Name: From Vision to Value: Applying Gartner’s AI Ambition Framework
Uploaded: 2025-10-07T16:01:04.186Z
Duration: 40 min 17 s
Description: From Vision to Value: Applying Gartner’s AI Ambition Framework

Transcript for "From Vision to Value: Applying Gartner’s AI Ambition Framework": Okay. To make sure we keep everything on time, I'm gonna go ahead and get started. So welcome, everybody. I'm Tori, our vice president of marketing at Thrive. Thank you so much for joining our webinar today, From Vision to Applying Gartner's AI Ambition Framework. Our goal is that by the end of this webinar, you'll have a better understanding of the basics that can help you create a solid foundation and actionable plan for your AI strategy. A few housekeeping items for today, we will have a q and a tab that's going to be open throughout the entire webinar. Please feel free to add your questions in there at any time, and we will be answering as many questions as we can at the end of the webinar. And any questions that we don't get to live today, we will follow-up with you via email. In addition to that, we will be providing these slides and the recording, via email as well. So anyone that registered will be receiving that. And we would like to kick off today's webinar with a few poll questions. So I'm gonna start those now. So our first question is, have any of your teams started using AI officially either for evaluation or in production? Yes, no, or you're unsure on that. Oh, the yeses flew off the handle right away. This is always very exciting. Yes. And not surprising. I mean, AI is yeah. AI is everywhere. Everybody's doing it. So not surprising at all that, the yeses have have run away with this one. But I love the honesty and the no's and the unsures. I'm gonna give this a few more seconds, and then I'm gonna open the next poll question. Okay. Closing that one. Next question. Do you have AI policies in place around acceptable use, preferred technologies, or use cases? Yes, no, or unsure. Okay. A bit closer of a race here between the yeses and the noes. That's really interesting. Are you more surprised by the yeses or the noes? I think I'm more surprised by the fifty fifty split. Yeah. It is very close. Well, the s's are are trying to pull away a little bit here. Alright. I'll give this another couple seconds, and then we'll go to our last poll question. Okay. Our final polling question is, do you feel like you have a clear idea of how AI can help you or your team? Yes, no, or unsure? I'm kinda surprised by this one. Yeah. That's good. I I'm glad that people feel they have a clear idea. K. I'm just gonna give this another couple seconds, and then we're gonna jump into things. Okay. So with that, I'd like to go into our agenda for today. So we're gonna cover a look at Gartner's AI Opportunity Radar, Thrive four phase methodology, and then questions to start your planning. So now I'm gonna hand it over to the people you actually wanna hear from on this. Michael Gray, Thrive CTO. Welcome, Mike. Hey. Good morning, Tori. Thanks everybody for joining. And then JR Dawkins, Thrive director of AI services. Good morning, JR. Good morning. Good morning. And finally, Emily Steen, Thrive's AI solutions developer. Thanks for being here, Emily. Yeah. Thank you. Okay. So I'm gonna hand it over to the three of you to take this away. Alright. Thank you very much, Tori. So I'd like to start a little bit with a definition of what are we here to talk about, and that's your AI ambition. So what what is that defined as? So before we talk about strategy or technology, we really wanna think about what this means. And Gartner defines AI ambition as your organization's attitude toward AI, not necessarily your road map or your strategy, but a mindset that you bring to it. And a lot of this, presentation is gonna focus on teaching a little bit on the business side and how we look at AI outcomes as it pertains to some of this new and very exciting technology. So as you walk through, you wanna really start to form what is your organization's mindset, to how you're gonna use use AI. Now let me walk you through this slide. This is Gartner's opportunity radar, and it's a way to look at your approach to AI in your organization. Now on the vertical access access, you see external customer facing operations versus on the bottom there, you see internal operations. On the horizontal access, you have everyday AI usage. And then along the other side, on the right side, you see game changing AI. Now as we dive into the different quadrants here, on the top left, you see front office activities. So this would be where you're using AI for customer service, or perhaps in sales. Then on the bottom left, you see back office activities. Perhaps you're using it on the legal side of things or in finance or HR. On the top right in dark blue, we have a product and services. So perhaps you're gonna use AI to develop a new revenue stream inside your organization. And then in the bottom right, you see core capabilities. This might be an area where you start to use AI to figure out how you can increase margin or perhaps even differentiate yourselves from your competitors. Now the really interesting piece here is, as you'll see later on, your organization could be shaded to any one of these four quadrants across either access. And we have some interesting use cases that Emily is gonna show you in just a little bit. But before we do that, JR, I'd like to talk a little bit about this idea of everyday usage versus game changing AI. Can you give us your thoughts there? Yeah. So, you know, in the beginning, it can seem a bit overwhelming, right, making the decision of how you're gonna move forward with AI. So I think it's very important to kinda simplify things. So I love this one way or the other choice here, improve existing business and operating models versus fundamentally new. Right? So what are you trying to do? Right? Are you looking to automate routine work, reduce manual steps? Right? Then you're leaning towards improving existing. Right? If you're looking