SPOTLIGHT: AI Isn't Just Faster Translation, It's a $40B Tug-of-War for Global Attention. | with Bryan Murphy, CEO @ Smartling

0:06Welcome to Topline, the podcast for the best founders, operators, and investors in B2B tech. Every week, AJ Bruno, Asad Zaman, and me, Sam Jacobs, will break down what's important for you to know to be the most well-informed professionals in the market.

0:24Bryan Murphy: Are you ready to level up your go-to-market strategy for 2026? Then don't miss GTM 2025, the only B2B tech conference exclusively for go-to-market executives. Join a thousand other revenue leaders this September 23rd to 25th in Washington, D.C. for an exclusive executive-only experience. This three-day event is hyper-focused on connection, strategy, and execution. Expect hands-on workshops, in-depth strategy sessions, and curated opportunities to build relationships with VPs, CXOs, and founders facing the same challenges you are. You'll stress test your GTM approach, align your team, and leave it real, actionable insights from top-performing leaders.

1:05Bryan Murphy: Don't miss the Must Attend conference designed to help you boost go-to-market results in 2026. Visit attendgtm.com, that is A-T-T-E-N-D-G-T-M.com to secure your spot today. And don't forget to use the code TOPLINE for 10 % off your GA ticket. Hi, everyone. Welcome to Topline Spotlight. You know Topline Spotlight. This is the episode where we sit down with a business leader and talk about a problem that they have solved in the last 12 months, an important, a difficult problem, one that we can all learn from by understanding how this individual thought through the problem and solved it. I am Asad Zaman, one of the co-hosts, my other co-hosts are vacationing.

1:57So you just have me, which is why this is the first time I'm introing an episode, but I'm very excited about it because we have Brian Murphy joining us. This has been a conversation I've been looking forward to. Brian, welcome. Thanks. Great to be here, Asad. Very excited to have you. So Brian, we're going to talk a lot about the problem you solve. But before we do that, let's get the audience to learn a bit about you. Tell us a little bit about yourself as well as the organization that you lead. Sure. Thanks. So I've been building SaaS and e-commerce companies for the past 20 years or so as a founder in publicly traded companies and within growth equity or private equity investors.

2:44So I spent a bunch of time building WHI, which was a SaaS e-commerce company. We built that to$2.5 billion in GMV and sold to eBay. So I spent a few years at eBay running a variety of businesses. My last role there was vice president of vertical markets. So I had responsibility for global product and strategy there. And now I'm running a company by the name of Smartling, and we're an AI translation company. We help companies like Apple, IBM, Disney, Tesla, Pepsi, Verizon, create multilingual experiences using AI and software that their customers love. It's one of those spaces that is huge. I don't think everybody understands how big and how important this is.

3:31So tell us, what's the size of this industry of translation and why? Why is Apple needing help with translation? What does it do for them? Yeah, I was surprised too. When we started looking at this space four years ago, I had no idea that it was a$40 billion industry globally. It's like massive. Just for context, by the way, you know how everybody is talking about code generation today as this massive market that AI companies are fighting over? So you've got Cursor, you've got GitHub, you've got OpenAI and Anthropik fighting over it. That market is supposed to become$27 billion by 2027. So when you think about the most competitive market in tech is about half the size of this market.

4:17That's kind of crazy. It's wild, isn't it? And the reason for it is fairly straightforward. You know, when you look at the data, 87 % of people globally will not buy from a website that's not in their language. Like, think about it for a second. If I'm looking at a pair of shoes on a German website, I don't speak German, I'm probably not going to buy. So our customers, the B2C, B2B, they do it to drive conversion rate and GMV. and this this works both ways right like if you are apple and you're looking to sell in germany it's going to be better if your website is in german but also you know i like buying clothes and if you find a nice brand in germany you you can't buy their clothes unless you can read the website no you just can't because it's like the size is the description all that all that stuff So we do a lot of business with the big retailers for that reason.

