Snowflake Summit 2026 | Glenn McPherson, Snowflake & Helen Crossley, Canva

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Glenn McPherson

RVP - Australia

Snowflake

Helen Crossley

Head of Data Science

Canva

play_circle_outline Snowflake-Canva partnership building a unified data foundation and governance

play_circle_outline Personalization in Canva via memory books and design-history-driven recommendations

play_circle_outline Democratizing AI at Canva with company-wide upskilling and hands-on weeks

play_circle_outline Importance of solid data strategy as prerequisite for successful AI initiatives

play_circle_outline Scaling Agentic AI: From Innovation Islands to Governed, Reproducible Paved Roads

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Glenn McPherson, Snowflake & Helen Crossley, Canva

Glenn McPherson

RVP - Australia Snowflake

Helen Crossley

Head of Data Science Canva

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Rebecca Knight

>> Hello everyone. Welcome back to the Moscone Center. We are theCUBE and we are live at the Snowflake Summit 2026. I'm your host, Rebecca Knight, alongside Dave Vellante, co-founder, co-CEO of theCUBE. We've got two very great guests for this next segment. I would like to welcome Helen Crossley, head of data science at Canva. Welcome.

Helen Crossley

>> Thank you so much, Rebecca.

Rebecca Knight

>> And Glenn McPherson, RVP Australia Snowflake. Welcome, Glenn.

Glenn McPherson

>> Thanks for having me.

Rebecca Knight

>> So Helen, Canva is such a success story. It started in Perth, Australia, and now, let me look at my notes, 265 million monthly active users, $4 billion in annualized revenue. When you look back, was there a moment, an inflection point when you realized that the data stopped being a supporting act and was actually the main business?

Helen Crossley

>> Yeah, it's interesting. So I've been at Canva only for about, coming up on a year now. And so for me, that really has happened through AI in the last few months. And I think what I've seen across the business is actually a step change in how people are using data even in that short window. And I think what's really causing that is just the velocity of how fast we're shipping product at the moment. It feels like there is a new product coming out every time you turn around. And I think some people in the industry are a little bit nervous about that, right? Because it means that maybe they feel like, "Oh, what about that traditional experimentation platform that we had, and the way we did things, and really inspecting everything very closely?" Well, now that still happens, but on a much more compressed timeframe, which means that every meeting that we're having, every product showcase starts with the metrics. What's happening this week today on our data, and it comes to our product, and what our customers are seeing and feeling. And that change in how we are operating is really something that's kind of filtered through the company in the last few months, that means that the way we're using data has really changed, I would say. And so that's probably, for me, what that looks like in the last little bit.

Dave Vellante

>> So what's happening down under this fall? You're going into your winter, and what's the market like? How do you compare it with the state of AI adoption, and what you see around the world?

Glenn McPherson

>> I think at the moment, Australia is one of the most vibrant countries in the world when it comes to AI. We've really moved from the experimentation of AI to outcomes. And AI at the moment is like, it's a board directive. So it's exciting that we're a part of it. I think in Snowflake terms, we're one of the top countries in the world. And yeah, we're just seeing customers really lean in and drive business outcomes. And they often start quite simply, just simple ways of finding, getting data access to the business folks, looking to drive automation, and that's spawning bigger and more sort of fruitful ideas, that as long as the business get involved in it, things always go well.

Rebecca Knight

>> So you are growing your enterprise customer base at a rapid pace. I mean, describing that the pace of business has changed, but you're also really growing your customers. How are you using Snowflake to help you better understand those customers, and where there are potential business opportunities?

Helen Crossley

>> Yeah. I'd say there's kind of three real ways that we're using Snowflake for that at the moment. So firstly is understanding how we segment those customers. So you mentioned the 265 million. As everyone comes into our platform, we need to know who are they, what's the job that they want to get done? Are they a big business? Are they an individual? Are they an education user? And we've got a great education platform for them as well. So we use our Snowflake data to segment them, and then to really, for those enterprise customers, how do we think about prioritizing them, scoring those customers for our sales and our partnerships teams to go out to business, really around that, and then tailoring all of our messaging. Of course, personalization is key to this. How do we use Snowflake and all of the data and the signal that we have to be able to really make that message feel personalized to that customer, in that moment, with the job that they're really trying to get done? So that's how we use Snowflake, all of that signal coming in, across product, marketing, our sales team in one place, so we can do those three jobs.