to build new revenue streams, new experiences, then you're leaning into the fundamentally new operating models. Right? So, you know, I think this is very important to start here, start with the basics before moving forward. But from there, you wanna look at some of the common approaches. So, Emily, if you could share some common approaches that you've seen out, that'd be great here. Yeah. Of course. So here we see that radar that Michael was talking about segmented into those three common approaches to new AI projects. So on the left hand side, we have the productivity pursuers. We have the not in front of my customers. And then the right hand side, we have the AI first everywhere use cases. So I would say in a lot of our conversations around use cases, most use cases are are within those first two categories, the productivity pursuers and the not in front of my customers. So these companies are ones that are really excited about the capabilities of AI to optimize productivity, but they're not quite ready yet to have it be customer facing. The last category, AI first is kind of a buzzword that you see floating around in the AI world. But, basically, these are the most ambitious companies. These are the ones that understand the capabilities of AI and then restructure their business processes around those capabilities. Michael, do you have any thoughts on how to run these types of projects? Yeah. So I I think what we're focused on here is that once you've figured out your ambition, and how you wanna approach AI in your organization, it's time to think about planning. And it's time about thinking about planning an an effective AI project. Now this is really the very beginning step, if you really have not gone into this world just yet. So the first thing you wanna do is you wanna do a lot of experimentation. And a lot of people are doing this, probably on the consumer side of things. They're using AI for recipes at home, things like that. One thing to do is and if you can do this in a controlled way, and we could certainly help you, is don't necessarily limit the experimentation. Obviously, you wanna be very careful with your data, but just starting to learn and understand the technology can be really, really powerful, especially when we get to use case development later on. Secondly, as you after the experimentation, what you wanna do is think about how this is all gonna work for your customers and your users. Do they like it? Is it confusing? Do they trust the data coming out of it? You really gotta start to get some education around how this works so you can understand what that perception is gonna be. And again, in this early phase, after you've gone through that sort of experimentation and learning about what you might what kind of impact you might have, then you wanna think about your top down guidance from your executive teams. You gotta learn an awful lot about where your organization can go and what your, goals can be and also what kind of impact you might have in your own marketplace. At that point, you can start to think about what your targets are gonna be for an AI project, what the risks that might be out there, and also areas that you might wanna stay away from that could be damaging to your organization. But the number one thing that we recommend is don't launch an AI project without a plan. And we see this over and over again. Money has been spent, investments have been made on putting some AI technology into an environment, yet they don't actually have a plan and it ends up being a waste of money and, more importantly, a waste of time. One of the ways that we can help guide you through this process is through our four phase engagement methodology, and this is how we help our customers understand the AI technology, how it can be used, and then ultimately, implement it. So JR and Emily, I wonder if you could help me a little bit, going through this methodology. And, JR, why don't we start on the discovery side? Absolutely. You know, I love this part. Right? So this is where you're kicking things off to dive deep. Right? To understand what are the strategic goals. Right? And, you know, it goes beyond, just, you know, pie in the sky, but we start to look at what is actual workflow. Right? And what are the steps from a to z for each workflow? And this is the point where, you know, a lot of folks realize, alright, let's go back to our documentation. Let's go back to the core of, you know, how we do things. This is important because until we understand the base and the details there, we can actually build something that's effective. From there, we go on to phase two. Emily, can you tell us a little bit about this evaluation step? I know you and JR have been, done quite a few of these. So, I'm sure you both have some stories in deployment, but let's walk through evaluation first. Of course. So phase two is all about getting your data and getting your systems ready. So we start by identifying the critical data sources, whether that be CRMs, vertical applications, knowledge bases, basically, wherever the truth lies that you wanna connect your agent to. And then we assess all these tools on data quality and availability. We also identify external data sources, whether that be websites or subscriptions that you have access to. These things can also add additional value to your agent. So ultimately, this step is where we make sure that your data and your environment can support AI at scale. Without this step, even the best models and the best agents will underperform. I always like to tell clients that your AI product is only as good as your underlying data. Well, I I gotta say I could not agree more, and we we spend a lot of time both internally and externally talking about, that data cleanliness. It really is the backbone of a lot of these projects. Jared, can you take us into deployment and and maybe, perhaps you and Emily could tell us a little bit about some of the interactions that you've had? Absolutely. So deployment, right, this is where it becomes real. So we we get it into the