380 million workflows & counting: How Alteryx is powering enterprise AI

Speaker A: This is the London Fintech Podcast, bringing you practical insights and approaches, stories and inspiration from the innovators who are building the new world of financial services. I'm Tony Clark, I'm founder CEO, uh, of Next Wave. I'm also the host of the London Fintech podcast. So this is a London Fintech podcast first Streaming live from Money 2020 in Amsterdam in a glass cube. My podcast advisors tell me this should be like a conversation in the pub, but this is the strangest club I've ever been in. But, uh, really looking forward to it. So if you've joined us for this session, this is under the AI and the Agentic age theme. We're going to be talking about the billion dollar business of AI ready data. And I'm here with Remco Denhaya who is, uh, VP International Channels for Alteryx. Hey Remco.

Speaker B: Hey Tony. Glad to be here.

Speaker A: It's good to be here with you. So Alteryx, if anyone doesn't know them, are exhibiting over in the corner of the hall behind us actually. So somewhere behind, um, a couple of stands. Alteryx is a billion dollar revenue company that's enabling the data foundations that the AI revolution relies on. Alteryx is extensively used to replace legacy data processes with governed versions, scalable and visible data workflows which we're going to be talking all about in the next 20 or 30 minutes. And the platform operates seamlessly with the major cloud data platforms, so we're talking snowflake databricks, et cetera, to serve and source AI ready data for the agentiq workforce. So there's going to be lots to talk about. But just before we get into it, Remco, good to see you. Is this Your first money 2020?

Speaker B: It is my first money 2020, correct. Um, but not my first podcast.

Speaker A: Good. Well, we both made it. So I came over from London yesterday and what was supposed to be a three hour trip took the whole day because of cancelled flights. And I know you've been dealing with cancelled trains and things today as well. Yes, I was in the nick of time. So what do you make of it so far? I mean I've got my 10,000 steps in just doing two, two laps so far.

Speaker B: Absolutely massive. This uh, this, this uh, this event is so big. Uh, I, I'm hearing that there's eight. Uh, yes, I'm trying to speak to a good proportion of them in the. Over the next two days we'll see

Speaker A: how we get on. You, me both. And this is in your hometown, so great for you to be here.

Speaker B: Right, yes. I'm, I'm so happy it's here. And it's uh, weird that I haven't been here before Money20 20, even though it's in my hometown.

Speaker A: Well, we haven't been to any parties yet. Well, I haven't, but maybe there's a chance this evening there's 2,000 firms represented at Money20 20. And I was over with the, the press folks a bit earlier, um, hearing some of the releases and news updates and there's so much going on around the floor. So we're doing our bit here to add to all of that and really, uh, want to talk about Data Bank.

Speaker B: I guess there's big money and money.

Speaker A: There is, there is. Um, so Remco, just before we get into it, a little bit about you, your journey to date, your career journey to date. I know you've come up through sort of big tech and international enterprise, uh, you know, prior to taking up the role with Ultrix.

Speaker B: Yeah. So, uh, thanks for asking that. Uh, yeah, my background is indeed, uh, uh, data and AI. Been in that business for uh, I think about 30 years now at about nine years at Oracle, uh, a well known company, of course. And I've been at SAS Institute for about 19 years. Company, uh, very well, uh, known for advanced analytics and being kind of the trailblazers in that area. And uh, uh, recently, the last year and a half, I've been at Alteryx as a VP of international channels. And international channels means Europe, Middle East, Africa and Asia Pacific and Japan. So everything but Americas is kind, uh, of like my remit with regards to all the partnerships, the alliances, the ecosystem that we manage that help us grow our business and delight our customers.

Speaker A: Yeah. So you're fully in the ecosystem and you spend much of your time talking to Alteryx's customer base, which is probably no surprise. And that includes many Fortune 500 corporations and many of the firms that are actually at Money 20 20, uh, this event as well. Um, how has the conversation with your customers changed with the accelerating pace of AI adoption and really the movement from strategy and buzzword to practical boardroom priority now?