Dave Vellante

>> So Canva and Snowflake have been working together for quite some time, right? We've seen the ascendancy of Canva. It's a good story. I know you'd love to take credit for all of it, but it's like I say, it's a good story that you can sort of share with other customers in terms of how you're helping a small company get to a big company. How do you tell that story, Glenn?

Glenn McPherson

>> I think we started out with Canva in very humble ways, as you say, and we were trying to solve the simplest of problems, which was that data fragmentation. And that kind of built up over the years, and we built a reputation over that time of being very dependable, very simple. And Canva started to leverage that data foundation for a lot more than just a great safe space to have your data. And now they're bringing a lot of work to that data, AI being one of the main ones. So keeping it nice and simple, nice and connected, and ensuring that governance and that security is there. So it's almost as simple as that. It's been a really fruitful partnership so far, and it's growing into other things, where not only they're our customers, we're their customers too.

Dave Vellante

>> It goes both ways.

Rebecca Knight

>> So what is that dynamic like? I mean, that's really interesting, because you are giving them ideas about what could be done, and you're also using their platform, but also selling to them too. How does that work?

Helen Crossley

>> It's a great partnership in actual fact. Because if you think about what Canva does really, really well, we make design incredibly simple.

Rebecca Knight

>> Everyone's an artist.

Helen Crossley

>> Everyone. Everyone can imagine, everyone can design, and we believe that so wholeheartedly. And then if you think about what Snowflake does really, really well, it's making that data simple. And so the partnership is kind of natural in that regard, in terms of what they're bringing into our platform to help all of our teams with. And then what Canva brings to Snowflake is really that design element into all of their teams as well, which is fantastic.

Rebecca Knight

>> So when you're using Canva for those design recommendations, and colors, and fonts, talk a little bit about how the Snowflake foundation that is enabling that, and how you're using that for that real personalization that you can have, where everyone can be an artist.

Helen Crossley

>> Absolutely. So any time you think about two customers coming into the same platform to design a poster, those two posters are going to look very, very different, because everyone is unique. And so the job of us is to be able to capture that signal and really deeply understand those customers, to make the next time that they come back even more delightful, even more personalized. And that's where we're partnering with Snowflake to do a lot of that work. At Canva Create a few months ago, you would have seen that we announced how we're bringing together Canva AI. And with that, one of the things that we've launched is memory books, which is all about how you can create that real personalized touch. We've got years and years and years of really rich design data, and we are using that to build up these memory books to make sure that everything that you do going forward is utilizing that knowledge that we have. And that's something that is a little bit different about Canva, because we do have that such rich history there of the designs that make it great for you as a person, and that should be different from the next person who shows up in the next. So that's really what we're trying to do on the Canva side.

Dave Vellante

>> So Helen, what's the workflow like? Is it traditionally that the data analysis and the design were kind of largely separate entities? Are those colliding? Are they coming together? Are you directly injecting the data into the design process, and how is it informing the design?

Helen Crossley

>> Yeah. So a lot of that's happening through our Canva AI platform. And so when a user comes in, there's a couple of ways it can happen on the personalization front. One way is just through kind of straightforward personalization. When you come into the platform and you'll see a template, that is something that should be for you. And so we know that maybe you're designing an invitation for a child's party, and we want to make sure that you're seeing something that's personalized for you and in a simple way. But really taking that to the next level. That workflow looks like, hey, you're coming into Canva and you're using the Canva AI system to naturally talk and tell us what you want to design. And in the backend, we're using that information, along with other information that we have, to try and really personalize that approach for you. And that product's getting better and better to be able to be a more personalized approach as we go along, really using those memory books, using that information that we have.

Rebecca Knight

>> Glenn, you said earlier that Australia is one of the most vibrant countries in the world when it comes to AI. What have you observed with Australia ... I mean, obviously Canva is a great proof point of that, but what have you observed in your conversations with customers about ... the most successful ones, that have moved from, "We're piloting this, we're experimenting," to, "We're deploying and executing?"

