Miro CIO Tomás Dostal Freire On Reclaiming Creative Time With AI

[00:00:04] How much of your working week is actually spent doing your work, the work that you were hired to do? New research for Miro suggests that for every single hour of creative, high-impact thinking, three more hours are actually lost to meetings, email, admin, and the kind of maintenance work that quietly drains the energy from you and slows down decision-making. Sound familiar?

[00:00:31] Well, I think we've all been there. Well, today's guest, he is the CIO at Miro, and he's going to unpack why that gap is growing, why it has become a board-level issue right now, and also how AI can help teams reclaim their time without adding yet another layer of noise. So we will explore the hidden cost of context switching, the shift from individual AI to truly collaborative intelligence,

[00:00:59] and also why the next talent war will be won by the companies that remove friction from the employee experience. And yeah, we're going to give you some practical ideas today that you can test immediately to protect creative time, speed up momentum, and build on what my guest calls a shared context for better decisions. So if you're interested in trying to turn AI ambition into

[00:01:24] measurable productivity, hopefully today's conversation will give you a clear and timely reality check. But enough for me, let's get my guest on now. We'll talk about all this and much more. So a massive warm welcome to the show. Can you tell everyone listening a little about who you are and what you do? Yeah, sure. So I'm Tomás Dostal Freire, originally from Argentina. I've been with Miro for about

[00:01:51] three years now. I'm the CIO, and also I'm leading our business transformation, as we call it, business acceleration. So we're moving beyond thinking of AI implementation and going towards how does this huge thing called AI can help us accelerate how we work. I've been working with Miro for about three years. And before that, I was at Booking.com, Netflix, Google, a few trailblazing companies. So now I'm having fun at Miro.

[00:02:21] Love it. And on the topic of speeding up productivity in work, there's so many misconceptions out there. A lot of people say that AI will remove a lot of the bottlenecks that we have inside the workplace, help us all work quicker. But you have new research showing that for every hour of creative work, people lose three hours due to things like meetings, email, admin paperwork, all that kind of stuff that we've all felt in the workplace. So when you first saw that data,

[00:02:51] what stood out to you most there? And why should leaders be taking this seriously now? Yeah, it was striking. I mean, we all know that we have to do, as we call it, boring stuff. But the ratio is just wrong. When talking to leaders about this, many also see this in their organizations. I believe that there are a few factors why they should really care about this.

[00:03:18] The first one is, of course, speed of decision making, like that velocity and momentum that you build when it comes to getting teams to work together and make decisions faster. But the second one is, I really believe that not very long from now, and I already started getting some candidates asking these questions. Candidates are going to choose employers based on how much admin work they need to do internally. The staff that they know that could be automated or AI could

[00:03:47] help, they will choose companies that reduce that for them because they are not hired to be taking down notes in a meeting or sending up a follow-up agenda. They're actually hired for their creative work, right? For what they studied, what they do best. So I think that there's a twofold, let's say, imperative to leaders. First one is related to speed of decision making and velocity of the business.

[00:04:11] And the second one, for sure, is related to employee well-being and employee satisfaction. And if we look at every tech trend, I think every technology has promised to allow us or empower us all to do more and do it faster. But we don't seem to have any more hours in our day. And many people feel they're actually working longer hours while achieving less. So that means frustration and

[00:04:38] burnout quickly become part of daily life. And I'm sure there'll be people listening around the world nodding in agreement there. So from your perspective, what is the hidden behaviors or habits inside teams that are quietly draining energy without anyone noticing? Yeah, I guess one of the biggest one is context switching. So something that now, let's say, our whole operating system is a browser and you have about 30 tabs open at any given point in time.

[00:05:06] And you actually need to do what systems should do best, which is to connect the dots across different platforms, right? So context switching is a huge cost. Moving between the screen, let's say, or the computer and the meetings in person and then going back and trying to collect all the context that has been shared in different places to make a unified decision is huge. The second one, I think,

[00:05:30] on top of context switching is the duplication of work. So a lot of teams feel they are busy doing something that they later realized somebody else was doing. And that has to do with the lack of, again, shared context or shared unified playground for people to make decisions. So I think that's the second huge one. And I think the third one is when we think about individuals, they are maximizing their

[00:05:58] individual productivity. So when we think about summarize this document for me or help me have a first point of view about the topic, that's what we call a single player moment with AI. And that's one-to-one, right? So you go to GPTs of this world, you type in a question or a prompt, you get an answer, then you copy that answer, paste it somewhere else. People don't know where you got that from, but they see the response and then they ask more questions and it's just tiring. It's exhausting.

