Transcript
Dave Littman: Hi Dave Littman with Truth in IT. Welcome to today's webcast. Today we're talking about the good, the bad, and the ugly of AI assessments, proofs of concept and ROI. And I'm here with Dave Bellows. Dave is VP of Strategy at Metal Toed. Dave. Great to have you with us today. Dave Bellous, VP - Strategy, Metal Toad: Great to be here, David. Dave Littman: Fantastic. Thank you. So, hey, um, I want to talk a little bit from a high level about everything we're going to talk about today. We're going to talk about AI assessments. We're going to talk about the business case and the ROI behind them. We're going to talk about the role that AWS plays in these, especially with metal toed proofs of concept. What makes metal Toe different? And a really cool segment I want to get to at the end, which is reserved for the skeptics in the room. Um, but before we go there, you know, a 36% of AI pox fail to uncover additional needs to address before even deploying a solution. You got the slide right there, Dave. So talk to us a little bit about that if you could. Dave Bellous, VP - Strategy, Metal Toad: Yeah, this is a quote from Gartner, our friends over there. And what typically happens is it usually shows up when there's a lot of excitement without a clear destination. So what that means is, uh, someone in the executive team has learned about AI and what its capabilities are. And you don't have a clear plan about how you're going to show value from it. So the idea is, you know, there's no success metric if you've got no defined place that you're trying to get to, no measurement criteria or KPIs and you're implementing AI, then it's likely you're going to be part of that kind of 95% of POCs that are not successful because you don't know what you're trying to accomplish. And so I think if you have a clear alignment and strategy around where you're trying to get to, that will help you be more successful. And most of those things, if you don't understand what needs you're trying to address, How on earth do you plan to to build something useful? Dave Littman: Okay, cool. So so that speaks to an assessment, I guess. So let's start, you know, if we could just sort of, you know, walking through what actually kind of happens, um, with an AI assessment, um, you know, if I'm a VP at a, at a midsize enterprise and I, and I sign on, you know, what would sort of like, you know, week one even look like. Dave Bellous, VP - Strategy, Metal Toad: Yeah. So what we typically do is we work through, uh, kind of a structured process. We start by getting as many of the, the right people in the room as we can. And what that typically looks like is a cross section. Uh, interestingly, a lot of the time when people think about their AI strategy, they bring the executives into the room, maybe the CEO and the COO and just a couple of folks. What I think really needs to happen, our philosophy on this is you need a cross-section of people all the way to the front line workers who are often left out of that conversation, but they're the people who understand that pain point the most often. And AI is interesting because it really crosscuts the organization. It's not a legal problem. It's not a sea level problem. It's not a front office problem. It involves everyone. If you don't have legal and financial and all the different teams working together to define the goals and what you're trying to accomplish, you're going to end up with a pocket, a POC that doesn't go anywhere because it doesn't actually accomplish anything. So we get all these people into the room. We start with what we call a whiteboarding session, where we get all the ideas out into a great big whiteboard, and we do what we call divergent thinking or brainstorming, and then convergent thinking or prioritization around that. And we try and bring all the ideas out. Every idea is a good idea. We love that peanut butter is kind of our little phrase internally to say, if you want to say peanut butter, we'll write that down, that's fine, and we'll figure out what that means later. And then we try and identify through a couple of different lenses, around complexity, around value to the organization and around kind of cost and effectiveness. We bring a few different prioritization lenses to identify what's the most valuable idea to bring forward and start to mature? Dave Littman: I love the concept of the gap. It sounds so simple, but the gap really speaks to where people think they are today versus kind of where they think they need to be today. And you know, everyone in every different discipline and every different member of these of this cross-functional team, you know, could have a gap that that needs to be addressed. So I love that concept. Um, so, you know, how long does an assessment typically take? Dave Bellous, VP - Strategy, Metal Toad: Well, we say 4 to 8 weeks typically. And there's a couple of different reasons behind that. Uh, they can be very quick. We can sometimes do them faster than that. But the reality of scheduling people's time, collecting people into a room and working through that process takes some time. And we do that whiteboarding session to kick off. We work through a couple of different questionnaires to understand data readiness, and that can be one where depending on the number of sources of data you have, the complexity of them, your own internal understanding of those data resources. Uh, we do a couple of different discoveries around that. And in that, we're uncovering a lot of information. Sometimes the organization isn't aware of the maturity and quality of its data. And so that can take some time to go through. Or there's someone who we need to speak with in a different department or