#315 Jarrod Johnson: How Agentic AI Is Impacting Modern Customer Service

0:00After all the work in Fortune 500, it was really, really fun. So I've been here since 2016. I run the go-to-market organization. For those people who don't know Taskus, we're a digital services company supporting some of the most innovative businesses in the world. So what does digital services mean? A little over 50 % of our work is in digital customer experience. So think about chat, email, in-app support for food delivery, chatting with your airline app to try to get your seat upgraded, all kinds of things like that. And then we do voice support, but it's 100 % cloud-based. Can you introduce yourself to listeners and give your background so far as it's relevant?

0:39And then I'll start asking some questions about Taskus. Yeah, for sure. And thanks, Craig, so much for having me. I'm excited to be here. Jared Johnson, Chief Customer Officer of Taskus. In terms of background, I spent my first 15-plus years in the technology sector. I was with IBM for 10 years as an application developer. and consultant. And then I moved into account management and large services contract management for Fortune 500 companies. So a lot of technology services in my background there, which was a great foundation for me in the early 2000s. And then I went to another IT outsourcing company.

1:16We sold that business to Xerox Services. And after that, I just said, I don't like these big companies. They're too slow. I need to do something a little bit more exciting and fast. And all my buddies were working in these private equity companies. And so I went to a SaaS business that was private equity backed, which was super fun. Spent a couple of years there kind of learning how to run go to market organizations in smaller and nimbler companies and building them from scratch. Then in 2016, I stumbled on Taskus. We were not very big. We were 4 ,000 employees globally, which for a services firm was not that big.

1:49The largest players were 300 ,000, 400 ,000 employees. But I really liked the services. It was a heavy mix of digital services. I'll talk more about that in a second. And then it was the coolest customer list I had ever seen. It was all these Silicon Valley tech startups. They had a contract with one of the biggest tech firms doing like$40 million a year. And this client was just on a rocket ship. And I was like, I don't know how these kids figured out how to land these kind of clients, but I can learn something from them. After all the work in Fortune 500, it was really, really fun. So I've been here since 2016.

2:23I run the go-to-market organization. For those people who don't know Task Us, we're a digital services company supporting some of the most innovative businesses in the world. So what does digital services mean? A little over 50 % of our work is in digital customer experience. So think about chat, email, in-app support for food delivery, chatting with your airline app to try to get your seat upgraded, all kinds of things like that. And then we do voice support, but it's 100 % cloud-based. So there are these critical moments in financial services, fintech businesses and such, healthcare and health tech businesses where you really want to get on the phone with someone.

3:00We do that support as well. So that's the bulk of our business. We've got a trust and safety practice, which we like to think of ourselves as the police force of the social web. We protect users. We take down inappropriate advertiser and user content. We do dating apps, social media sites, streaming businesses. And we also have a financial crime and compliance business that's in there too for our fintech clients where we do KYC work, know your customer, any money laundering, things like that. That's about 25 % of our business. And then the remainder is our AI services practice. That's everything from managing autonomous vehicles in the field, data tagging, annotation, building AI models, and emerging thing we call AI safety, which is helping make sure that those models are actually safe when they get deployed out to end consumers.

3:50It's a super fun, exciting business, historically one of the fastest growing in the services sector in my time here. So glad to talk more about it. Yeah, I mean, the safety thing fascinates me. How do you, well, let's, before we get into that, why has AI become central to your vision of next generation consumer experience. And I'll say that I've talked to a lot of customer service companies that are implementing AI-generated voice and text and those sorts of things. It seems like a fairly crowded sector right now. But tell us about how AI is central to Taskus. Yeah, it's a great question. In the customer experience space, it's all anybody's talking about.

4:39Like you said, it's a very fragmented marketplace right now, but everybody's trying to figure out, is there a better way to serve my customer and transform what I do through the use of AI and AI technologies, agentic AI, et cetera. Many of those clients are doing chatbot work today, but it's relatively primitive. And from a Task Us perspective, if that's what the market wants and that's where the market's going, we're going to try to go get there. We're still not the largest player in the space. we have an opportunity that if we can lead in helping customers get these kind of solutions right, we think we can continue to grow and take share in the marketplace from kind of the bigger, slower competitors.

