Actually, easy one, Malcolm. Maybe just talk a little bit about yourself and your role at Appian. Sure. We can take it from there, actually. So, Malcolm Ross, I'm the Senior Vice President of Product Strategy for Appian. I lead our go-to-market, meaning working on product roadmaps with our engineering, product management teams, and overall direction. You're kind of protecting Malcolm on all the financial questions. Exactly. Malcolm, like, you've been at Appian for a while? 19 years. 19 years, yeah. Quite a bit. Can you talk a little bit about the evolution of the company in terms of like what you set out to solve, you know, 19 years ago when you joined, versus like the much, much broader offering that you have, like, today? Yeah. I mean, I was there at the really instantiation of the company, when we were starting to become a software company. Mm. We set out to really become a, at the time, almost two decades ago, business process management. Yeah. We had a vision of helping organizations unify their business operations or end-to-end workflows. At the time, it was very much workflow driven, but we recognized our customers needed software that would be easier to use and actually get value out of. That evolved into Appian very much focusing on low-code in the 2010s. Yeah. We saw that manifest in the low-code market around 2015. Appian is very well regarded in the low-code market even today. As we saw, the process automation market overall is kind of this nebulous mix of things. Business process management's in there, low-code is in there. We saw the RPA days in the 2015 to 2020s or so, where RPA became very hot. Yeah. Appian made moves in that market as well. About three years ago, I worked on, myself, the acquisition of the RPA product that's now part of the Appian stack. Yeah. And now we're in AI. But at the end of the day, what we're trying to focus on is helping our customers achieve end-to-end process automation. How do I help a major bank, government agency, ingest a case, ingest a customer service request, take that all the way to resolution? And when it comes to orchestrating the human interactions of how customer service office handlers work on that, to automating the legacy systems and unifying that so it can manage that process end-to-end and drive efficiency over time as well. And where are customers on that understanding of, like, the broad offering? And that's. It's kind of probably better done out of one starting point. Mm-hmm. Yeah, I mean, we're, I think, past the RPA craze, I would say. Yeah. Customers are definitely much more focused on holistic automation now. Mm. RPA is a technology that's here to stay. It does something very unique. It allows you to automate interactions with client desktop applications and legacy systems, and that's still a needed component, but everyone's dipped their toe into that. It's now part of the core toolset for automation, and customer focus is now on what Gartner would call hyperautomation. Yeah. But it's holistic automation, and how do I achieve those outcomes in a holistic fashion using RPA, using AI, using rules to achieve my overall business outcome? So Appian, I think, is very well positioned. We made those investments in RPA, and focus on that end-to-end process and other companies. Yeah. Okay. And then, so as you think about it, over the years, we saw a lot of, like, scope creep in, in your space, you know, where it used to be like you guys and Pega, where you kind of were the local guys, and then you had, like, the UiPath and, Blue Prism, et cetera, you know, IPA. Now, over the last two years, you have, like, everyone is trying to do everything a little bit, and then you have, like, the Microsoft coming in with the Power Automate, et cetera. Like, how do you see this, like, this market playing out in the long run? And then I have a follow-up. Sorry. Sure. Yeah, I mean, consolidation is what we see. Yeah. Even UiPath is positioning in end-to-end process automation, although they're mostly regarded as RPA still. Yeah. The Gartner reports, Forrester reports that focus on this the most. It's the. If you saw recently, the Forrester Digital Process Automation Wave report that came out- Yeah S o they really cover that in that perspective. Appian is well regarded there. Forrester covers it as an aspect of low-code of business workflow automation use cases. But what we see is, you know, ServiceNow's moved into RPA. UiPath is trying to move out of RPA into end-to-end process automation. Celonis is trying to have a greater value proposition than process automation as well. Microsoft has the Power Platform, Power Automate, and a bunch of kind of other tools. I think Appian has always been in this space of business process management with a holistic view. Mm. Made those investments in AI, RPA, to have the automation technology in place to really capture that market share of yours, well. And then, like, there's competition. There's just- Mm-hmm Y ou know, big competition. There's, like, labor competition, but it's like then real competition. Like, how do you see that out in real life? Because I can, Mm-hmm I f I'm talking to practitioners, it's like, yeah, look, if I do something around with my ServiceNow platform, I probably use their platform. Yeah. But like, if I do solve some proper real-life examples, you