Glenn McPherson

>> I'd say, first of all, yeah, we've moved very quickly beyond experimentation into a real outcome focus. And yeah, like I said before, it's become like a board directive now for most enterprises to actually take on AI. And I think the stats say about 70% of enterprise in Australia now have moved from a pilot to production. And as long as there is that outcome focus, as long as there is the business involved and it's not seen as an IT project as much as a business strategy, where, again, they know where they're going with it. And it might be really simple. Again, that some of the simplest things that you can see with automation, or even putting the data into the hands of business folks with things like our Cowork capability, it just spawns some incredible ideas and capabilities.

Dave Vellante

>> I agree. It is a board directive, and kind of was. We were just talking to Kevin from Fanatics about sort of the top-down edict, "Thou shalt do AI." And people said, "Okay, what does that mean?" Well, now it's happening bottom-up. Canva's a great example. "Oh, I'm using AI. I'm becoming more proficient, and now I can do other things." And so how are you seeing that sort of top-down, bottom-up at Canva and then just across the industry?

Helen Crossley

>> Yeah. At Canva, when we think about our own teams, we just spent a whole week where the whole company spent time, not in meetings, not on their day to day, just practicing, using, exploring, experimenting with AI tools. And that's really an up-leveling that we do every year now at Canva, to be able to make sure that everyone across the company, no matter their role, this is not just for engineers, who, by the way, are using AI very deeply every day in their jobs, but every single person across the company has the chance to spend that time upskilling. And you're absolutely right. Some of the ideas that have come out of that platform are just phenomenal, that we never would've thought of on that kind of more top-down edict that's coming from boards, and from founders, and other companies. And so I love that, that we're able to get this exploration of the data, but then be able to take those ideas and turn them into real product for our customers is just something that it's fantastic to see.

Dave Vellante

>> And that really is, Glenn, what's profound about AI. I've never, I mean, we've been doing this now, this is our 17th year, and usually it affects the technology department, or the technology vendor. Now every interview that we do, we can talk about what you're selling and how you're consuming AI. Every single company has to think about this. And so do you think for the customers that you talk to, do they really, truly understand how this is going ... I mean, it's hard to predict. Maybe I'm simplifying it, but how well do they understand how this is going to change their operating model? Or are they more just, "Okay, let's figure this out and sort of tactically go about it day to day?" What's the conversation like?

Glenn McPherson

>> I think it's a little bit of both. I think there's some fundamentals that when you talk to, you talk to some executives now, and we talk to the simple message of, "You've got to have a great data strategy to unlock your AI," as opposed to just have an AI strategy. And they get that. They get that now, because I think in that experimentation stage, they figured out if you don't have your data right, there's a problem. Then you kind of flip it to, but we really do want people in the organization to utilize this, and let them free flow a little bit with that utilization, and great ideas will be spawned from that. We see that at Snowflake as well. We saw that with the launch of CoCo. I believe that we actually gave it to all of our engineers, and then just to everyone. And suddenly we were being very creative in the field, in terms of how we can utilize the tools to be more productive. So I think it is a little bit of both, and I think over time we'll see that really unfold.

Dave Vellante

>> We've been saying it's so hard to predict things these days, but one thing I will predict is what's going to happen is because of that bottom-up, you're going to start to get islands of intelligence. And so the challenge is going to be, how do you actually break down those silos? Because we are a siloed industry. We always have, we got data in one department and others. Now using Snowflake, that's a start. It's just the shared sort of data platform. Is that something that you think about internally?

Helen Crossley

>> Absolutely.

Dave Vellante

>> And are your customers thinking about how not to allow that to just create islands of intelligence?

Helen Crossley

>> And you don't want to stop the islands, right? Because the islands are fantastic.

Dave Vellante

>> That's what's hard about this.

Helen Crossley

>> You want people innovating, you want people on the tools, especially right now, where everything is so fast, and so new. I love the islands, but you're absolutely right that in my job, I also worry about how do we make sure that we get the best of that across all of the team quickly? And so what we're doing at Canva is building a lot on our kind of agentic platform, to make sure that we've got those paved roads, where we see something great, someone has the ability to pick that up and have a way to quickly scale that across the company. It's something we're still working on, in full transparency, in terms of really trying to get that right. But the absolute intention is that we love islands of intelligence, and how do we turn that from something that is in those pockets to being something that's really broadly understood across? I think we see examples of that, but it is something I still worry about. And some of that, I think, comes from everyone can be building something great, but they maybe not have the confidence to know that this is something that the rest of the business can use, wants to use. And so it's really our job to be encouraging that and giving them that confidence, and security, and platform around taking those ideas broader.

