Global Collaboration and AI-Enhanced Solutions with Molly Sands at Atlassian Team ’24

This is Techron tv. Hi everybody. Welcome back to, uh, Atlassian, team 24 here in Las Vegas, Nevada.

I am, uh, joined by Molly Sands, who is doing research on work Anywhere. Tell, tell the right title 'cause I don't want to get it wrong. Team anywhere.

Team anywhere. Okay. Well, we're gonna dive for sure into that.

So tell tell folks about who you are, the kind of work that you do. Yeah, absolutely. So I'm Molly Sands and I'm a behavioral scientist by training.

I lead our team Anywhere lab, which is our future of work research group at Atlassian. And we study how teams can collaborate better across the world and using digital tools and our products. Wow, I can't imagine every company has a lab and, and people have a team like you doing this.

How does that inform Atlassian? Yeah, I have a very unique job. Every time I say I am the head of the team, anywhere, lab people say, what's the Team?

Does that mean the lab's anywhere or the team is, or what, what is it? Right. It's true.

It's all, yeah. The team is anywhere. Um, yeah.

So what we are really focused on is trying to design solutions for teams to collaborate better. We're in a new age of work in a lot of ways, and teams are more global. They're more distributed, they're spending more time online.

They're more distracted than ever before. And there's a lot of research about those challenges and I think we all feel them in our day-to-day jobs too. But there's not a lot of research about how do we actually solve for better collaboration and for helping people do deeper, more meaningful work.

And so we are driving a lot of work at Atlassian to try to understand how people can work together, what kinds of work practices or ways of working really help teams stay connected and effective. Mm-Hmm. And we use that to think about how do we make work amazing for Atlassian so we can continue to innovate and build fantastic products and solutions for our customers, but also how are teams working across the world?

What does that look like for our customers? We look at their data to better understand that. And we also do a lot of industry research to understand teamwork and how it's evolving to make sure that our tools are meeting the needs of today and tomorrow's workers.

Interesting. Not everyone has an opportunity to work with or, or, um, even meet sometimes people who are really truly in research. And there's definitely a different mindset.

Um, you you approach that behavioral science being one dimension of that, but also just being a researcher. Describe to folks what is that, what is that mindset that a researcher has that maybe others don't take that same quite same, that same approach? Yeah.

I think part of it is just being really curious about how stuff works and wanting to get to the bottom of it. That's what drew me to research early in my career is that why, why, why is it, why is it like this? But I think also this desire, and I see this in my team so strongly to come up with new solutions, we really try to focus on what actually will work.

And there are certain conditions that we need to be able to learn about how people work better together. And so a lot of researchers, I think have a mindset of this gold standard of experiments, right? We really can control a lot of things.

And that's not, alwa controlling things is not the reality of an actual workplace. Right. But I'm in a really cool and unique role at Atlassian where while work happens, we actually do get to run experiments and AB tests where we ask teams to adopt new ways of working.

And we see how that goes in a real company where they are focused on solving customer problems all the time. And we get to measure the impact of those things. 'cause we have lots of data from our products and our tools that people collaborate in at Atlassian to learn more about what works and what doesn't.

You said, you said measure and I was gonna say one of the most important tool, if that's the right word for a scientist, is data. Yeah. Right.

Because you have hypotheses, you have problem sets that you're working on. Maybe you formulated what you think might happen or is happening, but it always comes back to the data and, and, and measuring human work and data. You know, we're not, we're not lab mice and you know, like we did a chem chemistry test and this drug work and that one didn't.

How do you measure those kinds of things? What, what are the key data points Yeah. That form your, your opinions and insights?

So when we talk about distributed work, we really just mean people working at different times from different locations. Um, but working together and really what matters there is for people to be connected and effective. And so a lot of the ways that we think about measuring behavioral outcomes are what are people really doing and how does that impact their connection to each other, their connection to the company, those things that we know are so important for being really engaged and motivated at work.

