Built in EMEA — GTM Operator Stories
Real conversations with the GTM operators who built EMEA's best revenue organizations. Too many SaaS companies scale the hard way — burning good people, missing targets, and wondering why growth feels more painful than it should. The answer is almost never the product. It's the architecture.
Built in EMEA is hosted by Pavel Novák — revenue enablement leader, GTM operator, and founder of the ROA Prague chapter. Every episode, Pavel sits down with a senior GTM operator in EMEA who has built something real: a sales process, a revenue org, an enablement function, a customer success motion. Someone who made real decisions under real pressure and has the scar tissue to talk about it honestly.
No theory. No frameworks that look clean on a slide and fall apart in the field. Just honest conversations about what it actually takes to scale a revenue organization in EMEA — and how to stop burning good people in the process.
For CEOs, CROs, RevOps, enablement leaders, sales managers, AEs, and CSMs who want to build better.
Built in EMEA — GTM Operator Stories
The Human Touch in a Bot World: Real AI Use Cases for PMMs with Valery Mezencev
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Product marketers are spending too much time overthinking AI and drowning in repetitive tasks. Instead of just listening to LinkedIn gurus promising 5-minute life-changing fixes, it is time to actually integrate automation into the workflow.
In this episode of Built in EMEA, host Pavel Novák sits down with Valery Mezencev to unpack how product marketing managers (PMMs) are realistically applying AI and automation. Leaving behind the clean slide-deck frameworks and theories, they discuss what it actually takes to build a modern product marketing workflow that balances automation with essential human intuition.
In this episode, we cover:
Stop overthinking AI: Why you should stop overthinking which tool to use, pick what feels natural, and just try it for yourself.
The irreplaceable human touch: Why the subtle intuition of understanding customer sentiment and messaging cannot be codified by AI, and why automation should be reserved for manual, repetitive tasks to make a PMM's job more enjoyable.
Moving beyond manual chatbots: While PMMs are already using tools like ChatGPT, Gemini, and Claude daily, the real switch is automating those AI workflows to eliminate steps that add no unique product marketing value.
Start with a problem, map the process: Why simply having a clear definition of a problem is enough to get started with automation, but why you must explicitly map out the steps before automating any repeatable process.
If you are a GTM operator trying to build the right thing in the right order, this conversation will challenge how you approach your revenue organization.
Follow Built in EMEA wherever you listen to podcasts. Because the companies that scale well in EMEA aren't luckier. They're better architected.
Welcome to the next episode of uh our podcast, Built in a MEA, which is focused on talking with really interesting people who built something tangible in a MEA and in B2B SARS, and it's my great pleasure to have Valerie with me today. Valerie's focus is product marketing, which is uh excellent angle we haven't spoken about yet. So really excited and thank you for taking your time, Valerie. Um, I have a few warm-up questions for you, and then we will go deeply into something that you built. Valerie, introduce yourself, please.
SPEAKER_01Sure. First of all, thank you, Pavel, for having me. It's a pleasure to be here with you to have this discussion. My name is Valerie Mezensev. I'm based in Prague. I'm currently a product marketing manager at Make, and Make is a workflow automation software company that focuses on uh AI agents and AI workflow automation. So I'm really happy to talk about that topic, product marketing, go-to-market strategy, anything that we would feel like discussing today. Fantastic.
SPEAKER_00If you if you look at the number of B2B SaaS companies existing in the market today, every one of them has a roadmap with six months' new releases, etc. What is the number one reason why product launches fail based on your experience?
SPEAKER_01I would say two reasons. One reason, more obvious, more straightforward one, is that oftentimes with big launches, it's very tempting to feel overconfident about your success, about your feature and your product being the best, that you only focus on the day of launch and just hoping that once the day comes, everything will click and it will be immediate success. But the part that uh can be can be a bit neglected is pre-release phase, pre-launch phase where you validate and you actually prepare, you test your angles, your ideas, your approach. So you so you work before the launch to make sure that once you launch it, it's not uh a question of yes or no, it's just confident launch. But the second part is that sometimes, of course, it's even more tempting to focus on launch as an end result. So, yeah, we did a launch, we uh received a positive reaction on LinkedIn, and that's it, that's a success. But what's more important is understanding what is the business metrics that you want to drive, and that's actually the what is showing whether it succeeded or not.