5:20And their content changes all the time. It's like, you know, it's a product information management issue. And so they have millions of SKUs with tens of millions of product descriptions flowing under that that are changing all the time. A lot of fast fashion customers are big customers are. So they need automation. They need very rapid turnaround on translation and high quality. So that's really what we work to do. And what's the history been of this industry in the sense that I imagine that the importance of this and also how this is fulfilled as a service has evolved a lot as the technology industry has evolved.

6:03And so 20 years ago, was it just a bunch of humans reading things and sending, you know, a written up translation that somebody would manually do and then it slowly became more and more? How did the industry evolve? Well, that's still being done a lot today. That's why it's pretty remarkable. We run into these massive enterprises and we're like, wow, I can't believe you're doing it that way. Okay. So it's really interesting. And that's one of the reasons why it's$40 billion. I've always looked for big verticals that have the potential to be disrupted, right? So we did it in auto parts, selling auto parts online.

6:40At eBay, we built a$4 billion a year business selling used cars online. etc. So when we started looking at this space, we're like, wow, it's 40 billion. One of the reasons for that is that rack rate is like 20 cents a word, and it's fairly slow and not automated, right? So it's literally like extract the content, put it into a spreadsheet, email it to the translator, bring it back, revise it back and forth at Wynn. And I've done this my entire career. And it's always expensive, slow, and takes a lot of resources. So I try and A, ration my content, B, do it as infrequently as possible, which is really to the detriment of the company, as we just described.

7:21If 87 % of people, whether we're trying to convert leads for B2B or we're trying to sell B2C, that's a problem, right? So when we started looking closely, we were looking at tech in the translation space because we recognized it was a big space with potential for disruption. And we ran into a few companies and we really like SmartLink because we're like, wow, these guys have a great SaaS platform. They're leaders in machine learning. They're leaders in neural machine translation. So at the time, we could do things faster and we could do them cheaper, but we couldn't do them better three and a half years ago.

8:00And that's really where AI came in and sort of it was like, aha, that's how we solved the problem. Interesting. When you said we were looking at companies, was that when you were in a previous organization looking for options or were you looking at companies from an acquisition lens? Give us some color there. Yeah, so I've had a relationship. So Battery Ventures is the owner, the investor of Smartling and I've had a relationship. You're good friends with the battery folks. Okay, they're the best. I've had a relationship for them for like 10, 12 years. And from time to time, we kind of look at things and decide whether we want to do something or not.

8:42And they called me about this space. And the more I looked, the more I got excited about it. In fact, I liked it so much. My original thought was like, well, come on, I'll be a board member, yada, yada. And I'm like, no, I want to run this business. This is huge. So you find the organization, and then you can do it cheaper, you can do it faster, but you can't do it better. What was the delta there? If one was using smart lane pre-AI or pre-this version of AI versus one of the traditional methods of what was the delta in quality that AI is now helping bridge? That's a great question. So we use a quality metric called MQM or multidimensional quality metric.

9:25Okay. It's a super complicated sort of qualitative, quantitative thing. But it puts out a score, right, ranging from zero to 100. So at the time, at the time, we could do machine translation that would give you a score of like 86 to put things in context. And I'll get yelled at this, but you can't say it's like 86 % accurate, but let's just call it that just for the sake of argument. So 86 % good, right? Human translation is around 98. So that kind of gives you an example. It's never perfect because people have preferences, right? One translator might say it this way. I might say it that way, but it's 98.

10:04So that was where we were. We were kind of like at this 86. And you could use that for lots of content that you could think about. But you couldn't use it for like medicine, let's say. No, no. You wouldn't want to use it for medicine. You wouldn't want to use it for like a landing page or a homepage or a marketing email, let's say. But there is lots and lots of content that you could, and we did use that for. And we had, by the way, we also provided other forms of translation as well, human translation, and we still do. And it is, in fairness, I should say, I'm going to talk about, when we talk about AI, there's two forms of AI.

