The Five Pillars of a Successful AI Strategy

Thank you for the introduction. And you did not do too bad on the last name, so thank you. Appreciate that. You okay if I introduce my amazing panelists?

Let's get to it.

Let's do it. Well, I'll start with the ladies first. So first up, I'll go with. Let's see. I'll pick on Christina first. So she is a dear friend of mine. She is the CEO and co founder of Lustre AI. One thing, I'm going to share some personal things. I know about each one of these people. I did some consulting for her a year ago or so on the tech and some of the strategy stuff, and I had the privilege of watching, I think, 10 of her sales calls, and she's probably one of the most talented salespeople I've had a privilege of watching. She is awesome. And her tech is specifically about what she calls predictive enablement, where they take the skills that are needed to be able to win in a sales process and then predict forward so that she can use her AI roleplay technology luster to make sure they can mitigate those risks and challenges before they ever happen. It's freaking awesome. When she told me about it, I was like, oh, I wish I would have thought of that. That's such a cool concept. So, Christina, thank you for joining. So happy to have you here.

Thank you. Awesome to be here.

Yeah. Next up, of course, the other lovely lady on the panel is my dear Ashley Wilson. She is the co founder and CEO of Momentum IO, which I'm a little privy to and biased, but we'll get to that in a second. Super excited to have her. She, if you didn't know, this is in another life, a voiceover artist because she has this such lovely smooth tones. She does all her voiceovers for our stuff. And she's probably one of the most brilliant and yet perfectly combo of humility that I've had a privilege of working with and a founder. I'm just honored to work with her. So, Ashley, thank you for being here.

Yeah, thank you so much. Really excited to chat with you.

Heck, yeah. Okay, now, one thing I didn't tell Gerhardt was that the requirement to be on this panel for a man was you have to be bald. So that's part of the thing. So we're all bald. So we'll start with. We'll start with Albert, who is the founder of the AI Circle. I think Albert, I don't know if this is true, but I think I was one of the first. I don't know how many people who sign up. But I saw it come around and I was like, oh, I love this concept. He has a deep experience inside the AI space, working at a lot of different places. He now advises a lot of different companies and helps fill gaps for AI talent. But what he's done is create this amazing community of AI talent from go to market to deep AI engineering technology people from OpenAI to perplexity to AWS. It's a phenomenal group. And he's. I've had the privilege of working with him in a couple of different places and very, very smart, one of the kindest people I've had a privilege of working with and just really excited to have him here for the deep AI knowledge he has. So welcome.

Thank you so much, Jonathan. Glad to be here.

Yeah, man. Next up is my bald brother who is also named Jonathan. Sometimes we get confused for each other, so I'll make sure it's very clear. I will go by Coach and he will go by. I call him jmo but Jonathan Moss. So he is the currently EVP of GTM strategy and operations at Expirity, if remember correctly, if I say it right, and which he's also leading their AI council. So I'm really glad to have him here. And then he's the co founder of AI Business Network, which you'll be talking a second. And then lastly, of course, I'm Mr. Coach here hanging out with everyone. So it's kind of fun to have this mixing of worlds with all of these different guests. So that further ado. Should we get into it? We only got a little bit of minutes. Should we go?

Cool, let's rock and roll.

All right. Let's rock and roll. So I'm going to start, actually, I was going to go through an agenda, but let's do this. I'm going to show really quickly this, this question.

Why is starting with this question the wrong place to begin? And I'll pick on. I'll pick on Christina first. What do you think?

