146 | From Portals to Agents: Reinventing Partner Success with AI

Speaker A: Foreign.

Speaker B: Welcome again to another episode of the tech marketing podcast, um, and another episode of our channel series. Um, so I'm joined in the virtual recording booth by my, my partner in the channel, uh, Seb. So Seb, welcome back.

Speaker A: Thank you, Joe.

Speaker B: This is an exciting one I've been looking forward to for a long time actually. And, uh, so I'm going to say welcome to our, ah, our guest, Divya Raja Gopalan, um, senior Director of Global Partner Strategy, um, and I'm going to say has got quite an impressive title of a, ah, channel Digital Innovation and Transformation Leader, um, which is what gets me really excited about joining. So Divya, welcome to the podcast.

Speaker C: Well, thank you for having me. I'm so excited to hear to talk to you.

Speaker B: We crossed paths back when we do a little work for ServiceNow, back when we were essentially having that initial conversation about ecosystems. But since then, of course, AI transformation, that was a few years ago. This has been a, uh, rocket ship that we've all been part of. So I'm really excited to go into this today because I think there's a lot of stuff I think we can all learn from what ServiceNow are doing. So where do we even get started? Divya, what does your role consist of today? Let's start there. What does channel innovation and transformation look like?

Speaker C: So we work very closely with our partners, um, because what we build has to suffice for how our partners can work every day that we're building the best experience. So it means working very closely with our partners, getting feedback from our partners. It's working closely with our internal sales organization as well as our operations organizations in order to build the best ecosystem in the world. Because what we want to do is we want to thrive and we want to make sure that our partners are not spending a lot of time, not a lot, don't have a lot of overhead in terms of the work that they have to do. Right? And then also for our internal organizations, focus on what you need to do, focus on selling, focus on doing that work rather than thinking about how to build spreadsheets and all of that. So, um, it consists of all of that. And I will say that it is one of the. Probably one of my most favorite things is just being able to contact a partner and say, hey, would you like to be part of my focus group? Because I'd really like to know what's not working. And let me tell you, it just comes out and they are so happy to be able to tell me what is working and what's not working. The good thing, John, is that they tell me what is working. Also, it's not always about what's not working.

Speaker B: I was going to say, though. But just having that, be able to have that honest conversation with a partner about what's not working is, I think, a question many vendors are not really asking themselves.

Speaker C: Absolutely. Our partners, um, when we ask them to participate, I mean, I probably get about 20 partners that are willing to come talk to me to tell me, hey, we have ideas. And they don't just come back, come in with, we've got problems. They actually have ideas that they give us, like, hey, maybe we should work it this way. Um, and it, it does really help us build that great partnership with them. Right. It's the collaboration piece that I love the most.

Speaker B: One question I love to ask all of our guests as part of this channel series, um, and Sev and I were actually joking about this earlier today. Not just because we were, we were mentioning another guest that we've got coming up. Um, you know, the channel doesn't. Isn't something we all choose. We kind of fall into it. Like, what was, what's your story? How have you landed in the channel?

Speaker C: Oh, my. Oh, my. Okay. So I began my career actually in the mortgage business. And my God, that's another story because I was there. I worked in what's called the secondary marketing department. I sold mortgages to our Wall street investors. Yes, that was one of the biggest problems that caused the mortgage bust. So mortgage bust happened? Yes, you can say it was partly my fault. Um, and the mortgage bust happened is when I really, being in the Silicon Valley, I mean, where do you thrive? Right? Silicon Valley equals technology. And so I decided at that point to really make my move into a technology sort of organization or a company. And I initially began my journey at VMware. Um, and I worked in the channel ecosystem at VMware. And that was what, like 15 years ago. Um, and I've done everything in the channel ecosystem, by the way. Right. I've done operations, I've done programs, I've done, um, everything around, uh, analytics and insights, and then was really asked to lead this digital transformation kind of organization because ServiceNow, um, and I don't want to say it wasn't leading, but we were just a little behind, right. In terms of how, uh, to best support our partners. We're asking them to do so much, right? Go sell. We want source revenue that we were finding. They were spending so much time in the tool itself, just manually having to do work and so that became the most exciting chapter of my life. And so here I am, I'm still in the channel business again. Have been doing it for over 15 years and I love it. I love our partners.

