AYX - Alteryx Inc. (AYX) Presents at Rosenblatt Securities Technology Summit: The Age of AI (Transcript)
2023-06-09 07:58:03 ET
Start Time: 11:00
End Time: 11:42
Alteryx, Inc. (AYX)
Rosenblatt Securities Technology Summit: The Age of AI
June 08, 2023, 11:00 AM ET
Company Participants
Kevin Rubin - CFO
Suresh Vittal - Chief Product Officer
Conference Call Participants
Blair Abernethy - Rosenblatt Securities
Presentation
Blair Abernethy
Good morning, everyone. It's Blair Abernethy, software analyst with Rosenblatt back again here this time with Alteryx. From Alteryx, we have Suresh Vittal who is the Chief Product Officer of the business as well as Kevin Rubin, the CFO. Thanks, gentlemen, for joining us this morning.
Suresh Vittal
Good morning. Thank you.
Kevin Rubin
Good morning. Great to be here.
Blair Abernethy
So we've had a lot of companies in the last, since day two and obviously AI has been a tremendously important topic and becoming much more so and certainly was a big topic at your user conference a week and a half ago. Maybe before we dive into AI, Suresh, could you just maybe give us a little high level for some people who may not be that familiar with Alteryx as to what the company does, its core products, and then sort of your background? Because you've just joined the company in the last two years, and certainly made a lot of changes since you joined.
Suresh Vittal
Yes. Thanks for the question, Blair. Yes, I've been at the company for just over two years, Chief Product Officer, responsible for product and engineering and design. Came to Alteryx from Adobe where I was kind of running products for the digital experience portion of the business. Alteryx and kind of a little brief overview, our goal is to make it easy for everyone in the organization to be able to discover actionable insights from every piece of the data that they have. And we call this approach, this philosophy analytics for all. And our goal has always been and our claim to fame has been that we take these unsung heroes in operational roles and give them the tools and capabilities that allow them to operate at a much higher skill level.
Our flagship products have for the longest time been designed in server. They're much beloved products that enable the business user and not just the data scientists to be able to work with data and do lots of complex manipulation of this data in the service of analytics, in the service of insight generation, integrating with other systems, but also machine learning and AI. Our interface allows our users to orchestrate data across multiple systems, whether it's on-premise, databases, ERP systems, CRM stores, or even the cloud application, the cloud data warehouses, and really create -- allows them to create kind of analytics and solve mission critical business problems, everything from marketing to HR analytics to supply chain optimization to pricing and packaging decisions, Alteryx kind of touches almost every one of those use cases.
Blair Abernethy
Thanks, Suresh. And an important component of the product set today is a platform you bought with Trifacta early last year. Maybe just kind of walk us through or remind us why did you buy that and why that's important to the future of Alteryx?
Suresh Vittal
Yes. As we were going on a journey to make our innovation available wherever our customers want to see it, so many of our customers use our desktop products but they also use our desktop products with connections into the cloud. And yet, so many of our customers want to experience our innovation purely in a browser. And so we were on the journey of making this technology available everywhere.
And we saw a unique opportunity with Trifacta to accelerate our cloud transformation journey as well from a product standpoint. So we made the acquisition last year, last February, with the intention of making Trifacta our cloud platform and bringing the Alteryx capabilities; the tools, the user experiences, the connectors, all that's great about Alteryx to the Trifacta platform. And so we did that and I think in really short time of a year and made all that innovation, including many new products, like Alteryx Auto Insights and Alteryx Machine Learning available to our customers earlier this year.
We -- combine this offering is Alteryx Analytics Cloud Platform and it's built on one common platform. The reason we picked Trifacta beyond kind of its ability to provide all of these cloud capabilities across a multitude of clouds; Google, AWS and Azure, available across regions, but also kind of deeply integrate with our customers cloud warehouse decisions, whether that's Snowflake or GCP, or Databricks.
Blair Abernethy
That's great. And talking to some of your customers recently, and I think you guys highlighted in a couple of presentations just how many data sources that customers are relying upon to actually get into the designer product. It's generally quite a few that people are using, right?
Suresh Vittal
Yes, it's staggering, Blair. I think you probably attended our conference a couple of weeks ago and you saw the customers talking about the multitude of sources. As we look at our product telemetry, it's quite revealing. 70% of the workflows involve four or more datasets. You heard a variety of customers talking about 30 data sources, 50 data sources, 70 data sources, that's not an uncommon occurrence in our business.
