Executive RoundtableAI Partner-Led Growth (AI-PLG)

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Julia Nimchinski: Thanks again to mass, Doug. We are transitioning to our next conversation, and it’s AI and partner led growth welcome, Jay Mcdain.

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Jay McBain (Canalys): Thank you. Thank you for having me. And I’m so excited about this this topic.

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Julia Nimchinski: We’re really excited, too. Let’s do a quick round of introductions. Everyone.

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Jay McBain (Canalys): Sure I I can go first, st I think. I’m on the on the left side of the slide. Jay Mcfain, chief analyst for Canalys. Canalys is Latin for Channel. However, the channel space has changed pretty considerably in the last couple of years, and I’ve got some of the people on the panel here who have been responsible for those changes so excited to kind of dive into this conversation and move on. I’ve got Bob next to me here, so we’ll kind of go to you next.

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Bob Moore: Amazing. Thanks for having me, Jay. It is great to be here. My name is Bob Moore. I’m the CEO and co-founder of a company called Crossbeam which is about 6 years old and we are a platform that helps companies collaborate across company lines with their data. So

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Bob Moore: we take the age old process of account mapping, which is basically how companies compare Crm data, and we bring that into the cloud in a kind of secure, tightly controlled manner, to to unlock the Venn diagram of

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Bob Moore: things like how many customers you and your partners have in common, or how and where your sales, pipelines overlap and kind of by unlocking that data layer. There’s an incredible universe of ways in which you can unlock new workflows and ultimately train and provide context for AI agents and do other exciting things. I’m sure we’re going to talk about today.

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Jay McBain (Canalys): Absolutely dina your next

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Dina Davenport: Jay. Hi, everyone great to be here. I’m the global partner marketing director at Intel Corporation. So I am overseeing this strategy and go to market development. For our, at this point, our top global system integrators.

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Dina Davenport: So it’s been pretty exciting. As we are integrating AI in various ways, and really, that being that collaboration champion, both internally and externally, within the industry as a whole, to bring success for our customers and our partner ecosystem so great to be here looking forward.

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Jay McBain (Canalys): That’s fantastic, Megan.

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Meagen Eisenberg: Yes, Hi, Megan Eisenberg, I’m the Cmo of Samsara. We’re all about improving the safety efficiency and sustainability of operations that power, the global economy. So all those folks out there frontline workers driving trucks, construction sites, keeping them safe and keeping the road safe. So excited to be here. Talk a little bit about growth, with partners.

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Jay McBain (Canalys): That’s great, fantastic! And Blake.

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Blake Williams: Awesome. I’m Blake Williams CEO of Demandy. And what we’ve been helping brands do. We’re about a year old, but we’re kind of took a different approach instead of the brand to brand co-marketing activity. We are doing a lot of brand to network co-marketing primarily happens on social. But we’re getting people because people buy from people. We’re getting those people in front of each other’s customers opportunities and prospects. So we can have that audience cross pollination. So that’s what we do.

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Jay McBain (Canalys): Great welcome. And what a what a great panel I’m excited about this, Julia! How long do we have time? Check.

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Julia Nimchinski: 54 min.

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Jay McBain (Canalys): 54 min. So let’s let’s jump right in.

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Jay McBain (Canalys): I wanna you know, kinda just frame this a little bit with you know lots going on. You know, I mentioned kind of the Channel world. There’s, you know, the partnership world. There’s the Alliance world, this broader ecosystem, and this kind of move over into the, you know, a much more important part of the go to market motion.

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Jay McBain (Canalys): And there was a point. And and some companies. There’s still a point where, you know, marketing, you know, is somewhat siloed from sales which is siloed from Cx, which is siloed from product who are building integrations.

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Jay McBain (Canalys): And then the the partners that you know traditionally been viewed as cash registers.

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Jay McBain (Canalys): 75% of world trade, the 105 trillion dollars of Gdp last year flowed indirectly.

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Jay McBain (Canalys): So how we bought a car, how we bought a TV, how we buy peanut butter, but across all 27 industries, the dealerships, the agencies, the brokers, the resellers, retailers, franchisees, the gas stations, the pharmacy, whichever industry you want to talk about, it’s always been viewed measured, paid

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Jay McBain (Canalys): at that point of sale.

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Jay McBain (Canalys): The the one thing that we’re learning, you know today, especially in the platform economy.

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Jay McBain (Canalys): is that the average customer going through their 28 moments before they make us purchase, actually have 7 partners.

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Jay McBain (Canalys): And that’s not according to me, me. That’s according to Mckinsey, who advises every one of the Fortune 500 boards.

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Jay McBain (Canalys): So if there’s 7 partners who have spent decades earning that trust.

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Jay McBain (Canalys): and are, the, you know, most important people around that customer.

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Jay McBain (Canalys): How do we think about marketing? How do we think about sales? How do we think about Cx

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Jay McBain (Canalys): through to and with these really important people

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Jay McBain (Canalys): that surround. Last thing I’m gonna say is, you know, in sales salesforce puts out a state of sales every year.

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Jay McBain (Canalys): and you know, this year’s state of sales report, which, you know, interviewed 5,500 cros and vps of sales around the world in all industries, in all geographies it’s at a point now, where 89% of salespeople

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Jay McBain (Canalys): are using partners every day for the 11% who are not 58% plan to

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Jay McBain (Canalys): within the next 12 months.

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Jay McBain (Canalys): and it was not a good selling environment in 2024, over half of the salespeople in the world did not make their numbers.

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Jay McBain (Canalys): but for those that did 84% of them pointed to partners as the reason why.

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Jay McBain (Canalys): So we’ve seen, you know, Hubspot came out with a report in marketing.

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Jay McBain (Canalys): and it’s close to 84% as well. And so we’re starting to get to this point of this decade of the ecosystem.

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Jay McBain (Canalys): where all of the go to market. Motions are coming together and and getting pretty obsessed

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Jay McBain (Canalys): over the 7 partners surrounding not only the 1st 28 moments, but every one of our renewals. Every one of our enrichment and upsell cross sell opportunities for this customer for life. So love to kind of move through the panel on the road here, and and how you frame up this moment. And I know, Bob, you’ve thought a lot about this kind of convergence of channel and partnerships into go to market.