Dave Vellante

>> And it is in part your job, as the head of data science, but you've got to collaborate with, I don't know if you have a chief data officer, you may or may not, you don't. You probably have a CIO, you've got enterprise architects, you have others that are interested in this. So together, you can't do it alone. You have to think about that future architecture of your enterprise.

Helen Crossley

>> Absolutely. It is absolutely a partnership across the entire company to get that right.

Dave Vellante

>> Let me ask you a question, because I was saying to Glenn off camera, day two we kind of test the day one theories. One of the questions I've been asking heads of data science, folks who kind of understand that, I'll say it, the sort of legacy Hadoop pipeline, we had highly specialized roles, data scientist, data engineer, quality engineer, business analyst. Do you see those roles collapsing, or do you see those roles getting more hyper-intelligent?

Helen Crossley

>> From my point of view, I think it's going to be a bit of both in some places. I think you've got a spectrum of skills, and even if you take a group of data scientists, they've all got things that they're different at slightly. Some of them will be fantastic about that data storytelling element of it. Others will be fantastic about, really thinking about what's the right way to visualize the data, build the pipeline, whatever that might be. I think what I am looking forward to seeing is people leaning into their strengths even more, because AI is helping them to get better at the rest of it, and really being able to hone in then and say, "Okay, how do I really sharpen that thing that gives me the edge on the AI, on everything else that has always been a core skill of mine, but now I can take that even further."

Dave Vellante

>> So where will those roles collapse?

Helen Crossley

>> That's something that we don't yet know, what exactly that organization of the future will look like when it comes to titles. I don't think about titles, I think about skills, and I want all of those skills in my team today and in the future.

Dave Vellante

>> Because today, this is getting a little futuristic, but today the organization, most organizations, I don't know about yours, are very hierarchical, built around people. And in the future it's going to be built around skills and intelligence. So as a data scientist, you might be able to traverse some of those other areas, as opposed to having to wait for somebody in that pipeline like you have had to for 10 years, you can't move on to the next step until the data quality engineer is done, or the business analyst gives you that feedback. You can probably more easily traverse to a point, I guess is ... Because earlier you said kind of a bit of both, and that's how I see it playing out. I don't know if you agree, or disagree or don't have a thought there.

Helen Crossley

>> Yeah. I think about it in terms of, how do we get to that outcome faster? And so that can look like one person doing more of that job all the way through, or it can also look like, how do we get those other jobs to be more automated, more efficient, make that as easy as possible? Whatever that looks like, I'm open to how we do that, but ultimately at the end of the day, we have to go from idea through to execution in a much more rapid timeframe than what we've done in the past. But I do think there's multiple paths to how we actually do that.

Dave Vellante

>> And it sounds like, I'm just sort of talking this through, that specialization is preserved, but agents potentially pick up more of that activity with the human oversight.

Helen Crossley

>> Absolutely. With human oversight, making sure that we've got those domain experts really thinking about what is that agentic skill, making sure that they're looking after that with their domain expertise. And so those fast-paved rows that we're talking about are something that can really enable the rest of the organization, absolutely.

Rebecca Knight

>> Well, as we are wrapping up here, you started out this conversation just talking about the pace of change, and how quickly you're shipping products, and how that can be inspiring and energizing, but also tiring, too. What is your best advice, both of you, for Australian or global AI executives about how they should think about this next wave of innovation, and at a time when the pace of change has accelerated so much? Do you want to start?

Glenn McPherson

>> Sure. I just have some very simple advice. I think the first is get the business involved, and make it a business strategy. I think the second piece is it always starts with your data, make sure that you have a solid data strategy, and then keep it simple. And let that simplicity drive the following steps and give the returns that they so desire. I think it's those three things.

Helen Crossley

>> Yeah. I think all of those are great. And I think one more that I'd build upon that is find your AI heroes in the organization. They're absolutely out there. They might not have knocked on your door yet as an executive, but you absolutely have to invite them in, find them in your organization, and really listen to them in terms of what that future could look like, because they're motivated, they know what they're doing, and really can help a lot in that area.

Rebecca Knight

>> And close to the work, too-

Helen Crossley

>> Absolutely. And-...

Rebecca Knight

>> so they get it....

Helen Crossley

>> that's what you need. Yeah.

