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The secret sauce to your sales success? It’s what happens before the sale. It’s the planning, the strategy, the leadership. And it’s more than demo automation. It’s the thoughtful work that connects people, processes, and performance. If you want strong revenue, high retention, and shorter sales cycles, the pre-work—centered around the human—still makes the dream work. But you already know that.
The Unexpected Lever is your partner in growing revenue by doing what great sales leaders do best. Combining vision with execution. Brought to you by Vivun, this show highlights the people and peers behind the brands who understand what it takes to build and lead high-performing sales teams. You’re not just preparing for the sale—you’re unlocking potential.
Join us as we share stories of sales leaders who make a difference, their challenges, their wins, and the human connections that drive results, one solution at a time.
Jarod Greene (00:00):
Hey everybody, welcome to another episode of The Unexpected Lever, this show where we talk to the best B2B leaders in the industry about the levers that truly move revenue, whether that's people, process or platforms. This season, we're exploring one of the biggest shifts across all three, the rise of artificial intelligence, sales, and go to market. Not the hype, not the abstract theory. We're focused on real life practical lessons from the leaders who are actively navigating the shift, the wins, learnings, and the insights that you can apply immediately. I'm your host, Jarod Greene, and today I'm joined by Martin Gomez, Chief Operating Officer and co-founder of Wing. Martin brings a strong perspective to the table and he's going to talk about that today. Martin, welcome to the Unexpected Lever. Great to have you here.
Martin Gomez (00:49):
Thanks, Jarod. Pleasure being here.
Jarod Greene (00:51):
Pleasure to have you. So for the listeners who may not be familiar, tell us a little bit about Wing. What do you do? Who do you serve and why does that matter right now?
Martin Gomez (00:58):
Yeah, happy to. We at Wing run a talent solutions company that basically has democratized the ability for people to build out extensions of themselves. And that extension is basically a digital assistant. And so effectively what we've done at Wing is created a company that allows you to hire anyone in the world within a few minutes. And we basically get built technology around people to supercharge them and basically build digital agents that honestly can take over entire functional operations on behalf of companies. And nowadays in the post-COVID world, a lot of the companies that run distributed teams run on wing. And we basically get built an engine for companies to get work done in one place. That's
Jarod Greene (01:40):
Awesome. Very cool. That context is perfect. I always like grounding the conversation and where you guys are and what you do and some of the problems you're solving. All right, this is a weird question, but we ask it to every guest to get the party started. Martin, what's the oddest or most weird or unexpected thing that you use AI for, personal or professional?
Martin Gomez (02:01):
It's a funny question. I was actually thinking about that the other day. So I actually use AI to track my nutrition in calories. So I actually, I do Ironman triathlon on the side outside of work. And I basically use AI to track my workouts, the calories I burn, if I've ate too much, if I'm under eating and what to eat next. So it's sort of something that I've been using it. It's almost like my nutrition psychologist and source to give me basically a sense of clear mind to be able to enjoy that Americano or enjoy that Chipotle bowl.
Jarod Greene (02:35):
Absolutely. And you look great. And I would say I've done a few Tough Mudders. I'm not quite the triathlete or the marathon runner. Yeah. It's a great use case. I've certainly used AI to plan workouts. Sometimes I'll use it to help my daughter with some of the things she's working through as an athlete and so can appreciate and understand that use case. What have you seen as far as results are concerned? Is this a lot easier to do than the old spreadsheet days or paper-based days?
Martin Gomez (03:02):
What's easy about it actually is the computation. So being able to calculate the macros and calories. The hard part sometimes still is actually telling you the raw truth, which is no, you can't have that. You've already ate too much. If you want to eat that, you have to go burn a little more calories. So that's honestly been the hard part, which is just an objective emotionally raw truth.
Jarod Greene (03:22):
Yeah, I love that. That's awesome. Let's talk a little bit about your AI origin story from the company perspective. We talk about the fact that every team, every organization has this moment where AI shifts from, that's interesting to this is absolutely necessary and critical. What was that trigger for you and the team at Wing?
Martin Gomez (03:42):
We've been around since 2014, so better part of 12 years roughly, if you count our college days with me and my co-founders. And we always saw the idea of, I don't know if you've ever seen the Marvel movies like Iron Man. Yeah, of course. We were seeing that. In fact, we grew up seeing the JARVIS of sorts, that convenience aspect. And so we saw a lot of the iterations with Alexa and Siri early on in our days. And we realized that the limitation with these systems was always like weather and music, that's about it, or just reading information. There was no operator getting things actually done behind the scenes and things that were actually valuable. And so early on, actually for us, the decision was let's build something kind of interesting that is capable of being an extension of oneself. And so for us, it wasn't like GPT came out and we decided to pivot.