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Bob Moore: Yeah, it’s very, very true, thanks, Jay, and thanks for that tee up. I think it’s pretty undeniable. The outside looking in just overall amount of influence and impact that ecosystems at large kind of have had historically and are having on the way. Companies build their businesses. But I think there’s a bunch of other

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Bob Moore: trends afoot here that end up, having these really interesting force, multiplier impacts on on all of that foundation from which we build right. And they’re all kind of converging in this era in a way that I think is actually going to radically amplify the impact of

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Bob Moore: whatever label you want to put on it partners or ecosystems or alliances, etc. The lines are increasingly blurring, but the impact is able to be significantly larger because of those blurred lines. So specifically, what we’re seeing is as the evolution of the Api economy and the widespread adoption of the cloud and kind of the late stage of the digital transformation movement have pushed virtually every business that is operating at any kind of scale into

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Bob Moore: being operated, and their data being primarily stored in these cloud-based applications and cloud-based locations. It means that there’s this incredibly high amount of interoperability that can exist between both technology tools and the services providers that bring those tools together. Whether that’s system integrators, agency partners, or anybody else in the traditional channel. And because you lower these barriers to

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Bob Moore: interoperability, it means that you greatly increase the way in which you can scale systems, workflows, playbooks, where that interoperability is a key part of how you make a sale get done. So what that means from a practical standpoint, is that companies that may have historically been partners because they issued a press release together, or had some customers in common or embarked upon a partnership because they had a multi-year joint venture development where they created a product together.

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Bob Moore: Now the bar is exceptionally lower where the amount of technical lift and the amount of effort required to build a product integration or potentially the amount of effort required to train a sales team or bring, go to market folks up to speed with. How such an integration or collaboration brings value. These things are going from multi-million dollar investments with multi-month commitments to

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Bob Moore: kind of the foundational operating system of how companies create value and kind of formulate new value propositions so just the general movement, independent of the channel of this whole digital transformation movement that enterprises have been talking about for the last 20 years, really reaching these late stages just creates this incredible unlock where the ability to do

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Bob Moore: the best of the best things that we’re creating value through partnerships and partner channels is now accessible to everyone. Right? If you look at. There’s 30,000 companies on Crossbeam. We’ve got plenty of large, publicly traded enterprises. But let’s be honest. 2526,000 of those companies are probably less than a thousand employees. They’re all doing this, too, and I think that’s an important thing, which is that it has kind of pushed

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Bob Moore: the the gatekeeping aspect of this that used to be about finance and about scale down into simply gatekeeping, around awareness and around the will to to actually do it.

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Bob Moore: I’m sure we’ll talk about AI later, because there’s a very obvious like what’s next component of this, so I’ll kick that can down the road. But I think it is. It is very, very exciting to watch

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Bob Moore: just the incredible accessibility of these ecosystem led growth strategies to virtually every company out there that is able to make their value proposition stronger by working with some 3rd party or second party. And that’s really at the root of why we’re seeing so much momentum in these recent years.

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Jay McBain (Canalys): Absolutely. And AI does really good in very complex environments, nonlinear environments. And when you think about your addressable market, your available market, your obtainable market. Multiply that by 7 partners. Multiply that then by 7 layers of a stack that they’re going to be buying to solve their problem, you get into some pretty large numbers, and you know, partnership seems to be a natural for

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Jay McBain (Canalys): for for AI. So Dina kind of same question over to you kind of how we’ve progressed and and where we are now. I sold Pcs. For 17 years. Work closely with Intel in in terms of that. But not only are you participating in the 7 trillion dollar build out of AI on the infrastructure side, you know, Intel has even a more complex

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Jay McBain (Canalys): go to market with Oem.

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Dina Davenport: We do?

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Jay McBain (Canalys): All the ways. The product is embedded in so many of the, you know products that we use every day. So how are you thinking about this this journey.

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Dina Davenport: Yeah, it’s it’s pretty interesting. And you said it very well like it. We are unique. If you think about us, we are what you’d call a core ingredient to an offering or a solution when it comes to the Isvs or csps the oems. So we are when it comes to go to market, we look at it and get get to market and go to Market End to end. But when we look at go to market

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Dina Davenport: that is truly critical as you. You need to align to the the partner ecosystem strategies. But you also have to understand the real aspects of

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Dina Davenport: You know these, you know what is your product, that value proposition that’s powering up and truly contributing to the success of that offer as a core ingredient. The hardware, the software, the services, and what we’re packaging.

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Dina Davenport: So that is where we’re at. And we look at the partner ecosystem altogether in terms of how we go to market to through and with our partners.

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Dina Davenport: And it’s it’s been pretty interesting. The one thing we are doing is putting in more glue in the internal infrastructure ecosystem to

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Dina Davenport: provide that to external. Because right now, as you had said earlier on, it’s been so siloed, and we are eliminating that that is not how we can survive even from an AI perspective which I’ll I’ll talk about a little later when we touch on that. But we are in such, I think what has led us to this point when we think of it from intel perspective, or I think from all of us.

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Dina Davenport: We’re in a hyper competitive market. Right now, data explosion is happening.

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Dina Davenport: And we really need to look at how can we assess our data platforms, our data estate. So we have the ability to use AI jump in and put your, you know whether you’re dipping your toes or you’re more advanced, really engaging in that right way from an objective and strategy perspective. So we’re doing a lot that way. But that’s how we look at ourselves and the approach to go to market when it comes to

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Dina Davenport: an ingredient into the core solutions.

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Jay McBain (Canalys): Yeah, that’s a that’s great. What a great segue to Megan. Cause you know, we’re in a world that is creating mountains of data. You think of Internet of things, and and all of these, you know, sensors and cameras and everything out there, you know, leading to safety.

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Jay McBain (Canalys): But it’s it’s the, you know, accumulation of all that data, the training and tuning, indexing, inferring of that data

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Jay McBain (Canalys): to get to that next action. So, Megan, you also have kind of a bifurcated view of this on the one side. You know, you’re driving so much change in this future of AI. And the second side of it. You’re you’re serving over a dozen industries

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Jay McBain (Canalys): and the partnership math looks infinite in terms of how all these pieces and parts, including when you inject public sector in all these things. So how do? How do you look at where we are in the journey that got us here?

