The ActivateCX Podcast

Learn AI Secrets of Business Leaders

• Frank Rogers

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Watch Frank Rogers and Special Guest Hardy Myers from Contact Center AI LEADER Cognigy reveal how businesses can use  ai contact center solutions to be more competitive and win in the market place. Here some great stories around how companies are using Ai ot GET and KEEP customers.


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Chapters

00:00 The Cognigy Origination Story

01:07  Having the Senior Leadership Conversation

03:00 Is There a Dystopian CX?

04:24 Are You a Human?

06:56 The Act of Pre-Training the AI

08:30 The Cognigy Source of Truth

13:18 A Lifelong Journey of Iteration

24:51 Innovation includes Ethics

26:33 Flying Cars and Contact Centers

32:18 Today is a Better Day

33:19 Closing


#podcast #contactcenter #ai #customerservice #salesstrategy

Hardy. Welcome to the show. Thank you. It's good to be here. Hardy your company. Cognitive is an AI leader not only just known in the industry, but also in the Gartner magic quadrant that didn't happen overnight. What's that thumbnail origination story look like? Many years of hard work, to answer your question. It started off with very much a horizontal Set of solutions in the conversational AI domain, including human resources and IT related use cases. But as I'm sure you're aware of the markets really evolved and the real opportunity in the short term, not to say that HR and IT use cases aren't important. Internally facing use cases aren't important, but the Gartner views, the real market opportunity as, as essentially AI agents augmenting the human agents in the contact center and in the customer experience, customer service domain. So when you look at the market opportunity roughly half of a 40 plus billion dollar market opportunity in 2028, 2027 is being derived by AI enabled automation, which is really what we do, which is where the conversational AI market opportunity resides. When you're speaking to a senior leader in a prospect or an existing customer, and perhaps they're struggling with the concept of AI, because you've obviously been working with this for a decade plus, but in terms of, for most consumers, whether it be a personal consumer or a business consumer, it's really come to everybody's attention in the last 24 to 36 months. And these senior leaders are hearing things both positive and, and maybe some disturbing things as well. How do you bring them into. kind of the proponent camp for you and assuage their concerns? Yeah, so that's a great question., I call it the benefit and the curse of generative AI. No one was talking about generative AI 18 months ago. Maybe some really deep knowledge guys, but to a large extent it wasn't sort of widely known. And, and then with the advantage at GPT, of course, it just exploded onto the to the market, if you will. And point really for about, from our perspective is that, , previous to that, the move towards more automation, the contact center was very kind of opportunistic. It wasn't a, I would call it. Market wide adoption trend at scale. And what generative AI did, at least the, I think it opened up in people's minds, the possibility of what could happen, what could be done to create new and very transformative customer experiences from the past. And that opening of that possibility really drove this interest in what I would call as an AI enabled automation. And then the. Key to this story, though, for for the listeners is that, , you can create amazing customer experiences with very little generative AI, but a lot of conversational AI, which is a very mature, very low risk technology, which is, of course, , one of our core areas of domain expertise. Indeed, you bring up a good point because AI is a generalized term and it covers a lot of things from unstructured data to structured data, of course, and and then even robotics. When we hear people like Elon Musk talking about AI and he can get rather dystopian in his commentary. And do you think that at least as we apply conversational AI and fundamentally in the contact center that we should have a similar concern? Well, I mean, generally, depending on the enterprise, you could try to use it as the entire solution. That's sort of a single shot type solution. But we're not seeing enterprise doing that. It's a little early for that. The large language models are still fairly new. Immature relative to where they will be at some point in the next few years. So I don't view it as a, I don't think of it as a real risk. I'm talking about the application of conversational AI to the enterprise contact center. And there are very specific use cases of the application of generative AI, where there's little or no risk as well, including the most frequently discussed one right now, which of course is auto summarization, call wrap up and upload to CRM which is a really nice high value use case. And it's doesn't impact the customer. It doesn't fade. It's not customer facing, so there's really no risk of. Anything, , happening, if you want to call it that. Yeah. I think risk mitigation is a big part of assuaging people's concerns around utilizing AI, because there's a lot of misconceptions out there in terms of something that is moving into a hallucination mode and creating a bad experience. Yeah. I, I heard the other day, somebody was talking about the creation of the conversational AI in terms of the voice bot in their business and how fundamentally they pulled it back off of being too perfect because too perfect was almost creepy in some terms. And that just the fact that it was really, really good. But ultimately you could tell that it was a bot was kind of the point they were trying to hit. Do you find that in your, like with the Cognigy? Yeah, sure. Yes. I think there is some concern that the quality of the voices and the speech to