5:17There's another side of this though, Craig, that is like an existential threat, right? So if 50 % of my business is humans answering customer contacts, I need to be relevant in the future. That may mean less humans and more humans in the loop combined with AI technology. We think if we can transform our business to be more that way, give clients what they really want and help them achieve their outcomes, we'll win in the long term. Yeah. And so the customer are enterprises, not BPOs, right? I mean, you're not plugging this in at the call center. You're giving it to customers. And then if they have a call center, it can integrate with this Taskus technology.

6:05Is that right? Yeah, we're using third-party technologies, but the concept you just said is correct. So the clients will select and implement that select and choose to implement the technologies. We'll advise them, we'll help with the architecture, we'll talk about the workflows, and that'll be the first line of defense to try to triage and solve these end customer issues in the most efficient way possible and in the way that the customer wants to be served. And then the humans in the loop are task us behind that first line of defense. So when there's something that's critical, maybe it's fraud related, maybe it's financially related, maybe it's a critical healthcare issue, or just an issue that the client feels like they want a human to help solve, either due to complexity or the emotional nature of it, that the human is still there to handle those exception cases.

6:58Yeah. And then you did a deal recently with two companies, Regal and Decagon. Am I pronouncing those right? Yeah, that's right. Can you tell us how those partnerships fit into your AI strategy? Yeah. We wanted to have an agentic AI platform to help serve our clients. And so the difference between just simple chatbots and agentic AI, the agentic AI means this agent can take action for you. The example we use is in the past, we used to build a chatbot to support customer interactions for a neobank, a digital card services company. And we could tell them how to lock their card, but we couldn't actually lock their card with the technology.

7:44The technology didn't have the ability to go take an action. Now we can implement that same solution and we can, through the chat interface, can say, would you like me to lock your card for you? And they can say yes, and we can just take that action. A human used to have to do that. So that's the big shift. These two players we found were the leaders in their spaces in agentic AI for customer service. We looked at about 25 different players. It's a very crowded marketplace right now, and nobody has a real dominant position. We looked at technical capability, willingness to partner, cultural fit, to be quite honest.

8:19Like we wanted somebody who was going to move as fast as we did, work with the kind of customers that we work with. And these two stood out. They're different. Regal is a legacy voice technology provider. They built a voice-first AI platform. So it's voice-to-voice on a voice-based model, which is unique. They were pretty early in the lifecycle of voice-based modeling. Decagon is more of an omnichannel platform. So you can do voice, you can do chat, email, different work types through that platform. And it's a full platform with quality management, analytics, recommendation engines, some other things.

8:55But it is in the case of voice, it's voice to text, to text to voice. That's how the technology works, because it's a text based LLM that's serving up the answers. We felt like we needed both. And they're both, you know, they both been fantastic partners, a lot of good client traction. And just against who I understand, Tescus is kind of like a systems integrator. You're not building these solutions. You're putting together a system with these solutions. Is that right? Yeah, that's exactly right. It feels a lot to me like how we used to build the first generations of websites at IBM, or we used to do ERP or SAP or Oracle implementation.

9:39Our job is to be the integrator who has expertise and knowledge on how best to implement the platform. We're going to configure it to meet the needs of the unique business case of the client. And then because of our history in the human operations of this work, we have the ability to write operating instructions and train the tech to do what we've done so well in training humans to do over time. deliver high customer satisfaction, quality results, while delivering efficiency in the interaction for the consumer as well. Yeah. And so you don't have, for example, a customer service workforce yourself.

10:19yourself? It's you're helping enterprises find the workforce and find the tech to work with the workforce and all of that? Or generally, does the enterprise already have all of those pieces and you're just helping upgrade it with Agentec possibilities? Yeah, so our agentic AI consulting practice does the implementation, business process management, outcome definition, and configuration of the platform to meet the needs. But luckily, because we're a legacy digital services and outsourcing provider, I have the humans also behind that so that when the technology either should not or does not solve the problem, we then kick that to a live agent who's in one of these task-less operations centers around the world to make sure that the customer is taken care of.

