Scaling AI: What Trace3, WWT, and Presidio Know That You Don't

We are here with a bunch of our partners. And Serbia's joined me from Cisco as well. We're going to do some intros in just a second. But honestly, our partners are our access to our customers, significant amount, nearly 100% of our business goes through our partners. So nobody's more important to reach out to those customers than all of you. So thank you for joining me here today. My name is Emma Carpenter, by the way. I run what we call our recurring revenue acceleration team. We're here to talk about AI. So let's go first. Josh, please introduce yourself. Thanks for having me. Josh Lindstrom, Senior Director for Data and Analytics at Trace 3. Been there about eight years running the practice, building it. We've been a data practice inside of Trace 3 for about 14 years. But when I got on board, we kind of had to revamp it and really get ready for this AI wave, which I knew was coming. Trust me. But ideally, responsible for all go-to-market, all partner ecosystem, and all technical pre-sales for data analytics in AI at Trace 3. Thank you for being here. Serbia, over to you. And thank you, Emma, for having me here. My name is Serbi Kall. I run product management in the CX organization. And as you all can imagine, everything is about AI. So Cisco IQ, and everything that we are doing there, everything is based on AI and insights and data that we have about our customers and partners, and very excited to be here and share some of those issues and challenges with you. Thank you, Serbia. Laura, over to you. Yeah, thanks, Emma. I think it's excited for this panel with some fellow partner minds. Laura Keener, I lead CX software life cycle for Worldwide Technology. I've been in role with this part of the organization for about five years. I've been with Worldwide overall nine. And man, I'm excited to get into the panel because this is such a crucial, critical, exciting topic for us. So I'm just looking forward to the conversation. Thanks. Brilliant. And last button, by no means least, Kevin. Yeah, thanks, Emma. So Kevin Corus with Procidio. I lead Procidio Evolve, which is our software services life cycle, goes everything from software renewals like you talked about, but also CX and the integration of data, automation, and AI with it. Brilliant, thank you. Well, thank you all for being here. Look, everything's changing so quickly right now. And if you think about all the different revolutions that have happened over time, the industrial revolution, you think about the advent of the mobile phone, you think about the internet. And you think about today with AI and how quickly things are moving, it's just incredible. And it means that you as partners have to work at the speed of light to really serve your customers and our customers together. We're going to get into the main set of topics here now. And Josh, I'm going to come back to you for a minute. When I think about what's changing in the industry right now, like I go back to some teachings by a guy called Charles Duhig, the wrote a book called The Power of Habit, because technology isn't a good without people. With the power of habit, you often talk about Q, routine, reward. And in any cycle of human psychology, you might want to change the routine. You want to keep the Q, what sets you off to do things, and the reward the same. But even in sports, you want to change that routine. Routines becoming workflow now. In the AI advent, we're thinking about how do we replace some of those workflows? From your perspective, if you think about how business models are evolving from a trace three perspective, what do you think about that? It's one of those things where we're seeing the enterprise actually embrace this a little bit more. It used to be, hey, I need to land all my data. I need to be able to present that in dashboards and reporting. But we're seeing a front end call it maybe like a semantic layer. We're now able to ask the data questions, and it's being able to provide answers back. So we're seeing a big shift, and customers really wanting to embrace that. And it's no longer like, hey, I deployed co-pilot or something like that. But we're actually seeing organizations say, hey, connect to the data sources, be able to pull back real-time answers, and leverage whatever model that is available to me. But the other side of that, we're seeing, even internally within trace three, where folks have built their family vacation in Google Gemini. And then they start dumping company assets into Gemini because they're comfortable leveraging it. That's what they like to use. So we've actually had to go in and put some guardrails around that. Because again, we want to help drive innovation. We want to help drive productivity. And we want to leverage the data that's available to us so that we can get the things done. But we want to put it in the hands of the people. And so when we go have conversations on the enterprise side of the house, we're not trying to solve all the problems. And everybody tries to say, hey, bring up a use case, drive that. But what we're seeing is the personas of the individuals dictate the use case. Let them-- it should be different for everybody. Marketing, renewals, CX, everything across the board. We're seeing that happen real time. And customers embracing that a little bit. So that workflow and that automation, you're going to talk a little bit about autonomous. But we're seeing that become real time. It's very true, actually. And one thing you touched on earlier was the importance of data in this environment as well. That's going to be kind of the new currency as we move forward and then accurate data. I think the fact that we actually have to make sure that we secure what we're doing as well becomes even more critical for, as you say, different people accessing things or putting things that you actually don't want onto. Come on, kind of place the other people can find it. Maybe