AI in Partnerships, Without The Hype

We explore how generative AI changes channel strategy when treated as an operating model shift rather than an IT upgrade. Justin Trombold shares practical ways to reduce risk, improve cross-functional execution, and turn partner sprawl into a coordinated advantage.

• Defining first principles for partner ecosystems
• Layering orchestration to manage data, quality and risk
• Shifting mindset from tools to operating model change
• Finding and fixing cross-functional bottlenecks
• Focusing GenAI on decision support in known workflows
• Five pillars of AI readiness and the biggest bottleneck
• Leadership behaviours that scale beyond pilots
• Lessons from successful and failed co-sell motions

Justin is the President and Founder of AnteSyn Advisors, a consulting firms that helps leadership teams overcome generative AI frustration to quickly design and execute a value-creating generative AI strategy with our goal to make our services obsolete.

Learn more about their research into the main drivers of Enterprise Generative AI Readiness and how you can think about creating value through GenAI today and tomorrow: https://www.antesynadvisors.com/post/generative-ai-readiness-1

Start your own value-creating GenAI Journey with our "rapid" diagnostic here: https://www.antesynadvisors.com/blank-3

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Transcript

Meet Justin And His Journey

Maciej 1:29

Justin Trombold, welcome to Channel Voices.

Justin 1:33

Thank you so much for having me. I appreciate it so much.

Maciej 1:36

Thank you for joining me. Justin, would you mind quickly introducing yourself and tell us a little bit about your channel background, please?

From Consulting To AI Value Creation

Ecosystems, Partners, And Rising Risk

Justin 1:45

Yeah, you know, thanks again for having me on and a quick thumbnail. My story is long and boring, but I'll just say that I cut my teeth uh as a as an academic teacher and researcher in biological sciences. I was working at various universities here in the U.S. for you know over a decade and transitioned into consulting and you know, have worked at some relatively large consulting firms doing everything from finance transformation to working with with ecosystem challenges for contract manufacturing organizations to growth strategy and everywhere in between. There's a lot of work that I did, M ⁇ A, strategy, you know, integration, so forth. But over the last few years, I I left that lifestyle and and and founded uh a consulting firm called Antison Advisors. You know, we do we do a lot of different things, but our primary focus is helping leaders understand and demystify the value creation uh path to generative AI. And one of the elements that we like to do is that it isn't just about delivering this thing and this deck. It's about delivering a solution that they can use and then equipping the team to then make us obsolete, meaning you're not reliant on us. Now we could continue to do some projects, but you're teaching, teaching clients how to fish and using generative AI as a key layer. From a channel perspective, in my own business, you know, there's a there's a critical element of that because generative AI, you know, we can only know so many things, right? And and ecosystems become very important in my own work to support whether it's implementing generative AI solutions for clients or doing it for myself in my own business. But what that looks like for our clients is as we get to that point where we're saying, let's teach you how to fish, oftentimes these companies we're working with, particularly if they're smaller or medium-sized companies, it's often their first introduction into thinking about partners and thinking about how they set up a collaborative ecosystem. And so then you layer the complexity of generative AI on top of that. And so a lot of times what we're trying to do is get them to understand that yes, there's some complexity of working with suppliers and perhaps having to work with several different suppliers and partners and being comfortable with the fact that it might be multiple partners coming in to deliver a solution and what that looks like. But then also that so getting them comfortable with what that looks like, but also making sure that when they form those partnerships, they're doing so in a judicious way. So are they are they investing in solutions with people they can trust? And a key part of that, and what we're starting to see a lot of, and this is more at larger organizations, but you see a different flavor with small companies, is you know, risk is becoming a board-level imperative overall or a leadership level imperative. It isn't just risk management. What generative AI is doing is it's basically putting that on steroids. And so what that means is that as these companies are forming their first ecosystems, their first sets of channel partnerships, or bigger companies are now proliferating to hundreds, if not thousands, of partnerships through Generative AI, you now, at every partnership you add, there's a risk element of to what extent are you responsible for the behavior of your suppliers, behavior of your partners. And that's a big pain point. But then also from a data standpoint, from an accuracy standpoint, how do you make sure that you minimize the risk and minimize of a mistake, of a reputational risk? And so what we we work with, and and when I'm considering my own channel partners to go to market, one key thing that I look for is is this partner not only going to deliver that value? So let's say help them develop this Shinrave AI solution, but are they gonna also help the client and my client? So what becomes our mutual client, not worry about these things, not worry about reputational risks, not worry about data and accuracy and other risks that go along with that. So there's a there's a really interesting level of complexity that's emerging that companies need to think about. And then as someone that's a partner for customers, yes, deliver value. That that's table stakes. You've got to deliver what you're gonna deliver. But the question I think maybe your listeners should ask themselves is how are they helping to minimize that risk for the clients, particularly if they're offering generative AI or AI solutions as part of that that stack in their in their uh in that in their ecosystem, in their partnerships?