SPEAKER_00So ship fast and fix later, or really invest in preparation before the launch and get everything right.
SPEAKER_01Yeah, I think that's this question is even more important right now than ever because ship fast is easier than ever, thanks to advancements in AI and AI coding agents, etc. I wouldn't say that there is a clear yes or no answer. I think it depends on uh what your company is better best at and also what works best for your specific target audience. Of course, right now we are moving more and more closer to shipping faster than waiting longer. I think it needs to be also ingrained into your DNA or your company that you are actually able to ship fast and integrate fast, and your audience is also able and willing to forgive you if nothing if not everything is perfect from the day.
SPEAKER_00Getting ready for the next really the next version being better, but actually accepting that the first version is not perfect, and that can be a very different audience to audience.
SPEAKER_01Exactly, exactly. Some, let's say, large enterprise customers they uh might not feel really well about this approach. They they are willing to wait a little bit longer about expecting already finalized, polished, tested, well-working solution. Again, it's not just a matter of uh customer segment, it's also like customer size, it's also a matter of what uh vertical you're in. Perfect.
SPEAKER_00You spoke about um the measurement of a successful launch. Um, how do you measure that whether a launch was successful beyond just the initial launch, the big bang recording a wave on social media and then crickets? What is the ultimate KPI that you're looking at that will tell you that that launch was actually very successful?
SPEAKER_01Yeah, I think this is also what it makes it a bit harder because yes, we can say that end goal is always uh added revenue, but this is this is something that is not usually that direct when we are in B2B SaaS. You cannot have this quick, fast way to immediately add new revenue. So then it depends uh also on what kind of feature or what kind of product you're launching. Sometimes, let's say specifically, because I specialize in product-led growth and product-led sales, there is a free tier in those products. So sometimes you want this uh this new feature and product to primarily drive new use, new usage. Other times the launch is about uh improving retention or adoption. That might be the key metrics because you're solving something that users have been missing in the product. But other times you are, for example, entering a new segment and your goal is we are trying to acquire new customers or new sign-ups from new segments. So it really needs to be tied logically to what you are building. But again, the way I prefer to think about it is not, hey, let's build a thing, let's plan the launch, and while we are planning the launch, we then only think, okay, what is the metric? It's actually should should start with the vibe, thinking, okay, this is the metric that we need to solve. Let's say we need to solve retention in this segment. What do we need to build to solve that? And how do we need to launch it properly so we actually can drive those results? Okay.
SPEAKER_00And then before the results are visible, there are some first indications that actually it's going in the right direction. So it's not only just prolonging contracts or new revenue in the door, but it could be starting with engagement of people and usage of the thing.
SPEAKER_01Yeah, 100%. 100%, because we have leading and lagging metrics, and that's important, especially when you have longer, let's say, sales cycles, when it's not just self-service SAS, when it's uh maybe faster for you to see whether there are more credit card purchases, it's important to measure those as well: product usage, uh product adoption, feature usage, etc. So you can you can spot early on whether it's moving in the right direction or not. You will not just wait for six months and see whether it was successful or not, and being just left in the dark until uh that moment comes. Nice.
SPEAKER_00Valerie, how does one become a product manager? What was your journey?
SPEAKER_01Yeah, I think it was quite uh interesting journey going from uh founder to product manager to then a product marketing manager. Um I think primarily and I think especially nowadays and going forward, it's about curiosity. So if you just enjoy great products, which I believe all of us do, but if you are really curious about what it takes to make some decisions, how they are built, uh, what it takes to actually build them and what are the different approaches, how to build them. If you are curious and excited about those, not just being on the recipient end when you use them, but actually what is behind making this happen, then I think that that's all it takes to become a product manager. And then from for me, then the extra move to product marketing was that of course, and again I will be repeating myself over and over again today, but with AI it's uh easier than ever to build stuff, to build products, build features, and that equally means that it's harder and harder every day to stand out, to make sure that if you build it just on your own, it doesn't mean that the customers will come. So that's why actually product marketing resonated with me so much. So it doesn't end in with building the stuff, but really making sure it gets to the customers and gets to the market successfully.