text. And the text to speech have gotten so good that, it's it's literally lifelike. But , the flip side of that is if anybody who's had the one dot.O bot experience would tell you that it was very robotic. And in my opinion, that lack of human like quality was one of the things that was holding back the deployment of more, more, more advanced automation solutions. So I would say from my perspective, I think that it's actually a really good thing that we're getting more lifelike. Recognizing that, , people do not want to people want their problem solved. Let's be honest. And if it can be done without having to deal with a human in an efficient and personalized and effortless way. We would all do that every single day. So and as long as I felt like I was having a great experience and it wasn't, the voice was either so robotic that I reacted negatively to it or it was so human. Like I didn't realize it was a bot. There's a lot of room in the middle there where you can create some really powerful. And by the way, personalized experiences, the number of voices that are available today from all the main providers of that technology, which include the hyperscalers and a lot of. Boutique cognitive services firms are, are really driving this forward. Quickly. I can agree with you more. You really hit it right on the head. People just want their problems solved. And really where people were getting, caught up in this context of human versus a digital bot lost that particular meaning that people just want to have problems solved. And at the end of the day, if they have their problems solved that's Supreme customer service. Your company has , this construct you call, a pre trained AI agent. What does that mean? So, we've obviously deployed hundreds and hundreds, if not thousands of these solutions globally. And from that, those learnings, what we've done is we've gotten very vertical market specific and taken, what are the typical common, most common use cases for a vertical and made it easier, even more or easier is probably the right term to, to deploy by coming up with some standard snippets of code. Things like that are. use cases, if you will, for that particular vertical. Microsoft used to have a similar type of approach long ago when they rolled out SharePoint, they knew that people would have to get their heads around it, so they created a solution accelerator, whether you were in the finance industry. So that's your approach to getting the pump prime, so to speak. Yeah. But the reality of it is of course and this is the power of the Cogsy platform is that every enterprise is. unique. And so and now do many airlines use specific databases for bookings? Yes. Do many enterprises use specific companies for ERP yes. And so those more frequently used databases, we've got integrations to all of those over 100 systems of record for enterprises. And of course, then you kind of move down one level into vertical markets. And as we continue to expand and scale, We're building out the integrations to those very those vertical market specific integrations, like, say, an airline reservation system is an example As part of your offering you use a term called knowledge AI. And when I think of that, I think about potentially a knowledge based tool or repositories. Is that what you're talking about there? Yeah, it's a customer specific knowledge base. Tool. It's kind of the next generation of that, I would say, and it's a vector database. And so what you're doing is feeding data from various curated data, by the way, from various sort, meaning you can't just, , throw unmanaged stuff in there. I guess the way to say, for example, FAQs is a good example. So you take perhaps some PDFs, some other content, Word documents, et cetera, that sort of are comprised the population of the information that you would want to have in your FAQs. And you upload that into a vector, a vector. Vectorized database, which takes all that data and chunks it, or they call it referred to as chunking it up, and then that creates all these pointers, if you will, to various elements of the information that you need to be able to address FAQs. And so then when you leverage that in the course of deploying your, your FAQ flow or bot, it enables the bot to very rapidly access data. And based on, , the level of recognition essentially. Stack it, stack, rank it and provide the most logical answers along with the attribution of the source. So it's really, really good from that standpoint. And this is kind of the next generation of source. Sorry, excuse me, of search. And it's, it's very powerful. It's really cool. It accelerates it and optimizes it really. That's what you're looking at. Yeah. That makes sense. And again, the magic answer right now, from my perspective, well, relative to AI enabled automation is really a combination of traditional, conversational A. I intent based, applications combined with large language models or generative AI I would call it infusion is probably a good way to say it. And you can do that in a way where, , it's a very low risk scenario and you're actually getting the benefits of the tech. Some of the technology today without exposing the company to risks and higher costs and things like that. Because some of the technology we're doing today Actually runs on, I would call it the previous version of some of the large language models. So you don't necessarily need to deploy the most recent versions, which are very expensive. And so, , one of the things we're always trying to do here is. With automation is optimize the cost structure. Efficiency is one of the goals of automation, obviously, and you're if you're spending, , if you're if the cost of the automation is more than the potential labor savings of not eliminating humans, but freeing up humans to do other things that doesn't make a lot of sense. So we have to be mindful and just to be mindful about, , in