Maciej 6:39

Thank you for that. Obviously, there's so much complexity, and generative AI is pretty much present in everyone's everyday life today. But from uh from a context of channel and the ecosystem, how would you describe generative AI?

What GenAI Means For Channels

Justin 6:58

Yeah, and so I'll stick with that example and we could dig into some other ones as well. But you know, one one area that we're starting to see emerge significantly is are are generative AI solutions that are first helping companies manage the relationships of their partners and their ecosystems, but also managing the the complexity that goes along with having so many different partners. And so just as a as an example, let's say, let's say there's one, there's one solution, but there are multiple partners, for instance, that are providing three different data sources that feed into a some sort of of generative AI application that is coalescing all of that and providing some solution for the client. What we're starting to see are solutions that layer on top of that that ensure that that process is managed effectively. And so ideally, what would happen is the application itself that the data is being fed into from these different partners is doing that, but often it might be doing it with that one application. But let's say you have that five, 10, 15, a thousand different instances of that across the organization. There are technology stacks that are being developed, and I'm blanking on a couple of those key companies that are doing it, that are helping to integrate across all of those. Again, back to that reputational risk, the quality risk, you know, managing data, making sure it can talk to each other, it understands the difference between data X, data Y, service X, service Y. And so, like an orchestrator type type ecosystem. So maybe to put a bow on that, there's a level of complexity from the generative AI itself that makes the partnerships more useful for customers, but also more complicated. And so as a result, there are solutions, generative AI solutions that are layering on top of those technology stacks. And it could be with legacy systems too. It doesn't have to be a generative AI solution, but basically, like how can we best unite the clans and make sure that we're getting a product that's usable?

Maciej 9:19

Got it. Every company out there today is trying to get their AI story right. When I think about uh you know generative AI uh within the channel, uh it's uh it typically serves you know ability to make decisions or make those decisions uh more efficiently rather than just add another uh tech layer onto something that we have, right? But you rightly so um also mentioned, you know, there could be legacy systems um that are in play which is which aren't that easily replaced. But what shift in mindset do you think like channel and alliances leaders need to make to truly act on that view?

Orchestrating Complex Partner Stacks

Justin 10:05

You know, I think it's you know, there's so much to that, but I'd say this is the necessary mindset that has to be established. Otherwise, the other part doesn't matter. And that mindset is if you treat generative AI like an IT tool, you'll get IT results. And so what does that mean? That's nothing against IT per se. That means that you're gonna get something that is a solution and assumes that you know how to use it and you can deploy it. And generative AI solutions are by their nature a little bit more open-ended. There's a little bit more variance, a little bit more uncertainty that goes along with that. And so the shift that that leaders have to have is whether they're you know it it through channel partnerships or even just in general, you need to treat generative AI like an operating model change. And what that does is as a result, you can get business level results. And so a little bit more detail on that is you know, this mindset shift is AI as a tool for your teams and as an ability to a redesign lever to help think about your partnership operating model and how that interacts with your internal operating model from a first principles perspective. So that ability to coordinate, have everybody on the same page and change the way you work in such a way that enables the generative AI solution to deliver value is absolutely critical. So it's it's operating model first, the operating model, including the ecosystem partners, and then it's technology that layers on top of that to address a specific solution. And what that does is it makes it to where you fall out of that trap, which is here's this solution that in concept could be very valuable. But if nobody really knows how to use it, no one knows why we're using it, the problem that it solves, maybe it solves a bottleneck, but there it just shifts the bottleneck to a different point. So let's just say, as an example, there's a scenario where you know you have a process that you improve that becomes faster and faster and faster because of the solution. But then if the result of that process is let's say you have more potential clients in a sales pipeline, but you didn't change, let's say, the incentive structure for the front office team, everything's just gonna log jam at that point. Yeah. Right. So it's going back to first principles. And, you know, for the channel partners out there, it's whether you're offering a generative AI solution or not, it's being proactive and thinking about well, where do I sit in my customers' ecosystem? And what is it that can I can I do to help decrease the potential negative outcomes or lack of positive outcomes from implementing a solution that creates more bottlenecks, shifts things to a different point in the life cycle?