It doesn't matter what tool you should buy if you don't know what problem you're solving, you know, and I think there's this idea that AI, it's, it's fun. You want to experiment with it. I think there's a lot of people that are just dying to say, like, I'm an AI exper. I know everything about this. I've heard people say things like, I want to enable my entire go to market team with AI and so what AI tool should you buy? And you should never buy anything without an issue. Right. You think back to the psychology of how human beings buy anything, which I talk about all the time, which is, do you have a problem? Should you solve it? Should you solve it right now and should you solve it with this tool? And so AI is a tool that should solve a targeted solution specific problem that likely cannot be solved without AI. And if you don't have org wide agreement on what those things are, you're going to buy a lot of technology, you're going to distract a large portion of your go to market team or your larger employee base, you're going to turn over a lot of technology and then you're going to be left with worse problems, distracted individuals. And that's going to erode revenue, it's going to erode morale like any tool. So what AI tool should I buy is. Is the last question that you should actually ask before all of those other come before it.

I love it. Albert, I'm going to pick on you next. So coming from the lens of both helping with AI talent, so you've seen both the inner workings of AI technology two on the other side of implementing inside of teams. Tell me why is this right or wrong? Tell me your thoughts. What do you think?

Yeah, I actually really like what Christine offered. You know, I would generally like to diagnose the problem first before I pursue any kind of solution. I think candidly I, I'm very interested in how people evaluate tools. So like one example might be if I'm like a, if I'm in an industry that's highly regulated like insurance or finance or healthcare or something like that. I hear a lot of people buying closed source models as an example, going through all of this diligence, some people actually end up purchasing it and then realizing like, oh, I don't own my own data and oh, I can't, you know, all of my data goes over and gets housed somewhere else and that becomes a big no no for people who are. Yeah. For other decision makers in the org. And so I think having a really robust evaluation process and knowing exactly what you're getting into is really important before you actually buy a tool. And that would involve like getting different stakeholders, like yeah, an engine like a lot of your engineering and cto. And if you don't have AI ML engineers to vet the tool, that's where. Yeah, like I, yeah, I would, I would consider having a very rigorous evaluation process before purchasing.

Yeah, I love it. You're actually teeing up the five pillars really well because that's part of what our five Thing is, so thank you Albert. Jmo, my man. So you're leading the AI council there. So I'm curious to like what, what was the lens kind of going off of what Albert said of how you guys thought about this particular question? Is it like buying a tool or is it more about like what were you focusing on with your council?

Yeah, yeah. So I, I would, you know, kudos to both Christine and Albert, what they said, because I don't know that I could say any better. But ultimately, you know, what we are doing is we are breaking down the company wide problems that we have both internally and externally. So you know, and we think about it Persona lens. So if I think about it, you know, externally, who my customers are, I have different Personas that our product, you know, or services and enable, you know, what they do from like an experties in health tech. So I'm just going to say like providers or MAs or billers or things of that nature. And so we're thinking about it from their lens. What, what is the friction points and problems that they have and is AI a solution for that? And I think the question is AI is also not a solution for everything and you don't need AI for everything to solve. So I think also understand that problem deeply and what is the right solution internally? The same thing as, you know, kind of Albert mentioned. So like once we've decided, you know, what is the problem that we're trying to solve, you know, is it an internal Persona or is an external Persona it's really about should we, can we build the capability? Is it easy enough for us to use a large language model today and some of the kind of building blocks that you can have in order for us to kind of build this and solve this ourselves? Or is there something else out there that we need to really bring in? Because we don't have that capability, we don't have that knowledge or skill set or the opportunity to if we build it. If you think about the opportunity cost over time, it's just more expensive than buying. And so these are all the kind of questions and things that we're really thinking through and analyzing before we even get to this point of is it an AI tool that I buy?

I'm gonna have Ashley follow up with this and kind of wrap this up. But I'm curious because obviously momentum does things a little bit differently. So I'm gonna have you answer in a little bit different lens, which is.

How should someone approach the problem as far as like how historically you would have used legacy tech in a specific way to solve the problem versus is there another way to solve it? Like, how do you think about this differently in that lens?