Speaker B: Yeah, um, that's such an excellent opening question. Uh, you called this podcast, uh, you know, Billion Dollar Business. Um, but you know, at the end of the day, yeah, that is what Alteryx is. Alteryx is a billion dollar business. Uh, but we are moving in a market that is truly a trillion dollar business, if not trillions of dollars of business. And so how is the conversation changing with regards to AI? Uh, that's the opportunity that organization, a few years ago, it might have been like, what is AI? What can it do for us? How can we experiment? And that conversation is now really changing towards, okay, how can we make AI work for us? How can we become profitable? How can we be more agile? How can we be faster? How can we do our, uh, work in a better way with our customers, uh, and operationalize, know, and make our processes more efficient? So it's really about that. Deploying AI at scale is really the conversation these days. Um, and, you know, it's interesting because, um, I was, you know, I don't know if you sometimes do that, but you watch your, uh, you know, what's surrounding your house. I'm rebuilding my house as we speak. And I was looking at my, uh, uh, my books and, uh, my. And I had a. I have a, you know, a wall full of books. And I was thinking to myself, that was last night. I was getting a bit nostal, and I was thinking, like, why do I even have those books still? Uh, in the Internet age, is that even needed? AI knows everything. What is in those books? Uh, but I'm still looking at those books. Like, which books I haven't read yet. What kind of wisdom is there? And so I was in this nostalgic mood, and then suddenly I looked at one book in my book closet, and I brought this with me here. Look at this. I don't know if you can see it. We have a camera here as well. This book is a book from the 80s, and the book is called A Portrait of Power.

Speaker A: There we go.

Speaker B: Now, what's so interesting is that in 2026, the meaning of this title is very different than what it meant back then, because this book is actually about Claude Wattie. Claude, uh, Wattie was for many years, I believe, 35 years, uh, in charge of a tropical island called St. Martin in the Caribbean. Uh, and, um, he got into power and he stayed into power for a. And he did some really, really good things and maybe also did some nefarious things. Uh, but he was able to put, uh, Saint Martin on the map in a certain way and to get St. Martin push, uh, you know, above its weight in terms of developing tourism, you know, getting a lot of, uh, you know, getting the economy to grow. And so I was thinking about the analogy with. With AI because of course, Claude, right. Right now is one of the most powerful AI systems in. In. In the world. Right? Uh, and this guy is Claude. What's he. And so I was think. Analogy, uh, with that title and with that book you can think of Saint Martin has beautiful beaches. Uh, but the beaches alone don't make St. Martin a top destination. What makes it a really great destination is the infrastructure around it. The infrastructure around it, the governance to organize all of that, making it all work. And in that same way, you could think of the beaches being the models. Claude is one of the best models. We have OpenAI models, we have Gemini models, but the models themselves are getting commoditized. And so the conversation you're talking about what is changing in the conversation. Conversation is not so much anymore about what model is the best model. The conversation is about what is the infrastructure around those models to make it work for us. Uh, same analogy as with, uh, the tropical island punching about their weight. What is the infrastructure putting in place to be able to actually get to those beautiful beaches and make this a thriving economy?

Speaker A: Yeah. And of course, infrastructure has to be built on solid foundations and have great scaffolding and all of those things. Um, so I love that analogy, by the way. I learned a new word a couple of weeks ago, which was claudification, when someone was describing that the sector had been claudified. I did try and get hold of the anthropic guy at the conference for one of these conversations, but he's been a bit busy. They had a reported 80x uptake in institutional uptake on, on anthropic in the first quarter of this year. So anyway, that's a different conversation. But I love the fact that you found the Claude book in your library. Um, let's talk about the data readiness problem. So this is where it starts. Um, you know, the opportunity for AI is underpinned by and fueled by AI ready data. And there's many stats out there. I think Gartner suggested that organizations abandoned 60% of their AI initiatives because they don't have AI ready data. And is that a credible figure for you? Is that what you're seeing across the Alteryx ecosystem? And I'd love to ask, is there an example of where data goes wrong that really puts that into contrast?