Speaker B: I mean that's a great origin story. He mentioned you. In partner operations, partner programs and digital

Speaker C: transformation, I've done insights. So I've done all of the reporting things. I've done analytics and insights. I've also run an enablement team. Um, and so now I'm also doing strategy. So. So yeah, I'm kind of getting the whole 360 holistic view which I love. Right. Because that's really where I want to be. I want to learn the entire ecosystem of the channel business and I've been able to do that.

Speaker A: That's amazing. Do you feel like certainly that having that experience around ops program analytics, um, is that what set you up to be able to do transformation? Because you already talked about focus groups but enabling colleagues to work better with partners, listening to partners like that must have given you an amazing grounding.

Speaker C: It's uh, it's funny you say that because that's one of the critical things that my team actually even said to me which is you have. Because, because I've had such a rich background in operations, like I know the end to end of everything. Right. I've had to, I've been able to see it. It's been so much easier now for me to be able to say, okay, I know how the system works. Cause I lived it. I know what the partners complained about because they came to me from an operations perspective. And so now you know what's my insights and making it better. So absolutely, you nailed it. It's having that experience in both programs and operations definitely did feed into where I am today.

Speaker B: I was going to ask what's been your favorite role in the channel? Um, but I can hazard a guess what your answer is going to be on this one.

Speaker C: It's been the digital transformation. I mean I don't know if you know John, but I've been, I have been able to build our own PRM from ground up. So my God, like talk about exciting times. So, um, yes, it's my favorite by far.

Speaker B: I knew that was going to be your answer. I kind of, I, I set you up there. But I would say, I would also say like having. So we last met at the partner Tech X. We were just talking about this in the pre call. Um, and having seen what, you know, you guys, you mentioned ServiceNow is behind. I think that's definitely not the case now with some of the digital transformation you produced and the experiences you're providing partners. Um, so it was amazing to see, to see how you've blended, you know, building your own PRM with some of the newer, uh, newer technologies such as AI. Of course, we're not going to get through a podcast talking about, um, transformation without talking about AI. Like, when did you, I'm going to say, when did you first start seeing that intersection about with transformation AI, with partner enablement? When did that start to occur?

Speaker C: For me specifically, it started probably about, I would say it's been a year now because, um, a year ago I all of a sudden just started having like different organizations. And when I mean organizations, I mean companies, vendors outside reach out to me and kind of start soliciting like, hey, we were able to do this AI, this AI. And I thought, wait a minute, what is this craze? Like, what are, what are we talking about? And so I actually went on and I did a course around AI, went out and really started to learn myself. Okay, what all can AI do for you? Then I became kind of the nerd in the AI space. I started using every single AI tool that's available. I have a Perplexity license, I go on to chat GPT, I use Microsoft. I am all over the place, by the way. Um, and when I started using it more and I thought about our partners and we started internally ServiceNow starting to build our own AI product, which is called Now Assist, it became even more clear to me that as we're starting to build this now this product at ServiceNow, I want to start utilizing that product to service our partners first. And so we are one of the first ones in ServiceNow to start taking that now assist and pushing it out specifically to our partners. Because I said, we have it like we've built it. Let's go get our partners on it. Um, and so earlier, uh, late last year is when we started building out kind of the AI foundation component to help support our partners.

Speaker B: I have the opportunity of seeing quite a few partner programs out there. That's one of the benefits of my role. Um, and I would say you're one of the few that has really been able to bring the Gen AI or agentic AI into the partner experience. Um, what do you, you know, I've used the term there partner experience. Like, what do you think is. I'm m trying to think the right way of phrasing this. Like, what do you think? Where do you think this is heading? Like where, where do you think, what do you, what's wrong with partner experiences today? And where do you think that this is going in the future?

