AI Roundtable: Tangible Takeaways to Help Leverage Artificial Intelligence

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I'm very pleased to be joined by Alex Kostrounis, Nick Capone, Ken McLaren and Keith Thomas. What we'll do briefly here is make introductions from each of the panelists, so you understand who's joining us today and the insights that they're bringing. To kick things off, Alex, can you give us a quick intro?

I'm also a professor of artificial intelligence at Northwestern University's Kellogg School of Management. As part of their MBAI program, I'm the author of a book called AI for People and Business. Great to be here, thanks.

At Access, we aspire and we're very early in the journey, but we aspire to build what we call the next generation private equity firm. And analytics and data is central to that. Thinking through how do we apply all these technologies to improve what we do as a sponsor across the investment life cycle, but then also for value creation in our portfolio businesses? Prior to access, I was a consultant. I was with McKinsey for six years, primarily focused on digital business building and innovation strategy, and started off my career as a software engineer and startup founder. Looking forward to being on this panel with everyone.

To Sean's point, up front, we believe significantly in value building. Our Center of Excellence team, which I'm a part of, actually has more members than our investment team, which is a little bit unique in the mid-market. But really, we're close to build. Value is what we do. Data and AI is the vertical that I lead, and so we've been investing behind data and AI as a strategy for the last three years. Just really believe in the value it will bring to our portfolio companies. I'm excited to be here.

Prior to Fraser, I spent about 20 years as an executive, building and leading data analytics businesses in the healthcare space.

I think people have to do it in working with private equity firms in support of this journey. We found a number of things should probably be done in advance. That are going to have maybe more immediate roi, but also are going to enable the effectiveness of your AI journey. To get started off, I'd love to get some of your thoughts, Nick, about what are some of these precursor activities that people should be thinking about?

It starts with strategy. I think that's the first point I'd make when it comes to this understanding where the value is. Understanding how you're going to prioritize things, the basics of a good strategy. It really sets your strategic compass for when you deploy these technologies, and that strategy needs to be tied to your strategy for using AI. Obviously, it needs to tie to your broader digital strategy, which needs to tie to your broader business strategy. As a firm, where the value is, where you're going to focus on.

I'd also kind of bifurcate it, because when we say AI, it's a broad spectrum of things. You have your traditional analytics, which is descriptive analytics, just getting stuff on a screen and reporting. Then you have your more predictive analytics for predicting the future, then prescriptive analytics predicting the future. And then actually doing something about it automatically.

With those kind of use cases, the value is well-known.

It's a bit more mature, so you could be a bit more systematic. With a strategy of taking some of these best practices, applying them to your specific business context versus something like generative AI, which I think is what we're all kind of excited to talk about here, like Chat, GPT and LLMS and things like that, where we know there's a ton of value on the table, but it's still new. The way you'd approach that strategy is a bit more iterative, a bit more experimental. Trying to get use cases, and then experimenting as soon as you can to figure out where the value is. But either case, even if you're in well-trodden waters or experimenting with something new. Just kind of having that strategic compass, knowing where the value is and how it relates to what you are trying to achieve as a business. And then the second level, below that, I'll talk about foundational enablers.

So the biggest one here is obviously data, you need to do analytics on something, so it's getting your data in order, oftentimes when you do some of these big. When I've done some of these big analytics projects in the past, 80 90 of the effort is just getting the data centralized and clean. And almost like analytics is like the fun part once you could actually get to that, once your data is all there. So that's definitely something everyone should be doing now as a no-brainer to really extract the value from some of these more advanced analytics capabilities.

And there's different ways you can do that, depending on how complex your organization.

Is there's a data platform? There's a Microsoft Azure kind of data platform that we roll out at all of our portfolio. Businesses that kind of ingest data from all their source systems services for analytics and then sends it over to us at Access for portfolio monitoring and fund valuations and things like that. So just getting kind of your data ecosystem in order is critical. Some of the other enablers here are talent and technology.

And there's a build versus buy kind of question when it comes to those things, how much do you in-source versus partner with? And the answer will depend on kind of your specific context, but definitely important ones to think through. And then, finally, my one last comment, kind of on the change management piece. Having done a lot of these digital transformations before. I think one of the biggest predictors of success is a champion inside the organization that could really own the vision and drive the opportunity.

And often that's kind of from the CEO, or someone the CEO directly holds accountable for the digital agenda. And really having kind of that leadership voice to set the vision and drive the organization and mobilize change is critical to success for analytics and any other kind of major digital transformation.

