AI’s Role in Revolutionizing the Tech Channel’s Future

We recently hosted a webinar featuring Tom Brunn, VP of Channels at Vation Ventures, and Troy Cogburn, Chief Technology Evangelist at Vation Ventures, alongside our host and Director of Strategy, Sam Grise. The trio discussed Artificial Intelligence (AI) and its significant impact on the tech channel, exploring every facet of the topic. 

The conversation covered how AI is a business game-changer, not just a trendy term. It’s transforming everything from data center operations to how companies engage with customers.

Our panelists shared their experiences and insights on using AI in their work, pointing out both the opportunities and the challenges. They discussed why having a solid game plan for AI is vital for any tech-focused business that wants to stay ahead.

They also explored AI’s practical applications, like boosting business efficiency and shaking up industries with fresh innovations.

Did you miss the live discussion? You can catch up by watching the entire video above or reading the transcript below.

Sam Grise, Director of Strategy, 4B Marketing

Tom Brunn, Vice President, Channels at Vation Ventures

Troy Cogburn, VP, Chief Technology Evangelist at Vation Ventures

Sam Grise:
My name is Sam Grise, I am the Director of Strategy here with 4B Marketing. We’re an outcomes-driven, tech channel agency focused on business outcomes, cost, cash flow, revenue-driving activities, asset utilization. Everything that we build, we want to tie to those outcomes and focus on the revenue growth side of it. And one of the pieces that is so important in today’s technology ecosystem, the big boom right now, is AI. We wanted to talk about how AI is revolutionizing the tech channel. And with that, I’m going to go ahead and introduce our panelists here that we’ve got for the discussion today. You know who I am, Sam Grise. But with that, I’m going to pass it over to Tom Brunn, who’s the VP of Channels at Vation Ventures.

Tom Brunn:
Thanks, Sam. Just wanted to introduce myself to everybody. My name is Tom Brunn from Vation Ventures. Vation is a technology consulting firm that consults in the channel. So we have a relationship from venture capital distributors, partners, SIs, GSIs, and we have a community of end users. We consult and drive innovation from money to money. So venture capital to the end users. And we’re very involved with the channel and their initial steps, as I would say, into AI. So look forward to talking with everybody today.

Sam Grise:
Awesome. And we’ve got another attendee from Vation Ventures. Very excited to have Troy Cogburn on the phone today to talk about the technology side of things. What does it look like from a tech ecosystem perspective, and what does it take to run AI, a platform perspective and having the correct infrastructure. But Troy, I’ll go ahead and let you introduce yourself.

Troy Cogburn:
Yeah, thanks Sam, thanks for hosting us on this webcast. Really thrilled to be here. Name’s Troy Cogburn, I am the Chief Technology Evangelist here at Vation Ventures. Primarily built out our innovation and emerging technology research team over the past four years. And now I’m doing a lot of consulting from that entire ecosystem that Tom had mentioned. So consulting from VCs to emerging technologies through the channel all the way down to the CIOs, helping them really take advantage of innovation, partner with innovation, and really become thought leaders in their industry for things like competitive advantage. And so super thrilled to be here. And prior to this, I did have six years at a solution provider, Trace Three, where I was there for sales enablement and new partner acquisition as well. So thanks for having us, Sam.

Sam Grise:
Awesome. Perfect. Well, I’m going to go ahead and stop sharing my screen here so that we can really just turn this into the discussion side of the house around AI and just a quick housekeeping item. I do have the Q and A up. I do have the chat up. So as we’re going through this discussion, we want to make it valuable for you as well. So if you do have some questions or some thoughts, please feel free to drop those either in the Q and A or the chat. I will be monitoring that during the discussion. So to kick things off, I really wanted to start with the background, if you will. AI has become this big boom, and my career before this, I was actually working for a conversational AI company, selling to Fortune 500 companies. AI has been around. It’s now become mainstream. One of the things that it really is is business efficiency. I know that for us internally at 4B Marketing, we’re using AI platforms, we’re using a platform called OneClick. And what’s gonna happen with that is this meeting is being recorded, and what OneClick does is it takes snippets of that so that we don’t have to go through and manually transcribe and all of those pieces to help with efficiency so that we can cut it up into snippets, post it on social, whatever we wanna do from a marketing perspective. But I would love to open it up, you know, kinda to you, Tom or Troy, and just ask you kind of your perspective of, you know, where do you see the biggest pieces of AI being implemented today? Right? What impact do you see it bringing to the space? Obviously, from a business user perspective, there’s efficiencies, there are some challenges with that as well, but I would love to open it up to you, Tom, just to get your perspective on where AI is sitting today and the impact that it’s bringing to the marketplace.