Speaker C: You know, I think what's, what's wrong today is we try and build a lot of capabilities for our, for our partners and we, um, m overly stimulate them. Okay. Think about a partner that's specifically focused on multiple route to markets, right? They're uh, implementation partner, they're a resale partner, and they're a tech partner. For all of these different partners, they are coming onto the portal because that is their home. And today what we don't do well is we just throw content at them. We tell them here, go here to go do your deal reg, go here to do deployments, um, make sure you're going here to look at your dashboard, make sure you understand your segmentation. That is overwhelming for any partner, right? Like, especially if they're trying to really grow in the ecosystem in their segmentation, you know, earn more revenue. It's a lot, it's a lot for them. They do it and they do it though with, with some this mentality of, oh my God, I hate my dog, because I hate doing it this way. So I started, as I started to learn more and more about this, what I realized is AI is able to take that 80, 60%, so 60 to 80% of the work that they're doing and automate through an agent. Okay, and let me just give you an example. Um, today our partners, when they're doing a deal registration, they have to come onto the portal, there's a form that they have to complete. Sometimes they don't complete it properly, it goes to internal, comes back, they have to update it again. Okay, uh, you have to actually hire admins in a partner, um, organization to do this work. Just imagine the overhead, right? And so one of the things we did was we build an agent. We built an agent. We're also doing an email to deal reg. We built an agent first. The partner simply comes onto the portal and says, hey agent, I need you to go create a deal reg for me. Here's some details, go create it. The agent can then just have a very normal conversation and say, well, what about this? Can you just give me this form created, submit it, we're done. And then we're also thinking about email to deal Reg. Like just imagine the partner doesn't even have to come into the portal. Simply send an email, agent creates it. I mean, these are the little things that we need to think about on how to get partners to focus on selling building revenue rather than thinking about, I gotta see, what am I missing in my criteria, like what certifications am I missing, what am I not doing to get to the next segment in order for me to get more benefits? Um, and so using AI, I think it's going to go a long way. The one key thing that we don't do well, I think in just the AI world is how do we use data because we don't use data in really helping us. And um, what AI can, can help support. Right. So one of the key things also that I've been thinking about is if you look at like the dashboards and things we build, uh, it's one thing to just give the data to partners. Here you go, here's all the data you need. But it's another thing to use AI to be able to tell the partner, we want you to know that based on what we're seeing on your dashboard, here's the data that matters to you, here's what's missing that you need to still do. Like from a segmentation perspective, like thinking about programs and things like that. We've got to use data more effectively using AI. And so one of the, one of the things ServiceNow is doing is the whole workflow data fabric plus AI. It's like the magic happens. Um, and then the last thing I will say is I'm seeing a lot more of this whole, um, voice agent. And that is pretty cool. And it's something that we're starting to tread on. Like it's, you know, I would love to just come on and be like, hey agent, do this for me. I don't even have to type anything. Um, so I do think that that's also where it's headed.

Speaker B: You've hit all the nails on the head there. Like, firstly my passion for the fact that deal reg forms are ah, one of the least optimized things as part of any partner experience. I honestly don't know why we're still doing it the way we're doing it. Um, and I love this concept of being made email to deal reg. We already have a lot of this data in our, in our calendars with who we invited to the meeting.

Speaker C: Exactly.

Speaker B: You know, we can plug together these different data sources, as you've mentioned, to provide a much, much better experience to these partners. Um, I think the bit that I can't wrap my head around at the moment, um, and this is speaking as a technologist as well as someone who handles transformation day in, day out, is kind of where are we seeing that interface develop like what you've described there, we're moving into a much more chat based interface and I think if we looked back a few years, most people would have said that's not the optimal way to do it. Give me a form. Now we, but now we are seeing, you know, comfort, adapt. You know, you've just mentioned voice. Like, I, uh, think this is going to be one of the most fascinating things to watch is like, how does the interfaces of our PRMs start to change as uh, some of this new technology develops.

Speaker C: Yeah, I mean one of the things that Michael park, who's our new leader now says to me all the time and to all of us is we have got to create an easy button for our partners. Like there needs to be, you know, the Staples commercial easy button. We've got to create that easy button for our partners and everything that they're doing because we, we don't want to have them worry about our technology. We don't want partners to sit there and worry about technology. We want them to go focus on their business. Um, so, you know, that's really what we're thinking. And one of the things you said to me really did, did resonate, which is, you know, the whole meeting kind of a conversation. Like what, what I see also when we talk about partners, it's one thing, but even our internal sellers spend so much time on trying to figure out, okay, I'm going to have a conversation, I have to have a conversation with this partner next week. Uh, I've got to go figure out like what do I need to mention to them, what do I need to talk about. One of the key things that we're thinking about within PRM right now is what's called a meeting assist. Like just imagine you have a meeting next week with partner X and you're like so involved in your sales right now and you're not, don't have time to prep the meeting. Assist function actually will help for your partner. Summarize all of the things that you need to know about your partner that are critical that you need to talk to them about. You've got a deal registration that's expiring. Hey, you've got a deployment, you've got this implementation going on and we're not seeing it move quickly enough. Uh, your partner certifications are expiring. Um, we see that there's a potential for an upsell on this opportunity. Let me give you the data set. Like even helping our internal sales organization is so important that you know, we started Thinking about this meeting assist, um, that you were mentioning just earlier, also in terms of how do you support our internal sales organizations with all of