(04:34):
GPT was actually a lot of what we actually had decided to. One, they've just made our lives a lot easier now. And so for us, it was always kind of from the get- go. We knew that that was something to do. We had the apparatus to collect data and kind of translate that into complicated processes later on, but it was actually not something that we came across it through a business problem. It was something that we actually started with that. However, we started a bit too ambitious and realized that there is an entire data infrastructure to be built. And that was just something that as a startup that was unattainable early on, but nowadays we've since moved past that.
Jarod Greene (05:08):
Yeah. Yeah. And it's probably allowed you to do some things quicker in terms of explaining what you do and how you do it to people. I would imagine this was a lot harder to do 10, eight, even six years ago. What has been the biggest sort of impact you've seen since I'd say GPT became commercially viable, the verb, if you will, that it is today. What changes have you seen across the way you guys work, and more importantly, the way you work with customers and prospects?
Martin Gomez (05:39):
That's a good question. I think for us, we have an entire workforce people around the world and we have thousands of people that work for us and work on behalf of us. And I think what we've seen on that front is we've been able to basically supercharge people to effectively make them find more productive. And so when GPT came out, the big disruption we saw is there is obviously a content creation piece that people are now able to take generic ideas and extrapolate into draft one, draft two type versions. So for us, it was being able to now create a layer of intelligence, both predictive stuff. We know that given ex-routine and every so often this happens, I think given why person, this person can have ABC capabilities. And now as a result, we've been able to take that, combine them, and effectively build a technology layer that allows people to, at least in this case, interact with some level of, whether it's a human or a human aided person.
(06:41):
And we've now been able to really go full onto this idea of digital agents, which is people around the world that are capable professionals and then now have access to the best tools to create, not just to create content, but just generally get work done and really function as high value stakeholders and operators in both high growth and startup committees.
Jarod Greene (07:00):
Got it. So you very much see, and don't let me put words in your mouth, but there's pretty much an augmentation you talk about as opposed to a full-on replacement. Talk to me about how you draw that demarcation line. There's one camp that says, AI is going to replace everything we do. There'll be no more need for work. AI will take care of everything. But what you've just touched on is the notion of augmentation, how it can supercharge humans to get the most out of the human capital investment and get the most out of the folks who are operators and strategic agents. Talk to me a little about how you balance those two, because you can hear arguments from both camps and curious to where you sit.
Martin Gomez (07:41):
Yeah. No, I think I've always been a people person. This company was built by people. So it's really been my life mission to make sure that everyone within the company feels heard and is empowered to do the best work they can possibly accomplish. I'm on the camp of the pro people side. For me, I don't see a situation where we ever want to get into a place we want to replace everyone with AI, not only because it's inefficient for us, but there is obviously structural limitations. There is the level of depth AI can give it to obviously is incredible, but there is obviously a lot of nuance because when you bring on the contract, you're still talking to a human. When you want to implement something, an in- person person needs to also think through the problems and actually get the work done. The scary day will come when there's actually humanoids doing that.
(08:31):
Now that we will have to come back for podcast number two for that. But I mean, I'm definitely on Camp people. I don't see a situation where AI is playing people all together. I actually see a situation people are just being more productive. A lot of the cool AI stuff that we saw in the early days was, "Oh, write me this email, write me this agreement, write me a message or a response." But I think that beyond that to us, there is sort of a structured way of thinking that AI has allowed us to compartmentalize and basically some extent democratize the speed at which people can have structured thinking into how they're going to approach a problem. But ultimately that problem output we believe is better assured by humans.
Jarod Greene (09:13):
Makes a ton of sense. Now, with your chief operator, Officer Hat, talk to me a little bit about how you've implemented AI within the business. How have you given AI tools, AI solutions to the team at Wing to drive some of that productivity, to drive some of that 5X, 10X output? What was that process like? Because again, you started the company many years ago and you get to a point where there's a sort of commercial practical application of these tools that they're easy to get, they're easy to get their hands on. How did you and your team come to the decision to give these tools to people and how did you manage that change?
