Bits25 I Andrey Khusid (Founder & CEO, Miro): Team x AI I Inspiration Stage

Yeah, I mean, hello everyone. It's great to be here again this year. So thank you for having me. For me personally, a human-AI collaboration or team-AI collaboration means that it's not anymore where AI and individuals are working on the side on topics. It's more about where AI is a part of that team collaboration, is a part of the decision-making. And over the last three years, we had this amazing moment with AI where everyone has their personal assistance, correct? And now we believe there is an opportunity to bring AI into the teamwork and accelerate the teamwork and accelerate how cross-team collaboration is happening. So that's a major shift from what we've been used to in the last few years.

Yeah, I mean, we started our journey with this simple idea of bringing the whiteboard into a browser. At that time, we just were thinking about a very simple thing: how people can have those collaborative moments similar to what they would have offline, in the browser. But since then, it's evolved significantly. In 2017, we introduced this visual collaboration category that became a major thing for a lot of companies, where it's not just a whiteboard, it's the way how people communicate and collaborate with each other. And last year, we announced Innovation Workspace, the place where you not just kind of ideate, brainstorm, but where you take the project from the very beginning of the discovery phase, create the solution, and then move it to the delivery phase. Now this year, we believe there is a major, major shift that can happen with what we've called originally whiteboard, but now it's a canvas. Canvas is one of the best mediums for AI, from my perspective, because everything that we use now with AI is in chats, in this kind of conversational formats. But the power of AI is actually to explore different edges of solutions. The power of AI is to see different kind of what-if scenarios, correct? And we believe that canvas is best used for that. And that's what we are building and shipping in a couple weeks from now: this kind of exploration canvas where humans and AI collaborate. So that's been quite a journey from just a simple whiteboard to the place where humans and AI can co-create together.

Yeah, we started our internal AI transformation journey just a year and a half ago. So we were not the first to rethink our approach, and we're still kind of on the journey. We still have a lot to do. But there have been a lot of great use cases that we internally discovered, both with Miro as well as bringing in external technology to Miro. So number one thing that I believe is critical on this journey is to ensure that people have all the right tools to do their job, because if people don't have access to tools or it's shadow IT, it will not accelerate as much use-case discovery as possible. So that's kind of number one: bring in the best tools and allow people to explore those tools. Now, not everyone would do it immediately. So you need to figure out those champions, those centers of excellence where people will start using best-in-class tools and figure out those initial use cases, and then you can amplify those use cases. One of the examples that I'm quite proud of what the team have built is routing the bugs and UX issues. So before, when customers would send us feedback like with a screenshot or just explain the problem statement, or internal teams would find some bug or UX issue, it would be a very manual process. People would take that screenshot, try and select to find who is in charge of that, like what team is in charge of that. And we have like more than 500 engineers and 700 people in product engineering. So it's actually quite hard. So what the team built is the routing mechanism where whatever is submitted can be easily identified through the code, through other areas, through other kind of information, who this bug or UX issue can be routed to, and it immediately finds the owner. So this is one of those use cases that we see is quite powerful, and we see more of those use cases that are adopted across the business now with AI that significantly accelerates our innovation velocity.

I mean, in general, it's a major behavioral shift. I think the number one challenge with AI is that we all need to learn new behaviors, and I think that's the biggest challenge to overcome for any organization, because we as humans are comfortable where we are, and we need to push ourselves out of the comfort zone. No one can push us out of the comfort zone. We need to kind of accept that there will be new ways of doing things, and almost every job of a knowledge worker can be rethought. And that's the same within our company, with myself, with everyone. It's about zooming out and thinking about, okay, how can I do this job differently from how I was doing it before? And I'm trying to ask myself all the time, like when I try to do something, how I can break the inertia of me doing it the same way as I would do it a couple years ago, in a new way. So I think that's number one challenge that we all have to overcome, and the best way is to lead by example. So for example, now I'm trying as much as possible in the meetings where we are doing creative problem-solving or we're thinking about some business processes that we have to reinvent for the team, for the company, I'm trying to think together with the team how it can be all done. So by bringing that into the context of the conversation, it's critical, because otherwise people would not kind of think about that in the first place.

Yeah, like, I mean, with leadership, it's like what we believe in. After speaking with a bunch of companies out there who are even more advanced than we are on the journey, but also looking at our internal experience, the reality is that AI transformation has to happen quite centrally, has to happen quite intentionally. So yes, there is this opportunity to give all the tooling to the team. So we see what are those best use cases that can come bottom-up and increase individual and team-level productivity. But with AI, you have to zoom out and rethink some operational processes in the company, some critical business processes, and that can't happen on the individual or team level. So yes, it starts with the leadership, and the leadership should own the transformation of the business processes and how the work is done in their respective areas. So that's what we are working with with a broader leadership team in Miro. Now, that's kind of where the KPIs could be and where kind of critical actions should happen. But for a broader organization, I think it's more about inspiration and showing the art of possible. It's less about KPIs. What we are exploring is AI adoption kind of maturity metrics, where like your basic experience is where you can go and retrieve information and think together with AI, but you can evolve your skills towards like something where you manage the group of agents or you build the group of agents yourself. So that's how we think about the evolution of individual development. But yeah, it's more about showing the path for people rather than KPIs at scale.