Speaker A: the advancements and everything that we can do, it still comes back to, uh, kind of the core essence of partnerships, which are enabling people to work productively together. And that bit I love everything you've talked about is I love the term overly stimulate partners. That's the politest way I've ever heard somebody say. We put way too much out there all the time. But, you know, I think of, uh, the principles that help us, uh, kind of work out what's a good idea and what's a good thing to do. And, and I think partner experience and what we're trying to do is be easy to work with. You know, that's everything you've said is about making it just easier. So when we're talking to a person or engaging them, it's we're specific, we're relevant, we're timely and actionable. And if we're those four things, it takes away all of the heavy lifting. And it's like, I know what I need to do and why and I can do it. And that's the best thing with AI, is that then the doing it can become that much simpler. Um, which is definitely there. But yeah, meeting assist, that is coming back to your data and AI potential. It's like we have all of this data, um, and obviously ServiceNow, you're an automation business. It's like knitting things together is what you're best at. Um, but I love that, I love that we have services and platforms around data and dashboarding. And again, we've had similar ideas of like, how do we provide information to someone that's actionable from this, rather than saying, you haven't logged in to look at this.

Speaker C: Exactly. I mean, I'd love to know your feedback on AI specifically. Like, do you. Are you. I mean, I still think that there's some hesitation in the market, right. About can you trust AI. So, um, I beat up AI like every day. Like, I use it for both personal and professional. Right. For my work and personal. I'm asking AI, how do I get my dogs to stop barking at squirrels? Now, I don't get a, I don't get a good answer for it, but I'm using AI all day long. So I'd be curious also to hear from you guys. Like, are you. Do you feel that in the market too? Do you feel like there's still the hesitation that's happening in different corporations?

Speaker A: There's definitely different approaches and um, governance around AI and that's something I think, you know, is very different from. You know, as John said, we're lucky to work with lots of businesses that are running ecosystems. Some have been far more I think open around experimentation and using AI. One area marketing for example. We've seen some businesses that have been very much like, you know, there's no external AI that's going to tell a partner information about our products. You know like that's an important distinction of going like so, so there's definitely that side. But I think in the areas we look at and the complexity of ecosystem co selling, understanding how to direct the right information to the right business in the uh, buying cycle, everything is very challenging. And AI kind of I think has the potential to help automate and make sense of a lot of information which is very difficult to at the moment.

Speaker B: I've got a slightly interesting angle on this. Uh, um, I'm going to come in with because we're living and breathing AI every day. Um, Seb, you mentioned quite an interesting word there of experiment. Um, and so I really think the key here isn't necessarily what you build but how you go about doing it and the kind of the how and the why. I think um, you know we've just talked about two great use cases here, right? We've talked about um, uh, deal registration which is you know m one of my big bugbears in the channel Meeting Assist, which is a tool that I feel I definitely need. But uh, um, trust me. Uh, but uh, the key thing here is like it's very easy to get caught up into like let's just sprinkle AI into everything. Um, like how I think the key and it really stood out for me in some of the presentations that we saw at Partner Tech X where some use cases were just so much stronger than others. Um, like Divya, from your perspective where you're going through this day in, day out, you're building out some of these tools. Um, what's the way that you try and make sure the how and the why is there? Like what's, what's your process to, to make sure you're focusing on the most important use cases.

Speaker C: A lot of it's obviously partner related feedback that I get. Um, but I heavily rely on, on metrics, um, because metrics will tell me where there are areas that need the most support. Right. So even when I was doing deal registration, I mean I was looking at deal registration, I was looking at deployment registration, I was looking everywhere but you have to look at in the ecosystem where is the bulk of your revenue going? And you want to focus on that specific revenue because you want to make sure that process is smooth because you got to make sure that that works. Right. And then I bridge that with partner feedback to say hey, here's some things that we're thinking about what is the most critical for you. And then based on that and running metrics, then you're able to come back with okay, these are the ones that we need to really focus on which is going to get us results. So I really focus on KPI's metrics first. I won't start anything until I have the proper KPI metrics. Because once you build something you still have to report out metrics on it. Right? Adoption metrics. Um, you know how well you know, is it, is it potentially getting more, bringing more source business into, into your organization? Um, so I generally start there and then I, and then I go about it. Yeah.