10:36There's what we call AI human translation, which is AI with a human in the loop, right? So this is about making our translators vastly more productive. So we're just talking about code generation. No one uses AI to write code and then release it to production. That happens very, very rarely. It's similar with AI human translation in the sense that a human translator pre-AI could do like 2 ,000 words a day. Now with the productivity tools that we're giving them through AI, it's like 8 ,000, 10 ,000 words a day and climbing. So we're making our human translators way more productive. And then not to complicate things, but then there's this version of AI translation, which is a click in quality below.

11:22So if that's 98, AI translation is like 96, 97. And that's purely automated with AI. And that's on brand, that is matching style guide. So terminology, tone of voice, localized, all these things at a fraction of the cost in turnaround time. So that's the big unlock. And that business now is growing over 50%. And is that the future of the business? Do you think this is where, because that seems like if it's 87, if it's 97%, right, like that could be something that gets a little bit better as the models get better as well. And these tools that people are using. So do you see that as the future of the business?

12:06Or do you think the future of the business is still going to be a combination of the human in the loop one for maybe a pharma client that wants some extra due diligence? or for a particular marketing leader who wants every word really thought about, whereas for most people, the other one would be the right service. Yeah, I think it's going to be a combination. And I'll step back and give you an example. Like the amount of content that's being produced looks like this, right? It's like exponential, right? So there's not less content. And most companies ration content to translate because of that cost, 20 cents a word, it's too expensive, right?

12:41and time involved, all that kind of stuff. So taking a step back, especially when I'm thinking about my days at eBay, we thought a lot about digital footprint and we thought about conversion rate. What is digital footprint? So if I'm a U.S. company, my digital footprint, the size of my website, all the content, the blogs, da-da-da-da, is probably pretty large. But in the markets that I'm growing, let's say it's Germany, France, Japan, those websites are teeny tiny. And so how does Google look at them? Google's like, oh, that's a teeny tiny website, doesn't have great engagement, so I'm going to knock it down in terms of SEO, right?

13:19Yeah. So the problem that we're helping our customers solve is not only are we helping them create localized websites, applications, et cetera, that boost conversion but also boost engagement, right? So I'm landing on this page. Oh, great. It's in German. I'm going to read this, right? But we're also helping them increase the digital footprint so they can be more competitive in those countries. Because now it's a large website. It's got great engagement, great conversion rate. It's sticky, less bounce, all that. So Google looks at it and says, oh, great, I'm going to move you up. See what I mean?

13:54So my point on that, going back to what we're talking about, is that I believe for a long time our customers are going to be – they need a combination. I need this volume, right, which we're going to solve for them. We also need very, very high quality for certain types of assets. So it's really going to be less about, hey, we're going to move everything to AI. It's going to be more about we're going to do a lot more with the right type of translation. That is fascinating. It's got me thinking about the future of RSEO as well. So I hadn't thought of this. This is very interesting. Thank you. Okay, so we're here to talk about a problem that you have wrestled with in the last 12, 24, 36 months.

14:32Tell us about the problem that you decided was the one you wanted to talk about today. So it really revolves around this conversation around this AI. So if I were like, you know, a couple of years ago, we were doing things faster, cheaper, but we weren't necessarily doing them better. Right. So I remember very distinctly when GPT or OpenAI, you know, announced, you know, hey, you can now use GPT. So that Saturday morning, I, you know, hopped on and logged in and start using it. And immediately the light bulb went off in my head. And we were, the leadership team, all had to have the same kind of epiphany.

15:05We were all slacking each other. One of the things about Smartling is that it's a SaaS, it's a cloud-first company, over a decade of machine learning, neural machine translation, which is a form of AI. So we immediately saw, wow, okay, this is a game changer, and talked about how we're going to apply it to boost the quality and solve a bunch of linguistic problems that we were stuck on. So that week, I pulled the LTE in and immediately reset the operating plan, put in place a project to go out and make sure we had the right talent. So hiring in the right talent and reorganized our tech team structure and process to enable rapid innovation that delivered outcomes that matter to our customers.

