Andreas Welsch: 0:30

Today, I have a special guest who helps share more about what the fundamentals are when you want to take your product further and scale. Leah Tharin. Leah, thank you so much for joining.

Leah Tharin: 0:42

Thank you for having me. It's very cool to be here.

Andreas Welsch: 0:45

Why don't you tell our audience a little bit about yourself, who you are and what you do.

Leah Tharin: 0:48

Hi, my name is Leah. I have a self inflated ego on LinkedIn. So that means about 60,000 followers that I have built up with a lot of sweat by pretending to be really smart about product-led growth. I'm on stages internationally, at least on planet Earth to talk about this quite a lot. I've been a product manager myself, and I've been with companies like small PDF. And that's probably the most knowledgeable one, and Microsoft as well. But that was way, way, back in the days. But I talk a lot about product-led growth and I try to talk about everything that I do very specifically, clear, without jargon and with somehow takes sometimes. Yeah.

Andreas Welsch: 1:28

Wonderful. In preparation I said I've been following your content for a while and I think you share it with an edge and I think that's very refreshing and, unique in a sea of people who claim to be influencers, who claim to be knowledgeable, but are oftentimes just regurgitating things. So that's why I'm super excited to have you on. Thank you for making the time.

Leah Tharin: 1:47

Thank you for having me.

Andreas Welsch: 1:48

Leah, what did you say? Should we play a little game to kick things off?

Leah Tharin: 1:52

I think we absolutely should. Hit the buzzer!

Andreas Welsch: 1:55

Perfect. So exactly, when I hit the buzzer, you'll see a sentence and I'd like you to answer with the first thing that comes to mind and why. And to make it a little more interesting, you only have 60 seconds for your answer. For those of you who are watching us live, drop your answer in the chat as well and why. So are you ready for, what's the buzz, Leah?

Leah Tharin: 2:15

I am super ready.

Andreas Welsch: 2:16

Alright, good. Here we go. So if AI were a color, what would it be? 60 seconds on the clock. Go.

Leah Tharin: 2:28

Should I give you the correct answer? Because there's only one correct answer for this one.

Andreas Welsch: 2:32

Absolutely. There's only one.

Leah Tharin: 2:34

Any color that LSD can also produce. So that means all the colors of the rainbows because whenever leaders are talking about AI, they're starting to hallucinate as well. So I think, so I've been raising money around AI pitch decks. I get a lot of pitch decks with AI in it as well. So that's why I'm saying that all the colors probably. I would say that's pretty accurate, so.

Andreas Welsch: 2:56

I love that. That's awesome. Yes. It sometimes feels like you're on a trip. You just know if it's a good one or a bad one.

Leah Tharin: 3:04

Yeah. And it's never the same color that it was like a second ago.

Andreas Welsch: 3:07

That too just don't stare into the sunrise with your eyes open. Yeah, it can be blinding. Now you already mentioned that, right? You get a lot of pitch takes. You talk to a lot of startups and I see a lot of startups that are jumping onto this agentic AI hype train. And what do you think is it just opportunistic? Is it truly game changing? Does the world need more AI agents?