As I said, the use cases span everything from the Office of Finance to the Chief Marketing Officer and how they're thinking about lead generation and pipeline building, to the supply chain teams as they think about logistics and optimizing the predictability of delivery, as you think about the merchandising teams deciding what assortment to stock the shelves with, Alteryx plays a role across these use cases.
And that means allowing our customers to bring data from all of these places and create really complex analytical automation processes and take all the complexity out of the system. So it's not uncommon that our customers will -- as they build these workflows and build these analytical applications will be touching 10, 20, 30 data sources just to make the -- enrich the data set so the analytics can get smarter.
Blair Abernethy
Excellent. And that sort of leads us directly then into artificial intelligence, which is data is the lifeblood of these applications and these ways to add value. So let's dive into that a bit if we can, Suresh, and just give us an idea of -- because you announced some stuff at the conference recently. Just give us a sense of what Alteryx's strategy is around AI and start with that, and maybe then we'll delve into some of the big areas of opportunity for you?
Suresh Vittal
Yes, for sure. So at Inspire, we talked about AiDIN, which is our AI platform that brings all of the artificial intelligence and machine learning capabilities that Alteryx offers in a single set of capabilities. We think about the opportunity with AI and large language models, it's really lowering the barriers to entry to ask analytical questions, breaking down these data silos, and really engaging the end users imagination on how they understand data. And we've always believed that's really aligned with our mission of analytics for all.
This domain work has not been new to us. We've been utilizing some of these technologies as early as 2019 with the intelligence of language models in our intelligence suite product. They drive several capabilities, whether it's kind of text mining, PDF data extraction, natural language processing. And since then, we've been kind of constantly iterating on new ways to incorporate more of this technology into our portfolio. So we see kind of three really kind of core use cases that help describe our AI strategy.
The first one is that we see the opportunity for multiple personas to wake up to the power of AI in how they do their job. So as we introduce these generative capabilities into the analytic stack, we're seeing something emerge uniquely that we call multimodal analytics where you can allow -- analytics has always been a challenging area of collaboration inside an enterprise. If you're an analyst, you may want to be in a local interface or an Excel. If you're a data scientist, you want to be in Jupyter Notebook or Python code. And if you're a data engineer, you may want to be writing SQL and only that.
And as a result, it's been really hard to kind of help these teams collaborate and really achieve breakthrough insights. Powered by AiDIN, we see each persona being able to leverage their tool of choice from no-code and code-friendly workflows to chat prompts to Python, SQL and all of this kind of powered by AiDIN into one single application. When we think about attacking the friction between these roles, this can offer a dramatic acceleration in how analytics happens inside an enterprise. So that's clearly one underpinning of our strategy.
The second thing we see is that there's a lot of appetite for generative capabilities within our Alteryx toolsets. As we've been researching extensively with our customers and partners, we see that the appetite for generative and AI technologies is to reduce tasks, not roles in the enterprise. And so that means they spend less time on these mundane tasks like documentation, metadata management, systems integration, and instead can really apply their potential to creating transformational insights. And so within our product at Inspire, we announced capabilities like Magic Documents like workflow summary and so on, which are really meant to enhance the capabilities of individuals by allowing them to focus on the core analytics that needs to happen.
And the third opportunity, if you wish the third pillar is we see contextualized generative AI applications based on unique datasets that the customer brings to bear. I would kind of like to unpack that a little bit. If you think about it, Alteryx is a repository of really complex workflow data created by many customers or partners and by Alteryx ourselves. If you think about it, millions of analytical building blocks that we've been helping create inside our systems. So we're starting with an industry standard foundational model, fine tuning it with our library of best practices and design patterns and partner customizations.
And then finally, allowing our customers to bring their own data to bear. This, we believe, will create a personalized proprietary AI for each and every customer, which will be really rich in context with the data that Alteryx provides around the Alteryx ecosystem and the workflows and also that the customer provides around the specifics of their business. And so we think the potential of analytical outcomes is something that nobody else can replicate or create. We are also focusing on this AI workbench to be able to train and deploy these models within the customers' firewalls, so we can bring generative AI to where the data sits with the customer.
As some of the early design meetings that we've had with customers on these topics, they're really careful about how this happens inside the enterprise. This is not a slam dunk in their minds of how these capabilities get fulfilled in an enterprise setting, given all the regulatory, the compliance, the transparency, the governance work that needs to happen hand in hand. So that's a quick summary of how we approach generative AI and machine learning capabilities with the AiDIN platform.