15:50Right. I'm making the sound like, oh, it's so easy. I think what you're saying is super interesting. How this was that moment for a lot of us where we're like, okay, the world is, you can, especially if you were in technology, once you saw the first version of it, you could extrapolate out and say, this is just going to get better and better. And if this gets better and better, what are some of the implications and how can the world evolve? Right. Let's just assume it gets 20 times better. What's that world? And so I think a lot of us went through this period of like thinking through that and saying, OK, now it's time to start making adjustments because you're still early in the curve.

16:32Right. And so you can start making adjustments right now and ride the curve. But a lot of organizations struggled with acting on their insights. And so tell me a little bit about you guys had this moment. You saw the technology like, oh, shit, this is this is this can change everything. what was the time period of pontificating about the future versus acting on like here's a plan now and this is what we're doing now let's go and implement this plan i think this is where you know i've been doing this for a while and i've been a founder for a lot of my life and i think this is where the founder in me uh really became a a benefit sometimes it's like dr jekyll and mr right.

17:14So I remember getting yelled at at eBay for my founder-ness, right? So anyways, but I think this is where I immediately saw the potential for disruption and what I, and so I immediately went into founder mode, which is like, okay, guys, timeout, break glass, plans out the window, right? And in a big company, everyone would be like, what, are you crazy? Right. You know, kind of thing. But in our company, and I had a, but, and I have very like-minded um i have a very like-minded lt uh leadership team so it wasn't that hard to to and people had this conviction but you know when i looked at you know i just like to me there's been kind of like i'm going to really simplify it three big moments in tech in my career one the first was the commercialization of the internet the next was the move to cloud and this i think is going to dwarf both of those or at least be on par with um uh with the commercialization of the internet.

18:05So when you see that as a founder, you know, you have to like immediately break glass and change the plan. It's, that's the one thing that I've noticed is very different in founder led organizations versus not is like the speed at which they can act that can with the, and the conviction with which they can actually see this in the market today, right? You see Apple that has really struggled to come to terms with this platform shift in so many ways. And you see examples on the other side. And Google is also an example, a non-founder-led business that has one of the best models in the world. Gemini is one of the best models in the world.

18:43And then try using Google Home. Google Home is the one voice product that is worse than Siri. But these guys own the best model. And so it's kind of crazy. Whereas you see companies like Meta that you might not love their strategy, but there is conviction and strong action, right? and there's a speed at which they're moving. I find that really fascinating between like the founder mindset versus not. So you got the troops aligned. Everybody saw it. Everybody instinctively figured it out themselves as well. You guys decided we need to move quickly and started moving in a particular direction. What were the biggest hurdles as you were going forward?

19:23What were the ones, what were the problems that you knew you were going to face as you solve this? and what were some unexpected things that came about that you had to wrestle with? Yeah, that was a big part of the problem, right? So everyone was really excited about this. And so everyone wanted the cheese, right? You know, the who moved my cheese thing, right? So everyone wanted to do it. And so one of the really hard things I had to do was recognize, okay, this can't be jump ball. I've got to have some structure here. So we always start with, at least in my opinion, I always like to start with and we like to start with focusing on delivering outcomes that matter to our customers, right?

20:02So what are those pillars, right? So in our particular case, it's quality, speed, cost, and ease of translation. Those are the pillars, right? So that's the North Star that drives everything in our business, including the product roadmap. Yeah. So once we aligned on the we aligned on those, obviously, and then how do we think AI is going to affect it? Now, how do we actually integrate it and build it? And when we say build it, something that's never been built before. Right. So I had to make the decision to sort of create almost like a production line within our tech organization that started with R &D.

20:42Right. So like this R &D department. Also, once again, like our engineering. team is like one of the best in the world. I mean, we do 3 ,300 production releases a year, right? We're a very efficient team. 99.99 % uptime, 98 % on time delivery. This is one of the best run engineering teams I've ever had the privilege of being involved with. I didn't want to disrupt that. So I, we hired in some really great AI talent. We structured a process that we would do R and D, but like, once again, the R and D has to have guardrails around it. Can't just like spin around in a loop. So we set up basically, um, confidence levels and time boxing.

