Microsoft Copilot and the Threat to MSP Margins: Ryan Morris on AI-Driven Channel Shifts

[00:00:12] Here's something strange about the AI moment the whole channel says it's living through. Walk the websites of the managed services market right now and roughly six in ten of them don't mention selling AI at all. Not a service, not an offer, not a line item. The loudest transformation in a generation, and most of the providers supposedly delivering on it are silent on it.

[00:00:39] And of the minority who do say something, the most common thing they say is a vague AI solutions banner with nothing productized behind it. Where there's something real, it's overwhelmingly reselling one vendor, Microsoft's Copilot.

[00:00:56] And the question I want on the table today is, is AI coming for the channel? It's not that, it's this. Has the channel actually built a go to market for AI at all? Or has it just agreed to be the fulfillment arm for the people who did? My guest has spent his entire career designing how this channel goes to market. There's nobody I'd rather ask.

[00:01:21] Welcome to the Business Effect Lounge. This is where we break down what's changing in the IT services market, what it means for providers and vendors, and what to actually do about it. If you're running or supporting an MSP, this is about making sense of the environment you're operating in, not just the headlines. Now to make this conversation possible, a message from our sponsor.

[00:01:43] This episode is brought to you by Control Map. Growing MSPs are using Control Map to build recurring revenue by expanding their GRC services. Starting now, Control Map is offering a free plan for MSPs looking to get started with providing compliance as a service. Create a free account and run an assessment. Track key items like policies, risks, and evidence in one place. It's a practical way to prove value to a client before deciding to expand your compliance offering.

[00:02:11] Try Control Map for free today. Visit scalepad.com slash Dave to get started. That's scalepad.com slash Dave. Ryan Morris is Principal Consultant and Chief Go-to-Market Analyst at Morris Management Partners, where he spent 25 plus years designing go-to-market and channel strategy for vendors, distributors, and solution providers alike.

[00:02:35] He's built channel programs for some of the biggest names in tech, and he's a featured voice in Scalepad's 2026 MSP Trends Report. If you want the person who can tell you whether a go-to-market motion is real or theater, it's him. Ryan, welcome back to the show. Thank you very much, Dave. Very glad to be here. And audience, drop your questions in the chat as we go. We'll put them in live. Now, Ryan, you're my man for thinking about go-to-market strategy because it's your world.

[00:03:05] Now, a little over a year ago on this show, you did draw a line. The opportunity for MSPs in AI wasn't reselling tools. It was enabling adoption. That's the onboarding, the training, the integration, the human in the loop. That's the value that reinforces rather than being replaced. Resell, you said, and you get commoditized. Enable, and you differentiate. So we went out and we looked at what the market actually did with that.

[00:03:33] We ran our web positioning data across the entire MSP market. About 61% of MSPs aren't selling AI on their sites at all. Of the ones who are, the sing its largest category is this vague AI solutions language with nothing scoped. I'm calling that AI washing. Only about a quarter of even the AI forward firms have a named productized AI service.

[00:04:00] And anytime there's a specific vendor, roughly 84% of the time, it's Microsoft co-pilot. Right? So I got to tell you, you told this audience to enable, not resell. And a year later, the data says the market that moved mostly went the other way, co-pilot resell. And the majority didn't move at all. So I got to ask, like, what did the channel get wrong here about AI enablement?

[00:04:25] Well, I think that it's just a question of the legacy of the last 20 years in our industry. What we have taken to market, packaged products created by a vendor, taken to market through us as a channel, that we use either as products inside a customer's environment or tools inside of our stack that we use to deliver package services. That is just not where we're at from an AI tools or solutions perspective.

[00:04:54] I like the word AI harness. I think that's an effective way that I've heard this described in the last several months, where we take the tool, the brain, the algorithm that does the analytics. We match that with all of our data and the technology stack that makes it available and accessible. And then we run it through logic that we use to actually do something in our business. See, the algorithm is packaged and available.