Leah Tharin: 3:34

So I think it's always funny because I think I'm the only person on the internet that does not know what an AI agent really is because like everybody has slightly different kind of definitions of what the term is, and we're really good at inventing new terms in this industry. You know that, right? Oh yeah. And but I think okay, if we say that, about three years or two years ago, I was talking about this with Kieran Flanagan, where we talked about that AI will get to a point where it does not need an interface anymore to operate a product. And I think that's a fair summary of what an agent can do. And I wanna say move the mouse, but metaphorically speaking, it could move the mouse and it can take some of your tasks. Now that's already the case also with some LLMs. But I think the point here is that it can navigate interfaces or systems in some kind of way that automate something that has not been automated from before. So without going too much into detail, I think we have all a rough idea that we're talking about the same kind of deity here. So I think we are still in a very early stage. I think that's not a very hot take in that sense. But what's important is that whenever you have a technology jump like this, and this is a one that, this one is probably a little bit unprecedented. You have usually an early segment and then you have a late segment, right? So the longer term segment and then the earlier segment, and then there's people who are selling shovels. In any gold rush, right? Like people are selling shovels and then everybody's saying oh what's the mantra on the stock market? Sell on the news or what, yeah. I'm not good in the stock market, but I think it goes a little bit into the same direction. Yeah. So right now we have a lot of early hype, which. Is not very sustainable. And you can see this when you're looking at investors and how they think about the investments that they do in these companies. So I'll give you a specific example. So let's say you have an AI agent that says that, Hey, you can replace a, BDR, an SDR with whatever agent we have. They can fake the calls, they can handle all the email outgoing conversations and so forth. What this is missing in as usual. Is that these are very siloed solutions, right? So it's oh we're trying to replace a salesperson. We're trying to replace a designer. We're trying to replace my wife when she's making a restaurant reservation. Like something like this. And this is going about it the wrong way, but it's a very natural kind of thing because you're always trying to kinda replace what has been there before in one kind of person. So to answer your question, finally, I think it is a hype when it comes to the long term. Value still. But there are some really good integrations that are also gonna come. And I do think it's gonna change the space dramatically, but AI as a whole, and I do not know whether we actually need the term AI agents, but for the purpose of this, I do think, yeah. Yeah. So yes and no.

Andreas Welsch: 6:35

And sometimes that's the answer, too. Yeah. That, just speaks to the complexity and the newness of things as well. I really like what you said about we look at something that we know that we can grasp and we say, let's see if, or how we can make that better, faster, cheaper, with the help of a new technology like AI. What I also think we are, we're missing if we primarily take that approach, is that the much, much bigger potential of. How can we actually do this? If we actually sit down and we start to rethink, is this not only a way that we can automate, but is it even the best way to begin with? And I think many organizations, whether it's the one adopting the technology or the ones building it are, struggling there as, as well because it's so big and so broad on one hand, but also so difficult to, conceive and, then to, implement if it's such a radical change as well.

Leah Tharin: 7:29

You know, what's interesting about this is let's say you're having this agent again, that is like an automated BDR, and let's just say you have an agent that can do this, right? Like it is satisfactory. Good, right? To whom would you sell this agent? You would usually sell it to a VP of Sales, right? Or like a CRO. But let's say now you really have this fantastic idea that you have an AI startup that makes selling a product. So not just like the sales process, but like selling a product in a way so much better that it not only covers what the SDR does, but also the, usage the usage metrics from the product and everything else that you also need to whom do you need to sell this agent now? Now you need to sell it to the CEO. Yes. So now you have changed the audience that you're actually selling this entire thing to the complexity of what it is, and your entire thing is now starting to fall apart a little bit. So I think one of the challenges that we have is it's like this incredibly difficult shift of, okay, there's one thing about something being possible, but then the second thing is, how do we sell it to the market? Yeah. Because that's the thing. If it's hard to explain. You cannot do a PLG solution anymore. So like product-led growth, right? That is just like having a freemium and hey, just try it out a little bit.'cause people do not understand what the context of it is.

Andreas Welsch: 8:44

Yes. And, I think then also to your point, who do you sell it to if you sell it to the VP of Sales? But the solution largely makes them redundant as, as well, or may makes large portion of the team redundant. What's the incentive to do this if you are losing, have your kingdom and Yeah, and your castle, right? So that always argument.

Leah Tharin: 9:04

Yeah. It's like we, we get you out of a job.