SPEAKER_00Basically fighting for the attention so that people even notice it could be a great product.
SPEAKER_01Yes and no. I think that the way I tend to approach product marketing is not fighting for the understanding the problem that is existing, and of course, researching whether it's existing on the market, and then making sure that we are able to match our solution to the people that have the problem. So it's not just about making noise, it's actually about being very deliberate and understanding deeply that you have something unique, but then of course, at some point you need to make sure that those ideal customers find you.
SPEAKER_00Resonates, Valeria. I just recently discovered Bob Mesta and April Dunford and these people finding the underserved market and then packaging so that it makes sense, and and it's like within 10 seconds the customers understand oh, this is something that we've been missing, and it's wonderful. What is a product that you are excited about? You're crazy about maybe a recent discovery or something that you've known for a long, long time, and you go back to as a as the golden standard of this is how it's done right.
SPEAKER_01Yeah, I think it will not be a surprise or something super uh super unique when I say that I'm super excited about what Anthropic has been launching and the speed and quality of their products that they are adding. Of course, clothed code is a first of mine for me, so this has opened a new world for me, which is which is super amazing, obviously. But beyond that, of course, what I uh what I'm spending a lot of time in is our make product as well, and I really like the combination of using cloth code and going really wide and wild with what it can do. But then, actually, as soon as uh I want to create something that is repetitive, that is reliable on the ongoing basis, and I need to have uh clear control over it and set it once and don't think about it anymore, then I actually turn to make when I can combine the power of cloth with uh deterministic workflow automation, depending on what what are my needs. And then there's a third one that's a very personal one that I've uh discovered just recently, and it's called Akiflow. And it's uh it's just a personal productivity tool that combines task management and calendar, and it's purpose-built for time blocking, which is very important for my weekly uh routine. So, yeah, that's a that's a nice one to uh to explore as well. Fantastic.
SPEAKER_00Fantastic. So let's transition to what you build at Make. Your company's transformation and AI uh automation lead said this is one of the most impactful agents that you run at Make. So could you take me to the beginning? Well, first of all, what did you build? And then let's start with the story, how it all started, and what was the reason for building something like that?
SPEAKER_01Yeah, uh I'm pretty about pretty excited about what I've built because I started from zero. I I had no experience building it a couple months later before, but I started with a clear problem in mind. And what I arrived to is uh creating a system of product launch, I call it product launch agents. So it's a set of workflows and AI agents built in make using different uh different LLMs and AI tools as well, included, that really help me and our internal product marketing team to keep up with the pace of product launches being launched every week. So that's something that really is right now super important for our team because it all started with myself joining the company to help manage the load of product launches that are uh happening in make, but clearly with the pace going up and up faster and faster, uh we were looking for ways how to actually make it scalable and more sustainable and more realistic to achieve. So that's why uh I started building uh product launch agents.
SPEAKER_00So the problem defined in the problem terms, what was the definition at the beginning?
SPEAKER_01The problem, uh the problem was very clear. It was just that we have so many super cool, super exciting features being built and released, and our internal product team just couldn't keep up with it. And we got as as the scale grew bigger and bigger, we were more and more stuck. I mean, as product marketers, we were stuck in doing repetitive manual work again and again and losing time and losing to focus on more higher level strategic thinking. So that's um that's what was important and what was the the clear problem where we started this.
SPEAKER_00Okay, what did you think was the what what was the hard what you thought was the hardest thing uh before you started and where you write about the assumption?
SPEAKER_01Uh yeah, there were many challenges, I would say. One of them that I was um I was expecting, but it was um really interesting shift of how I think about product marketing, is actually that in order to create a system, create agents and a relationship between them to actually for them to start working properly. Of course, I I it's not just about connecting them in between. I creating AI slop for our product marketing. So the the absolute number one priority was we need to find a scalable way using make workflows and AI agents while maintaining the same level of quality. So in order to do that, I actually had to completely shift the way how I think about it, trying to reframe and capture the frameworks, the thinking. My best practice that I use day to day when I'm doing launches manually to create some kind of repeatable, repeatable frameworks or repeatable context and repeatable prompts for the system so they can do it themselves with the good enough quality. Of course, we are still involved for the review, and it's uh it's not completely fully automated. That was never meant that way. But yeah, that was a real challenge to take something that is integrally ingrained in my brain after many years of experience and actually putting it into paper, like to computer, and creating a clear structure that is yeah, that is the core to the approach.