[00:05:23] The rest of the harness, it's do it yourself. We're building these things as we go. And we're just not that much of a builder audience from an early market deliverable point of view. My rule still is that the way that your customers will take you seriously is when you are, number one, delivering a packaged engagement type.

[00:05:48] It doesn't have to be just a skew or a line item that has a fixed price, but it has to be a defined and packaged engagement investment that delivers a measurable business outcome by changing the workflow or the business processes that they do in between. That means it's going to be much more effective when it is targeted to a vertical, aligned to a specific function within a business and designed against the customer's data,

[00:06:18] not against some general algorithmic data lake that we might have access to. That's what I consider to be success. Custom specifically defined engagement, measurable business outcome that makes a measurable change in the way they do their business workflow and business process. That's just literally not out there yet. And to be fair, it's early enough that people haven't figured that part out.

[00:06:46] But I will still say one year later, if that's not where you are, then you are reselling a commodity product that if we're not paying attention to the headlines is commoditizing at light speed compared to what previous industry phenomena have done. Now, I got to ask though, because I feel like this AI enablement idea, like in my mind, it's kind of obvious, right? That there's an element of you need to get some your customers data ready.

[00:07:16] You need to be talking about what's appropriate for AI, what's not like that feels like a sellable, manageable service. But, you know, the market isn't necessarily responding that way. Maybe they're viewing it as this kind of unplayed glue work that comes around that. Like, give me your take on, you know, what the actual opportunity is right now for providers. You're hearing the vendors and what they want to push out there. But like from a customer side, what do you think there? What's the actual opportunity?

[00:07:47] See, I see these opportunities in three very specific buckets. And there are early offers that are doing very well in each of these, but we're not yet at kind of mass market distribution or coverage. The first bucket is the governance bucket. In other words, not just that you have data, but that you're not using private or protected data in an inappropriate way.

[00:08:13] Because when you apply an agent into the data lake, it does not have morals, ethics, standards, awareness of or give a shit about whether or not they're doing the right thing with the data. It's going to answer the question. So governance and risk management around the AI implementations. That's the first and most obvious bucket. And I see some people doing that very, very effectively.

[00:08:43] The second bucket that I see is actually around what I will call the financial control of this category of resources. Think about it, right? Like go back in the way back machine in the world of business telecommunications, right? We used to sell long distance service and then we would switch people from one provider to another.

[00:09:06] And the tactic that we got to somewhere in the late 1990s, early 2000s was, well, just show me your phone bill. I'll take a look at it. I'll review it and make sure everything is accurate. And then I'll see if I can find a way to save you some money. And if I could save you 10, 15 or more percent on your bill, would you be willing to switch over to me? Right.

[00:09:30] We got there because it was an ungoverned resource that had unspecified application. It could do something. It could not do something. Right. But it had unlimited potential. And as soon as people started using it, they went, whoa, that's a really expensive and highly variable and unpredictable. So we put in these control systems to say, here's how you can actually control the cost of telecommunications.

[00:09:59] You will note that we are already seeing those services around token management and AI spend because number one, the AI companies make more money when you use more tokens. So they can make it really easy for that to happen, even sometimes what you perceive to be accidentally.

[00:10:18] But also when you are applying agents into a data set, the way that agents work, they are just designed to answer the whole question and they will review things over and over and over again. We sometimes see 20, 30 calls into a data set to answer a single question as the agent checks, rechecks and tries to get to a legitimate answer.

[00:10:43] You can just feel the tokens churning through the slot machine, right? You can just tell that this is an outrageous expense. And so now there are people who are out there saying, listen, you tell me what you're trying to do. I'll set up your infrastructure right. We'll say who in your company is authorized and then who is not authorized and what is appropriate and what is not appropriate usage.

[00:11:06] We'll assign budget controls, we'll assign throttling mechanisms and we can help you control the cost associated with AI. I think that is something every customer would pay for yesterday if you offered it to them and if it sounded smart. And by the way, I happen to know a broad line distributor who has packaged something like that and it is quite literally available as a billable service today.

