What does it really take to sell an AI-native product into the Fortune 500? In this episode of Founded & Funded, Madrona Managing Director Matt McIlwain sits down with two founders deep in the trenches of enterprise AI adoption, Yoodli’s Esha Joshi and Gradial’s Anup Chamrajnagar. Their companies are selling into some of the world’s most complex organizations, like Google, SAP, Snowflake, Databricks, and more. And they break down what founders can sometimes underestimate about enterprise AI sales.
Listen on Spotify, Apple, and Amazon | Watch on YouTube.
This transcript was automatically generated and edited for clarity.
Matt: So Esha, tell us a little bit about the original wedge and how that original wedge allowed you to enter the enterprise market.
Esha: Yes, my name’s Esha, and I’m a founder of Yoodli AI Roleplays. What we are building is a native AI roleplay and conversational coaching platform for customer-facing teams. We started the company to help people become more confident communicators. And what we do today is we are enabling sales account executives, customer success managers, sales engineers, and support specialists to practice high-stakes conversations and get personalized real-time feedback in a judgment-free zone without a human manager physically in the room. So it’s a very fun time. Our wedge is with sales enablement, helping our enablement teams ramp hundreds, if not thousands, of salespeople very quickly and onboard them without having to worry about consistent training and the lack of consistent messaging across an organization.
Matt: Anup Gradial is also a young company, about three years old. Tell us a little bit about the original wedge for the company and how you won those first couple of enterprise customers.
Anup: Gradial is an AI-native accelerant for your content supply chain. What that means is, generally speaking, a lot of AI for marketing tools that we saw a couple of years back, first when ChatGPT recently came out, was mainly used for things like content creation. So blogs, article pages, base level, simple images, but nothing else that happens after that, which is actually 80 to 90% of the bottleneck on why things take so long to get out in enterprise marketing to begin with. So we tried to build what we call the configuration co-pilot to help software companies configure workflows faster, where you have a configuration problem at the beginning and a continuous configuration problem throughout while trying to update your site and make new designs and code new components. And so that was really the problem we set out to solve first was solving content execution at scale for some of the largest companies in the world to help people get content out faster and get more scale and get more efficient at the same time.
Matt: Well, let’s talk a little bit about what the state of the world was a couple of years ago. It was a time when a lot of people were piloting things and trying things, and some of those worked and some of those didn’t. What did you learn about buying behaviors to be able to tell the difference between somebody that was just purely a tire kicker, somebody who was serious about the pilot and then what you ultimately needed to do to be able to get that person to be a committed long-term customer?
Anup: Yeah, I say this thing around the room that we’re not selling a solution, we’re selling pain. And I think that was something that we realized early on that if we could get to the actual day-to-day pain of our customer as quickly as possible and have them recite back to us that they basically empathize with what we’re saying, then we can find ourselves in a situation where we can actually co-opt a solution together in the early days. And they started saying, “No, that’s not it.” And they give you little tweaks over time, but it was a mixture of selling the pain very accurately and then being able to basically show them a demo of something that was very consistent across the industry, and I’m sure you guys find this as well, of enablement. I was joking that I should have probably used Yoodli before this entire podcast, but it’s industry-wide, vertical wide, you try to find this similar pain and then that’s how you start achieving scale with that kind of stuff.
Matt: Esha, what was the experience for you with some of those early customers that were piloting things? I remember some of the times where you had these really small pilots and their buying process and how that has been evolving in this era of AI-style solutions.
Esha: I wouldn’t say it was easy for us to get in those pilots, but we were certainly doing more feature selling and showcasing what the value of AI functionality could look like, which in the Yoodli realm was all about these contextualized back-and-forth conversations for people to be able to practice ahead of a conversation. So a pitch, practicing skills of discovery that had never been done before. Previously, what was status quo is humans and human managers and their direct reports have these manual, awkward, in-the-room conversations, roleplays, they were inconsistent, they were a little bit stressful. And then when you took technology and put that in the mix, you then had very static talking to Smart Mirror with a very canned response. So nowhere seen before had they had these really contextualized conversations that took in the context of company collateral.
So to answer your question, it was still hard because it was enterprise sales, but getting people to really see the value and dream was, I would say, easier back then than it is now.