client's environment. And, you know, there's there's been different examples even this with us interviewing clients and potential clients. Right? So, we've talked to clients who already deployed a solution and, you know, found themselves a little stuck. We've talked to clients that did the first two phases and, you know, have seen some success there already. So for us, the deployment part is where we're getting into the training. Right? We're fine tuning the models, and we're building the systems to bring the use cases alive, one of which is, like, RAG. Emily, could you share a bit more about that in detail? Yeah. So a good example here is we built a due diligence agent using RAG. So RAG is a common term in the AI space. It stands for retrieval augmented generation, and it's basically a very efficient way of optimizing an agent and training an agent on your specific company and your specific data. So in this specific example, we created due diligence agent that was connected to two data sources. One was the company's input documents to their due diligence process, such as, like, legal, compliance, financial documents, etcetera. And the second were those output documents, which were the actual due diligence reports that the company had created before we introduced AI as a tool. So using this rack application, the agent is actually trained on your company's specific due diligence process and optimized for that process. The other things that we also include are we choose an, LLM, which is a large language model, and we choose one that's appropriate for your specific use case. So there are a ton out there, and it takes some experimentation to choose the best model for the use case. Some are like OpenAI models, cloud models, which are good for long documents, etcetera. And then we also choose how you interact with your agent. Whether you want a direct connection to something like SharePoint for your documents or whether you prefer to manually add files to have a little bit more transparency there in the process. Yeah. I know this phase gets pretty exciting because that's when they start to see things come to life. Now, I think JR mentioned earlier, you know, one of his favorite phases was was number one. You know, for me, it's this step around analyze and refine. And one of the differentiators that Thrive really offers is we can show the usage across your organization, around how your employees are using these models. And not only that, we can start to understand who's having the most success and present that data back to you in a very, very clean way. And so when you start to look at this data, then you can start to see other opportunities. You can see where to invest future AI deployments, and really start to transform your business. It's actually very, very exciting. Now one thing that people do get really excited about it and and what I'm talking about here is the hype. So, an interesting slide from a Gartner study, interesting statistics. Really no surprise that 96% of CEOs expect AI to boost productivity. I've not heard those who those 4% are, but that's interesting. But on the other side, of this gap here is 70 cent 77% of tech employees, I e IT people, say that AI is increasing their workload. So the what we can see here is there are some AI implementations that are that are not planned out properly, and they're actually causing the employees to get a little frustrated and doing the opposite of what a lot of these projects were originally done designed to do, which is increase productivity. They're actually decreasing productivity. So, unfortunately, failure does happen a lot inside, AI implementations, and we've seen that time and time again, just even over the last eighteen months. JR, can you talk to a little bit about why that failure happens? Yeah. I mean, you know, it's different from past technology rollouts, like, you know, when we introduce cloud and you're moving your files to the cloud. So what I believe is happening here is basically we're getting lost in ambiguity. Right? So we look at this these numbers here. 62% of organizations can only map 25% or less of their processes. Right? That's a that's a huge, challenge there. So if you can't map the process, you can't actually build a AI solution to, complete that process. Right? So it starts with what you are actually giving it. 47% of employees don't understand how to use the AI tools. So they have deployed, but there's not enough training and education around that. So, you know, there's a lot of pieces to the puzzle and, you know, that's why, you know, we have our Thrive AI services team to to really guide this this process and and partner. Yeah. And and I gotta tell you, JR, that having a dedicated team, internally to help our customers has been a huge advantage for us. And I know we're making just lots of progress and and helping everyone on their AI journey. But we do wanna talk about what the foundation should be as you start to look closer at what the next step is in your AI journey. So, JR, let's JR and Emily, let's look a little bit closer here. So why why are those some of those other reasons? Emily, let's start with you. This unknown or incomplete processes, what have you seen, from your time working with customers on this? Yeah. So I would say a lot of companies or customers come in being very excited about the potential of AI, especially for those productivity use cases, but they haven't actually flushed out, exactly where they want AI to exist within their systems and what specific use case they want, to use AI for. So when we come in, we actually start with the use case and we build our whole agent around the use case that is most high impact to your organization. So having that really, flushed out at the beginning of the process really helps the whole process move forward in the most optimized way. Absolutely. JR, let's talk a little bit about communicating value and expectations. I know we just spoke on that, on on the previous statistics there. Can you tell us a little bit more about those expectations? Yeah. You