207. From Managing Partner to Startup CEO: Mariana Hagström (Avokaado) on Automating Legal Workflows, Building AI That Replaces Repetition (Not Humans) & Why Corporate Professionals Must Learn to Trust AI

Speaker A: We have lot of formal discussions about how AI is going to replace who. I probably have seen those lists as well. I mean, am I in the list? I mean in previous Microsoft list, lawyers were not there and I don't believe, I mean, I'm advocating something different. I'm not saying like I shouldn't be thinking there on your chair and think when I'm going to be replaced because I mean this replacement happens anyway during our life cycle. So you're replaced by the better partners, you're replaced in employment, you're replaced all the time. We get replaced. If we are not good or keep up the work we should do, then we are replaced. It's obvious. I mean it's not A.I. uh, that's how life works. Hunger, they hunger for change and then to discover and also go out of the comfort zone. Because that's also a huge thing for, uh, people if they are, let's say they built up the career already and then they say, okay, now I'm going to the beginning again. It's huge. Which change you have to really be ready for, go out of this comfort zone. Your salary, your living style, your friends, everything, everything will change.

Speaker B: Hello, hello, hello, dear listeners, welcome to another episode of the Pursuit of Scrappiness podcast. Whether you're building a business, running a team, or just starting out in your own, we are here to bring you scrappy and actionable insights to help you become more productive, more successful and usually have some guests along to help you on the way. But before we jump into the episode, please don't forget to subscribe. On YouTube, on Spotify, on Apple Podcasts, you will find more than 200 episodes of ageless wisdom to help you become a scrappier and better version of yourself in life and in business. So click that follow button, check out the catalog. We have a lot of amazing guests and all topics, uh, relating to building a business. So please do, uh, check out and subscribe and you will be the first one to know when the new episode comes the Tuesday morning. So, about today's topic. So we want to dive into the world of legal, tech and um, also AI. So in an era where AI and automation are supposed to be transforming industries, we'll find out if it's, if it's true. Um, uh, then contracts and legal work and documents, those time consuming beasts remain a pain point for businesses and also law firms alike. But if you could slash all of those tasks and all of those efforts from days to minutes, and that's the promise of platforms like Avocado, a Cloud based contract lifecycle management tool Founded in 2016 by today's guest, Mariana, uh, Hagstrom. Uh, hi Mariana.

Speaker A: Hi Wildes. Hi Yanes. Hi to our listeners. I'm happy to be here.

Speaker B: Good to have you. And shortly, about Mariana, a former attorney with over a decade in corporate and IT law, uh, spotted the inefficiencies in traditional legal workflows while working as a managing partner. Drawing on her legal roots and expertise, she bootstrapped Avocado for the first years of operation, raising more than $2 million later on from such investors as Teta Ventures, Particle X and Thunderbeam, and probably some more. Uh, today the platform serves law firms across the Baltics, Nordics, Poland and beyond. Uh, automating everything from drafting to oversight. So, recognized as the European woman of legal tech in 2020, Mariana, uh, has been vocal about AI's role in legal teams, ethical innovation and empowering uh, women in tech. And uh, today we're going to talk about some uh, traditional business topics like bootstrapping, selling, fundraising, but also quite a bit about AI. So let's kick things off and we'll start with an origin story. So actually let me phrase this question, uh, and then uh, you can go. So um, we've found that you've shared publicly that you have never planned to leave law after over a decade in the business. Yet here you are founding a startup, avocado, and 10 years later, still going almost 10 years later. So what drove you to change your mind, uh, after a successful career in law?

