Channel Voices
Channel Voices is The Podcast for Future Channel Leaders, where we learn the ins and outs of partner ecosystems through casual conversations with channel professionals from a variety of industries, partner types and geographies.
Channel Voices
How AI Will Reshape Partner Ecosystems And Keep Human Trust At The Core
We explore how AI can actually help partner programs today, where it still falls short, and what data foundations are needed to turn dashboards into decisions. Margaret Adam, Director Product & Partner Marketing at Channelscaler shares practical wins in co-sell matchmaking, MDF automation, and personalising enablement at scale while keeping trust central.
• AI use cases that already work in channel
• Data readiness and workflow standardisation
• PRM as the real-time window for partners
• Personalisation with next best actions
• Co-sell partner matching beyond certifications
• Attribution support in multi-partner deals
• Generative AI guardrails and brand context
• Balancing automation with human relationships
• Measuring ROI through time saved, engagement uplift, conversion gains
• Future interoperability across ecosystems
Channel Voices is currently sponsored by Meter.
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Until next time 👋
Hello, welcome and thank you for tuning in to Channel Voices, the podcast of future channel leaders, where we learn the ins and outs of partner ecosystems through casual conversations with channel professionals from a variety of industries, partner types, and geographies. My name is Maciej, and I'm your host. Margaret Adam, welcome to Channel Voices.
Margaret Adam:Hey Maciej, great to be here.
Maciej:Thank you. Margaret, would you mind introducing yourself and telling us a little bit about your channel background, please?
Margaret Adam:Yeah, sure. My channel background was pretty much my whole career has been involved either directly or indirectly with the channel. I kind of started out in vendor and a hardware vendor, and I was dealing with retail channels. I was a product manager. I then held various roles in distribution. I was funded headcount at a couple of distributors. I then moved into partner land. I worked for an ASP, as it was in those days, kind of pre-cloud, as well as a SI. I then spent a good 14 years as an industry analyst. And you'll be unsurprised to know that I looked after all things partnering. So had a team that looked at channels and partner program best practice, had a team that looked at the services ecosystem, had a team that looked at ISV and marketplace. And really, my role as an analyst was really to analyze what was happening in the channel and what this means in terms of strategy. I then went back into vendor land. I was in a partner marketing role at Salesforce and then back into product marketing. And the last two odd years I've been with Channel scaler, as you know, and back in product marketing again. So, and obviously what we do is provide the technology to automate your partner programs, right? And engage with your partners. So yeah, partner is very much my DNA, I think. Um never really left.
Maciej:Thank you so much, Margaret. Before we get into our main conversation, we have the segment where a previous guest leaves a question for the next guest on the podcast. And the last guest on uh on channel voices was Adam Ulfers from Meter. And he left a question for you, and it goes like this What will it take for channel partners, particularly resellers, to push past the 20% threshold and you know significantly grow their share of recurring revenue?
Margaret Adam:Okay, so we're talking about traditional resellers moving into the cloud, moving into managed services. I think there's got to be a will, to be honest. Um, it's a hard, it's a hard transformation, right? We we saw this with the cloud. Um, it has a very real impact financially to manage that transition from your one-time lumpy fees into a recurring revenue model, requires a different sales motion and a different structure. And so, you know, both financially as well as culturally, it's a big change. And honestly, if they haven't done it by now, maybe they don't want to, right? We've been talking about the transition. When I was at IDC, going back 14-15 years, we were talking about this transition, right? So I think there are some businesses who don't want to make that transformation, and that that ultimately is their decision, right? But there are others that have embraced it. I think the easier motion is to move um into smaller managed services, moving into SaaS resale, but that doesn't give the volume to move them out of that 80-20 mark. So it does mean delivering and developing new services and a new portfolio and a new way to go to market, which some businesses quite rightly don't actually want to make the transition. It doesn't make sense to them. And I think you know they will continue to run as they are. So I don't know the answer to that one.
Maciej:I don't think this is this is for Adam to basically decide whether you know he he is okay with that answer. I am I am very good with that answer. But a follow-up question from my side do you think these traditional resellers are they going to be left behind given everything that's going on?
Margaret Adam:We've been talking about their demise again for 15 years, and they still play a pretty critical role in terms of the relationships that they have. So, no, I don't. I think what might change is the relevancy of the portfolios that they offer and what they do, right? Tech is moving incredibly fast. So I think maintaining growth does mean being in the cloud, it does mean adopting new technologies, um, new cyber solutions, AI, you know, all of that. So the portfolio, um, and of course, when a product is very commoditized, your profitability is under pressure. So they'll need to embrace the new in order to remain relevant, profitable, and growing. And if they don't, I think they are gonna struggle in the longer term.