region, and just really understanding your ecosystem and what you have and how it can be applied, that takes some time. Then we work through a methodology of looking at a number of different, um, algorithms and identifying what is the right method for your organization. Is your kind of prioritization cost or speed or efficiency and quality. And what are you working with? Are you working with text or numbers or video or audio? Each of these different parameters brings back a different method or algorithm that will be the best fit for your organization. So we take some time working through those, and then we bring back sort of this really clearly prioritized list of, of the use cases based on what's really going to move the needle for your organization, unlock more revenue or reduce costs or make your team more effective. We identify what is the most effective way to do that. What are some of the methods and algorithms are around that and bring back a recommended plan. Dave Littman: Okay. And let me go a little bit deeper into that plan. So at the end of the assessment, you know, what are they, what do they actually hold in their hands? Is it a, is it a, a report, a roadmap, a prioritized use case list? Dave Bellous, VP - Strategy, Metal Toad: Great question. So it's all of those things. We do two things. We bring back sort of an executive summary and a presentation. So you get a slide deck that summarizes some of the work products that are behind the scenes. So the slide deck touches at a high level across all of those. So we bring back a prioritized use case list, a number of different recommended methods that would kind of help you move forward on your journey. We identify some timelines and some POC candidates, what that would look like. We bring back a recommended architecture to accomplish what you're you're going to tackle. And we also have some things like a governance recommendation. We have maturity models. We have an ethical summary and kind of recommendations in that category because depending on what you're you're looking to do might have big ethical implications. We talked to someone who was trying to record audio recently, and what ambient sounds might you pick up in the room that you need to deal with? There's, there's lots of considerations in what might seem like a very simple use case. You start to realize, understanding how that ethically could be treated and what you might be capturing. If it's not my voice, but someone else's in the room, what are the implications of that? If I'm going to record and upload that to an LLM? Dave Littman: Yeah. Nice. Okay. Well, Dave, so you, um, have all of these high value AI use cases that you, that you prioritize that you give to your client, but, you know, there must be some situations where you have to tell a client, hey, there are some on the list that, that, you know, the client has expressed a desire to pursue, but that really just would never really survive reality. Dave Bellous, VP - Strategy, Metal Toad: Yeah, that does happen so often as we come into these consultations, what we encounter is that someone already has something in their in their mind about what they'd like to accomplish. And there's two flavors of that. Sometimes either it's too complicated, their data doesn't yet support it. They've got a clear ambition, but they don't have the data resources to back that up. Or it's unlabeled data. It's messy. It's in too many different locations to kind of pull it together in the way that it needs to be. That's, that's one problem we encounter. The other is when you ask someone what they want to do with AI, they typically think of the least risky thing they can do because they're nervous about it. And so what they'll bring forward is a use case that doesn't really matter to anybody. So even if we accomplish it, it won't move the needle in a significant way. The way I like to reframe those problems and ask a bit of a different question is to say what is difficult or what's the most annoying problem in your business right now? What slows you down or takes your time? And then could we automate that because it's just a subtle reframing around like, what's the friction point in your business? Most people know that pretty clearly. And if you ask, hey, could we automate that? It just reframes it and says, instead of saying, what could we do with AI, which often people think small about that or get stuck in in feasible contexts, just talk about what is difficult in your business today, what slows you down and takes your team's time. Dave Littman: Yeah. Okay. Well, you know, um, building on that, have you ever encountered a situation where you told the client the best move is just not to pursue a AI right now? Dave Bellous, VP - Strategy, Metal Toad: Yeah. We've had a couple of situations where either, uh, a company was in the middle of a acquisition, so they'd just been acquired. They were moving into a different system. And that in terms of instability and just logistics wasn't the right fit. We also had an organization where their data was just a mess, frankly. And so in terms of pulling any real value out of it, they needed a data kind of cleanup project. That's what we recommended. Get your house in order, clean up your junk drawer, and then by the time you've got that done, then we can do something meaningful. But if you have a lot of dirty signals coming in and you're trying to automate that, Bill gates has a great quote that says an inefficient process accelerated becomes more inefficient and an efficient process accelerated becomes more efficient. So if you're if you've got something messy and you apply accelerant to it, you're just going to make a bigger mess. Dave Littman: Okay. So let's before