know, expectations can be very different depending on the group, their goals, their their jobs. Right? So it's good to have expectations. Typically, we aim to start from the top down. So that way, there's, like, global expectations, and then everyone could fall in line with within that. When there's no expectations, you know, it it folks are going in different directions, and it could be a little unwieldy unwieldy to have, progress as a complete organization. Yeah. Absolutely. And it can mismatch expectations can really tear things down. Emily, let's move on to tracking success criteria. Why do you think this is important? Yes. This is extremely important because building an AI product is is a very iterative process. We actually take a lot of your feedback when we're building these agents and we get feedback at various points in the project. So at the requirements development stage, as well as after deployment that we saw in our slides earlier. So we really want to keep that human in the loop as we're developing these solutions to ensure that the actual product that we're giving you is optimized for your specific use cases and for the people who are actually using the tool. Yeah. It really is amazing when you start to measure some of this stuff, what what some of the things that come to light. JR, I know use case development, and use case strategy is something that is very important to you. Can you can you tell us a little bit about some of these poor use case alignments that we've seen? Absolutely. You know, coming from the world of product, you know, use cases are everything. So if there's not alignment on the use cases, then that, you know, continues the the missed, or mixed up expectations. Right? So the use case, it needs to be aligned with business goals, needs to be aligned with the opportunities. Otherwise, you're just creating maybe cool interesting things, but they're not actually solving, the business endpoints. Yeah. We we've seen some of the coolest use cases, but, in the end, they didn't actually help the business move forward. So you really have to be very careful on how you're judging, what do you wanna do for the next step. And lastly here, these changes in the market and technology, this is a it's been a very interesting, eighteen months, two years. This technology changes every day. So something that perhaps, you thought wasn't possible three weeks ago is suddenly possible today or will be possible tomorrow. And so you really have to keep up with this changing environment so you can start to understand where you can go because, this is changing technology, in a very, very large way. It's likely the biggest transformation since the Internet and some are saying even bigger. So you really do need to keep up with the changes, in this technology because things are advancing very, very quickly. Now I mentioned earlier we wanna provide a foundation. So Thrive believes you have to build this foundation for success and then measure it. And we're gonna give you a couple of ethos here in order to, plan this out. So the first thing you wanna do is create a goal that you really want to achieve. If you create a goal that isn't really all that interesting to you, not only will you not put the time into it that's needed for it to be successful, but others in your organization probably won't bother to join in either. Don't assume you know where you are. You need to measure it and track it. This one is really important. We really wanna drive this home. It's perfectly acceptable to go out and buy some licensing from a large provider and hand an AI subscription to a variety of people. But if you don't have a method to track it, you have no idea if it's been successful. You have no idea if the money that you've spent on that licensing was even worth it other than asking around, hey. This is cool, or are you using this to write an email? And then don't be afraid to change course. I mentioned earlier that things are changing literally every day, in this technology. It's really interesting. It's really fun. But you really have to forget about might look like a failure because, in this world where things are failing and then improving the next day, you really gotta be present about what things what you could do differently tomorrow because that's how you really start to innovate within your organization. And lastly, ask questions to get started. This is very, very important. You need to ask around your organization, what are you using AI for? Do you have any ideas? And we certainly got some sample questions here. Tori? Yes. So our team put together, some really good places to start, some good questions to ask, and I would love to hear from each of you on what your favorites are from this list that our team put together. Michael, you wanna go first? Absolutely. So, this was an easy one for me. Who owns this workflow and who would own the supporting AI project? I'm sure a lot of you have heard the statement, if everybody owns it, nobody owns it. So if you look at a workflow and you wanna automate it, maybe everybody in the company agrees that the workflow is far too manual and could be automated with AI. You really do need to select an owner. You need to select one person that can at least document everything if it's not already documented and then help guide a project through each step. What we've often seen is, maybe let's use an example of a 17 step process. And somebody says I wanna automate step seven in that process. Well, if you bring in somebody that owns it and can really look at it at a high level, it's possible that you could automate 15 of the 17 steps or maybe the whole process start to finish. But if you're too focused in on just one step, you're you're probably missing out on some opportunities. Great one. Who wants to go next? I could jump in here. For me, I look at the first one primarily. Right? What do you what are you trying to solve? Because there's there's there's been many conversations where that has not yet been identified. And, you know, it takes some reflecting, and