Andreas Welsch: 9:08

That's the scary side of things for many people as well, I think. Is this going to come is this going to replace me? But the part that you mentioned about packaging, pricing who do you sell this to? I was recently teaching a course on maven.com about how do you actually price your aI solutions, and I'm running another cohort early in June to build the monetization model because I think to your point, many organizations, many startups are approaching it as a, here's a task and this can be replaced with AI, or here's a human equivalent that can do similar things. But I think what we're missing in, many cases is, actually more of this outcome driven monetization because. I, ideally we need to caveat this always. Ideally I can give you something, I can give you some technology that delivers predictable outcome or predictable quality. So from transactional pricing or user-based pricing to outcomes, that ranges as wide. But I'm wondering what are you seeing? What are companies missing today when they do price their AI solutions, their gentech AI solutions. So what you're asking is in some ways where do companies leave money on the table in a way, right? That's one way to look at the problem. And the other one is so how do you set yourself up for long-term success? So on the second question, I have no idea.'cause nobody knows where this market is going. That's the first thing. The second thing is I do not believe into any pricing models or monetization strategies. Let's put it that way, where. What you charge from the customer is inhibiting their customer success in some way. So for, you said it already, but let me give you an example. Let's say OpenAI as or like Chachi pt, whatever you're using as your favorite LLM is not costing you per month,$20 or whatever the subscription price now is, but like per chat, you're gonna pay 50 cents. Just like 50 cents. What this does is it encourages you to do some really shitty practices. You know that, that's why you get messages internally as well from the companies. Hey, write everything into one prompt. Don't use it for your personal shit and so forth, right? So like we start to design systems around the monetization models of the products that we are actually having. That is problematic though, because yeah as I said already, right? Like you're actually stopping the customer from being successful. Now a customer is very glad to pay you for something like a share as we are doing with sales as well, if the outcome is proven at the end of it. And but this is a challenge that is, quite hard to crack as well because we also need to be honest, right? I have a couple of people who always come with some evergreen ideas, unlike, hey. I wanna make a platform where you can sell we facilitate like the selling of cars. This always works until the moment where people can start to abuse the system and then go outside of the system and you're not getting a sales commission anymore. This is a challenge that has been always there for forever. So in theory, it is correct that you should charge, if possible, towards the outcome. So if a deal is closed, then you also get a share. But how do you make sure this is actually happening? And this, ironically, this is not that big of a problem in B2B companies because in B2B companies like the fraud, there's just not, they're not gonna short charge you too much. So in a way, I'm thinking that the more a market that you go, try to be more really on the outcome as much as you can. And on the lower end, try to keep it simple. That's, just keep it simple. Do an average. Define some like you some borders where you just say Hey, you look, if you fire off a hundred thousand chats per day, then you know you're not gonna use our stuff anymore and so forth. But keep it simple on the lower market. And if you go up market on the B2B side try to target it towards the, outcome. The other thing is that you need to understand for enterprise solutions, and I've seen some of them already that are coming, that are quite exciting. Let's say what I said is true before that, it is a solution you probably need to explain to these customers as well. So it's not always as easy as, Hey we're gonna replace your salespeople. Let's say you're having Salesforce and you're a huge company, and we're now bringing this entire stuff into that. What that means is, if I'm Salesforce and I'm trying to sell you now an agent that is helping to sell on top, you understand that, oh shit, that means we're even more locked in now to Salesforce. So be careful that with the monetization, the packaging that you have, that you're not even it sounds like a nice thing, right? Like that you're locking in your customers, but don't lock them in just because you can only if you really have to, get to the value of, whatever your agent solution is. And what I mean with that is, is that it should behave. More or less independently and not just like from, your own product platform. I, hope that makes sense. Yeah, it is. It is a very confusing space. It, certainly is. Right? And it's it's very nascent. It's just emerging. Different companies are. Yeah probably throwing spaghetti on the wall and see what sticks at, this point is the impression that I get as well. And there was so much good information, so much so much meat there. Last couple minutes, what you shared and I remember about what, two months ago? Almost. At beginning of March, Sam Altman's send out this tweet. Hey folks. Hello World. We're thinking about. Changing the monetization model of chat CPT from$20 a month to credit based. So you buy some credits and then you use it exactly what you shared. And I think you know that, that sends a very troublesome signal to the market and to your customers. Probably OpenAI went in with a$20 price point in most of the Western world,$20 a month is something that you can pay, that is reasonable that doesn't break the banker or your budget. But then when you look at the additional features that OpenAI has, added models that are more capable, that are probably more energy intensive and, resource intensive in research. And this and that. Then$20 per user per month might not be the ideal price point anymore if your costs exceeded and, if the usage exceeds it. So it, it can get tricky if, and if, the usage is hard to forecast as well. If you have a good product,