Tom Brunn:
I think the impact is broad-based. The market and enterprise and mid-market companies see it has applications in their industry, repetitive tasks, customer service, HR, across the board. It’ll impact every functional group within companies. And so everybody recognizes that. But then they don’t necessarily know what to do next, right? They don’t have the experience with it. They recognize it’s, I have to do something with it, but they don’t know exactly what, they don’t know where to start, and then they don’t understand the underlying technologies are driving these because they’re radically different from the standard data centers that have been built for the last 20 years. And so I think people recognize that, but it’s, in my opinion, it’s more disruptive than even the current people recognize. Nobody’s moving fast enough because if you look at what John Chambers has said, this is the biggest technology refresh since the industrial revolution. So the analogy is moving from horses to steam trains. That’s the level of change that AI is going to drive. And I don’t think people really realize the overall potential impact to their business.

Sam Grise:
That’s a great point. And I want to dive just a little bit deeper there because when I think of it in my lifetime, I think of it as the cloud boom, if you will, everybody moving directly to the cloud and then, oh, man, it’s expensive. We need to go back to hybrid or go back on-prem for security. All of those different complexities that come into play. I would love to get your perspective on the short term. We’ve mentioned OEMs and the technology of adopting fast enough. It is coming, it’s happening. What does the short term look like to either get started or the impact that it’s going to have on the business? But then what does the midterm and the long-term impact look like for AI and getting things moving?

Tom Brunn:
I think from a tech stack perspective, this is my opinion, Troy, it will be better suited to answer this question, but it’s going to evolve. So the tech stack associated with AI is just different then? It uses different technologies. It uses different storage technologies. We all know Nvidia, it uses different compute technologies. So it’s different. It’s a little bit different than, it’s a lot bit different than the current technologies. And the major players in data center and networking. I think short term are being impacted negatively as customers pause, customers are pausing their spending as they’re trying to figure out what their strategy is with AI. So they’re not building the new data center. Boards are pushing back on IT teams saying, well, where’s the AI strategy? You’re not doing anything until you give me the AI strategy. So I think it is impacting the big tech firms negatively in the short term. I think in the long term they’ll be fine as they adapt and build the technologies that run modern technologies, but I think it’s negatively impacting them in the short term. I’d be interested in what Troy thinks.

Troy Cogburn:
Yeah, I think you’re right on the money. And there are so many pieces that I was pulling out there. So I want to have a few different talking points. I really want to talk one, Sam, to your original question, what are people doing today? What does the long term look like I really have this simple kind of view on it, which is, I think today there’s this copilot model. I think it’s a bit of a marketing buzzword, but it’s a bit of a way to say that it’s an AI-powered application. I think organizations today, the easy low-hanging fruit is get a copilot type application for things like customer service that could be customer-facing or internal employee service, desk applications automation. Tom had mentioned how can you automate certain workflows? Certainly marketing and content is getting automated. Then developer will be the last one. I’m interested in. Coding, copilots, code, explainability. These are the low hanging fruits. You just sign up for a SaaS application and they’re AI-powered. And now you have this AI engine to help boost productivity long term. I think the strategy is to actually incorporate AI into your own products and services. You have the low hanging fruit which is adopt these productivity boosts and application long term. How do you power your actual business? Through AI? And that’s a lot more difficult. And that kind of leads into that tech stack that Tom was referring to, which is it’s interesting if you look at current tech stacks today, the high-performance best of breed infrastructure network storage compute is actually being run in the production environments for applications. But if you think about AI, it flips it to where now you need the best of breed infrastructure for training and developing because that is the most compute-intensive process. Now you have these GPUs and different types of compute coming out. You have these high-performance storage, and then you even have photonic computing and optical networking. Now your training environment is really your best of breed. But even beyond that, once the application is running, there are different requirements there. I mean, you have different types of databases, vector databases, which are really good for AI based off of the way that they do the math and the underlying processing. You have this thing called an inference compute engine. You think about typing to chat GPT, you want it to type back to you as fast as possible. Well, that’s actually using an inference compute that will continue to improve, to improve customer experience. But it’s across the board. I mean, the whole infrastructure stack is changing. And I think a lot of people will either move to a cloud type model which is like getting AI provided through an API, but the folks that are trying to do that long term enterprise value which Tom was mentioning, they’re going to bring it all in house and build it out themselves because there’s a lot more control over things like security, speed, cost, and will probably give you the better competitive advantage in the end. Interesting, interesting. One piece that’s a hot topic in the industry, just in general, is security. So that’s an interesting thought there from a perspective of bringing it in house so that you can help with that security side of it. Obviously, we’ve seen some of the legalities going on and what’s happening in the space of utilizing AI. I know that there was a case a couple of years ago of somebody putting the case into chat GPT, and it came up with random solutions, if you will, that were not accurate. So it is interesting to talk about that security side of the house from a perspective of the future of AI. Something that I want to ask is that tech stack that we’re talking about in my past when I was selling conversational AI, one of the challenges that I had from a seller’s perspective, Washington oh, we’ll just build this in house. And my thought was, well, there’s a lot more to it than just building an LLM, right? There’s a lot more to it than just having some learning, if you will, around that. And so I would love to hear your perspective of from an enterprise business that has the capabilities, the resources, the financial stability to be able to build it on their own, what does that look like? How does that begin? How do you start with that kind of tech stack? And then also from a perspective of those that are already providing AI solutions, if they want to have it in house, if you will, on all of their items to help with security, what does that look like from a structure perspective? I would love to get your opinion on that. Troy.