Yeah, I mean, I agree with what everyone's saying. I'll start by saying that I think you want to probably align your internal kind of expertise and understanding and ownership of said tools with what you're going to buy. Because I think that we've experienced this on both sides where we've been probably more advanced than maybe a tool would be able to offer us, just because, like, we do a lot of AI ourselves and we've been a little bit disappointed. And then on the flip side, there's been times where we've bought tools that just have not had a good strong owner internally. And so then you just don't get value from it. And I think that that's where we see with our customer base, where we maybe fall short on adoption or we lose the initial champion that brought us in. It can be hard to kind of like build that back up just because people don't always have the kind of level of expertise that you need to really partner with them. So I think probably how that relates to your second question is, again, it's kind of the same thing, aligning what your needs are with, like, does the person who's going to own and operate this tool internally have the right skills? Like, I think about using something like Clay, which feels like, oh, everyone should go sign up for Clay. But the reality is Clay is a very technical product. When I looked at Clay, I was like, I don't know what to do. Whereas, like, Santi, our CEO, who's also an engineer, spends hours basically functioning as our go to market engineer because he wants to do that. So we're getting a ton of value now from Clay and it's replacing some of our legacy tools. But that's because we have the ownership and the expertise to do it. If we didn't have him, we probably shouldn't touch clay and we should keep doing what we're doing and then maybe do like a real evaluation on a tool that would be maybe a step in the right direction using AI without going like full in, you know, play. Go to market, engineering mode, turbo mode, almost.

Yeah. I'll also add this, the reason why I wanted to start with this question, because I get people hitting me up on LinkedIn all the time saying, hey, what's the cool new AI tool? Or what should I get? And I always tell people, what do you want to solve? Because otherwise you're wasting your time and but, but literally this is a question I get asked 10 times a day at least. And I was like, I want to talk about this because this is not where people should focus. Okay, let's shift into the five pillars and kind of tee us up. Albert, I'm going to pick on you first because you talked about data. I'd love to have you double down into like, what does that mean? Documentation data. And then not only that, but who should be involved from like both the technical perspective and then running the, running the play with data. What do you think?

Yeah, it's a great question. So a little bit of context. I previously worked at Invisible leading a team of several hundred data annotators, working on about 50 different campaigns that were fueling some of these large language models. And then now I'm advising AI startups on how to basically build their in house human data teams. And so one of the things that I find here is like a couple of things in terms of LLMs. Like everything is getting pretty commoditized, right? Like generally speaking, like, what do you use? Oh, I use anthropic or you're using Cohere or OpenAI and what have you. But like, as that world gets increasingly commoditized, I think what makes.

Your tool or your approach differentiated is actually the quality of your data, the kind of data your model is fine tuned on or trained on. And so yeah, a couple of things I'm finding is like when it comes to data, it's how.

Upstream can you go to fix your data, right? So like I would argue that this problem goes up to the very top, like to the CTO and arguably CEO level where it's like, how.

How seriously do you take your data hygiene and quality? And then after you do purchase a tool and integrate into your system, how do you add even more data to make the tool.

More aligned with what your organization needs? And so yeah, I see it as like one of the most important things on par, candidly and very biasedly to like your engineering team.

And so yeah, when you say data, let's conceptualize that just a little bit more. Because AI has the ability to taking all sorts of what used to be unstructured, unseen data and creating it to some different way. So.

What would you say is that, what is the, I guess the core fundamental data that you should, you think is fundamental to this whole thing from your perspective?

Yeah, so like, let's take for example, you know, you get ChatGPT or Claude or whatever out of the box, you're going to get a tool that everyone else has, including your competitors. And so.

And it is going to be general, not specific. And so the question is, how do you get that tool to better serve you and the unique fingerprint that your organization has, whether it's internal or external. Right. And so.

Let'S use the messiest data you could think of. Like, let's say you have a tool that helps you.

Input data from your emails or your meeting minutes over to like Salesforce or something. Right. In order to get that really highly unstructured data to do a very specific task, you need to show the model what good looks like maybe like 10,000 times in order for it to perform that task really well. So one example might be like, hey, you have minutes from a meeting, a transcript, and you need to show it what a good summarization looks like. You show it what a good pulling out of what the extracting what the tasks are or what the highlights are and then doing another showing it what a good operational step looks like or a function calling or tool use step looks like after you get that summarization. And that just requires a lot of candidly a lot of people power to show the model exactly what good looks like.