all of your competitors, Emma. We actually did not. Go ahead, sorry. I was just going to add that you are talking about data. And it's so true that your model AI models, no matter what you create, the output is as good as the data you put into it. So if you don't have clean, clear data with the right information, you'll get garbage out. And that's not going to be useful. So when I talk about the autonomous world, that's one of the big focuses for us. Rubbish, isn't it? Rubbish, actually. Rubbish. That's OK. But what's crazy is that that's a real story. We kind of assessed our data internally within Trace 3. And we do this for a lot of our customers. Are you ready to actually do AI? We did our own internal analysis. We've never done that within our company. I think just because we're moving so fast, we found several people that were storing their W2s in SharePoint. Which is easily accessible by everybody. And that's like real time. It's like, what are you doing? Come on. But the data's there. And it's available. And can we leverage a model to be able to answer those questions? Now you're certain to see that happen in real time. So the message is remove your W2 from the access. By anything else, OK. I'm going to move to Serbia for a minute. So we were talking about autonomous there. Like everything is getting quicker. We're really thinking about what we need to do for the future. What are we doing at Cisco to enable our partners to succeed in this new era? Actually, we are doing a lot of investment and innovation in this space where we are taking just like we're talking about the data. The one advantage that Cisco has is that we have tons of data. Not just data that is being produced by our business units about every product, every feature, but also a lot of data from our tech. We get about 1.6 to 1.7 million tickets every year. And we can inform, take that information, and consume it in the interface that we have been talking about, which is Cisco IQ, and truly build insights for the partners, for their customers, to say, how can you leverage this data to guide your customers, the workflows you're talking about, to quickly get them to the outcome and the resolutions that'll get them to that resilient state that we want to get them to? I think it's worth talking about as well, because certainly as I met with Kevin Alley, we were talking about how do we get some of the insights that we have as Cisco out to our partners as well. And when you talk about tech data, that's critical, right? Whereas a caseman raised with that customer in the past period of time, what's top of mind for them that we need to recognize when we together are going to kind of try and solve for that customer issues we move forward? We've actually put a digital assistant or an AI assistant for all of our recurring revenue acceleration team, both specialists and the geo teams, to actually help them get to the data quicker. Where is our risk and where is the charm potential that we may see in advance? Do you want to just talk a little about that and how important that is as well? Yeah, so I think one of the things that I want to share with all of you that when we think of building Cisco IQ, this digital interface, the first thing we are thinking about having complete landscape clarity of all the sets. But then on top of that, we want to build this resiliency. So you're not just thinking about tech data when an issue happens, what am I supposed to do? We want to take that learning and say, how can I avoid that issue from ever happening? So that is the world we need to go to. And that has its challenges. Because then you're saying that you trust my ability to take this data and be very deterministic in how I give your resolution. And right now, we may not necessarily be there in the trust we have with our customers and partners. So we have to put a human in the loop. But we do want to start moving, shifting left where we become resilient with human in the loop and then autonomous when the trust is there in our own models as well as partners and customers thinking that Cisco can deliver that autonomous world. >> Thank you. There's some common themes coming out in there. Kevin, I'm coming out. >> Yeah, I would agree, right? The whole human AI approach and something that the Presidio has been pushing with customers for a while now, we all have to be honest that we're not going to get it perfect in the beginning, right? And that this whole AI piece is going to be that journey, right? Around the data, around the governance, around building that model out. It's just one of those things that we have to, I think, accept. And then figure out, what's the right balance, right? The balance between the automation, the AI, the freedom it has, at the same time with what we want to see around data, governance, and still human review, right, and intervention. >> I'm going to keep going for a question for you, Kevin, if you don't mind. So there's been a real speed for people to adopt AI. And it's interesting because I go back to the human nature of all of this as well and you say, Okay, some people will use it. it once and then go back because we're all in a comfort zone at the end of the day to do the way that they always did before. Like how have you gone about making sure people adopt it in the right way and what are you learning at Presidio? Yeah, I think you'd see this big difference, right, between the people that have like a very well-defined outcome and a plan to get there, right, in the adoption piece versus the people that are like the me too, right? And like we're at a point now where anyone can take data and within 30 seconds you have a clickable dashboard that's filterable and everything like that. And my favorite thing to do is ask them about it and drill into the data, right? And that to me is kind of what separates that gap, right? Who are the people that, hey, I just wanted a quick way to be able to show this better versus who has the