Thank you, operator. Good morning and thank you for joining us to review Appian's fourth quarter and full year 2023 financial results. With me today are Matt Calkins, Chairman and Chief Executive Officer; and Mark Matheos, Chief Financial Officer. After prepared remarks, we will open the call for questions. You can follow along with our earnings presentation by downloading it from the main page of our investor site at investors.appian.com. During this call, we may make statements related to our business that are forward-looking under federal securities laws and are made pursuant to the Safe Harbor provisions of the Private Securities Litigation Reform Act of 1995. These include comments related to our financial results, trends and guidance for the first quarter and full year 2024, the benefits of our platform, industry and market trends, our go-to-market and growth strategy, our market opportunity and ability to expand our leadership position, our ability to maintain and upsell existing customers and our ability to acquire new customers. The words anticipate, continue, estimate, expect, intend, will and similar expressions are intended to identify forward-looking statements or similar indications of future expectations. These statements reflect our views only as of today. They do not represent our views as of any subsequent date. They are subjected to a variety of risks and uncertainties that could cause actual results to differ materially from expectations. For a discussion of the material risks and other important factors that could affect our actual results, please refer to our most recent annual report on Form 10-K, quarterly reports on Form 10-Q and other filings with the SEC. These documents are also available on our Investors section of our website. Additionally, non-GAAP financial measures will be discussed on this conference call. Refer to the tables in our earnings release and the Investors section of our website for a reconciliation of these measures to their most directly comparable GAAP financial measures. With that, I would like to turn the call over to Matt.

Thanks, Sri and thanks, everyone, for joining us today. In the fourth quarter of 2023, Appian's cloud subscription revenue grew 26% to $81.3 million. Subscriptions revenue grew by 24% to $115.8 million. Total revenue grew 16% to $145.3 million. Our cloud subscriptions revenue retention rate was 119%. And our adjusted EBITDA was a gain of $1 million. For the full year, Appian's cloud subscription revenue grew 29% to $304.5 million. Subscriptions revenue grew 21% to $412.3 million. Total revenue grew 17% to $545.4 million. Our adjusted EBITDA was a loss of $44.8 million. I want to mention two milestones at the top of this call. Last year, for the first time, our revenue exceeded $0.5 billion. Second, and an interesting complement to the first observation, we achieved the highest non-GAAP gross margin in our public history last quarter at 78%. In our presentation deck, we've included one last time the special bonus metrics we tracked quarterly in 2023. We didn't get the recession I expected, but there were some macro complications, and you can see it all starting on Page 5. Last year was the year of AI talk. Now the conversation will shift to more tangible things: shift features, successful deployments, practical value. That change will be good for Appian. We have a distinctive approach to the AI market based on years of leadership and existing technology. We are focused specifically on the application of AI to data. We're leaders in data fabric, which is like a virtual database, uniting the customer's enterprise. And we are leaders in AI, and now we will be leaders in the combination of these two things. I think everyone understands by now that AI is only as good as the data behind it. More data, better AI. Appian has an open data strategy that allows AI to benefit from information scattered across the enterprise. Ask a question, and your response will be informed by everything known to the business, not just the contents of one silo. Same for generating new content; the more data that supports the AI, the more sources you bring to bear, the better the output. Let's explore this with an example. The purpose of this example is to show you the advantage of having AI draw from multiple data sources; that AI is better that way. We work with a large U.S. state government entity that awards hundreds of millions of dollars in contracts every year using Appian. Its procurement processes are highly regulated and must comply with federal and local laws. In Q4, this organization deployed Appian AI to optimize its awards management process. As you know, contracting usually involves many data objects of different types, in different formats, typically in many different locations. Now AI will understand thousands of regulatory and procedural policies from various sources, so employees don't have to manually search for information. Appian AI is embedded in the customer's workflow, doing work and making the customer's data more usable than before. Procurement officers and supporting staff can ask AI questions in real-time and get great answers. This allows them to advance their procurement speed and accuracy. Our second advantage is simplicity. Appian takes a practical view towards technology. Our goal isn't just to make extraordinary software but to make it accessible. Programming in Appian is done with a mouse, not a keyboard. We