Speaker B: Yeah, honestly, uh, I. That number may even be conservative. Uh, at the end of the day, um, most AI projects don't fail, uh, because the models are bad. Just the analogy that I just gave with the beaches. The beaches are beautiful. Uh, the AI projects fail because organizations are not able to consistently apply business logic context to the models in order to make them work at scale. So, uh, there's a lot of excitement around agentic AI. Uh, and all of that is good. But the real challenge the real challenge is trust, uh, because if the data that is being queried with AI and if the AI is being put to use but you don't know, uh, you don't have the visibility around it, you don't know that there's an understanding of what that data means. Can you repeat how you got to those um, results uh in the first place? And is that stuff auditable then? That's really what it comes down to. So that's to me uh, those things are becoming more and more important than just having the models in and of themselves. But you can see that being behind models, you know, project AI projects failing.

Speaker A: Yeah and I thought, you know, before the show kicked off this morning, I wondered what the Most used phrase, Money 2020 was going to be. And I thought is it going to be crypto? Is it AI, is it stablecoin? And the word I've heard the most since this morning is actually trust. And it's in all of these conversations and that's really what it boils down to. So I think um, fundamentally that's what we're talking about. Because if you're going to put ass on chain or you're going to be uh, um, trusting your customer base with AI driven, agentic driven processes, you've got to be able to feel uh, comfortable and have confidence and put your business and your in many cases on a regulated industry, put your personal uh, career and personal status on the line, uh to trust the data and the uh, and the processing and the, and the AI that's delivering those outcomes. So AI ready data then as the foundation. Let's talk about that for a minute. I've mentioned AI ready data about 4 times already already and uh, without really explaining what it is. But Remco, this is really what Alteryx does. So what's your definition? AI ready data?

Speaker B: Um, well I can tell you what it is not. AI ready data is not data that is clean. Uh, AI ready data is data that you can trust. To your point about trust, it is data that you can trust. It is data that you know where it came from, uh, you know how the data may have been transformed, uh, you know, uh, that you can repeat that process, uh, and it can be audited. Right. And so that is what AI ready data uh, is. And uh, when I talk about this visibility and being able to understand and repeatability and being auditable, I uh, call that VURA v U R a visible, understandable, repeatable and auditable. That is at the end of the day is what, you know what trust Comes down to if you have those things in place, then that's how you can get to AI ready data.

Speaker A: Vooora. Visible, understandable, repeatable, auditable.

Speaker B: Right, Yep. Uh, that's ultimately trustworthy.

Speaker A: Pretty easy to remember. So can you just unpack that a little bit? Perhaps in the context, let's pick a use case, uh, perhaps claims, uh, handling for an insurance company. When we're talking visible, understandable, repeatable, auditable data, what are we talking about?

Speaker B: Um, uh, where did the data come from in the first place? Uh, what was it? If you look at a data set, what rules were applied to actually get to the data set in the first place? Uh, it may have been combined with other data. And then you look at that data, what does that data even mean? Uh, which rules were applied? You can, may think of uh, who approved those rules. Uh, can it be reproduced? If you look at uh, you know, claims process? Yeah, it's, it's, it's exactly the same kind of concept. Regardless of what the use case is, whether it's about um, fraud recognition, whether it's claims processing, whether it's about giving somebody a loan. Yes or no. Uh, it's all, it always comes down to those, to uh, those points.

Speaker C: Yeah.

Speaker A: So I guess in that example it's going to be well, where did the policy data come from that you used? Where did the customer data come from that you used?

Speaker B: Yeah, and it might have come, it might have come from different systems because maybe, maybe you've acquired uh, you know, another insurance company. You try to roll it into your ye, into your systems, but maybe it's not completely integrated with your main, you know, with your main system yet. Right.

Speaker A: So and this is, you know, fundamentally for me there's been a lot of debate about agentic workers and agentic models, single agent models, multi agent models, synthetic workers, digital twins and the human robot workforce. But for the regulated industries where we're sitting, you know, today and where everyone is in the sector, uh, for me it's the power and the acceleration of generative AI, but on governed rails, so on defined workflows. And this idea of really at this stage, uh, twinning your employees and giving them the keys to handling your customer base and all customer comms, um, it's not there yet. Ah. And I think there's a lot of debate about human in the loop and you can have maker checker models and all those things, but uh, this idea of deterministic rules, uh, at the core, but with the acceleration and the power of agentic and uh, gen AI over the top, I feel is um, the best of both worlds.