right outcome in the planning to say, all right, here's what I need to get to, and then identify my steps and then one of my gaps, right? And I think the way that we approach that and like help customers through it and just showing up and talking about, well, what's your strategy? Are you trying to take a existing data governance model and apply to AI? Are you actually thinking about something brand new and a new way to look at it? I think there's a very significant difference. I think that's really true, actually, and really thinking about it, how do you get them over the hump? In other words, they use it once and they go back to doing the way they always did before. How do you do it in a way that is conducive to them learning at the same time? We've had to build detailed prompts that, hey, here's where we've had really good sort of output from to help our, the rest of our teams go, okay, I get it now and actually now I can start to think about building some of my own as well. Yeah, I think, you know, Josh, you talked about like the whole workflow, right? Yeah. It's the repeatable motion. Yeah. It's not a one and done thing. Exactly. Right? And I think coming back to employees with the right model of leveraging it, using it, educating how, right, in the immediate what to avoid, right, no more W2s on SharePoint, but really being able to say, okay, here's the right model, here's the right way to use it, here's the education. Yeah. And then how do you do it in a phased approach? So start with something basic, start with that piece and then as people mature, as they gain the skills and as they gain the understanding of what AI is great at and what it's not, then you start to evolve with more. Yeah. It's our own group, right, so we've started with, you know, just very basic AI office hours for my team, right, and you have the entire org show up and start to learn and learn from each other on what they're doing and what works well and what doesn't work well. And we keep coming back to, it's okay where you're at, you're always going to get better. And everyone admits, we always ask the question, did you get it right the first time? And we've never had a single person say yes. It's going to be that iterative approach. I love that iterative approach and it's about the people too. And those early adopters are key to the success of as you move forward at the end of day. So Laura, I'm going to come to you now and you and I were talking earlier about the importance of human intelligence, in other words, critical thinking and how that links into kind of using the technology, et cetera, just talk to me a little bit from your perspective and WBCs perspective on that. Would you? Yeah, it's been really interesting and I think exciting, similarly, we have opportunities for teams to get together and share, innovate on the fly, learn, and that is incredibly exciting. I think at the same time it's easy to walk out of that, but there's so much more. There's more we need to be doing. We need to do it quickly and how do we get to these insights? And I think one, just the hey, it's okay to learn in phases. I think that's something that we're trying to reinforce. I think the other thing that we're ultimately trying to reinforce is the reality of having a wide swath of individuals able to innovate on good quality data is a tremendous opportunity. And I think there's governance that's required. There's this opportunity to instill a change management and adoption approach for organizations. We're doing that internally and then being able to point that out externally is I think a real opportunity and we're investing in that space as well. I think the other thing I would just offer on the point on visualization and but do you really understand? I think we've all been in that situation. I was in this situation recently where last-minute prepping for a presentation, hey, thought let's challenge ourselves. Let's use Claude to put some content together based on some other information we already had. And I'm confidently rolling through these slides. I hit one slide that had some information that was clearly a hallucination. I didn't know it. No, W2, though. Not W2. No personally identifiable information, but in that moment it was this great opportunity because a hand was raised by the group. I don't recognize that. What is that? It's like, well, learning opportunity. We generated this content with Claude. We are owning the fact that we generated this with Claude and we are acknowledging that the data is bad. And we need to resolve that and we need to find the systemic reason that data is bad. But I think overall just that visualization, positive, understanding the depth, gathering the insights, being able to leverage that for strategy and planning, that's the human element that doesn't go away. But I think we still have a struggle because not everybody's using it. That's true. The right way, right? I was just having a conversation with one of our sellers the other day and he's, I don't need any of those tools. Email search works just fine for me. I'm like, whoa, okay. You know you have all this capability and we've given it to you. But you're right, one, the data has to be key, but then enablement and then engaging it and using it the right way. I think it's a thing that we're all going to be struggling with as we, this thing is moving so fast. Yeah. We'll go up with the train moving and how do you jump on and make sure that we're doing all the right things and going in the right direction, right? Yeah. And I think I see both sides of people, right? People who are adopting it blindly and just trusting everything and going forward with it, like the lawyer, I don't know if you heard about it, who used some fake cases as years. Yeah. So that was me like that. That was presentation. That's right. And then there are people who are completely aware of it, no, no, I know. You know, they're very complacent in the workflows that they have