take the same approach to AI. One of the nation's largest universities uses Appian to improve graduation rates; it recently deployed Appian AI to its student advisers. AI will recommend actions to take on student cases and propose meeting agendas to advisers before they meet with students. First, I want to emphasize how important it is that such a system draws on all the data in the enterprise. If you're trying to help a student complete a four-year degree, you need to know about all the threats to their progress. You need to know if they're failing any courses, that's in one system. You need to know if they're late on tuition payments, that's in another system. You need to know if they're missing classes, that's in the attendance logs. You need to know if they have friends who recently dropped out, and that's in another place altogether. You get the idea. These are different data sources. And AI needs them all to identify risks and make a good recommendation. My other point is that such a system must be easy to set up and use. Counselors just want to ask a chatbot some questions about their students, not become AI technologists themselves. We made it easy. And it was easy to deploy as well, going live in under two months. All this talk about drawing on the full enterprise of data sounds great until you consider the implications: merging data sets within the enterprise, uploading massive amounts of data, and training AI algorithms you don't control. Appian requires none of that. We offer insights across the widest amount of data, but we do it while disclosing the least of it. We specialize in private AI; we use our data fabric to provide just the information that's pertinent for every question, rather than pretraining an algorithm on everything an organization knows. It's more cost-effective and much more respectful of our clients' architecture and privacy. Another example to make this point; a top pharmaceutical company manages several core processes with our platform, including ones related to clinical trials and manufacturing logistics. In Q4, the company named Appian its standard enterprise workflow tool. It will now deploy our platform to over 50,000 employees. The company aims to bring new products to market faster. Our AI is privy to their confidential documents, their lab results, et cetera. This is sensitive work and, needless to say, they take the privacy of their data very seriously. This global firm has decided to trust our technology to make the most of what they know while keeping the highest commitment to protecting it. Appian landed some of our largest seven-figure deals this quarter. The total contract value of our top 10 net new software deals increased by 70% in Q4 2023 compared to the same period last year. Here are some notable stories; a U.S. military branch wants to unify its operational system so it can better mobilize its forces. Appian will integrate data from 14 legacy systems into a single modern platform. In Q4, it purchased a seven-figure software deal. 100,000 analysts will use Appian to train, manage and equip personnel globally. Another example, a financial services company managing hundreds of billions of dollars in assets became a new customer in Q4 with a seven-figure software deal. The company is growing quickly and wants to modernize legacy systems that are too costly to maintain. Appian's data fabric will unify data from over a dozen core systems into a single view, so they can run end-to-end workflows like customer onboarding and wire transfers. Appian will help the customer scale and process more than 4 million transactions annually. Next, a national police force recently adopted new strategic priorities to optimize operations and improve public safety. In Q4, it selected Appian as an enterprise-wide platform to improve productivity and unify the group's disparate systems, starting with an incident management app. Desk workers will triage inbound requests, open cases, and route them to field officers for investigation. This process had always been manual. Now thousands of officers will be able to respond to incidents faster using Appian. Last example now, a top health insurance provider is running a company-wide initiative to modernize systems and save $1 billion. It selected our platform to aid this effort, starting with its enrollment process. The provider made a seven-figure software deal in Q4 and became a new customer. Appian's data fabric will consolidate Medicare and Medicaid requests into a single application, so employees can do eligibility checks, fix discrepancies, approve applications and initiate the billing process. The organization expects to process millions of requests annually on Appian. Now a few final notes. Appian expanded our credit facility this quarter with participation from existing and new lenders. The aggregate principal amount of the term loan facility is now $200 million, up from $150 million. And the revolving credit facility is $100 million, up from $75 million. We welcome the additional financial strength. Appian has made progress in our intention to aim high in this market and sell more large deals. We've held to our financial discipline and done so without taking anything from growth. You'll hear more about our plans at our Investor Meeting on April 16 at Appian World in Washington, D.C. We'll talk strategy, results, technology, AI and more. I hope to see you there. With that, I'll hand the call to Mark.