Speaker B: Uh, yeah, I mean if Netflix gives you a wrong recommendation, it's annoying. If an AI agent gives you a wrong banking decision, you might have a regulatory event and you can't have that. So that's exactly why you want to have the good of the AI agents, which is the ability to reason, the flexibility, you know, the intelligence behind it. But you need to be able to when it, you know, when, when it is about that data and it is about informing decisions, whether humans are making decisions or whether it is, you know, subsequently a decision within a process that an agent may, may run, that needs to be correct and it needs to be again auditable and it can, there's nothing that can go wrong with that. And that's the deterministic part of that. And that's what we indeed call the guardrails of the workflow. So y got to be able to uh, apply the business logic which is the context like what, what, you know, who did what to whom and uh, you know, and to which data and, and then you got to be able to uh, be able to govern that in the right way. And then you know, that brings me back to Claude, the portray of power. Uh, it is a lot of power in AI and I feel that more and more it is about uh, you know, there's more and more power going to come in, into the, into the hands of organization, going to give the AI a lot more power and then you got to be even better able to govern that power and have it within the guardrails that you set up front.

Speaker A: Yeah, and I think this is going to come into sharper contrast with the advent of the EU AI act of course where you've got to be able to evidence the risk ratings of your AI models and it's, and it's evidence based proof. So you've got to be able to measure and show where the data is coming from. And this is really where Alteryx fits, which is why I think this is such uh, a relevant conversation. As I understand it, Alteryx ran some 380 million data workflows last year. So you've got, it's not just fs but you've got a multi sector client base and you are really that data layer that's pulling from all sorts of uh, data sources, some of them spreadsheets and underlying databases and other systems of record. But you're pulling data, you're normalizing it, you're cleaning it, you're getting it ready for AI and Crucially you're able uh, to audit and measure those workflows. Was that a decent description of where you fit in the whole um, infrastructure stack?

Speaker B: Yeah, those 380 million workflows, uh, was actually also a massive growth versus the year before. And what that tells me is that we're looking at an industrialization of analytics. Uh, uh, businesses don't run on prompts, they run on processes, they run on workflows. And so what this tells me is that organizations are going from having uh, maybe separate use cases, uh, that are being automated to automating them at scale across multiple use cases across multiple departments. And that's why we see such a growth in these workflows across our platforms. Ah, that we can measure so clearly. And so yeah, that's at the end of the day, uh, what we see. Uh, and I, um, what I would also say is that um, uh, that then plays into what our platform is

Speaker A: right now you're solving a very practical problem. One of our mutual banking clients who I think probably at the show runs 24,000 spreadsheets in their financial month close. And they do that because they are embedded in the month close process and they can't actually throw them away, but yet they've uh, committed to the regulator that they will move off what the sector calls end user compute, which is uncontrolled spreadsheets, ess on something that's on platform. And that's a very real problem. I mean that's actually a regulatory commitment to go on platform which Alteryx can solve for. But of course it's also the foundation, the stepping stone for AI because once you're pulling that data in a uniform, normalized way, then you can use AI to get more value from it.

Speaker B: Yeah, we have so many examples like this. And uh, in banking in particular, we have for instance bank of America that is doing regulatory reporting, testing processes and it, you know, uh, before they started using our platform, it took them 1700 hours to do all that whole regulatory testing. They automate and they automated all of that, all of those processes. Right. When you talk about workflows applying the right business logic to the data and then being able to do that and automate it and they took and they moved it from 1700 hours back down to one hour.

Speaker A: Whoa.

Speaker B: So it's not like a 50% increase or 90%, you know, ah, implant improvement. We're talking about 1700 to 100.