done for years. So we have the two sides and we have to balance it. And that's why I'm saying that, you know, you have to learn as you go, as in, you have to inspect and then trust but inspect and then help the AI get to the point that it is also learning from us. Yeah. So we have to train it to give better outputs and train it and coach it when it gives incorrect information. And I think that's where I look at our partners as that barrier between what we are producing and what goes to the customers to take that information, sanitize it, build additional layers of subject matter expertise that you're talking about before we give any solution to the customer. And I think with that, it's also about some of the discussions we're having is, what's the right way to meet that user that you talked about in their native tool set? So like when you talk about like their workflow and things like that, how do I prevent them from just going and uploading data into a public platform and instead take something that's very controlled, very methodical with no data hallucination, putting that in an internal model that has the data, the governance, everything, but kind of integrating it with that natural tool set that's part of their workflow so that you're going to get the adoption. And that's what's exciting to see is everything changes. Well, it's one of those things that we're, from our point of view, what we're trying to do with Cisco. I'll do a plug for you. Oh, thank you. I didn't pay him for this, but we have a secure AI factory. We're always talking about these use cases and these high level vertical use cases. And we start having conversations more with the Cisco team and the Nvidia team in regards to what sort of blueprints should we go drive, everything from healthcare to manufacturing, line, identification, all these things and we're just like, wait, why aren't we doing pointed use cases? Can we leverage AI to go identify if our network's going to go down and go present that? We're not doing that. We're trying to figure out these big high level use cases, but that's a purpose use case that may just fit that one customer and we're trying to go present that. Let's go talk to the folks that are living and breathing this every day and then show them a blueprint leveraging AI so that they can get an outcome to your point, the persona, because there's AI built in all the technology. Let's go leverage it, but show that person the right way to go do it. Yeah, I think the other piece of what you were describing there and we think about this a lot at WWT, the idea that automation can be driven through AI, I think, was the initial boom. Everyone was so excited we could automate all of these capabilities or reports or things that are happening and while there is power and strength and we're seeing that come to fruition with reporting and some other tools that we're using, the piece that I think we really want to instill now when we think about both our employees and how we extend that out is that it's not about the automation, it's about the augmentation of information that human-led insight that comes from I have good data and now I can use my power of thinking in context that you could not put into a model reliably and augment that insight with people. That's the shift. That really is the shift, actually, because I know you and I were talking about this earlier, but I was talking to some friends of mine very recently and they were talking about anithetus using AI to basically define what drugs were required for a patient. Think about that for a minute. Do you want a machine, and I'm thinking about a film at this point, right, by the way, that there was a film where the computer took over the world, but it has to be balanced with people because you imagine the critical thinking skills that are needed for an anithetus, I have to watch your body language and watch what's happening to see if I need to do something different. By the way, we're all different, so it might be the case that a patient reacts differently to a drug than somebody else. I don't know, I want AI choosing what drugs I'm going to have on the operating table. But it goes back to your point, you said at the beginning of, do I trust an Uber driver? or do I trust somebody that's not, like, - No, I trust-- - Should I eat that actually? - No, that's a whole different thing. - Who do I trust in regards to getting in a car and driving me across the strip, you know? Somebody that has like all this personal stuff that's happening and who knows how fast they're gonna go or somebody that's calculated as a machine, leveraging AI and looking at everything across the board. - And it's probably gonna take every safe opportunity they can, I mean, it's a pretty good, maybe I will get one of those. - There you go. - So, there's some use cases like that where we can, you know, debate at Nossium. - But there's some workflows like you're talking about. That are the low hanging fruit that we absolutely should outsource to AI and say, taking summarizing tech cases, correlating what issue caused which problem. All of that is table stakes and low hanging fruit that we can give it. But then higher value efforts and workflows, that's where, you know, we come in and you all come in, which is where we want to ensure that we give you the lower level stuff, very easily in a digital interface and then you go and build your value at services. - But I also think about the power of the info you have as a partner on the customer linked with the power of the info we have around that customer. That coming together is a unique place back to the data, bringing those data feeds together that we've never been in before. So, what's the opportunity do you think in terms of us being able to bring that together, Kevin? - Going back to what you talked about that hurdle. - I think a lot of this comes down also to like the approach. Okay, so when you talk about these approaches, you're talking about