We released many of these capabilities that I talked about, workflow summary, which kind of creates a layer of metadata management and governance on top of designer and server. We released that into our customers' hands with the latest release of our products. We released Magic Documents which reduces the time from insights to data storytelling and getting the insights into the hands of the variety of business users as a part of Auto Insights, we released that as well. And so a lot of innovation already in the hands of the customers and more coming here shortly.
Blair Abernethy
That's really interesting. And the idea or sort of the approach of having each of your customers build their own models, their own tailored models on their own tailored data is quite intriguing. So is that going to be something you will do through the AiDIN part of the platform, or could there be additional like standalone products from Alteryx?
Suresh Vittal
Yes, we see the opportunity to provide standalone products that allow our customers to focus on that specific initiative of using our data in conjunction with their own unique datasets. I kind of used the word AI workbench to describe that effort. It would be things the customer wants to do in addition to the work they do with designer and auto insights and all of our other products. So it will be its own product that customers can leverage and deploy to train using their unique datasets.
Blair Abernethy
What did you hear from your customers in the last two weeks and sort of -- is this something they're looking at -- they're just trying to get their head around, is it something they want to do soon? I guess I'm trying to gauge sort of I guess the level of interest? And of course, budgets are tight everywhere. So how do you think the market is going to move on this technology?
Suresh Vittal
Yes. As with a lot of this stuff and as you hear the industry kind of talking about this, there's growing concerns around the transparency of insights, the bias, some of this may introduce the ability to audit and explore and govern the models that may drive decisions is becoming critically important. We are in conversations with several of our peers and with regulatory bodies as they think about how to approach AI as a topic. So our customers are rightly careful, right? They're exploring, they're learning, they would love to do some early tests. And as I said, we have several dozen customers in early stage pilots with us on these topics.
But there's so much that -- enterprise technology stacks kind of tend to move slowly. And CIOs have to focus on the risk there onboarding with some of these new technologies as well. So the transparency and the covenants are super important. That's something that's come out loud and clear in our early conversations -- in many of our early conversations. And as they work through the product definition and as they start using the early products with us, that's becoming highlighted even more so. Also the ability to serve these multiple stakeholders is something that these organizations are very keen on. So I think there's interest -- there's probably more interest on the investor community side than there is on the customer side. But there's definitely interest from the customers, but they're all moving carefully and slowly as they rightly should.
Blair Abernethy
Yes, interesting. If you look at applying LLMs within your designer tool, for example, it's a very complicated -- it's a sophisticated tool, but it's also -- it's built as a low-code, no-code kind of product for the less technical user, if you will. Do you see opportunities there to make that even simpler to use to drive more automation? We've seen a number of companies, software companies looking -- talking about building copilots within their own applications. Is that an approach you might take?
Suresh Vittal
Yes, Blair, as you think about it, I kind of talked about those three pillars of AI strategy. And the second pillar was about reducing the time our users, the analyst, the business user, the data engineer, the business owner, the amount of time they're spending on doing the mundane tasks of analytics, I think there's a great way for us to help solve that and take a lot of that repetitive task out of the equation so they can focus on the core value-added work, Blair. So I think that's super important question that we ask as well. And we've kind of -- a big part of what we delivered early into the product was to do exactly that was to make it even easier for them to be able to do this stuff.
We call the power user in our community an ACE, right? He or she kind of gets that advanced certification because they're really skilled in how they build workflows and do analytics automation. We think there's an opportunity to make everybody an ACE through these technologies, which means you have kind of more people using your stuff to do more productive tasks and add more productive capabilities. So we kind of -- a lot of what we released more recently, the OpenAI connector, Magic Documents, Workflow Summary, flow tree [ph], the generative prompt to start writing workflows, all of that was aimed at exactly that goal.
Blair Abernethy
That's great. Maybe one for Kevin here. Kevin, how are you looking at ways to monetize some of these new efforts in and around AI for the business? And how is that impacting your sort of stated goals here of driving your margins up over the next couple of years?
Kevin Rubin
Yes, it's a great question. And I think as you heard Suresh go through the variety of capabilities that we see from an AiDIN perspective, in particular, it's going to cross over from capabilities and features that we think ought to be in the hands of everybody. And so they'll become core to their respective products. So think Magic Documents, as an example, is really core to the auto insights product. It will be features and capabilities that are added to that product. Workflow Summary is something we think is important and will be part of the designer product.