Rebecca Knight

>> Excellent. Well, Helen and Glenn, thank you both so much for coming on, a really interesting conversation.

Helen Crossley

>> Thank you so much.

Glenn McPherson

>> Yeah, thanks very much.

Rebecca Knight

>> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBE's live coverage of the Snowflake Summit. You're watching theCUBE, the leader in enterprise tech news and analysis.

Glenn McPherson, Snowflake & Helen Crossley, Canva

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Rebecca Knight

>> Hello everyone. Welcome back to the Moscone Center. We are theCUBE and we are live at the Snowflake Summit 2026. I'm your host, Rebecca Knight, alongside Dave Vellante, co-founder, co-CEO of theCUBE. We've got two very great guests for this next segment. I would like to welcome Helen Crossley, head of data science at Canva. Welcome.

Helen Crossley

>> Thank you so much, Rebecca.

Rebecca Knight

>> And Glenn McPherson, RVP Australia Snowflake. Welcome, Glenn.

Glenn McPherson

>> Thanks for having me.

Rebecca Knight

>> So Helen, Canva is such a success story. It started in Perth, Australia, and now, let me look at my notes, 265 million monthly active users, $4 billion in annualized revenue. When you look back, was there a moment, an inflection point when you realized that the data stopped being a supporting act and was actually the main business?

Helen Crossley

>> Yeah, it's interesting. So I've been at Canva only for about, coming up on a year now. And so for me, that really has happened through AI in the last few months. And I think what I've seen across the business is actually a step change in how people are using data even in that short window. And I think what's really causing that is just the velocity of how fast we're shipping product at the moment. It feels like there is a new product coming out every time you turn around. And I think some people in the industry are a little bit nervous about that, right? Because it means that maybe they feel like, "Oh, what about that traditional experimentation platform that we had, and the way we did things, and really inspecting everything very closely?" Well, now that still happens, but on a much more compressed timeframe, which means that every meeting that we're having, every product showcase starts with the metrics. What's happening this week today on our data, and it comes to our product, and what our customers are seeing and feeling. And that change in how we are operating is really something that's kind of filtered through the company in the last few months, that means that the way we're using data has really changed, I would say. And so that's probably, for me, what that looks like in the last little bit.

Dave Vellante

>> So what's happening down under this fall? You're going into your winter, and what's the market like? How do you compare it with the state of AI adoption, and what you see around the world?

Glenn McPherson

>> I think at the moment, Australia is one of the most vibrant countries in the world when it comes to AI. We've really moved from the experimentation of AI to outcomes. And AI at the moment is like, it's a board directive. So it's exciting that we're a part of it. I think in Snowflake terms, we're one of the top countries in the world. And yeah, we're just seeing customers really lean in and drive business outcomes. And they often start quite simply, just simple ways of finding, getting data access to the business folks, looking to drive automation, and that's spawning bigger and more sort of fruitful ideas, that as long as the business get involved in it, things always go well.

Rebecca Knight

>> So you are growing your enterprise customer base at a rapid pace. I mean, describing that the pace of business has changed, but you're also really growing your customers. How are you using Snowflake to help you better understand those customers, and where there are potential business opportunities?

Helen Crossley

>> Yeah. I'd say there's kind of three real ways that we're using Snowflake for that at the moment. So firstly is understanding how we segment those customers. So you mentioned the 265 million. As everyone comes into our platform, we need to know who are they, what's the job that they want to get done? Are they a big business? Are they an individual? Are they an education user? And we've got a great education platform for them as well. So we use our Snowflake data to segment them, and then to really, for those enterprise customers, how do we think about prioritizing them, scoring those customers for our sales and our partnerships teams to go out to business, really around that, and then tailoring all of our messaging. Of course, personalization is key to this. How do we use Snowflake and all of the data and the signal that we have to be able to really make that message feel personalized to that customer, in that moment, with the job that they're really trying to get done? So that's how we use Snowflake, all of that signal coming in, across product, marketing, our sales team in one place, so we can do those three jobs.

Dave Vellante

>> So Canva and Snowflake have been working together for quite some time, right? We've seen the ascendancy of Canva. It's a good story. I know you'd love to take credit for all of it, but it's like I say, it's a good story that you can sort of share with other customers in terms of how you're helping a small company get to a big company. How do you tell that story, Glenn?