SPEAKER_00So externalizing your best practices, you know, and your process so that it delivers and formalizing it so that it delivers quality. Yeah, I mentioned there was a LinkedIn post about this from from the leader, and they mentioned mentoring one-on-one. Did you reach out to anybody to help you build that or to guide your thinking into what is possible or how did the mentoring work?
SPEAKER_01Yes, that's that's actually one of the amazing things at Make is that uh at Make we have our uh internal team of uh automation and AI specialists. And Sarah, who posted this LinkedIn post, uh, she leads the team and she's doing a really amazing job. And as soon as I had this clear problem defined that I need to solve, that had a clear business benefit for our company, and especially not just for myself, but for the entire team, then it was just uh completely natural for us to connect and start our weekly mentoring session because where I was starting at with the process is that I have my product marketing and product launches deep experience. I can do it manually, but I was a beginner into how it actually how I can how I can rebuild it with AI and automation, while Sarah is expert in that. So I where I really leverage her help is actually finding the ways how to think about it in that systematic AI workflow agent way. So that was really nice because I was coming in with my manual expertise and she was opening my my mind with how it can be solved systematically.
SPEAKER_00How long did the whole thing last and how many iterations of the agents did you did you build before you said before you saw, well, actually, this is producing really good results, reliable quality.
SPEAKER_01I think what made it all much easier, and also it gave me absolute confidence that this is this is very much achievable, is that uh at Make and in other companies, myself and other PMMs, it's uh already an absolute common standard that we use AI chatbots when we are working on our uh on our product launches or on product marketing. So ChatGPT, Gemini, Cloud Chat. This is something that we use daily every time. And the only difference was that we still kept using it manually. So the switch was not about can AI actually help us if we if we instruct it properly, it was just about how can we reduce this manual, a manual process that doesn't that still involve AI, but in a manual way. But how can we actually automate it, especially the steps that are not adding any any unique value from a product marketing perspective? So um, so yeah, but of course, going from from there, I've had many iterations and I will have many more. And the reason is the reason is that just continuously improves. So similarly, I think that it becomes more and more useful when it's trying to work as close as possible to having a colleague. So let's say having a junior product marketer in our team that, of course, doesn't sleep, doesn't need a break, and just waits for your instructions. So that way, actually, similarly, as we as product marketers evolve and learn new things and adapt. The same way it needs to happen with this product launch agents as well. And of course, there is a never-ending backlog that I have with ideas of how how further it can improve and how many more actually manual work it can capture on our end. So continuous discovery and delivery process.
SPEAKER_00That's so cool. Have you measured any of the early indicators? Is there like this much time saved, or how do how did you see that this is so promising? What was the metric?
SPEAKER_01Yeah, I think right now we don't do any hard measuring because I I don't think it will it will change our approach because also we need to acknowledge the fact that AI models get improved almost every week, every month. So this is also us already getting tons of value from what we are building, but also we are building towards the future future where we know that it will only get better and better. Then also, of course, already now, especially with smaller launches that require that are totally fine with a more repetitive approach and it they don't need that much individual care compared to huge tier one tier one launches. For those, I already save at least 50% of time when working on it. Sometimes it just with some launches it even cuts the time that I need to spend on manually to uh just 20% of uh what I would normally do. But of course, it's fair to acknowledge that one thing is what I save when I actually work on the launch, but the other thing is time I'm spending on building and improving the agents. So we need to be completely honest in there. It's also something that we need to uh count in as well. Yeah, sometimes actually I spend more time working on launch because why while I'm doing that, I'm improving the agents with uh with the benefit of uh yes, when I find fine tune, we are able to grow and scale our team as well. But all of those people will be able to uh leverage the same agents and it will be just a scalable way.