And then how does that impact effectiveness usually at a team level. So are we able to come up with great ideas, you know, can we innovate together? We have measured that in a lot of different ways.

We recently did some research about our internal hackathon called Ship It. It's a biannual event that we have where teams can assemble and build something or fix a problem. And we had about 4,000 teams come together to work on, uh, new problems.

That's a lot of teams. Yeah. A lot of teams.

And we then look and see if they create a demo and we have other people rate the projects. And so we get really rich data about innovation there. Um, and what we were learning from that was that distributed teams tended to be more cross-functional and more innovative and more likely to get to that goal of completing a demo.

So we have examples like that where we have a lot of information about behavior. We also look at product data. So how much work is being completed and how quickly, and we try to balance that with quality outcomes.

'cause we want high velocity teams, but we also wanna be solving the right problem. That's true. We wanna make sure that this is tied to delivering real value for our customers.

So when we think about effectiveness, it is really a confluence of confluence, not in the product sense, but it is a confluence of I seen that Okay. That unintentionally, um, it is a confluence of factors, right? We've got a lot of different metrics that we balance.

Um, but because we work together in such a digital way today, we get a lot of really rich data. And then we supplement a lot of that with surveys because we wanna understand the experience for people. And usually the survey data is really highly correlated with other types of outcomes.

Interesting. Do you publish that anywhere or is that internal kinda information? How does that work?

We do, we do, we are always learning new things that we share. Researchers Gotta publish, right? We gotta publish.

So I don't publish in academic journals anymore. That was a different part of my, my career. But we do at Atlassian have opportunities to share a lot of this.

So we recently published a guide to distributed work called A thousand Days of Distributed Work Lessons Learned. And it's all about our journey in team anywhere. Um, and that has a lot of great data and resources for companies that are interested in learning more about how we're rethinking collaboration.

Let's talk about asynchronous communications. Um, or asynchronous work. Yeah.

Yeah. For a while I think a lot of us thought of as, you know, sort of chat or slack or those kind of things being asynchronous and they certainly are one, one mode of communication, but I think we're in a new era of what asynchronous work looks like, not just communication. Tell, tell me what are your thoughts About that?

Yeah, I do. So one of our big hypotheses is around asynchronous work being so key for the future. That is, and when I think about asynchronous work, I really think it's using the time that we're apart to better support the time we're together.

Okay. I I really had a big question about that is that seems to be the, is it an ebb and flow and how do you kind of make, do you need to rise the times where you're not working together? So things are happening or, or is just thinking about it or working on it or Yeah.

How you See that part of It. I think one of the biggest changes that's gonna happen in the future of work is how we think about time. And I really want to see us all shift to thinking about investing time rather than just spending it.

Mm-Hmm. Right now so much of modern work is back-to-back meetings and trying to reach inbox zero and responding to a million notifications all the time and being super reactive to things that may or may not actually be urgent or important. And workers feel that I've been having amazing conversations here, our team with so many of our customers, and it's hard, right?

When your whole day feels like you're sucked into meetings. We hear from people all the time, about 80% of knowledge workers say that they can't get their work done because they're stuck in so many units. Yeah.

And the technologies that are emerging, AI at the front of them are really going to help us take more control of our time or create more time. And I think we need to pair that with being really intentional about how we reinvest it and that our work days need to shift to more meaningful collaboration and work when we are together. But also a lot more deep thinking and true creativity in the times that we are of apart In interesting.

In a way, our calendars are our friends and our enemies. Right? I mean, it's you, you measure your work by how full your calendar is, which isn't a great measure, right.

Because it doesn't mean you're working on the right things. Totally. I think that's how so many of us learned to work, right?

With being in the room was really important and that was the goal. And I think we are shifting early in this shift, but towards really outcomes being the goal. Mm-Hmm.

And asynchronous work makes meetings a lot less valuable. Um, and, and if you can put the information that people need where they can access it any time, that's amazing for team velocity, right? It moves so, so much faster if I don't have to be dependent on interrupting you to ask a small question or, oh, I had that document somewhere.