Matt: Now, so why is that? Have the expectations gone up now in terms of somebody being even willing to do a pilot, and how have those expectations been changing?
Esha: I think the expectations are different from different companies. So certainly IT, tech, SaaS companies, they have different solutions, different founders metaphorically knocking on their doors, outbound messaging, cold calls, maybe they have an AI bot doing outbound AI-driven emails. So there are a lot of opportunities, and there are a lot of solutions that you can look at.
So now I would say buyers are not just looking at features, they’re looking at the outcomes you are providing, and if the user experience bar is raising? I feel like it’s gotten a lot harder to make sure that your user experience is high enough. It’s very difficult to keep people delighted, and I think there are incumbents for certain solutions that have been doing this that are not necessarily an AI-native platform. I think you heard both of us say that we’re AI-native platforms, meaning that the flexibility of adding on capabilities that drive certain outcomes, whether it’s time saved, revenue attained, or whatever the equivalent is for you, it’s a bit easier, I think, for both of us to do that because we can add on AI functionality.
Matt: Yeah, so you’re moving on from the wow factor and you’ve got durable outcomes, durable impact. That is what the customers are looking for today. Is that what you’re finding as well?
Anup: Yeah, exactly. I mean, in the form of a pilot, so I mean, you either go in with preliminary engagement, or if the scope is very large for what the client wants to do, then you obviously run a pilot of a microcosm of that, and then run success metrics. But I think the thing that was successful is that many clients or prospects will initially use words like, “Let’s do a POC.” And we try to typically circumvent that to at least say, “Let’s do a targeted pilot.” Because I think the difference is everyone asks, and I’m sure you guys get this all the time, “Do you guys have a sandbox we can play with and test out a couple scenarios?”
And so for us, we know we’re solving an end-to-end problem for folks. We know we have enough reference ability; we know everyone will come in and say, “We have such a complex environment, can you do it?” At this point, we know we can. And so what we try to do is say, “Hey, here are the three things that we’re going to get out of this pilot as an objective. Do you guys agree? Can we build a mutual success plan together and say, ‘Okay, if we come out of this, we’re going to roll it into production and actually get a real thing going here?'” So I think you don’t let the pilot die on success.
Matt: Right. Right. That’s a great way to say it.
Anup:
Because I think a lot of AI companies will be successful, and then it’ll die out. Right? And so you need to make sure that you get that commitment upfront so that when the work is actually put in, it’ll result in a good outcome.
Matt: Esha, do you do a similar type of thing to validate that, “No, we’re serious about this. We’re not just doing pilots to kick the tires. We’re doing pilots today so that this company can be an impact player with us”?
Esha: Absolutely. I would say before we enter into a scoped pilot, call it 60 days, 90 days, I think anything longer than that, you’re starting to extend the time with which you can get a clear answer. We try to scope it ideally like 45 days. And we say, “During the 45 days, here’s what we will give to you and here’s what we need to have in place before we get into it.” Ideally, you align on pricing, but even that’s asterisks because procurement will get in the picture, and then it’ll become a negotiation all over again. But align on pricing, align on success metrics, be very declarative on the outcomes you can give to them within a 45-day period, what you need from them. And then also beyond that, tell the story of what this is, just the starting point of, and what this enables in the future, with this existing use case with 3x the number of people or even more use cases in the organization, depending on how you roll out.
Matt: Let’s spend a couple of minutes on that because we talked about the early wedge, the initial problem that you were each trying to solve. What are you starting to see maybe even with some of those earliest customers that you won, how they’re starting to pull you into adjacent use cases?
Esha: So I talked about how our wedge is sales enablement. It’s still a primary wedge for us, and we find that we start with sales enablement in particular. But there is go-to-market enablement. So it’s very easy to make the case of moving into customer success managers, moving into sales engineers. So that’s one direction we’ve moved in. Similar types of folks, very similar skills and competencies and even onboarding process.
There’s another area we expand into, which is not just onboarding and real-call preparation, but also knowledge acquisition, and that is definitely enabled by a lot of the advances we’re seeing in AI today. But if you think about it before you get into even a podcast like this, I’ve obviously practiced for this podcast, but in order to practice the content, I need to know the content I need to push myself.