11:16So the agentic AI consulting practice is the tip of the sphere to help get clients where they need to be. And then we bring the human talent behind it when it's required. And when we envision a world where 65, 70 % of interactions may be handled by technology, and then we would just focus on the complex, critical interactions that customers need to talk to a live human. yeah i see and yeah so you have both ends you have the the tech the gentic end and you have the human end and as part of your training of the human workforce how to use these tools or how to interact with them because that's one of the big issues with company kind of bolting on you know voice on the front of a system that's relied heavily on human call centers, that the handoff can be very, very unsatisfactory.

12:13Yeah, that's right. I mean, it's certainly the handoff from the technology to the human is still a challenge. Nobody likes to be transferred, right? Oh, I'm sorry, let me transfer you to the airfare desk, right? Nobody likes that. But because we've handled the human interactions from start to finish for so many years for so many of our clients, we have almost a better understanding of the process flows than our clients do. And we also understand some of the shortcomings in how they will need to evolve what they do in order to make the technology work so we can help fill those gaps. Yeah. And in the past, for example, closing a card, that would be done by the call center or they would send a message to someone else in operations to do that.

12:58And now that you have that agentic capability, it happens right away at the front end. Yeah, that's right. So I would say, you know, most clients turn over to Taskus or other outsourcing partners 90 percent of the contact volume. There may be 10 percent of things that are like escalated refund or, you know, some dollar amount that they want to handle in-house. But 90 percent of the work would come to a, you know, a human capital talent partner like us. Now, you know, we're seeing we're hoping that we can take 50, 60, 70 percent of those front end interactions and then pick up the bulk of the remainder and execute on those for clients.

13:37Yeah. But in the past, something like, did you say what's the verb closing the card or, you know, if you have a credit card that's been lost or stolen in the past, your call center representatives would do that? or does it involve more than that? No, so in the past, in kind of the legacy world, only a human agent who had logged into a system had to access rights in order to turn off a card for a customer, right? A customer may or may not be able to do that. Some of the self-service apps can do it. You know, you can do some things yourself. But if a customer called in, a human would have to go take the action to log into the system, validate the user, and say, yep, I'm going to close this card down.

14:25And they might even try to save them, offer them a lower interest rate, you know, do some other thing. Now we can train the technology. The old chatbots could not take that action. They could send you to a help article or they could try to walk you through the steps to do it yourself if it was available on self-service. But normally the chatbot would have to get you to a human agent to make that action. Now, with agentic AI, you empower the chatbot to actually go into that system, authenticate, and take the action on behalf of the customer without a human having to be in the loop. Yeah. So from your perspective, how do you define agentic AI?

15:05Because there's a lot of automation out there that isn't truly agentic. And why is it so pivotal in the way companies like yourselves are deploying technology? Yeah, the primary difference in GenTik AI is the ability for the virtual agent to go take an action or have agency, right? That's where that kind of term comes from. These systems on the back ends that our human teammates use are very complex, right? You might have to log into a CRM system to validate somebody, then you might have to toggle over and get into a financial system in order to lock and close a card. and we have to train teammates for two, three weeks before they can get on the floor and be proficient.

15:49Now with agentic AI, I can train technology to overcome these previous systems limitations and actually take action on behalf of the end user or the customer or the member to deliver the result they need without so much human intervention and frankly, error. You obviously have to train the technology to take the right actions and there will be things you have to tune over time. But that's the primary difference from, you know, the old websites you go to and like you're going to a help center and you're looking up a knowledge article to do something yourself or the chat bot who might feed you information but can't take an action on your behalf.