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Meagen Eisenberg: Yeah, you know, I think about your number 7 on average. And then I think about Samsar. We have over 300 partners and integrations for the platform. But I think the bigger point is, you probably without AI only have the bandwidth for 7. If you think about your teams and the go to market. And how do you scale? And those 7 partners probably have 200 or 300 partners trying to vie for their attention. So

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Meagen Eisenberg: the partners are going to be most successful are the ones where you can create the content. You can enable their sales teams, they can enable yours. You can really paint the vision of why these joint solutions matter for going to market. So I definitely think a lot about this at Samsara. What can we do to move faster, because that does scale a business not just direct. But they’re out there talking to, you know, especially as you go up to Enterprise.

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Meagen Eisenberg: and they’re used to working with partners for technology and solutions to help modernize their businesses and their stacks. So this matters a ton for us. And this topic, I think, is really relevant for us, and how we’re going to scale go to market with our partners.

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Jay McBain (Canalys): That’s fantastic. And and Blake, you know, kind of I know I’ve known you as an ecosystem leader. Ecosystem led growth leader since I think the day I met you you’ve been out there with a big brain, thinking about how all these things form.

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Jay McBain (Canalys): you know. A few years later, as as the napkin we had back then is starting to form and and create really large markets.

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Jay McBain (Canalys): You know, with many more buyers and than we think before you know. How do you think about this? You know where we are, and and leading up to this? And and how do you position this.

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Blake Williams: Yeah. What I see, even the brands that we work with and what Megan just talked about is, you really only have the bandwidth or resources. You’ve got this long tail of partnerships and a choke point that happens between

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Blake Williams: all the other go to market functions and the rest of that partner base. It’s rife with traditional friction. Right? So agentic AI and new approaches going brand to to network allows you to start to incorporate far more people into that conversation, and for the time being, until it’s data and agents talking to each other, buying on our behalf

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Blake Williams: for this little blip period of time incorporating more people to get that message out allows you to share their own perspective on your value proposition together, and then deploy that. So

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Blake Williams: I see, I see that network, the kind of the footprints in the snow, the technology that a lot of people are building are now supporting the processes that people typically use to buy software anyway, at any level.

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Blake Williams: right? Whether it’s, you know, getting referred or eventually bought. So

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Blake Williams: I see AI just accelerating that that curve faster than we can keep up.

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Jay McBain (Canalys): Yeah, that’s fantastic. Well, let’s go into AI next. I know Bob was asking for it, and you just finished on it. So you know most of us in tech have known that AI has been around a long time

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Jay McBain (Canalys): when I was, you know, a young person I own I joined Ibm as an intern out of college. We’re just getting ready to play Gary Casper off a chess, you know, the 1st time a computer had ever beat a grandmaster, and since that time in 1996, a grandmaster has never beat a computer since.

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Jay McBain (Canalys): You know, it’s very simple brute force that a computer can play the whole game back and forward, and and figure out every permutation, and make the best move every single time.

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Jay McBain (Canalys): And then that’s beyond a human that might even be able to see 3 moves ahead, you know. 15 years later it was taken on jeopardy.

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Jay McBain (Canalys): and that’s not as much brute force now as it is generative. AI.

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Jay McBain (Canalys): When you’re asked a question you get about, you know, a millisecond to press the button, and then you’re given 4 seconds to go read the Internet cover to cover

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Jay McBain (Canalys): and and tell Alex, you know, in the form of a question what what the answer? And you know, Google went on and beat go, and you know this has been going on for 30 years. But here we are in March of 23, all of our non technical, you know, family friends and neighbors saw chat, gpt, writing poetry, recording music, deep fakes.

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Jay McBain (Canalys): And this was the moment that we all got the phone call in a panic. Did we just light up? Skynet is is Arnold Schwarzenegger, you know, coming back to shoot things up. Is this the end of humanity? You know we all had to quickly, in March of 23 kind of come up with

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Jay McBain (Canalys): what it is and frame it out for folks.

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Jay McBain (Canalys): But it created a new 20 Year era in Tech. We went through 20 years of client server. We went through 20 years of cloud, and this is now a a 7 trillion dollar build out of that next era.

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Jay McBain (Canalys): Even the last couple of weeks you’ve seen 2 trillion dollars of that 7 being committed by governments and and companies. So here we are, in this new era. And now all the professionals, you know, in go to market across every you know, part of every division needs to figure out not just what their co-pilot might do to help their current job role.

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Jay McBain (Canalys): but how their company competitively is going to stay ahead and perhaps get an advantage by being a 1st mover.

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Jay McBain (Canalys): And what this means? Agentic AI generative. AI! What’s that next cycle? So I’ll change up the or order a little bit. Here, go to Megan first, st and you know kind of where we are now, just looking forward a little bit.

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Jay McBain (Canalys): You know. How would you guide folks where we are now? I mean, it’s you know, our co-pilots aren’t doing things like Blake was mentioning for us yet. It’s not booking flights. It’s not changing our lives or the apps aren’t dropping off our phone or our, you know, the Internet, you know, isn’t dropping sites yet.

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Jay McBain (Canalys): But what do we do now, Megan?

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Meagen Eisenberg: Yeah, I mean, I think so I think about partner growth. There’s kind of 4 areas that I’m looking at and how we use it and and moving forward. Certainly one content creation. We all know that you’re, you know, and that leads into really the second one. But creating the story better together. You can parse their websites, you can add it. How do? How would we match really? Well with this partner? What would be the best? Go to market message. What do people care about?

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Meagen Eisenberg: Let’s look at joint customers on their website compared to ours. What’s the customer, DNA, that matches that we can go after in the content creation. You’ve got case studies. You’ve got blogs, ebooks, sales, enablement content. There’s so much that it can produce right away that you you would take weeks before to get done. You can get it done in a day or 2, I think the second thing or 3, rd if you do story creation. But the 3rd would be.

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Meagen Eisenberg: let’s figure out who to recruit for partners. Let’s ask it. Who would be a natural partner for us that’s out there that maybe has similar joint customers or DNA, and use AI to generate a list of potential partners to go after increase your 7 to 200 or 300 do a swot analysis, and how it makes sense, right? There’s so much

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Meagen Eisenberg: thinking and stuff that can be done with it. And we’re doing some of this already at Samsara. And then, lastly, I think, performance analysis, how’s the partnership working? Let’s look at the data. What’s converting what you know? What’s attribution. And can, you know, rank our partners, who are our best partners, to be working with rank, our partners by industry. So when I’m working with sales, teams say, these are the 3 partners you should work with, or the top partner you should work with.