Matt: Right. So there’s a learning dimension to it too. So you’re getting pulled that way.
Esha: Exactly. Before this, I would’ve talked to an AI version of Matt and have rehearsed an answer and been like, “Matt, does this align with the story that you’re trying to tell with the Madrona ecosystem?” And you might’ve said, “Well, maybe you think about pilots and scoping it out this way?” So that’s the experience that we now get to give our customers before they then practice for the podcast.
Matt: Anup, tell us maybe about one of those early customers who you won and now they’re pulling you into adjacent use cases.
Anup: I can think of a large telecom provider who was using us primarily for web authoring across their digital experiences. And there was a particular instance where they had their largest partner changed prices on them, and usually this very large partner doesn’t tell any of their partners about the price changes because they do it overnight. And when that happens, you have to change all of your digital experiences to reflect that new price or else you’re technically liable for that price increase. And in this case it was $7, so it was pretty substantial across their customer base. And so it usually takes three weeks to go through the digital experiences, sweep everything, actually replace the content with the right thing, go through brand compliance and legal and get it approved, it took them 30 minutes with prompting with Gradial. And the email team took note of that and they were like, “Can we use this?” And the creative studio team took note of that moment and said, “Hey, we have assets that we have to replace all the time. Can we use this technology to do that?”
So seeing the use cases in action and broadcasting that across the customer bases will naturally fire it up. And if they don’t have to go through procurement themselves and there’s already an MSA in place, they’re much more likely to be pretty gung ho about it, so…
Matt: Absolutely. So maybe let’s pick back up then because that was really helpful as well. But I want to pick back up on this notion, okay, I’m in this pilot, I’ve set the ground rules of the pilot, gotten alignment on the objectives. Is there anything else in your playbook that you think other people should understand about how do we get through that pilot successfully and then fully upsold into some kind of an annual commitment?
Esha: So my background was not in sales, starting Yoodli. I had a background in engineering and product. But of course, founders become the first salespeople of the company. And I remember with one of our very first deals, we found a champion, she was excited, saw the value, saw even the potential outcomes, and I was like, “Great, my job is done!” And then it turns out that you have to then enable this whole ecosystem. So champion is step one, then you have to convince IT, you’ve got to convince procurement. Now, with a lot of SaaS IT companies, there’s an AI governance council; you’ve got to convince the budget holders. So to answer your question, going from a pilot to a full 12-month contract, really anything with enterprise sales is you’re convincing the whole ecosystem. And every single person in that ecosystem has something that they care about, whether it’s a discount, risk mitigation, outcomes to the company, onboarding, whatever it might be. And I definitely did not think… It makes sense intuitively, but I did not think about that when I first started.
Matt: How about you, Anup?
Anup: Yeah, I think reading, listening to any founder talk about it could not possibly prepare you for all of the things that Esha’s basically talking about, of needing to appease the people who are higher up that the champion reports to, needing to also get the lower rung. So there’s a three-legged stool to enterprise sales. You need to get your champion, you need to get their VP or the CMO, and then you need to get the person who is the power user of the product. Right? And to get all of those, you need to work fast, right, during the pilot to actually get them all engaged. And so you start learning how to expedite and some tricks up your sleeve to accelerate parts of those.
And there’s this whole new concept in a lot of enterprises called the AI review board. Right? This wasn’t a thing two years ago. Many of the people are just procurement people that basically got a new job at their own company to lead this board. And so, anticipating some of those questions that just get asked because there will be added steps because you’re an AI vendor that will come up, but there are also other things because you’re an AI vendor that people want you to win. You try to make people win with you as well, and there are different tricks we can get into that we’ve thought of to try to accelerate that.
Matt: What strikes me here as interesting is outside of the AI review board dimension and probably some of the additional data security layers, because it used to be more software security validation, how similar this enterprise, understand the different players that are in the mix is to classic selling. What I’m interested though is it seems like both of your companies, you both had spectacular growth last year and on a great start again this year. It seems to be happening faster. And so what is it about how you run a sales program in an enterprise? Maybe a lot of the same techniques that are around, but how you’re doing it with more agility and with more speed?