Troy Cogburn:
Yeah, great question, Sam, and I think you hit on a very hot topic just in terms of strategy. You have these folks like Anthropogenic Coherent, OpenAI that have these proprietary models that you can consume and integrate into your applications. And so it’s partly like a pre-built AI. You kind of skip a lot of the training pieces, and then you also have the open source, which is a lot more cost-effective, a lot more transparent, but a lot more of an uplift to get up and running. So it’s like how much time do you have to get to market? If you have a small time to get to market, those API providers could be better. But if you want more control over security costs and things like you can do the open source models as well. I think I’ve been talking a lot about just the natural language processing, which is the hype. I think chat GPT is really what spurred the hype into AI but there’s so much more in terms of AI than just natural language processing. I like to think of AI as these are human-like capabilities. Language processing is like reading and writing. That’s one capability. And there are still things like computer vision, and so there’s going to be these multimodal pieces. But going back to your question around why build it yourself when there’s stuff that’s off the shelf a lot of times? And I think this is what Tom was talking about, is there are these industry-based use cases, and you actually need these proprietary data sets in order to train these models for the specific industry. And so this could be some type of law or legal type of AI, this could be life sciences biopharma. And these are datasets that are not readily available to these proprietary models on the market today. And so for these specific industry verticals that are really going to help disrupt their own businesses, they kind of have to build their own off their own data sets, because that’s not just generally known information. I think that answers your question. I don’t know. Did I miss anything there, Sam?

Sam Grise:
No, I think you answered the question, and it goes into a question that was just asked in the chat. So from an organizational standpoint, who owns AI strategy? And the reason why that question is so important is because there’s the contact center we talked about, virtual IVR, if you will, from a natural language processing perspective. Then there’s the copilot side of it for an executive assistant, if you will, to help with scheduling and those kind of items. And then there are deeper use cases for AI. But it is multifunctionality in multi-department, if you will. And so either Tom or Troy, I would love to get your perspective as the end user from an AI perspective, the Fortune 500s or any business really, who owns that strategy? And from your perspective, how should that trickle down? Because it is a new technology, everybody is starting to adopt it. But how does that get solved? Is it the Chief Digital Officer? Is it the CIO? Is it the CISO? Because it touches all different areas of the business. I would love to get your guys’ perspective on that.

Tom Brunn:
I think it’s a combination of all the above. The CIO’s will own foundational data stack type of work. Hey, what is it going to run on? How are we going to secure the data? How are we going to protect the data? What data are we going to keep those type of messages? And then the functional teams will own what they’re mapping their processes to AI and then funding the specific projects that are in their functional groups. I think the ultimate owner in this new world, the successful companies are going to be the CEO’s that really learn this and figure out how they can disrupt their competition using AI and then driving results through the organization. So I think the most successful organizations are going to be the CEO’s that really, really understand the impact of their industries.

Troy Cogburn:
I’ll add on there, Tom, I totally agree. I think the CEO is going to be ultimately responsible, especially from that second variation, what I was talking about, which is AI to enable your business from a productization or a service which is actually going to end up becoming revenue generating. That’s really going to take a lot of vision and strategy from the top, from a business and technical perspective to that point, you probably will partner with your CTO, you’ll partner with your CIO, and then the CEO will probably ultimately responsible for it. And the other piece is you think about the regulations that are coming out. I think those regulations are ultimately going to hold the CEO responsible, just like some of the security use cases that you see hold the CISO responsible. So you’re going to see a lot of these executives held responsible by these regulations coming to market. But it is a challenge and I don’t think it’s going to be solved very easily. I like to equate AI to also SaaS applications. It used to run all kind of internal tooling applications, email, what have you. And now that they’re SaaS, any BU or business department can really sign up for any application they want as a swipe of a credit card for their organization, which created this kind of idea of shadow it. So what apps is our organization using and how do we manage that? You’re going to start to see the same thing for AI as well. What is the shadow AI? What are the AI tools that our organization is using and how do we start to put guardrails around those? And so it’s kind of like the same problems we continue to see, but just a different flavor of it. SAS shadow it. And then AI, shadow AI is kind of what they’re calling it. That’s a great point. And I think that that could get us into a wormhole of digging into what that actually looks like. And maybe we’ll have some time at the end to really dive into that of what roles regulations internally, so on from a shadow it perspective, to understand how to manage those applications we can dive into. One question that I want to take a step back on is, Tom, you were mentioning from a perspective of the OEMs and some of the OEMs being behind the eight ball, if you will. From a perspective of AI strategy, which may be impacting purchasing or building that new data center with that organization, I would like to take a step back and focus on the channel side of it. Your traditional systems integrators, your value-added resellers, MSPs. How does that adoption look? Because they’re focusing on the OEMs with that technology, but also trying to service those clients. Whether it’s building that new data center or helping with that AI strategy. What is the impact that you see on those traditional resellers, service integrators and MSPs? How are they integrating AI into their sales strategy?