Maciej 13:16

So you obviously are an advocate of a you know first principles approach. And that's you know, this is typically how alliances create value. What are the most kind of critical joint decision points you think in a typical ecosystem that you believe that Gen AI should focus on first?

Mindset Shift: AI As Operating Model

Justin 13:36

Yeah, it's it's actually it's it's interesting because you know, often what I see is that and and we all do this, I do this as a consultant. I'm so caught up in what my solution is, I often forget about what it is that the client is trying to solve. And so the the first principles approach there is to focus on what it is the client's trying to achieve and where are those joint decision choke points, let's say, where ecosystem friction is highest and value leakage is most obvious. And so, what do I mean by that? So if you think about the interaction between, you know, let's say uh a partner and and other partners as well as the partners with with the customer, it's thinking about well, where are the choke points between your partners, right? If you have like it isn't necessarily a co-sell solution, but if you have a multi-partner ecosystem and you're either delivering all of those aspects or you have multiple partners that are delivering into one thing, it's how can you think about the challenges that are manifesting within the partnership, like between the partners. And then as that solution gets to the client, it's how is this alliance creating value for the client? Right? So whether it's you know sourcing demand or shaping deals, you know, assembling the partner stack, um, you know, delivering outcomes, whatever that is, and really think carefully about what it is that can be done in the way that the solution is implemented that doesn't just solve problem B, but also thinks upstream and downstream and helps clients solve the peripheral problems or at least not create additional problems. And then if there is a generative AI aspect to help to do that, then think carefully about how that's being deployed. So it's it's the same way that a company internally would think about it, but you're just thinking about it in the context of as a partner, where do you really need to focus on implementing, let's say, a generative AI solution to help solve the client issue?

Maciej 15:55

You've done some research, and given your academic experience, you've done it quite well. And if you could please um let us know where where people can find it, how can they grab it? That would be fantastic. We can put that in the show notes. But in that research, you highlight five core capabilities. Which of these five do you see as the biggest bottleneck in most channel organizations today? And and maybe back it up with why.