the context of space., half of the world is run off of BPo. Solutions or BPO's provide half of the contact center agents the world, I would say, roughly, and that that means that there's a lot of lower cost labor out there that's being leveraged. And so, to automation has to do better than that, frankly, and or augment that in a way where it's complimentary and not cost prohibitive. Just occured to me that you really have to moderate that change, don't you? This is a really interesting point because I think people's perception is that the end state of automation is the total elimination of humans, I guess, or whatever, back to your sort of dystopian world. Correct. And, what we see, we don't see that. What we see right now is customers. Obviously wanting to be more efficient, but as importantly, reducing the load on the agents. And as we continue to automate the more, the simpler tasks that today are really putting a lot of burden on the contact center agents, change my password, change my address, things like that, theoretically automation is really well suited to handle every call that our chat that ends up coming into the contact center is a harder discussion conversation. It's. my router is down and my child's papers due in two hours. And it's a nightmare, the dog's loose and, whatever. And so it puts a huge amount of, so what happens in the old days, of course, is agents had kind of a mix of call it lighter, Conversations or journeys from customers easier is probably the better term and then these more difficult ones. But as we continue to automate more and more of the easier, lower hanging fruit of the journeys, you end up with this a much more complex set of journeys that the agent has to solve. So that means. Basically we want to bring tooling to them to that makes their even makes their ability to answer those harder questions easier. And that's where the whole agent assist part of the story comes into place, which is which the auto summarization of post call that I talked about is an element of that agent assist tooling, which is a think of it as a bot or a co pilot for the agent. Right? Unlike , our customer facing our AI agents or the customer facing version of that. Right. Personally, I think that's the low hanging fruit. If you look at your cost structures of technology and people, , people are the primary costs that you have right now in a contact center or in all your customer facing people. And I think there's going to be a little bit of a sliding scale. There's going to be a point where maybe there's a meeting where those are equal, but for right now, the low hanging fruit is, For the amount of money that you're putting out for your human resources, if you can make them incrementally better, in terms of their performance, their ability to create a better experience for the customer, it seems like that's the first place that you would start. And then the second place you would start is taking those high volume, low value interactions that you can automate more effectively with a bot. Moving into those first and then going through iteration. Is that kind of how you're seeing the go to market? Yeah, what's interesting is the adoption strategy is really critical to success in this space. I was actually just talking to someone about this earlier today., I, at one point I was in charge of a huge corporate I. T. transformation and we would go to the well to present the story. But every version of the story was always, don't worry. We're going to hammer on this thing for three years or two year, two and a half years. And then in the last six months, you'll get all the benefit of this two and a half years investment. And needless to say, We got turned down like four times because we could not get value fast enough in the transformation because people had to believe that we were actually gonna pull it off. And I'm not saying they didn't trust us to do it, but it was just such a long journey that the outlay of tens of millions of dollars to solve this problem before they saw a result was not. optimal, I guess the way to say it. And hence we, we didn't, we weren't successful at getting it approved a couple of times. The results were not going to happen on their watch. So that wasn't right. So, and they would be blamed for it, but they would blame the other guy for it. Exactly. The old three envelope rule. So the beauty of what we're doing today and what the, really the firms that are adopting this technology and seeing the greatest success is they're identifying use cases that can be deployed quickly. So fast time to value, which we can because of the low code tooling nature of our platform and all the integrations I talked about and all that. So we can get the other solution deployed quickly and then very high value. Return. So, and I used identify authentication, identification, verification as an obvious version of that. One of the largest insurance companies in the world, Allianz, which is public information has, , they are a customer of Cognigy and they're, they are just implementing that one use case globally on 100 million calls and they're saving 90 seconds a call. So just that simple use case. But what that that's doing, of course, is it's generating a huge pot of savings that they can then plow back into or invest back into the service Delivery for more use cases because of course with like all insurance companies in a perfect world They would be able to automate the claims process completely which is a bit of a holy grail thing Obviously and that would be on the other end of the spectrum of very complex automated use cases if you use an airline example, for example, Lufthansa has been a customer for many many years they've now automated flight rebookings and if you've used some of our friends in the u. s If you've experienced an airline, cancellation, flight cancellations in the U. S., we've all had this