Tom Brunn:
I think with all this discussion and change we’ve talked about, it creates opportunity for the channel to monetize, bringing those skills on implementing AI and growing AI, and helping business transformation. Companies transform from how they’re doing business to an AI-enabled business. Those are all opportunities for the channel to monetize. And now it’s different than selling infrastructure though, because the skill sets on configuring networks and servers and storage they’re going to adopt that will still exist in the modern data stack. So they’ll need to transform. So how data is accessed and stored in AI environments, it’s all a little bit different depending on the application, but that, you know, there’s a huge opportunity for the channel to monetize this. And I think the skills are different. So they obviously have to learn the AI technologies. And I do, I personally believe that partners need to do more development work because these AI-type of application, observability, security and how the AI application itself runs. They need customization and every customer environment is a little bit different. The AI partner of the future, whoever it is, is going to have to do some development work. You see the Accentures and the big GSIs, they’re positioned pretty, they’ve jumped into the space immediately because they had those dev skills. Now I think the rest of the channel will pivot to it quickly, but I think that is the skills that the new partner needs.

Sam Grise:
Interesting, interesting. And we did have a question come in the chat. So I want to jump into that, which is from an agent model perspective, the Intellisys, the Talarises of the world as they’re maturing their go-to-market strategies, recommending AI-based or AI-ready solutions to end customers. Is that very similar to the traditional VAR systems integrator, if you will? Or do you see them being ahead of the game because of their agent model, if you will. Troy, can you answer that?

Troy Cogburn:
Yeah, I’m trying to fully interpret what the agent model, my interpretation of this agent model. We talked about copilots, which is like an assistant for you within your day job, an AI-based assistant to improve productivity. We’re moving to this agent model where it’s not just an assistant, but you actually have an AI coworker, the developer examples, you can actually assign it tickets, you can assign it tasks, and we’ll actually go out and figure out what tasks and workflows it needs to complete that. That’s my interpretation of what the agent model would be. But recommending AI solutions to end customers, that’s always going to be a challenge. I think there is this need for a vetting process, and I think we’re starting to see companies come to market that can vet AI on different types of performance metrics, whether it is like a security metrics, a transparency metric, actual quality and accuracy metric. And so once that starts to happen, some of that might actually ensure more trust with end users if it’s gone through this kind of certification phase or verification. I’ve seen a couple of companies come to market that are actually doing this thing. Hey, this AI is ready for market. Go ahead and feel free to sell it downstream to your customers. I’m not sure I entirely answer the question, but I think that’s what I’m seeing today is there’s this kind of pre vetting, and it might be through some partnerships that you would want to do before you actually resell these type of applications to your customers.

Sam Grise:
Yeah, I think you kind of hit the nail on the head there, right. Is from that perspective of understanding where it’s sitting today and actually doing some vetting before, and it opens up another line of discussion of within the channel ecosystem and emerging technology from a cloud-based perspective. A few years ago, if you will, there was a lot of cloud optimization or cloud readiness assessments that were going on a lot, and there were organizations that were stood up to just do cloud readiness assessments. Curious. With your experience and what you’re seeing in the marketplace today, do you see AI ready assessment organizations, if you will, starting to be stood up or disrupting the marketplace from the traditional OEM and reseller model, is that something that you see happening or something that you could see happening in the future? And I’ll ask that to Tom.

Tom Brunn:
Yeah, so, yes, yes and yes, but potentially in a good way. Vation has an AI readiness assessment we do for partners, dis customers, and it essentially produces a strategy for a company and when you produce a strategy for a company, the foundational element it says is, hey, you need this level of infrastructure to run AI. Here’s all the use cases. You potentially can move to AI and things like that. It could be done per department, but when you do these things, it drives huge pipeline for the OEMs, because most companies, their infrastructures aren’t ready to do any AI at all because AI accesses data and produces data at unprecedented rates and nobody has ready. You can’t just install something and run it. You have to plan it out and plan it out to run it in the future. I think for the partners, short term, as companies develop a strategy, it will drive pipeline and short term opportunities. But as it matures and moves into the modern data stack, the partner that pivots that way, that has developmental skills, is going to be the one that comes out on top.