Fixing Bottlenecks Beyond The Tool

Justin 16:24

Yeah, so I'll give, I'll just give a really quick thumbnail. So appreciate you bringing uh bringing that up. If people want to read the white paper, they they can, and we'll provide a link to that. And we have a quick enterprise generative AI readiness diagnostic that your listeners can take. And it's a little bit fun to go through that. It's five or six questions, but that there are five pillars. The first one is the ability to develop a strategic AI vision that aligns with an enterprise strategy, a business unit strategy, a partnership strategy, whatever that is. There's cross-functional generative AI execution. So the ability for an organization at all levels to be able to collaborate well. And this is a critical part that particularly siloed organizations have a problem with. And this shows up a lot with vendors, which are almost by their nature, you know, siloed aspects of an organization. And so that's a key element in that case, which we'll come back to. There's end-user generative AI proficiency. This is back to that aspect of unlike a typical SaaS solution, you usually need a lot more expertise to both develop and deploy and use a generative AI solution to increase value. There's the ability to scale and adapt. And so the proxy we see that's best for that is how well does an organization or how well does a vendor historically extract value from a technology solution? And then the last one's governance and compliance. And that's that's table stakes, but it's important. Now, in the channel context, the most important part that we see is cross-functional execution. This is this is the biggest bottleneck. And so as I mentioned before, the siloed nature of organizations internally is what causes a lot of generative AI solutions to fail. When you start to introduce partners, whether there's a generative AI solution as part of that partnership or not, that that ecosystem failure, that failure to collaborate magnifies. And so these ecosystems start to fail at handoffs and who has decision rights and how different parts of the business fit together, it doesn't fail in terms of ideas of solutions and so forth. And so being able to help facilitate cross-functional collaboration, be a partner. And what we're seeing with vendors is as they're able to more closely understand their customers, understand the degree to which this isn't just a solution that you're handing off to the client. It's a solution that they have to really understand and they have to integrate into their way of working. How can you be a facilitator to help them facilitate, you know, help them integrate that into the way that they work? So the cross-functional execution is a key element of that. And so as a as a vendor, we mentioned, you know, thinking about that from the customer angle, but as a vendor itself, let's, you know, the questions that you should be asking yourself is, you know, how are we implementing generative AI solutions to not just improve our products, but improve the way that we work? And so having that first principles discussion, are we siloed? You know, are we do we have this area where we have a like a customer service division and then we have like a like a product division, and we're trying to deploy something that fundamentally needs to interact in a more collaborative way across those functions at the company? If you aren't doing that internally, that's going to make it very hard for for them to do that and have that benefit.

Maciej 20:08

Right. And following on that theme, AI reaching out externally. And when you think about, you know, vendor-to-ventor collaboration and multi-partner co-selling, where does Gen AI have the kind of clearest opportunity to improve decisions right now? What do you see? Have you have you given it any thought where that opportunity may lie?

First Principles In Joint Decisions

Justin 20:32

Yeah, absolutely. And and you know, I think everybody's exploring this to some extent. In in my experience, that the highest confidence opportunities are in decision support and workflow acceleration inside existing partner motions, inside existing partner relationships. So, what are those known data and known established processes that are currently involved in the partnership? And from an organization standpoint, you know, they need to be thinking of well, do we have the right partner in order to enable what it is that we're looking for and our objectives? From a vendor standpoint, it's about being very clear of what it is they're trying to accomplish. Look at the solution that you provide. And I think there are two layers. There's one, what is it that we're providing? And so, as an example, let's say uh a vendor, a vendor partner is um, I'm I'm thinking back to my my days of doing some some contract manufacturing support work. So more of like a supplier in that in that case. But let's say they're providing a key, you know, O-ring within a cooling device for a medical device or something like that. You know, that's their core business. And and let's say within that relationship, one of the challenges that they have is making sure that you know there's a a clear expect, you know, supply and demand matching is is met. And maybe there's a historical problem that there have been shortages or sort, what in terms of being able to source materials and get them to clients on time because demand fluctuates. Let's just imagine that scenario.

Maciej 22:17

Yeah.

Justin 22:18

And so that's a core problem, and and they're working on that. So starting with that and saying, okay, here's something that I know being able to match supply and demand for a client is important. As a supplier, what are those areas where there are the biggest friction points that are preventing us from doing it? First, looking and saying, is there an out-of-the-box generative AI or AI solution that can help augment that process? If not, digging in a little bit deeper, mapping out your process of how you track supply and demand, and then forming partnerships to help develop solutions for you to do that. So that's about optimizing what's already being done. The second way to think about that is well, how can we reimagine what it is that we're providing to the client, right? Now, when you make things more efficient, a strange knock-on effect is that as a supplier, you often now might have more time to think about more complex problems. And so there's an element there. But sticking with the same example, you know, let's say you've improved supply and demand and you've been able to be a better partner in that way. Now it's you can get back to that first principles conversation internally of, well, what is it that we can do to really differentiate? An example of that again could be well, yes, we're we're providing this O-ring to them, but are we doing anything? Is are there any opportunities that once the O-ring gets there, that the client, let's say, is having trouble with that we could help facilitate and decrease the friction associated with it? So one example that comes to mind is that you know, often what we would see is that, you know, an O-ring would get there, but the other parts wouldn't be there, things would be shipped to one place and not the other. And so are there opportunities to form partnerships with those other organizations? And then think of well, to do that in a seamless way, is there a coordinating solution that didn't exist five years ago when I was doing this client work that could help, you know, again, back to that unite the clans aspect. And now you're really talking about a shift in mindset of how you think about your business as part of this ecosystem. You know, so you're efficiency first, find those opportunities, get better at what you do. And then it's where those differentiating points, and as we talked about before, as as supplier proliferation you know continues to grow for organizations, I would expect to see that starting to become more and more painful uh for companies. So being able to think about things like that and differentiate.