Glenn McPherson

>> I think we started out with Canva in very humble ways, as you say, and we were trying to solve the simplest of problems, which was that data fragmentation. And that kind of built up over the years, and we built a reputation over that time of being very dependable, very simple. And Canva started to leverage that data foundation for a lot more than just a great safe space to have your data. And now they're bringing a lot of work to that data, AI being one of the main ones. So keeping it nice and simple, nice and connected, and ensuring that governance and that security is there. So it's almost as simple as that. It's been a really fruitful partnership so far, and it's growing into other things, where not only they're our customers, we're their customers too.

Dave Vellante

>> It goes both ways.

Rebecca Knight

>> So what is that dynamic like? I mean, that's really interesting, because you are giving them ideas about what could be done, and you're also using their platform, but also selling to them too. How does that work?

Helen Crossley

>> It's a great partnership in actual fact. Because if you think about what Canva does really, really well, we make design incredibly simple.

Rebecca Knight

>> Everyone's an artist.

Helen Crossley

>> Everyone. Everyone can imagine, everyone can design, and we believe that so wholeheartedly. And then if you think about what Snowflake does really, really well, it's making that data simple. And so the partnership is kind of natural in that regard, in terms of what they're bringing into our platform to help all of our teams with. And then what Canva brings to Snowflake is really that design element into all of their teams as well, which is fantastic.

Rebecca Knight

>> So when you're using Canva for those design recommendations, and colors, and fonts, talk a little bit about how the Snowflake foundation that is enabling that, and how you're using that for that real personalization that you can have, where everyone can be an artist.

Helen Crossley

>> Absolutely. So any time you think about two customers coming into the same platform to design a poster, those two posters are going to look very, very different, because everyone is unique. And so the job of us is to be able to capture that signal and really deeply understand those customers, to make the next time that they come back even more delightful, even more personalized. And that's where we're partnering with Snowflake to do a lot of that work. At Canva Create a few months ago, you would have seen that we announced how we're bringing together Canva AI. And with that, one of the things that we've launched is memory books, which is all about how you can create that real personalized touch. We've got years and years and years of really rich design data, and we are using that to build up these memory books to make sure that everything that you do going forward is utilizing that knowledge that we have. And that's something that is a little bit different about Canva, because we do have that such rich history there of the designs that make it great for you as a person, and that should be different from the next person who shows up in the next. So that's really what we're trying to do on the Canva side.

Dave Vellante

>> So Helen, what's the workflow like? Is it traditionally that the data analysis and the design were kind of largely separate entities? Are those colliding? Are they coming together? Are you directly injecting the data into the design process, and how is it informing the design?

Helen Crossley

>> Yeah. So a lot of that's happening through our Canva AI platform. And so when a user comes in, there's a couple of ways it can happen on the personalization front. One way is just through kind of straightforward personalization. When you come into the platform and you'll see a template, that is something that should be for you. And so we know that maybe you're designing an invitation for a child's party, and we want to make sure that you're seeing something that's personalized for you and in a simple way. But really taking that to the next level. That workflow looks like, hey, you're coming into Canva and you're using the Canva AI system to naturally talk and tell us what you want to design. And in the backend, we're using that information, along with other information that we have, to try and really personalize that approach for you. And that product's getting better and better to be able to be a more personalized approach as we go along, really using those memory books, using that information that we have.

Rebecca Knight

>> Glenn, you said earlier that Australia is one of the most vibrant countries in the world when it comes to AI. What have you observed with Australia ... I mean, obviously Canva is a great proof point of that, but what have you observed in your conversations with customers about ... the most successful ones, that have moved from, "We're piloting this, we're experimenting," to, "We're deploying and executing?"

Glenn McPherson

>> I'd say, first of all, yeah, we've moved very quickly beyond experimentation into a real outcome focus. And yeah, like I said before, it's become like a board directive now for most enterprises to actually take on AI. And I think the stats say about 70% of enterprise in Australia now have moved from a pilot to production. And as long as there is that outcome focus, as long as there is the business involved and it's not seen as an IT project as much as a business strategy, where, again, they know where they're going with it. And it might be really simple. Again, that some of the simplest things that you can see with automation, or even putting the data into the hands of business folks with things like our Cowork capability, it just spawns some incredible ideas and capabilities.