[00:11:32] It's something that we got to a decade faster than we did with any previous technology because the costs have just exploded that much more rapidly. And then finally, without going into too much detail, the third category that we see are offers that are actually business specific. Help me run my financial services company more effectively.

[00:11:55] Help me serve more clients through my law firm with the same number of headcount by utilizing AI tools. Help me get to market faster with marketing materials and translated in language websites, et cetera, et cetera. If you can do a business function that is tied to a specific vertical and you can show people I can actually make you more productive. That's where you're going to make really big money, right?

[00:12:22] Because governance is insurance and we know how to sell that cost control. We all understand how to sell that and it's your earnings are a fraction of the savings that you generate for your customers. But all of the upside is in that third bucket, right? It's all these things that we do to say, you've heard everything about AI. You've been told it will just do your jobs for you. That's not true. Let me show you what it can do, how you can use it and actually improve business performance.

[00:12:52] There are good examples. But as you said, it's just right now people are waiting to be handed a packaged offer and told, just put your logo next to our logo and go sell it into your install base and everything will be magical. We'll both make recurring revenue. That's just not where we're at evolutionarily in terms of AI at this point. So let's dial it back one level, right? So because I like I'm a dog with a bone on the 61% that aren't even putting it on their website.

[00:13:22] Right. And because I look and say like, OK, let's say for a moment everything you've just said, the aspirational stuff. OK, that might be too far reach for many. But I'm going to pull in a comment that Vadim Vladimirsky put on from the, you know, in prep for this. And he focused on the fact that the 61% seems to replicate what he's hearing from partners. That makes sense to me.

[00:13:44] And his his justification here is, is that most are using AI heavily internally for ticket triage documentation, but they don't want to put an offer on the website. But I want to qualify what my 61% is. 61% don't even mention a eye on their website. And so I would sort of say, like for this, this 61 group, like if you're using it heavily, your messages.

[00:14:12] Hey, customer, we're really good at managed services because we have used AI machine learning, large language models, automation, like all of the magic words. We've done all of that work to be really good at it. So I would sort of say, OK, let's go with Vadim's right here. Sure. They're using it heavily. I will smile and go, I don't believe it until you put it on your website.

[00:14:35] And so I want to I want to ask you as a go to market person, like, am I highlighting a gap or are MSPs like deciding there isn't margin or reason to talk about that? Well, and again, I think that if you if you position that correctly, internal usage that improves my quality, accuracy, productivity, that actually is a very effective marketing story.

[00:14:58] I can convince a customer to choose me instead of an alternative because I use the latest, the most advanced, the most polished, the most automated resources to ensure that we will cover things that others cannot cover, that we will produce results others cannot produce. And we will do it with a confidence that others cannot deliver. That's a quality offering.

[00:15:21] That is a question that says, I use the best raw materials to enhance the caliber or the quality of the offering that I bring to market. But you've got to understand that that's a reason in a competitive marketplace to say everybody says they do the same thing, but I do it better. Therefore, I am the quality alternative and I will charge you more for the services that I sell.

[00:15:46] But you and I both know that that's just not the way that the vast majority of MSPs sell. The vast majority of MSPs say we all do exactly the same thing and I've used some tools that everybody else has access to. And therefore, I've reduced my cost and I can do the same work for less expensive than other people can do. If what you're saying is I'm using a lot of AI and therefore I can give you a deeper discount.

[00:16:13] You know that thing we've all been dealing with and managed services for the last 30 years where customers get to the point of satisfaction where nothing breaks and everything goes fine. And then the customer starts to scratch their head and go, wait a minute, why am I paying you to manage my systems? Nothing ever goes wrong with my systems.

[00:16:33] Okay, if that was a little bit of a problem in the classic sense of manual services that led to tool stacks that are now being empowered with AI. If that was a little bit of a problem before, it's a thousand times a problem right now because what you're saying to people is you don't need me.