Five Pillars Of AI Readiness

Maciej 25:03

Thank you. And thanks for giving the examples as well. That that makes it more real. In terms of the change management, which is needed, right, to to happen to facilitate any of this. Like what from your experience, from working with the clients, from going and educating others, from your research, what specific leadership behaviors do you see that would distinguish those who are succeeding from those who are stuck maybe in pilots and just slideware?

Justin 25:37

Oh, yeah, I I'm I love that you you not only asked that question, but you framed it up the way you did because pilots are easy. Now, not for everybody, right? So the getting over that first hurdle to do pilots is sometimes hard. But once you do that, that's really that's like that's like parking in the parking lot before you go to do what you're trying to do. Like you have to be able to park, but obviously that's not sufficient to stay in the parking lot, right? Right. So pilots in in many ways are are easy, but changing the way that people work is the hard part. And that's really the whole point of this first principles um idea. And so what we're seeing with leaders, there's a concept that that's emerging that I think is I think is right, as we're starting to see, is that generative AI is, in my view, a leadership test. And so why do I say that? If if we think about what has to be true for generative AI to generate value, and we go back to those five pillars, if you think about those pillars, whether it's cross-functional collaboration, scaling, and adaptability, governance, all these areas, those are very characteristic of organizations that have a very agile or Silicon Valley type mindset, meaning that it doesn't, it doesn't mean that you're flat, but it means that that leaders are very good at making data-driven decisions. They're very good at at relying on their people and empowering their people. They're very good at being okay with not taking the credit and being able to identify. So all the things and you know, if something isn't working, admitting that there's a loss and then and then pivoting, so all these all these agile processes, that type of business model is very, very hard for a lot of leaders that are used to a more command and control mindset. And what we're starting to see is that my organizations that have that mindset, they're gonna be at a tremendous disadvantage with generative AI. And one of the main reasons they are is that because you're so reliant on the end user being able to not just use the tool, but help to identify where those opportunities are, be involved in the experimentation, and then also having upside in their business to be able to, or in their own work, to be able to do that, develop the solutions, as well as let's say that, let's say that a business unit or a person becomes 50% more efficient. Okay, what then?

Maciej 28:16

Yeah.

Cross-Functional Execution As Bottleneck

Justin 28:16

Right. So as a leader, I think we've all struggled with the idea of once you get outside of the typical work that you're used to managing, how do you then set up a structure so that your people can use that remaining 50%, that that bandwidth they have in an effective way? And a lot of times for leaders, that's venturing into a whole new territory. And it is for the people that are doing that as well. So, you know, leaders need to treat generative AI as transformation. That's what we're hearing in a lot of our conversations and what we're seeing with our research. And that in that change management program, they're focused on a few outcomes. There's there's enforced measurement and there's clarity on how decisions are made. And there's an investment and a clear path to investment, a clear path to adoption. And they're very, very clear about what has to be true to scale for something to scale. I subscribe to the mindset that you have to have the experimentation. But if it's just experimentation and there's no clear path to how it scales, what you're gonna get is not just non-value creating solutions. You're gonna have a disillusioned workforce that feel like they're just like cranking away. And it's like, well, I thought that if I proved that this worked, we would invest in like a bespoke agentic solution, and I could, you know, go from 10% efficiencies to 60%, and I'd be able to do this other thing. But the leader's not returning my emails, and it's they don't get it. Right. So there's there's there's so much responsibility and so many things that have to be true. And it does get back to that transformation element. And it's I'll wrap it up with this. The the challenge as a consultant in this area that we're seeing is that least positively received answer in any consulting engagement I've ever done is that you need to change the way you work.