Speaker A: So actually Ulis, I just want to make one remark uh, before we dive into my career change, which is a funny story. But um, we definitely, we're working with the law firms but um, I work harder today working with most of the like enterprises, medium sized companies and transforming their daily operations with the business intelligence, contract intelligence. So when we started, yeah it was uh, first because I came from the law firm, I really wanted to actually apply the same things. What I learned, uh, um, and I built as mvp, if it's possible to sell it to the law firms as well and they could benefit what I built and what I believed is going to uh, improve the work of the lawyers a lot. Um, but if we go back to that statement, I said it in one LinkedIn poster, that I never planned to leave law. I mean in general I haven't left law. I'm working every day with the legal documents and processes and basically it is also what I'm doing currently. But I never meant to leave law firm practice or legal practice. I mean When I built up my career in a law firm, it was actually a huge achievement. I mean as a woman, uh, as coming with no whatsoever relationships, uh, with um, like I don't have anyone in my family who has practiced law or has been an uh, attorney. I mean when I went to study law, I even didn't know, I mean, what I'm going to be. It sounded like a profound thing to do, I mean to study law. I mean most of my family are ah, study um, or practice medicine. And I went to like I'm a black sheep in a family. I went to the law. I didn't have any expectation, honestly, just to get a good education. Um, but um, yeah, in law practice I felt like I'm going to circles. So basically, I mean I built up different law, um, practice groups. I gave them over to another new partner, new attorney. I became a managing partner. I felt like, I mean, what's next? I mean, what's really next? Because I mean I tried so many things already. What's possible, I achieved what's possible here. When you are in the law firm and your attorney. I mean, I, uh, think I broke many ah, class ceilings to do what I did already. And then, um, I continued thinking like how he can do better because I'm not the person who will be satisfied sitting in one place and getting the salary. I mean, I'm constantly uh, urging or I, uh, need to change. I mean to do something to deliver more value, not just to do repeatable work. I think it's two different people. Some want to all the time grow and learn. And they are um, insatiable. They hunger for change and then to discover and then also go out of the comfort zone. Because that's also a huge uh, thing for uh, people. If they are, let's say they built up the career already and then they say, okay, now I'm going to the beginning again. It's a huge uh, change you have to really be ready for. Go out of this comfort zone. Your salary, your living style, your friends, everything, everything will change. I mean you will change. You will enter into a huge catalyst of change.

Speaker C: 2016 is like now with all the last three years with AI, like 2016 sounds like a different age back then. Uh, you probably had some vision also in the future that okay, the technologies will only become better. But how did the last three years, did they surprise you? The speed of the AI or was it something you kind of expected that has to happen at some point?

Speaker A: Yeah, definitely. Ah, AI changing a lot. How? Um, I mean we even don't imagine. I think at this point, I mean, we have lot of formal discussions about how AI is going to replace who. I mean, I probably have seen those lists as well. I mean, m. Am I in the list? I mean, in previous Microsoft list, lawyers were not there. And I don't believe. I mean, I'm advocating something different. I'm not saying, like, I shouldn't be thinking that on your chair and uh, think when I'm going to be replaced. Because I mean, this replacement happens anyway during our life cycle. So you're replaced by the better partners. You're replaced in employment, you're replaced all the time. We get replaced. If we are not good or keep up the work we should do, then we are replaced. It's obvious. I mean, it's not AI that's how life works. Uh, and definitely AI I think from my point of view, will bring a lot of more opportunities for lawyers, like, I mean, to change what has been cemented for decades. Our billable hour, our way of working, I, um, mean working in the world. I mean, everything we have believed as a lawyer. But how it has to be, I mean, uh, it's basically the same change. I mean, from the horse to car or something. I mean, it's going to fundamentally change because I think, uh, in general, legal is going to be a lot blended into that general business process. So it doesn't have a privilege to sit alone into that tower where you have to climb up and get some advice. It's becoming really part of the whole business process and the role itself. It's going to look more like, uh, orchestrating the full business processes. I mean, how we want to. What are the quadrails, how it's going to look like everything around that. Not so much of being a reactive contract reviewer, advisor, uh, when someone calls you, but you are going to be rather. I mean, you can still sit on that tower. What I said. But then this tower is different and you are like God, who is setting up the rules of the game. I see that.

Speaker B: Yeah. What was the initial problem that you were solving since AI uh, was nowhere to be seen, uh, at least at such scale back in 2016. So, so what was the problem that made, uh, you jump the ship?