SPEAKER_00Okay, so there is an investment in the beginning, but ultimately as the time moves on, um it's it's uh saving time. Something that caught my interest, Valerie, is it seems like the the driver was internal or internal bandwidth or capacity of the team. Was there in any way an external customer facing challenges or problems connected with with the product launches that were part of the initial assignment? Or was it purely the internal play to manage the ever-growing speed of innovation and new releases?
SPEAKER_01Yes, there were actually three key drivers for this. One is, as you said, internal motivation to increase our capacity, because we just in product marketing we knew we want to cover as much as uh product updates as possible. The external one was present uh as well, because our users and our customers are super excited about what's new and super excited about our product. So they just wanted to know, they wanted to be informed about what's new because they want to improve how they work, they want to adopt the latest best technologies. And of course, when we don't communicate it to them uh SP release, they just have no way how to find about it. So they just wanted to be more informed, and that that was the direct feedback that we received as well. Like you build so much, so much cool stuff, and you don't tell us. You call you tell us about just the biggest ones. Uh we want to learn about all of that. So that was also like clear input that uh drove the focus for us. And there was also the third, very deep my personal uh motivation that that I'm having still to this day, which was also the reason why I was excited to join Make, which was I've been doing uh product marketing already for some time, and uh what I'm really curious about now is actually what what is the part of my work and product marketing work that I do that is actually very repetitive and that is very easy to delegate to AI systems, and what is actually the what might be the rest of it that requires really my deep expertise, and this is something that is not really replaceable by AI, and that's something that uh makes most sense for me to to just nurture and focus on. And if not, yeah, if actually I'm I will find out that I'm not adding any value to the process anymore, then actually I would not be enjoying doing that, anyways. So that's that's my extra motivation that I put into that project as well.
SPEAKER_00What is what have you found out as as of as of April 2026?
SPEAKER_01Yeah, I I think that at least still, but I believe it will remain this way as long as our customers are people, not AI agents. The super unique value is and will remain this like human touch and personal feeling, because of course. AI can create you different kinds of copies, sales enablement, etc. Whatever you want, whatever framework you want it to follow, etc. But still, at the end of the day, I and we in product marketing are always the ones that have this have this sense for it. What is right, what is wrong. Hey, right now we need to take a different approach, or right now we need to communicate it completely different way. This like those subtle things that right now it's very hard to codify and formalize into something, some generic rule, but really this this feeling for the situation, for the customer, for what is the sentiment, that's that's something that remains super important. And yeah, on the other hand, what I've already proven myself to be working perfectly is those manual repetitive tasks that are just repeating themselves. That's actually that's perfect place right now for for the system and for the agents, and it's actually like making my work so far more and more enjoyable. So that's really nice.
SPEAKER_00What was the one assumption that uh that was challenged in the in this process of building those agents? Something that you didn't think at the beginning, and that really forced you to re-evaluate or or kind of go back and think. Actually, it's different than I thought.
SPEAKER_01One of those things is where again mentoring can make from the people super experienced in automation AI space that I didn't realize at all is actually when our end goal is that we want this system to be working for our company and for our entire team, not just for myself personally, start building it for that purpose from day one because I have experienced that what I thought that I will build it for myself, because that was just a natural tendency of mine. I will build it for myself, and then I will just teach on a personal level, I will teach other product marketers to use those agents or maybe to replicate those agents to work for them. So everyone would have their own product marketing launch agent personal. But instead, but instead, what actually showed to be uh much a better approach when when working in a larger team is from day one build them as uh not one-on-one uh product launch assistants, but really a team-wide uh uh team-wide colleagues, let's let's call it like that. And uh that really shifted the approach of how how I actually need to build it, but at the same time, it really improved uh the way it can perform and help uh scale our product.
SPEAKER_00Talking about team, do you have to win the people over? Or what was the what was the biggest challenge there in the team approach and and thinking for the not for the whole team or the whole company from the get-go?
SPEAKER_01Yeah, this this topic specifically is very easy at make because we are trying to push, we are trying to really push boundaries on how much we leverage the latest technology and also our own product to help us be more efficient, faster, but with the same quality. So that was uh completely natural within our team. It's just and I wouldn't worry about this being issue with any team actually, that hey, I don't want to use it. You just need to prove and show that it's actually working and it's actually saving you time and hustle of what you do usually. So it's not about no, I don't believe this is yes, of course, we are right now last year, it might have been different, but right now the the technology is so good that no one really questions whether it can provide some great results. It's just a matter of yes, show me how it works for you if I see the results. I'm the first one in the road to start using it as well.