Leah Tharin: 15:28

I can give you, I can give you a pricing model that I think is quite clever in this regard. So I think a company that does this really well is Buzz Brow. So Buzz Brow is not an AI company in any way. But it is a podcasting platform where you upload your files and then they're being distributed into other. Stuff. So what you're getting with a subscription is you get a specific limit. If you go over it, you are not blocking the users at all. Then you're gonna get charged like per gigabyte that you just go over like a specific fee. And I think that's good practice. So we do not wanna stop the users in doing something, but with the subscription you get a very hefty kind of like base kind of quota that you can go with. The problem is, that as well as we said before, what is a successful chat with an LLM? Are we gonna charge you for like when they're, when it's hallucinating data, how would you know that something is good or not? So the challenges are gonna remain for sure, but I think keeping it simple is, probably an art going forward. Just one final point on this, if you do believe, and probably there is no one right now in the world that thinks. That any of the solutions that we have right now are not gonna fundamentally change right now. So all of this is in a book, big flux and a lot of stuff is we don't even have any good solutions so far. Do not try to lock in any customers with your early stage solution with yearly long contracts. It's not gonna work because your customers are not stupid. Nobody's gonna commit to a two year plan on an agent where they're like, Hey, you know what? I don't know whether in three months AI, open AI is coming out with another model that is gonna beat you. That's an important one as well.

Andreas Welsch: 16:59

Plus, if you're a vendor and if, you need to change your pricing model, you have some customers that are grandfathered in with their plan and you need to figure out how do I migrate them? Do they want to keep it? If it's a three year, five year long contract, that can get pretty difficult as, as well. Before you move them to something that's more un vCAN at that point. Yeah. Let's say you've, built your AI, product agent, AI product, what have you, and you ask your sales rep to pitch it and, sell it. What's the next step where that vision of changing the world with agents keeps falling flat for most companies? What are you seeing? Where are we on the maturity curve and is it more like the internet internet of the nineties, or is it completely different?

Leah Tharin: 18:01

So I think there's two major points in this one. The first one is that in almost. Maybe even in every consulting client that I have ever worked with, and I'm working mostly on product like growth. AI is also a topic that's coming a little bit, but I'm not an AI leader specifically. But the principle is always the same. The biggest opportunities that we have in companies nowadays are by and large cross-functional, meaning that if you need to optimize something, it's not the problem. That we need to solve inside of sales or marketing or product. It's usually cross-functional in the sense of Hey like sales is not talking to product. They're not doing this. Sales is using their own tools, right? Like every silo. I almost wish swearing right now. Every silo, every f silo. Is using its own tools, right? HubSpot is using sorry, sales is using HubSpot for their data stack. Marketing, Google Analytics. I don't know why in 2025, but hey, I've seen it too. Product is using amplitude or whatever, everybody has their own tools. So you and the problem is that the customer doesn't care about this and they're landing in all of these individual tools at a different time because the workflows are different. So if the opportunities are cross-functional and you're going out there and you're selling silo solutions, as we said before, right? Hey, we're replacing your salesperson, we're replacing this. This cannot be a winning strategy going forward. I'm gonna pause here just for a second, but I think that's the very first, most important point in that you have to make sure that you're showing the value of the cross-functional kind of journey. Because if this automated SDR makes it harder for someone from support to call them up, right? And I like figure out, hey, what did we actually talk about in this particular call? Hey, what did you actually promise to them? Then you're starting to create cross-functional problems rather than solving them.