Yeah, I love that. Ashley, he's teeing you up perfectly. So I'm going to have you kind of be the, be the next step. But answer if you don't mind, because what momentum does is taking unstructured conversational data and puts into structure in a very fluid way. So if you want to double tap in that we'll go into the next step with Christina.

Yeah, yeah. I think what you're talking. What's interesting is to think back to, because I think we've been doing this now for two years and so a little bit longer. And when I think about the beginning steps to now, just over two years feels like nothing. But in the AI world, it's like we're light years. It's. We're moving just so fast. And yeah, I think that the. It's not enough to just feed it. This is why if you take a transcript and you feed it into a model, you get some initial outputs that you're like, whoa, it summarized it. But then if you want, you actually have to go back and forth many, many times to pull out the information that really matters beyond the initial wow factor. And I think the wow factor of a year ago is not the same as today because we're all just accepting that AI is taking our notes, doing our things. So it's the bar to have it be Our expectation is not to be wowed. Our expectation is to actually say is this something that I would trust to do what I would do as a human? And so the level of sophistication and expectation around something like hey, pull out my med pick fields and make them be exactly what I would say so that when I go talk to my CRO, he's not like.

And I think that that's where from us what we've learned in building in this space and having to keep raising the bar and the quality is like you have to keep building on top of the large language models and like you really have to be. I mean this is all driven by our product and engineering team. This is a R and D effort. That's why I think that like slapping on AI or calling it like, you know, we have AI features, this is where I think we're like hitting in 20, 25. Rubber meets the road of what is actually going to meet the level of quality that you will buy it to replace real work versus saying it's just not worth it. Like I just will have my sales team keep inputting. But I think that's an exciting place to be in because I think for all of us building in this space it's like wow, this is a very fun technical challenge to go after along with saying this can have a real impact on revenue team's velocity so you're able to at least tie it to a real like business outcome.

So.

Yeah, going in and there's obviously a lot of agent building tools, even a GPT you could feed in a transcript too and get some stuff back. But like that type of analysis like you said, is not the level of a CRO would expect out of a salesforce field. It's not the specificity you would expect. You know, so having the expert level, there's a big gap in between there. Christina, because of time I'm going to keep on rolling here. I want to bring you up on for this one which is in mind specifically on gap and priority based or gap analysis. Just because this is like what your product does is this. So if you can double tap in this as far as this pillar of end in mind, not starting with the tool but starting with the priority, what would you say?

Yeah, I, it actually it's. I like these pillars because they all tie together, right. So like when you look at data, what we're talking about is data is not going to solve your problem. Data is going to show you where it sits. And I think a lot of people, if you get data and you have nothing to do with it, or you don't actually have the means to solve the problems that you are seeing within the data. Then you have a lot of data and you feel super empowered. But the end in mind is missing as to what do we do with that data and what kind of solution do we deploy. And so it comes down to any time that you are going to plug in any new tool, whether it's AI, non AI, AI, feature based AI infrastructure, AI vector database search, AI predictive enablement, whatever it actually may be, it's why this thing for this person right now. And it's also determining there's two main big use cases for AI that have a lot of people saying a lot of things in the market. And use case number one is should I use certain forms of AI to replace people? And the other is should I use AI to make people stronger and better at what they do? So it's supplemental, right? To draw metaphor. I can either if there's a marathon coming up, I can deploy a bot to run the race for me, or I can deploy a bot to be a really, really good treadmill. And your people are consistently thinking about that. I hear a lot of people talk about like, is this AI going to take my job? And so the end in mind is what are we using this technology for? Do we have the means to do that? Is this going to make our people stronger? And can we measure it? Right? So the AI use cases, we deployed a whole bunch of AI. Can we actually measure the outcome of that? Can we make it scalable? What happens if something changes in our business? Does this still work with us? And so yeah, like that's, you know, what we do is this entire idea of how do we take a skill based proficiency map and use that to predict revenue impacting mistakes that are about to happen. That is something that is always relevant. And so it's taking the data and applying it to a revenue use case. So I would say take your AI and are there processes that you can streamline that are going to make your people stronger? Are there people that you have sitting in positions where they're not utilizing their strategic brain and they're not able to be the best version of employee? You actually do need to replace that function. I will give you an example of that. A lot of people hire additional enablement and learning and development headcount simply to create content for salespeople. Right? Their entire job is to create content for salespeople. And if you ask them, they say, well yeah, I'M good at that. I'm good at making content for salespeople. I'd love to be so much more strategic. You can deploy AI to scan your entire business and build really meaningful content for your salespeople and then deploy your human being to make sure that they're helping to ingrain that knowledge. Right. So the end in mind is, how do I use AI to be stronger and then use the people like your most powerful resource at your company? I don't care how many AI tools you have. Your most powerful resource are your people. Are you supporting your people in the right way? Can you measure it?