SPEAKER_00Yeah, it just dawned on me that that make is the automation first company, so there will probably not be a resistance for no no no, we don't want to automate things. What translated to a go-to-market team at a SaaS company. Nowadays everybody needs to think AI, anyways, but um they're not necessarily automation first. So, what would be your recommendation to companies which might face some resistance because, for example, they're dealing with sensitive data or repeatability and reliability every time is critical. What would be your advice based on your experience with building these agents?
SPEAKER_01My first advice is, and this is even at make, even the in the automation first company, it's always about whether you have a clear plot problem that you want to solve in a more effective way, or maybe, if not, if you are happy with the way that are at the moment, there is no need to change it. And that's applicable for every company, every team, every kind of state that are in of maturity and using AI and automation. It's about hey, if this is large and important problem for you enough that you see it might worth automating, and we understand the problem or the process enough that we are ready to actually start exploring how to automate it, then it's then you are already there. Because then it's just a matter of implementation and building it, and already, for example, uh with make as well or uh other solutions, you can of course build systems that are very deterministic, very reliable, very repeatable every time. But you need to just be really convinced and indicated that this is something that you need to solve. I think it doesn't make especially for important parts of your business, it doesn't make that much a difference if you just explore it casually and don't take it seriously. Then maybe you are not ready yet. But if you are solving a real problem, a lot of them are solvable now.
SPEAKER_00Would you say you have to have a process ready uh to automate, or just having a clear definition of problem is enough to get started?
SPEAKER_01It's definitely enough to get started. Then it depends, of course, on what kind of problem is that. If it's something that is repeatable, so in that moment you would like to use not only AI but also automation, so it it goes on repeat. Uh, then if you actually do it implicitly and then you don't really know explicitly what the process looks like, of course, you start with problem, and then the first thing before you build any automation, you need to actually map out what is the process. So that's that's the important part. Uh, but yeah, always you start with the problem and uh you go from there. And that's that's actually completely natural, and it's also different in different industries or uh different companies that in some cases, like in my case, it was very easy to take a pause for longer to really understand hey, we do it, it works naturally, but we actually it was not designed as a process, it was it's just human-led, and we right now need to look on the inside and understand how it actually works, what the process looks like. But you always start start with the problem. Okay.
SPEAKER_00Well, my understanding is that sometimes, and that comes with experience and the number of years in the business, sometimes you know what kind of problems you are solving and what is the solution, it just repeats. So then the automation is kind of it offers itself and you get really excited with all the tools that exist nowadays. But what about a new person on the market or an unknown problem where you don't have that experience or you don't really know how to do it well if I'm familiar with my question? Do you see do you see like putting like these tools putting new entrants to the job market as being at a disadvantage, or is that a false fear, and actually it's a question of really looking at the data and doing the steps and the tools can help you figure it out faster.
SPEAKER_01I think it heavily depends on the specifics of the role and what you are trying to achieve and what kind of problem you are solving, or what kind of job are you trying to perform. But of course, there's right now a huge advantage of also starting fresh and not being burdened with how it has been done in the past because right now, of course, everything is changing very fast, and we will see whether customers and customer preferences will be changing faster and faster as well. So, actually, it's totally a problem or some kind of task or some kind of challenge for them to solve with their fresh mind and those new AI-native tools, they can approach it very differently and achieve even better results. So, again, if it's just if you start, I have no experience, but I know that let's say I need to introduce this new product that we've built. We have a new startup, we need to promote it, we need to get our early customers in. I've never done it, but I have a bunch of super great uh tools that are newly available. There is there is a chance, there is a high chance that you will find a way actually how to leverage them in a very unique different way that will help you achieve the goal. Okay, okay. But again, again, I would say that in that moment, the this will not be about this will not be a process automation that much. So it's not just you are a you have a process and you are trying to make it more efficient, it's about being able to do some type of tasks or work or approach it differently because you can automate some stuff or you can use AI agents or AI to help you.