Speaker A: I mean, uh, I think it's quite obvious, of course, maybe obvious to me because, I mean, or the lawyers who work. But I think the problem, uh, it still exists. And it's the same problem we see with the big companies that still struggle with the same problem. Actually, when I started, I mean, launched Avocado, eight years ago. And right now, could you imagine right now we're seeing they are jumping on board and they want to resolve it. But um, repeating same thing thing Losing uh, uh knowledge when people leave. Um, I mean ah, um. Inefficiency. I mean um, uh like all these manual workflows. I mean where you have to. I mean it's not. It doesn't require a um, um expensive high skilled lawyer to do those things again and again. And of course um, as a managing partner, I mean there was yeah two side of it I saw like when people are leaving the firm, we lose all this knowledge. I mean basically we didn't capitalize anything. I mean they're leaving. Of course there are uh like bunch of files regarding the clients and uh, the work is done. But who has time to read it? Who? No one. And then we just uh. Start from scratch. When someone is of course joining already knowledgeable. But when they are not so much then you're starting from scratch. Actually you could already scale but you do it again and again. There was also customers. Customers were not happy. Like why this takes so much time because you know Billabillauer is a guard here uh driving the law business. Uh, we want to get rid of and there's like a lot of discussion uh when we are going to get rid of Billa Blauer and uh, what different groups of people believe about. But yeah, definitely customers were not happy about. Like why. I mean getting the timesheet and then it took like 10 hours, I don't know, 100 hours. Why you don't. Why you're more efficient. You should have it somewhere, somewhere. Why you do you really do like redo it everything? Um, for me, yeah. And uh, on the same time uh also from customer side I saw like legalism and legal documentation is actually something I mean no one, I mean in a business side never reach. Definitely true. Because they don't understand it. It looks like I don't know uh, what cosmos. And then um, when they get these documents and they have to go and start working with that. I mean they even don't read it. So how they negotiate it. So I understood that um, to solve the problem that the business really can work with legal documents. The complexity should be built in vertically or like by design. You cannot just give a word document to business and assume that they can work with that. Ah, so if they can delete wrong things they don't understand um interconnectivity or different clauses, what one drives another etc. So it should be built in and that was something I thought if we have all this knowledge around, let's say it is a um, shoulders agreement and we negotiate it in different ways, dependent on the situation, dependent on how many shareholders are there or how um, I mean where they are located. I mean what's the business case? So why can't we implement all this knowledge into one workflow? So we make decisions rather than writing and based on those decisions, like two shareholders, um, I mean what is the percentage of shares, et cetera. So everything is built in. So the people who are putting it together, which are business people, for instance, they don't have to think about legalism, but they have to think about business decisions and it's so much easier. And I saw that as well. If we change the process from writing to making decisions, then people actually understand what they agree on.

Speaker B: Uh, yeah, that actually uh, resonates with uh, my experience as a business person working with lawyers. And there's some kind of contract, an important contract we're working on and then I get a write up of like for example, you know, 100 marks and comments and stuff like that. And some of them range from your company name is not spelled correctly or uh, the wording on this should be uh, changed words, places. And then there is some kind of critical key uh point where you need to really pay attention and make a decision. And after you have read like 70 uh, for comments about grammar and like other uh, things then suddenly you're just like ah, it's just another comment like uh, maybe I'll, I'll skip that. And then, and then actually that's the most important point that you actually had to spend all your time on. So, so, so yeah, that resonates with me for sure.

Speaker A: Yeah. And it's also what we see as well. I mean lawyers. Uh, one of my first uh, contract negotiation workshop I went and was held a very, very good um, English uh, lawyer, a senior lawyer who actually told what to negotiate and I think he mentioned as well and what I have recognized a lot as well, lawyers really like tick boxes so they cannot hold them back like doing every single box ticking. And actually what we should negotiate, exactly what you said is the major topics, I mean and make it clear to other parties as well why this is important, me, why this is important to our business, why we have to negotiate it and turn it around from redlining versioning, uh, as you said, like I mean point and uh, comma changes to a, ah, really uh, um, like really major discussion. Like maybe there are three things we need to negotiate. But I mean we get the same uh, distracted because it's a lot of redlining somewhere and go away from their first business goals and then we go to legalism.

Speaker C: Can you reveal a secret from a legal perspective? Are uh, lawyers or some lawyers is on purpose making the language hard to understand the only ones who can do it.

Speaker A: Yeah, uh, exactly. Um, I had a very good talk um the other day and we were exactly talking about. And then it was a very good point. I mean when the lawyer gets a ah, contract and then he, I mean starts redlining and reviewing in a very like neutral, let's say position and immediately when that lawyer recognize like oh, the other lawyer has actually hid it there or put there some clause which could be a tricky one, then they will like angrily start redlining more and more. And that's why it's very important as well between the parties to really build that trust and clarity. It's not like who red lines more, creates more value. It's not. I mean the best contract is with both on uh, like understanding what in the like why we even doing that contract. And more clear, more in center, more balanced, more trust, better relationship better.