16:25So the big leap is the ability to actually create the integration. The Decagon or Regal system can actually go talk to the CRM and validate. You can actually go talk to the financial system and lock your card and then tell you, hey, you're taken care of. So it's a big leap in functionality. And I imagine this sort of, this will continue to evolve and they'll be able to take even more actions on behalf of consumers. Yeah. And how does Regal and Decagon work together? It doesn't depend on, yeah, tell me how that works. Yeah. So they, in theory, they could. We have clients who are using multiple agentic AI platforms for different use cases, or sometimes they want to test both and see which one is performing better than the other.

17:07But in most Most of our cases, our clients are, maybe it's because it's early days, but our clients are picking one that best meets their needs. And again, in the case of Regal, if you have a use case like a voice interaction that is costing you a lot of money that you think can be handled by technology, they're a great fit. Decagon might be a great fit for multi-channel interactions where you're looking for a different set of solutions. So we generally don't present both at the same time. We try to recommend one that we think works best for the client. Right. But you do recommend one or the other, I mean, as opposed to just making that decision on your own based on the case?

17:47Yeah, I mean, we normally try to look at each customer use case and fit and make a recommendation for, you know, where are they trying to get to? And then based on our set of partners, and frankly, there's others we're not as close to, but there's others we've evaluated that we understand that maybe there's even a third option. So we're trying to act as that consultative partner to help get them to success in their project or implementation. Yeah. If 60 to, did you say 60, 70 % of call interactions are resolved with technology, with either a Gentec or some voice technology? Is that what you were saying?

18:29Yeah. So the example I was giving is in the old chatbot worlds, about 40 to 50 % of interactions are handled by the chatbot and resolved, which is kind of surprising, right? We have one client, you know, maybe a million interactions a month were handled by the chatbot, which is great. And that didn't exist five years ago, really, right? The difference with agentic AI is we're able to push that, what they call deflection or the ability to resolve inside the tech, we're able to push that up to 65, 70%. And I think over time we'll get even better because now we can take that action for the end customer that before the chat box just couldn't do.

19:09So 70 % is kind of our benchmark. That's where we think we can get clients to if they give us all the volume they have coming in. We think we can get them not only to the old 40, 50 number, but get them up to 70. And then that incremental, we think is 20, 25 % incremental resolution with the technology, which is significantly cheaper cost per interaction than the humans are, even if you're doing it in different parts of the world, low cost parts of the world. Yeah, absolutely. So is your workforce then shrinking your human workforce? Or as you grow, you just distribute the 25 % that require human interaction across that workforce?

19:51Yeah, that's phenomenal. Yeah. Well, because this is the big existential threat part. Like, are we just accelerating our demise? You know, am I the MP3 player and I just created the iPhone or something? You know, I don't know. But what we've seen so far with clients is they're able to drive that 10 to 20 % incremental efficiency on the simple contacts, the high volume, simple contacts you want to allow a technology solution or a bot to handle. But then what they're doing is they're taking that savings and maybe they pocket some of it, but they're coming to us and they're saying, Hey, you know what we really want to do now?

20:25We want to put your best agents into a tier two group that is just going to handle my most valuable members, my most valuable customers. So we're getting these premium cues or fraud cues, or maybe they deploy more people to saving customers who are canceling or retain, you know, retention activities. We're seeing them redeploy the bulk of that cash. And so we're still growing in the most case, but I will say that the agent profile is getting more complex. So because the work that we're getting that's replacing the simple stuff is getting more complex. Yeah, I mean, I'm at a conference right now where there's a lot of research conference.

21:04So they're talking a lot about advances and agentic capabilities. Where do you see it going in your industry? Yeah, I mean, I don't know that I have a crystal ball on this one. But if I look at the most advanced users of the technology that we've deployed and that we've kind of studied in the market, I think that Agentec AI will take these simple interactions that are already probably enabled for self-service, but the users don't know how to do it. I think it'll almost wipe out that simple contact volume. We think that represents maybe 20 to 30 percent of our interactions that we get today over the next three years.

21:46So I think that that what I'll call level one work, that simpler work will go away pretty fast. But I think on the horizon, what's really interesting is the translation technologies are becoming more advanced on top of these agentic solutions. So things you couldn't automate before because they were in they were in Japanese and the technology didn't couldn't handle it. all of a sudden this advanced translation work is going to mean that you can solve even more work globally than you could with your old chatbot solutions. And even 18 months ago with the LLM based AI solutions, the translation wasn't quite there.