Esha: Step one, we have more salespeople.
Matt: So that’s more capacity. That’s one.
Esha: Capacity. So we’re going with more speed.
Second, specifically for Yoodli, some of these big enterprise accounts, Google, Snowflake, SAP, we have initial success stories. And so we have little groups of champions across the organization and they’re speaking and we’re sort creating these serendipitous moments where we’re getting them to speak up by planting success stories and intel in their laps that they may not know from other parts of the other side of the organization. And to your point, we have an MSA and we’re through the security process. The AI governance process is from my experience, a royal pain in the butt. And a lot of these companies-
Anup: It always comes in at the last second, too.
Esha: Exactly. And a lot of these companies are like, “Oh man, I hope we don’t have to go through that again.” And so I think the fact that we’re already in through the organization and there’s some success stories, capitalize on that momentum and make them feel the pain to not move off and go somewhere else.
Matt: Anything on your end, Anup? I completely agree with Esha on adding more capacity, anticipating some of these nuances. On deal velocity, anything that you can offer there?
Anup: I think the concept of revenue operations or sales operations, I think everything has a KPI tied to it. And I think in enterprise selling, the concept of ops should be to reduce the sales cycle as much as possible because that’s where they can actually influence it, right, filling in all of the gaps.
I mean, I’m sure you and everyone went through the same thing; it’s all founder-led sales at the beginning. You do so many things naturally that you don’t really think through the gaps that may emerge for a person that’s newly coming into your company and needs to sell. I’m sure you guys have it on lock with using Yoodli to actually train your sellers. We don’t, I wish we had that, but basically giving them the runs, that’s having them shadow folks, you can actually start spreading that around a little bit more.
And we have a go-to-market hub at our company that basically has anything and everything you would need to know to sell Gradial, and we’re always adding to it. And it’s a collective knowledge base, so everyone can add to it. I’ve assigned owners to each section of it that gives them ownership over a particular section of enablement, so they really take pride in that. And one person owns workshops, one person owns different kinds of way of communicating pricing, one person owns the BVA deck, one person owns the pilot cadence. And you can have them be the centers of excellence and try to scale what used to come to us. All the questions that come to us, you try to scale that a little bit better. So that’s been much easier.
Esha: Good idea.
Matt: And I think I would describe that almost as AIOps. You’re using collaborative systems, but also AI to help make your own internal business better to help you accelerate sales cycles with your customers.
Anup: I mean, it was pretty unbelievable. With Opus 4.6, we plugged it into HubSpot, and I asked it exactly what I asked my VP of sales every week for a full Q1, Q2 forecast. It gave me every single detail I could possibly have wanted, and it made it an interactive app that I can just click through and see whatever I wanted. So it sent it immediately to me and the other three guys, and it was pretty awesome.
Matt: Now, in your customers, though, that kind of story might make them nervous. And I think one of the other aspects of this AI world is the customers you’re selling into worrying that by adopting your technology, you might be replacing their jobs or facilitating other people losing their jobs inside their company. How do you navigate that issue?
Anup: I think the most important thing is we talk about the three-legged stool and the champion, but realistically, everyone is a champion. And in this case, the power users need to be champions of the product. Right? And I think something that I model after is the way Clay built its ecosystem, Vercel built its ecosystem. People are so proud to talk about the way that they’re using these agents and this technology that they write pieces, they have training videos that they… And this is completely unprompted by the company themselves. Right? There’s whole communities that have risen up from the ground on being an expert in Clay, right?
Matt: “Claygents” I think they call them.
Anup: Yeah, exactly. And you need to build that camaraderie and basically that base up somehow because you’re not going to get replaced by AI, you’ll get better by AI. Right? And you’ll be one of the people, one of the few people in the world who can actually be a superhuman with AI. Right? So partner with them through the journey of actually having that super remote control, I think has been a key focus of ours this year and the end of last year, for sure.
Esha: I think Yoodli has given our enablement team at companies, at our customers, like superpowers that they never had before. So first, we’re able to now quantify the improvement of people in the organization in a way that we couldn’t. And so we’re making our champions, which are enablement leaders and enablement stakeholders, look like superheroes because they’re able to go to their CRO or go to their people leader or talent leader and talk about here’s what this tool system has done to enable folks, and here’s what this actually means from a revenue attainment standpoint. So they feel really great about that.