But when we get to the other end of the spectrum, and Suresh talked about an AI workbench, talked about multimodal analytics, those are large monetizable standalone products that we would expect to separately skew and charge for. And look, as we think about the growth opportunity going forward, in particular, over the longer term, the ability to keep adding additional products to the platform and provide customers with a variety of capabilities at their disposal as we think about analytics for all, it just becomes another opportunity to capture additional dollars at the account level.
Blair Abernethy
That's great. So reinforcing the value prop for the existing core designer product but also creating new skews, that's great. And Kevin, just maybe touch on for people in the audience who may not be that familiar with the current state of the business, but just on the margin side of things. Obviously, there's a lot of spend going on, on the R&D side, but you've set some goals out there to drive margins for the business in the next couple of years.
Kevin Rubin
Yes. So at our Analyst Day, we updated our long-term model framework and aligned it to fiscal year '28. And we're looking to drive operating income margins to 25% to 30% and free cash flow margins 20% to 25%. And we spelled out a framework that investors can look at in terms of where we are today. So the midpoint of the guided range for the full year is 9% operating margin, and we expect to deliver 3 to 4 points of leverage each of the subsequent years as we kind of march to that 2028 period. Additionally, we talked about the model assumes an ARR growth rate over this period of time of 20% or more. And so when we think about growth, a lot of it is around innovation, having more opportunities to sell more products to more users and more use cases within our customer base and our prospecting.
Blair Abernethy
That's great. And maybe just back to you, Suresh, for a moment, maybe you could just describe sort of the competitive landscape a little bit more for Alteryx as it sort of stands today. So as we move into some of these newer areas that you guys are exploring, how does that shift or maybe improve? It looks to me like you might be improving the stickiness of your product by customizing LLMs, if you will.
Suresh Vittal
Yes. So analytics is a really large category, generally speaking, and so there's kind of many different players. I'd say, by and large, the biggest competitors for us has been kind of homegrown solutions or systems to do analytics automation in the absence of an analytics platform like what we've been assembling over the past multiple years. And then on and on, there's kind of the introduction of maybe a prep and blend capability, maybe an analytics automation capability, maybe an orchestration capability, and so on. But the ability and increasingly, as our customers are dealing with fragmentation of data just as much as dealing with consolidation of the data, Alteryx platform really shines in helping our customers kind of solve for those challenges. As you know, we don't maintain a persistent layer ourselves, right? We want to help our customers do that.
And so when a customer wants to bring data into Snowflake or Databricks or Google BigQuery, they'll often turn to Alteryx to help kind of bring the business use case onto those platforms and use Alteryx on that journey. And that's clearly showcasing some of the deep partnerships we have with Snowflake, Databricks and Google. When our customers want to kind of build reporting environments and visualization environments with Tableau or Power Bi or others, Alteryx is a key enabler of that. When our customers want to integrate back into systems of engagement like sales force or workday or Adobe, Alteryx becomes a platform in that. So the breadth and variety of use cases is such that there isn't kind of one single company I'd call out as a competitor. It's largely been kind of homegrown systems that teams have built in-house. And increasingly, they're kind of turning to Alteryx to help solve that.
Blair Abernethy
Got it. And you mentioned Snowflake. Maybe give us a little bit of an example of how a customer of yours would leverage your platform with the Snowflake data warehouses and do you play directly in the Snowpark area of the platform, or how do your customers tie in to that data?
Suresh Vittal
Yes, great question. I think the real kind of -- at Inspire, I actually invited the field CTO for Snowflake to come and talk about how Snowflake views the partnership and more importantly, how our customers view the collaboration across these organizations? And the words he used to describe it, which kind of was actually a really good way of describing it is that if Snowflake has the data, Alteryx has the analytics, right, and together we help brands like Herman Miller, Polaris, Great Clips is another one that comes to mind, CUNA Mutual is another one. So we've got about 1,000 customers together.
Snowflake customer will often want to kind of bring the data to Snowflake, access the data from Snowflake. Alteryx provides that access to the analytics, all the analytical building blocks and blend, transformation, analytics automation, feature engineering, data science and so on that customers want to do. And so it's that ability to access that data and access the analytics through one common interface. And, indeed, Blair, we are doing interesting things with Snowflake around Snowpark and Snow services [indiscernible] partners to embrace Snowpark. They have a conference coming up here around three weeks or so, stay tuned for some interesting things we'll be announcing jointly there.