experience, , hour long lines and this and that, where it's not an automated process, whereas Lufthansa, because of their sort of investment in the automation over time and really focusing ultimately on this really solving this complex use case,. Tremendous value. I mean, I was in Germany, my flight was canceled. I got immediately three options to pick picked one. It was done. It was that it was that I'd have to go to agent and I have to make a phone call instead of the whole way for people to call me back. None of those normal things that we still experience today. So the Typical adoption curve, if you will, of a customer on this is starts with a high value, low risk use case, or if you want to call it quick to deploy use case, all identity and verification and then continue to move to the right of complexity over time, ultimately with the goal to achieve, , a very high level of automation throughout the whole customer service, a set of use cases that a customer might have. Is that one of the successful use cases that you have out there for. Cognigyh or is one you would cite as being something that had tremendous impact to the organization? We've done, I mean, literally across, a dozen verticals, we've done all sorts of different use cases. But if you think about, okay, what are the ones I can do quickly to get some value and start moving? And this is by the way, certain verticals,, may have a particular pain point. And what I always say is figure out what your pain points are and where the highest value would be for you. And it may not be economic. It may be., we're putting our customers through so much trouble because this is so painful. We didn't intend to it to be that bad. So that kind of depends on the on the vertical and the customer. But, now, , just in general, three quick use cases that we see a lot of customers gravitating towards early in their adoption curve of AI enabled automation. One is this identity and verification and intent recognition. So authenticate the customer, find out what they want, connect them, either solve their problem via automation or connect them with a human agent that is able to handle their problem most efficiently and hand that over to that agent, fully contextualized and authenticated. That, and you and I have been through this, all of us, hundreds and hundreds of times, and are having to call. Enterprises, contact centers know that the most frustrating thing is this, I would call it two factor authentication, which isn't the right term you get, but it's the, I just spent five minutes punching in numbers and doing all sorts of stuff only to be handed off to an agent who then makes me go through the same process again and that chews up tons of time. And of course it leads to just tremendous customer frustration. So if that's an easy use case, the other one that's really interesting customer facing that we see a lot of today is languages. So the ability to support multiple languages, say, with an English speaking contact center. And so through a combination of obviously the Cognigy orchestration tooling our technology and then leveraging some of the more advanced translation tools that are on the market today, which plug into Cognigy, we can do this in real time where you can have customers speaking to us in Call it any language and either voice or digital. And then we're, the agent is in their native language, getting the question and the responding in their native language that's being translated back to the customer in their native language. And so that eliminates this need to have to have, , pockets of people that speak Spanish or French or whatever. And you can literally have an entire contact center of people that speak. Let's say English and they can respond to customers across multiple languages. And that's very appealing. The other way we see this happen is,, during the day, for example maybe a custom enterprise has native speaking agents during the day that are talking to native speakers, if you will. So let's say German to German. And then at night. When the German team goes home, those calls, the less calls, obviously, but the calls are routed to another supporting contact or somewhere else where say they'd speak English and, it's a seamless from the customer standpoint, they don't realize that the calls are now being routed to a different location where perhaps the actual agent is not a native speaker of German. It's such a paradigm shift and it's a really big subject for organizations to pivot on. When I think about. AI organizations there's a lot of them that are evolving on a daily basis and coming into the marketplace, kind of like pop up stores. And ultimately I look at the industry around the contact center as like three basic things, maybe four that you see, like one there's agent assist. The second thing is really a voice bot, something that you're going to encounter when you Drive into the voice channel. And then the third thing is really a chat bot. Something that you might have on a specific touch point, like a, maybe it's a portal for customers or a support portal, and then finally,, underpinning all of this really is a knowledge base that's sitting out there. And there are some companies that they'll provide one of those things or they'll provide a combination of all of them. You seem to really have the whole kit and caboodle when, when it looks to differentiating yourself, are there three or five things that you say, all things being equal, all of us doing similar things. We're different at Cognigy because we do these three or five things better. Yeah. And multimodality. So it's really important. So you have your traditional. Kind of voice or digital ingress, if you will, customer facing. And then you also need to have be able to support both of those paradigms on the agent side via conversational AI or AI enable automation. So, yeah, I would say for us, it's I mentioned