Speaker B: This is one of the reasons why for some less important contracts I have just not involved lawyers because I felt like it's gonna, you know, add a month to the signing when the redlining here and back is going to be done. So I just like, you know, read it myself and like yeah, it looks, looks fine. It's not like a million dollar contract. Whatever, let's go.

Speaker A: Yeah, sure. And that's actually also the problem. I mean the lawyer stem cell create a friction between business and uh, legal because they overcomplicate things. Sometimes it's good enough we don't have to go crazy about amending something because we don't like the wording at the moment. It doesn't change much.

Speaker B: Speaking of going crazy, I think companies used to have uh, 100 Excel sheets for different kind of workflows and different kind of departments. Now it feels a bit like uh, companies have 100 different SaaS, uh tools for all kinds of parts of their business, uh, and processes. And when selling avocado, have you kind of uh, felt like it's like not another SaaS kind uh, of uh, tool that we need to get or is it like still that people just very, let's say unemotionally say we have this process, this can be improved. Uh, it's going to be another subscription. Add to the list of subscriptions and go like what are you seeing, I

Speaker A: mean in general, uh, yeah, um, when we are coming to the customers uh then definitely we see some kind of chaos there. Like a legal chaos, documentation, I mean uh, no controls et cetera. Um if I mean companies are testing doing pilots at different SaaS products because lots of teams want to test automate different things. I mean plug them them together etc. Yeah it is possible. Um, then it becomes uh also from the legal point of view a um infrastructure chaos because you even don't know you have to control now regarding the AI act, everything what everyone uses, I mean what is the use case? You really have to map it down, you have to create policies. I think it also a little bit cools down. I mean go crazy with all the different products. But if we speak about um, I mean specifically avocado and when we are going to our customers and um. We so much, we are not so much, I mean we're SaaS but not so much of the typical SaaS because um. We also have platform. So we want to centralize all different uh teams onto one data driven platform which you can really integrate as well with your existing stack. And when the customers coming to us then they also say, I mean they also going more and more towards that that we want to one united ah ecosystem ah we want to have a um single source of truth or system of record where we know somewhere we have to know what is true, I mean what we can trust again so we don't create like I mean we, we think more about infrastructure and then I mean somewhere you have to plug in different tools you can test let's say AI as well super vertical AI you can plug in and. But somewhere you really have to have the code Rails rules, I mean where you um, manage uh things where you collaborate. I mean uh, that's what you have to have and that's why we haven't had a really difficult talks because we are just not another tool in the tool set. But um, we go company wide so it's used by legal sales team compliance, procurement um, HR really dependent on the company and what we do as well. We bring lot of or replace legacy products into the data driven platform where you can actually access and build different data driven workflows and get full transparency actually what's happening in a company. So we actually have a bit um. Opposite approach here and unique selling point for the customer. So not another SaaS but have one platform which will support your growth goals and uh, maybe buy next products and AI products on top later on the go. But you need to have some solid foundation here.

Speaker B: You mentioned um, the key letters AI. So you've spoken about companies and legal teams being stuck in pilot mode when it comes to uh, adopting AI. Do you have some advice or playbook on how companies can uh, be more kind of aggressive and more assertive in their AI strategy and AI implementation?

Speaker A: I think it really dependent depends as well on the company maturity stage size definitely. I mean there's not advice what fits to everyone, uh, uh, but when we are looking or building the strategy for our customers and building the strategy to the teams actually how to adopt AI there are like different things you really have to keep in mind. I mean uh, you shouldn't start with just I mean oh this is so cool, let's test it. I mean, I mean it looks like, I mean you get a wow immediately but you don't have any understanding. I mean why we are doing it. I mean this why still. I mean in all this startup why is the most important question. But the why with this AI adoption as well, very important question. So what it is actually going to change. I mean why um we are doing it not just I mean ah, we can now uh, it's so cool. We can plug in this uh, HubSpot to this slack and this slack to this and then clay and then I mean we can oh wow. We can build that workflow and I mean why what is the big thing actually it's going to change or deliver or I think it's previously so many tools were talking about efficiency and I mean faster to be faster, everything faster. Uh, I don't think that, that being fast is not relevant anymore. I mean of course efficiency is relevant but when we talking about the products the outcome is more important. I mean what we are getting out of it, not how fast we are getting it done. I think the talking or narrative has changed a lot with AI and um, how we are looking at different challenges in the company. What we can do. As I said, uh, we can also use a bit kind uh of similar uh tools as we do when we build product. Because we should look at um, um legal team. We should look at different teams as also building some kind of products now so we can also uh differentiate

