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Foreign.
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Welcome to today's episode of the AI to Roi podcast. Today I am joined by Yaakov Vanderkoy, the founder and CEO of Winning by Design. We'll be covering three main topics with Yaako today. Number one, the catalyst for their AI project within Winning by Design, some of the initial hypothesis to justify that initial budget. And then we'll be talking about the measurements that Jaco and his team used to say this was a great investment. And here's the roi. So that. Jaco, would you please take a moment to give a brief background of your journey to becoming my guest here on the AI to ROI podcast.
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Ray, welcome. How are you? How are you doing? And it is the first time we're speaking in the new year and we should be careful with that because it's already February, ray. We're already 10% and down of the year.
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B
It's amazing. Time flies so quickly, especially AI time.
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A
Oh, you better believe it. You know, if anything, what AI, does it make things go faster? Well, it's a pleasure and a treat. I'm here today because I'm here to share some of our experiences that we had with our AI sdr. And I, you know, like, I often, you know, embrace the opportunity to share with your listeners that what we go through in the hopes that they can benefit from it in whatever form and shape it it serves them well.
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B
I'm looking forward to this because once upon a time someone said, ray, you don't know Jack. And then I'm like, I do know Jack. It's an AI SDR that Winning by Design is deployed. So why don't we start at the beginning? Can you tell us a little bit about kind of why you decided to go down this AI SDR journey?
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A
Yes. Well, as you know, and as some of your listeners may know, Winning by Design, a company I founded over 15 years ago, we help companies with the GTA motion. In the early the mid-2015-16, SDR was the hype, you know, later on it became process and so on. And today, obviously, AI is where most of the motion is. So we believe that if you deploy a technology like AI, you actually have to use it. We can't recommend it in such a new world if we're not having tried it ourselves. Now we wanted to figure out what role would we do right? What is the right role to try it out in a customer facing role. And we picked the SDR role because it's the most, you know, arguably one of the highest risk roles. It leads to first impressions, trust Empathy, all that comes from that, you know, like. And any form of failure instantly could damage the brand and also our pipeline. So we thought to ourselves, if AI can't work here, and then it can't work anywhere. But the opposite is also true. If it can work here, it can work anywhere.
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B
Yaku, do me a favor, because the AI and ROI audience, it has expanded beyond the B2B tech industry and includes people from financial services, manufacturing, et cetera. Can you just explain a little bit about what that SDR role is and why it's been so critical in the B2B tech industry?
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Yeah, the role is so critical because in most cases it's the first point of contact that a company has with your company in which there is a form of a significant value exchange, for example, the invitation to a workshop or the sending of a reasonable research paper. So it's that first point of contact between the company. Now the SDR generally is a person earlier on in their career that performs the pre work needed for a sales call.
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B
Great. And you know, in the tech industry, we've been using these sales development representatives for about 10 years and things like Kadence software from an outreach, et cetera, really kind of made it even more popular. But I know that companies are having some challenges with the performance. What were some of those business objectives or challenges that you were trying to address by using AI?
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A
Yeah, well, the first step is to realize that the ROI on those SDR functions had starkly declined over the past years. In other words, where previously an SDR would set up about 20 appointments for a software for a seller a month, we with the certain conversion rate would make it make sense that dropped has significantly dropped over the years to about three to four appointments per month. At which point in time the ROI of an SDR no longer was as valuable as it once was.
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B
Well, so you know, doing benchmarking a lot, we used to see 10, 12, 14 appointments per month per sales development representative. And you're saying you saw that reduced down to three to four.
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Yeah, and on top of that the conversion rate would be hit harder. In other words, what a conversion rate originally was 28, 29% it declined to the low teens percentages.
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Well that tells me because as I do benchmark for an entire industry, I need to see customer acquisition cost, which is measuring how much sales and marketing investment you're putting into every dollar of new revenue. You know, even in large public sized companies that went from like $1.31 at median up to over $2. So were you facing Some of those same, I would say, financial return challenges in your outbound sales development reps.
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A
Absolutely. And you know, like, not just us, all our customers experience those metrics. And what we notice is when that happens, Ray, is it highly, you know, like, makes the seller, in this case the sdr, makes them very unmotivated to do so. It is, you know, that level of rejection and success is not sustainable for a human being.