[00:06:28] So the movement from this single player type of approach to artificial intelligence and collaboration to multiplayer where everyone shares the common context and that context becomes common for people to make decisions together is critical to move forward. And a large share of workers, they also described something called maintenance work as the biggest drain on their motivation during the working week. And we are recording this on a Monday where many of that drain will be felt in

[00:06:58] offices all around the world. So I'm curious though, how do you define maintenance work? And how can teams tell the difference between that work that truly matters, delivers value and work that just simply keeps those systems ticking over? Yeah, I think you just nailed it. Maintenance work is anything that sustains the systems, that sustains the way of operating without necessarily moving the

[00:07:22] business forward. So from status updates to formatting documents, to logging tasks across platforms, to checking dependencies, all that type of noise that you have that is part of your day to day. Arguably, when you're working on this, it feels productive because you feel you're doing something and therefore you're spending many hours on certain activities. But when it comes to the real impacts on decisions, it's not necessarily so directly correlated.

[00:07:48] So what we see is a growing frustration in, I am this year, I'm doing more, I feel I'm doing more things, but ultimately the outcomes are not necessarily as fast as the input that I'm putting in. So we need to rethink a bit of a few things. One is not to throw AI on top of what we do today, but actually take a moment to rethink what is it that we should be doing as humans? What is it that we could automate? And AI is already ready to help us with this.

[00:08:17] There's a very simple quadrant that we always use, let's say, as a design principle, which is complexity of the activity versus the impact of having a human involved in the decision making. So if it's a high complexity and the impact of having a human involved is high, you keep AI as your co-pilot, so to speak. But if the impact of a human decision making is low, and the complexity of the task is high, you start looking into ways to automate. So redefining and

[00:08:46] rethinking through what AI does, what is co-built with AI, and what the human remains as the center of the action is critical for this. And your research also suggests that outdated tools and scattered systems, these are things that are also making the problems much worse. So in practical terms, what does misalignment look like inside modern teams? And how does it show up in the day-to-day collaboration? I'm sure you've got many stories that you've come across there, but what does it look like in the average office?

[00:09:16] Yeah, I would say it all boils down to shared context. You know, it's just that. So there is this constant, and I think many will relate, this constant element of figuring out what's the latest version of this document. Where was the context that I needed to actually make this presentation? And we are not aware of how much context is vital to decision making. If I think of a marketing campaign,

[00:09:43] they do need to get the financials, and they need to figure out what's the latest version of the Google sheet or a spreadsheet that has the latest numbers. They also need to look at the numbers for sales. Sales needs to get information from marketing to position the product in front of the customers, and I can continue for hours. The problem of that lack of centralized context for faster decision making is critical. What this results in is a lot of confusion, a lot of delays,

[00:10:09] and a lot of frustration because I thought that I was using the latest version and it wasn't, or the latest file and it wasn't. And it really stifles innovation. It slows you down in, again, what you were hired to do, which is to be creative, to be the human that makes decisions and speeds forward the business. And I am a solutions, not problems kind of guy. I don't want to be doom and gloom here. And there's a flip side to everything we're talking about, and that is

[00:10:35] that many employees are increasingly optimistic about how AI can help them with decisions, reporting, and ultimately finding information faster. And anyone that's ever tried to find a document that they filed away somewhere six months ago will know what that pain can be like. Based on what you see at Miro here, where does AI genuinely save time and where should teams be more cautious about their expectations? Where is it working? Where is it not?