[00:16:51] You just need somebody who's configured these tools and then managed services could be on a path to actual zero in terms of the incremental cost needed to operate these things. I'll go out on a limb and say that's not what we want to convince the marketplace of. We do not want to advertise, hey, we're not necessary. You could totally do this with machines and without us.

[00:17:17] But if you could use it for everybody does the same thing, but we've gone to the nth degree and we've added the brains and the power and the automation to make our services even better. And that's why we can be more personalized, more relevant and compelling to you as a customer. Therefore, we charge more. Cool. Do that like use technology to be better.

[00:17:40] But don't tell me that it's like I use technology and therefore I'm going to give you another discount. Gotcha. Well, I want to get into the economics of the channel here. But before we get into that, let's hear another word from today's sponsor. This episode is supported by the Small Biz Thoughts technology community.

[00:18:03] Small Biz Thoughts is designed for IT service providers who already know the technical work and want to get more intentional about how they run their business. The community focuses on the operational side of managed services, things like service agreements, pricing, process design, and the day to day decisions that determine whether an MSP scales cleanly or stays reactive.

[00:18:24] Members have access to a deep library of practical resources, but more importantly, they're part of ongoing conversations with peers who are actively running services businesses and willing to compare notes on what's working. It's deliberately practical, deliberately focused, and built around helping MSPs make better business decisions over time. If you want to see how the community works, you can find the details at smallbizthoughts.org.

[00:18:53] So I want to pull in an argument that I've been working on, and it's quite possible that you and I are not on the same page here, right? And I think that tension is interesting and something worth talking about. So on the news show, I've been making an argument that might be really uncomfortable for anybody who's been a channel person for a long time. And my thought is that AI isn't a new product category, but it's actually a structural threat to the channel's economics, right?

[00:19:19] The idea of moving to seat plus consumption pricing just devastates the recurring revenue model that MSPs are built around, right? And that when it comes to the AI for the small business market, MSPs, I don't necessarily think will be the product, the primary monetizers here. I think the vendors may actually become that. Like the channel becomes the delivery layer and the vendor starts capturing much more of the value.

[00:19:47] Now you've spent 25 years designing the indirect channel. So I kind of want you to grade my argument. Like, I want you to say like, how much am I on to something here about this shift and tell me where I'm wrong? Well, see, I think you are 100% on target with the economics model. It is a question of maturity and speed with which we arrive at the extinction point, right?

[00:20:14] So on the first part of it, the model consumption pricing, and we've been arguing about pricing models and managed services quite literally since we got started all in per seat, per user, per whatever, right? We've been trying to figure out what is the metric against which we measure or scale the value that we deliver.

[00:20:36] Got to embrace the reality that the metric for AI is opposite of what you've been trying to do. Whichever unit you've been measuring as your per calculator in your pricing scheme as an MSP has been designed to make it go up, up, up, up, right? I want to have more users using this. I want to have more authorized access. I want to have more servers, more data volume going through the pipes.

[00:21:06] I want to go more and more because if you're using it more than by definition, it's providing more value. And that means that I should be able to charge you a higher price. Except that AI is by definition designed in a consumption model to increase consumption of things beyond the control of the MSP and eliminate the consumption of things that are within the control of the MSP.

[00:21:31] Think about it this way, right? If I am going per user pricing in my managed services and somebody comes in and says, well, wait, if you use an AI agentic model and it can do the work of all of these people, they just went from 10 authorized users who need access to a system to one. And now the other nine are doing something else. But why would they continue to pay you for people who are no longer needed to have access to the system?

[00:21:58] It goes to a core psychology of consumption based modeling. And trust me, that's not a new model. Everybody likes to say consumption modeling as though this is something we've just invented in the world of technology. But you get a utility bill every month for electricity and water that you consume. You also pay for gasoline at the pump or electricity that you put into the battery of your car based on consumption.

[00:22:27] Consumption based models have been here since quite literally the dawn of mankind. The more you use, the more you pay. But you know every bit as well as I do that the psychology of consumption economics, as soon as I recognize that using more costs more, I am constitutionally required to figure out a way to minimize or to optimize the utilization of that. We do that with electricity.