I think personally, and one of the reasons why I’m really excited about what we’re doing and why we started it is we have stories of people saying, “I was not replaced by Yoodli AI, I actually got promoted because of Yoodli. I practiced, and I became an AE or a senior AE, and before I was just an SDR or BDR, and it feels good.”
Matt: The adage that you’re not going to lose your job to AI, you’re going to lose your job to somebody who’s embracing AI.
Esha: Yes.
Matt: So you’re looking for those kinds of champions that are, “I’m going to embrace, I’m going to invest in this technology to help advance our company and inevitably help advance my career.” What happens though, because I’m sure whether it’s in the pilot stage, we are working with non-deterministic models and non-deterministic systems. There’s probably some good stories. I don’t know if you want to tell any of them of like, “Oh, this is when things didn’t go quite right with our system, with our models,” and how do you recover from that with a customer? And so because this trust element of I trust not only this company, but I trust their underlying application and the models they’re using, you have a good story or two to share there and how you navigated it? Do you want to start, Anup.
Anup: There are a number of examples of this. People claiming that the tech earlier on, especially not as much anymore, but that it didn’t work, that something that they ran just clunked out and just didn’t do it. And we ran the same thing and it did work and you had to try to show them, but they were like, “No, it didn’t work when I ran it.” And there’s no way to prove that other than go back. And so it was a little bit of trouble here and there of saying, “Okay, some people think that they see some writing on the wall and they’re reacting in a certain way.”
And we’ve learned from those lessons of how do we bring everyone together on a culminating success plan? And that’s what led to the mutual success criteria here of how do we actually beforehand establish if we completed these things, we would be successful with you guys. And get everyone’s agreement. The VP was in the room, the director was in the room, so now everyone feels like it’s an actual assignment that they need to do versus, “Hey, I tried this thing and I tried some things and it just didn’t work.” So, you’ve got to keep a close eye.
Matt: That’s great. Yeah, there’s always going to be bumps in the road along the early adoption phase, and so if people are focused on the end goals, then if somebody takes that mistake that happened and tries to use it against you, it’s like, “Well, let’s focus back on the goals we agreed to.”
Anup: Yeah, the pilot doesn’t stop either. I think we’ve learned that the hard way as well is just because you have a production contract doesn’t mean that they won’t still treat it as they want tangible goals, they want tangible ROI. And so we have a philosophy that the pilot never stops. Right? We want to keep having this mutual success plan keep — is the thing you want to accomplish at the end of the year and how do we help you get there?
Esha: Yeah, what you were saying, it’s either the pilot never stops or really the renewal is actually in the first three months. That’s the other one that I keep hearing. It’s rarely happening throughout the course, those first touch points.
But going back to your question around what’s a story that you can share. Early on in an early adoption phase, one of our customers was using Yoodli for a product certification and had used Yoodli. And this was a company that had several thousands of sellers using Yoodli. And one individual person reported that the product made a comment on their clothing, like the AI referenced the clothing.
Anup: Wow.
Matt: Yeah, a fashion review.
Esha: A fashion review. A fashion review. And I talked about that to my team and various members of the team were really upset. Other members were, when they heard it, they were like, “Well, I should go have AI comment on my clothing.” But long story short, it was obviously very disappointing. And so what we needed to do is, our engineering team was figuring out what kind of model magic could happen on the backend to serve that. But then we had to go talk to that individual user, hear them out, reassure them that they’d be good for the future. We had to go talk to our champion and the leader above and basically regain trust. It was a process, it was nerve-racking for a second, but I think we’re on a better path now.
Anup: That’s interesting. I guess sometimes in these situations, I mean, it’s better in a sales training and not a product sort of situation, but sometimes people on the other side are very unprofessional. Is that part of your guys’ work? How do you react to someone who’s being super callous? I mean, that happens all the time, especially in sales situations.
Esha: 100%.
Matt: Yeah. It’s maintaining your respect for them as a customer. And even if it’s like, wow, that seems to be a little bit out of bounds, but we’ll navigate it through and then solve those problems.