So I thought that was a great point.

You're going to get a lot of roi out of visualizing it and analyzing it, and don't forget that type of really, really good precursor things. And then it's the change management we can talk a little bit later about. Also, you know, AI is going to be part of your strategy as a tactic.

It's not your strategy. So I think there was a lot in there that was extremely helpful. And so if we think about that, you're on this journey, you've got your data, it's clean, you're analyzing it, you're getting ready to kick off the AI. Keith, What are some of the things that any company should be really thoughtful about in terms of protecting the castle? As you're building kind of these data assets up?

So we want to make sure that organizations protect their data, that they're actually ingesting and definitely using. A change management process is highly important as these dependent as these AI systems get rolled out. And Nick, you had mentioned that you had met your different organizations. There's a dependency on AI at this point. And if you don't get that data, if you don't get those analytics that you're looking for. Because the AI system goes down or becomes unavailable, then how does your organization respond to that?

And so it's a very important piece that you want to make sure that you have some type of disaster recovery failover capability. Even if it is to go to a manual approach, that's okay. Having the plan is the most important part of that. And then, finally, I'll say one more thing, Sean, which is. We're going to invest a lot of time, a lot of resources into building these data models, into building these systems. And we want to make sure that we protect them from theft. Making sure that if someone gets into our organization, that they can't pull that model out and take it with them to use somewhere else. And there's some ways that we protect, using different security tools and different security capabilities to support the idea of a model theft by attackers.

And there are things that large companies certainly have bigger resources for, but I'll tell you just through the everyday. Here. There are all sorts of resources available to SMBS and any size company that can do a lot to protect your organization. And in some ways, it's increasingly most critical assets your data. And so if you're looking for this, you can go to almost any. MSP can take you a long way as a business, and so I think this is a great way to start is doing the precursor work. That's maybe not as glamorous, but that's going to enable you to do a lot of the really cool, fun stuff in the days ahead here.

So you have your systems protected, you're cleaning your data, you're visualizing, you're analyzing, you're change management, and you're ready to start putting AI in place. Let's talk about some of the tech that people and business leaders should be thinking about. You know, considering beyond, above and beyond, maybe even some of the consumer available LLMs like Chat, GPT and Board, etc. So Ken, I'd be curious, what's your thoughts on some of the best in class tools and tech stacks that businesses should be thinking about as they get going on this journey?

So once you've got your data in place, you know, just using Jupyter, Notebooks, Python, your toolkit, you know, our Scikit, PyTorch, Tensorflow. Everyone has their own tool of the trade. But we do a lot of prototyping on desktops. And, you know, as we prove the value and the use cases, we then start getting ready for production. But, you know, don't build in production. First, get the kind of proof value with your customer market in place before you start building. When we're ready to build, we do. Like Microsoft Azure, we actually say we're cloud agnostic, but the reality of being in mid market healthcare is the majority of our portfolio companies in Microsoft Office, the majority of our companies are already using Azure Cloud in some ways.

So it's a natural step, and we do really love the tooling that Microsoft has been putting in place. We work with other cloud providers as well, but Microsoft Azure is just an easy path for us. And we really like to Keith's point before that. We've got a lot of good terms and conditions cybersecurity, data privacy, active directory. A lot of the stuff you don't want to be thinking about with AI kind of comes out of the box with Microsoft.

From there, the business has been investing, Microsoft is investing heavily behind AI, so not just the OpenAI partnership, but there's your ML environment. Before that, you know, the cognitive services, the open source tooling that they bring in. So you can natively work in Databricks and Spark, you can use Kubernetes and Kafka. And so whether you're streaming or batching, your tool of choice is pretty much available for you there. So it's a good platform and a good chase you for all of those reasons.

A lot of good success working there. The number one thing we always will start with is just having a good data lake infrastructure. You can pick some of your tools around that. But it's not just the clean data, it's that that data is hosted and production ready. So having your data pipes with things like Azure Data Factory, having good storage, whether it's Azure Data Lake storage or using Databricks Delta Lake. On top of that, having a production ready data environment is important.

And then once you've got your models ready, really thinking about the productionalization again. You can do a lot through Azure ML, but you can plug in a lot of open source tools, so there is. There's really no one platform to rule at all. There's a lot of best of breed tools. But whether it is Azure or Google or AWS, you've got good environments to work in. And we just probably spend 80 of our time in Azure. For the reasons I mentioned before.