[00:22:56] How do you optimize, get the most performance with the least amount of consumption? How do you do that with gasoline? Oh, the mileage goes up and the efficiency of the machine gets better. How do you do that with AI? Well, you eliminate the things that are driving consumption. Consumption and the things that are driving consumption tend to be humans or the agents that are replacing humans in which both cases, those are the things that an MSP actually charges for. Right?

[00:23:25] So, if we put ourselves into the natural human consumers mindset and we insist on getting paid by consumption, okay, just because it's good doesn't mean that people are going to go, well, at the same price, I'm going to consume even more of it. What it means is they're going to look for a way to cost optimize that. They will consume more AI. You and I both know that, right? We're at the early stages of the consumption in this technology.

[00:23:55] We see data volumes just absolutely mushrooming as people figure out, wait a minute, this agent can actually do something. Let's apply it to our business. It goes from 1x to 100x to a million x very quickly and the AI companies love that, right? Everybody is making money on that who is running the infrastructure and the pipes.

[00:24:18] But you on this end are the face who is issuing the invoice to that customer and they're going to say, okay, so data consumption times what? Well, it's data consumption times servers. I'm going to consolidate my servers and have fewer licenses. Data volume times users. I'm going to license fewer users. I will cost optimize against the consumption model 100% of the time.

[00:24:47] Your theory then is 100% correct that we're going to commoditize or even optimize to zero the things that we currently get paid for. I would just argue we're so early in the process that we have time to adapt.

[00:25:05] Microsoft recently made a big announcement about, hey, we're going to stand up this service offering to put forward facing engineers in the field to service customers around AI design and harness development. Cool. They're going to put $2.5 billion against that initiative. Cool.

[00:25:28] That will give you roughly, I don't know, 6,000 to 8,000 engineers depending on where they are housed, which shore they are homed on. Right? Divide that across the top 20 fortune companies out there. You know, Fortune 10, Fortune 50, whatever. Get to the Fortune 50 and 8,000 engineers are barely going to be able to ring the doorbell of all of their customers and all of their needs.

[00:25:57] It's everybody says, oh, the vendor is going to be the one who accrues the value here in terms of these services. But even in the most common one that you've already identified around Copilot and Microsoft said, I'm going to put $2.5 billion out there and we will do the services.

[00:26:15] And everybody goes, oh, no, that means they're going to take our business from the top 50 customers for the other, I don't know, 9.995 million SMB entities that you have access to sell to. Microsoft never has been able to touch them with direct professional services. They will not be able to touch them with direct professional services. The model is dangerous.

[00:26:40] The vendors will never scale to a point where they will come and directly compete for your end user install base. But somebody else is going to step in there. It will be one of us. It will be another service provider, local, customized around the AI business model who will come in and steal your install base. It won't just accrue to the cloud. Well, so I'm going to offer a middle theory and I want to get you to take on it. So if I think about where the margin has lived, right?

[00:27:10] So if we think about the traditional kind of per seat version of this or per user or per like there's been additional margin in the services component, right? That's the bit that it's heavyweight. But if the dream of agentic AI lives true, we're going to be able to automate a good portion of the human bit of that. We'll be able to do it at machine speed and we'll be able to do it at considerable scale, right? Which so, okay, cool.

[00:27:39] Microsoft may not be able to move in and do that, but I don't know. Well-financed large players may be able to take in a good portion of that. And you end up with a large portion of the market that can get just driven to commodity services, squeezing everybody out, right? You're going to have a group of people that want to pay on outcomes. They're high value customers. Yes.

[00:28:02] But you're also going to have a very large portion of the business that is, you know, the SMB world that is enticed by just simple do it yourself, you know, eliminate all of that. And I would make an argument that a lot of MSPs are trying to do both at the same time. Like they're kind of middle of the road, service the space. And I think you're going to have to get really focused on one of the two. Does that resonate with you to think that kind of framing?