Speaker A: And I think I said top of the conversation. Um, aside from hosting this POD show, uh, I run a consulting company called Next Wave and we do partner with Alteryx. Remk and I know each other already but there are numerous use cases of one of the other big US banks. We took libel reporting from five weeks down to five minutes. So it was a smaller contained use case but it was something that was reported after the fact five weeks late, um, because it had to be reported. But it went to something that was pseudo real time and the death sends started making credit decisions on it. So it actually got um, a whole new value, uh, dynamic out of the data that they never had and also saved some headcount as well in the process.

Speaker B: Yes, yes. Yeah. So uh, we're very happy to have those happy customers and many of them uh, indeed here and we're very happy to partner with Next Wave, of course.

Speaker A: Before we jump right into today's episode,

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Speaker A: Coming back to Alteryx 1, what does it do?

Speaker B: Uh, we sit between the raw data and business outcomes. Right. So Alteryx one is a platform, obviously it's a piece of software because we are a software company that takes data and it transforms them, transforms the data into business logic and then operationalizes that logic into um, business actions into business decisions. That's what it does and it does

Speaker A: it on a very visual canvas. You can build your data flows by dragging things around and connectors to connect to data sources, connectors to uh, connect to data visualization or analytics outputs and you can build um, scalable repeatable governed workflows for your data in the tool inside the cloud data platforms as well. So you're integrated with the databricks. Correct.

Speaker B: So the data does not have to leave uh insurance companies or banks preferred cloud data platform. It can stay in there. The visual canvas uh, enables a business analyst that might be sitting in a FPA or might be sitting in the risk department or might be sitting in the marketing department uh, to be able to work because it is uh, a canvas, a visual canvas. And so uh, you don't need to be a programmer to be able to still have a very enterprise grade environment to work with all to have all those capabilities at your fingertips. That's why this software is used and loved by a community of hundreds uh, of thousands of people. We have a community that's 750,000 people on our website. They can all go there and they help each other out with all the use cases that are out there. Now having said that you describe it as a visual canvas that people can work with. Uh, but what we increasingly see is the AI is that actually the agents start using uh, those workflows that may have been built by business analysts or actually the agents can help in creating those workflows. And so that's really where we see a true acceleration of uh, the application uh, of the software and towards all the use cases that we see in

Speaker A: the financial services which is something that we're investing in together. So I talked about that insurance claims example. We have a working, working uh solution whereby we actually have a couple of insurance agents. I think one of them is a claims handler and the other one's an inbox, uh, triage agent working on top of an Alteryx platform connected via MCP and the fraud scorecard. And everything sits in the Alteryx, uh, Rails and the uh, rules based deterministic flows and the agents are working over the top and we've built that one for front office, back office rec. So anyone out there from the banking community, this thing called Foborec, we've got one running for that as well. This is, and this is where the industry, where the sector's going, isn't it?

Speaker B: Yeah. I love your example. You know your insurance example is first notification of loss. Right. It's a real use case that we can help insurance companies with. And so uh, and you know we were happy to partner with uh, you but obviously uh, our business partners play a Very, very important role. You can't get to um, uh, AI readiness as an organization, as a bank, as an insurance company. You can't get to AI readiness just by deploying one technology. So in that you need a business partner, a business partner, uh, knows the particular use case, has the industry expertise, the business partner that we work with has the knowledge around the systems in the organization, has the expertise uh, around the product as well. And it brings it all together and maybe also about other products that are adjacent, other technologies that are adjacent to it. So that's why we love having an ecosystem that work with us to solve our customers problems.

Speaker A: And you're hearing it in all the conversations here. So the other, the big theme is the great rebundling and it's this new uh, ecosystem component stack to build end to end services on new technologies and using new uh, service components. And you're hearing it everywhere. And I was in a fantastic stablecoins conversation earlier on today in MoneyLab and uh, the amount of partners that were in that particular initiative, you know, it was dozens of firms involved in building the stack out. And see you're very much, much connective tissue in that context, aren't you? Because you're connecting to underlying systems, apps. Um, and we're just starting to talk about agentic flows and you're building that as those with a variety of technologies. I mean the first example of first notification loss we built, we built using Google adk and we showed it to one of the big banking clients and they said, oh, can you rebuild that using Anthropic? So as we rebuilt it on the

Speaker B: cloud platform, fine with us.