Thanks, Matt. I'll review the financial highlights for the quarter and then we'll provide guidance for Q1 and the full year 2024. We closed 2023 on a strong note with revenue and adjusted EBITDA coming in above the high end of our guidance range. We saw continued healthy contribution from existing customers and strong growth from key industry verticals. Let's go into the details. Cloud subscription revenue was $83.1 million, an increase of 26% year-over-year and above guidance. On a constant currency basis, cloud subscription revenue grew 23% year-over-year. Total subscriptions revenue was $115.8 million, an increase of 24% year-over-year. On a constant currency basis, total subscriptions revenue grew 21% year-over-year. Professional services revenue was $29.5 million, down 9% year-over-year. As previously noted, services revenue can be volatile from quarter to quarter and a few large projects can influence performance. Our professional services will continue to be a strategic offering, focused on enabling partners and driving customer success. Long term, we expect professional services revenue to continue to decline as a percentage of total revenue. Subscriptions revenue was 80% of total revenue, compared to 74% in the year-ago period and 76% in the prior quarter. Total revenue was $145.3 million, an increase of 16% year-over-year and above our guidance range. On a constant currency basis, total revenue grew 13% year-over-year. Cloud subscription revenue retention rate was 119% as of December 31, 2023, up from 115% a year ago and 117% in the prior quarter. As a reminder, we continue to target a cloud subscription revenue retention rate of 110% to 120% on a quarterly basis. Our international operations contributed 36% of total revenue, compared to 34% in the year-ago period. Our cloud software net new ACV bookings were approximately 80% of the total net new software bookings in 2023, consistent with last year's mix. Now I'll turn to our profitability metrics. Non-GAAP gross margin was 78%, compared to 73% in the year-ago period and 75% in the prior quarter. Subscriptions non-GAAP gross profit margin was 91%, compared to 90% in the year-ago period and 89% in the prior quarter. Professional services non-GAAP gross margin was 26%, compared to 27% in the year-ago period and 30% in the prior quarter. As noted on prior earnings calls, we continue to invest in customer success management. These advisers help our customers achieve the most from our technology and increase adoption of our platform. As a result, professional services non-GAAP gross margin should decline to the low 20% range in 2024 and beyond. Total non-GAAP operating expenses were $114.1 million, down 4% from $119.1 million in the year-ago period. Adjusted EBITDA was a gain of $1 million, versus our guidance of a loss between $16.1 million and $12.1 million, compared to an adjusted EBITDA loss of $24.8 million in the year-ago period. In the fourth quarter, we had approximately $11.1 million of foreign exchange gains, compared to $8.5 million of foreign exchange gains in the same period a year ago. We don't forecast movements in FX rates; therefore they aren't considered in our guidance. Non-GAAP net income was $4.9 million or $0.06 per diluted share, compared to non-GAAP net loss of $20.6 million or $0.28 per diluted share for the fourth quarter of 2022. This is based on 75.3 million diluted shares outstanding for the fourth quarter of 2023 and 72.7 million diluted shares outstanding for the fourth quarter of 2022. As noted above, fourth quarter 2022 non-GAAP net loss was aided by $11.1 million in foreign exchange gains or a gain of $0.15 per share which was not included in our original guidance. Now before I turn to the balance sheet, I wanted to briefly update on the recent amendment to our credit agreement. On February 12, 2024, we increased the aggregate principal amount of the term loan facility by $50 million and the limit of the revolving credit facility by $25 million. The total aggregate term loan facility is now $200 million and the revolving credit facility is $100 million. Additional details on the terms of the financing will be in the 10-K filing. Turning to our balance sheet. As of December 31, 2023, cash and cash equivalents and investments were $159 million, compared with $196 million as of December 31, 2022. For the fourth quarter, cash used by operations was $8.2 million versus $12.6 million for the same period last year. Total deferred revenue was $240.7 million as of December 31, 2023, an increase of 20% from the year-ago period. As we have stated on past calls, the majority of our customers are invoiced on an annual upfront basis but we also have large customers that are billed quarterly or monthly. Due to the variability of our billing terms, changes in our deferred revenue are generally not indicative of the momentum in our business. I'll now recap our full year 2023 results. Cloud subscription revenue was $304.5 million, representing 