Andreas Welsch: 19:58

Reminds you of the time before a lot of that technology emerged and people were talking to each other, had spreadsheets and, these things. And in a way similar.

Leah Tharin: 20:07

Yeah, no, 100%. And maybe to your second question on where we are in terms of it's, is this the same as two thousands or 1990 or whatever? I think I'm a big fan of understanding the jobs to be done right from people. So what is the job that someone wants to do with whatever they're doing in their life? And there's always this one job in every sales conversation, in everything, when you're talking about, Hey, we want to get a new product into our life, whether this is on business or in new consumer life, and that is. If you make someone look stupid or they think they could start to look stupid because of something that you're selling them, they're not gonna adopt it. They're not gonna pay money for it, and they're not gonna use it. For example, if you say that, Hey, you can click this button and I'm gonna send Andreas automatically an email where I'm just saying thank you. I wanna see that email. I'm not trusting my AI yet to just do it without me having any oversight. And that's a very important one. You need to make sure that whatever flows you're building, that there is a human element of control in terms of quality. It doesn't matter how good your model is, and that's an very important point. It's not about whether your model is good enough. It is. Can you overcome the distrust from people that people still have? Of course, rightfully by the way. To automate 80% of the flow while giving them 20% of excellent control over the output. Because that's what this is about, right? You do not wanna make someone look stupid because otherwise your pitch is gonna fail. And I think that's, that has been the same in the two thousands, 1990, right? So if you're, starting to introduce more stuff you always need to make sure that the people who are working with it also start to appear professional and so forth. And I would say, like on this entire curve. We have the same challenge that we had back then. Enterprise data is not ready. So whatever you're thinking about with your AI, what it's gonna do, it's gonna take us another 10 years to get the data infrastructure in place for all these B2B companies. To use your agents and an agent without access to data is useless anyways

Andreas Welsch: 22:17

Lots of powerful things. Definitely the part that AI needs data and, that your data still isn't where it needs to be is one that I keep coming back to as well, and to me it's, mind boggling that, again, to your point, we've been talking about this since the early two thousands or, what have you and, yet there were always more important things to do or shinier project shinier objects to pursue than. Getting your fundamentals in, place and getting your foundation in place. Where I see the opportunity though now is that AI can be that that, that hook if, you will, to say we need to do a phase zero, which is getting our data in order to do phase one. That takes courage and it takes budget and, it takes expertise as well.

Leah Tharin: 23:01

I'm 100% with You I just think that people really underestimate how hard it is to get your stuff under control. Specifically the data, the big difference to back then is that let's say you have a sales department of 300 people. Okay, so you have a sales department of 300 people, so you need to have 300 people who are selling your stuff to outside of the market. That means you need to have a very specific infrastructure that is capable of servicing these people. Now, these people do not talk to each other as well. They may be in the same team like you have an entire organization managing this entire stuff. If you do have an agent solution for something like this, it doesn't matter. The throughput that you have, right? So like whether an AI can make a phone call even, or like writing an email is not dependent on like how much you're using it, right? So like the bandwidth is just more that's more compute power rather than anything. So there's a big difference in this, and there is a cost argument in there, in that I think that existing companies have data stacks built for humans. And their limitations. So for instance, access control. Why do we have master branches and, forks and everything because people are just messing with this shit constantly, right? And that's going to change in ways that I'm not sure whether existing companies can really like fix, so to speak, their existing data stack to make it compatible with AI agents or whatever the future is going to be, because. The requirements are gonna be so different that it's probably easier to build it up from the ground new.

Andreas Welsch: 24:30

I think that's a very important and powerful message, right? That, yeah. The systems that were built for humans might not be the same that we need to operate AI and genetic solutions. But I'm wondering there specifically from your experience working with a lot of founders, working with a lot of product leaders, what's the one thing that they need to do right now when it comes to AI and when it comes to agents?