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Meagen Eisenberg: There’s so much analysis that you can do, and when you look at your competition, you can even do things with AI. Look at their reviews, look at your reviews, look at your partner reviews, look at your competition, you can run g. 2. Through it and say, what are their strengths? What are their weaknesses? How would we complement each other? How do we position ourselves against them. And it’s awesome to see. Our CEO has looked at a lot of this data, and he goes out there and

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Meagen Eisenberg: analyzes a lot of the data out there and comes back and says, Here’s what I you know, I was having fun over the weekend.

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Meagen Eisenberg: and I’m looking at this stuff, and and you know, Mining, all this really amazing information that we can go act on on the go to market front.

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Jay McBain (Canalys): That’s fantastic. I love how you jump to measurements. Because that was one of the questions we got. One of the 1st questions we got was on metrics and and things like that, because that’s pretty much. The 1st thing is, you know, how do we monitor and measure and manage this?

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Jay McBain (Canalys): We’ve got a lot of people around us, you know. Financial folks want that those measurements to be reliable. They want them to be repeatable and, like, I said, in terms of scale. The force multiplier partnerships. They have to be scalable, too. So you know, this is not an easy thing to do, and that’s probably the 1st thing before we get too far down the path. Dina, what? What’s your thought? Kind of

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Jay McBain (Canalys): on what’s the next step? And you know how important are measurements and metrics and and other things to that.

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Dina Davenport: Yeah, I think I have 2 angles on this. The 1st is about how we harness AI, and it really is about looking at AI, I mean first, st what I’ll do is Caveat, that there’s just so much hype going on in the industry right now. How do we break the noise? How do you take a step back and truly look at your go to market strategy.

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Dina Davenport: understand where AI can start to contribute, treat it as part of your community treat it as a team member.

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Dina Davenport: So literally, I encourage our teams. Let’s let’s look at. Let’s have that team member help develop a market analysis segment analysis. What is customer segmentation?

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Dina Davenport: Do we need a team member to help write an agency brief? You know things of that nature. So I look at it from that perspective. Look at AI as a collaborator versus a tactical task, and bring it in as part of the strategy. We still need human judgment

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Dina Davenport: you still need to ensure. You’re aligning to your strategy, internal your messaging your framework, so you need to always have that overlay as well as with your partner overlay as you’re working with them.

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Dina Davenport: But really look at it holistically, end to end and really look at it as a team member. I mean, we’re looking at it in our sales. We’re looking at it in content creation, as Megan had mentioned, but literally in not just content. But really that market and segment analysis as well as we’re putting business cases together and our go to market plans together. So it’s been invaluable that way. The one call out, I would say.

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Dina Davenport: in terms of action we should be taking. And I think, Bob, you mentioned this. And I think even the previous panelists actually brought this up.

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Dina Davenport: It’s going to be AI. We’re we’re like, if we’re looking at the web and we’re looking at how we are going to be using AI.

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Dina Davenport: It’s not us going in anymore. It’s going to be AI, that team member. So the most important thing we can be doing is sharpening our current skill in terms of prompts.

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Dina Davenport: prompts is going to be the best thing to do right now. So I’d encourage everyone on that because and refine them and refine them and refine them. It is for internal and external collaboration with your AI resource or community members. So that’s that’s what I would offer for us.

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Jay McBain (Canalys): Yeah, that’s great. There’s not a lot of people at Intel that are dialing end users directly.

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Jay McBain (Canalys): So when you’re putting together the marketing, it’s, you know, intel plus a dell HP. Lenovo asus acer plus a distributor plus a bar. Msp, maybe multiple.

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Jay McBain (Canalys): Yes.

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Jay McBain (Canalys): Then, picking up all of that, and then serving a a Midsize bank in Inverness, Scotland, who’s having a particular problem. When you add all that layers to AI. It could get smart enough then.

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Jay McBain (Canalys): you know, to actually make something personalized and customized. And then again, how do you scale and and measure that Blake kinda over to you in terms of you know the the state of AI today, and what you would recommend people, you know kind of think about. At this point in 2025.

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Blake Williams: Yeah, absolutely. I mean, like, I said, partner programs are traditionally just rife with points of friction. And the value of your ecosystem is a rumor. And until you’re activating it.

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Blake Williams: Right? So there’s some key things that need to happen, right opportunity, identification.

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Blake Williams: then opportunity, evaluation and then opportunity, justification, and then the enablement of going after that opportunity. So I think today some of the things you can do. Yes, prompting. But you should also be thinking about, how do you create agentic tests that can communicate with each other? We’re doing it internally today. It’s not in production. But we’re testing it today to see how agents can offset some of those points of friction. For us

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Blake Williams: to start to get a faster throughput from good data that we think means something into justifiable action that people want to take

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Blake Williams: and go into market. So I think I think that’s what you need to be doing. Just test.

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Jay McBain (Canalys): Yeah, another great segue. Because you know an agent world, you know, everything becomes headless.

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Jay McBain (Canalys): And I start to think of the power of data and data matching and and mapping.

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Jay McBain (Canalys): And if you go across those moments and they read my ebook, and they listen to your podcast and they went to my website, and they went to your event. And you start to collect these. You know very important things out of your marketing platforms, out of your sales Crm platforms. And and, like you said, pulling this back into partnering

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Jay McBain (Canalys): and and guiding people, getting, you know, salespeople connected and working to to move that customer through the rest of the moments, you know, to ensure a positive outcome. So you know, Bob, I think, is the inventor, and obviously the the person that thinks probably more about this than than anyone in the world.

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Jay McBain (Canalys): So this is your AI question, Bob, that you were asking for.

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Bob Moore: Thanks. This is so fun. So the

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Bob Moore: I think one of the really important things to remember about all of this is the way that these Llms get trained in the 1st place, and the initial body of training data, at least in the large scale models, the ones that are widely consumed from from Openai and others.

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Bob Moore: They’re trained on basically public data, right? And it’s a lot of public data. It’s an absolutely impressive, monstrous amount of collected data across a limitless number of modalities. But it’s trained on data that is, by definition commoditized because it was able to be ingested and kind of put into the core knowledge base of these things. And when you go to Chatgpt and ask it a question to analyze your market or say something about your company.