Speaker B: how

Speaker A: easy it is to build and then impact and on the same time now it's also important we also check how risky it is like how it will create uh, like I uh mean yeah. What is the like a risk implementing it? Because I mean let's say we implement like I mean customer service something. I mean how um Customers getting the automated emails, responses, everything. It's not so high risk. Yeah, so we're getting a lot of workload, I mean away from customer support and etc. So but on the same time if we are looking at the contracts this is high impact definitely. But on the same time it's also high risk. So I cannot put my contract workflows immediately into some like outcome uh through an agent and say like do it raise prices to all, I don't know, Lithuanian clients 10% and then I sit there and watch. So the impact and the risk to the business is much higher. Um, and that's why as well what we recommend is also building this kind of uh, roadmaps and strategy maps where you can really pick, I mean of course first you have to talk to the teams first you really have to collaborate and understand, I mean what, what kind of impact of uh, different um, uh AI implementation or the product changes or process changes would have. I mean we have to understand what would be the impact or maybe something uh, I mean really easy to implement. Low risk. Let's do it. I mean just it's a pick it and get it done, fill it done. Uh, but high impact things as well. We have to be mindful that I mean if we don't start now, if we start building now, if we don't think start building those like start small, iterate small then they never become like the big projects. Uh and you have to really understand as well this high impact, high effort projects take like year two horizons. I mean even though in the AI age it still feels like I mean we can just, I mean you hear all the time like companies at the top, it's just a one man show, everything is AI running. But if we look more enterprise, if we look more mature companies like telcos, banks, I mean it's never going to be like okay, let's test it, let's implement it. We still start with low risk, we still start with uh, winning small. We build roadmaps, uh and it takes much longer time actually to release um these high impact things uh for the company. So it may feel like everything changes overnight but uh, it still takes a lot of time uh to implement it right build the right code, rails be uh, safe, great trust inside the companies, great trust with their customers, I mean to feel secure really to launch those things. But yeah, what I recommend really building uh roadmaps um and roadmaps could be built only through collaboration with the different teams across teams. Have your uh, IT guys, product guys there, have you legal team there, have your business people there discuss, um, I mean, how we can impact what's the problems, what would be like easiest thing to actually implement, kill many stupid ideas, which doesn't create more much value, et cetera. So um, it's like a traditional uh, work behind it which uh, I mean should be done and then most important, yeah, like I said, collaboration, uh, have those meetings, start with those meetings and then you are able to start creating those roadmaps as well, not just doing things in the corner.

Speaker B: Yeah, ah, you mentioned risks quite a few times. And I guess one of the kind of obvious risks when working with AI is that at least from experience, uh, talking to ChatGPT and similar models is that they are pushing out complete nonsense and wrong information with absolute confidence. And the question is like how to implement, as you mentioned, proper guardrails or quality assurance. Do you have some experience, uh, that you can share, uh, how to prevent getting some complete bullshit ending up on your papers or in your workflows.

Speaker A: So um, actually there are two things here I would like to mention. I mean first of all, uh, you really um. I lost my thought right now, but anyway, I start somewhere else. So basically that's very, very interesting thing, what's happening behind when we talk about risk versus trust. So you see how people who never trusted anything they even didn't trust put things into the cloud. Now sudden they're trusting uh, these uh, different models and putting most confidential things into um, a, um, public uh, uh, chat boxes. And why this happening? Because, um, I thought and I figured it out why this happening? Because I mean all this uh, chat, it is designed so that it's also trust built in somehow because they're so empathic, they never judging, they always answering politely. So how they speak to us creates us as a human, such a trust that even we don't go there and think about it. And then I uh, thought uh, um, that's something actually when we speak about collaboration, collaboration inside the team, collaboration with the customer, we can easily copy. That's how you build trust. Because I haven't seen such enormous uh, trust created in anywhere as those chat is doing right now. Like ChatGPT, you go there, it's always like, I mean, help me. We are putting most person on most confidential things. We even don't question where this is going. But will this, um, your question, I mean, I lost my thoughts. Or maybe you could just one time, uh, get me in that one again.