And they continue to invest in and innovate in that platform space. So we see a lot of great benefits because of the healthcare world that we live in. Privacy, security, easy access, reasonable, scaled, production, ready tools is really important to us. We're not a hyperscaled organization, so we can get a lot of good kind of bang for buck and move fast.

So good tool for us to use. Most of our vendors and partners have experience with it. So it's pretty easy to get talent in there and move pretty fast.

Many of our portfolio companies are concerned about using some of the open tools. Because it's not clear what will happen to the data that goes into a prompt. Will it be used to fine tune or train a model, and eventually, what happens to that data? I think the open tools are starting to catch up and share some better language around protecting user data. But we still guide our portfolio companies for sensitive business data, customer data. Keep it out of any open tool. And use the protection of the Microsoft Azure environment, where open AI services and Microsoft has strong protections for business and customer data.

And again, to the healthcare point. Sean Yes, for like, HIPAA and PHI. We do a lot of work just to make sure those environments are protected in the right ways. For not just our businesses and teams, but our customers and patients, that are all impacted by the data that's there, so it's pretty critical for us, and we certainly do guide in those directions. And like the Microsoft terms and conditions around the commitments not to use open AI data in any of the underlying models that they train.

Like model governance and data pipeline governance, and so on. So, in addition to the kind of Azure offerings that Ken had talked about. I think Google has released recently their generative AI studio as part of Vertex. And it's really quite interesting. Because I think, if you think of like the superhero movies, right, Iron Man with Jarvis, and I think in Black Panther they have something like that.

You know a lot of the way that AI could be really powerful, using, especially the conversational interfaces and natural language interfaces. Isn't so much just having the parameters of a model like GPT-4 or Chat GPT to generate texts like if you're trying to write an email or summarize something. But rather to create this interface, that becomes sort of an information retrieval system or a question answering system on top of your data. And I think that's really we're going to see a lot of the real value in the already, but really in the near future. And I think some of the platforms out there, not just like Google, but also some of the open source stuff like Lane Chain, which I'll talk more about in a minute, are really sort of pioneering and pushing that a lot forward. So in the case of the generative AI studio, what's really amazing there is, you know? It gives you the ability to load up your data in different formats, whether it's sort of CSP or PDF, or text in other formats. Or what have you?

And then you can put on top of it. They have a model hub, and in that model hub you can choose either. You know, Google has their own model barred, which is similar to Chat GPT. The underlying model is actually Palm, but so you can choose different versions of that. But it's also a hub that has open source models like Mama and other models, so it really gives you a lot of flexibility there. To put a LLM interface like I'm talking about. On top of your data for information retrieval and question answering, as well as needing Google Search itself on top of your data, which is pretty powerful.

And it allows you to buy this actually between how much of the output of this generative AI is generated by the model parameters. In the same way. Like if you just went into Chat GPTs user interface and, you know, asked it to summarize something versus from your actual data. So you can kind of bias one way or another. Because obviously, one of the main concerns a lot of organizations would have are things like trustworthiness, hallucination and so on. So, you know, trying to get more towards a true informational retrieval system that you can rely on, and that's, excuse me, trustworthy. As well as it's all very containerized, where you can have your own encryption key that Google doesn't have access to. So it can be HIPAA compliant and everything else, and you can't get access to your data except yourself.

So it solves a lot of those issues that I know a lot of organizations are wondering when it comes to proprietary data, confidential data and intellectual property. And just kind of sending that off to sort of a closed source, proprietary API in the case of, like, you know, some of the open AI stuff. And then also allow us for a lot of that other sort of OPs and governance type stuff in terms of model tuning and iteration, and so on. One of the other things that's interesting with a tool like Lanechain, for example, is, you know, if you're just going into a user interface like ChaddBt. It's hard to track your prompts right, and like, versioning them, and as you're doing prompt engineering and so on. And so these kinds of frameworks like Lanechain really help you, excuse me, to templatize, if you will, your prompts and sort of also create variables in your prompts.

So you can sort of inject different things, you can iterate on the prompts, you can version them, and then you can also chain prompts. So, output of one LLM call becomes the input of another one. And it allows you to then set up these pretty sophisticated systems that not only do that chaining, but also can integrate with outside sources like the web or Wikipedia or other types of database. So on, APIS, so I think a lot of that, and, of course, Hugging Face, one of the really big open source options out there that's really compelling, has done a lot with making transformers widely accessible to folks.