Speaker B: Where do we even start with KPIs and metrics? Like how do you know you've picked. I get. Okay, let me put this a different way. Like with, especially with AI it normally result revolves around two main items like efficiency or revenue. Like is it going to help us drive more deals or is it going to help us make us more efficient? Like how, I guess, how have you built some of those, some of those metrics in? Because it's quite difficult to measure time saved with a partner.

Speaker C: It is, it is difficult. You'll never like some of these examples. You probably won't get um, the direct numbers. Okay. Because there's some of this you are going to have to guess. Uh, so sometimes what I do is internally specifically, um, for individual from an operational savings it's actually easy, right? You've got each of the individuals that are, get, get paid a certain amount for the number of hours that they work. So let's say we're going to save them an hour a day. You take that hour a day and, and take the, the amount of money they would make that hour a day and you're able to actually rationalize into dollars saved. Right. Because now that person is working on some other things with partners it's more, it's difficult to go and ask them so how much do you pay your people? So I hung to that. I basically just ask them, okay, what are the number of hours you think it'll save you? And then I try and do some of my own cost estimates based on what I think. You know, admins like what was their normal pay? And then I'm able to round it. I try and build dollars around everything. Even hours can turn into dollars. So um, my measurement and what I report out usually is always either dollar saved adoption metrics, because you want to know how many people are adopting certain tools. Um, uh, or it's more of like usage. Right, like maus and things like that

Speaker A: would come into place now that makes, that makes sense. And we've used a similar one, looking at time efficiency as being something that you can calculate and you can play with it to kind of go, we're conservative. So you underestimate, um, you always have

Speaker C: to underestimate because when you have results, you were, you're gonna be way off. You'd rather show way better results than say, oh, I did not meet my expectations.

Speaker A: So yes, no, no, it's totally that. And then it's also something you can say if we invest more time, we can probably get a more accurate analysis, but you'll find it will be higher. Um, so it's almost like the emphasis is prove it. So, ah, that makes good sense. But I'm thinking back to the question you had about AI adoption and what we see. And I think one of the really interesting, um, parts of TechX was the great presentations of which yours was one on the Tuesday, which is almost like his actually what we're doing, you know, in some really amazing practical examples where we, you know, The Deal Ridge 1 We all look at and there were some other people who had great examples. And then the second day almost felt like the Doomsday squad came in to say, and this is why it's not possible for many of you. It was really interesting that juxtaposition of like is here's where we're going. Um, but then it's like, well to get here you have to get your house in order first. And so it's interesting because when you were asking about examples there was something nagging me and it was generally that it comes back to data, um, and processes. It's like if you've not already got a good automation process happening in your business around areas you know, of channel and partner engagement, it's quite hard to kind of build something transformational. Um, so one of the use cases we can see, and it talked about often is leads that are coming through inbound to a business. It's like, can we work out the best partners to assign those leads automatically using AI, not just based on their tier or on their kind of, you know, information we can see about them? But based on vertical expertise, based on capacity, based on investment.

Speaker C: That's a great idea.

Speaker A: Yeah. But if you don't have good data around those partners and you haven't got it organized, you could send those leads to the wrong place and they're dying.

Speaker C: Which is why the first thing I said was data has to lead. Because if you don't have the right data, um, yeah, like you know, data and AI have to go hand in hand. Like you have to. Right. That's the most critical thing. We launched, ah, an um, AI leads process also. But I like what you said because we haven't gone that far yet. We haven't gone as far as, because we have leads that come in through Partner Finder, we have leads that come in through campaigns or we have leads that come in through our store. But that's really customer saying I want to talk to this partner, whereas you don't. Leads that are just generated in the system where you have to basically see whether those leads should go to a partner or uh, which partner it has to go to. And using like we can use AI for that. But you're right, you have to have the data for the partner correctly in order to say this lead could match this partner. If your data doesn't work, that lead will not work either. So yeah, you're absolutely right on that one.