Bam. I love that. We're gonna keep on rolling. Thank you for that, Christina. Boom. Okay, Jono, you're up, my man. Because you're on for two things. One, because you're on the AI Council and you lead the charge for expirity with this particular topic. And then two, because you and I are going to be releasing in a humongous list of AI tools to help people select what in the world is out there, because there's so much stuff. So talk to me about the selection process from both angles.

Yeah, absolutely. So, you know, Christine is a hard act to follow for sure. So ultimately, yes, selection. Selection is key. And what I'll say here is you have to select and understand again. And this kind of ties into something Albert said. And I'll give an example for us is like, do I need a tool or not? And one thing that he mentioned, you know, around your data. So I'll give you an example. Like, at experity, we went down the route of what's called an ambient scribe. So similar to some of the examples here, it's really a dictation of what a doctor is talking to the patient about. Right. And allowing them to get a structured output and things. And so what we found was training it on our own data, because we are in a unique specialty, was actually more effective than getting a large language model public. Right. And so you have to think about, number one, the problem that you're trying to solve. Number two, do I have the data? You know, is the data unique to me and is that what I should actually go after? Once you've kind of figured out what problem you're solving, you're going down that process. If you do go to the tool instead of building, you know, it's. It's super critical to me in understanding, like, what is, you know, what does your current tech stack look like and understanding how, you know, what gaps and deficiencies that it has today. And is the tool selection that you're going to go with, is it complementary to something, you know, something the other things that I have and does it integrate well, or is it a replacement of something that I have and understanding that because we already have and go to market. Too much tech stack bloat today. And as Ashley eloquently mentioned, there's an adoption challenge already with the tool stack that you have. And so ensuring that you have, you understand what it is that you need, making sure that you have a plan around the adoption and hopefully you get support from the vendor on that. The other thing is what's important to me here too is understanding the difference between an AI native tool, or, you know, software, if you will, or company versus one that's got the bolt on features, right? Because if I'm going to go get a tool, I want to make sure that what I'm going to implement is also set up for scaling, for getting better and ongoing and not going to limit itself in the future of certain things that I may need, you know, for it. And so I think what's important is to make sure that you have all of your problems that you are wanting to solve. And going to Christina's point is like, which one should I solve first and which one do I solve right now, but knowing the other ones that I want to solve, does this tool help me solve some of those later on or is it going to limit me and I'm going to have to go buy yet another tool? And so you've got to have all of these types of things in mind as you're going through your selection process. And then I think again, important to get involved with your, you know, your AI, if you got AI or ML experts as well as engineers, because the other side of this is understanding, you know, what the rope, what their roadmap looks like and kind of the core, you know, what they've built their software and tools on. So again, you're, you're setting yourself up for success and not only the problem that I'm solving now, but also the future of what other problems that I may need to solve with this tool.