Blair Abernethy
That's great. And then maybe just along those lines to Kevin, are there partners more on the go-to-market side of things. That's an area that Alteryx has been investing in a fair bit in the last year. Maybe walk us through sort of what the status is there and kind of where do you see that going over the next couple of years?
Kevin Rubin
Yes, so we've got a pretty transparent strategy to really attract a much higher, more skilled partner as part of our go-to-market program. You've seen that with the likes of companies like PwC. And we had Ian [ph] represented at our Investor Day talking about the relationship we have with them and how they're going to market in some of their areas, specifically leveraging the Alteryx technology. And then when we think about certain customer segments and areas of the market, we really do lean on partners there as well.
So our direct go-to-market efforts are clearly focused on the largest companies in the world, both customers and prospects. But as we get down, the company size and that segmentation, we really do rely on a different level of partner to be able to serve those markets more efficiently. A lot of companies in that area need real solutioning and need partners to be able to deliver a complete package for them, as opposed to some of the larger organizations that have more resources available. So partners are incredibly important as we think about our ability to efficiently scale.
Blair Abernethy
It's interesting to what's happening in this company in the last two years. Since Suresh has come on, you've really broadened the product offering and it sounds like the product offerings getting broader by the month here. You introduced a new licensing program, the ELA. So maybe you could talk to -- help people understand that a little bit? Because I think that that really is going to -- is creating this kind of virtuous circle when Suresh is putting up a lot more product to deliver, right?
Kevin Rubin
That's exactly right. If you go back two years ago, we basically were a designer server company. And those were the two primary products that we had to offer. And while their flagship beloved products have taken this company a long way, it's time to be able to innovate and offer a much broader suite of products and much of what we talked about today, having a cloud platform to allow customers the flexibility to deploy technology, how they desire to deploy it to be able to innovate on these new technologies, like generative AI, et cetera. So we're actually coming up on the second year anniversary of when we launched our ELA program in Q3 of '21. And they're basically packages of products and technologies that we offer to customers. It's a really easy way to purchase different tiers, different quantities of products.
And the unique aspect to our enterprise license agreements are, we give them 50% more capacity for the first year of the ELA to really trial, prove out value, and explore areas within the business that Alteryx may be more helpful for and can drive a real ROI without having to go department by department and solicit a purchase requisition and go through the process of purchasing. So think of it as like a yearlong free trial for those additional licenses. So companies can really explore the art of the possible. For us, the attractiveness, obviously, as we get significant visibility into that use, we've seen the lion's share of the customers that purchase these ELAs get into the burst capacity. So they're using more than they've contractually purchased, which means when that burst capacity expires, there's a high likelihood that they're going to upsell to that higher tier.
And then we will give them burst capacity again in that next tier. And to your point, it's a very virtuous cycle where we're able to continuously provide more and more technology and value to customers with little risk, right? They're not taking that risk that we're going to put software in the hands of people that it's not going to land or it's not going to deliver value. This gives them the confidence that they can prove that out. And once we do, it's kind of like dry cement. The ability for customers to kind of rip it back out of users' hands is incredibly difficult. So it's been a really powerful motion.
Blair Abernethy
That's great. And Suresh, maybe just back on the large language model question or area, maybe you could talk a little bit about what are some of the technical challenges that you're seeing from your perspective, and your customers are going to have to get over in order to really make this technology workable from an enterprise perspective?
Suresh Vittal
Yes. Several, Blair, and it all stems from the pre-training, the fine tuning, and how you get the model ready for deployment across an enterprise. To do that correctly, you need to have a period where customers are bringing their own data, adding a bunch of the context to the model building process. But then, once they've gone through that exercise, being able to deploy this takes on a whole different life of model operations, constantly measuring the efficacy and the performance of these models. And being able to open the blackbox that's the model and understand what variables are driving the behaviors and therefore the prediction, and if that's in line with the company's objectives, or if they're inadvertently introducing a bias into the model. There's a lot of industry talk about summations as well, which is the model kind of flat out often wrong results [ph].