Speaker B: I think for me though, like we're diving into kind of some of the uh, really interesting use cases where AI helps us make those better decisions but doesn't necessarily replace a partner. Right. We're not looking at partner expertise is going to be very difficult to replace. Like they've got to come in and in ServiceNow, implement an automated process but you're not going to automate, uh, those elements. Like the aha moment for me with AI was when you started to realize, and actually we still haven't got there yet with this. Um, we currently process all of our emails by the time they come into our inbox. That's how we still think about things. But AI should allow us to say like, what is the most important thing I need to do today? Um, and that was the moment where I was like, when that comes in, that's going to be game changing, isn't it?

Speaker C: Yes, I agree with you. Um, my iPhone actually started to do that. It tells me whether do you want us to. But it doesn't necessarily tell you what's the most important yet because it doesn't know how to use data yet. Right. It just tells you we can give you a review or summary of your emails. Um, but the data aspect of it and being able to determine what's the most critical that you need to know. Oh my God, that would be so powerful.

Speaker A: This is the challenge as well that we have in ecosystem and partner experiences. As we all get used to these everyday capabilities that we can access, we will start to expect them to be the way in which we're working. And it always feels like that's always one of our big challenges is it's super complex trying to deliver experiences for so many different users who are all coming at it from a slightly different kind of business.

Speaker B: Need you raise a good question there. A good, a good point there, Seb, which is these as experiences as we get used to ordering a taxi from our phone and not having to speak to someone or order something and it arrives next day, or emails get automatically categorized and prioritized for us, um, everyone expects that in their lives. But we still set the bar so low for PRMs.

Speaker C: Um, for part of portals, we do you hit it. That's. That is, that is, that is correct. Which is one of the reasons like, as we started thinking about PRM for ServiceNow, um, it almost felt like, wow, I feel like there's still things missing out and out, you know, in the industry for prm. Let's just build it. Like, I can see where there's gaps. Let's just go build it. Um, but you're right, that was one of the critical factors that I took into account before we made the decision to build versus buy.

Speaker B: I want to recognize this as well, Divya, because this is something that, you know, m my job as CTO sounds, uh, like I should be building a lot of stuff and I do spend a lot of time building things, but it's also to understand our clients technology. Um, and you've achieved something which I have been, I, uh, see as almost the holy grail, I think of partner experiences, which is you've been able to dog food your own product for partners to use. Like that is. No, that is no mean feat. Like that is. That's an incredible thing to, to, to be able to get out there to say, like, we are already using our own product to be able to deliver this for you, and I think it just makes that even more important. Um, you know, as you started to build that prm, because you mentioned it's all home built, like what got you most excited about that intersection of AI and partner experience? Like what. When was the moment when you were like, oh my goodness, this is going

Speaker C: to be game Changing, ah, when I started to build prm, it was just going to be the native. Make sure you're able to do this work and that work. Um, as soon as we got our handle around prm, which is when I started to really dive into. You can't just build a PRM system anymore that houses all of these transactions. You just can't anymore. You have got to embed AI into every single piece of prm, which means that whether you're doing deal reg, whether you're doing deployments, whether you're looking at your certification data, whether you're trying to understand your segmentation, every single piece of it has to have an AI. So as I talk to my product team today, because I built, uh, our own PRM more from my own use perspective, right? I wanted to build it and I wanted to use it for our partners. It wasn't necessarily to say, hey, ServiceNow, now go take this PRM and let's go productize it. In the beginning, it wasn't that I just wanted to build something for our partners to be able to utilize. So as I started to think about it more, I started to build it. Um, you know, and I love Erica because Erica was my biggest supporter and she said, oh my God, we have to productize this. This is going to be great. And that's when we started to really think about, okay, how do we build now the product that we can use that ServiceNow, you know, so where we've already built CRM, we're going to focus on PRM and build it as a package. But as I thought about it and I was talking to my product team, that one of the key things I said is you have got to have AI in this PRM tool. Like, every single element of this has to have an AI touch point. That became probably the most critical factor. And as I talked to a lot of the industry experts, that that is what they mentioned to me also. Like, make sure your PRM has an AI model embedded throughout the, the, the system. Because you can't just have it in one place, you've got to have it in every single place. And that has been the most exciting thing for me because imagine having to look at this big thing and then trying to figure out, okay, how can AI make this easier? How can AI make this easier? How can I. Like that is, that is like, uh, that's been really exciting for me to be able to build out within our PRM system.