Maciej:Thank you. And speaking of new technologies, everyone's talking about AI right now. Um, but from your perspective, how is it actually transforming the way that channel programs operate? You know, from recruitment to enablement, sales, and beyond.
Margaret Adam:I'm gonna, you know, I don't think it's transformative yet. I think it's gonna be though. So I'll caveat that. Um if I look at what where most vendor activity is, it's still a lot around the edges, right? So they really kind of testing AI in different use cases. We are seeing some things come to market, but I wouldn't say it's a holistic transformation yet. Where it is showing up quite a lot is in kind of the partner identification, recruitment, and prioritization piece, right? Kind of using AI to rate different partners for their industry fit for gaps in portfolio, um, a need for integration partners, you know, using AI to analyze those and identify those gaps, and then go out and help to identify those partners. We're seeing it in supporting the PAMs, so kind of this idea of an AI PAM. So, you know, looking at internal workflows where there's a lot of overhead on the partner account manager, things like basic support requests or how do I register a deal, how do I apply for NDF, you know, those kind of questions and bringing that into your PRM and into your workflows. Another place is showing up a lot, and I talk about this quite a bit, is in the co-cell piece. And I would say that probably the one area where this is it where I'm seeing it be the most mature, actually. So using AI to look at all kinds of different signals, you know, points of data, certifications, experience, customer case studies, customer references, um, to identify and match and align vendor sellers with partners around shared opportunities, right? So kind of matchmaking for co-sale opportunities, but then also matchmaking for kind of partner-to-partner type engagements. And linked to that is, and I don't think anyone's nailed this yet, but certainly seeing the role that AI can play around attribution and scoring always being very complex, right? Particularly in multi-partner deals to associate a value that a non-transactional partner brought into a deal, right? And so kind of using AI to help with that scoring. Um, and ultimately that would help with attribution. And then, of course, we're seeing it in marketing all over, right? Um, I've seen some really cool use cases around creating like an AI interview for a partner to create a case study that's very templated and very quick to do. Um, so creating you know context-driven case studies. Um we're seeing it around content creation, obviously around personalization. So we're seeing pockets, I guess my message, we're seeing these pockets of innovation. But is it a whole-scale transformation yet? Probably not, but it's certainly heading that way.
Maciej:I wanted to touch a little bit on the on the PAM side of things. So you said you know, you AI can be used to support partner account managers to properly manage their their partners and making sure that they're supported with all with all that data. And I even made a a short episode, one of the Channel Tuesday ones, which talks about you know coaching partners by using real-time data. So all the data-driven channels, it a lot of times it means you know, there's a lot more dashboards that are being created. But how would AI help, especially you know, PRM platforms, turn that data into that decision-making power?
Margaret Adam:Yeah, no, this is actually a really exciting space. And you know, I used to work a Tableau writer. I love a good dashboard. I'm a bit of a data geek. Um, but what AI brings to it is really excited, uh, you know, it's it allows you to do things like the natural language. So you can actually explore that data without being a data analyst, right? You can start bringing things like predictive modeling and what-if analysis and scenario analysis. A great example is if you want to adjust your partner program from silver to gold and you're gonna raise the thresholds, you can very easily with AI play around with that data and say what is the impact gonna be, how many of my partners are gonna shift, for example. Um, so it allows you to more intuitively explore that data, and also you can start to bring things like NextBix Actions. It can it can give you the actions you need to do, right? So moving from that data to insight to action. And then it's also really handy if you've got lots of data for doing things like anomaly detections, um, for doing things like correlations, right? Really important in the channel. So kind of you'll be able to start to see. We've seen a drop in engagement in this region. What could that be? What is the correlation there? And and start to get answers to those questions a lot more easier. So, you know, it's that shift from visibility and transparency, which really is really important and has added a lot to our lives, um, but into that actionability. I will caveat this though, right? Because I think one of the biggest barriers to AI adoption by channel teams is the data, right? Because AI is only as good as the data foundation that it operates on. You can't, age agents can't trigger actions from systems that are disconnected. If you've got inconsistent workflows or like partially automated workflows or different workflows for different regions, you're not you can't automate what isn't already standardized or already digitized, right? And so, you know, to do all of this and to get the value from that data, you do have to get your data house and your data estates in uh clean and and and and AI ready, right? And I think that that is one of the limitations from what you can apply once you've got the data foundation place is really exciting, actually.