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Bob Moore: or take a look at what it knows about your partner ecosystem, or how you should run a strategy. The interesting thing to remember is that your competitor or a upstart kid at a Y combinator startup that wants to compete with you can ask Chatgpt that same exact question, and then it will get the same exact answer right? And therefore the commoditization is not just in the training data. It’s in the ability to extract the knowledge and insights and leverage out of these models, which is why there’s so much

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Bob Moore: disruption in the air, and everything’s kind of so scary, all at the same time as it’s exciting the thing that’s so critical and gets to your question about unlocking these new data layers is that these Llms and the kind of the underlying model of using transformers to create these generative models. In the 1st place.

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Bob Moore: part of what is so awesome about them is the existence of this concept of a context window, right? Your Llm is trained on all this public knowledge. But depending on the model you’re using, you can create a context window where you basically provide it with a certain amount of awareness that is specific to you and your session and your conversation or set of reasoning that you are doing with it. And inside that context window

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Bob Moore: you can put things that by definition only, you know, and by definition are things that are only sitting inside of your walled gardens. You can put your Crm data in there. You can put your crossbeam data in there. That’ll tell you every single customer you have in common with all your partners and every single sales deal. You have going on right now, where a partner has also had a call with them in the last week. And it’s when you start to get into this realm where you’ve got these Llms, where there’s a specific

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Bob Moore: agent that has been created, whose context window is something that is proprietary to you, where you start to get really proprietary leverage, and you start to be able to really build things that can make a really big impact. So I’ll give you an example. Acrossbeam. We’ve completely, internally, with no outside tech outside of kind of a commoditized like Openai’s 0 3 model that we use. For this

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Bob Moore: we have built an internal agent fleet, where every single sales rep at the company. They have their very own agent, that is constantly looking at the fire hose of buying signals that are coming out of our product and product led growth signals. Who’s doing what in the product publicly available news sources through Google news ecosystem led growth data from Crossbeam about how all of our partners are interacting with and engaging with their book of business

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Bob Moore: along with all your classic marketing signals like, Who’s landing on our website? Who’s reading blog posts? Who’s downloading forms? Who’s clicked on an ad etc. And this fire hose of data gets back to my original point about the confluence of all these factors. We’ve had all this data the whole time. This, this fire hose of information. We’ve been operating this company 5, 6 years. We set up 90% of what I just described in the 1st year, when we were 3 people in an apartment. Right? So what’s different now is that we have

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Bob Moore: a infinite amount of

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Bob Moore: intelligent labor that is capable of reasoning with and consuming this fire hose, and making discretionary decisions about among the 1 million events that have happened among the 500 companies that are in this reps book of business in the last 24 h, when they show up at their desk in the morning at 9 Am. What are the 5 companies that they absolutely need to make sure that they reach out to. And what’s the context around? Why.

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Bob Moore: and that’s what they’re getting. When our reps log into slack every single morning they get this briefing that is kind of like a dossier of this is the processing that’s gone on, and the Llms are extremely good at doing it, and good reasoning because of all of this large public data that they’ve been trained on and kind of the magnitude of

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Bob Moore: like the foundational models, but they’re only able to be useful to our company because of the context window that they possess and no competitor, no Y combinator startup, no anybody could go and build that specific implementation right? So when you talk about, why bring this all back to partners and ecosystems? A really key piece of that? And frankly, the most proprietary piece of data that I mentioned in terms of what our agents consume is that ecosystem led growth data right? It’s the fact that

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Bob Moore: we’ve got this opportunity. That’s stuck. But we had a partner that just had a call with the person yesterday who hasn’t returned our calls in 3 weeks right? It’s like these pieces of intelligence that can work their way into

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Bob Moore: the next best decision that needs to get made by a worker at your company just creates this incredible force multiplier that no amount of brilliant coding would have been capable of doing if the Llm. Was not sitting there behind the scenes being capable of reasoning, prioritizing, etc. And like that is the magic. That’s where the magic’s happening and where it’s all coming together, and where I think this ecosystem data goes from being something you really had to understand and grok and have all your cells

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Bob Moore: reps like, know what to do with to being something where it is a very, very big part of the equation of what actually matters, and the agents are going to be way better than the sales reps at knowing why, and then the agents can actually, to Tomash’s point on the session that just ended when he talks about sales training and sales accountability being like maybe the most disruptive thing that might happen in the next 5 years.

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Bob Moore: That’s it, right. This is like

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Bob Moore: the amount of silver platters that information, and next steps are about to be served up on for sales reps is astronomical, and that creates a level of commoditization that makes training and assessment also really, really kind of a new beast compared to what it was before. But I’ll end my rant there, but that’s at the core of it.

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Jay McBain (Canalys): Yeah, that’s great. So let me take all that and add a wrinkle.

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Jay McBain (Canalys): You know, we just are going through a demographic shift

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Jay McBain (Canalys): where the majority buyer, for example, in the 5.4 trillion dollar tech industry just changed over to a millennium born after 1982.

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Jay McBain (Canalys): So here’s a cohort that you know. Gartner just did a study, and, you know, surveyed them all. 75% of your majority buyer now doesn’t want to talk to a human.

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Jay McBain (Canalys): They’ll go buy a car in their personal life. Second, most expensive thing for many millennials who can’t afford houses. The most expensive thing they’ll ever buy

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Jay McBain (Canalys): without ever talking to a person without any human interaction going to buy a million dollars worth of software ending up at a digital marketplace which are growing at 82% compounded, almost doubling each year.

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Jay McBain (Canalys): So in this world of 75% of people may not want to talk to a human in the traditional marketing sales and customer success journey.

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Jay McBain (Canalys): Now, we’re gonna have to do much more digitally. So AI, in terms of making these matches, you know, connecting the dots across all these moments and guiding the customer.

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Jay McBain (Canalys): you know, without human interaction. So that becomes kind of the next phase looking forward is a demographic shift that’s causing, you know, all kinds of interesting changes as well. So, Blake, I’ll come to you 1st on this time. You know. How do you think about this? And I know you’re building, you know a company that kind of is at that intersection, and the good news is we’re all millennials on this panel. So we can really talk from 1st hand. That was a joke.

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Jay McBain (Canalys): Yeah, it’s like, go ahead.

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Blake Williams: I’m a millennial elder, elder millennial. So if we just take a so software just did a product launch with us and the way that we do things right. It’s all about short form, video and text on social. And they had their investors, internal people. They had experts, 60 experts that are, you know, small agency owners as well as integration partners

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Blake Williams: all create content around their feature release and their rebrand. And so it was something like 700 videos that started to trickle out actually from yesterday throughout the end of the month. And what we’re doing to Bob’s Point is capturing that ecosystemic signal. Everybody has their own point of view, and they’re dropping that in front of their audience. And we’re pulling some of that engagement. The comments and then surfacing that for high intent. And obviously, if we had an integration with cross beam, I’d go find out. You know.