Martin Gomez (09:53):
Yeah, so the first version of our AI for us was kind of what I call a Delighted customer initiative. So we used to see customers coming to us at 5:00 AM on a Saturday morning because obviously a lot of the companies don't stop operating because a lot of the companies that work with for SMBs, startups where the founders are working through the night and to the day, 60 hour weeks. And so we really kind of saw a need to be able to delight customers. And so one of our customers a long time ago gave us some vouchers into a nice hotel and we stayed at this hotel and we realized when we got there, there's a name, "Hi, Martin. Welcome to this hotel." Our name is on the screen floating, right? There's a chocolate basket waiting for us. So there was an aspect of delight that we saw.
(10:36):
No one in our industry was really aiming for that. Everyone was aiming for these giant BPO operations where everyone is just back in office outsourcing, but we didn't see this identity aspect of it kind of being met with delight. And so what we started to do is we started to create systems that would predict what is the customer probably going to do on this date given the past. So they worked on their Asana project management updating last Wednesday. Instead of just, for example, us waiting for that work or that particular agent working for that work, we would effectively build out a plan that would already take last week's work and kind of structure the update for this week's work. And so the initial idea was to delight the customer by anticipating not just their needs for that day, but anticipating that stakeholders required dependencies if they needed to go out and get information from people.
(11:30):
If the input required was very big, like, "Oh, I'm on PTO. I'll get back to you. " For us, that was something that wasn't enough. We wanted to build out something that could effectively enable people to continue to operate, even if the person there wasn't actually physically present. So it was really delighting customers and creating that. And then switched over a little bit to what I call firefighting, which was obviously a company our size is no perfect by any means. And when we do have situations arise, we want to be able to anticipate like, "Hey, this customer hasn't used us for X time, or they've seen ABC issues, we should probably be able to do ABC resource." And typically, most companies would refer to a blank knowledge base, like here's articles that you can read. We wanted to actually do very curated response plans that were basically picked up.
(12:25):
Any minor satisfaction like, "Hey, this wasn't done correctly for whatever reason by the human worker review, we wanted to take every kind of inefficiency and build a plan around it to be able to surface that, action that and course that to the right channel to get the open solution."
Jarod Greene (12:42):
Got it. Got it. And then talk to me a little bit about, because again, there's this great transition, great sequence, but there's some bumps along the way. There's some hurdles to jump. There's some things you learned. So what were those lessons? What were the things that maybe were unexpected or unanticipated that got you through the journey, but prepared you for the next initiative, the next AI project, the next AI-driven process orientation? It's
Martin Gomez (13:08):
Very easy to think about AI as just like, if I type ABC, do ABC for me, it's going to do ABCD for me. But for us, we just recognize customer education is so important. And customer education is just one of those things that is not something that just happens. You have to be an active player. And for us, anticipation, in one of those hurdles that we saw often is customers effectively had the power, should be human power, but they didn't know what to do with it, which some of the problems that we saw with Alexa after weather and music, that's about it. You don't know what else to use it for. It becomes more of a gimmick and toy for your children to play with right on the weekends. There wasn't this kind of depth of presence in the customer's routine. So we figured the best way to think about that is instead of asking the customer, "Hey, well, what do you want to use AI for?
(13:58):
" It was like, "Talk to us about the three big hurdles. They're taking up the most amount of time and why is that similar hurdle to begin with, given that you've been at this for years?" And it was a lot of those conversations that got us to think there is effectively problems that can be categorized into people don't have time, people have too much work on their plate, people have time, but don't want to put in the work to actually transcribe everything they have in their head onto something that a team can take on. And so for us, that big hurdle was adoption. It was education. It was, how do you tell someone that they can effectively ask us anything? And I think that's probably some of the problems that Microsoft Word had in the 90s, which is you can write anything. And then of course right now, if you're in college, you're writing papers, if you're writing contracts.
(14:50):
But in the capacity where you effectively have the world's best talent at your disposal that is aided by human and AI, how do you get things done? How do you know what to give them? Does that make sense? Yeah. And I think for us, it was taking a lot of the data from millions of hours that we saved from customer time, picking up giving X industry, giving X title, given their size, given how much work they're giving, given the types of challenges they're seeing, what are the types of work streams that they're interested in taking on? And instead of just collecting that and using that to train our model, we would surface that to customers like, "Hey, how does this sound?" People in your space, in your industry during this time often are using us for ABC curated. And there was an aspect of being able to engage commercials that way.
(15:43):
And so early on, it wasn't about asking customers like, "Hey, what do you want to use AI for? " It was still going back to that same thing, what are the mundane things that you don't really want to be focusing on that you don't really enjoy in your business that have to get done?