know, someone like Appian, is that kind of how, kind of how you see it playing out? Or like, how, how would you think about that going forward? Yeah, competitively, we see a lot of established vendors. So Microsoft, Salesforce, ServiceNow, customers already have those investments. Yeah. And they say, "Well, we want to do case service management automation. Sounds like maybe I can do that with ServiceNow." So they try to, but with Appian, our value proposition is quite different, I would say. With Salesforce, ServiceNow, for example, typically, the value proposition is you take your data, you take your systems and the logic, you put it into ServiceNow, you build applications around it. Mm. Appian's value proposition really around y ou have Workday, you have NetSuite, you have ServiceNow, you have these technologies. You need to unify those, stitch those together, a new composite, individual customer service center for contracts management, whatever it is. So the core value proposition is actually quite different as far as a unification platform that says, "Keep your assets, Mm-hmm. But unify them into complete digital experiences. That said as well, like ServiceNow, Salesforce, we compete against them a lot as they're established vendors. We never see them as, say, there's a greenfield opportunity for both vendors. Like, they don't have ServiceNow, and they don't have Appian. They're considering both tools, that never happens. Mm. It's usually Appian is well regarded in that whole significant process management area, and they regard us more against, I'd say, Pega as the primary competition Yeah in that space. Yeah. And then, like, quickly, you mentioned AI a little bit, like, can you just talk a little bit about how that plays into your, your space? Yeah. AI is, well, aside from being, you know, the top of your- Yeah Hyperautomation is the legitimate investment degree of automation that every organization is making right now, and we're very much on top of investing that ourselves. So in the early this 25th year, we announced our release of our AI Skills Designer. Yeah. Which is really a low-code design experience, which allows our customers to not have a data scientist, but train machine learning models for AI skills that drive automation inside their business. Yeah. Specifically, ingestion of emails and automatic routing in document classification, document extraction. We have a roadmap of building this out further and further, but it's all about low-codifying AI. Mm. to make it approachable and easy to start applying that inside their business applications. Now, furthermore- Sorry to interrupt you. Is it a little bit like a copilot? Like, Microsoft calls it Copilot, next one calls it Autopilot. No. But, like, is it, how? Is it? When we bucket our AI in three categories, we have our AI skills to specifically be building new AI machine learning models, in a low-code design experience that a customer can fully drive AI automation in their enterprise. Okay. We also have an AI Copilot in Appian, which is typically based on LLM, that is an assistive feature for building applications, guiding a user through a certain path. That's another pervasive feature of the Appian product. And then finally, it's all built on an architectural promise, what we call Private AI. Mm. Which is to ensure that when you're building these AI services, we respect the privacy, data retention, and to make sure that the machine learning models also remain under control of our customers, in that regard. Like, where are you on that AI journey? Like, you know, obviously, like, like non-generative AI has been around for a while, and people were doing- Yeah A nomaly detection, et cetera, et cetera. Yeah. Like, so where are you on that journey, and how has that accelerated in the multiple course? Yeah, we started working with OpenAI and with LLMs first half of 2023- Mm. -as we all saw them come out, in order to offer an integration to those platforms right away. Then we started benefiting some of our embedded features in August this year, where we have LLM, specifically initially OpenAI, that is an assistant feature that helps build application experiences. In just November tenth is where our last GA release was, just a couple of weeks ago. We released the integration of the LLM with our Data Fabric technology, which is really interesting use cases. So first, we're moving towards more of an embedded LLM strategy- Mm. which says, instead of integrating the LLMs, we're just going to offer it as part of the Appian stack. Yeah. A complete private AI. And then second, we're going to tightly integrate it with our Data Fabric to allow you to have natural language conversations with your data set, and it's really trying to solve the hallucination challenge with LLMs, which is, if I want to. Like, one of the use cases we often will focus on is, imagine I build a composite application using Appian, that utilizes data from SAP on order history, and Salesforce customer data, and worker information, and Workday. And then I say, "Well, write an email to my customer about their order history of the past three months, and then tell them what product opportunities they can take advantage of in the next quarter." Common thing every account executive does. Mm-hmm. Well, by having the LLM understand