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Blake Williams: what? What partner has this this person as a customer and then surface up a nearbound play so I could trigger an intro what I think is gonna happen is that you know my 1st job out of college. I actually worked for Goodyear Tire and Rubber Company in their procurement part department. It wasn’t long I didn’t last there, but I know a lot about the buying process, and as soon as we start to see agents start to interpret our own intent for us and surface that intent.

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Blake Williams: It’ll be able to visit those those marketplaces on our behalf, and up to a 3rd certain procurement, threshold.

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Blake Williams: execute on our behalf, and then surface up triggers that we just need to approve. And then I think we’re gonna see the purchase volume instead of these annual contracts, it’ll go down to buy the drink happening, and then I don’t know what happens to my business sales or marketing, right? Because at the end of the day you don’t need it. You just need to talk to your AI agent. Communicate your intent with your business. It’ll probably tell you what you should be worried about and thinking about, and then you can react, and it’ll go execute on your behalf. But I think that’s a

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Blake Williams: that’s a i don’t want to think about that at least, not for the next 3 years. But that’s probably.

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Bob Moore: I’ll see you in Cabo in 4 years. Blake.

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Blake Williams: Let’s do.

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Bob Moore: That’s the only thing left to do.

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Blake Williams: Yeah.

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Jay McBain (Canalys): To to extend that to extend the vision is, you know, when your car self reports that it needs tires.

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Jay McBain (Canalys): and your co-pilot goes and does all this work on your behalf, and makes decisions on your behalf. That self driving car at 3 in the morning drives itself out to a warehouse near the airport, and by the time you go to work in the morning you smell new fresh rubber when you open your garage.

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Jay McBain (Canalys): So this is the whole, you know idea where we will all be fishing in Cabo. And you know, life will move on. But, Dina, let’s kind of come back on a little bit on the demographic. You know you’ve got kind of bifurcated. Go to market at this point.

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Jay McBain (Canalys): With Intel. Intel is also having a moment. You’ve got you know. 4 years ago next month we all went home with new laptops because of Covid.

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Jay McBain (Canalys): you know, we try to find Pcs. Anywhere to to give to our employees. But all of those Pcs are now 4 years old. They’re gonna be replaced with AI enabled Pcs. That you showed off at Ces, and all your partners showed up at Ces Microsoft pulled back support for windows 10, which is another cycle for this year. That’s a double and then kind of the triple is this whole move towards building a more robust

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Jay McBain (Canalys): edge because 80% of processing of these models will be done out on edge devices, whether a smartphone or PC, so it’s causing double digit growth in the PC market, which you know, doesn’t come around often.

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Jay McBain (Canalys): But now you’ve got kind of a new buyer. And and how do you kind of play all this out from a Channel marketing perspective?

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Dina Davenport: Yeah, I think a number of ways. And I guess, depending on the persona we’re talking about, whether it’s consumer or commercial. We think the elements are similar. One of the things that we’re looking at, and we’re very excited. By the way. When it comes to the the new

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Dina Davenport: processors that are coming up. It’s just amazing in the power that we’re implementing. And really, truly, when we think about the end user really contributing to what they’re looking for. When you think of that demographic or all of the demographic segments.

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Dina Davenport: So

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Dina Davenport: one of the things we’re looking, I mean everybody I like to call it the let’s disrupt and cut the clutter, and the noise that’s out there, that sea of sameness that everybody’s talking about. We really are cutting into that. And really looking at, how do we do more experiential interactive approaches, especially in the segment where you were talking about the demographic you were talking about.

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Dina Davenport: That’s very key to them. It is about personalization. Now, there has been hyper personalization. But it’s also about how they make decisions, whether it’s, you know, through their forums, that word of mouth. And the way that decision making is flowing, we’re really taking that into account. But let’s take the developers. We’re highly engaged with developers in market as well. That

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Dina Davenport: that audience, those partners, developer partners we are working with are truly interested still in in person. In many ways. Get them into the tech workshops, get them with hands on. Get that experiential and interactive feel going with with product. And how do we enable them the heroes. It’s about the hero and putting on the super cape for them

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Dina Davenport: and enabling success for them. So we’re doing that, call it the hero makers.

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Dina Davenport: And then when you think of the consumer same thing, everyone wants the long battery life. Everybody wants the type of thing. But really about that end, use on productivity and other essential uses for what enables the consumer, whether you’re a student, whether you’re a shopper, whether you’re, you know, whatever you’re doing.

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Dina Davenport: the automotive buyer, you know, really enabling the bringing it to life in a more automated fashion, and how AI built in can help securely, productively, and fulfill that better together, packaging that we really need in market right now.

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Jay McBain (Canalys): Yeah, it’s fantastic. So, Megan, you know, when I think about hyper personalization, I think about safety. It’s not just a broad, based banner like, hey? How many days since our last accident? It’s every single person on a job site, every single person kind of in the connected. They all have very unique

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Jay McBain (Canalys): and and specific guidance on how to keep them safe

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Jay McBain (Canalys): at an individual level. And then, you know, kind of the second part of the question is again, evolving buyer.

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Jay McBain (Canalys): you know, not going to the trade show industry show the same way they made up in the past may maybe not reading the the magazines, the the way they did in the past. How do you think of peer groups? And all these, you know, digital groups coming together with AI and go to market.

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Meagen Eisenberg: Yeah. So I mean to your point on safety and vehicles, and a little bit back to the the vehicles that drive themselves to fix themselves. We’re not too far from that. I mean, if, when we look at Samsara, we provide a list of preventative maintenance, we see your tires, your braking all of that on your trailers, and and

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Meagen Eisenberg: if I’m running a fleet I log in, and I can see immediately what needs to get done. We use? AI to say, Okay, what’s that code mean? Okay, what does that mean that I need to do? We distribute training. We’re ranking drivers helping them be safer. So all of that’s already in play. We haven’t pushed the button to go drive, and change the tires yet, so that will come to your point next. I think that’s exciting. When I think about the buyer, your second question.