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Yeah. And let's talk about something else that especially for large enterprises, but for any size company, this is an issue. And that is the infamous CRM hygiene issue. How often that salesforce or HubSpot instance just has a lot of bad data that executives are trying to make decisions from.
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A
Yeah, yeah. And mind you, to add on top of that, because the role is so challenging, Ray, that role transitions a lot from person to person to person every six to nine months. Then CRM hygiene starts to become a real issue. Then, you know, like inconsistent qualification, the lack of standardization becomes a real issue. Like all these things start to compound on top of each other. Right. And for us, a side effect was that we have offices in Australia, Latin America, Sao Paulo, we have offices in London, we have offices all across Europe. And what this means is that as a global company, on top of all this, we were not acting like a global company because our SDR hours were only within a certain limited hours. And so, like, all this gave us a problem that we were hitting what we would call our human limitations. Right. Like these are all. Fatigue is a human limitation. The inability to cover multiple hours was a human limitation. The CRM hygiene issues are human limitations. So we felt we were hitting human limitations there, Ray.
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So can you tell us a little bit about your journey? So you knew that the current state of operations wasn't delivering the performance you wanted. But AI SDRs, there's a lot of noise out there, whether they're high quality, whether they lead to better performance. So how did you justify the decision to go after this new AI based structure? And what was your hypothesis as far as what the actual financial benefits would be?
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Okay, so our first approach was not to look at it as buying a new tool and replace a human being with it, which is the common thing. But our first hypothesis was replace it as a system. You can't see a single tool. And the system meant that we needed to work with products. So in a second, you will use a particular product from one mind as the tool. But then combined with clay as a tool, we started to create a system combining with our knowledge. So for us, it Wasn't a series of tools connected just with some glue. It was an entire system. The mindset behind that system was not that it was a point of first contact, but it was a point of first impact exchange. The first time that a customer really gets a positive experience with an AI system.
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B
You know, I really like the way you talked about this as a system and it reminds me, and a lot of our listeners may not know about your revenue architect and really kind of the revenue factory. Would you mind just taking a step back and kind of share that revenue factor that you talked about and then how this really did leverage that concept?
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Yeah, we believe, Ray, that once companies surpass $10 million, whether it's 100, a billion and whatnot, that they mimic the operation of a factory. The same goals such as production efficiency and quality in the form of retention of your customers. The same processes that also mimics in the form of factory production lines, revenue production lines, which are often mimicked in the form of GTM motions. And it also mimics the factory behavior of PPT people, process and technology. All this comes back to understanding that when you run a revenue operation, you actually have to run it as a factory and factories run as a system. You can put more inputs in, you can improve the throughput, or you can develop closed loop systems in which output generates inputs. This mindset of thinking is called system thinking. And when we move to an AI centric approach, you're generally very quickly have to move to a system centric approach.
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B
You know, a long time ago, I led the automotive industry for division of GE and we looked at reengineering our manufacturing process, leveraging just in time kind of processes and technology. So tell me, did you have to look at your current state process, kind of map that out and redesign it before you even thought about how to leverage the tools?
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Yeah, you're actually coming to the conclusion, but I'll leave with it so that your audience understands it. What we initially were building, Ray, we were building a system to replace the seller, to make the seller job better. And the conclusion of this entire episode that you and I are working on today is actually we learned that we have to build a system to make it better for the buyer, not for the seller. That's the major conclusion that we came to because initially you go like, oh, let's do the same behavior that an SDR does or that the rep does, and then copy that. In AI, that did not work as well as we will see in the metrics in a second. You got to actually think of how do we make the buyer's journey, the buyer's experience better?
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B
Well, can you do me a favor? Can you kind of walk through the early part of this project, who you involved and if there were any of those aha moments, even at the very beginning before you went into the actual proof of concept or pilot.
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Yeah. Being one of the first to deploy a human like AI system, we picked that human to be me. And therefore, hence Jack, we made him look and feel like me. And the thought behind that was, since we're entering a new territory, we didn't want the AI system to reflect a sense of unfamiliarity. Instead, we want it to be a familiar experience, reflect the trust and experience of the brand. So we made that that AI system in reflection of me. That's not an easy decision to make because, you know, many people would perceive that as an ego trip or something like that. We again looked at it from the perspective of the customer. Right. And what we learned, Ray, is that that also includes the personality of me. So you can ask it, in my case, many people of you know that I'm a fervent Red Bull drinker. But so you can ask it and lob questions about Red Bull in there and it shows you the character similarities of my character as it responds, well,
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I don't want to get into the outcomes yet. I want to save that. To keep the audience engaged here. Was there kind of a first big aha moment? It's like, wow, we didn't really think about it this way.