Maciej:Super cool. The other thing that I would uh want to mention here is that having all that AI functionality being available on the vendor side to being you know able to grab that full ecosystem view of what's happening, the performance, etc., is all well and good, but you do need a mechanism, typically a PRM, to share that data in real time with with partners as well, to create that transparency, right? And that's what where PRMs like Channel scaler would typically come in.
Margaret Adam:Absolutely, absolutely. You've got to make it visible and you've got to make that data as in real time as possible, right? So that it's not a report that you're emailing to them months after the fact, right? That actually and and we've seen that right with Channel scaler, where you show dashboards in that portal, you get much higher engagement because it means something to the partners, right? They're able to see how they're performing. You're one of many vendors that they're working with. Now, the next stage of this is that they can go and explore that data, right? So, okay, if we had to do this, what would what would the impact be, right? So they can start to play around with the data rather than just having it as as visible. But you're 100% correct. And and PRM plays a critical role in being that that window to partners, right? And and and vice versa, bringing partners back into your organization.
Maciej:We've seen this trend for for a long time now, and it's all about personalization, and especially when it comes to you know onboarding partners and providing them with the the right enablement for them, and on the marketing side as well. Like personalization has become pretty much an expected thing today. How can AI make it possible to deliver that you know very tailored experience to hundreds or thousands of partners at once?
Margaret Adam:Yeah, so first of all, it can, right? I think that's a beauty because it, you know, once it's able to trigger those actions, it can work on these huge volumes of data. I think what AI can do is add context, right? So it's not just what partner type you are, but it's actually we know who the partner is, but we also know what they're doing, we know what stage of maturity they're at, and we're able to advise them on what they need to do next. So that's highly, highly personalized based on the triggers that that individual partner or the individual user within that partner has actually taken, right? So you can get to much more granular journeys over time. And then you know the AI system learns from every interaction, right? Who's opening watch, who performs well. So you start to be able to see and be able to get trends in terms of okay, our best performing partners do these six or seven things. Let's bring that into that onboarding journey, for example, because we know that these are um the actions or activities that drive the most results, right? So I think it's that context in terms of where that part individual partner is and their journey with you, and then providing that next best action or next best recommendation. So moving again from that visibility to that action, actionability, that's a word.
Maciej:Are you able to back it up with any real-world examples? Um, any stories or cases where AI really made a big difference already in something like, I don't know, lead distribution, deal registration, or even marketing development funds?
Margaret Adam:Yeah, I'll talk, I'll talk from two perspectives. So, from our perspective at Channel scaler, right, we recently launched as part of an MDF concierge service brought in AI document intelligence, right? So AI can analyze proof of performance. Now I've been in partner marketing and I know what a pain doing that is, right? It's an overhead that nobody needs in their life, right? But it it's there for a reason. So actually bringing in AI to be that proof of performance, and it can analyze the majority of it, and where there is a problem, it's typically because there is a problem with the proof of performance, right? So just taking away hours and hours of work, right? And so that you're able to focus on on more creative work. We've also brought AI into deal rich, actually on the partner account manager side, right? So being able to help them to manage the deal-rige pipeline to which deals to prioritize first in terms of approvals, where there's actions taken. So just you know, speeding up that process and again taking that overhead away from the partner account managers. Another place where I've seen it be very effective is around matchmaking. Um, and this has had a huge um impact. So I think AWS one of the first to do it, but I know many, many vendors have started to do this now. So AWS is one is called a um partner matching engine, and it looks at all sorts of data points, right? Not just certifications, not just tier status. It looks at you know what case studies have done, past businesses, how engaged they are, what kind of marketplace activity partners have done, you know, so all sorts of data points and activities to really get a very rich view of that partner, but then start to recommend partners in the seller's workflow. So if you can imagine AWS seller has an opportunity in Citibank, okay, and they're going into their CRM and you know, putting in that deal or that opportunity, it will look at which partners have worked in similar accounts, have done similar solutions, um, have submitted case studies, have transacted on the marketplace, and look at all these things that would make that ideal partner and will actually recommend to the seller in their flow of work, right? These are the partners you because that's a problem with post-sale often. There's interest, but the sellers are, you know, they're motivated to close the deal, right? They don't necessarily want to go hunting around for a partner. So actually making it so easy for them to identify the best fit partner for that opportunity, I think, is um really having quite a big impact.