Sam Grise:
Awesome. It sounds like there’s a couple of things that come into play here. One, it sounds like channel organizations and OEMs, they can’t afford to not invest into an AI strategy, if you will. Whether it’s assessments or reselling the technology or building new platforms, if you will. There has to be an investment from a channel organization or an OEM to be able to stay relevant and stay on top of the market, if you will, for a reseller or for a VaR. What is the first step to get started? Okay, this AI technology is out here, right? Everybody, it’s the big boom. Everybody’s talking about it. We know that it hits all of those business drivers for driving revenue, reducing costs, reducing risk. Right. There is some security concerns there, but obviously, if you build it in house, how do I get started? If I’ve got a reseller that’s been traditionally selling Cisco, Dell, EMC, all of those technologies, how do I revolutionize my channel business so that I can start getting into this AI game? Tom, I’ll take a stab and then I’ll let you go. I’ll do a quick answer for that. I just wanted to mention that I think this is just a complete shift in the way that there’s a go-to-market. And if you look at, I would say in relation to the data market, the data management and analytic type workloads was a different sell for the channel, but a lot of the channels started getting into that. The challenge with that is this consultative-based selling, where it’s going from speeds and feeds and building out an infrastructure stack to more of this. How does this data strategy impact the business? And so the buyers actually ended up being a little bit different. You still had CIO buyers, but you had COO buyers and different types of audience. And so you are going to have the different audience, but it’s going to be very similar for AI as well. And I’ll bring this around to answer your question is they’re very business objective outcomes and it’s really required in building that strategy and consulting piece. One of the biggest challenges with AI right now is the business doesn’t necessarily know that they’re going to get return on investment on it. And so developing that strategy, developing the KPI’s, the use cases, if you have the infrastructure ready, that way you have a clear picture of how you actually obtain that ROI. And that’s all a consultative layer. And so for me, you start with the consulting piece of AI. That’s really how you gain kind of market share, thought leadership. And then once you start having those conversations and services with your customers, the suppliers or partners that you’re going to bring to market are going to come based off of that consulting effort. And so for me, consulting is the first thing I would do if I was in the channel. But I’ll toss it to you, Tom, I’d love to hear your perspectives as well.

Tom Brunn:
“Well, I think any channel partner today can work with their distributors on developing their own strategy. The distributors have already, all the big global distributors have invested heavily in developing AI enablement programs. So those are good places to start. And so call your distributor contacts and start there to start building the foundational knowledge and a partner wants partners grow up different ways. Some grew up as Cisco partners, some grew up as Dell EMC partners, some grew up as Oracle storage partners, NetApp partners. And so there’s a natural foundation to the business. And I think start there, start where you’re at, where you grew up from, and figure out how AI impacts your core business, your Cisco business, your NetApp business, your HP Dell business. And that would be a good place to start and just start to learn about it. And the partner leaderships, channel leaderships, they’re very good business people. They will see the business opportunities naturally, I think say learn more about the, about the technologies.”

Sam Grise:
Awesome. And that’s a good segue into a question that was asked in the chat. And the question is, what is distribution’s role within AI? And are we going to see a cloud type of marketplace for AI APIs, so on and so forth? So Tom, would love to get your perspective on that. And then, Troy, also your perspective.

Tom Brunn:
I do think that distributors will be the aggregation point for AIH. They have relationships with some of the big AI players like Nvidia, Dell, IBM, AMD, intel and those I think will be the center point for huge ecosystems of AI plays. And the distributor is in a position to be in the middle of those things and to orchestrate those together and develop which use cases, use these vendors with. So I do believe they have a role. I do believe it will be a solution element on their cloud marketplaces. It will be the AI section of the marketplace will have pre tested AI solutions integrated together that are tested in the Disti lab and things like that, that partners will be able to go work with their disti and get a I’ll add just a couple of thoughts there. I totally agree. They’re going to be that solution aggregation point. I do think the marketplace and API is going to be one of the delivery routes for AI. And that’s really just my main comment that I wanted to add is we’re starting to see two different elements, which was you have these open source repositories of models, which is like a marketplace, but open source of course is free download and use at your own risk or what have you. But you also have these API providers that were probably going to get aggregated as well. You start to see different models run on different infrastructure. You’re going to get different performance based off of which API you’re using. Then there’s also this idea of using different models for different use cases. While you may use an OpenAI model on one, maybe you use a Google model for another use case. I don’t see a huge risk of vendor lock in within an AI API, but I think folks are going to be using a lot. But they’re probably going to have struggles on the finance side of how much are we spending with Google versus OpenAI versus cohere. There is probably going to be this unified financial and billing element that will be very beneficial to the end customers if they are using multiple AI providers. That’s my two cent there. I could be wrong. Who knows where this goes, but I hope it goes that direction.