21:25So we would come up with an idea and we would say, okay, we think if we do this, it's going to solve this particular customer problem. Okay, great. You have X amount of weeks to prove that out. And you're going to be able to measure it with a confidence level. And you have to achieve a certain confidence level within that time period, or it's you're out. Interesting. It gets brewed and we move on to the next thing that way we don't get into like, it's really key. Uh, let's just work through a bunch of ideas, right? So you're like, okay, we don't know, like, let's try a bunch of shit. And so So then you're working through it, but you have a mechanism to not get stuck in a loop where it's just not working.

22:04It's not good enough, but you keep at it. And then you're too distracted. Like you don't have the resources to do all the other ideas. So it's like, that's really interesting. Yeah, that was really key to it because you could literally, people, you know, like human nature, you get really excited about your idea, right? And you think, no, no, no, I just need another work. I can make this, I need another week. I can make this work, right? Right. We give a little bit of latitude on that. But at some point we got to let's move on. We got something else on the list that we think will work. And so that way we're able to, you know, continuously move forward.

22:34And then once it gets to a certain confidence level, then then it rolls into the product and engineering world. Right. Like, OK, roadmap. We've been talking about this. Great news. We're at 80 percent confidence level. We're very confident that we can get done what we need to do now. Let's start moving it into the machine. And then it gets developed and then it gets released. So as you look back, you know, I find that usually some of the best learnings come from I'd come at the end of having wrestled with some really interesting problem that wasn't an easy problem to solve. And then you kind of reflect back and you're like, okay, this is how I would do things.

23:18This is what I have learned that informs my view on how I would solve other sorts of problems moving forward. When you look through this experience, and you're probably still in it, right? Because the technology is still rapidly evolving. And so what you can do with it for your customers is probably rapidly evolving as well. So you're probably in some way still in the midst of it. But you have shipped some really interesting innovations into the market. And so at this point, when you reflect anything, you know, the way that you thought about creating this unit within the organization, that was your R &D and experiment unit.

23:56And they had a framework with which they were looking at these things. And once it got really good, they would ship it out. I imagine you learned that somewhere else and have perfected that over time. Any other things that you've learned through this experience? Yeah, I think the key for me is always having that North Star. And I learned this at, I think I learned this at eBay, really. They were probably, I was really fortunate to have a great mentor there, Christopher Payne. Like he used to, he would always terrify me. He just beat the heck out of me. It was always like, okay, the two things you have to keep in mind is what is this going to do for the customer?

24:34And is it material? Like, is it going to move the needle, right? You know, is it big enough? And so that's the challenge. that's the gauntlet I always lay down for my team, which is we talked about what is that North Star? So everything that we do has to have a material impact on quality, speed, cost, or ease of translation.

24:56Bryan Murphy: Right. And if they can't demonstrate that, if they can't communicate that as part of like the BRD process, right, it gets it gets it doesn't even get off the ground. So having that customer outcome, those pillars of a customer outcome are super helpful and create a great discipline. And then on the softer side, right? So when you have to go through this type of change, like I'm joking, like this was incredibly disruptive and painful, right? I mean, I remember, yeah, it was painful. Because it's somewhat of a traditional industry as well, right? And so it's different when you're trying to evolve a traditional industry versus something that's at the cutting edge of innovation.

25:40Yeah. Yeah. And for us, there's two elements of that. Number one is making this change across an organization that was used to doing things for a long time and had a lot invested in that. So I'm sure I went tops down too hard at first. right um the founder in me i guess uh and you know that doesn't really you can't go tops down it doesn't really work creates a lot of agita so i had to back off a little bit and spend time talking with the team listening listening to the team um and making those adjustments you have to be a good listener a good communicator but you also have to like have like the steely resolve on what your vision is and that's really that's a tough balance because otherwise you just get stuck.