different things scaling from, hey, someone went out and just tried creating their own thing which is very different than the other end of the spectrum of here's a specific agent that we've built and it's cookie cutter, it's out of the box and things like that. And what we're seeing is kind of also the flexibility a little bit in between, right? So one of the things about that building a trust and things like that are you look at like what we've developed for Studio AI and some of the stuff that we're using and what my team started to consume and things like that. It's a totally different approach of one individual trying to build something from scratch versus, hey, here's an agent that I built and like packaged. Here's how I'm using it and oh, my team, this is how you can use it and we now kind of have that the collaborations increased of people are getting better about leveraging the technology. And Emma, back to the point of trust and kind of that hurdle, we have to get there with the data, right? We have to get there from the data perspective and like what you talked about and doing the hard work and the cleanup, the pieces that like a lot of times we don't wanna talk about, right? And I think that's really important in terms of getting able to overcome that, build the trust with it. And when you look at partners, this isn't necessarily something completely different than the approach aspect that we've had for a long time. You know, as having the discussion internal, we've been doing AI and contact centers for years. It's just now evolved into generative AI and things like that and it continues to evolve. But when we have that experience across a lot of different large customer sets and being able to kind of aggregate it together and say here's what's working and here's where we've had challenges and here's what we're not gonna do again. You're bringing that approach, that trust. Hey, we've done it internal, we've done it before, here's what we're doing and really how we're building on it. >> I think we've done, we've done a couple different, to your point, that programmatic approach. What's our strategy? What are the priority use cases? What about AI governance, agent to agent, all these things? So we're looking at that more from a program standpoint, but that has to run on something. And as you build agents, you're going to start paying the token tax and that's gonna start driving up. We started programmatically, we have a digital team, they're really focused in on driving those programs for those customers helping them accelerate that. But what's it gonna run in? Is it gonna run in the hyperscaler? Is it better to run on-prem? Does it run in the neoclouds? And then all of the things that you need to tie with, storage, network, compute, the software layer, all these things are things that customers are struggling with. So I can have the best strategy in the world, but where's it gonna run? How's it gonna be optimized to be able to run? And that's what we're seeing firsthand. I like that comment Kevin around CX, because you guys have been doing AI for a long time in that. >> I mean, I think the partner capabilities have evolved. We talked about the gap in the customer, but you're talking now about like the gap that we have in the partner of the community. And you're gonna see customers need that from partners that show up with the full stack capabilities, able to educate them, able to start with, hey, let's talk about your outcomes, and then we'll talk about the right way to do this. And have that ability to solve an application hardware, you name it. >> Yeah, I think Kevin and Josh, one of the points that keeps coming up is the ability to create a clean landscape of data, and that then turns the tables from being reactive to being very proactive. And I think for partners, one of the exciting opportunities is to serve our customers in a different way, in different ways as partners differentiate from one another, right? In the way we build out that how we respond proactively or engage with our customers proactively. So while the data set, we're leveling the playing field from a data visibility, and Cisco's investment in the tooling and telemetry, and all of that kind of shared landscape, I think on one hand, you could say, oh gosh, well, then what do we do that's different if you're already serving that information up? But that switch to saying, now I have insights based upon the landscape that we as WWT sell into, or our customer base. Now I've insights and repeatability and messages I can bring to my customers differently. I think that proactive piece is huge for the next wave of opportunity around AI for us. >> Absolutely, that's where we are leaning, just to build that proactive resilience for the customers, because it's not just about every time every incident you're in that reactive mode. And I think the question that you have is very valid then how do partners enhance what we're building. And I think this is where I was trying to create the point that we want to take away the most basic thing, bring all the data together, cleaning the data, building the model, and then you are driving the outcomes for the customers, right? That's how we want to be the provider of that capability, that digital interface, and now you're free to go and create those outcomes that are truly driving value for the customer, and not just saying reacting and chasing KPIs like time to resolve things like that. >> I love this. We could get very, very carried away, but I'm going to summarize a few things, and I'm going to come to our last question. So we've talked about outcomes. We've talked about changing the workflow. We've talked about bringing the data together from all the different sources ourselves, you as partners from the customer, and bringing that for a better outcome, basically to our customers at the end of the day, because we are all here to delight our customers no matter what, no matter what happens