we shift gears and talk about the business case and the ROI of AI assessments, um, I want to come back to the sort of the human part of it, which is, you know, what do you find is the hardest, the hardest thing to get clients to be honest about during an assessment is, is it data quality? Is it budget? Is it internal politics? Something else? Dave Bellous, VP - Strategy, Metal Toad: It's a the complexity and data and complexity. And what I mean by that is often people misrepresent their data quality and not out of malice or ill intent. Uh, it's sort of like how, how clean is your laundry room right now for all of us to know? Sometimes you do or don't know that. Often when you start to look at your data and unpack it, either everyone has you know, if you're in the leadership position, everyone has told you it's in great shape and you haven't dug into it to really understand what's in there, which is often the case, or you just haven't looked at it in a long time, or you have so much complex data, it's really hard to get a clear handle on what it looks like. On the other side of complexity is just the nuances of being in a large organization, each motivation being different. Because AI crosscuts the organization, you have to come up with a clear strategic goal that everyone is aligned around and whatever that might be budgets, spending, personnel, AI, it can be hard to get that consensus and clear strategic goal across everyone. And so that's one of the complexity pieces that you need to be really clear and overcommunicate around to make sure you have alignment there. Dave Littman: Okay, cool. Okay. Well, let's, let's get into the, um, a little bit of discussion about business case and ROI. And then we'll talk about AWS. So, you know, not every AI outcome is a cost story. Sometimes it's speed, quality or a new capability. So how do you help clients think about what success actually looks like before they build anything? Dave Bellous, VP - Strategy, Metal Toad: It's a great question. And that's one of the ways I talked about kind of brainstorming and then prioritization. And that's one of the lenses we use around prioritization is what new capabilities does this unlock for your team? I think that AI does not replace people. It gives them power tools. And so if you could give your team a set of power tools that would make them more effective, what would that be and what would they unlock? We had one customer where they went through through a old legal contracts. They had to go through and kind of identify these, these scenarios. And we automated that through AI. And so their team could refine, double check, clearly communicate around this. They were able to then kind of reduce their, their effort by 95% and speed up from instead of taking two weeks to come back with an answer, they had it within about 15 minutes. And so what could you do in terms of quality of communication with your team? If they weren't looking through documents all day long and then rapidly firing off an email at the end of the day to give someone the answer they discovered if they had this data, can pull out more valuable insights. Think of the quality of communication and customer care you could give in that kind of a scenario. Dave Littman: Okay, great, great. So, you know, a lot of executives are skeptical about AI, ROI timelines. Obviously, they're skeptical because so many of these projects, you know, never work out. So they have good reason to be. But, you know, what do you find is a realistic expectation? Like when should a company expect to see measurable business impact after an assessment kicks off a roadmap? Dave Bellous, VP - Strategy, Metal Toad: It's a great question. And typically executives have a reason to be skeptical about that because they've had some bad experiences. If you have a clearly defined goal, one of the things we look at is kind of this matrix around complexity and transformational impact. So if you have a low complexity and low transformational impact, you're not going to kind of move the needle as far, but you'll have a faster response in terms of seeing that value. If you're really shooting for the moon in terms of transformational and high complexity, it's going to take longer. And that's one of the ways we prioritize our use cases. And I often say to one of the executives in the room, you know, your culture best, you know, kind of if I've got these three use cases that I'm recommending, this one is a quick win and we'll kind of show some value quickly. This one's a big reach, and it's going to take you a year to see some value out of this. Which should we start with. You know, your organization, you know the culture and kind of how leaned in you are. Help me choose between these two as what recommendation is the best fit for you? Or do both start one long term, one short term and see the value kind of happen quickly without quite so much transformation. And then that will align people toward that other one. That's one of the lenses we help customers make good decisions around is you. You know how ready you are for this kind of change and what change management looks like at your organization, and pick the one that fits best to what your culture is. Dave Littman: Okay, awesome. You know, I didn't forget I do want to address the elephant in the room, which is where the name came from, but I don't I don't want to, I don't want the answer just yet. I want our audience to, to, to wait until the end. So let's, let's shift gears a little bit and talk about the AWS funding angle. Um, you know, this is really genuinely surprising to most people that AWS will actually fund the cost of an AI assessment at no cost, at no cost to the client. So, you know, how does