and some surveying and getting everyone on board with that that question. Right? Because if we don't know what we're trying to solve, then it really puts you in a space of, you know, anything can be the solution. And even within the AI realm, you know, of platforms, there are many different ways to approach it. So if you don't know what you're trying to solve, it makes for a huge gray area to start with. You don't wanna start in the gray. No knock on you, man. But, JR, I know you and I know you and Emily have talked to, over a 100 customers here just in the last two months. I think what we see is a lot of people what they're trying to solve, their goal is to put AI into their company. They're not actually trying to solve a problem. So that's a really, really interesting one. Emily, you wanna wanna pick from the list here? Yeah. I actually think that ties in well to my favorite. So as a developer, my favorite was definitely number three. Is this workflow well understood from start to end? So, again, as I said, when we build these solutions, we are building it from the use case first, and then we're building out the entire process from that as a starting point. So I when I'm developing these, it's really important that we all understand what workflow we're trying to optimize and automate here, from the very beginning to the end, like, how your business currently does this workflow. And this includes the software that you use, the data that you use, any internal logic and reasoning that your team uses. So all these are essential for me to understand so that I can, optimize that solution and actually add value with the AI tool that I'm building. I love all of those. All three seem very important. So with that, I do wanna just quickly say before we get into some questions, you will all report and our ebook, the AI readiness playbook with the email that is going to go out with the slides and the recording. And now let's get into some q and a. So pop in there. Michael, do you wanna go into some questions? Yes. We've got quite a few here. Yes. We have a lot of questions. Alright. Okay. Hold on. That's not actually a question. What would be the biggest piece of advice for an organization looking to introduce AI moving on from employee scale to a custom to to customer facing? The biggest piece of advice, and and we kind of, you know, beat this in earlier, but is have a plan. People are treating this, a little bit AI like maybe a bit of a toy. Let's bring in AI and it's gonna magically solve some problems. It's not really gonna do that. If you look at it that way, you're gonna end up facing losing a lot of money and and a lot of time as well. Let me just jump in, JR. One for you here. Let's see. As a policymaker at a facility with rather strict data sovereignty and data protection requirements, what type of AI, whether it's cloud based or hosted, in other areas would you recommend for us? JR, I think, we certainly run into this question quite a bit, and and maybe it makes sense to tell them a little bit about our our, sort of flagship solution here. Yeah. So, depending on, I guess, what the data sovereignty requirements are, we so we we have a solution that is a Azure based, private install. So the the LLM is deployed into your own instance there. And the benefits of that is, you know, it's not even in the cloud. The data doesn't leave your environment. So it's not in another entity's cloud. Because sometimes, depending on the data requirements, you need to get a business agreement with this third party. But if it's in your own environment that you're managing and, we have the security protocols in place, then you will be able to comply with the the ARM data prod the data Yeah. Protection requirements there. And I think, the most, you know, popular, implementation that we see is an AI environment running, solely inside your Azure tenant, a customer Azure tenant, and then you control all the boundaries and you control all the data. And the the really interesting thing is, you can see all of those resources inside your Azure tenant. It's not a mystery, as to where things live. Let me jump into the next question. Emily, certainly coming to you. I hear a lot of implementation of AI projects and agents. Can we also engage with the front end policy and readiness side? So absolutely. But, Emily, you wanna take a shot at that one? Yeah. So in terms of the policy and readiness side, we actually do have in the front end of the application. We have a terms and conditions that the customer or the user actually has to, acknowledge and sign before they enter the UI of the tool. So that can be totally customizable based on your specific policies within your organization. Also, as a broader picture, we build the agents custom for you. So we build the agents around your specific policies and requirements, whether that be the way that we store and access your data, whether that be how we, permission the agents themselves. So it's totally customizable around your AI policies. Yeah. And I I just highlighted a question here. There's someone asking, what do we mean not in front of my customers? There are a a pretty large cohort of organizations that wanna keep that human to human relationship, when they're dealing with their own customers. They don't necessarily want their customers interfacing with an AI bot because I believe it it they feel it erodes some of that the value that they bring to the marketplace. So that that's what I think, Emily, the the radar slide where we saw not in front of my customers. Mhmm. We have seen that occasionally. Correct? Yeah. I would say a lot of people are more focused on the internal processes than that customer facing side as of right now. Mhmm. Yep. Yep. Absolutely. How do you empower your employees to do more? Absolutely. JR, why don't you tell us a little bit? We've got a a a question here. How how does, how long does an AI implementation typically take, from phase one to four? And I know there's some technical bits in there that can, you know, perhaps just slow things down