They're just trying to decide how they want to kind of put the cards on the table for a while. Are you seeing some of that in the Google stack?

And one thing worth mentioning, too, is there's also part of the data store, or the database. Kind of talked a bit about having a data lake and so on. One of the things that's also becoming really prevalent with these new generative AI sort of solutions is the idea of a vector database. And so Pinecone is a good example of that, and Promodb is another example of that. And these are ways to. I don't want to get too technical here.

So, try and summarize a very high level.

You have sort of unstructured data, like your text data, your documents, whatever it is. You can convert that into a very specific kind of numerical format that gets stored in a specific kind of database called a vector database. Which essentially sort of encodes all the semantic meaning of your unstructured natural language data and the contextual meaning of it. And that becomes very handy later on to do certain things, like putting that conversational interface on top of your data. Because now it allows your when you're doing queries by whether it's through a chat interface or something like that, to translate your query into pulling the right data out of the database to pass to the large language model without having to do like SQL queries or anything like that, or writing any sort of code. So it's almost sort of automated in that way, once you have your data stored, one of these vector databases.

And we started with the best in-class tools that were available a year ago, and so there's going to be requiring agility.

And new tools available. And you're going to be on a journey. And I think it shows you that there's some major titans helping. But there's some new things coming as well. And so Nick, let's jump as well into. You've got these tools at your disposal.

You've done your precursor. What are some of the most addressable use cases that companies should start thinking about this for most, like everyday companies can start doing today?

So similarly, you can kind of think of bucket use cases for generative AI across those dimensions. So when it comes to information, this is really, and Ken touched on a bunch of this. It's really the power of these models to rapidly collect and synthesize data, information. And you really get, you know, something differentiating when you could connect that to your proprietary data. So this would be things like, you know, marketing customer research for marketing and sales function, report generation for customer operations. You know. Collect all of your customers data, feed it to them, and easy to read report automatically.

On the sponsor side, we do a lot of market research and we have these documents that we prepare using the Analyst associate capacity.

You know, generative AI, so I think about that, these kind of gen AI for information is almost like a, you know, Google on steroids kind of use case. And the value prop there is really when it comes to time savings, kind of. The next level for these kinds of models is, you know, languages isn't just information, it's not data, but it's also expressions, being able to convey, you know, thoughts and ideas. And you know, that's really the power of these models to generate kind of creative content on any topic, in any voice, in real time.

So these could be things like, you know, content marketing and sales is kind of the obvious one to start with, so content generation for personalized marketing campaigns. Can you create, you know, sales agents or chatbots for customer operations, you know, things like that? So kind of being able to create, kind of generating a question list for expert interviews and is one of the use cases that we've deployed on the sponsor side.

And then, finally, and this is where I think, you know, there's going to be a ton of value here.

Especially down the road would be, you know, language as instruction, so not only can you, you know, intelligently create language and content, but you could also use that to do things make to effect change in the world. So being able to generate code using these LMS, that's a perfect example. So being able to, you know, do software development, you know, cheaper and faster than before. I think you're going to start seeing the rise of agents that, you know, autonomous agents that can automate.

A lot of you know tasks that you use, so I've been hearing about, you know. Salesforce is launching Salesforce, GPT. And all these models that you know not only generate content for marketing campaigns, but can actually, you know, execute campaigns. It can bid on keywords, you know, things like that, to really drive automation. So I think there's so that's kind of exactly how I think about the different use cases. When it comes to what function I think we'll have, we'll see the highest value.

There's, you know, a few really obvious ones, so marketing and sales, like we said.

Marketing, customer research, sales agents, customer operations, so, you know, ports, chatbots, customer support. I think R&D and product development could be another big function where this stuff will add a ton of value. So being able to, you know, quickly, you know, test ideas. But also for rapid prototyping. So being able to use these models as kind of a man in the box, kind of MVP for product development. And then, you know, obviously software engineering, software development too, is going to be seeing a big uplift from Gen AI.

And then also data analytics, I almost see kind of some of these models as a really, well you know, a smart kind of analyst in your organization. That you could feed it data and have it run kind of ad hoc analytics and then use it in your data science, data analytics work. Yeah, so a few thoughts on use cases and some of the value is going to be in the early stages.

This now has had some AI generative aspect to it. And so there's going to be really huge increases in productivity. Kind of the magnitude that we saw on the internet in the 90s. That are going to take place. And then the real question, and maybe we'll talk about this a little bit later, is who is the beneficiary of that? If your coders, you know, should your coders be cheaper or faster, or both? you know? should your lawyers be cheaper or faster, or both?