or how fast we're going to move, things are moving at a rate of not, so our opportunity is to lean in together and really find an opportunity to deliver that value. So the last question is, what things are going to change, what's going to be in that film in the next five years that really is going to revolutionize this space from an AI point of view? What, one message would you leave for people as they think about using AI, et cetera, and Kevin, I'm going to come to you first. - Come to me first. (laughing) - You weren't ready for that, were you? - No, I think it's great, right? I like it. I think the story of the journey and seeing how people start it, and you're going to have to show up with a new approach. It's not going to be, I'm going to take a very rigid, structured framework that I've used for previous compliance things, and just think I'm going to automatically apply that to AI, right? And the ones that kind of start off with, hey, what's the outcome I'm trying to achieve, and then really do that self-inventory, you know? Where am I at, and what, being honest with myself, what are my gaps? And then you can talk with a partner, and you can talk with Cisco about, hey, these are the gaps I'm seeing in my environment. How have you seen other customers handle these? What's the right way to approach it methodically? Going through a journey on AI, knowing that I'm not going to get it right for the first time, okay? I'm going to have some struggles and challenges, but I'm going to leverage you for expertise to get to where I need to be. And that's kind of, you know, following along that journey, I think that'd be a great movie. - Phenomeno. - Laura. - So I think tying those things together, both the iteration of quantum leap, which is a stretch in my mind, but also what you would leave with, I think about from a culture perspective, core values from a worldwide technology, we talk a lot about embracing change and facing reality. And I think the elements of AI coming at us so fast require both of those with strong conviction. And then if I tie it to quantum leap, again, which is a leap in and of itself, the storyline there was definitely that you could go back in time and you could create a different result. You could produce a new outcome. And I think when we look forward and we're embracing change in facing reality about gaps, we're being honest with ourselves about where we need to improve, you look forward and take that proactive kind of lens. I think there is just limitless opportunity to take what was a predictable outcome and turn it into something innovative. So kind of ties to quantum leap. - Yeah, that's great. - That's a great answer. - Thank you. - Thank you all. - Serby, coming to you. - So I think from a partner and Cisco point of view, what I would say is that I want to see us working together to figure out which use cases and where AI makes sense and where the value at makes sense, right? What we're talking about. Because it's very important to be able to understand where what is the right place to use AI and where is the value, the SME in-depth knowledge that you bring to the table. And then the second thing I'll say too. On a personal level, I think each one of us should be thinking about how to reinvent ourselves in the AI era. I think that's very important. Cisco partner customer, anybody, we all should be thinking about how do we step out of that complacency and start adopting AI in our day to day lives? - As long as we don't have to adopt karaoke, which was what Josh was suggesting earlier. I'm just saying, but no, thank you. - Yeah. - Well then. We'll create an AI agent that will just sing the song for us. Even better, 'cause you definitely don't wanna hear me sing. - Every single one of these folks said something key. You gotta change your persona. You can go change the way that you approach and going back to the future. I want shoes that will just tie it for me. And what's funny is that your perception of that was like, isn't that sketchers? - Sketchers slip on. - Yeah. - And then back to the future sketchers. - They had Nike's that tied themselves, right? I thought about that for a second, and people's perception is different. - Yeah. - And so how they leverage it and how they use AI, I think is gonna be key. And it's up to us as partners working collaboration with Cisco, how we can reinvent ourselves to be able to approach that, because I'll walk into rooms and you'll have the smartest guy in the world that just knows everything about AI. And then another person is like, we gotta get our data in order. How do we do that, right? And so we have to be able to pivot, again, jump on the train 'cause it's moving fast. But now the government's gonna look at every model that's being built before it actually goes out, right? And there's some things that are happening in real time where anthropic can't be used in the federal space. Nobody in the DOE or DOJ or any of those folks can leverage AI to even respond to an RFP. So these rules are coming. You gotta change the persona and it starts with the partner ecosystem collaborating with Cisco, I think. - I wanna thank all of you for being here today. I hope our audience really enjoyed 'cause I certainly have the back and forth conversation that we've had and the real talk about what's going on in the industry right now and what we do for our partners. Clearly, if you take nothing else from this podcast, you take that you will not put your W2 on your computer and make sure that it can be seen by any level of AI. Apart from that, I think you're gonna also take journeys just as important as the starting point that the human capability, we gotta use that critical thinking skills combined with the technology. And look, if I can leave it with this, we're all in with you. This is about us joining together to do the best that we possibly can to delight our customers. Thank you very much for being here. - Thank you. - Thank you. - Thank you.