Speaker B: Well, basically about some kind of uh, quality assurance or guardrails when implementing AI to Make sure that actually you get uh, veracity in your data and in your documents.

Speaker A: Yeah. First of all, I think most important, um, is uh. As I said before, you should evaluate. I mean according to the AI, um act and everything. I mean what is the risk category? I mean what's something we are doing right now. And let's test the things which doesn't have such a high risk. And we can really. I mean if something goes wrong, we can iterate, we can improve. And that's how as well we as a human build trust inside the team to test more so we don't take m too much risk first. Right. The other thing, what I have seen like people go to. I mean people who don't want to change, they go to there and they find the case. Oh, it's calculating wrong or it does it. I think, ah, the page is wrong. And then you say, oh, it's a bullshit and let's never use it. So what's. Again, why. I mean it's. We haven't seen such powerful tools actually to automate our work. And um, we really, as I said, we should start. Why we should understand its capabilities. We should understand the risk tolerance, we should understand where to start and understand how we can really implement it so that we don't um, put too much risk into the business. But on the same time get more and more confident. I mean, for instance, I mean what um. I mean you just cannot do it like linear. We take just one process but AI there and then that's it. I mean, as you have seen as well. I mean this process get. I mean I build one data extraction agent and then I build a uh, agent to do Q and A. I mean who knows what is good data. And then I build another agent who will uh, check that one. That the Q and A is not hallucinating. Uh, basically, yeah. I mean it's building. It's so much bigger than the first maybe iteration or what you actually want to do. So first when you see it working, then we start building also, um, backup. I mean we want to launch it, we want to scale it. Then we also have to understand what different other quadrails and agents should be in place which is controlling the. That the actual protocols and process is running as we seen. I mean we talk a lot about um, AI and human collaboration. I mean that's also something. I mean that's what we as humans really believe. Like agent is our associate and uh, we are ah, the ones who are driving the process. I think in many cases it's really going to change. So we will be out there and then the result is better than we have. Uh, it said so many different um already coming up the use cases. So leave the human out and actually the result is going to be better. Um, um, but if we talk about um, real use cases, how to build it. I think I already said be a really open mindset, try different use cases, build the roadmap m, discuss with the team, be ready. That's going to be wrong many times. Uh, uh, and build it uh, step by step. So you don't scale it but you can scale when the trust is created with a system with the guardrails, everything.

Speaker B: Final topic, we want to talk a bit about fundraising. So you raised a bit more than a year ago I guess last time. But companies uh, in the space usually always raising. So what about, what do you feel is the fundraising environment for a company that has AI uh quite deeply in the mix. At one point it felt like that all you need to do is put AI and you get funded. Um, now it's so many companies, so many different layers, applications etc. So obviously not everybody, nowhere near everybody gets funded. So what do you see as the environment and has it changed since you last raised?

Speaker A: I think we actually ah came into the stage where mentioning that I mean it's AI empowered or AI native is uh, it's not a cool tone anymore because it's almost like I mean it starts feels like you're saying oh it's uh a like Internet company or whatever.

Speaker C: It's electricity.