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Yeah. We quickly came to the conclusion, Ray, that Jack is not an sdr. He's not an early stage first jobber. That is placing a hesitant call to a customer. Right. Like that first outreach, cold call often is. It is a full fledged operating GTM operator. It is capable of talking about the product. It can take the customer's conversation anywhere it wants. Way more so than a human being can. It can pull up a slide. It will pick out of ten thousands of slides. It will pick one of the right three slides at the right moment where a normal human being would not know where to find the slide, have to look it up, Boom, instantly pulls up slide. It is a fully trained GTM operator. That was the first big takeaway.
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B
Interesting. So you didn't view it as automating a sales development representative. You looked at it as automating this kind of revenue factory. And that this agent needed to be able to be so broad as an entire go to market operator.
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Is that absolutely right? And that mindset, now you switch back as the founder of the company. Now we put the company's best foot forward first. The best person in the company, the founder. Not necessarily the best, but you get the idea. The founder, founder led insights first rather than, you know, like SDR led insights first.
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B
Let me ask you this. When you, when you had to get this first GTM operator, this AI SDR ready to go, how did you go about training them on all your data, your processes, your policies, and probably most important, your experiences?
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A
Yeah, this is actually where, where we went wrong the first time and had to do a restart. And so here's how not to do it. I'll start with that. You can't start with by pointing it at your entire database of research and say like, okay, here's a folder with all of our materials in it. Go learn it. Because that weighs too much and it can, it doesn't give you an accurate answer. So what we did is we gave, we started slowly growing it and we started giving it the first batches of insights and call it like 20 files or something like that, 30 files. But here was the key. We then started learning after two weeks what the common, most 20 common questions were that Jack got. We then says, okay, why don't we tune those questions up with great answers? So then we retrained and iterated Jack saying, okay, These are the 20 most common questions. These are the best answers, the best slides that you can pull up, the best case studies that you can pull up, and so on. And so we iterated that in, you know, like over time. The understanding that it takes humans months to do that, whereas it takes Jack an hour. What? You know, like take an AI system an hour. That's a big aha. Seeing how quick you can learn is just an amazing thing.
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But Yaako, even though it may only take AI agent an hour to learn, I would suggest that the quality of that learning is based upon the quality and the appropriateness of the material that they're trained upon. Is that right?
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A
That's absolutely right. Ray, you're touching on such a fantastic point. Because essentially what happened when you are training an AI system, it tells you and it spits out instantly the things that you don't want. And you come to the conclusion, I am training this, this AI system wrong and you need to correct it. You can't tell the AI system, just learn to live with it. And so as a result, we learned that our systems and processes were so immature, our write ups were so incomplete, as a result, we had to actually level up our insights. And today we Call that WBD files. It contains the deepest insights of our company. So our AI systems are all speaking the same language and coming from the same source. Pristine source when we train them.
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B
Yeah. You know, that's interesting. You also mentioned something early on that I wanted to see if we could double click on. And you said to me, ray, this isn't just a chatbot. It goes beyond kind of just chatting. And I think you mentioned something about learning or self learning. Can you tell me a little bit more about that aha moment that you had?
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A
Yeah. So one of the key aha moments in the. Because we ran into one after another and one of the key aha moments that we were into is that, hey, first of all, Jack is not an sdr and he. And then second of all, he's not a chatbot. Not something you just type in. This is a full fledged learning system. In other words, it will learn from what the customer says to it. In the process, it will pull extra information on it. But its goal is not to qualify you. The mindset is not necessarily just that. Its goal is to make sure are both companies a full on match. Can we help identify the impact qualifying? We often say, oh, company's the right size, right person, right this, right that. But that is not necessarily how our AI systems work. They go like, well, if the customer churns, they're not going to be a good customer anyway. So it uses a way more advanced knowledge base in order to make sure that there's a match for the customer. And it does so by giving the customer along the journey valuable insights. It makes it more of an exchange.