[00:11:04] I would say that where we already see significant impact of AI is real-time savings for day-to-day admin work. So if we think of summarizing meetings, clustering feedback, if we want to draft content, so how to, going away from that paralysis that you get of the blank page, where you need to build something, you need to create something, but you have a blank page in front of you, having what we call this first bad version that you can iterate and improve. Those things are really speeding up

[00:11:33] creation. What we see is that the areas where AI requires the lesser amount of context is where you're more likely to automate and simplify. For example, taking a summary and next steps and actions of a particular meeting, the context is the recording of the conversation, then that's straightforward and something that you can bring AI to the loop. However, where companies are still struggling and

[00:11:58] we're all struggling to be honest, is where you need that shared context layer. So what I mean by this is when the context goes from single player, which is me and my meeting, to company knowledge, for example, that's where it's a massive complexity because AI is going to be as good as the context that you give it, right? So if it doesn't have enough information to make a decision, it will make

[00:12:23] a decision biased on the information that it has. So this is where we are trying, the way we're trying to approach this is the more we bring to the same context layer, in this case for us is the canvas, the more you bring into the same space, the more shared intelligence you have and shared context you have, then every decision or AI action that is performed is out of the shared context that everyone in the room has. Then it becomes a lot smoother for decision making as well and for velocity of AI.

[00:12:51] And listening to you here, embedding AI into teamwork every day sounds incredibly promising, but it could also create a lot of noise if it's done the wrong way. So to give people listening valuable takeaways, what principles should leaders listening be following to make sure AI supports creative work rather than just adding another layer of complexity? What should they be doing here? Where should they start?

[00:13:16] Yeah, that's a great question. I think the first thing would be to make sure that AI is embedded where work actually happens. So if we are talking about simplifying context switching and we add more tabs, so to speak, of AI and more fragmentation of the brain into more and more and more things, it's just going to make the current problem we're trying to solve worse. So making sure that the AI

[00:13:41] comes in where you work is the first one. Then give teams control over like freedom within a framework. So what we want to make sure is that we don't want to be prescriptive 100% on how to do things, because again, we hire humans to think with us, right? However, we want to make sure that there's a controlled environment or a controlled playground or a framework where people can actually have that freedom of creativity, of decision making or whatnot. So I would say the first one is bring it where

[00:14:10] people make decisions. Don't create another, let's say, fragmented or siloed AI specific solution. The second one is give that within a framework and give freedom for people to think through. And I think the third one is focus on reducing the noise. So what we were talking about, the busy work, like if you're able to, again, you assuming that you're hiring the best talent for making business decisions for whatever area of the business it is, what you want to make sure is

[00:14:40] that they have as much time as possible for what you hired them for. So if it is a salesperson, you want them to for sure spend as much time as possible with prospects and customers. So get that off their plate, the busy work off their plate, so they can spend more time for what you hired them for. So again, AI should ultimately make our creative work, our uniquely human work, a lot more open and flexible and should just remove the stuff that is already boring for us to do

[00:15:08] and that does not really value. Fantastic advice. And if I was to ask you to draw on your own personal journey at Miro, can you share maybe a lesson that you've learned along the way about how you maybe help teams reclaim time and focus even before introducing any advanced technology? Because very often, it's not even about the latest shiny tech tool. It can be a culture change, but anything you can share around your own journey here?

[00:15:37] Yeah, a hundred percent. I think it's good that you mentioned this because what we are doing at Miro is preparing employees or focusing on the transferable skills that are required, not particularly on the current latest technology. And the reason why this is essential is because right now it's AI. It could be that in six, 12 months, we have AGI and then in 18 months, we have something else. So it's more about understanding how do you bank on your uniquely

[00:16:06] strong strengths, such as cognitive agility, ability to really think first design principles for the problems that you have. Therefore, you understand who does what versus AI automation and human. I think in our case, from very early, we gave that, let's say, freedom within a framework, which actually worked well. So we had a full year of exploration as a company. We allowed people

[00:16:31] to play, let's say, play and not so much, but to get comfortable with AI without a particular productivity objective in mind. Because what we thought was, okay, here we are redefining how people think, and then we will go towards particular business outcomes. In my journey in particular, I'm just playing with every new shiny new tool that comes out, but I don't play with it from the angle of, let me learn that technology in particular, but rather, what could this technology do