That's perfect. I know we could dive into that for, for a bit because of time and I keep on rolling here, make sure we get some things. So I actually want Ashley and then Albert to tune into this one real fast. That's okay. So, Ashley, obviously Momentum offers a testing process, but what I've noticed about you, obviously working with you now for a couple years, is that you do the same. You require the same thing of employees and tech to have a testing period just to make sure it fits. Right. So this number four pillar, I thought you'd be attuned to get to this. And then we'll tune over to Albert. So your thoughts?

Yeah, I think with, with AI, we, we are still in the kind of see it. Well, I think we've gone beyond the see it to believe it. Because I think that we started doing POCs in the early days in like 2023, because we were like, if you see it, you'll want to buy it. It just was like so magical to do a call and then see a note and have it go in slack. And everyone's like, oh my gosh. Now it's like, yeah, show me more, show me more, show me more. So I think that now we're in the stage where again, it goes back to the quality bar is very high and people want to make sure that the tool will deliver on their specific needs. And I think with AI, you need to be able to prove that you can adapt whatever you're selling to actually again provide the value that people are looking for. And I think that we're moving in a world where we're not measuring adoption by whether you've logged into the product and you're adopting it, but we're moving to the world where you're measuring adoption well by our. What's the, what's the value that I'm getting from this? And so it comes a lot more around like, what is the tool? How is the tool impacting some key metric? And I think that having that kind of test, experimental phase to just make sure that this is the right fit, the risks on both sides, the company being the right fit and the vendor being the right fit. So I, I am such a fan of like very short.

Trial periods. I don't love, like, I don't mind like a one year agreement if we have a trial agreement, but I think it's pretty hard to expect companies to buy sight unseen. I also think we have so much competitive pressure now with like so many companies going after the same space. And plenty of companies do offer a trial or offer a lower pricing. So you have to be really, I think, adaptable kind of as a vendor in this space and also as a buyer. Ask for more things that will fit for your business, which might be, hey, we only buy if we can try.

Yeah, Albert, I'm gonna have you continue with that then loop into the last pillar of just the feedback loop. Because I know Obviously as an engineer, invisible, you, you depended on that. So what are your thoughts on this?

Yeah, I would say if I were buying a tool, I would, to Ashley's point, I would, I'm incentivized to do bake offs with all tools. So like what are like some common things people might do? It's like hey, I look at like an LLM leaderboard or I hear a name brand that everyone says I should buy and that's cool to like field calls and start there. But nothing is quite like actually using a tool. Nothing is quite like using it in actual daily use against other tools and then coming to your own point of view versus relying on what people are saying. And then I think when it comes to just kind of that feedback loop models all require retraining. There's what we call model drift where things just generally decay in performance over time. And so I think having regular feedback loops.

I, I call them when I call, when I say something like evaluation, it's basically like doing A, B, C, D testing across tools across the same.

Problem to be solved and see which one solves the best. Right. It doesn't have to be super scientific or super whatever. A lot of people have this vibe check like oh, did that generally summarize my meeting better or worse or did it generally give me the task? And so you don't need to get super scientific about it, but just at a high. But I do think having some mechanism in place to regularly evaluate tools is helpful and I would say there are so many early stage startups out there, I would never be afraid to ask. People like to give feedback. Right. Like I think a lot of people are like, you may represent an entire company's ideal Persona and they are very incentivized to make the tool work for you. And so I would be, I would approach things very open mindedly.

Yeah. One thing I'll chime in real quick that I agree with, I think it's important I want to just, just double down on it is you should, if you're not doing this, go demo and use one new tool a week, two new tools a week, whatever you have time to do. Because everything is moving so fast to the point there's so many tools out there and understand and, and just, and it's really good learning and experience too, especially if you're new. And so I would just wanted to double down some on that specifically that Albert said because it's super important to get started and go.