the semantic question there, parse it out to the context of the Data Fabric, retrieve the data from the Data Fabric, so we reliably report what is the actual order history, not gonna hallucinate and make it up, and then compose that email. Yeah So that it has confident information, but structured in that semantic email that can be sent out. Really cool stuff coming out that we introduced already on our November tenth release, that we're continuing to build on, that combines LLMs with competent data from the Data Fabric in Appian. Do you need to have vector database in there then as well? Yeah To kind of do that? Okay, yeah. Yeah, we definitely semantic vector searching capabilities is a very important part of it, as we overlay that on the Data Fabric, to correlate natural language semantics to specific table names and specific kind of information by the Data Fabric. Do you have to develop that themselves, yourself, or are you partnering with someone there? We're partnering on the LLM side. Yeah. Specifically, we've been working with Microsoft Azure on the OpenAI in the past. Yeah. We're now more aggressively working with Amazon Bedrock. We wanna make sure we also have that portability of LLM, so that we have. We can swap out the LLM if necessary. Bedrock is the one we're banking on right now as far as deep concern. Yeah. Yeah, yeah, yeah. Okay, makes sense. And then, last question, and then, I don't know, Sri, he wants to kind of jump in. Like, how do you think about that monetization or how you charge for that? I think we're looking at multiple ways. There are different aspects, and what we've talked about is we're going to have a premium pricing for the AI continuity that could just- I nvolve having a separate SKU or having some element of usage-based pricing, but that is something that will come out later next year. Yeah, yeah, yeah. Okay, and then so if you think about it, like you, you know, one of the things that happened for you guys is you, you were more on the cloud journey- Mm-hmm. You have more and more of your customers working in the Appian Cloud. Does that impact, like in a way, like your like choices there in terms of which type of skill set to go with, et cetera, with all the AI coming up now? Now, does that kind of create like a new discussion, given like how strong Microsoft and OpenAI is at the moment? We've always had the strongest relationship with AWS. Yeah, yeah. We host our cloud with AWS primarily. We do have a preference towards AWS. Yeah. But we always want to integrate with AI services that are free. So, we've historically had business partnerships with Google on document understanding. We've had integrations with OpenAI. So at the end of the day, we want to be a platform that focuses on process orchestration. Yeah. Recognizing our customers will always have a heterogeneous mix of cloud scalers or enterprise systems that we want to integrate in-house. Yeah, yeah. But you're more in the AWS platform? Yeah. Yeah, from the infrastructure perspective, primarily AWS shop. Yeah, yeah, yeah. Okay, makes sense. And then, last question. No, not last question. Shifting gears a little bit, like, I think historically, you guys been heavy, relatively heavily on the federal government side of the world. Does that kind of create, like, different challenges around AI, given, like, the greater need for security, understanding stuff? Like, how should I think about that? Well, that's exactly why we're focused on bringing the LLM and AI capabilities inside the firewall environment of our customers' cloud. Yeah. So that we can give that confidence that you know, it's interesting, with AI, you don't need to just protect your data anymore. You also need to protect your machine learning models- Yeah, yeah. because they have an awareness of your data set. So we want to make sure our customers fully control both the data and the machine learning model and the scope of their Appian Cloud environment. Yeah. And then by embedding it inside, when we introduce AI services, they can inherit our existing FedRAMP certification and IL5 certifications that we have for our customers to have that data confidence. Yeah, yeah. And then just, will AI, Like, if you think about it, how big are those LLMs in terms of if they're in need, like, can the customers still do it themselves in their, in their data set? Or do you really need the hyperscale to do that? And a follow-on question would be, would that drive more Appian Cloud actually in the future? Because, like, you need to provide a lot of extra compute there. To answer the first question, it's just, it's easier for adoption by embedding the LLM because, yeah, they could, you know, bring together different components themselves. Yeah. But there's a lot of technical complexity in that. So on the value proposition of Appian is simplifying it through our low-code experience and having it all right there to rapidly gain value. Yeah. It does introduce, you know, additional compute costs, things like this, especially as we do machine learning model training, GPU time, things like this. And we have premium pricing and other SKUs that we offer to capture that value as they upgrade their services to using more AI services. Yeah, yeah. So but could I do it myself? Could it be, like, at home, or does it need to be