[00:16:59] that I hate doing? That's kind of the question that we keep asking our employees as well. So if you were to hire an intern for you, regardless of the level you are in the organization, but you have a graduate or an intern that is helping you with certain activities and you outsource some of these activities to them, what would those activities be? Because they are repetitive, because they don't need as much context as you have, because they don't need as much seniority as

[00:17:24] you do. All those things are ripe for AI to come in and play. So that's a bit of the thinking that we have now, which is we're not implementing a technology, we're actually accelerating the business and rethinking how we work, which is a much more of a longer term bet as well for us. I absolutely love that. And I always urge anyone listening that feel overwhelmed by some of the technologies and the many, many different tools out there, it's just have a play with them,

[00:17:50] get used to using them, what works for you, what doesn't. So completely agree with you there. And for people listening that do feel somewhat stuck in a cycle of meetings and admin and email and not being able to do the work because they're pulled in so many different directions, what is one change that you think they could test this month to protect creative time and help their teams feel more energized about their work again? It would be great if everyone listening had that

[00:18:17] takeaway, went away, tried it for a month and see what they could improve. But what would you recommend there? I think the first question is what is something that you do every single day that you hate doing and a 23 year old fresh from uni could do? Ask yourself that, make a list. You hate doing it, someone with little contacts and no work experience could do it for you. From there,

[00:18:44] I would like you naturally go towards usually some people get like they don't like, let's say, reviewing emails and categorizing them to understand which are the activities that need to actually perform versus spam. Some others enjoy doing that, but they are not very good at drafting emails. Some others are good at, for example, going through Slack and the conversations, but some others really need to summarize that for them because it's overwhelming amount of messages. What is that one task?

[00:19:12] And start with just one. The highest, if you think of highest that in terms of that bores you or demotivates you or de-energizes you versus highest probability of a 23 year old to be able to do it well, I would start there. And then I would play it backwards from, okay, what is the outcome that this, that I need to achieve with this? A clean inbox or cut prioritized emails for me to respond on a

[00:19:38] timely fashion, whatever it is. Then from there, start working towards what are platforms that could help me with this? Whether it's a platform like Zapier, for example, that is this, can start as simple as if this, then that type of automation and can be as simple as working on a canvas for the process to something more complex, which could be building an agent for yourself, depending on where you stand in that journey of understanding of the technology. But again, guiding question is,

[00:20:03] do something for you selfishly, something that you hate doing a 23 year old could do. AI can most certainly have a very first take on it and assume that you're not going to go from three hours spent during this to zero overnight. It's very likely that it's going to be a journey. So you will need to have that patience of refinement, which is critical. Until now, everyone has been talking about prompt engineering, but one of the critical things going forward is going to be

[00:20:30] refinement because in refinement, you are actually giving more context that you maybe forgot or just did not give at the beginning in terms of how exactly you want the AI or the automation or the agent to do it. So assume that in the first couple of days, you're going to spend more time than you usually spend on something that you hate, but it's a short term pain for a long term gain. I absolutely love that. And of course, Miro is the AI innovation workspace and you help bring teams

[00:20:58] and AI together to do things like plan, co-create and build that next big thing faster. It's something that I know you're incredibly passionate about. You've got a huge following online. You also serve, I think, something like more than 100 million users across 250,000 customers. It's incredible what you do, but for people listening and hearing about you guys for the first time and how you might, may be able to help about some of the things we're talking about today, tell me more about what you

[00:21:26] do, how you help and, and also how people listening can find out more information on this stuff. Yeah, for sure. So it's, uh, it's interesting because Miro has, uh, and this is probably the main reason I joined, uh, I left Netflix to join a Miro. Uh, it's defining a category every single time. We are defining how people collaborate. And now it's that extension of human collaboration to human with AI collaboration. So some of you may have heard about Miro starting as a whiteboard, an online whiteboard

[00:21:55] that exploded, has one of the highest growth rates in history of companies during COVID. Um, but it's, we are at some point we were reflecting how people collaborated, which was okay, a whiteboard, then we digitize it and we bring it on, uh, to people. But now we are actually defining ways of working going forward. So if we think of, uh, of the platform now, we, we call it the AI innovation workspace, very simply put, imagine a blank canvas that is infinite, meaning that you can just go