SPEAKER_00So let's say you are not super clear what you are doing, but you have a cool Did you notice you approach your personal life slightly differently with this experience of automating and and and removing the manual no-added value tasks?
SPEAKER_01Yes, yes. Definitely it has shifted the way how I see things uh even in personal life, and definitely there is no way coming back because as I'm review removing some of the repetitive manual tasks at work that I've just never enjoyed, what I really like is that hey, the agent does the work based on my instructions, and then I will just review and say, hey, do this and that, and it works on its own. Uh, of course, work or work tasks or situations like this happen in a personal life, so I need to uh do some like fix some errands or etc. And it's something that I just don't enjoy, it's manual, I don't add any value in there. I just wish that I have agents for that as well, or some kind of automation for that as well. So of course I have my personal tasks and personal calendar, etc., and those kind of personal product level. I have my automations as well, so I don't need to, I don't know, go through my inbox and find actually what is pending, etc. So it's creeping in there very much as well. And you bet that I always think that hey, I just finished finished work today. I've automated this kind of process that before I had to go and do manually, right now it's just uh set and done. And now the day uh the day is over and I go to wash dishes, and I feel like hey uh AI is creeping into what I do as a product marketer, but can it help me with doing some kind of manual stuff uh at home as well? Let's see, I know that of course there are heavy advancements in robotics, but it will take much more time. Yeah, there are still parts of a personal life that uh, of course, are manual, are meant to stay that way and are the essence of uh the life, I mean family time, etc. Of course. But yeah, there are a couple of things like washing dishes that I would gladly delegate to someone else.
SPEAKER_00I mean, Valerie, what is what is kind of one or two books that you would recommend to everybody to understand a unique viewpoint of a product marketing manager?
SPEAKER_01April Dunforth, who is a legendary product marketer specialized in positioning. So her book, obviously awesome, is just a super great read. And yeah, it's it's really from the product marketing perspective, and which sometimes can be perceived differently because you have also brand positioning, etc. It's really great, and that's the kind of perspective also that is very important for us product marketers to actually build, because it's also very common that product marketing gets easily misunderstood. You feel like product marketing is just you have a product and you slap some marketing at it. So you are doing LinkedIn posts about your product. But what this book is doing great is actually going into deep details of how much strategic thinking is actually behind real product marketing and how much actually how close it is to product strategy and value proposition and how you win the customer and go-to-market strategy. So that's that's the part of product marketing that I do and enjoy the most. Yeah, I think that of course I will stay busy with uh with the product launch agents because there is so there are so many ways how to further improve them. What excites me but scares me at the same time sometimes is just the pace of innovation, just the fact that Anthropic this year is is on fire and keep releasing a new groundbreaking feature almost every week. So what is a bit uh sometimes overwhelming and scary to me while I'm just trying to get and try hands-on these features as soon as they are released. It also sometimes makes me think it, hey, like almost every month I question what I've built already. So hey, maybe this is already an old, obsolete way how to do it. Maybe I need to completely rebuild it from scratch using this new approach and new type of agents that is uh newly available, etc. What scares me a bit is this not always clarity on maybe I'm building a legacy system already because the technology has moved on. Maybe not even technology but the mindset, and maybe I'm doing it still too much the old way. But at the same time, when I there's also even potentially a bigger risk that I might be trying to move to newer and newer technology while actually I will be move moving further and further away from solving the problem. Because maybe the problem is already solved and uh like there is no reason to panic about it. So that's that's a bit scary about understanding when to move on the next thing or when to completely pivot and exciting at the same time. For some reason, if it doesn't feel right for you, if it doesn't click, you feel like you need to push hard yourself to do it because everyone is doing it and you you are not in that space yet. I think that right now, today, I would say that's actually not that big bigger deal. Don't worry. Because everyone is pushing the narrative of if you you are falling behind, you need to be to keep up with what's new, otherwise you are falling behind. But already, like this year, we've seen that the technology is evolving so fast, is that with the new approach, you actually might not even need uh to learn how the previous approach worked. So you don't need to worry.
SPEAKER_00That's a wrap on today's episode. I'm Pavel Novak, and this was Built in Emea. Go to Market Operator Stories. If something landed, share it with one person who's building right now. No, thank you. See you next time.