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B
Yeah, I have a question because I was just thinking about this. One of the challenges I had when I ran revenue organizations was getting feedback from my account executives and my SDRs about what were those interactions like with the potential customer. Right. And how can I use that to maybe make us a better company in our sales process more effective? Did you have a similar kind of finding with trying to get feedback that you could use to improve that customer experience?
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A
Yeah. What we were finding is that when the AI system did not give much feedback to the customer, therefore it was more one who took information but did not share back, the responses were not as favorable. Essentially it started performing as bad as a regular sdr, a regular salesperson. However, once we engage it with experiences for the customer, such as, would you like to get a summary of today's diagnosis? Would you like to get a copy of this slide? Would you like to get some more information on this topic? Would you like to see a research paper on this. Once it gives the customer an opportunity to get something out of it, you can see what does the customer value right from the front and how can we engage the customer with it. For example, if you want me to send you a research paper, if you exchange, if there's an exchange of an email address, I can send you the paper. If I pull up the slide, you can take a screenshot, and so on and so forth. This level of engagement actually gives Jax something way more useful. We're not just responding into what the company wants. We're also helping the buyer, the human being on the other side, to get what they want out of the call.
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B
So now we're going to get into the main part of the AI to ROI podcast, and that's the return on investment. And I'm going to ask it a little bit differently. But when you went into the project, I'm sure you had some success criteria that you had defined that you were going to measure. You know, is this working or not? And then I'm going to ask you, as you move from that initial pilot to production, you know, what were the return on investment metrics that you've captured and share some of your results, if you don't mind.
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A
Yeah. So one of the main things is obviously the salary of that we would have of a human being. Now, in our world, the salary of an SDR is in the range of anywhere 60 to $80,000. And in order to make that work, you can't deploy the human being full time on that because then the SDR calls because of the bad conversion rates are too small. So we would operate an SDR person in SDR role about two to four hours a day. That's like 25 to 50% of the workload. And so what we notice is that the response rate to that was relatively, you know, like, right in the range of like. In our world, it came down to about like a few percentage points that we're talking about the first deployment. In the first month that we deployed, we had 831 conversations in the first 30 days. Right.
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B
Hey, Jakob, how do you define conversation?
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A
831 people talked to Jack and started the conversation for longer than. Hey. Hello. So assuming that we just cut off all some of the basic but actual conversations, we rated 831 in 30 days. From that, we serviced about 1200 pain points. But the customer said, I have this problem. We got about 70 qualified lead out of those 831. So that drops slightly below 10% right. It's 8, 9%. And then we have what we did see is all the data was accurately captured. So we started to see, okay, CRM data gets logged. Now our email capture was relatively low at 8%. And what we noticed is that all that the conversion rate to the conversations to conversion was relatively slow. So in other words people said hey, I'll give you an email. But later on we had a hard time following up with them. Now we do not know if this was just because of the novelty. This is a year ago, this is February 2025. But we noticed that it didn't, it, it started to work, but it wasn't working optimally yet.
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B
And I want to make sure that I define this right for our listening audience. Jack, the aisr, he truly was an AI avatar person. They were having voice conversations with your target audience. Not text based conversations, correct?
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A
Absolutely. This is a full on engagement like you and I are sitting. But one of us would then be in this case not an avatar, what we call a humanoid, a fully human looking me version including all my idiosyncrasies in my face and whatnot.
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B
So 8% email capture across these initial 831 conversations. A little concerned about the conversion into an opportunity. So what did you do to try to enhance that and what were the results then?
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A
Yeah, what we needed to do and as you know, like we needed to think that it was not, you know, like in order to improve the experience we integrated feedback points that we would give our customers information back. So one of the key things was making sure that we pull up visuals like slides and not like highly, super highly detailed slides, but slides with information that customers could obtain information from. Because as you know, winning by design, we're a very visual company. We you know, we integrate a lot of our visual elements and we wanted to make sure that, that come back the moment we did that Ray, the moment we enhanced. And the second thing is, sorry, the second was important is that we improve the quality of the question answers to the question. So we optimize every 30 day now we take a look at the most common questions and we verify if the answers are quality based. Okay. That layered up to the response rate. So following that we have seen email capture, you know, go up from 8% to 20% one out of every five ish.
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B
Right.
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A
We see that our MQL conversation rate has, has gone up significantly to about 30 ish percent, 36% to be.