Speaker B: And when you say AI needs to be embedded in every part, like, what does that, what does that mean.

Speaker C: So, um, for example, you've got, you've got content that loves, right? You've got so much content, you got to use generative AI to be able to not have somebody go find a document and read the document. Use generative AI to summarize, tell you what you need to know about the content, right? So today you've got sales plays, you've got so many playbooks. Who wants to read like a 50 page document? Tell me the gist of what I need to know. So Judy is using generative AI. That's why I say it's overstimulating. I really do mean the content. It is overstimulating for any partner or um, you've got to be able to use the agentic AI feature. You've got to be able to use machine learning plus AI in many areas. You've got to now be able to think about the voice piece. So there's a lot of different components of AI that you can build into the system. And I mean use all of it, use all of it where you think it needs to go. And that's what we're really trying to look at because we have generative AI right now. We've got the agentic AI, um, we've got the AI summarization piece. So we've got a few AI components and now we've got to just figure out where it makes sense.

Speaker B: I think this is the other point I like to make around AI. Um, you know, one point here actually. Let's just for our listeners, because it gets thrown around a lot like how would you define gen AI versus agentic AI?

Speaker C: So when you think of generative AI, what generative AI is doing, it's looking at an actual content document, whatever it is, and it's actually reading through the entire thing and it's able to then summarize for you what you need to know in that content. It's basically taking the content. So, so I'm asking the question, I want to know more about the sales program rather than taking you to the document that talks all about the sales program. What the AI functionality is doing is it's looking at the sales program guide and it's then answering your question and saying here's what you need to know about the sales program. And then maybe another question then happens through kind of the agentic AI where now it becomes conversational. And then you're asking the question of, well, I'd also like to know what I can do to be progressing in my segmentation. So now you're starting the conversational AI where you've got the, the, um, more of the chat, you know, generative AI and then with the agent. By actually running through an agent and saying, hey, Agent. Like, hey, John, I want you to be able to take my data and I want you to go tell me what are the things that I need to do in order to become a premier partner. The agent is then able to take all of the data set and then come back and tell you, John, John, you need to get five more certifications right now. Do you want me to show you want me to, um, automatically enroll you in the courses? You need to get two more deals completed. I see that you have two opportunities in the system already, so it's actually going that far. And I found that I've utilized all these different components of it to actually make AI be really powerful because not just one of them will work.

Speaker B: And I think you hit on something that I want to make sure we dive into, which is you personalize that experience for the partner. It's not about AI, is not the, the product here. The product is, is the outcome.

Speaker C: Absolutely.

Speaker B: It's the, uh, we've made this, we've, we've come in, we've given them an action of, you need to go and perform these five different certifications. That's personalized based on the data that we talked about a moment ago for that particular outcome. Um, I think that that's worth recognizing where, you know, and we, we've talked a lot around different use cases in. On today's podcast already. Like, where do you think the, the next biggest gap is in prms?

Speaker C: Well, number one, every single PRM needs to have an AI component. I'm going to just say that I think that is the most critical. Um, I think where some of the leading PRM vendors lack is they only specialize in like a few things and, and the other things that are missing. You now have to go get an outside vendor to fill the space, another vendor to fill another space, another vendor to fill another space. Now you add the cost for the PRM vendor plus this vendor, plus this vendor, plus this vendor. You're playing an arm and a leg. So the PRM vendor is not a pure play vendor because they're just focused on maybe deal registration and maybe like account management, but they're not really looking after these other things. Now I want to just make sure I'm making this statement that by no means a service now, saying, we are not going to be using some of these vendors because if I'm going into the market and um, somebody's already using your product. We've got to make sure we have an integration with your product, right? So we've got to also build that integration so that companies can utilize the vendor that they have now, plus our prm. So we're already starting to build, we're trying, we need to build that relationship right now with a lot of these vendors that already have their footprint out in the market. So we can make sure we're doing that. But you've got to have a PRM system that's a pure play. There are certain things that we do need other vendors for. You know, I will say that, um, but I don't see a pure play vendor yet. I just haven't seen it.