Dave Vellante

>> I agree. It is a board directive, and kind of was. We were just talking to Kevin from Fanatics about sort of the top-down edict, "Thou shalt do AI." And people said, "Okay, what does that mean?" Well, now it's happening bottom-up. Canva's a great example. "Oh, I'm using AI. I'm becoming more proficient, and now I can do other things." And so how are you seeing that sort of top-down, bottom-up at Canva and then just across the industry?

Helen Crossley

>> Yeah. At Canva, when we think about our own teams, we just spent a whole week where the whole company spent time, not in meetings, not on their day to day, just practicing, using, exploring, experimenting with AI tools. And that's really an up-leveling that we do every year now at Canva, to be able to make sure that everyone across the company, no matter their role, this is not just for engineers, who, by the way, are using AI very deeply every day in their jobs, but every single person across the company has the chance to spend that time upskilling. And you're absolutely right. Some of the ideas that have come out of that platform are just phenomenal, that we never would've thought of on that kind of more top-down edict that's coming from boards, and from founders, and other companies. And so I love that, that we're able to get this exploration of the data, but then be able to take those ideas and turn them into real product for our customers is just something that it's fantastic to see.

Dave Vellante

>> And that really is, Glenn, what's profound about AI. I've never, I mean, we've been doing this now, this is our 17th year, and usually it affects the technology department, or the technology vendor. Now every interview that we do, we can talk about what you're selling and how you're consuming AI. Every single company has to think about this. And so do you think for the customers that you talk to, do they really, truly understand how this is going ... I mean, it's hard to predict. Maybe I'm simplifying it, but how well do they understand how this is going to change their operating model? Or are they more just, "Okay, let's figure this out and sort of tactically go about it day to day?" What's the conversation like?

Glenn McPherson

>> I think it's a little bit of both. I think there's some fundamentals that when you talk to, you talk to some executives now, and we talk to the simple message of, "You've got to have a great data strategy to unlock your AI," as opposed to just have an AI strategy. And they get that. They get that now, because I think in that experimentation stage, they figured out if you don't have your data right, there's a problem. Then you kind of flip it to, but we really do want people in the organization to utilize this, and let them free flow a little bit with that utilization, and great ideas will be spawned from that. We see that at Snowflake as well. We saw that with the launch of CoCo. I believe that we actually gave it to all of our engineers, and then just to everyone. And suddenly we were being very creative in the field, in terms of how we can utilize the tools to be more productive. So I think it is a little bit of both, and I think over time we'll see that really unfold.

Dave Vellante

>> We've been saying it's so hard to predict things these days, but one thing I will predict is what's going to happen is because of that bottom-up, you're going to start to get islands of intelligence. And so the challenge is going to be, how do you actually break down those silos? Because we are a siloed industry. We always have, we got data in one department and others. Now using Snowflake, that's a start. It's just the shared sort of data platform. Is that something that you think about internally?

Helen Crossley

>> Absolutely.

Dave Vellante

>> And are your customers thinking about how not to allow that to just create islands of intelligence?

Helen Crossley

>> And you don't want to stop the islands, right? Because the islands are fantastic.

Dave Vellante

>> That's what's hard about this.

Helen Crossley

>> You want people innovating, you want people on the tools, especially right now, where everything is so fast, and so new. I love the islands, but you're absolutely right that in my job, I also worry about how do we make sure that we get the best of that across all of the team quickly? And so what we're doing at Canva is building a lot on our kind of agentic platform, to make sure that we've got those paved roads, where we see something great, someone has the ability to pick that up and have a way to quickly scale that across the company. It's something we're still working on, in full transparency, in terms of really trying to get that right. But the absolute intention is that we love islands of intelligence, and how do we turn that from something that is in those pockets to being something that's really broadly understood across? I think we see examples of that, but it is something I still worry about. And some of that, I think, comes from everyone can be building something great, but they maybe not have the confidence to know that this is something that the rest of the business can use, wants to use. And so it's really our job to be encouraging that and giving them that confidence, and security, and platform around taking those ideas broader.

Dave Vellante

>> And it is in part your job, as the head of data science, but you've got to collaborate with, I don't know if you have a chief data officer, you may or may not, you don't. You probably have a CIO, you've got enterprise architects, you have others that are interested in this. So together, you can't do it alone. You have to think about that future architecture of your enterprise.

Helen Crossley

>> Absolutely. It is absolutely a partnership across the entire company to get that right.