Speaker A: Yeah, absolutely. I mean uh, and definitely there are uh like AI native companies which has grown super fast. We also see like I mean uh, their numbers, their funding. I mean for instance lovable from Sweden. It's extreme like story how fast they're growing so big. But mostly with the AI. What we see as well are different. Like, I mean also have seen like all these AI startups. I mean they definitely attract a lot of interest and protop pilots and people coming in, they're interested. I mean what we can do with that and what's the result? And then now the pilot they go out and they say like okay, we're not subscribing, we are not becoming customer and not good enough doesn't solve big problem. I'm missing a lot of the maybe platform or connection or infrastructure features. So you go there, it's a lot of interest and it also drives uh, the numbers and KPIs will show how many pilots or how the revenue going up but actually a lot of this revenue doesn't stay and it churns while this pilot is over I think it's um, ah lot of uh FOMO and uh these numbers in many cases are not correct neither. And of course investors I mean they always want to predict what is the next hot or next big thing and definitely it's a very interesting space. Uh but um. Uh yeah it's also AI native. I mean companies like Clava will going up within one year. Um I don't know uh, uh it's huge and on the same time there are um more mature companies who bought in AI and it's like empowered AI. It's AI layer on top which also very good. I mean different numbers but now you can apply AI on top of all these platform features which are already there and then uh. I think the more uh focusing on sustainability metrics there I mean how we can drive another revenue stream uh actually launch new feature, monetize those features like also different stretch and then I can go to. I have seen as well like it's a longer term um, I mean maybe CD uh round already and then they just launch one and then they haven't fundraised a long time and then they launch just an AI functionality they rebrand it, name it differently. I mean this is our new um functionality feature on top of the big platform M and then again it attracts so much interest and so they can put together the next round. So there is I think lot of different strategy how you can use AI uh tools to drive revenue attraction. I mean get PR around it. So actually I read doesn't go so much into that talk but it was super interesting when uh um company I mean the strategy how to make a ah to the big one uh uh that Chrome got like M and a proposal like we're going to buy it so you're going to get a lot of pr. You get headlines of the uh biggest newspapers and I mean all these kind of things. I think it's. But it's again how we play everything what we do as a founders, how we actually play it big and how we show it how it's going to actually impact overall growth goals and company growth. I mean how we can do a big jump with that. I think it's still very much boils back like I mean the business, I mean how it impacts our business and how we as a founders can play it to the investors. I mean how it really sounds. I mean with of course real proof how it's going to be like more customer sticky longer uh Lifetime values or more revenue per customer. I mean uh, we even, I mean as an example, I mean if we launched any uh, AI features all this data extraction, I mean building all different features and uh, launching a operational intelligence platform on top of that. And then uh, our revenue by ARR per customer actually grow like 10 times. And I mean that's also. I mean the investors want to hear signals like what's your signals and if there are a lot of signals coming up then you want to be early on who's investing. Because when it's already a business case and proof then everyone are interested. So that's um, maybe my take care.

Speaker B: By the way, do you have any uh, tricks that you use in your personal or professional um life that you not just random prompts to solve some kind of uh, question but maybe some kind of ongoing things that you use AI to really help you uh daily?

Speaker A: I think personally I have such a good teams in place so I don't have so much of like a different business goals to achieve how to automate. So my team's automating everything around so they know better what they need and how to automate how to use different AI to actually use our resources most efficiently. Uh, but for me, I mean my meetings, um, my I don't know, daily writings, I mean all like a simple things. I think I uh. And all what's happening around. I mean my other different tasks, I think my team uses those for me. So I'm not uh, like orchestrating uh fully my life over like AI. Ah and putting everything there. So I think think like not too much. Let's say not too much. Personally I like um, cut as many tasks. I mean I don't want to have on my plate though so it's possible I'm delegating and then my teams are looking after those themselves.

Speaker B: Your teams of human and AI agents?

Speaker A: Yeah, basically.

Speaker B: Got it, Got it. All right.

Speaker A: Definitely when I would be like a uh, one man show or two people. I mean I uh, would tell you absolutely different story. I mean how to really scale the team uh and to get the jobs done. I mean you have heard as well the stories like I mean it's a one man show, everything is run by AI. AI is coding, AI marketing, AI meeting, uh, I mean lead generation meeting, uh, booking. I mean that's also interesting would be see like how this uh, one man plus uh, AI whatever 100 AI agents are working. But um, But I, I don't have that case at the moment.

Speaker B: It must be buzzing sound in the office. Like, it's like a bunch of. A bunch of AI agents doing worry.

Speaker C: Yeah.

Speaker A: Yeah.

Speaker B: All right. All right, Marianne, thank you very much. It was, it was pleasure talking to you and, uh, a lot of good insights. So, uh, so, yeah, wishing you, wishing you good luck moving forward, keeping some humanity exactly. In your products and in your organization and helping your clients also grow in such a way. So, so thanks a lot.

Speaker A: Thanks, Uli. And thanks, Janice, for having me. Hopefully was, ah, very nice. One hour. Thank you.

Speaker B: Yes. And to the listeners, I hope you also had a nice hour. And, um, see you next week. Thank you.

Speaker C: Thank you.

Speaker B: Bye. If you like this show, remember to

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