Anup: Yeah, that and a real-world scenario that you train for is someone being mean. It’s just like on the other side, they’re just like, “Yeah, I don’t like this.” Or they just came in with a bad mood. Is that something that you guys train against too?
Esha: 100%. We account for that. You never know how somebody else is going to react in a conversation. And there have been instances where I’ve left conversations or co-founders left conversations where that person was really mean, and you just deal with it. Not everybody has maybe as much thick skin. And so that’s actually what we say, we can build personas with different personalities ranging from really timid and meek to really aggressive and mean and rude.
Anup: That’s awesome.
Esha: And it’s not a bad thing, it’s just realistic. That’s the whole point of this.
Matt: Well, let’s switch from some of these product and selling cycle experiences to something closely related: pricing and packaging. And so maybe you could first tell us about how your product is priced and packaged today and how you’re thinking about continuing evolve. But let’s start with how it is today.
Esha: We’ve been experimenting with pricing for a very long time, so I would say our most frequently used pricing and packaging paradigm is seats-based model with a fee associated. Fee can be anything posed as a platform fee, which is the cost of AI features and functionality and also customer success support as part of that. We typically use that. We’ve had some pushback from customers, and then we can switch it up. Typically, the customers who push back or the ones who themselves don’t value and don’t sell to their customers seats and another fee, they’ll position it either as purely just seats with add-ons on top or usage-based pricing models. And so, we haven’t yet really seen an AI outcomes pricing model approach, which I know is causing a lot of excitement. But we’re keeping our eyes out.
Matt: But you don’t have much of a consumption component to your pricing system today. It is more on the seats-based and a platform fee type of a model.
Esha: Correct.
Matt: And then not an outcomes-based either.
Esha: Not an outcome-based model.
Matt: And, Anup, yours is a little bit different, so let’s hear about that.
Anup: We’re almost the opposite. But we do have a platform fee and a support fee. Those are two parts of it. The bulk of the fee from basically a volume-based metric because at the end of the day we are measured on output. So it’s like you’re more efficient with Gradial and you’re able to get more stuff out the door with Gradial and people have these output goals that they want to reach at the end of the year. Right? I want to have 200 more personalized blog pages, or I want to get 5,000 more emails out the door to targeted. So there’s I want to do this much more analysis. There’s an output measuring of it. And the good part is in marketing, there’s actually a very small finite list of those things, of those artifacts that people want to actually push out the door.
And so ours is a little bit, we map out a tentative plan of what they want to do and say, “Can you get more ambitious than that?” And they’ll be, “Amazing if we reach this.” And then we package that up into basically a volume-based metric of output. Right? So we don’t charge for consumption per se. If you’re asking it like a simple query, you can get that query out, but if it actually produces something of value, then that gets action.
Matt: And my sense is that that is generally working for you and that is what you plan to run with. Are there areas around the edges you’re exploring or experimenting like Esha was saying?
Anup: So, I think version one was seats-based module, use-case based. The module use case was how providers were doing it. And so we modeled after the best that there was, right?
And now we had the general pool of actions. And I think where we’re experimenting now is, we actually realize that when a customer tells us they want to get this host of artifacts out the door, why translate that list into some amorphous definition and then read translated when you’re trying to communicate the business value? Why don’t you just say, “We’re going to pledge to deliver that. Exactly what you want.” Right? And then find a way to, because we’re lucky that we are in a space where that list is very finite and it’s always going to be finite. It’s an artifact that you’re creating, whether it’s a page or an email or some kind of analysis report or a buyer report or an update to something. It’s always going to be like this finite list. And so if you can have this uniform list of categories, then why not just align it directly with what the customer wants to see at the end of the day? So that’s a little bit, it’s like it’s still volume, but it’s more tangible business value volume that is easier to explain to procurement.
Because the other thing we found is when we had the amorphous definition, oh, it was…
Matt: Tough for procurement.
Anup: Yeah. They’re like, “Okay, please walk through multiple types of prompts and use cases and how that would result in this actions, and do you guys have a dictionary?” And then, “Oh, well, how many actions would this be.” We are starting to get those questions a lot, so now we’re getting a little bit more simpler and saying, “Hey, it’s basically what an agency does, honestly, it’s how they price.”