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Meagen Eisenberg: and I think about 6 months ago, for the 1st time, I saw a technology. You may be familiar with Amanda Collis. She’s kicked off a company called One Mind. And it’s actually creating the human like experience where you can interact with them. And so I think about the buyer. Okay, I’m talking to an Se. Where I don’t need a human se like the knowledge is all there. I need to talk to an Adr

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Meagen Eisenberg: to understand and ask questions, or qualify or see a demo. That’s all automated. I think about chat on a website. I’m a partner. I’m coming to learn about the solution. I need to learn more. I can do enablement through these chat humans. I can do selling. And you know, when she showed it to me, I think about 6 months ago, I was like but what if it’s not accurate or hallucinations? And she was.

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Meagen Eisenberg: She kind of laughed because, she said, do you think your adrs are 100% accurate? And I was like, Oh, good point, you know, they’re ramping. They’re right out of college. They’re learning our material. They’re only as much as we’ve enabled them. But

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Meagen Eisenberg: all these sources that we hook into these these interactive chats. They’ve got access to all of your material, unlimited information. They’re probably even if they were 95% accurate. That’s probably be 10 x if not 100 x better than what the human can think about. I think Tomaz is talking about that as well. So when I think about the buyer experience. Yeah, we’re going to use a lot more AI to interact and

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Meagen Eisenberg: answer questions. And that’s also going to be huge cost savings to have that. And I can think of a thousand ways that we can do it across web properties and chats. And just any of the interactions that we’re going to do.

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Jay McBain (Canalys): Yeah, it’s fantastic. And you know, I think about hallucinations. I think about the 1st 2 years of this era. And you know Bill gates back in 96. You know we overestimate what’s going to happen in 2 and then underestimate what’s going to happen in 10 in our industry. And it happened, you know, all this flurry of hype that Dina talked about hasn’t really resulted. The Gsis are growing at like 2% underperforming in industry growing at 8, and all the people we thought would have immediate success with AI.

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Jay McBain (Canalys): Are not so, you know. It’s been a little bit of slow roll. One of the things a chat bot in Canada

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Jay McBain (Canalys): cooled the market globally.

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Jay McBain (Canalys): It’s when Air Canada’s Chatbot told somebody who’s you know, asking about a bereavement

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Jay McBain (Canalys): and and gave them advice, and it they totally made it up. It wasn’t anywhere in the training materials. It wasn’t anywhere in. They couldn’t find it. So it went to court and air. Canada lost, and and they kept moving it up. It went to the Supreme Court

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Jay McBain (Canalys): and and air Canada just use its standard defense which every airline uses, which is, it’s a tech glitch.

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Jay McBain (Canalys): If flights went to a dollar on the website, it’s a glitch no one would expect to get a flight for a dollar so therefore we’re free. And then the Supreme Court said, No.

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Jay McBain (Canalys): going back to your inside reps your chat, Bot. You own the training. You own, the tuning, you own the hallucinations

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Jay McBain (Canalys): you need to treat that. So the the this became for every bank and for every you know, insurance company and other industries.

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Jay McBain (Canalys): you know, when the Supreme Court starts looking at your AI as human.

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Jay McBain (Canalys): and you’ve got to take responsibility, and you got to put that into your risk factor that somebody on subreddit says, Hey, go ask the chat about this question. You get a free flight.

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Jay McBain (Canalys): It becomes a material thing. And you know, there’s things like that that are gonna change, you know, compliance, legal everything else. But I thought it was just an interesting tidbit when you said, You know, do you think your adrs get it right all the time? What if you’re held to a different standard than just you know the tech glitch that we’ve always, you know, kind of backed into in the tech industry. So you know, Bob, kinda bring us home on this one, you know, new buyers, new kind of go to market digital first.st Digital only marketplaces.

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Jay McBain (Canalys): you know. You might be sending these matches, and you might be sending this early journey advice.

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Jay McBain (Canalys): you know, to non-humans. And and how do you think about that?

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Bob Moore: Yeah, I feel like I saw a New Yorker cartoon or something recently, where there’s some tech worker. And they’re saying, This is so great. I just sent

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Bob Moore: one sentence over to Chatgpt, and it made it into this amazing page, long email. And then on the other side, there’s another tech worker saying, Oh, this is so great! I just received this page long email, and Chatgpt condensed it down into one sentence for me. And I do think there’s a little bit of that, a little bit of that potentially starting to go on that should get really really interesting where, as language starts to become

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Bob Moore: almost abstracted in some ways, especially as we get into all these extra new modalities of of voice and

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Bob Moore: and imagery, and the blending of that in things like these kind of fully video enabled Sdrs and things like that.

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Bob Moore: It’s very natural to ask the question like, Are we just going to have a bunch of robots talking to robots, talking to robots. And what’s left when you have that? And I think in those closed loop systems the real thing that ends up remaining is this core question of like value proposition, right? And I think significantly, more things will drop down to lowest common denominator kind of commoditized.

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Bob Moore: The exchange of information, the efficiency of doing that, the idea of training and kind of entry level work maybe getting modified. But what it does is it unlocks this period where you can actually start to work on higher order problems which start to be really fun. And if you think back to as an analog the days in which the mobile web started emerging. Right people love to compare this to the dawn of the Internet. But I think, like the dawn of Mobile is a more recent one. We can all access.

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Bob Moore: you think, about applications like Uber, where Uber is incredible, but it stands on the shoulders of giants. You can’t have Uber unless everyone is holding a mobile device. And that mobile device is connected to a non Wi-fi source so that it works when they’re standing on a street corner. They also need to have GPS enabled, like, you know, we had to have, like a multi decade, long effort for government funded satellite systems get launched into low Earth orbit, so that these little devices we hold in our

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Bob Moore: pockets could know where we were all the time. You’ve got to have mobile payment and transaction systems that work really well. It’s the confluence of all these things happening at the same time that then unlock

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Bob Moore: this this magic

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Bob Moore: that allows us to change the way we even think about realizing value from the things we already know. Right? And it goes from the automobile to the taxi to ultimately kind of the Uber model. And now it’s going to go into the autonomously driving version of that Uber model that knows where you need to go, and when before you even necessarily need to interface with it. That doesn’t mean that jobs are going away. And it doesn’t mean that efficiency is going down. I think it just means that

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Bob Moore: it is a net contributor to

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Bob Moore: quality of life and the efficiency and high leverage use of of human capital and and human time. And

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Bob Moore: if humans have been really really good at one thing, over and over and over again throughout history, it’s being terrified by new technology that’s going to take away all the jobs and then being amazed by how the most exciting, highest growth, fastest growth, industries and innovations that have happened came very much out of that same exact set of technologies, and as interesting and as many unanswered questions as there may be about this wave. I think this is.