26:28Um, so those are, those were some of the big challenges then with customers, they don't believe you. They're like, Oh no way. I've been doing this for 20. That sounds too good to be true. So we kind of have to put our money where our mouth is. And the way that we've solved that is generally with a proof of concept. We'll say to them, fine, great. Um, I remember having a funny, you know, having a funny conversation with a customer, like where I bet the customer a hundred bucks that, uh, that we could deliver what we said we could deliver. And we did a proof of concept on our nickel and we delivered.

27:00Now they're a multimillion dollar customer. Wow. That is fascinating. This is, this has been great. Brian, thank you for sharing all of that. I think I learned a ton along the way. A couple of questions for you as we wrap this up. I'm going to do different questions than the ones we thought I think these are better. What is some, who is a person or a book or something you have experienced or consumed earlier on in your career that has had a very lasting impact on the professional that you are? Well, that's funny. I actually have them right here on my desk. So Jim Collins, obviously good, great.

27:43um execution by larry bossity uh lessons from the top by jim citron and uh and jack straight from the gut jack welsh so like those are all great books on how to um you know really to you know when i was young in my career like you gotta you look at these icons and how did they execute how did they do so well and i think that these were these are the ones that uh formed me quite a bit that's brilliant. What about as you are in a world, you're a technology executive, you've got to keep on top of things. How do you do that? You're really busy. And then there's all of this change that's happening.

28:22What is your mechanism or method to stay on top of things so that you can make decisions maybe a little bit before your competitors do? Do you like to read a lot? Do you carve out time for it? How do you do this? I do. That's a great question. So I always tell new hires, it's really important that you be intellectually curious. And I always have. And I think that that's a really important strength. So I carve out time every morning. I think it was Jamie Dimon talked about this. He gets up at like five and he reads like seven different newspapers, something like that. And I don't get up five, but I do get up early and I make sure that I read a number of different periodicals.

29:11I've got newspapers, et cetera. I have a number of different newsletters that I get. And then I try and spend a lot of time talking to customers, right? So that's sort of like those are the two big things that I like to do. Morning time to just take a second, like a slow morning. Bezos talks a lot about slow mornings. And I think finding a way to have like a slow start to the day is really helpful. where you can just like take stuff and think, and then you go into that thick of things, right? Yeah. Last question. You're in a stressful job. You've been in stressful jobs for a long period of time.

29:45What are your, what's your favorite or what are your favorite ways to manage your nervous system, to disconnect, recharge, to not burn out?

30:00I just cram it all. I'll just cram it down. I just compartmentalize it and fingers crossed. Yeah. I think, you know, like I learned this early on when I was as an entrepreneur, like you have to learn how to compartmentalize. I remember when we were first, when I first was launching companies, I would, I was so stressed. I couldn't eat. I couldn't sleep. I would not be able to sleep. And then finally, I'm like, this is not going to work, Brian. You either have to find a new career or you have to learn how to compartmentalize. And I, and I think that that's probably the number one thing that they do.

30:29And then, you know, other things I like to, you know, I like to exercise and, you know, do some yoga or do something like that. What's your favorite form of exercise?

30:41Probably, you know what I'm really liking right now is yoga. I'm terrible at it, but it's like, it just makes me feel good. I think there's a masochist in most founders where they like doing things that they suck at because it's like a humbling but also very rewarding experience. because you start and you're like shit at it, but you're so good at like being an entrepreneur and you're really bad at this thing. And there's an interesting dynamic there. It's so embarrassing. I'm so unflexible. Brian, this was fantastic. Thank you so much for your time. Thank you for sharing this with us. We'd love to have you back and talk about how this industry that I'm sure most of our listeners do not really know much about is evolving with AI.

31:21So thank you for your time. Thanks, Ishan. It was great to talk with you.

31:30Thanks for listening to Topline. New episodes come out every Sunday. If you like what you heard today, subscribe so you never miss an episode, leave a five-star review, and share it with your friends. Don't forget to join our Topline Slack channel to connect with us and discuss the topics we cover with other listeners. Click the link in the show notes. Lastly, if you're interested in joining Pavilion, you can learn more about membership at joinpavilion.com forward slash membership. Thanks again for listening.

31:58We'll see you next time.