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B
So let me define that. So for each hundred conversations, 36% of those that is 36 are becoming marketing qualified leads that a human then takes over?
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A
That's right, yeah. And then what is also important when you say when a human takes over? The human handoff is now better. The AI to human handoff. Because previously the SDR to the AE handoff was. Has been problematic. And not because of the humans are bad people or anything. Just because they lack consistency in the diagnostic thing. They lack the consistency writing in the CRM. Humans are just not very consistent and quality driven human beings if they're only in the job for six to nine months.
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B
Let me ask this question because it's the first time I've spoken to someone who's deployed their aisdr. Could that salesperson partner, whoever takes that lead that's been generated by Jack, can they ask Jack questions and help them prepare for that conversation with the prospect?
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A
Oh right. What we do, that entire conversation is now fed into the CRM and you can query it back. You have an AI system that you can query. Okay. And so like this might, this, all this, like I said, it's like once you in, once you introduce the customer to an AI system that helps them, they start giving more valuable information because it's helping them along that decision process. And you know, like that these findings that we had were kind of like, let me tell you what most of us will work on. We go like, hey, hey, it's an $80,000 SDR and the total investment of us probably also worthwhile product wise and so on. Cost is probably 100 grand, you know, give or take 80k startup cost with 20k or something like that, you know, like usage cost. But this thing starts to, you know, like the quality starts to increase the consistency. Ray, I cannot tell you I do no longer have to worry about whether I have the right SDR or if I should hire an sdr. I no longer have to wake up like, oh my God, this is the sdr. Resigned. We have nobody in the SDR role for the next four weeks. None of that. All gone, gone. Nothing that I even have to worry about ever again.
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B
You know the other thing I think you mentioned to me another kind of finding was who Jack is really supporting. Is Jack supporting winning by design in your sales process or is his primary job to support something else?
- [26:22]
A
His primary job is to help the buyer qualify themselves and it is okay if they disqualify right now of all the things we found, there's also one big aha moment that we didn't realize. One big thing that I started this to come into Full play. Okay. Because I can tell you the conversions were great, the handoffs were clean. You know, the qualified opportunities it creates. Right. I can tell you that. But what I noticed is when I started to talk about cut to customers about their AI experience. Right. And the one thing that I notice is. And you know, like. And I know we'll have stories and all of you will say, like, okay, B.S. that didn't happen. We have one deal that progressed significantly, a $200,000 deal. It progressed significantly to near closing. So the only thing that needed to be exchanged with the final closing of the paperwork and so on and so forth. And here's what had happened. I talked to the customer about what had happened. Do you want to know, by the way? I'm just making sure this is a good thing.
- [27:24]
B
Oh, I definitely want to know about this because one of the questions I wanted to know and have you share with the audience is when do you know to transition the conversation from Jack to the human on your side?
- [27:35]
A
Yeah. Okay. What had happened here? And then I showed a transition. What had happened here. The customer was going through the discovery process, had a good experience with Jack, and came back a couple of times. Clear signal. Right? Came back a couple of times. But then what we noticed is that other people from the company were coming in and not 2 or 3, 10 to 20. What we learned later on, but from the. From the buyer, what they had said is they. They told their entire team who was involved in this project, go talk to Jack. And so all those people got in. Jack. Now let me tell you, if you're in a buying committee, you can have maximum five, six people in it. You cannot have like 20 or 30 people in it. But when people can go, and you can tell anybody in the company that is associated with that, why don't you go in and check it out. Suddenly more people get involved and they got their beaks wet on what they. What we're talking about. And as a result, what had happened is the customer came back to us and says, yeah, you know, we're ready to buy. And we go like, oh, should we have a discovery call with the team? And they said, no, we already had that call.
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B
Good.
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A
And so like, then it escalated. And when the escalation come was one call, one or two calls, making sure what is still relevant. In this case, it was a larger company is the security profile. They wanted to make sure that, you know, that. That their data was protected and secured within winning by design, which we had answered. But they want a human being because we're the AI is not accountable. Right. Humans are accountable and can be sued in this.
- [29:08]
B
Right. You know, one more question and then unfortunately our time's coming to an end. But I'm just thinking about what you just said. Oh, we need to have a discovery call. And the potential customer said, oh no, we've already done that. And that is, have you learned or any advice to share how to make the humans who are taking over from the AI agent to feel comfortable and not make that customer feel like they're repeating themselves?
