatever that might be, you still have shifts and schedules. You still have expectations of work that just great against the very fabric of the demand that's out there for the more advanced stuff. So if you have a general, we always have this requirement that this type of work, this type of skill set, this type of language, then it still makes sense to use outsourcing and find the people who are there that you can rely on. But that's not what's happening in the AI training world. The demand is all over the place, all over the time, all over the subjects, all over the levels of qualification. And by design, it requires these bursts of activity around trying to find people. Freelancing is just a fabulous way of finding that. And flexibility, while people are to your point still trying to figure it out. Chris, thank you for taking time on Friday out in the UK. I know that it's been a long week and you're just getting your kids down to the weekend. It's nighttime there. Thanks for your time. You guys are not just adding features to your software, you're rethinking systems. And that's one of the things I've really been interested in following the journey from 2016 from Limitless is not only pushing the gig economy model, but really thinking how technology and now AI integrates and how you can create value for companies and for customers from those systems. So thanks for your time. And as always, everyone, thank you for tuning in. Keep questioning, keep experimenting. But most of all, stay curious. Thanks, Chris. Thanks, Paul.