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Bob Moore: This is history repeating itself in in that regard, and I’m significantly more excited than I am scared.

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Jay McBain (Canalys): That’s awesome. I I think I agree with you so kind of to wrap this up. We’ve got 6 min. So let’s do kind of a rapid fire. Here, look forward. We will save this video for all of eternity to come back and see if you’re right or wrong. But let’s look back. Let’s look beyond tomorrow. Let’s look, maybe even beyond, you know, 2025, 2026 longer term partnership led, you know. AI driven go to market.

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Jay McBain (Canalys): Where? Where? Where does this end up? And if you’re thinking about that career in the future, what skills you know? Should you be building? So with 1 min each. I’ll start with Megan.

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Meagen Eisenberg: Yeah, I mean, I think about the future. In some ways perfect solution and matchmaking buyers are going to find sellers. And it’s going to be that much more efficient. So I think, to prepare for that is, you know, make sure you know how to use the technology you’re enabling it. And you’ve got good content out there, and you know how you’re being crawled by AI tools and Gen. AI.

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Jay McBain (Canalys): That’s fantastic, Dina, bring us home.

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Dina Davenport: I’d say, work on those prompts. Back it up. Look at your entire go to market strategy. Think of AI as a contributor, a team member, and where you can get the biggest output and Roi, either from a productivity or investment standpoint.

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Dina Davenport: And lastly, you know, train train. I mean, it’s part of it we’re merging. And I have a book. I’d recommend life 3.0 Max Tegmark great book, and gives us a lot of possibilities to be thinking about across cloud, computing healthcare, everything, and much more so I would leave it at that. It’s here with us. So let’s welcome them and collaborate with it.

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Jay McBain (Canalys): Awesome great advice. I I jotted down the book Blake.

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Blake Williams: Yeah, I think it’s gonna come down to AI readiness. So a lot of this is about data and infrastructure. And like to Bob’s point, the more proprietary data that you have to accomplish, whatever your mission or objectives are. And you have it structured and ready to be able to feed either an agent or Api or another person. That’s gonna dictate the speed and the ceiling with which you can rise with any AI, because it’s just moving so fast. So that’s that’s gonna be a core thing.

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Jay McBain (Canalys): Awesome bob.

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Bob Moore: Yeah, look, I think I’m most excited for getting rid of adjectives, right? I mean, look at the title of this session. It’s a it’s a real mouthful, right? You’re talking about an AI driven future and partnership led. Go to market, and

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Bob Moore: I think in the future we won’t have to say, the AI driven part in the same way. We don’t say that the way we buy stuff is computer driven, or the way in which websites are cloud driven right. It’s just the new operating model and operating system for how people exist on the planet in a modern technological society. And I kind of feel the same way about like partnership driven. Go to market like.

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Bob Moore: go to market to your opening point, Jay. 80% of it is already partnership, driven like go to market is partnerships. It already is. And I think

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Bob Moore: you know, the farther we get along here with tightly coupling the work of partnership teams and the work of go to market teams into a singular entity where it just kind of functions, because that is the operating model by which you grow. Businesses like that’s that’s the future. I think we’re headed to so hopefully. This exact conversation, if we have it again in 3 years, is just called, go to market, and the AI. And the partnership stuff is is implied.

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Jay McBain (Canalys): Yeah, that kind of central, you know, Cro, taken marketing sales. Cx, and of course, partnerships. And then, obviously a big part of this is the integrations that that new buyer that I mentioned is an integration. 1st buyer. Remember, a couple of years ago, when Apple was announcing their new iphone, they mentioned that 79% of people won’t buy a car unless it has apple carplay.

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Jay McBain (Canalys): I’m sure Tim Cook meant to say Android auto as well, he missed it. But can you imagine being in that industry, you know a 3 trillion dollar industry? You’re taking over a dealership from your great great grandparents who had it for 120 years. And you’re just about to lose 80% of your buyers because you don’t have a tech integration.

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Jay McBain (Canalys): So the product teams are are coming in to go to market like like no time. And so this platform economy, which is synonymous with partnerships.

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Jay McBain (Canalys): is where the AI piece of it works. And it’s pulling these tech integrations, the services partnerships, the Channel partnerships which I mentioned is over 75% of world trade, pulling together the co-marketing, co-selling Co. Innovation, co-development, co-keeping along with reselling and referring, and all the other motions that we know

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Jay McBain (Canalys): is complex

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Jay McBain (Canalys): and connecting the dots is, is probably more complex than than humans can do repeatedly. And this is kind of the story of AI. Long term

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Jay McBain (Canalys): is, the whole organization has to come together and serve the buyer at every moment.

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Jay McBain (Canalys): and renew that buyer for every micro consumption, every subscription for every moment. You know, thinking about that and surrounding that buyer. But the key word is surround. When you think about platforms

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Jay McBain (Canalys): and go to market won’t work without partnerships. Julia.

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Julia Nimchinski: Phenomenal discussion. Jay, thank you so much. Dina. Bob Megan Blake, let’s do shameless minute plug

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Julia Nimchinski: work in our community. Follow your work. Latest research companies thinking, Jay, let’s start with you.

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Jay McBain (Canalys): Linkedin is probably the best place.

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Julia Nimchinski: Both.

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Bob Moore: Yeah, find me on Linkedin under Bob Moore. I have a relatively new book out called Ecosystem Led Growth. You can pick up at any any bookstore published by Wiley. That goes into all this stuff, including the AI topic.

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Julia Nimchinski: Amazing! Dina!

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Dina Davenport: Yeah, on Linkedin Dina Habib O’mara, please link in with me, and happy to collaborate with you, share best practices and so forth together. Thank you.

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Julia Nimchinski: Megan.

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Meagen Eisenberg: Yes, I think we’re all on Linkedin.

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Jay McBain (Canalys): I’ll be there too.

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Julia Nimchinski: Like company, plug.

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Blake Williams: Linkedin. Yeah, if you want to build with your ecosystem on social company plug for demandy, let’s create some content together.

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Julia Nimchinski: Amazing. Thank you so much. Again.