that work and why does AWS offer this? Dave Bellous, VP - Strategy, Metal Toad: Yeah. Well, there's this stat that MIT put out, that 95% of POCs fail to generate even $1 of value. So there's a lot of these proof of concepts being run. People are trying to get their, their toes into the water around this, and a lot of people are struggling to be successful with it. Uh, we've had really good luck. So AWS, uh, has a number of different partners that work in this space. Uh, and there's been a couple of different programs around this, and you have to kind of be qualified into it, but they see the long term value. If you start to really transform your, your work, if you can reduce your work and increase your impact and you start to spend on AWS, they see the long term value on that. They want to support their customers and having putting a good first foot forward. And so what they're doing is they're bringing in partners like us that have a methodology and a process to help customers be successful at this journey, because it's a win win for them if their customers are successful and impact these tools that allow them to be way more effective in their work and they start to spend on AWS, why wouldn't they incentivize that? And one of the things I look at and kind of try and call out is for the customers, for the people out in the audience here. Use a AWS resources to de-risk your AI strategy. Dave Littman: Okay. And when you bring up the AWS funding in the conversation, when you bring that up, when you mention, hey, you know, this may not cost you anything to start. Do you find that it opens doors that were previously closed? Dave Bellous, VP - Strategy, Metal Toad: Yeah. Look, I think everyone knows they need to do something about AI, AI and build a strategy. Um, it's a priority for many people, but so are many other things. And so if we can identify, we try and identify the value of what the strategic plan brings you and what the assessment will bring you and say, hey, if it helps and there's a way we can make this even easier for you. Aws will bring some funding to the table as part of this, and they've got some good strategies around that. Uh, everyone has tight budgets. Everyone's trying to do more with less. Uh, and that just makes one thing a lot easier. It's also a way to kind of test the waters. Uh, and we can build a lot of trust through these organizations, through these, uh, implementations, which allows us to kind of show you, uh, we like to show that we provide value. And if we don't, hopefully we don't continue after that. But we've got a lot of relationships where we've had really good back and forth interactions over time that are quite valuable. Dave Littman: Okay, great. Okay, so we're going to talk now about proofs of concept and beyond them. And then we're going to get into differentiation and, and we're going to address the skeptics in the room. We want to make sure we, uh, we cover them. So after the assessment, you know, you move into proofs of concept, let's say you've got one to maybe three of them on the table. How do you decide which use cases get piloted, and how do you avoid the trap of building a POC that will never make it into production? Dave Bellous, VP - Strategy, Metal Toad: Great question. So typically we've gone through that prioritization, we've done our brainstorming, we've done prioritization, and we have that alignment where hopefully the executives have given that us that sense of, do you want a quick win or something strategic and transformational? And we use all those conversations to narrow down to the the proof of concept that is most likely to be most effective for that organization. And we identify, do you want to do something that's three weeks long, ten weeks long, one year long? We understand what their expectation around return on value is, what they'll see. We've gone through and looked at their data, their security, their kind of compliance posture, or do they have to comply with HIPAA? We understand the complexity of these things. We've used a couple of models and identified what will be most effective for them, what the costs of those will be. We can identify what the cost category of if they're running this long term, what is that going to cost them? That's something that often people will say is, hey, if I'm trying to replace this process, what are my costs down the road? If that's a surprise, once you figure it out, it may make that infeasible once you get there. That's all of what we're trying to kind of build in and validate with a proof of concept. So if we bring back a really easy to make decision, because we've given you all the different factors to make that decision, then we've done our job well and we've kind of brought together an ideal proof of concept that leads into an MVP that can make it out into production and actually impact your organization effectively. Dave Littman: Awesome. All right, Dave, now, you know, you and I were talking a little bit earlier about the Wonder Heart case study, and I want to ask you about that specifically. So just to set the stage, an AI game master that eliminates 20 to 40 hours of human procession. And it's a very creative, unexpected use case. And my question is, how do you encourage clients to think beyond the obvious in AI applications? Dave Bellous, VP - Strategy, Metal Toad: Yeah, it's a great question. So this is a really fun case where I'm not a big gamer myself, but a lot of our team is, uh, and this is where if you're, if you play Dungeons and Dragons, someone always has to play the role of game master and kind of lead the conversation and walk them through that. So we brought forward an agentic experience. So bringing a number of different agents