[00:22:24] across the canvas wherever you want and put information. All teams can work there within their existing artifacts. So you can have a Google doc, you can have a Microsoft sheet, you can have anything that you want where you actually work. So connecting back to what I was mentioning, bring AI to where people work, the finance will still have their sheet and marketing will still have their presentations. But the difference is you are bringing everything to a shared context layer or shared context. Um, and then the decisions become a lot easier because whenever you introduce

[00:22:53] AI to the room, to the table, AI has all the context that everyone has in the room. So very simply put, if we imagine that you're working constantly in a meeting room physically with people, right? And all the information that has been discussed is, is being recorded in that, in that shared context meeting room. Then whenever you ask AI a question, it's going to have all the information as context, then the decision is going to be much faster. Then you can still have this moment of individual work

[00:23:18] where you can zoom into a particular, uh, document and work in a one-to-one single player, but ultimately everything contributes to that shared intelligence and shared knowledge. So Miro is breaching that gap between the single player mode, which is individuals with computers or individuals with AI to a multiplayer mode where you have multiple individuals at the same time working on different things. And AI as a context aggregator that, that helps with decision-making. Um, so it's a massive change, uh, in, in how people work. What we're doing now is indeed,

[00:23:49] we, we have, uh, if you go to Miro.com slash research, you can find the latest Forrester, uh, study, which talks a lot about what you just, uh, uh, mentioned about the busy work and whatnot, but there's also very practical applications of how you can get rid of that busy work, uh, leveraging Miro. And we have some templates and what we call blueprints that you can just go and use for free right away. Awesome. Well, I will have links to everything you mentioned there in the show notes to this.

[00:24:15] And anybody listening, if they head over to techtalksnetwork.com, there'll be a blog post associated to this episode. And I will look for a good, good quality video, uh, of, uh, Miro in action there. So people can understand how it works and how they might be able to use it as well, but more than anything, just thank you for bringing all this to life today and hopefully giving a lot of people listening, some valuable takeaways. Really appreciate your time today. Yeah, my pleasure. And something I checked, uh, before joining, uh, I know you have a Spotify

[00:24:44] playlist as well, which I found very cool. Yes. Are you going to add a song to that list for us? What would you add? I was ready for it. I was waiting for the question. So, uh, so yeah, so I'm not sure if it's there. I haven't checked all the songs because you have a few, but I would say that the one that represents this has come together, uh, by the Beatles. Uh, I, I do think it's, uh, it's the time to come together. So these past two years of being in front of one computer by yourself is, are, it's just gone. What you have now is just to come together and make something faster,

[00:25:13] uh, create something great, uh, together. Oh, what a great choice. Oh, that will be added straight to the Spotify playlist. It's probably going to be playing in my head for the rest of the day now. It's one of those songs that when you mention it, you can just not take it away. Yeah. It's amazing. Love it. Well, I'll get that added to the Spotify playlist, uh, add links to everything for people to find out more information about you and your, your work there. But again, thanks for joining me today. No worries. My pleasure. Thank you very much.

[00:25:42] I think one of the things that stood out for me in today's conversation is that productivity challenges are less about working harder and more about working in an environment that allows people to think, create and decide together. And the organizations that can solve that context problem, reduce maintenance work and embed AI where work already happens. I think they'll be the ones that

[00:26:07] move faster and will feel very different to work in. And my guest shared a simple, but powerful starting point. You need to identify the tasks that you do every day to add little value and think about how they could be automated, delegated, or just redesigned. It's a small exercise, but one that could unlock hours of creative capacity across a team. So you'll find links to their research and

[00:26:36] practical frameworks we discussed in the show notes. And if today's episode maybe sparked a few ideas about how you're rethinking collaboration, productivity, or AI in your own organization, please share it with a colleague who's facing the same challenges that we're talking about. It's got to be just about everyone. Join the conversation. Let's see what we can work out together. And if you've got any questions for me, head over to techtalksnetwork.com. But that is it for

[00:27:04] today. So thank you for listening as always. And I'll speak with you all again tomorrow morning. Bye for now.