Leah Tharin: 24:54

I'm gonna start with a very basic mistake that Canva is doing right now. But if you go to a Canva's pricing page, for instance, and Canva is touting that they are a very straightforward like they're a very forward thinking company. In the HTML, you cannot see all the features, right? So if an agent is going in there or an LLM to research their pricing page, it has to expand the boxes that looks nice for the human eye, but it's not very helpful for a bot to go through. Now you can say oh yeah, but maybe it's gonna find it in the code eventually, right? So or like it's gonna expand the difference and so forth. You need to start to expose your stuff, not just to human users. And we're not just talking about an API, we're talking about actual websites because what agents are doing is they're imitating human behavior, as we just said before. So that's the very first one, right? So like we will have some kind of, I don't wanna call it search engine optimization, but it's gonna be like agent optimization. There's also gonna be some shitty behaviors that we see already, right? So like an LLM cannot differentiate between. A review site that says that Andreas is awesome between whether that's a true one or a false one, right? So like we start to have programmatic false review pages come up and so forth. So that's not a tip, by the way. Don't do that. But I'm just saying you need to expose your stuff to agents, right? So like you need to make it sure that, that it can be used there. And then the other thing that we have also a little bit on the pricing is that. Make sure that you have fail saves. What you definitely do not want to have is, for instance, if you have usage based pricing, is that there are no access controls where companies, for instance, can decide, for instance, Hey, let's treat this like an ad budget. Don't spend more than a hundred dollars, and give me a warning rather than just like spending over this. So this also needs to be baked into that and realize that people who want to use AI agents for themselves, which I highly recommend if you're selling some of them as well. Try to figure out an organizational structure first, and your data infrastructure, what that actually means, rather than just going to companies and hoping that they can figure it out themselves. So some AI agent solutions that we see right now have to have in their offering also help. With setting up the data infrastructure, which means sometimes also warm bodies like people, engineers that are helping these companies to facilitate this so they can see the value. But leaving that just to the customer and their data team is usually a very bad approach.

Andreas Welsch: 27:24

So I think what I'm hearing a little bit of is eat your own dog food. Coming back to that as well.

Leah Tharin: 27:28

100%. Yeah, 100%. There's, it's mind boggling to me how people just think that, oh my God, like my idea is the shit. It's important that founders are thinking that, don't get me wrong. But in order to really understand what challenges are, you also need to drag these out into, the light if you wanna. There's a good book by about this that has nothing to do with AI. Which I just forgot the title about. No, it's a sales book. I'll figure it out in a second, but maybe we can also add the point is this, that in a, good negotiation, you wanna highlight the negative points before the customer is coming onto this objection by themselves. And that's what you should do with AI agents as well.

Andreas Welsch: 28:08

It sounds that on one hand there's a lot of opportunity and promise. On the other hand, everybody's still figuring out what does it actually mean for me and my business? But it's important that you do start to figure it out and, work with your customers and also seeing and learn what resonates with them. What is it that they actually need? How are they planning and using it? So it informs your product, your marketing, your sales strategy. Now Leah, we're getting close to the end of the show and I was wondering if you can summarize the key three takeaways for our audience today.

Leah Tharin: 28:37

You are not just selling a product, I think you're selling a solution into an existing data stack. That is probably a mess. I would say that's the first one. Yes. Second is monetization will hurt. But make sure that you are aligning yourself as much as you can with the customer success. A good monetization model is one where you maximize your own gain, but also maximize the gain of your customer. That's a very classical ICP thing that we say. So like ideal customer profile. It never works if that's in an imbalance. So don't try to fleet your customers in that way. And the other thing is that one of the reasons why so many of these AI agents or like the smaller companies are not getting investments is because they're focused too much on the early on the silo solutions. So try to think cross-functional as much as you can with the constraints that your customers have, and you have a really good chance in the market.

Andreas Welsch: 29:34

I love that. That's awesome. Super actionable and practical advice. Leah, thank you so much for joining us and for sharing your expertise with us today.

Leah Tharin: 29:42

Thank you for having me.