- [29:34]
A
Yeah, the thing that humans hate the most, that's where you deploy AI. Humans don't like to respond to phone conversations 24 hours a day. So availability is a great thing. Humans don't like to enter data in CRM. AI does that. Humans don't like to sift through hours and diagnose hours worth of calls and come to the conclusion what a human being really wanted, the customer really wanted. AI has no problem in doing that and it does it real time. Once you realize that AI is not there to replace the sales experience, but to help the buyer to buy and that the great sellers in here are always the one that will rise to the occasion. Look, we have many sellers today that we need and we are in a way too dependent in B2B on too many human sellers to perform all kind of actions that shouldn't be done by human beings in the first place. Spam emails shouldn't be done by human beings in the first place. Right. And so on and so forth. So once you. And entering CRM data on a mobile phone is not, you know, typing. It's not supposed to be done by, by a human being. AI systems do that naturally.
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B
Okay, let's, let's back up. Let's end today's session with kind of a summary of those do's and don'ts that you take away from your real world applied experience. Implementing an AI agent for outbound sales development purposes.
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A
Yes, and that is a great way. I think the first thing to think is we must make sure that there's a value exchange. Build your AI experience for the buying experience, not for the selling experience. Make sure that you look at it as a system and not just as a, as a single function. Right. It's a system. Okay. What you want to make sure is, is start narrow. Remember what I said? Don't upload a folder with 20 to 40. Sorry. With 200 to 2000 documents. Get the first 20. Learn from the customer and follow that. Right. Start narrow and expand. Iterate, iterate. Iterate because the system can iterate very quickly. We did it in two weeks intervals and essentially slowed down over time to a one month interval. Right. Understand that an AI system doesn't fix a bad process. It will make sure that it reveals it where your process is bad. So in our case, when we trained the AI system and we didn't give it the right information because we didn't have prepared for that, it essentially told us we trained our SDRs over the past years always wrong because they had the same problem. They just didn't spit it out.
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B
Right.
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A
So these were a few things that I want to make sure that the do's and the don't. I do want to make sure that people realize that at first you will look at as a financial exchange, a pure roi, but that that ROI starts to expand way more beyond the salary. It includes hiring, recruiting, onboarding. So you have to take all that into account. And then in this particular role, it was a clear decision for us. And the last point, Ray, that human being that we replaced, his name is Ian. Ian just rebuilt our entire website because Ian was able to do a lot more than just SDR calls. And we did not want to let Ian go because he was a fantastic human being and a fantastic contributor to the company. We just deployed his human skills elsewhere. And if for those of you who go to the winningbydesign.com website, Ian built that over the no. And took him a while but you know, like he built your hard work. This is, this to me is like, let's deploy the human beings where they can make the best benefit.
- [33:03]
B
Okay, we're going to wrap up. Final thing, give the audience a chance to know you, but through two quick questions. Number one, what advice do you have for a senior executive out there who's saying, I just don't know if I'm comfortable outsourcing engagement with my potential enterprise buyers with an AI tool. What's advice do you give to them?
- [33:22]
A
Go to winningbydesign.com Jack will pop up, have a conversation and then at the end ask for the Spice summary. Can you give me a Spice summary? Ask for it and then ask yourself, will 80% of your people do better or worse than what the AI just did? We're not here to replace superstars. Superstars are superstars. We want to re embrace them. We will always need them. But 70 to 80%, will they be able to outperform the AI system as you see on our website winningbydesign.com okay,
- [33:49]
B
and the second question is for those early career professionals who are worried about AI and how it impacts their job opportunities, what advice do you give to them? Right now?
- [33:58]
A
I would say open up a Gemini account, open up a cloud account, and open up a ChatGPT account. Spend $20 a month, $60 a month. I know that can be a lot of money. So you know, like. But tinker around. Build something that you're very passionate about and that you're good in, build it in AI and then go pursue. Your best career move is to pursue something that you're passionate about and that you can become an expert in.
- [34:22]
B
Jaakov Vanderkoy, Founder, CEO of Winning by Design thank you so much for sharing your AI to ROI journey.
- [34:30]
A
Thank you for having me. To all the listeners out there, thank you for listening to me. Ra.