There must be a page about this. What call? I can't ask you yet until by the time I see you, I forgot what I wanted To ask.

Exactly. You, you're not online, you're in a meeting. I can't.

So tho those types of friction points, as we have information just accessible and more transparently available and more clear, people can start to get that on their own. And that really unlocks a lot of that deep work that can happen and freeze up a lot of time. Talk about, I know you're not in the product for, you're not in a product role, but thinking about robo, you could look at it as a, um, and one thing and one part of it might be as an automation assistant, right?

Yeah. But you could, I think you tell me if this is right, it seems like you could also look that as a way of asynchronous work, right? So wait for work to happen when I'm not doing that work, or it's preparing something that will help me do the work Yeah.

And maybe eliminate the context switching or whatever, a cognitive overload to get to that work and get it done. Yeah. When I think about asynchronous, I think there's really three key things.

One is making that information available to everyone. Scaling knowledge across the organization that's available any time, which I think VO unlocks a lot of already being able to simply question and search, you know, what about this project? Who's working on it?

Mm-Hmm. I have an idea about it. I wanna reach out to the right team.

How do I get up to date on the latest that becomes unlocked? So that information sharing is really key. Getting super clear on what work actually matters, what work matters most.

I think there'll be lots of advances in the AI more generally in, in the future around getting clear on bowls and measuring how we're progressing towards those goals. Mm-Hmm. And then really reinvesting that time, right?

And so actually doing that, that deeper work. And I think there's a huge role for AI to play in helping us be more creative and get faster, you know, faster feedback loops and being able to share our ideas in forms. We saw that today in, um, you know, Mike's talk about Rove was, can really quickly get to some ideas that are very concrete.

And for teams that's huge because so often we go a long time with a lot of assumptions that haven't been made explicit to other people in the team. And you, oh, you've probably been involved in projects where you get way into it and then learn, oh, there's actually this fundamental place where we were not alive. What are we gonna do now?

And so I'm really excited about the potential of AI to make that process much faster and so teams can truly align on what, what they are trying to accomplish. And, um, Yeah. Kind of filling that, that knowledge gap.

What, what is the phrase about, uh, nature of, of boards of vacuum, right? We'll make, yeah, we'll fill it with what we think something is. If we, even if we don't know yet or the push is not there to tell us or Yeah.

Explain it. What what's your time horizon for your research? How far out are you thinking?

Um, so it really varies depending on what type of research we're doing. So we do some experiments that take, you know, a few weeks, right? We design something, we have teams use it, we learn a lot about how that works for them.

Uh, we also have ongoing industries studies that we do year over year where we're looking at trends and how people work together. Mm-Hmm. A lot of the product research we do, um, is ongoing as well.

Great. How do you come up? How do you decide what things to research?

Such a good question. Yeah. There's so there's so many things, right?

There's so many things. There are so many things to study. So it is really a mix.

We have some hypotheses about what helps teams be really effective and we've learned a lot of that from other research, from organizations that have adopted really new ways of working. I think the remote work community has a lot of great inspiration, particularly when it comes to sharing information. That's something that companies who have really embraced that work model have had to iterate on because you can't rely on us sitting next to each other.

It's true of large enterprises as well. You can't assume everyone's available all the time. Um, so there's a lot of great inspiration there.

We look at challenges our customers ha are having in collaboration. I work really closely with a lot of our customer teams to understand those pain points. Um, and then we look at Atlassians too, and we ask our own employees what, what is hard about work for you?

And we work on solving those problems. Now, as part of what I wanted to ask you is, as I understand it, you probably wouldn't consider what you're doing primary research, right? Where you're kind of not, not, um, biased by a lot of, by its outside factors like a company would bring to it.

But you have that perspective. How do you balance the, the interest and the biases of the company? Because the products and the thinking and the models that, that people have about what work Yeah.

Looks like to, okay, let's look at this and see if there's another way to get different school of thought about it. Yeah. So I think I'm in a very fortunate position.