Matt: Any other thoughts on this of where it’s evolving?
Esha: We’re seeing it evolve more to volume-based pricing because our tool is associated with behavior change and with educational learning. And because it’s behavior changing and practice and learning is hard, it’s less based on some folks, again, who sell this way to their customers want it based less on how many seats they’re buying, but the usage of those seats they’re getting. So different ways we’re doing that is of the actual seats that are being used, can we price on that, or of the unit itself, which is the role play practice in our case. Based on that you have a per-units cost.
Matt: Consumption type. Got it. Maybe let’s just touch quickly on two different things. One is an example of a key go-to-market partnership. You all are living in ecosystems and so maybe, Anup, you can give us an example on that front of how you’re partnering. I often talk about the AI ecosystems are really an important part of the equation. And then love to hear one from Esha as well. And then we’re going to go into a couple quick rapid fire questions.
Anup: We have a multi-pronged approach to partnerships. I mean, as we all know, enterprises are tough beasts and you need to attack them from multiple angles in order to conquer the mountain here. And so agencies was how we basically started off. So agencies dominated our space. Outsourced marketing spend is the biggest outsourced services spend category in the world, so about half a trillion dollars of spend in just the Fortune 500 per year. So agencies basically reign supreme there and they have all the relationships, but they’re also called upon to implement AI for their clients.
So we caught onto that train and started making relationships with a lot of agencies, the blue chip top agencies in the space. We purposefully chose, you could go in two directions there, you could go with blue chip top tier, you could go with the commodity volume players. And we went with the former just because we saw ourselves as sitting in that camp of people associate blue chip with similar things, and we wanted to continue to be in that. So agencies like Dentsu, Accenture Song, Deloitte Digital, folks like that that of top tier in that.
Esha: A lot of similarities in what Yoodli experiencing and what you’re saying. So the equivalent of agencies for you is coaching and training companies for us. So this can be anywhere on the spectrum of smaller Mom-and-Pop coaching companies. When I say coaching, executive communications, interview training, sales training. So anywhere that has 10 or fewer people these kinds of companies will use Yoodli to augment the coaching they do, to something like a Toastmasters, which is one of our first partnerships that has incredible distribution to like 4,000 Toastmasters, to anything now on the other end, like Sandler Sales or Corporate Visions, which are two of the top most well-known sales training companies. And in either case a lot of the bigger ones, like you said, have a mandate to use AI to help with their training and enablement and reinforcement of whatever they teach. And also there is additional exposure into their clients of Yoodli that’s sort of branded by them.
So I would say it’s been a really interesting process with the partners because you have to enable them so that they can go and sell your product, and also you have to work together symbiotically. So we’re learning a lot by that every day.
Matt: Do either of you use this concept of a forward deployed engineer or is your product so ready out of the box that that concept doesn’t apply?
Anup: We definitely have a forward deployed team. So every customer gets an account director or a deployed PM and a forward deployed engineering team. And there’s a lot of help that’s needed at the beginning to kind of introduce them into the agent world, because for some of our customers we have, I know we talked about some of the technology customers that we have, but most of our customers are actually legacy incumbents in financial services, healthcare, industrials, automotive, telecom. A lot of users in our platform oftentimes have never actually used ChatGPT before, which is kind of crazy in this year and day, but it’s the truth. And so how do you get people through a complete paradigm shift at the beginning has been interesting. Obviously, that gap is shortened, but it’s good to have. And everyone, all these enterprises like having a helping hand, they’ve always paid for carte blanche white glove service, and you need to give them that.
Esha: The equivalent for us is a sales engineer slash solutions engineer, also experimenting with professional services, to your point around white-glove service. Technical account manager for some of the really, really incumbent legacy IT that need that. And so, it’s all kind of a derivative of the same thing with slightly different skills. But yeah, certainly needed.
Matt: Well, we could talk all day. I’ve learned a ton just listening to both of you. So I think I’m going to end it there and hopefully in a couple of years when you all have continued to learn all these lessons of being truly agentic AI-native companies selling into the enterprise and making those customers successful, we’ll have another conversation. So thanks, Esha and Anup, very much for joining me.
Related Insights