[00:28:30] Well, it resonates because what you're describing is the lived experience of business model transformation, right? You were born and raised in one business model. You look around and realize we need to change to another business model. Light switch events do not work in terms of business model change. You have customers that cling to the old way of doing things. You have a sales team that's not qualified or highly motivated to sell the new thing because it's confusing and it's difficult to do.

[00:28:59] It's difficult to persuade customers. You will have a prolonged period of time where you are one foot in each camp as you navigate the transformation into the new business model. That the science of business transformation, by the way, is something that it's affecting everyone in the channel. And very few people have figured out how to do it effectively.

[00:29:22] We've been teaching around that concept in our partner engagements through vendors and distributors for a number of years. We've done very extensive research and training and coaching and actual business project development where we help a business transform from one model into another. It is never fast. It is never clean. It is never clean. It is confusing.

[00:29:47] And as a result, most people sit and look at the pain and suffering of a transformation and they go, nah, I'm out. I'm not going to do that thing. And so I'll just volunteer to ride the slide down as the model that exists commoditizes. I'll just allow myself to slowly, gracefully wind up spending more days at my lake house than I do at my real house.

[00:30:14] And I will be, I will admit I am a dinosaur and I'll be fine with that. I will just ride the slide out for as long as I can. For those that aren't okay with that, or if you're not close enough to the lake house to actually live with that transformation decision, you have to go through from one to the other. And it's hard. It is a difficult thing for businesses to navigate. Keep the old business running.

[00:30:42] Don't piss off any of your existing customers. Spin up the new business. Don't overtax your existing employees and resources. And then gradually we remix the model agent agentic AI will help us in that process. It will need to be done internally for an MSP, but it is a service you should be able to provide for your customers. But it will absolutely never be 100%. Right.

[00:31:10] It's just, it's not going to be clean enough where we just say pick one or the other because the vast majority of customer consumption for the next, I'll give it five years, even at AI speed. The vast majority of consumption will be customers who themselves are running the legacy business model using AI, trying to move into the business model of the future. It'll get muddy in the middle.

[00:31:36] They'll be there for an extended period of time and they'll need service on all three sides, right? Legacy model, new model, messy middle. All of that will need to be done. As an MSP, you need to make an offer that says, I understand where you are. I can appreciate where you're going. I will join in the fight with you and agree to get paid for the value I produce in the form of outcomes.

[00:32:01] But the way we get there is a little bit technology, a lot of business process to accurately scope and utilize that technology and then transformation of humans to bring them along in the conversation.

[00:32:18] Everybody wants to say, AI is going to replace humans and it's going to take away all the entry level jobs or all the white collar jobs or you wouldn't want to be a radiology technician reading x-rays in the world. That was all supposition in the beginning. Lived experience is proving that that's not actually eliminating the need for those people. And in fact, we will see an increase in the need for those kinds of specialized technicians.

[00:32:47] You're not going to get rid of human employment with AI. You will change it. You will change the productivity calculation. You will change the expectations of output and the volume that a single human can produce. But you're not going to eliminate the humans. Right? Boston Consulting Group. Right? The guys who are famous for their BCG matrix, the laggards and the cash cows and the diamonds in the rough. Right? Everybody's kind of familiar with that concept as a business principle.

[00:33:16] BCG came up a number of years ago with a model around technology that they call 1027. Right? And it's been true for everything. I believe it will still be true for AI. 10% on the functional technology that's needed. 20% on the data and the environment where it will operate. 70% on the humans and the process and the workflow that they use. Okay?

[00:33:42] If you're selling Copilot and the algorithmic capabilities of agents, that's the 10%. If you are standardizing data, creating data lakes, creating the queryable environment where you can get relevant, personalized answers through your own GPTs, that's the 20%. The 70% is how do we redesign business process?

[00:34:08] How do we bring humans out of the dark ages and make them not freak out and want to quit because we're eliminating their jobs? How do you make them capable in the new world? If they're wrong. Right? Let's just say that was how we did client server and that's how we did cybersecurity and that's how we did the transformation to the cloud. But AI, oh, that's going to be different. The 10% is going to be much larger.