languages. Deployment paradigms is important, like which hyperscale we were available and all the hyperscalers were available and multi tenant sass were available in single tenant sass and were available on premise. So, for certain parts of the world, that's really important. The interoperability of the platform is critical. So the fact that we have all these prebuilt integrations really drives the time to value. And that's a differentiator. Our gen AI interoperability is super critical. Today we support. Roughly 15 large language models, and I'll come back to why that matters in a second. But the fact that we are continuously adding new integrations to the model, the ability to pick your speech to text, text to speech, translation, cognitive providers. Is is a differentiator. And then the tooling, of course, the low code , the interface, the UI, if you will, of the product that enables developers to rapidly build these really transformative customer experiences. That's another really, really important differentiator. And then I would say the last one, from my perspective, is our natural or at least of the top five or six is our natural language understanding engine. And the quality of that, which is really tuned. For this particular set of applications. So customer experience type applications. I mentioned GenAI. Let me just take one comment on that or make one comment on that. The really the other thing about the product, which is very interesting and cool, frankly, is you can leverage different technologies at different parts of the journey. So it's not, for example, you come in and we can only use one large, large language model for a particular journey, we could actually pick different ones based on,, you know, let's say we need to render a picture at some point in the journey. We would use a different large language model than we would perhaps for optimizing the human like experience of the conversation. So there's a really powerful interoperability and future proofing value proposition of the product, which again is the reason why we're ranked so highly by Gardner, because we really just nail This set of different criteria, which there's a lot of competitors that have, I would call it similar. They, they all play in these domains to your point, or at least to a certain extent, but , I would argue that we do it better across more of the domains than anybody else in the market. Malleability probably is what you're talking about there more than anything. Yes. Interoperability, flexibility, scalability enterprise, quality of features Deployment flexibility, all those and future proofing. The, in my opinion, that's the checklist for an enterprise. And I would say nobody does that better than Cognigy. Lots of ethical and compliance considerations around AI. How do you delve into that subject and address it? Yeah. So, , because our core platform has natural language understanding, so conversational AI , is a domain that we're involved in, on the scale of aI risk, natural language understanding is very low. Risk element of the area where the risk would come in, of course, is by leveraging the leveraging large language models. We don't do large language models. We, , customers either we, or customers provide the ingress from the large language model into our orchestration platform. And of course the let's use the term hyperscalers here. They're the ones that own the domain of the large language models and their policies for privacy, data retention or not, as the case may be, et cetera. Model training, all that. That's all spelled out very clearly. And of course, , there's so much scrutiny in this area right now., they're all kind of bending over backwards to try to make sure that they address these issues with customers. The other thing I just highlight is because of the flexibility of the platform. If you're on a zero risk tolerance on generative AI then you don't have to use any generator at all with Cognigy. That's the beauty of the product. You can absolutely just do traditional conversational AI, which, as I said, is on the scale of risk of AI. Conversational AI is like a zero, almost, almost a zero. So, yeah, I think the way that you described that just brought also in the mind, just what a shared responsibility, the ethical and compliance, How that gets addressed is not just solely on your shoulders. It's really a partnership across all these different technologies. When you and I were younger, we were told in the year 2000 that people would be,, and at least everything that we saw in some sort of visualization, people were in silver suits and they had flying cars and well, obviously that never happened. Right. I don't know what you're talking about. Yeah, that's the flying cars piece. Like, I'm really happy about that whole piece because then we would have to have force fields for our homes. I'm looking at how drivers are. But if we look at the contact center world and conversational AI, you made the mention, we're into this really a solid 18 months in terms of like everybody's just radical awareness of the topic. And so now we've got this next 5 to 10 years and you even brought up the context that we're immature really in some of our technologies, even though they're doing radically incredible things. What do you think a contact center looks like maybe in five to 10 years? It's a great question. Well, if you think about the context center of the past and the future, has a human element for sure. Okay. The legacy ones, that's all it had was a human element. It was all voice. Then we added digital. But it was predominantly a human element. And then we added a little bit of automation. So IVR speech enabled out of 10 socket stuff. That's right. And that was, , 10, 15, 20 percent of the journeys. But, , if you fast forward five years from now, I would say you're looking at 50%, 50, 50, maybe even 60, 40 digital versus