I study things that Atlassian cares about, right? So I study teams. We care about unleashing the potential of every team.

And so all of the work that I do focuses on that. Um, but we we're actually pretty open to learning. Um, the lessons learned guide that I mentioned is all the things we've learned so far.

And some of them are, Hey, we've learned we don't know what to do here yet, right? Mm-Hmm. And there are definitely areas where we still have a lot to learn.

And I'm very grateful to be in a situation where we get to be really transparent about that and where we do get to share what works, what doesn't work. We recently did some amazing research about Loom. We had managers use Loom to send check-ins to their team.

So at the beginning of the week they said, here's the top three priorities for the team. These are the things that are really top of mind. This is what needs to happen this week.

And then at the end of the week, they checked back in and they recognized the team's accomplishments. They expressed how appreciative they were for their work. Um, and people felt 30% more connected to their managers.

Most people were much more clear on their goals. And we tested this against written messages. And what we found was that for connection and recognition, loom blew the written messages away.

Makes sense. It was sense. So much better.

Makes sense. Yeah. But for getting super clear of a list of three things that mattered most, the messages actually performed a little bit better.

Mm-Hmm. And so what we ended up recommending to teams at Atlassian, and we published this publicly too. Um, it's available on our work life flog, if anyone wants to read, you know, more about it.

But, um, we told them to do both. So at the beginning of the week, have that list of three things written down, book compliment it with that loom video that makes it a much more personal connection moment that people really want with the leaders of their teams. And it's great that we can share, like, this is exactly the right use case for that.

And I think everyone at Atlassian is, you wanna solve, we actually do, we wanna solve problems for our customers. We want teams to have a better time at work. That's Not research for research's sake.

Yeah. It's not research for research's sake. It is, it is certainly.

Um, I like to think we are advancing what we know about teams. Um, but in a really practical sense. And I'm most satisfied with what we do when I can come back to teams and say, Hey, do this thing.

Try writing a page in this way. Try using this robo agent that we designed. Try, uh, you know, doing these simple loom check-ins when it's something where you don't have to, you know, upend your entire workflow, but you can actually make some really meaningful progress and collaborate better.

That's the most satisfying outcome for research for me. Well, speaking of coming back to teams, you kind of set me up for my last question, which is different kind of teams, teams 24. Thinking about Teams 25, when you come here next year, what do you hope we're talking about?

Or what do you think we might be talking about that that, you know, interests you in the problem set that you're Yeah. Going after? I hope we are talking about successes in creativity and time.

I hope that we are at a place where we have realized a lot of efficiencies, but also are reinvesting the time we get from those in solving harder problems. That's really what brings teams together. That's the stuff that matters.

I hope that a lot of the work about work, the like challenges of coordination are starting to lift a little. And that people are thinking through how do I really do things that are really meaningful and getting to do that more in their work days. Great.

I hope we get a chance to check in next year and Yeah. See what the next things and maybe we're thinking about that or new things as well. Yeah.

Molly, it's been a great talking with you. Yeah, Thank You. Thanks for checking in and, and spending the time and sharing, uh, as well as the other ways that you share your research and publish it, um, externally, but helping folks understand the thought that goes behind the research that you're doing.

So Awesome. Thank you. Molly's sand, uh, working in the, any, any work anywhere lab?

Is that team anywhere? Lab team, Anywhere? Lab?

I, I get so many names going on. Oh, it's hard. I, for me to keep 'em all in my head by the end of the day.

So it's been a pleasure talking with you. Yeah, thank you. Thanks for joining Us.

So you get, you get, you get such a wide variety of perspectives from somebody who's an advocate in the community, people doing research, leading product, leading strategy around what's happening at the company, creating it, the AI researcher that we talked to, uh, building products. So it's just a lot of fun talking with so many parts of the organization and understanding the perspective that goes into creating, uh, an amazing company like Atlassian and the technology that Great. So it's been a pleasure talking with you and thanks for joining us.

Yeah, thank you.