Who gets that beneficial aspect? You know, the spoils of this productivity is going to be something I think humanity will be working through a lot of. And I think the points that you're bringing up are spot on use cases. What I think is kind of interesting is, in some ways, it's opposite of probably what they thought it would be.

They thought it would be kind of more, you know, robotic process, automation and plans, and then it would get to, you know, kind of mid-organization. And then creative. It seems like it's starting at creative.

Through the rest of the organization, with content and imagery like Dolly and whatnot. So I think those are really important things that every business should think about in terms of their own organizations. NICK So Keith, let's talk about this. So you've got these great engines that are amazing that you can feed things into, and they give these great distillations back to you. What are some of the things that companies should be considerate of in terms of using, you know, particularly the publicly available models and the implications that they could have on a company?

And so, you know, to Ken's point, they're building out their own infrastructure, they're building out their own platforms. And that's a way to reduce the black box problem of knowing what's behind all of these generative AIS, knowing that's behind all the reports that get built. So we want to make sure that you have an architecture and a design that people can follow and that we can look at as technology experts. And help align not just security, but align the integrity and the availability of these systems, so that they stay online and they stay available. And then to the point of what we put in there data privacy, and you know, I'll kind of keep going back to this. What is the data privacy rules that are in place to protect your organizations and your customers?

And how do we align that with our AI models, and how do we align it with all the different pools that we are going to build out our AI platform with?

It could be something like malicious in there as well, and then that gives adversaries the ability to do an attack. It could be an attack to modify or corrupt the data, it could be an attack to try to take the data. There's lots of different ways that they could come in.

Vectors is what we call them, that they can come in. So those are a few of the ways, or a couple of the ways actually that you want to be aware of. When you're building out these models and looking at the overall architecture and design.

So your company has multiple pools of data, we want to make sure that each employee only has access to the data that they need access to. That will prevent them from actually taking data inadvertently and putting it into an AI model.

Of where is all this going? And we're going to talk crystal ball time. The crystal ball on my desk is pretty hazy and it seems to be changing shape and form every time I show up in the morning. And so let's talk about where you all see kind of AI going in the visible future. And so, Alex, what are some of the things maybe that you think may take shape?

To the extent you can't.

Adding multimodality to Bard and Palm. They did a demo where they took a picture of a software screen, like a mobile app screen. And then code was automatically generated, like actual functional code, just based on pixels of an image. So it's pretty remarkable, I think. Not only are we going to see these large language models keep evolving on the tech side, but we'll also see that multimodality. So, adding audio capabilities, which are really just sequences of images at a certain frame rate, and so on.

And so we'll go from right now generative models like CHAgGBT and GBT-4. Essentially, just keep iteratively predicting the next word, and that's what generates or the next token, and that's what generates text. But we could see that happening with video, for example, so given a sequence of frames of video, what's the next image or frame of video predicting? And then just keep recursively doing that or not too long. Instead of just saying hey, write an email, say, hey, I want a 30-second company explainer video. And the next thing, you know, it creates the whole script, the animations in it, the background music, the whole enchilada all at once.

Things are just advancing very quickly, as well as the real-world applications and use cases of some of this generative AI stuff. So, yeah, it's going to be moving quickly where we'll be even three months from now. But people need to get started, need to start learning about it. Don't wait, at least look at what's happening right now and start building small and prototyping out specific use cases.

Don't wait.

I believe Nick made this point earlier is more than just generative AI. The good thing about, I guess Chad DBT's release and being so exponentially increasing public awareness and interest in AI, is that it did exactly that. But on the other hand, it's also over-indexed for people on AI as being just LLMS. When in reality it's this massive field. And so when I say get started, it isn't just go all in on LLMS or generative AI. Don't forget, there's a lot you can do with AI and machine learning.

Salma Altman, CEO of OpenAi, was interviewed just after ChatGPT came out. And he was asked by Lex Friedman on a good podcast show, What is GPT for? And he said It's a system that we'll look back at in the future and say it was a very early AI. It's slow, it's buggy, it doesn't do a lot of things very well. This is the CEO of his big new product launch, saying it's not that good. But now there were computers when they first launched.

And so they're going to get a lot better, and they're going to get better fast. So the other point I'd raise is which has really driven innovation for the last almost 60 years. So the number of transistors in a chip, or the cost per compute going at a rate of change of every two years, the power doubling or the cost halving? What we see now with AI algorithms is the power or the pace of change is almost every three to six months, the performance of AI algorithms is doubling.