[00:34:33] Even if the 10% became 30% and the 20% stayed the same, you notice there's still 50% of the human cost of adopting, leveraging and benefiting from AI because we don't just apply AI to theoretical data calculations. We apply it to business. And businesses, as you know, are just groups of humans who do some things for money. Those humans are still there.

[00:35:03] You're going to need to service them. That's where the MSP is going to find and like just an unlimited amount of opportunity. Do not just sell the tool. Do not just standardize and organize the data and let somebody else pick it all up. You will be the implementation arm for the 10 or 20%.

[00:35:25] But that's where all the value comes from is in the implementation, integration, customization, aftermarket support, human training level one, level two. Right? Like we know there's a massive amount of work to be done post sales. That's not going to change. So let's wrap up here and try and make it a little, we have some advice that's concrete, right? So we've got an MSP who's listening or watching to us. That is very typical, right?

[00:35:53] 60% plus of revenue or percentage points in their revenue is flat rate managed services. They're in the 61% that haven't put AI on their website. Give them some a little bit of what would be your tactical like, hey, this is your initial go to market strategy. Like what's the thing that they should be thinking about in the next 12 months? Yep. Experiment, package, price, test, validate scale, right? Pardon me. That is, that's the methodology.

[00:36:23] You need to experiment internally to figure out what it can do and can't do. And I think even you and I, there's still things like on a random Wednesday where I will use AI and go, Ooh, that's cool. I didn't realize that I could do that with that. So we're still experimenting and we're learning. The mistake most people make is that they tell their entire team, experiment, go crazy, just use AI, figure out what we can figure out.

[00:36:49] And then from this big bucket of maybes, we'll filter out the ones that we want to package. That sounds an awful lot like token maxing. And that sounds an awful lot like a really fast way to have your CFO take away the keys to the car and tell you no, you don't get to go out on Friday night. Right? Like that, don't have everybody make it a SWAT team, make it a very specific set of individuals who have targeted functions in your business that they're thinking about,

[00:37:19] and tell them to experiment. Figure out what that is, use it yourself internally, consume it, then say, okay, this is the kind of an engagement that it would be. We'll package it. We'll do it for somebody and go, would you pay a dollar for that? Would you pay a hundred dollars for that? We have to figure out the pricing. Then we go out there and we test it with a handful of customers. We drive the consumption and validation. We move into a scale model.

[00:37:44] You got to start with the experimentation, but move aggressively into the customer engagement model and the packaged offering. It doesn't take long, right? You should in 30 days or less of focused experimentation be able to come up with two packaged offerings that you can do that are relevant to the stack you already have, that are meaningful to the customers you sell to, that sound like they got buzzwords associated with them.

[00:38:12] But you can come up with two offerings and then you can use AI to help you do the documentation and all of the how-to manuals for your techs internally, right? Get to an offering. Two things that you can put into the marketplace as rapidly as possible and then actually promote those things, right? Tell them I'm using technology to improve myself and that's why we're better, not why we're cheaper. That's why we're better. We are experimenting.

[00:38:41] We've found something. We want to use it internally and then test it with a customer. We'll use that to build the packages, test the pricing and actually go to market. Okay, it's people want to look at this and go, okay, I need to develop a go-to-market strategy. That's nine to 12 months.

[00:38:59] No, that's one of the really nifty things about AI is that you should be able to come up with a go-to-market strategy for two structured offerings that are relevant to your environment in 30 days. That's it. That's fair. And I would also caveat and say like, by the way, one of the things you can definitely do is one of those two can be to make your offering incredible.

[00:39:20] Is that you can make your own offering so much better than it was by using data and automation and fill in the gaps in your own org. Ryan, we could do this all day. I know that's for sure. If people are interested in continuing the conversation, what's the best way for them to get in touch? Best way for me still on LinkedIn. So I'm at Ryan Morris 303 on LinkedIn and would love to have a conversation. I know you would. I always love having you on.

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