Maciej:Yeah, co-selling is definitely a big topic these days. Yeah. And the other side of AI is the generative AI tools, and they're everywhere. We uh pretty much everybody uses it in their daily lives. But how are partners actually using them in their you know, in their regular channel business? And do you see any challenges or risk that's come with that adoption?
Margaret Adam:Yeah, I'd say they're probably using it the same as we are. Um as yeah, I think everybody is. However, where I'm seeing really intelligent use cases are particularly with your MSPs and your service delivery partners, is bringing AI to improve help desks, to improve customer onboarding time, also to do things like predictive analysis and things like that, right? Doing root cause analysis. So applying it into their service desk, their health desks, um, to improve and optimize on service delivery. Then they're using it for things like campaign creation, social media, your BDR outreach, you know, um, as are others. They also use it for things like solution documentation and customer QA, and obviously partners often first line of support or a gold mine for that kind of information, right? So using generative AI to actually not only capture that, but actually respond and be able to learn. Um and that brings me to the next point. Whereas it goes wrong, context, context, context, context. You always say like AI without context is like an intern with no onboarding. You know, they can have multiple degrees, right? But if they don't understand how your business works, what your business does, it's pointless. So if you've got it's not pointless, but you're never gonna get this the right kind of results, right? So context is absolutely everything. And there is a risk that if they're just using a general open AI, that they're not necessarily teaching it the institutional knowledge. Um, and then for you as a vendor, right, you want those partners to understand your branding, your messaging, your position, your joint value proposition. So they are creating market content, marketing content, talking about your partnership, that they're doing it with context. So I think that's a risk. And then of course, there's always over-reliance and automation, you know, not checking, not fact-checking, you know, putting out stuff within accuracies, there's always that risk, right? And I don't think it's limited to partners. I think the same is true for individuals within any business, right? But I think it needs that governance, those brand guidelines, and that institutional intelligence in order to be effective.
Maciej:And you mentioned automation just there, right? And automation can be a double-edged sword in partner relationships, especially. Like how do how does one strike the right balance between using the AI to scale, but keeping that human connection and that partners typically value?
Margaret Adam:Yeah, that's such an important point, right? And we've all been frustrated on the other side of that, right? I think it is about the use case. You know, you should be focusing AI on areas where there's friction and administration, right? And so that you're able to free your people up to focus on things like co-selling, on your joint strategy, on being creative in terms of how you go to market together, um, on relationship building, right? Moving away from a very transactional relationship to actually getting to know multiple stakeholders within that partner. So, you know, for me, AI should augment those relationships, but take away the overhead. And I think we know, right, partner teams are always under-resourced, right? I've always. And so, and there's a lot of overhead and a lot of administration, right? So I think AI hopefully can improve that quite dramatically. Um, and for me, that's where the opportunity is. But I think it's always a risk of over-automating and becoming too reliant, and it's uh I think vendors need to very, very carefully balance that because this has always been and I think always will be a people business, and you you need to retain that.
Maciej:I agree 100%. Margaret, you're a product marketeer, so you know the fine line between innovation and hype. Like, how do you communicate the AI value to your audience and you know, in a way that is credible and isn't just you know buzzword heavy.
Margaret Adam:Yeah, exactly. Yeah, slapping AI onto the end of your name doesn't make it a product that works right. So I think I think you need to be really focused on tangible use cases and measurable outcomes, particularly now, because there's so much noise and there's so much buzz and there's so much hype, right? Is you know, moving away from the buzzwords and actually saying this is what it's gonna do and this is how it's gonna help you, right? These are the outcomes that it's gonna help you achieve. And guess what? Here's how it's gonna do it. But also being honest, you know, I've caveated every conversation I've said here where the potential on AI is amazing, but if your data state's not not sorted out, you're gonna struggle to actually bring anything, you know, you have to have the fundamentals in place. I so I think that's the one piece is you know, really focusing at real examples, making a tangible use case, measurable outcomes. But I think what we also need to do, particularly now, is explain the role of AI and the role of the human in it, right? And really starting to say this is what the human does and this is what the AI does. And as a marketer, that's really hard to do without a very long format piece, right? But I think it's really important to do that because this is the balancing act I think many of us are trying to figure out is you know, what where does human judgment need to come in and and where will AI play a role? Um, and then again, showing the measurement, showing the outcomes that actually achieve, right? I think that's how you cut through the noise right now.