Speaker B: I don't disagree. I think my, the way I've been phrasing that at the moment, Divya, is I think there's too much entropy in partner experience. Um, now what I mean is like you start off thinking, okay, this is going to be, we're going to create a great partner experience. We're going to deploy a new prm, could be off the shelf, could be something you build and then you go, oh no, now we've got a gap with deal registration. You build the deal registration form and then that form becomes three or four pages of ten fields that partners never want to fill in. Then you add another thing. We're like, okay, now we need an opt reg form. It becomes this sprawl of entropy. Um, and actually that might be that you still have to fill in these items with different vendors to get a best in class solution. But you've got to consider the whole and the impact that has on partner's time and a partner's experience. And I think AI's got a great place to play in that, but it's got to be performed intentionally.

Speaker C: That is a great, that's a great way to say it. Actually. You actually nailed it. I agree with you there.

Speaker B: Like you can't just kind of let it happen by adding, adding different solutions. So I completely agree with your points, but I think there's got to be a structure to get there.

Speaker A: I was thinking about the description you had Divya around the agent role and I think it is kind of telling the story that you need to tell to a partner to motivate action, which is that piece around. You need these certs, you need these deals. And I think a lot of the time when we're looking at data and how we present it the way We've done it in the past has been to lay out information, almost those dashboards that can tell that story and make it easy for a partner to come Go, okay, I've got a gap, here's my pipeline, here's my top five ops. Like you can, you can lay it out. But I feel like that future is definitely how we tell that story in the partners space. You know, like that's gotta be one of the future pieces is probably the idea of websites and portals is challenged in the future, you know, because ultimately what you have potentially is just something that's always there for a partner to get what they need. The world would be crazy because most partners will have 50 of those. So it moves the battleground, but it presents interesting kind of experiences. The way in which we'll just keep trying to do what we're all trying to do, which is being useful, helpful and driving action.

Speaker B: We've covered a lot of things in today's podcast talking about AI. Uh, we've covered different types of AI. We've covered some of my most favorite use cases to optimize. Um, by the way, if you still have a deal reg form and you're listening to this, please uh, find ways to replace that. Um, and uh, I can't wait for email to deal reg to come out. But for someone listening to this in the channel, wanting to get started, what would be your one piece of advice for them, Divya? Like how would they, how do they even get started bringing AI into their prm?

Speaker C: I would definitely say first to start, do your own research. Um, because when, when AI came out first I actually became a consumer of AI. I went out and I used AI features. I used them like I said, professionally and personally. You have got to understand AI. You've got to understand how AI works. Because the term AI is used very loosely, but there's so many different components of AI. You have to understand what is a generative AI. What's the difference with that versus agentic AI versus, you know, the voice AI. Like you have to understand it. So um, I did all that on my own. So I would definitely say first understand the definition of AI. You've got to understand the mechanics of it. Then I would start looking into your ecosystem and really start thinking about after you understand AI, where are the biggest problem areas in your ecosystem and then think about how AI can help you fix that. And I wouldn't go straightforward and just say, I've got 10 things I want to do with AI. Focus on one, take one, focus on the one and see if it'll work. If you get the adoption, if you get the feedback, then you can really start pivoting towards many others. Um, but I. But the most important thing is learn AI, because a lot of people just assume AI is one thing versus they don't really understand all the different variations of AI. So you have got to be an expert there.

Speaker B: You've got to get started somewhere. And. And actually that's a process that doesn't involve AI, is doing some learning. It's, um. Yeah, it's. I think the biggest thing for me is always to experiment, like, find ways that you can, you know, understand what the difference between a prompt is versus a rag model. And if you don't know what the difference is between those two, that's okay. Um, but it's. That's part of the journey you've got to. You've got to go on.

Speaker C: Yeah, you're going to get very techy when you have these. These specific conversations because, uh, I don't know that anybody would understand the rag model until they actually go and learn AI. So, yes, those are all useful things for you to know.

Speaker B: True story. When I first was in my first AI meeting, when I got involved, I thought they meant, like, I was coming from a project management mindset, was like, okay, we need a red, amber, green model. Like, why do we. What are we measuring? But it's, um. I'll let our listeners do their own research if that's something they're new to. But, you know, Divi, it's been an absolute pleasure having you on the tech marketing podcast today. Thank you very much for joining us. This has been a long time coming. I've been waiting to get, uh, waiting to get you on the podcast for quite a while. So, um, thank you very much for joining 7me in the woman booth.

Speaker C: I appreciate you having me. I love the conversation.