voice. I mean, part of this is demographics. Obviously, the more mature elements of the population, may prefer to use voice as a voice channel. Whereas, Perhaps our kids, they don't even, they don't even answer the phone. We know this, right? They don't like to talk on the phone, frankly. So the last thing they want to do is call a contact center. So they're going to go digital as early and as often as possible. And, , I mean, Gartner's forecasting a,, I'm going to call it a nominal decline over time of the number of agents I actually, , this is going to sound, people probably think I'm nuts because of how. challenging customer experiences today. They hold times and all this in my mind, the perfect paradigm is a whole lot more automation and maybe even more humans to create these really amazing experiences because we all know there's a direct correlation between superb customer experience and higher revenue in cohorts of companies. I mean, this is all empirical data. It's not like Hardy's data. This is like the real world data. So. What puzzles me sometimes, and I have talked to many, many, many customer experience professionals over the years they all want to deliver an amazing customer experience. So the real challenge is what's preventing them from doing that. And sometimes it's, , Sometimes it's,, architecture of the tech, et cetera. And so, , what we're doing with our technology is we're eliminating some of those obstacles to improving the customer experience. And that's what gets me excited., day out, I was talking about multimodality earlier. I mean, to me. A multimodal experience with a full contextual handoff to an agent and being able to have your whole issue solved in one call. That is like, for me, that's like the greatest thing of all time. I mean, cause it, if, and when you had a customer experience where it was so amazing, you have the phone like, Oh my gosh, that was incredible. That's what we're trying to do at Cognigy every single day. And we're doing it by the way, and it is awesome when we do it and which we do it a lot. So it's, it's not that we can't do it, it's just back to this point about, , what are the other tools we're working with to make it happen. So, but , everyone I think in, in customer experience aspires to do what I just outlined, which is give the customer the experience they want on the channel they want, at the time they want, and, and get it done in one, one shot. That's, that's really what we want. Some sort of follow up to make sure that we were happy or, , do we need anything else or whatever, or if I call back context, we saw you just call back and you called an hour ago and you had an issue with this, is there something else we need to do about that? Or do you want to talk to us about something else? Those kinds of things, , are all. In the domain of possibility now, and it's really amazing what we can do. And I'm super excited about it. Yeah. I'm pretty excited about it too, though. So first of all, I literally do not like to call and talk to a person. I would rather be in some sort of chat flow, because It's gotten so good. And if I need to escalate to a person, I'd rather not call them. I'd rather just chat with them because I can multitask more. This is what my thought is, you said, 60, 40, I think it's going to go to 70, 30. Yeah. I mean, I am not married to a, a ratio, but it's, yeah. No,. I know, I know you're not. I mean, I think that you're being kind and I think the big concern for people is well, what's gonna happen for people? I really believe that people are gonna have a better life. And I think that fundamentally the big blocker inside of an organization, and this is what has to change. Like, I don't think it's technology and I don't think it's a customer behavior. Customer behavior is naturally changing. It has changed dramatically in the last 36 months. What has to change is the command and control structure inside of businesses because people don't all necessarily need to be in the contact center , Maybe some of those really, really exemplary people should be in sales, or maybe they should be in business development, or maybe working on the professional services team where there's much more of a direct customer interface that needs to be very curated and coaching and bespoke, but, but all the other stuff can change. And I think it will change. The economics will force it to change. So I think that there's almost like a, , a stoic mindset around that nature. Manages itself. And that nature will be to push people into other areas of the business. And I think it's going to improve how businesses function. But we've got to break down the walls of the command and control structure. Yeah, I mean, what we're doing today, was sort of possible before, but it was super painful and required investment of services to get there. And then if you did the slightest, , tweak it, you broke it. So the stuff we're doing today is easy to deploy, incredibly powerful, obviously leveraging all this advanced and compute availability, cloud, et cetera. And so, I mean, and I will say at recent, I just recently saw some sort of surveying on, on agents. The agents are loving this stuff. In general, once they get past this view that this is an existential threat and they understand that this is designed to make their life easier, they are embracing the technology. If you think about call wrap up, auto summarization, call wrap alone. I mean, who wants to sit there and type up notes for three minutes?, I mean, it's really kind of lost time. So the ability to have that You know, done for you and then you're doing what you just said, sort of command and control of your own little world, then I think it's an incredible, an amazing opportunity. Hardy, you're a super pro. Thanks for being on the show. It's great. Thank you for having me. I appreciate it.

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