Maciej:Perfect. Rightly so. You you you talked about measuring the tangible results. And and I think a lot, a lot of channel professionals, channel chiefs, they're scratching their heads because they want to kick off an AI initiative within within their channel program. They need to be able to measure it somehow and prove the ROI. So from your perspective, what should they, what KPI should they be really concentrating on to prove that ROI?
Margaret Adam:Yeah, and I mean this is a real thing, right? So I think it was that MIT study that came out that says something like 95% of gen AI projects are failing and also failing to deliver any kind of measurable ROI. So I, you know, I don't think it's limited to channel teams, but channel teams have limited resources and limited time, which makes AI a very interesting proposition. But again, you don't have time to experiment indefinitely, right? So I think the my first bit of advice on measurement is being very clear on what problem you're trying to solve, right? Um, and starting there. Um, so if you are looking to save time, if you're looking to improve engagement, if you're looking to improve lead conversions, whatever the objective is, that you are able then to measure that as a result, right? It might have other impacts, but you need to be able to tie whatever AI activity you're doing with something that that can show that result. So I'd say probably those are the three that I'm seeing the most: time savings, um, conversion improvements, and and like any uplifting and engagement.
Maciej:If you were to take a stab at predicting the future a little bit, what do you think that AI enhanced partner ecosystem might look like in five years from now?
Margaret Adam:Yeah, well, I can't in five years. There's no way. I think um, yeah, I mean, I could imagine anything in five years. I genuinely think it's moving so fast. I two years ago, I wouldn't have even thought we were in this space, right? So I think there's people smarter than me that are able to see that. What I do think we will have is I mean, and I've kind of been thinking about this before AI became such a thing as well, is moving away from kind of individual systems to having platforms of collaboration, platforms of platforms or ecosystems of ecosystems, right? And really being able to create actions and triggers across different systems of different platforms, right? So if you think about multi-partner deals, is a really great example of this, right? Right now, let's say you've got a manufacturer in Brazil who's moving to S4 HANA on AWS, okay? So you're gonna have an SAP, AE, and SE team there. You're gonna have an AWS, AE, and SE team on that. You might have a marketplace transaction in that if that that company does private offers. You're gonna have a GSI, let's say it's Accenture, it's Deloitte, or it's Wipro, right? They're leading the implementation of change management, and then you probably because it's in Brazil, you're gonna have a regional Brazilian reseller to do all of the contracting and the first line support, right? And all of these partners are in different systems, right? And so it's very hard to get that visibility, it's all going to be living in in separate systems, right? And this is a big challenge. You've got the attribution when the deal closes, everyone's claiming that they did it, but you've got no way of actually seeing that. So I think what we will get to is a lot more interconnectivity and interactions between these different systems because that's how AI needs to work, right? And so I think it'll, you know, and I don't know how they're gonna do this, but I think what this will give us then is much greater visibility outside of our own systems and our own systems of record. So I think that data interaction is going to become a lot more interesting.
Maciej:Appreciate it. Thank you very much, Margaret. There's one question that I ask of every guest, and that's what's the one thing you wish you knew before you started your career in channel?
Margaret Adam:That's a great question. Yeah, I guess I would have known how hard it is. It's really not an easy space to be, but I think you know, related to that is that it has always been and will always be about trusted relationships, and that trust is really hard to build and really easy to break. And certainly I've made mistakes in my career, I think we all have, right? But that you know, the minute you understand the criticality of trust in any kind of partnership, I think the more effective that you will be in that role.
Maciej:Thank you. And just like Adam left a question for you, would you mind leaving a question for our next guest, please?
Margaret Adam:Yeah, and this is selfish works. I don't know how to do this. So my question is, right, with all the noise, with all the activity, with how fast things are moving, like how do you individually avoid the AI overload, right? Make sure that you are staying on top of it and being able to apply it, but are staying focused and purposeful in terms of how you do that and not getting distracted by all the AI activity. So I'd love thoughts on that.
Maciej:Thank you very much, Margaret. So much appreciate you coming on the show. Thank you so much for your time and sharing your insights into AI and partnerships. Thank you very much.
Margaret Adam:Thank you, Maciej. Thanks for inviting me. Great conversation and yeah, some hard-hitting questions here. I really appreciate the opportunity.
Maciej:Thank you. 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.
