Why AI is More Than Just Models (Eddy Chen)

Full transcript

So welcome back to the podcast, guys. Today we were joined by Eddy Chen. Eddie, welcome to the podcast. Thank you. Happy to be here. So do you mind giving us a little bit about yourself and what you guys do? Sure, sure. So I currently lead customer success at a company called Edge. What we do at Edge is we work with customers in the medical, dental, and healthcare industries. Thank And you. we place talent, remote talent, to do a lot of the administrative type work for them so that their in -site workers free up their time to do some of the more high High-value work. So we've had a lot of success working with customers and driving not only cost savings, but also efficiencies. And the talent that we have are really well -educated, high Quality, in many cases, almost over Over-qualified for the role. So our customers typically are pretty happy with the end results of the partnership. And most founders in AI are raising hundreds of millions before they have a product and you actually bootstrapped to billion in revenue, right?

Was there ever a point where you thought, like, maybe I should just take the money? you. I just should clarify. I don't know if you want to record this part. I should clarify. So I'm not a founder of the company. I work at the company. I lead our customer success team. The company was bootstrapped. That part is accurate. And in talking with the co -founders as I've been at the company, there was never a plan to kind of take the money, as you say. I think there's a greater mission and vision that we are behind. And that's connecting talent workers across the world who have the skills, have the qualifications, but just don't always have the opportunities in their native countries. Connecting them with the opportunities in the U .S. So it's always been about the mission and vision for me and I think for the co -founders as well. Certainly the money aspect is important, but for us, a lot of it is trying to create that, call it, world where, again, qualified, educated, talented people have the opportunity to better their lives, their family lives, etc. Through these roles in the U .S.

And you've said that the data quality is the number one constraint in AI today. Could you break that down for someone who thinks that AI is all about the models? I think AI is all you. the rage. And I think there are a lot of opinions on it. And I think the way I think about AI now is we have evolved in the last couple of years beyond just efficiency. So I think when AI first came out, it was, now know, look how great it is. I can input this information into AI and the it'll spit out this, you know, document, this output that's really polished and it took 10 seconds, something like that. I think it's gone beyond that now. you. I think efficiency is table stakes. We all assume that AI is going to do that for us. I think where AI is headed and I think where AI is exciting for us at Edge is how can it help us make better decisions?

So it's that critical thinking and analysis aspect of AI that is now becoming really attractive. Being able to look at our customer base, look at their activity, look at trends, look at various indicators that then inform us what are the next steps, both for my team, both in terms of growing an account. where So are the opportunities to expand, but also where are the risks, where can, what do we need to do to make sure that the accounts that we do have, the customers that we do service, stay on as customers and addressing any churn concerns as they come up. So I don't know if I'm fully answering your question, but I think AI has gone beyond just saving time and being more efficient. I think it's now focused on being that critical. It's almost like the, the advisor to you. How do you want to, how can you better do your job? How do you want to spend your time, your team's time and resources to ensure that you're able to achieve the goals that you, you're the goal. And what are your guys' favorite use cases?

How you use AI in the day -to -day? Yeah. Transparently, we are still in relative early stages. I think we initially were doing it for those efficiency use cases that mentioned. So things like, you know, we'll, we'll talk to a customer and they'll mention, or maybe a prospect. They'll say, they'll mention, oh, Hey, All you right. Thank for dost know, one of the things that's really important for us is to understand your solution is able to do, and they'll give us some sort of use case. We'll use AI to then generate a case study based on the customer data that we have, based on the accounts that we already work with and say, okay, hey, customer, we heard that this is something that's important to you. Here are three examples of how we're currently doing that. So you. how we started. One you. of the other things that I think we're starting to, to focus more on is that more of that critical thinking you.

and planning. So a different example would be from a hiring standpoint, as we look to add on to our team in different ways, you know, where, where should we look? What type of territories or areas make sense for the go To -market team? If we need to invest in folks, of know, we don't, it'd be great to be a hire everyone that we need to at the same time, but we have to be a little bit more methodical and strategic about it. So we use AI to look at our, our, our data set and determine, you know, for the next hire, right, where should we go? And what kind of territory quota can that person have? So we're starting to go a little bit beyond the efficiency gains, but right now most of it is centered around saving time, just being able to generate output quickly and efficiently. Yeah, I think the you. of the role I, I, i, you know, as a, as the leader of the customer success organization, I talking to customers very frequently.