Dave Vellante

>> Let me ask you a question, because I was saying to Glenn off camera, day two we kind of test the day one theories. One of the questions I've been asking heads of data science, folks who kind of understand that, I'll say it, the sort of legacy Hadoop pipeline, we had highly specialized roles, data scientist, data engineer, quality engineer, business analyst. Do you see those roles collapsing, or do you see those roles getting more hyper-intelligent?

Helen Crossley

>> From my point of view, I think it's going to be a bit of both in some places. I think you've got a spectrum of skills, and even if you take a group of data scientists, they've all got things that they're different at slightly. Some of them will be fantastic about that data storytelling element of it. Others will be fantastic about, really thinking about what's the right way to visualize the data, build the pipeline, whatever that might be. I think what I am looking forward to seeing is people leaning into their strengths even more, because AI is helping them to get better at the rest of it, and really being able to hone in then and say, "Okay, how do I really sharpen that thing that gives me the edge on the AI, on everything else that has always been a core skill of mine, but now I can take that even further."

Dave Vellante

>> So where will those roles collapse?

Helen Crossley

>> That's something that we don't yet know, what exactly that organization of the future will look like when it comes to titles. I don't think about titles, I think about skills, and I want all of those skills in my team today and in the future.

Dave Vellante

>> Because today, this is getting a little futuristic, but today the organization, most organizations, I don't know about yours, are very hierarchical, built around people. And in the future it's going to be built around skills and intelligence. So as a data scientist, you might be able to traverse some of those other areas, as opposed to having to wait for somebody in that pipeline like you have had to for 10 years, you can't move on to the next step until the data quality engineer is done, or the business analyst gives you that feedback. You can probably more easily traverse to a point, I guess is ... Because earlier you said kind of a bit of both, and that's how I see it playing out. I don't know if you agree, or disagree or don't have a thought there.

Helen Crossley

>> Yeah. I think about it in terms of, how do we get to that outcome faster? And so that can look like one person doing more of that job all the way through, or it can also look like, how do we get those other jobs to be more automated, more efficient, make that as easy as possible? Whatever that looks like, I'm open to how we do that, but ultimately at the end of the day, we have to go from idea through to execution in a much more rapid timeframe than what we've done in the past. But I do think there's multiple paths to how we actually do that.

Dave Vellante

>> And it sounds like, I'm just sort of talking this through, that specialization is preserved, but agents potentially pick up more of that activity with the human oversight.

Helen Crossley

>> Absolutely. With human oversight, making sure that we've got those domain experts really thinking about what is that agentic skill, making sure that they're looking after that with their domain expertise. And so those fast-paved rows that we're talking about are something that can really enable the rest of the organization, absolutely.

Rebecca Knight

>> Well, as we are wrapping up here, you started out this conversation just talking about the pace of change, and how quickly you're shipping products, and how that can be inspiring and energizing, but also tiring, too. What is your best advice, both of you, for Australian or global AI executives about how they should think about this next wave of innovation, and at a time when the pace of change has accelerated so much? Do you want to start?

Glenn McPherson

>> Sure. I just have some very simple advice. I think the first is get the business involved, and make it a business strategy. I think the second piece is it always starts with your data, make sure that you have a solid data strategy, and then keep it simple. And let that simplicity drive the following steps and give the returns that they so desire. I think it's those three things.

Helen Crossley

>> Yeah. I think all of those are great. And I think one more that I'd build upon that is find your AI heroes in the organization. They're absolutely out there. They might not have knocked on your door yet as an executive, but you absolutely have to invite them in, find them in your organization, and really listen to them in terms of what that future could look like, because they're motivated, they know what they're doing, and really can help a lot in that area.

Rebecca Knight

>> And close to the work, too-

Helen Crossley

>> Absolutely. And-...

Rebecca Knight

>> so they get it....

Helen Crossley

>> that's what you need. Yeah.

Rebecca Knight

>> Excellent. Well, Helen and Glenn, thank you both so much for coming on, a really interesting conversation.

Helen Crossley

>> Thank you so much.

Glenn McPherson

>> Yeah, thanks very much.

Rebecca Knight

>> I'm Rebecca Knight for Dave Vellante, stay tuned for more of theCUBE's live coverage of the Snowflake Summit. You're watching theCUBE, the leader in enterprise tech news and analysis.