So I love that kind of goes back to there's A Deloitte report, another report going out on how many CEOs and board level members have never even touched an AI yet. They're making decisions around it. You know, it's fascinating. I'm like, you better change that quick. Well, we have a couple minutes left and so what I would like to do if we can, I want to make sure I give you all time to talk about what you're all doing to make sure we have time for that. So let's start with the ladies. So Ashley, Christina, then Albert and then Jonathan. 30 seconds. Just make sure you have time to kind of talk about what you do. And before everyone leaves, and one more note, I will send a follow up to the group so they can send it through selling power about these pillars and hopefully help everyone out and have everyone's links in there as well. So go ahead. I think it was actually first. Go ahead, Ashley.

Okay, this is our pitch moment.

You said your pitch moment, your commercial. Yeah.

Okay. Yeah. So Momentum is doing something called enterprise listening. And we talked about how we take unstructured data, structure it and put them in your systems of record. Kind of think of ourselves as sort of the eyes and the ears for Reps and CSMs and then leadership to know exactly what's going on on all of your customer conversations and your sales conversations. So if you want to try us out, we do offer a free poc so you can try before you buy and you can reach out to me at Ashley Momentum IO or go to our website and just sign up for a demo.

Love it, Christina.

Awesome. So yes, I am Lustre Lustre AI. We are trademarking and have trademarked predictive enablement. We use AI practice session data to build a comprehensive skill based proficiency map for your entire go to market team. We then have predictive technology that takes that proficiency, identifies the deficiencies that are about to show up on the actual customer engagements that you have on your calendar. And we proactively prevent you from making revenue impacting mistakes, changing the way that go to market teams sell potentially forever. You can learn more about us. You can find me on LinkedIn, you can email me christinaster AI or you can visit our website and fill out the demo form if you want to talk further.

Love it. Albert, you're next.

Amazing. Yeah. So I'm the founder of AI Circle. It's the premier community for folks who are building the frontier of AI. We have members from frontier labs like Cohere, OpenAI, Anthropic, etc. And we host weekly speaker series from people like The Magnificent seven and you know the, the most up and coming early stage startups in AI and we have in person events in London, San Francisco and New York City.

Love it jmo.

All right. Along with, along with my go to market strategy and operations, I am the co founder of a company called AI Business Network and our mission is really just to bridge the gap between AI literacy and business impact. A lot of things that we talked about here. So really providing resources, education, research as Coach mentioned mentioned earlier, you know, giving you a tool that can help you find the best tools that are out there and what to use them for because it's very hard to navigate. I love the market maps that are out there. You can't a bunch of little icons and you can't really tell what to do. So ultimately it's really kind of that, that one stop shop for you on your AI journey.

Love and I'll wrap it up. So I'm Jonathan Carford, AKA Coach K, founder of GTMI Academy. We have on demand education for go to market professionals and leaders and then working with Jonathan on the AI Business Network which is like a extension of that to make sure we bring in business leaders and help with AI. I've consulted with Luster, huge fan of them. I'm a member of AI Circle which I love the community and I'm working for momentum as the head of GTM Growth and just absolutely love the tech because to me it's like the epitome of everything I've tried to do in AI which is a blast. So I can do nothing more but endorse all these people, find people and I'll send a follow up with all the links and stuff of everyone. So there you go.

And I want to endorse the endorsement. I like the advice that you should least look at one AI tool a week. And I love Momentum IO. It's an incredible tool. I have had the privilege of looking at Lustre AI and it's an amazing tool. And Jonathan, what I'm looking forward from you is you promised to send us a collection of AI tools that we want to share with our audience. When can we expect that where?

So just to be clear, it's an agent. It's based on a humongous list that Jonathan and I have put together. So the agent's going to be one going through tools as 600 plus tools in there, something like that. So we'll be releasing think next week, right? JMO is what we got. Yeah, next week.

Can we share that with all the attendees?

Yeah, we'll share it with you once it's live. Yeah, we will.