together to help kind of do that game, play that game master role and lead people through a narrative, which is kind of tricky because AI is is good at some things, but kind of that creativity and kind of following along and building into memory. There's a bunch of technical problems of, of loading things into memory and responding quickly. We were able to solve in a really interesting way. And so it was a problem that was complex and had a lot of different nuance to it. And for this particular customer, the quality of the experience had to be at the, at the top of the cost and kind of effectiveness and, and speed, speed and quality were two really key things for this, this customer, because in any kind of a gameplay experience, a narrative, the longer the time it takes, the, the more quickly you lose someone. You have to be really crisp on everything. And so that was a really interesting thing where we spent a lot of time ideating. We built a really cool proof of concept for them and proved that it was possible. And we've just finished and launched the MVP. It's out in production if you should be playable within the next couple of weeks, but it's a really great experience to kind of walk through and see that customer figure that out. And that's something we try and do all the time, is not take small bites and low risk things, but work at really big problems that's actually impacting the organization and try and automate those and smooth those out through, through AI. Because if you can kind of take those big swings, remove the risk from them, then that has huge impact on an organization. We love to see that happen. Dave Littman: Awesome. That sounds like a lot of fun. Fabulous. All right. So, um, let's talk Dave, a little bit about metal toads differentiation. Um, you know, I think that, you know, from our conversations, you guys describe yourselves as client zero. Um, and that you, in that you use your own AI tools internally before recommending them to clients. So, you know, what has that internal experimentation taught you that you couldn't have learned in any other way? Dave Bellous, VP - Strategy, Metal Toad: Great question. So we, we, every quarter, we run what we call a hackathon where we take 2 or 3 days of our team's time. We talk to all of our customers and say, we're just going to take a couple of days. And internally, everyone in our team, from our HR folks to our finance folks, gets involved in building, using AI through agents and actually trying to construct something, and we get together at the end of that and show each other what we've built. And there's that energy, that kind of interest. Every company has problems that can be solved through some of these workflows, and there's a lot of things like creating a statement of work or sending an invoice that have parts that you can automate. So it shouldn't be fully handed off to a machine. There's always a human in the loop on those things, but in my day, there's a lot of drafting an email or putting some stuff together or taking notes that if you use AI responsibly and ethically, you can really speed up your organization. And in working through those hackathons, we try and build processes for ourselves to be more efficient, deliver more value to our customers, and enjoy our experience in the day a lot more. One of the things we built was just a little icon that automatically creates tickets in GitHub so that you have to type less. You spend more time doing the tedious tasks. When we can remove those, it's better for everybody. Dave Littman: Fabulous. Okay, so one more question. Then we'll get to the skeptic in the room. So, you know, Gartner has a stat that says 70% of enterprise agentic AI initiatives will fail by 2029 due to agent washing. So, um, help us understand what agent washing is and how do you protect your clients from buying into it? Dave Bellous, VP - Strategy, Metal Toad: Yeah. So agent washing shows up where someone comes pushing a specific technology to you. This, this problem will solve this, this, this, uh, kind of particular piece of software or agent will solve all of your problems. Uh, and I think that's never really the case, as you well know, the idea that you have to find the fit to purpose solution for your organization's needs and go through that consultative strategic process. Uh, if you drop some agent into your ecosystem and hope it will solve all your problems, you're likely going to experience agent washing. That's where one solution will do everything. It doesn't matter kind of what you're trying to do. If you're working in finance, sometimes you need a QuickBooks and sometimes you need a big ERP system depending on the size of organization, one piece of software won't solve anything. Everything. Pardon me. And in that, if you can get the right solution to solve the right problem, you're going to be set up for success. And if you try and use the same technology, whatever it is for every problem you have, it's not going to be effective. And I think a lot of the solutions right now are being sold as this will solve everything for you. That's not the ideal circumstance because even if you're working through finance and working with numbers and creating an invoice, you might need one type of AI for that. And if you're in marketing, trying to create, you know, new messages and working with customers, you might need a different methodology for that entirely. And so trying to use the same tool for both solutions isn't the right fit. And that's, I think what a lot of people get sold these days is this one solution will solve all your problems, which hasn't worked for a long time. Dave Littman: Okay, so that's healthy skepticism. So that's great. Dave. Thank you for