One of the learnings I have come to realize is AI is outside of where I'm at in the San Francisco Bay Area. Uh, there's a lot of misunderstanding about what AI is. You know, I think AI outside of the professional work setting, you think, oh, look at this funny, you know, meme or image that was created or this video that was produced. That's what people think about AI. And so our customers, when we talk with them, I think they're influenced by that. And so they will say things like, oh, tell me about your AI, AI solution. And we'll ask them, well, what are you looking for? And they have trouble answering. They're like, well, I, you know, I know AI can create these funny videos, but I'm not really sure how it applies to what you do. So the learning is that there's a lot of education that is still needed with AI, especially outside of, call it professional tech bubbles. I think those, those of us inside those tech bubbles are very versed and very knowledgeable about AI you.

relatively speaking, very knowledgeable. But, you know, call it, um, regular, regular towns or regular places. think I AI, the understanding of AI, it really varies. And so education is important. Being able to address both the assumptions that have been made, but also just what's realistic and feasible. Like what can you actually use AI to do? That's come, that's a big learning for us. That's why we've been, um, a little bit more methodical about how we approach AI. We want to come at it from a, we're assuming you don't know much customer. So here's how we're going to help you, um, using AI. And here's, here's how we're going help our team, uh, better service you knowing that you'd look at AI right now with not necessarily like, um, you know, full understanding. You know, we were trying to meet you in the middle and help you, uh, be more comfortable with it. Oh, that's a great question. It's going be a while. I think you. running out of data is something that, um, yeah, it's going to be a while, but.

You know, I think what's going to happen is there's going to be more refinement. So what's going to happen over time. We're already starting to see it where I, this might just be maybe within my circle. In my organization, but when things are produced by AI, it's almost caveated with an apology. I, this, you know, AI helped me create this. And they say it with almost like, I'm not embarrassed, but I'm just starting, I just want to let you know, like, I didn't do you. this myself. AI did it. And so what we're seeing is this sort of, oh, well, it's AI. So it must not be as good. You know, I think some of that speaks to the data that you're mentioning where after over time, like the model start to all sound the same. You start to read something, go, that must be AI. So I think it'll be a while we run out of data, but I think what's going to really, my hunch is going to be what's going to become more and more important is that organizations start to really think about, okay, what kind of bespoke or custom unique data can I produce for the learning model to

you. leveraged? It's got to start to be a thing where you invest in your own data in specific ways that are unique to you. That's how you differentiate yourself because otherwise you're just like any other company, just, you know, throwing things in the clod or, you know, Gemini or something. I think that's how it'll be, but I think it'll still be a while. There's a lot of data out there still. Ah I think there are a lot of industries that haven't fully grasped or figured out AI. So probably quite some time before we actually run out of data. Think you. do What you think differentiates you, your guys' company from other companies that are in the same industry? Think you. Yeah, I think there's a couple of things. I think the way I'd answer that is when I think about our customers, a lot of them, they're not familiar with this model, this idea. So I mentioned medical dental insurance companies. So if you go to, you know, your doctor's office, right, you have someone, let's say, answering the phones, they are handling appointments and that type of thing.