Speaker A: Um, and so it goes, right? Everyone's um, assembling the parts. It's kind um, of a technical Lego.

Speaker B: Yeah. What you need, what you need is interoperability from a technology point of view as well as from a, you know, a business partner point of view and

Speaker A: switching it around, we also found that AI can help us build the workflows.

Speaker B: Yes.

Speaker A: So, so when you're facing into tens of thousands of spreadsheets that need to be refactored, um, if you can actually get AI coding to help you refactor some of those into governed workflows, there's a huge advantage. And we found that if you can uh, if you can context prompt, prompt the agents with standard operating procedures and you do have to do that, but they can actually build flows for you as well.

Speaker B: Yes, that is exciting. And that's why you see a massive increase in productivity of people that are dealing with these challenges. And opportunities.

Speaker A: Yeah. And as I said a few minutes ago, very, very practical challenge. So hopefully you're getting, uh, some great conversations on the stand. Um, this is sort of where Alteryx fits in this whole equation. Everyone's talking about data, talking about trust, talking about visibility. And you need a very practical way of knitting the data together and being able to measure and audit it. And that's, uh, really where you guys sit. So if there's anyone, and I don't know who's outside listening to this, um, uh, uh, I don't want to look in case we've lost some of the audience, but, uh, if a data leader is listening to this podcast, um, what would be one concrete step that you would recommend something you could do next week to make your data more AI

Speaker B: ready, I would say for the data leader, is to look at a. Just take one business decision that needs to be taken anywhere and then look at the lineage behind that or a business decision that was taken and look at all the lineage that happened behind that. Follow, uh, the data, huh, follow the rules, follow the approvals, uh, follow all the transformations. And most organizations will be shocked at all the complexity that you discover or sometimes even the inconsistencies or gaps that are sometimes behind, behind it. Uh, and so if you try to circle this back to AI and AI agents, uh, you can't trust AI. Back to the trust, uh, word if you don't trust the journey that this data took. Right. So that's my one recommendation for the data leaders.

Speaker A: Follow the data. Map the journey.

Speaker B: Start there, Map the journey. And I'm in a situation right now as we speak. I told you earlier, we're in a renovation, which is, I was looking at my book closet in the first place. Uh, and so we're renovating our house and I wanted to get an extra mortgage.

Speaker C: Mortgage.

Speaker B: I wanted to get, uh, a little bit of extra money. So, uh, I applied for the mortgage. We go through all these processes these days. Everything gets lifted and you need to give the bank so much the detail. Uh, and we did a pre check and it turns out, uh, that I can actually indeed have an extra little mortgage. Not even that much. Push comes to shove. We got pre approved. Four weeks later, it's like, okay, press the button. We want to get the loan. And then suddenly out of the work works. Somebody in the mortgage provider had told my, my, my mortgage advisor, no, uh, it's not possible anymore. It's like, why? Well, we have this rule that we cannot give anybody a bigger loan than X amount. Uh, and so, uh, and you know this, your combined, you know, mortgage will now be higher than that amount. Well, first of all, number one, that rule is already broken because my existing mortgage with that same provider is already higher than that amount. And then, number two, that business rule was never applied in the first place when they did the pre check on the mortgage. And so now when the push comes to shelf, you actually can't do the mortgage. And it's like, oh, go figure it out. Even though it's not about whether I have enough financial room to be able to pay the mortgage. So what are you faced with here? You're faced with an unhappy customer. You're faced with a lot of work and inefficiencies because the organization got to work, uh, and it could have been sold if you would have mapped all the business logic, all the context that is there in the right way.

Speaker A: Yeah, well, I think we're straying into customer experience territory, um, and, uh, an equally important topic, but not one that we'll have time for. I want to just switch the angle slightly because we've been talking about the data, the data flows, agentic readiness. Um, and Remka, in your role, you are a head of alliances and channels international. So just thinking about the ecosystem and the people that you're dealing with, and often at these events, you talk a lot about models and agents, but actually it comes back to transformation. It comes back to people change. It comes back to. That dynamic is so important. And I'm curious what you may have learned with your international footprint. And we're sitting here at, uh, European money 2020. So what have you seen in other geographies in terms of, um, AI readiness, um, data infrastructure, challenges and progress, and, uh, are there meaningful differences in the regions and anything that Europe perhaps could learn from other people?