In a test setting, that's okay. In a production setting, that could be disastrous to get it wrong. And so giving the right governance and the right guardrails is something that's super important for these customers, and for them to be able to change things. Very often our customers also share with us that they're going to be asked by regulatory bodies and by their audit teams to show these decisions, how they were made, why they were made, and what was driving the decision making process? And so providing the transparency and visibility is going to become more and more critical as people think -- as we get closer to making these work in production environments. Now also on top of all of that, there's the cost factor as well, right? Do we need -- the cost of getting it wrong could be very high in some decisions. And therefore you want to be absolutely right with that and invest what you need to.
Some decisions might be so low value that either you don't need to train too much, or you don't need to get it right all the time and you can kind of go through the process. So if you think about the decision making spectrum, not every decision gets treated equally or should be treated equally. And there isn't really yet in the genitive AI world a way to kind of decide how to make these decisions, which decisions need to be held to a different bar. And therefore, how do we deploy the technology stack to support that multitude of decision making that exists within the enterprise? And so that's going to also become a challenge, because not every decision can be super expensive.
Then the point of using this becomes lesser. So I think as enterprises go through this journey, the transparency question, the governance question, governance on the building question, the cost factor, and then the integration with the other enterprise systems and the other interfaces in the organization is going to become more and more important. So there's a layer of this interface. There's a layer of kind of embedding this decision making processes. And there's a layer of kind of training and auditing on unique datasets. Enterprise has to get all of these three layers right, the interfaces as well as the decision making.
Blair Abernethy
It sounds like -- I agree with you, and it sounds like the customers may have to be leaning more heavily on you at Alteryx and/or skilled partners in order to affect and build these systems, because this is a relatively new area, right?
Suresh Vittal
Yes, for sure. You've seen this evolution with other technology phenomenon as well, whether it's cloud, mobile, social, AI, this is not the first time this has happened, probably won't be the last time it'll happen either. I think we have a -- I'm excited about our opportunity to help enterprises navigate this journey. I'm also really excited about the opportunity to make more people ACEs, right, to lower the barrier to entry into Alteryx products and get the benefit of an analytics decision powered by Alteryx across the enterprise, I think that's worth getting ambitious and excited about.
Blair Abernethy
Well, that's something that you've been working on with your ML product, right? And maybe just before we finish up here, just help the audience understand what is this ML product you already have and who's using it?
Suresh Vittal
Yes. So within designer, we have a series of what we call tools that allow for models to be built. And by the way, if you think about -- I talked about the product telemetry. Nearly 60% of the workflows in Alteryx use some kind of an ML, an advanced analytics tool, right, whether it's a machine learning tool or a data science tool or a regression tool or a spatial tool, and so on and so forth. And so we always kind of enable them to do that. But what was interesting was also the ability to give a business analyst, a business owner, a citizen data scientist, if you want to call them that, the ability to build models and share that with their peers inside the organization to interface with the data science team and get their feedback and so on. And so earlier this year, we released a capability called Alteryx Machine Learning that's really aimed at helping a business user build a model using the predefined, preconfigured set of machine learning models and libraries that we make available to them. And therefore reduce the time it takes.
If you speak with our customers or frankly any brand out there, the biggest supply they have is of data science talent, right? And so inevitably, a business will open a ticket with a data science team and wait for six to nine months to even get the simplest of models. We believe there's a whole up scaling opportunity by allowing the business to be able to build models, maybe not to put them immediately into production, although you can do that, but at least put them in a place where they can now start to discuss with their data science teams and their governance environments and other teams before they start to deploy this into production. There's a whole step change in how you bring some of these machine learning models into production. And we think there's a real opportunity to empower the business users who understand the business, who understand the context behind the decisions they make, who understand the data, who understand the objectives to be able to build those models, even if it is to share and distribute across their stakeholders.
Question-and-Answer Session
Blair Abernethy
Well, that's brings us -- thank you very much, Suresh and Kevin. That sort of brings us to the end of our 45 minutes. I really appreciate the insights and just seems like you're in the right position at the right time here with the amount of innovation that's coming at us and your customers are looking to you to help guide them through it. So it looks like looking forward to a lot of interesting new products coming out over the next year.
Suresh Vittal
Thanks for having us, Blair.
Blair Abernethy
Thank you.
Kevin Rubin
Thank you very much. Have a great afternoon.
Blair Abernethy
You too.
Kevin Rubin
Cheers.
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Alteryx, Inc. (AYX) Presents at Rosenblatt Securities Technology Summit: The Age of AI (Transcript)