just a little bit. But I I think you wanna answer this question with all those technical bits all sort of wrapped up. Yeah. So if everything's wrapped up, we're really just looking at, con like, confirmation, and and fact checking each step. So it could be a matter of two weeks. Right? So depending on how many stakeholders have to approve, right, and and get every all our t's crossed and i's dotted. So our solution actually deploys very quickly. So you can be up and running in a in a short order of time. So it all depends on the platform. Yes. I think the other note there is use case development and talking to the business is the other thing because if your organization's not prepped to be sort of planning to do this, it can be somewhat of a difficult question for the the organization. And the technology part of it is actually the fastest part. The business transformation, is a little bit more difficult. I think it is worth mentioning we, do help with training and adoption, and we do help with that more consulting side, that more human side. That actually seems to be a more difficult piece than implementing the AI, because these solutions are quite modern, and they are not tremendously difficult, to deploy. It's just deploying them in a thoughtful way, which can can really be difficult. Just a quick question, JR. We'll go back to your deal out customization by our customers, or require reengagement. This is a managed service from Thrive. JR, why don't you give us some thoughts about how we'll continue to work with people, once we've implemented their, their AI environment? Yeah. I mean, that that that's a broad question. So it really depends on what you're trying to customize and how. Right? So there is a visual UI piece, so the UI could be customized to brand. If you're referring to the agents themselves and how they function, we typically work that out for you with you. If you have someone that's really the technical on your team, we can provide, like, additional admin access. Fantastic. Emily, I think we'll just grab one or two more here and, and we'll wrap this up. If you need to interrogate data outside a safe data environment, I think is what they mean, how does the agent protect internal data being used in the Internet to return data or questions being asked? I think it it's ultimately about how we build the different agents and assistants in the platform, and how they can have different data sources. Can you tell us a little bit more about that? Yeah. Of course. So the data that we connect to, it's all internal to your environment. So the the movement of the data from your system, to the agent will all be within your environment. It's all totally secure. And so what you also mentioned is the option to connect to the Internet. So there are actually a wide variety of ways to do this depending on your requirements and your data requirements as well. So if we wanna connect directly to the Internet, we can do some, add some intermediate agents for some encryption. We can limit the agent access to the Internet to only access specific sites. We can we can basically, use APIs instead of connecting directly to the web. So the agent is only pulling from specific data sources that you want access to. So there's basically a wide variety of ways. It depends on your specific requirements and exactly what you want the agent to have access to in terms of data on the Internet. Alright. Fantastic. And, I'll just take one more here. How can we manage what our employees are using AI for? Can we restrict them to a specific use case? You certainly could restrict them to a specific use case, just by writing a very specific prompt that says, if they ask me about any other topic other than whatever the use case is, just declined to answer. Just to share some feedback, we also offer the ability through a variety of other services, Thrive, most of which our customers already have in place. We will block all other AI services and redirect end users to sort of the corporate solution. We have gone through this both internally and with, some of our customers. It's very jarring at first, But employees, after they realize that there's an official, corporate solution around using AI, they actually start to use it even more, because they do believe they're in a safe place because they are, and it's something that they feel like they can trust. And we've actually seen a lot of use cases almost come alive because, there might be people in your organizations or people on this call who are your end users who might be using AI and unfortunately feeling like they can't show it to you or hiding it from you or just simply feel like they're cheating at their job. Well, once you give them an official solution, now they're starting to share what they're using it for. Hey. I used this prompt to, deal with a difficult situation with a customer, or I was able to walk through a complicated decision making process with an AI assistant looking over my shoulder and giving me a little bit of advice. And it really does change things once you have an official solution. For what it's worth, our solution is branded to your organization, so there is a lot of trust that is instantly built just by them logging in with their traditional credentials and not having some AI solution out on an island. Tori, I hope we hit enough questions. Yes. We do still have quite a few questions, but we will like I said at the beginning, we're gonna follow-up to those, after via email. So we will we will answer your questions if we didn't answer them live. Yep. Absolutely. So, yes. So thank you to the three of you, Michael, JR, and Emily. This was incredibly beneficial, and I hope that all of our attendees today found that to be the case as well. So thank you everyone for joining. We really appreciate the time that you've spent with us here today. And as always, if you have any further questions, please reach out to us, and you will get this all, via email. So thank you so much, everybody. Have a great day. Thanks, everybody. Thanks, everybody. Thank you.