The big tech have about 400 billion of operating cashflow annually that they're going to be investing behind this. And so you see, all of these dollars divert towards rapid innovation. From the first iPhone launch to iPhone 14, it took about 16 years to go from Gen one to Gen 14. Think about what would happen if ChatGpt's 14th version came out 16 months from now, instead of 16 years from now, the pace of change is going to be much faster.

And so for our businesses to be ready to adopt the new technology, but not anchor on ChatgPt. Or you pick your tool being the last and final. It's going to be the start of a long journey that's going to move quite quickly. So be agile, be ready for change, be have good foundations in place. With all the stuff the team talked about here today, be expecting of a pretty rigorous pace of change, and the future is really in front of us.

We're just getting started with this.

And you'll probably be users of Generative AI, even if you're not trying to be like Google Search, Bing Search, your favorite CRM, Microsoft Copilot. These tools that we're already using are going to get enabled by Generative AI. And it's going to become like electricity or the internet or analytics, right? It's something foundational to something that businesses just need as table stakes. I think the way we're thinking about it is still very in the early days, but it's about experimenting. It's any time you need, obviously being careful of privacy and data and all that.

But any time you need to google something or search for something, try an LLM. Every time you need to generate content, try experimenting with an LLM. And it's through that you'll uncover really powerful use cases that can be scaled and productionized across your business. So I'd say, don't be afraid of it, experiment with it, find exciting use cases responsibly because it's transformative.

And if you aren't, you should. I see some pretty big areas, actually, where AI is going to be very supportive for us. I think it was Bill Gates who said that the winners in AI are the ones who are going to create, the personal assistants, the ones that are going to help us. And so we think about how AI might be used in training, taking someone through a training program and being able to answer questions on the fly. And then also there are certain industries that don't have enough professionals to staff.

The position Cybersecurity is a huge industry, a tremendous industry where there isn't enough skills and skilled position workers to fill all the spaces. And so we'll see AI come in. And it won't necessarily replace those professionals, but it'll relieve these professionals of additional work that could be handed off to an AI platform to support. And then they can pivot and start working on things to improve and grow the business in different directions. So those are a few of the ways that I see AI helping us in the future.

A rapid fire of really insightful information. Here we have a few questions that have come in, so let me try to paraphrase if we can get to one or two of them. And so one of them is if you think about these hallucinations in different places during this conversation, and there's this propensity for errors. How do you think about applying some of these generative AIS in different use cases, and what use cases maybe are more appropriate than others? Given the fact that we don't quite know how it comes up with some of these answers, and why they're right, and why they're wrong in certain ways. Does anyone have a take on that?

You heard Alex talk a bit about vector embedding. You can put the LLM on guardrails, still with human on a loop, but you can train it so it does what you kind of direct it to. So those are two initial thoughts and things that we spend time talking about. But responsible AI You heard a lot of different versions of that. Today is critically important, so that's definitely needed in our space.

I think we have one more question that we can probably get to here, and it's more of the practical side. What do you think are some of the AI applications that can be used to maybe optimize some of the mid and back office functions within companies and drive savings? That might not necessarily be LLM, but could actually improve the efficiencies of companies through the middle of the business.

Investment memos. Or go into a data room and list out all the interesting things, like the things that I kind of think of. A LLM model, really strong performing kind of analyst or associate, just another member of the team that you can point to different paths. And I mean, sometimes they're wrong, just like real people are wrong, but you have to kind of evaluate that. So I think time savings in the back end for things like knowledge discovery, information synthesis, report generation, that could have a huge impact.

And so I think what we're seeing is, you know, it can have a cost savings impact. But it can also enable you to serve your customers in a better way, faster. And so that you know that 10 minute wait time can go to 30 seconds and you can get access to better information faster for your customers. And so that's something that we're, at least, you know, from what's coming in perspective. Seeing some helpful use cases, the only last question I have This sounds like a plug, but it's not. Are there any partners that can assist you with building out use cases?

And the answer is yes, a lot of them, we know a lot of them. But we'd be happy to help with this ecosystem that we're building out rapidly. That are addressable for, not only, you know, larger companies, but particularly SMBS. So that is all we have for today. I want to give really, really special thanks to Alex and Nick and Ken and Keith here for sharing these insights.

And maybe we'll get the band back together again in a few months and we'll say so, thank you so much everyone for joining us today.

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