Troy Cogburn:
That was a great point to the financial side of it because I wanted to get both of your takes on what this looks like from a financial investment. Because when we think back to the cloud boom if you will, it was move everything to the cloud. Then they started billing and they started saying wait a second, its not actually cheaper sometimes. Then they moved to hybrid or they moved back on-prem and vice versa. Really it was a cash flow optimization, if you will, from a capex versus an opex spend and so on. AI is being built in multiple different ways. For example, in conversational AI, it’s how many calls are coming in how many minutes, how much time are we spending on recording? That could be a flat fee for just doing each recording, if you will. Would love to get your perspective on the financial side of it. What does this look like from workflows and so on? And one, because the investment, if you’re building it internally, is a lot from a time perspective, a resources perspective, but also a technology perspective. But then if we get to the API side of it, or API cloud marketplace, if you will, how does that look? So I just love to get your guys kind of perspective on what the future of this looks like. Tom, I’ll open up to you first on that one.

Tom Brunn:
Yeah. The standard business return on investment type of scenario still applies. And so companies will have to apply their general principles for investing in a workflow or technology. And what’s the headcount required? What’s the capital investment, what’s the opex? All the costs will need to be built into each type of project. But the potential of AI is to allow a business function to scale in a huge way without adding lots of headcount. And then the headcount that you do add are AI specialists that train our prompt engineers for training the models. But then the model then can scale in a big way so you don’t have to add agents, for example, on a customer service piece, and so that all can be modeled. And headcount costs and infrastructure costs and recurring costs associated with owning applications, all that stuff can be modeled and I think companies will do a pretty good job with that. And there’ll be some mistakes and they’ll over go to the cloud for too much piece and it’ll be too expensive and there’ll be pullbacks, but that naturally happens anyways.

Troy Cogburn:
Yeah, and then one thing that I’ll add, and I think Tom’s completely right, people are going to figure it out. I think the challenge is a very similar challenge. It’s kind of my thing about history repeats itself again, is you have this. For most AI today, it’s consumption-based pricing. They’re doing it by tokens, which could be a word, a character. You have this allotted amount of tokens and then you consume those tokens. That becomes very challenging to understand. Just like I’m not sure how much storage I’m going to need in the cloud, I don’t know how much bandwidth I need in my networks. They figure it out when they get there, but then they can base it off. Oh, this is how much consumption we have on average. We can start to budget that. Its not necessarily this recurring SaaS type of monthly billing that we’ve seen. Its going to be more of a consumption base. But that’s really purely, I would say, on the AI as a service or API consumption. If you’re building it yourself, there’s going to be some capex expense for your infrastructure and things like that. I think industry as a whole is moving towards this consumption-based and so your AI-based applications are also probably going to be consumption-based as well. So that’s kind of my thinking there. Love it, love it. There’s a lot of different ways that it can go for sure. And the correlation from a SaaS to a consumption-based model, if you will. We’ve seen that in the collaboration space as well. When we’re talking about Webex or we’re talking about Microsoft Teams, we’ve seen some different strategies with that as well. So it’ll be interesting to see how this plays out and shakes out, especially with the different use cases of AI, from conversational to agents to all these different models, if you will. It’ll be interesting to see what leads the pack from a financial standpoint on how this is consumed and how it is purchased. We did have another question come in the chat, Tom, and I’ll direct this one to you. I know we touched on it a little bit at the beginning. What are you seeing as far as deal velocity today on traditional infrastructure deals? Is AI delaying the acceleration of deals? Have you experienced OEM vendor movement to solutions that are already perceived as AI ready? Not specifically that I have seen, but you look at the macroeconomic conditions in the market, and we’ve seen technologies from a lot of the traditional vendors slow down. Look at Cisco’s last order earnings. They had a pretty significant slowdown in their core business, and I believe that is probably due to enterprises slowing down a little bit to try and figure out what to do with AI. It’s not going to be a stop, it’s just a little slowdown as they try and develop a plan. Once the plan is done, they’re going to start moving forward on adopting these technologies. Enterprise-wide spending will increase. I think there’s a little bit of an AI pre hangover. It’s not a hangover. It’s like pre planned development slowdown. Interesting. And I’m curious if maybe some of that also coming into play, if you will, is the COVID years, if you will, getting things purchased, getting things in bulk, if you will, delays on product and so on during COVID we are seeing lead times of 160 days. So purchasing things, getting it out there as quickly as possible during those times, and now, oh, man, we over-purchased, right? We overextended, if you will. So let’s take a pause really quick as this new technology is really becoming mainstream and making sure that this is the correct strategy from a financial perspective to make sure that we’re hitting the mark. Troy, anything that you have as ideas on that as well, we’d love to get your perspective if you have anything.