pointing that out. So, you know, definitely It is important to be skeptical of agent washing, and that's going to lead us to our last segment, which are address for the skeptics in the room. And so, you know, what would you say to the executive who's heard the AI pitch a dozen times and is genuinely, like, genuinely fatigued? So, you know, who believes the hype cycle will pass and can they wait it out? You know, what would you say to them? Dave Bellous, VP - Strategy, Metal Toad: Yeah, I think in the same way that when desktop computers or the internet was rolling out, this is kind of a fundamental change in how technology works. And the longer you wait and do nothing, the more likely some of your competitors who are trying to grapple with this will outpace you. So I think the challenge there is, while there is fatigue, well, this may be a bubble that we're in the middle of. This is also a fundamental and transformational change, and your teams need to grapple with it and do some work with it to better understand it. So doing an assessment or finding some way that you can move forward in one area with your organization is one way to move forward. But whatever you're doing, just making sure that you're you're trying, you're opening up these tools, you're experimenting with them and doing so in a safe and responsible manner so that your team knows not to put personal, identifiable information in there or company sensitive data. You need to have these kind of conversations. And if you're not having these conversations with your team, likely someone in your organization is is working with it and you just don't know how. So the risk there is, someone's engaging with these tools in a manner that you might not be okay with. So I think figuring out a clear, well structured, strategically aligned roadmap forward is your best bet. Dave Littman: Okay, awesome. A few more questions, Dave, and then we'll get to the metal toad question. You know, what's the, what's the most common reason a company almost does an AI assessment and then doesn't pull the trigger? And is that reason usually valid? Dave Bellous, VP - Strategy, Metal Toad: Yeah, it often is. An organization needs to be ready for this. There's a couple of different reasons. One, in that they realize what's involved and that they don't have the right people at the table, or they don't have the right person or someone they're about to hire who will lead this initiative. Those are reasons why people feel like they need to pause. But at the end of the day, this is something that you need to keep moving forward on. And so my recommendation is always to start to try to move forward in some way and to explore and experiment and kind of touch the edges of this new technology and see where it might take you. Dave Littman: Okay. Awesome. Um, so Dave, if a potential client is listening to this right now and thinking, you know, this, this might be relevant to us, what's the one question they should ask themselves to know whether or not they're ready for an AI assessment? Dave Bellous, VP - Strategy, Metal Toad: I guess the one question would be is, if you have a really clear strategic plan on how to move forward with AI already, then you probably don't need one. If you don't know or there's some sort of a uncertainty, messiness, multiple different departments moving in multiple different directions, That's the perfect time to kind of engage with one of these. But if you already know how you're going to proceed, how you're going to measure your success and how you're going to move forward. I haven't talked to many customers who are in that place that haven't gone through an assessment, but if you've already got that clear plan, then it's not something you need. But if you're unclear on any of those points, then it'd be good to have a chat. Dave Littman: Okay, awesome. So let's do this, Dave, let's wrap things up. If you could. We we are dying to know where the name Metal Toad came from, and then help our audience understand where they can get more information for sure. Dave Bellous, VP - Strategy, Metal Toad: So, uh, we, we had the name for about ten years and talked to one of our customers and said, you know, like they asked us that same question. We said, well, well, what do you think? And they thought, oh, well, you know, you're pretty humble and there's a high, high utility. You guys get stuff done. So the metal. Uh, and, you know, toad is kind of this kind of self-deprecating a little bit. That sounds like you guys, uh, and we love that. And we love the description our clients give us. But the true reason is our CEO and founder's name is Joaquin Lippincott. And he thought no one's going to be able to spell that. And he had a little, uh, paperweight on his desk that was a metal toad and metal toad.com was available, and hence Metal toad was born. Dave Littman: Very cool. Very cool. Great story, great story. And of course, you know, where can people find out more information? Dave Bellous, VP - Strategy, Metal Toad: We'd love you to visit our website at toad.com. Track us down on LinkedIn. There's a lot of information we're putting out there all the time. We've got a number of different information resources about the assessment. Reach out an email to hello at metal toad.com if you'd like, and we'll get right back to you. Dave Littman: Fantastic. All right, Dave, thank you very much. Dave Bellows, VP of Strategy at Metal Toad. If you are contemplating an investment in AI, make sure you take the right first step before you take the plunge. Make sure you check out Metal toad.com. Dave Bellows thank you very much. I'm Dave Littman at Truth in IT. Thank you all for watching and we want to wish you all a great day ahead.