Maciej 30:13

Right.

Justin 30:13

Like that that's the last thing anybody wants to hear. And so, well, what does that do? That means as a consultant, it's hard, but as a business, whatever you're doing for your customers or in your own business as a supplier and a partner, is it's an opportunity to differentiate. And so that's why when we're working with clients, we start with, well, let's see where you are today. Let's try to get a sense of how well you have this natural operating model that may be congruent to create value with generative AI. And let's start from there. Let's look for some solutions that get the ball rolling, but let's also try to address these bigger picture transformation elements. You know, what we don't subscribe, what we don't do is say you have to transform from top to bottom and then start experimenting. Right. You can do both in tandem because one can drive the other, right? So seeing the experiments create value and get and stop can start putting a lot of pressure on leaders to say, because the the leaders' leaders, so the board and the CEO and everything will be saying, Well, you've been experimenting, why have they not been working? And then if if those leaders do their job and they talk to the people that were experimenting, they're gonna find out real quick that you're the problem, meaning the leader, like you're the bottleneck and the reason why things aren't scaling.

Where GenAI Improves Co‑Sell Today

Maciej 31:34

Thank you very much. There's two questions I still need to ask you. One of them is standard for for every guest. And what's the what's the one thing you wish you knew before you started your career in channel?

Justin 31:49

I'll couch my answer of saying that I'm cursed with a a professional wandering eye. And so I think when I introduced myself, you know, I was a I was an academic, decided to change gears, became a consultant, decided I didn't want the big firm consulting lifestyle. Now I'm uh now I'm doing independent consulting. So I'd say, you know, and and it's it's specific. I'll answer it more generally than specific about being the extent to which I work in different ecosystems. You know, what what I wish I knew before I even started as a researcher or even started as a consultant, I I wish, I wish I had understood the degree to which consulting isn't about the analysis, it's about relationships. Now, now you need to deliver value, right? That that's obviously the case. And I think for any vendor, it's the same thing. It's like, I have to deliver value, but I need to let go. It doesn't matter if I'm right. And and one benefit from being a researcher was my bar of being confident about something is very high because I know even if something is statistically significant and published, that doesn't necessarily mean it's true. And so I have a little bit more humility than most consultants do, but I think knowing that, yeah, we are we're trying to find a solution, but it isn't always about the solution. It's about the solution in the context of the client's problem. And so, for yeah, from a channel perspective, you know, that's a bit of a more recent phenomena for me. But I think I think what I wish I would have known uh going in as I form these partnerships is to somewhat to the advice that I was providing earlier is to have been more clear and defined on my end of what it is I'm trying to accomplish. And essentially having the table set before I started having conversations and partnering with people. And so, what I've ended up doing is I have a series of partnerships that are fine, but as my business has evolved a little bit, they're becoming less and less valuable for me. And so, like my own partner ecosystem is still okay, but I I could have I could have spent a little extra time thinking back to that first principles thing. What it is am I trying to do? Is there demand for this? Test it in the market, and then start bringing in partners that would be better fits for what I'm ultimately ultimately providing.

Maciej 34:21

Thank you for sharing with us. And the other question that I need to ask you is um one that's been left by a previous guest on the show. And our question was asked by Alex Buckels, and it goes like this Can you share an example of your most successful coastal program? What made it work and what results it achieved? And on the flip side, is there one that maybe failed or didn't take off? And why?

Leadership Behaviours That Scale

Justin 34:51

Yeah, and and I think I'll start answering that question with an element that I left out a bit on the last side of I wish I would have appreciated the value of going to market more effectively with my channel partners. And so, as an example, like if my business model has a series of generative AI readiness and training, and then I bring in the partner at the end, you know, one of the advantages that we have with having partnerships, particularly if they have an established business and you're, you know, an emerging consultancy or emerging business is leveraging their wins, right? So let's say I have some wins, but maybe they don't resonate with the client. But that channel partner that's implemented the generative AI solution, so my partner has a lot of really great wins and a lot of really great case studies. So being able to pull them in earlier in the process instead of it being more of like a terminal handoff. So going through a co-sell, like I wish I would have, I it's something that I knew, but sometimes when you're sitting at the computer by yourself, you forget. But but to answer your question, so a successful co-selling partnership was through through a podcast, not unlike not unlike this one. I had a chance to connect with a group that does some, they they're more on the side of having board-level discussions and offering bespoke out of the box generative AI solutions for front office. And I actually have a few different partners that have somewhat serendipitously, they have that profile.