in, like, with AWS or Azure if I do that? Well, specifically like machine learning models? Yeah, yeah. Yeah, yeah. Well, you can do whatever you want with AWS, Azure, and simply integrate it as an integration point through your Appian process. Yeah. But for the stuff that we roll by the Appian Platform, it's all kind of white-labeled, wrapped inside the stack. Yeah. They don't even when they train a machine learning model for document extraction in Appian, they're not aware that we might be using different services from Amazon behind the scenes because it's all simple experience that you, a user who's not a data scientist, are using and adding value to their business. Then last question on AI, and since you're a product person, I hope I'm not kind of going too far. Like, what is If you think about AI, more natural language, et cetera, what does it do to your AI end-to-end UI, kind of how you think about your user interface and, you know, how might it connect to Appian going forward? Yeah, it's trending. You know, I would say over the past decade, people have had to have AI chatbots for a while. Mm-hmm. What the modern LLMs is really making those actual, actual value, we'll say. We've always had frustrating experiences, right, with, you know, dumb chatbots. Now, they're actually very intelligent chatbots. So in the most recent release, we introduced something called our Data Fabric feature, which starts to introduce general business intelligence capabilities on data that you bring into Appian or customers that do data recovery. It has the general BI capabilities to point and click configure a pie chart, but we also introduce what we call our AI Copilot for it, which allows you to simply have natural language conversation data set. So I can say, "Hey, you know, from this report, can you give me a summary of expected orders I need to share with folks?" You can just have very free form conversations, and it's able to give you very reliable responses back, which have context that you're looking at. Right. It does start to evolve the interface to be much more natural language-based, Yeah Rather than more perspective UI designs. We're leaning into that in some of the latest features we introduced. And then, truly last question, like, where, where are customers on that AI journey? Like, I mean, obviously, there's a lot of like- Mm-hmm Oh, we need to do AI. Mm-hmm. But like, then, you know, doing it in practical terms is always, like, a different story. Like, where, what are you seeing in customer conversations in terms of that AI understanding and AI adoption as well? Well, it's about finding those use cases. Yeah, yeah. I think a lot of customers are still learning about what's possible, and there's some really cool things possible now. For example, like in our we have a government acquisition management suite. Really, you know, if you've ever written a clause for government procurement, it's a boring job people don't really want to do, writing 100-page government procurement docs. AI is actually really good at combining the data sets of government procurement contracts to do automatic clause writing. Yeah. And automating that. So we're seeing lots of interest from customers like State of Texas investing in that area, 'cause it drives real productivity gains in just procurement contract, Yeah With government agencies. So people are really exploring the boundaries of this, investing in kind of prototypes and where that area is, while also simultaneously being very cognizant of the data privacy issues on especially things like the European Union, the EU AI Act, which is, proposed right now. Yeah. Ratified recently, very much aware of watching what's going on. Shifting gear a little bit, it's like, historically, like, I guess, your heritage, you were kind of very heavy on professional services and kind of did a lot of the stuff yourself. The last year, a lot bigger partner both partners. Like, where are we on that journey of, like, what are you doing yourself? Where you kind of heading out new partners? I think we're still early in the journey. Now, what we have talked about the past few years, just look at this mix. At the time of the IPO, it was around 50%. So services mix. Yeah The most recent quarter, it's 25%. We expect the professional services mix to continue to decline as a percentage of total revenue. It's partly because, A, our subscription revenue growth is much faster than professional, but also we're leaning more on our partners to deliver some of those services. Having said that, we've always maintained that professional services will continue to be a strategic offering for Appian. What we have inherently found is when we look at our customer bases, that when that first implementation goes very well, the upsell that happens, it happens very quickly. Mm-hmm. And at very large amounts. So there is the incentive not only for that, but even for the partners, for that first implementation to go very well. Yeah, yeah. And so in instances, our professional consultants are embedded with the partners, and these are not even billable folks, but we have a common goal. Yeah, yeah. for that first implementation to go well. Yeah, yeah. And then what are you seeing on the partner side? Like, given the times we're in, like, in terms of building Appian practice, kind