22:25Now it's there. The other big advancement has been voice. So I anticipate that translation and voice on top of agentic AI will continue to drive more and more adoption. And then I imagine that today we're taking sort of one-step actions for clients on simple things. I imagine you'll get to a day where the workflows will allow the Agenteke AI technology to take multiple steps and actions for you and understand the context of your entire trip or your entire customer journey or experience that you're going through, and then actually be able to start making recommendations and interact with you. I can see a day where they'll have contextual history on you.

23:04So when you call in one time or chat in one time, when you come back, they know you're booked on the next flight to Chicago. They know where your seat assignment is and they start to predict, you know, what it is you may be needing based on your historical interactions. So I think there's a lot of potential for this to continue to evolve at least over the next, you know, five to 10 years. Yeah, and I was going to ask about getting from pilot to production because so many enterprises are experimenting, but so many of those pilots are failing. And you guys must have gone through that arc. What challenges do companies face when moving into deployments of the GenTech AI?

23:51What challenges did you guys face? That's one question. And then are you, is Taskus moving toward, when you were talking about more complex agentic workflows, are you moving towards maybe beyond customer service to helping companies implement multi-agent systems that do more than respond to customers. Yeah, so on your last question, we absolutely see a day where there will be multi-agent activity handling customer interactions. So a lot of the model developers today and the Decagon and Regal examples, they're trying to say laser focused on I am a customer service chatbot or AI solution because they know that market's plenty big.

24:47They want to stay hyper-focused. We don't want to spread their resources. But then we talked about other complex interactions like fraud and financial crimes. Someone's going to go build an AI solution for just specifically that problem that will know how to handle your disputing and charging your credit card. I need to investigate that dispute and understand from the merchant, were they really supposed to charge you or not? Somebody is going to build that, right? So you can see a day where you've got multi-agent orchestration handling the complexities of what really has to happen to solve a customer's problem and get to end of job.

25:20So we see us going along that journey with our clients. And we can see it when our longest standing implementations, it starts with simple, starts with the baseline, goes to the next, then you tackle a new problem. In terms of challenges, I'd put them into three buckets that are pretty consistent. Number one, everything you're training an AI solution to do is based on the information that you feed it. So these knowledge articles, which are your operating procedures for this is how you open an account, verify that it's you, check the account balance, all those instructions have to be accurate. And most clients do not have enough up-to-date knowledge management systems and policies in order for an AI solution to ingest them and then execute correctly.

26:12So we find one of the first things we have to do is get the scope tightened, work on those knowledge articles and policies and procedures, document them correctly, and then come up with a way to consistently update and manage those over time. So challenge number one, the base knowledge management's got to be in place. Challenge number two, our clients are some of the most sophisticated technology companies in the world. And they're most of the apps on your phone. And they're beautiful. When you log into them, the user experience, how you select an item, and do my tip, and auto refund, all these things, it's beautiful facing the customer.

26:47They don't exactly put the same kind of engineering talent on the back office systems that a Task Us teammate might use to go execute work every day. I'm never more fascinated than when I watch a task as a teammate and I sit side by side in the centers and they're going between tabs and multiple systems and copy paste here, this in order to make the process work. So they generally have limitations in what they can ask an AI agent to do because they have their own system limitations on the back end, which maybe humans can work around. but a technology solution was a little hard. And sometimes they say, you know what, I'm not going to solve this right now with agentic AI because I need to go fix my backend system first.

27:28Once I fix that, then I'll deploy the AI on it. So that's bucket number two is backend system sophistication, I'll call it. And then the third is just an industry reality, which is the sophistication of the platforms and the partner technologies are still evolving. So two years ago when we started deploying agentic AI systems, the partners did not have the same level of capability that they have today, right? So they're constantly adding features, functionality that serve the clients, which is great. But you're going to continue to have this sophistication curve on the platforms where they're continuing to build out more functionality.