Maciej 36:24

Yeah.

Speaker 1 36:25

But be yeah, we we were able to, and and you know, some some serendipity and fortunately

Justin 36:31

for me of of them, you know, bringing me into some of their conversations. And so they, you know, they had these existing client conversations, saw a bit of a gap in how they were working, that they were a bit of a hammer in search of a nail. And so they were wanting someone to come in and bring that first principles approach. And so, yeah, having having connections not unlike this, building those relationships, and then they felt confident and you know, took a fire on me as more of like an independent consultant. You know, I'm not a a Deloitte or a Big Four or Bane McKenzie or BCG or whatever. But then going in and that resulted in some really promising initial conversations that are ongoing today. So that, you know, building that trust and building that that partnership of partnerships, you know, and having a co-selling motion has proven to be really important. And particularly in the age of generative AI, where what I'm seeing more and more with clients is they're wanting a thing, they're wanting a product, they're wanting a solution. They aren't necessarily wanting a generative AI readiness diagnostic unless they can see that carrot at the end. And so the more clear that I could be with that carrot, the better the rest of it flew or would flow through. So that's success. I'd say the a part that maybe wasn't so successful. I had some existing relationships with people, and I was back that idea of what I wish I would have learned. I was a little bit over-eager to find some people that I had relationships with that could build those solutions for me with my clients. And I didn't commit to like major, you know, with like legal ramifications, but I had some verbal commitments and I've had to, and I had some client meetings that really didn't go so well when I when I brought them in to try to serve that purpose, to like give that credibility. And it, regardless of how much value I thought I was bringing, it it kind of eroded that because they felt like, well, okay, we like what you're saying. And maybe that's conceited of me to say that they liked what I, but let's just say as an example, they liked what I was saying. But it was clear when this partner that I hadn't thought through the fit both for him and I, how well he would interact with clients, how how well you know well situated is for the solution that I was going to provide. It just led to really disjointed co-selling motions. And, you know, unfortunately, fortunately, it didn't create a lot of problems. And that was a good time to have that point of failure. But again, reiterated that idea of like, get that, get my own ecosystem right, you know, take my own advice of what I'd say to the to the client, get a really cohesive group, leverage their expertise, you know, use their credentials, go to market together, and have a really nice unified sales message and give clients something they can really wrap their head around.

Maciej 39:28

And then there's the last piece for for this show, and it's what would you like to ask of our next guest?

Justin 39:35

Yeah, I I think an interesting conversation point would be how has generative AI either caused or forced, or you know, how has generative AI led you to rethink not really how you formulate formulate partnerships and co-sell, but how you think about value delivery for your for your clients, for your end customers.

Lessons From Wins And Misses

Maciej 40:01

Got it. Perfect. We'll make sure to ask that question of our next guest, and we'll let you know when that question gets answered. Justin, thank you so very much for coming on the show. It's been very informative. I learned a lot. I really like the the research that you've done, but also how you how you break down um these um these five principles and what you've been seeing in terms of how leaders need to behave to make this successful, right? I think there's a lot to take away for for people listening to this. So I do appreciate you for coming on the show and sharing this with us. Thank you.

Justin 40:43

No, it's a pleasure. I I I always learn a lot from these discussions, so I appreciate the opportunity. Just thinking out loud and engaging. So thank you so much as well.

Maciej 40:55

Thank you for tuning in to this episode of Channel Voices. I hope you enjoyed today's conversation and gained valuable insights. Don't forget to subscribe, rate, and leave a review on your favorite podcast platform. Every bit helps us grow and reach more future channel leaders like you. Thanks again, and we'll catch you in the next episode.