Speaker B: This is super interesting. Obviously. Indeed. I work for an American company. My direct boss is American. I've lived in Singapore on the other side of the world. I've lived in other countries as well, next to the Netherlands. And so I can confidently say that I can kind of compare a little bit. Uh, but you know what's so interesting?

Speaker C: It's it.

Speaker B: The differences are not that big, actually. Uh, the difference, the differences that you do see is between organizations that treat data as a strategic asset and organizations that don't, and the organization that treat data as a strategic asset and everything that comes with it. They're the ones that are the winners in this AI age. And so, uh, now, does that mean that there are no, uh, regional differences between Asia Europe and America. And what can they learn from each other? Yeah, sure. I mean uh, I think what we can learn and here in Europe from the Americans is fast innovation and just quick, you know, just experiment and go, go, go fast, uh, and don't be too afraid to break things. What can Americans maybe learn from Europeans? Europeans think very deeply about governance. Uh, what can we learn from the Asians? Asians uh, are very good at execution and being very rigorous around execution. Now obviously I'm generalizing, right, this is a big general statement, but I'm a big believer in uh, in keeping your eyes wide open, uh, having a 2020 vision, pun intended with the Money 2020 conference name here, uh, but keeping your eyes wide open, uh, and learn from uh, everywhere where you can learn from as an organization, whether it's from China, from Europe, uh, uh, from America.

Speaker A: And where do you think we are on the AI journey now overall? Sector wise and opinions differ.

Speaker C: Right.

Speaker A: If you listen to the Frontier Labs and the existential warnings there, then maybe life as we know it is going to be over this time next year. And I went to a analyst event a couple of weeks back where they had a timeline that went out to 2040 for identifying multi sector industries and that was only work in progress even at 2040. So it was sort of 15 year timeline. And I thought, crikey, it can't be that. For me I think the answer's somewhere in between. There's an enormous amount of um, latency and inertia around big corporations and you know, um, automating front to back using AI, which is going to take years to do. But from your touch point with the sector, you know, how far into all of this do you think we are?

Speaker B: Oh, honestly. Okay, a couple of points. Number one is I think we're at the beginning still. Uh, having said that, I think it goes much faster than you think. Uh, uh, point number three is can we actually predict what's going to happen? Well some people are arguing we might hit a singularity if we're not already in it, which is the point beyond which you can't see because the speed of AI innovation is accelerating, accelerating, accelerating. So we're on this exponential, uh, and uh, people find it hard to understand exponentials truly. Um, there's a lot of innovation happening outside of the big corporates and they're sometimes overtaking uh, the big organizations faster than we would ever see. We're witnessing as we speak. Back again to Claude Anthropic, the company behind Claude. Uh, what are they projected to have in turnover, uh, by the end of this year, 100 billion, uh, revenues by the end of this year. And for a company that's maybe not even three years old, this is unprecedented. So where are we? We're n at the beginning. Plus acceleration goes faster than we can even uh, think. And uh, I'm, I'm, I'm the last one that's going to predict what's going to happen. But what I can say is the core concepts are quite firm. I talked about this framework. Vora, visible, understandable, repeatable, auditable. You want to use the data and make, you know, to your advantage. Those are key concepts that you want to apply to your business processes. Uh, when you're in a big company or a small company.

Speaker A: Yeah. And the pace of innovation is extraordinary. You've only got to walk around this event to get a sense of it. And I remarked to someone earlier that it seems like the world's largest banks are all up on balcony one overlooking the floor having private meetings because they are, you can't have a look around there and uh, on the floor. So we've got every flavor of agentic innovation and, and um, white label ready to build crypto architectures and so forth. Yes, it is quite a head spin actually. And I, and I did find myself wandering around thinking well if I was uh, you know, a uh, mid tier bank wanted to get into crypto, I'm not sure um, who I would turn to to plug it, plug that infrastructure in because there's so many options.