Or maybe you have someone else handling billing for that physician. And a lot of our customers are not used to saying, okay, you're telling me I can do that with a person who's not in my office. How does that work? A lot of them, it's just the first time. And so the differentiation that we have comes down to a couple of things. I think one, our talent themselves. Our talent, they are based outside the U .S., but they, outside in their home countries, they are practicing physicians. They are nurses. They are pharmacists. They have education and experience beyond what a typical, typical, quote, you know, billing person or front desk officer would have if you were to try to hire one hire locally. So there's a differentiation in terms of quality. There's a so, a differentiation in terms of service. I think there's you. this perception with this remote workforce model or i call it offshoring model that, you know, you get wherever you get, you. you know, they may or may not be good. But since you're paying less money, it's okay. But, you know, you're kind of getting what you pay for. We

you. try to not lower the bar from a service standpoint. So every one of our customers right now has a dedicated, we call them relationship managers. They're essentially CSMs. They also have a dedicated account manager. So we treat it like kind of what, like a SaaS organization, how they would treat their customers. And we pride ourselves on providing a high level of service, both from a reactive, you know, if you have a question, you need help, we'll help you. Absolutely. But also proactive. Well, we're reaching out and scheduling business reviews. We're reaching out to share analyses of your account and offering our best practices and suggestions on how you can do better. So I think that's the other piece that you. is a differentiation. And then the last one I would say that is different is we are very much looking at it from not just like a staffing model where it's like, say, just give me a worker. And then, you know, that's it. You know, we try to cover end to end. To the talent that we have, aside from being educated, they are certified. Like we put everyone through you.

a certification process. We train them and make sure that they are basically ready to go when they hit the ground running. And then as the talent continues to work with the customer, the relationship manager that I mentioned, they continue to serve as a resource for them as well. So it's not a situation where you have a worker and you have to kind of figure it out yourself. You have a relationship manager who's also not only working with you, but working with the talent to make sure that that process runs well. So, you know, I never like to the ill of competitors. I think Thank what they, everyone's trying to do the best job they can with their company. But I think our company stands out in those ways I mentioned, and I think it's worked out well so far. Thank you. when And you look into this year, what's when vision for you? Yeah, I think there's AI piece as mentioned. So we're all in on AI now.

We're a little earlier on, or still in the early stages, but I think we're all in and we're going to really utilize AI to achieve operational gains as well as start to think about more effective ways to were our customers. But besides AI, you. I think the other piece you. we're starting you. to shift towards is, like many smaller companies, we started off with sort of the SMB segment, and a lot of our customers are on the smaller side. They're great customers. We will continue to service them in the best way possible. But we're starting to realize that large you. That organizations you. have a need for us as well. And we're starting to make an active push to engage with more of those types of organizations, work with them, and really shape our strategy and model to better service those types of customers. Prior to my time at Edge, past companies, the majority of the customers we worked with were of the enterprise variety. And so it's a world I'm familiar with, and I'm familiar with the fact that when you talk enterprise, more complexities, more rules, more hoops to jump through.

Everything requires, you know, three different people to sign off on that type of thing. So those are things we know that we need to be ready for. We're starting to work with a few of the larger organizations, and it's starting to help us better define the strategy we need to have. That's going to be a big push for us for the rest of year the is to enable us to better service those larger organizations in a similar way with the same differentiation that I mentioned earlier about the talent quality and high level of service. But also, though, refining our strategy to make sure we are best set up to work with those types of customers in addition to those smaller SMB customers. Find me. Well, I'm on LinkedIn, Eddie Chen, E -D ME -Y -C -H -E -N. So that's where I am on LinkedIn. Edge is on edge .co, O -N -E -D -G -E dot C -O. You you. want to learn more about Edge?

Yeah, I'm always happy to connect. I look at customer success specifically as something that, it's interesting, it has evolved over time. I think when customer success first came out, the focus was time, okay, I just need to kind of make my customer happy. I think as CS has evolved now, it's a lot more of a commercial revenue -driven function. So I'm always interested in connecting with other folks in the CS world to talk about how to optimize for that, how to better support our teams in working with customers. So always happy to connect to and LinkedIn I really appreciate you taking the time to chat with me today. Thank you. Okay, great. So thank you for listening to this podcast and we'll see you in the next one. Great. Thank you. Thank you.