Troy Cogburn:
Yeah, I think Tom’s right on the money in this AI hype, but it’s not really being actively implemented across the board. We’re still really, really early days and seeing that value to these businesses. And so as we’re trying to figure this out, I think we’ll start to see more and more come to fruition. But I did want to pull just a couple of stats from a survey that we did. So we surveyed roughly 300 technology executives, CTOs, CIO’s, CISos, and we ask them, how would you rate your maturity on AI? 32% are still in awareness stage, meaning they’re aware that there’s benefits to be had, and then there’s 30% that are in the planning stage. So they haven’t actively implemented anything yet. And so if you look at those two numbers combined, that’s 62% of the executives that we surveyed haven’t even deployed anything AI-based yet. Really early days. And I think as folks start to figure out those use cases, the actual partners and OEMs will actually start to realize those benefits once the end customers start to actively implement it. I think there is just this market delay of we have the suppliers and partners trying to figure out what to bring to market, but at the same time, the markets not quite ready yet. We’re still planning on what that looks like. So I think we’ll start to see, once a year or two, we’ll really start to see the AI sell through the channel. Definitely, definitely. And that’s interesting. The awareness kind of phase of where this is is everybody’s aware of AI being so important and it going to be a major driver for business moving forward. And they’re aware of it. But how do we kind of get this going, if you will, is what I heard from that, that kind of topic. And actually, we just had a question come in the chat asking about that survey, and it was what were the titles and what industries were the executives that you surveyed? Any of what that looked like? So there’s a bunch and I’ll try and scroll up and look at my data here. But it’s like financial services, healthcare, they’re primarily CIO’s. So while I said there are ctos and other type of titles, CIO’s are the top title at 42%. We also have cisos at 14%, ctos at 12%. The top industries, it’s kind of across the board, manufacturing, insurance, insurance law are kind of the big ones there. So we can, if you want, we can share the survey. And Sam, if you want to get the email, we can certainly share it. It’s free. Happy to do that. But yeah, primarily CIo’s.

Sam Grise:
Awesome. Thank you. We did have another question come in the chat when we were talking about the financial side of AI. The question was, what is the unit of sale on a consumption-based model? Now obviously that’s specific to a very specific AI technology, but maybe you could just touch on what that would look like from a consumption-based model for AIH.

Troy Cogburn:
Yeah, it really depends. I mentioned kind of that token-based pricing, which is crazy. It’s, I think it’s a certain amount of characters for a certain amount of cents. And so the more you input and output, the more cents you start to rack up. But for more of an application type of consumption-based pricing, it could be the activities that happen, and so it could be the amount of automations that you have generated, the amount of tickets that you have answered from an AI perspective, the amount of code that has been generated, and they’ll all have different pricing models behind that. But it does make things a lot trickier. And I can even say from like a vendor perspective, some of these startups are trying to incorporate AI in their strategies, but now they don’t know how to necessarily price it out to their end customers because they’re the ones responsible for the consumption, but their customers are the ones consuming it. And so it does cause a lot of confusion for the market, and there is a trickle-down effect there. From a reseller perspective, or systems integrator perspective, is how do we handle this financial model? Is that on the OEM and they’re billed straight to the OEM for that, for consumption, or is that coming through us? What does this look like? So from a financial perspective, those are all things that need to be ironed out and will be ironed out like we’ve talked about. And it’s really because we’re in that awareness phase right now. We aren’t in full implementation. Everybody’s got this. It’s still a technology that it’s being discovered. What is the best process and procedure for not only those emerging organizations that are building AI platforms, but also for the channel? The channel follows suit, if you will. Really, it sounds like for the channel, the best opportunity that they have right now is obviously having the conversation about AI, but helping with the strategy side of it more than anything of what are we trying to accomplish here? As opposed to just going in and saying, look, we’ve got an AI solution for you. Let’s actually take a step back and understand what it could mean for the end user and what the benefit could be and then build the use cases from there as opposed to just going in and going, we’ve got AI. As much as all of us would like to do that, I know I wanted to do that when I was selling AI a few years ago. Unfortunately, it didn’t work out that way, but get going. I think that’s the message to the channel, which is don’t shy away from those conversations. If your customers are asking you about them, jump in, bring in any available. You can always just call nation. We can come and help you with customers and help them develop strategies, but get going because if you’re not, somebody else will. And the analogy in today’s day and age is. My analogy is when they’re building the transcontinental railroad. The current businesses are horse and buggy hauling supplies across the country on cow trails. And the end, the transcontinental railroad is being built. Right. So you either got to figure out how to get it, you know, start driving an engine or do something else. Right. Because it’s going to be that disruptive to the entire industry and we’ll figure out all these. There are a lot of challenges, but what we will figure them out, of course.

Sam Grise:
Of course. So we’ve got about twelve minutes left here. I want to open it up to Tom and Troy. Both of you, kind of final thoughts and then we’ll open it up for questions, if there are any additional questions. But final thoughts on AI from an OEM perspective, a channel perspective would love to just get the final thoughts.