Speaker B: Options, yeah, they are. And uh, yeah, no advice from me on this one here but innovation is accelerating for sure.

Speaker A: But meantime that whatever it was, 360 million, 380 million workflows Number for Alteryx, which in itself is a startling number. And you noted it's probably 30, 40% up on the same number last year.

Speaker B: I said it was significantly up, but you're right on the percentage. It's a ballpark between 30, 40%.

Speaker A: So your business is accelerating as well.

Speaker B: We're our business accelerating. Yes, absolutely. Great.

Speaker A: Okay, well we've only got a few minutes left and I just want to sort of wider note, um, because you wear several hats. Remco. Right. So you alliances and international um, with Alteryx and we talked about some early career but along the way you've also been an angel investor in startups. So this sort of conference floor you're probably somewhat familiar with. I think you're sort of got your finger in multi sector. So it's not all financial services. Um, and I sat on A media session earlier today where there were, uh, um, uh, press release updates, product updates from big firms and small firms, and we're doing another one tomorrow, um, which is um, seven of the brightest startups that the conference has selected. So I'm looking forward to that. So I have a window onto the innovation sector. Uh, so I sort of have a view on kind of what I look for. But for you, what do you look for when you're walking the floor and there's 2,000 companies here and some of them are really quite small. What do you look for in the companies that you fly. Invested in?

Speaker B: Yes, uh, that I've invested in and that I will invest in. Uh, that is correct. I, I'm an angel investor and I'm in, you know, several companies. You need to build a little bit of a portfolio because some of them will fail and ah, we can talk about that. Um, and it might even be a large percentage, but that's, that's inherent to the, to that business. Um, what I'm looking for, I, I don't necessarily invest in ideas alone. I, I look for or founders that can learn faster than the market changes. Okay, and so what is key Founder quality, grit, uh, being able to adapt, uh, you know, total, total market size. What's, you know, what's the, what's the, what's the problem that's being addressed? And how big is, can that market become now? Big of a, uh, you know, of a, you know, how big of a slice can, can, can the company get? So the execution power behind that is really important as well.

Speaker A: One thing that perhaps from your career, that for the entrepreneurs out there, you wish you'd known at the start so you would like to pass on.

Speaker B: So yeah, uh, that question makes me feel old, Tony, you, me, both, but I do think it's an excellent question and it's great to pass on some, uh, nuggets of knowledge and wisdom. Um, look, technologies change, markets change, um, companies change. But at the end of the day, relationships, uh, is what is still at the heart of what matters. So, uh, build relationships early and soon in your career and nurture uh, those relationships because those relationships will compound over the years and uh, that will get you to the next platform.

Speaker A: Yeah. And they come back when you least expect them. My, um, head of commercial, um, always reminds me, he says, oh, we're going to see so and so. And I think, oh, okay, well that's a new introduction. And on more than one occasion, or maybe it's just my uh, my limited uh, memory capacity, I'VE walked into a room and someone said, oh, I know we've met before. We did this and we did that and uh, sometimes I forget. But, uh, if you stay hungry, stay

Speaker B: curious and build your relationship.

Speaker A: Yeah. You're going to meet them on the, on, on the other side.

Speaker B: You're going to meet them somewhere.

Speaker A: Yes. Okay, so just wrapping up. If anyone wants to find out more about alteryx, we talked about it a bit, little lot or indeed come and talk to you. Remco. Um, I think the booth is just over there, isn't it? Um, in the middle of this hall.

Speaker B: Altrix does have a booth here and uh, we are sponsoring this for the first time, this event and already having a lot of fun. Uh, so, uh, people can find us there and obviously uh, they can find us on um, the Internet. Alter.alteryx.com and um, my name, Ron M. Kudenhayer, of course can be found on LinkedIn if they want to connect with me. And please do reach out.

Speaker A: Perfect. Thank you. Okay, so I think that's, that's a wrap. This will be um, out on the London Fintech podcast on the usual channels. And thank you to our audience,

Speaker B: Sam.