of expanding capabilities and capacity there, like, where are we on that journey? I think we have a very strong interest, not only from the global SIs, but also from some regional partners. Yeah. You know, we've talked about the last investor day with a couple of partners have established some very aggressive hiring goals in terms of having Appian practitioners at the respective firms. Yeah. We still think early. Mm. We've recently talked about, you know, building dedicated partnerships with some of the global SIs, where they have specific incentives in terms of how many new logos they're going to add in any given year. Yeah, yeah. So we both of us have some common goals, but clearly, given our product expansion, platform expansion in the past few years, a lot of partners are more excited to work with us. Yeah, yeah, yeah. Okay. Yeah, makes sense. And, I'm staying with you three for a little bit longer. If I think about guidance last quarter that you guys gave, you know, there was an extra layer of conservatism because we didn't know what the government was going to do or what's going to happen here. Like, now it looks like that decision was pushed into next year. Does that give you an extra level of comfort, or how do you think about that? Yeah, in general, like, you know, we've always maintained some element of conservatism with respect to guidance. Yeah. You know, clearly, there were a number of factors that were going into Q4, not only the macro, the geopolitical, but also we had the additional overhang of potential federal government shutdown. Um, yeah. We did build that into our guidance, but we generally feel good about where we are. Yeah, yeah, yeah. No, fair enough. And then, Malcolm, the, it must be nice to have a boss like Matt there, because I remember, like, last year when everyone was thinking about, like, reducing headcount, et cetera, he kind of made the, like, at the time, somewhat controversial decision to say, like, "Look, there's so much good engineering talent coming out to the market, we kind of want you in that. Mm-hmm. Like, if you think about your product R&D organization, like, what did you see there over the last year in terms of, like, expanding capabilities, especially around AI, et cetera? We're expanding aggressively. We had good growth in our Chennai office, which has more engineering capacity for the team. Yeah. But, you know, we believe very strongly in the value proposition of the market moving towards, I think, hyperautomation, end-to-end automation, making sure we have the most attractive product in the market to staff partners, coming demand. I think it's been well represented also, again, by the Forrester Gartner report, so just in the past month. Mm. Appian's been the most highly regarded vendor for business workflow automation and digital process automation for that market. Yeah. And then, how tough is it at the moment? Like, you know, you're doing a lot of work around AI. Like- Mm. What's the skill availability situation there? Yeah, AI skills, we've been investing in that specific area before 2023. Yeah. So it's, it's not new. We had established engineering capacity, already building AI skills. From the talent pool of 2023 is actually more prompt engineering- Mm. As everyone is working, learning how to interact with LLMs effectively, do fine-tuning of LLMs, prompt engineering of LLMs. We continue to hire on that. And again, Chennai has been a good focus of ours to build up the additional engineering capacity for those specific skill sets as well. Yeah. If you're like, if you think about, as you mentioned, Chennai, is there a? Do you like, if you think about the classic AI, AI guys, they seem like crazy, brainy guys that have start working at Google or whatever, like in the Valley. Like, if you do prompt, is that, like, from a, from a skill level that is needed? Is that a different level than just the, the guys that build the LLM? Well, prompt engineering is just one part of it. Yeah, yeah. So, fine-tuning is certainly looking into more of our classical AI data scientists as well. Yeah. Training engineering models with extra context and awareness. But yeah, it's prompt engineering is more approachable to a non-data scientist to learn how to manage what they call tokens, as far as the token capacity of LLMs. One thing, so like. It's more of an architectural conversation. So, for example, features that we worked on is, if you want to have a conversation with an AI system across a very, very broad set of documents- Mm. LLMs can only really digest a few pages of a document at a time to give you contextual conversations about. So you combine that with an architectural approach of additional semantic AI search to refine the specific categories, and then have that refinement for context this area. So this is a design architecture called RAG. Yeah. Retrieval Augmented Generation Capabilities. Yeah. So it's not as much around classic data scientists, about architectural design, about how we combine AI services together to get the outcome the customer want. Yeah. Yeah. Yeah. Okay. Then, like, look, from my end, that was my list of questions. So, unless there's someone from the audience, I think I'll give you back two minutes. But hey, it was a great conversation. I really enjoyed it. Thank you. Thank you. Thank you.