Troy Cogburn:
All right, I’ll kick it off, Tom, and I’ll hand it to you. I think for me it’s just overly apparent that this is the next thing, this is the next market transition. And obviously the channel needs to get on board and be the ones really the thought leaders in AI to help their customers. I always think of the channel as these trusted advisors. Become the trusted advisor in AI and help your customers because this is the way the markets going, and it’s very obvious based off of things like Nvidia, Market share and other things like that. For me, just start to take in what we were talking about today, do what Tom is talking about, have those conversations, start to build in internal expertise and build those AI teams for consulting. That’s really where we’re going to start to see the value and figure out where the partnerships and different things come to place. Just get going, dip your toe in the water, figure out where you can find value. Have those conversations. That way you don’t get left behind when the market’s actually there. We’re still in the planning phase, we’re still in the awareness phase. There’s still a lot of time to become a thought leader on this.

Tom Brunn:
I agree with that, Troy. But I would also agree that I think it’s not just the next wave or thing, it is a revolution. So it’s seismically bigger than the Internet, than cloud, then everything we’ve gone through the last 25 years since really the mass adoption of the Internet. I believe the AI is bigger because there’s much larger potential to transform core functions of business beyond just Internet, enabling them like we’ve done the last 25 years. And so I just think it’s partners need to recognize that. I believe there is plenty of time to adjust, but you have to start turning the ship because businesses that grew up, as I discussed before, will take a while to turn. And so we got to start put the rudder over. I’m a Navy guy, got to put the rudder over a little bit to start the turn.

Sam Grise:
Awesome. Awesome. Okay, I’m going to share my screen again here. Can you guys see that? Just give me a thumbs up if you can, Tom. Perfect. So we’ll open it up to questions at this point. I know that I did see a question come through saying they’d love to get connected with you, Tom. So really what you need to do to get connected with us on these topics is feel free to email me at Samour B marketing AI webinar and we can have an extended discussion about what this looks like for the channel, it looks like for OEMs, and what it looks like from a marketing perspective to get this out, because that messaging is going to be important so that you can build that business and ultimately drive revenue. Feel free to email Tom as well to get a little bit deeper into the technology of what that looks like@tbrunvationventures.com. dot I did see that one come through and I did see one other one that’s kind of a fun question, if you will, to keep this thing going. Is, has anyone cracked AI yet? Has anybody hit the golden, the gold pot, if you will? Has anybody cracked it and just absolutely dominating the marketplace from an AI perspective yet?

Tom Brunn:
Nvidia.

Sam Grise:
Yeah, Nvidia. Anybody else kind of dipping at their heels or anything like that?

Tom Brunn:
OpenAI.

Sam Grise:
All those guys have, definitely have a significant lead on everybody else. Microsoft Copilot, OpenAI and Troy mentioned a couple, there’s a couple of definitely, um, short term winners in the market right now, but I wouldn’t say the, the dominant player has been decided yet.

Troy Cogburn:
I agree. I think there are some winners that you’ve seen in some of the public markets, but still too early to tell. I’ve heard the equation or the analogy of this is kind of similar to search. When people were doing search on the Internet, there was a bunch of players that were in the market, and then out of nowhere, here comes Google, and it disrupts everyone. So I think it’s still early days, there still could be a Google-like company that comes to market. And so I don’t think anyone’s fully cracked it. And I think everyone’s just trying to move towards this thought of artificial general intelligence, where we’ve got this, basically a computer is better than a human, is my very simple term. We haven’t gotten there yet. I think we’re far away. Some people think we’re very close. I think once we get to that AGI, it’s going to be a little bit scary, but that could be the person that dominates the market.

Sam Grise:
Love it. Love the perspective. Well, awesome. I don’t see any other questions coming in the chat, so we’ll go on ahead and call it there. I want to thank you both again for the insightful conversation. This was a great discussion, really diving into the impacts that it’s having from an OEM perspective, what it’s having on the channel, and also what it’s having on end users. I think that this kind of encompasses the full ecosystem, right? We talked about distribution and their, their roles in it, the traditional VARs and resellers, if you will, and system integrators, their role in it, as well as the OEM side. And at the end of the day, it comes back to the customers, right, the end users that are adopting this technology. And I think it was great that you brought up that survey that you did. Feel free to email Tom for that as well if you want to see that link for that survey. But really, you know where we’re at with this AI journey. Having that data of 62% of C-level executives, if you will, from a multitude of different industries are kind of in the discovery phase, if you will, the awareness side of it. So there still is time to develop this practice and get after it, if you will, from a business perspective. But the time is now, right? 62% are in awareness. That means that they are looking at investing into a strategy, and investing into this platform doesn’t mean that they’re going to do it tomorrow, but it does mean that it is on the roadmap, and they do have insight into that future. So definitely want to thank you guys again in for the time. Really appreciate the insight there. Awesome. Sweet. Well, thank you guys so much. Look forward to seeing a few emails from some of the attendees. Thanks, Sam, and thanks everyone for joining. Yep, thanks everyone.