Jarod Greene (15:57):
Yeah. Makes a lot of sense. Makes a lot of sense, Barry. So let me ask you this before we wrap, what practical advice would you give sales and go- to-market leaders about that, given what you've learned along the way, given what you've seen, given what you interact, engage from customers every day, this is really about making everybody else better. So from your experience, what would you give to go- to-market leaders about starting the AI journey?
Martin Gomez (16:25):
So I think the first thing is don't build a GPT wrapper. And what I mean by that is don't build another system that just leverages an open AI API to create emails or to send outreach. We have too many companies like that. I think the biggest thing is instead of trying to copy what other companies are doing and are already doing well because they were the first remarker if they're ready in that space beforehand, what are the deeper problems that are very specific that you have insight into that no one else does? And so if I give you an example, if you're in an industry, say you have tenants, you're in real estate space, the most important thing for tenants is retention. You want them to pay on time and you want them to be happy, generally speaking. Oftentimes, there's a lot of portals where you submit tickets and things of that nature.
(17:15):
If a company was building an AI system that takes a lot of these tenant issues and actually formulates real solutions for tenants based on their niche, that mean by something that's a little bit more valuable. But I guess the bigger picture, I guess what I'm trying to say is you really have to think about what is the painkiller that is going to be the thing that that company would be willing to pay for and not something that just kind of supplements that. And I think in the AI sense, it's very easy to build AI GPT wrappers to send emails or craft messages or replies, but that is something that is a nice to have. What are some of the core problems that in your industry that you can win magic wand would be solved, could be solved, whether it's through AI, whether it through other technical solutions, sometimes it doesn't have to be AI because that's some of the nice things because a lot of the learning models obviously require tons of data.
(18:11):
And oftentimes it's the small things. If you're building a company that is interested in solving a particular problem, I think that's the most valuable thing. Whereas versus if you've built an AI solution and you're looking for the problems to solve, that's sort of the wrong way to think about it. And I think we often at Wing had that problem where we would build all these really cool solutions and then we would go look for the problem and try to match make.
(18:36):
And so we flipped it and we would pick a problem that we felt was super painful. Whether it was like, hey, onboarding new team members is painful, knowledge transfer, granting access to the tools, making sure everything is set up correctly. And of course, there's a lot of SSL systems for HR systems that do that for you, but then there's a knowledge transfer aspect of it that could be eaten by AI. So I would think at your business for any company, just pick two problems that are super painful and I guarantee you, GPT is not solving them today. Otherwise, it would've solved them already. And I think that's the best way to think about go to market for any company.
Jarod Greene (19:10):
Yeah, that's awesome advice and insights. Appreciate you. All right. Well, let's finish up with the lightning rounds. Quick set of questions and I think this will be pretty painless. So short answers, don't overthink it. If you could automate one part of the business, whether it's sales or go to market immediately, what would it be?
Martin Gomez (19:29):
Yeah, I think employee onboarding. That's a no-brainer for us.
Jarod Greene (19:31):
No brainer. Gotcha. Question two, what book, podcast, or resource has influenced your thinking about AI most recently?
Martin Gomez (19:39):
Blitzscaling by Reid Hoffman with a great one.
Jarod Greene (19:41):
Very cool. And then last final question to the lightning around. Where can our listeners and followers find and connect with you?
Martin Gomez (19:48):
Yeah. So I'm on LinkedIn. LinkedIn, Instagram, wingassistant.com. martin@wingapp.com is my email. So I'm pretty active. Typically reply to messages. And I guess that's actually fun. That'd be another cool AI. One that replies even when you're sleeping and kind of funk, use your knowledge graph to kind of reply what you would say, that'd be another cool idea. Just kind of follow up on that last question actually now I thought about it.
Jarod Greene (20:09):
I actually need that myself. We'll get your information into the show notes here. So Martin, thank you so much, man. This is fantastic. We appreciate you sharing your insights, your learnings along the journey, your perspective. For everybody else listening, this is exactly what we do on this show. We serve as practical, battle tested insights from leaders who are actively navigating the transformation. If you enjoyed this episode, follow us, share it with someone else and leave us a review. We're very open to feedback. I'm Gerard and I'll see you next time. Thanks everybody. You've been listening to The Unexpected Lever. This stuff gets you fired up and you want to talk about it with other leaders, join the Powerline Slack community. It's a modern open access community for go- to-market professionals and AI enthusiasts alike. We're all ready to turn buzz into business outcomes. Join by going to vivun.com/community.
(21:03):
We hope to see you there.