29% growth year-over-year. On a constant currency basis, cloud subscription revenue grew 26% year-over-year. Total subscriptions revenue for the year was $412.3 million, an increase of 21% compared to 2022. On a constant currency basis, total subscriptions revenue grew 19% year-over-year. Professional services revenue was $133 million, an increase of 4% compared to 2022. The total revenue was $545.4 million, up 17% compared to 2022. On a constant currency basis, total revenue grew 15% year-over-year. Adjusted EBITDA loss was $44.8 million, compared to $76 million loss in 2022. Non-GAAP net loss was $59.2 million in 2023 or a loss of $0.81 per diluted share, compared to non-GAAP net loss of $89.2 million or a loss of $1.23 per diluted share for 2022. This is based on 73.1 million and 72.5 million diluted shares outstanding for 2023 and 2022, respectively. For the year ended December 31, 2023, cash used in operations was $110.4 million versus $106.6 million for the same period last year. Adjusting for the one-time payment of $57.3 million in the third quarter of 2023 for the Judgment Preservation Insurance Policy, 2023 cash usage showed a substantial improvement compared to 2022. As a reminder, we continue to believe cloud subscription revenue is a better indicator of our business momentum than billings or remaining performance obligations or RPO. The latter metrics can fluctuate based on the timing of invoicing, seasonality of on-premise license revenue, and the duration of customer contracts. The true scale of the business is represented by subscriptions revenue, which includes support in all software subscription revenue regardless of whether the customer deploys to the Appian Cloud, their private cloud, or on-premise. Now I'll turn to guidance. For the first quarter of 2024, cloud subscription revenue is expected to be between $84 million and $86 million, representing year-over-year growth of 21% and 23%. Total revenue is expected to be between $148 million and $150 million, representing year-over-year growth of 9% and 11%. Adjusted EBITDA loss for the first quarter of 2024 is expected to be between $9 million and $5 million. Non-GAAP net loss per share is expected to be between $0.21 and $0.16. This assumes 73.5 million diluted weighted average common shares outstanding. For the full year 2024, cloud subscription revenue is expected to be between $364 million and $366 million, representing year-over-year growth of 20%. Total revenue is expected to be between $615 million and $617 million, representing year-over-year growth of 13%. Adjusted EBITDA loss is expected to be between $25 million and $20 million. Non-GAAP net loss per share is expected to be between $0.73 and $0.66. This assumes 73.8 million diluted weighted average common shares outstanding. Our guidance assumes the following. First, Q1 professional services revenue will decline by a low double-digit rate year-over-year. For the year, we expect professional services revenue will be flat or will decline by a low single-digit rate compared to a year ago. Second, on-premise license revenue will grow year-over-year by a mid-single-digit rate and will track to seasonality that is consistent with prior periods. Third, our Q2 adjusted EBITDA loss will be bigger than Q1's adjusted EBITDA loss. This is due to the combination of on-premise license seasonality and the cost of running our global user conference, Appian World. Fourth, total other income and interest expense will be approximately $3 million in Q1 and $15 million for the full year 2024. Fifth, capital expenditures will be between $2 million and $3 million in Q1 and between $10 million and $12 million for the full year 2024. Finally, our guidance assumes FX rates as of February 13, 2024. In conclusion, we are pleased with the performance this quarter. We are investing in growth opportunities that drive long-term value and optimizing costs to drive profitability. We continue to balance our cost profile to prioritize investments in R&D, innovation, CSM coverage, and strategic go-to-market areas such as global partnerships, demand generation activities, and targeted sales capacity. And with that, we will open up the line for questions.

Our first question comes from Sanjit Singh from Morgan Stanley.

Congrats on a solid end to the year. Matt, as you think about 2024 and coming out of 2023, what are you seeing in your demand environment in your pipeline as it relates to this momentum around automation and getting practical value of AI? What are some of the use cases that customers are starting to pursue with Appian versus maybe some of the other initiatives out in the space?

Right now, AI is a fantastic door opener, but it’s best done with a very simple proposition. So we're equipping our team to be able to approach customers in an easy-to-understand, easy-to-implement way to get in on AI and show rapid benefits. I believe that keeping it simple and having a short period of investment before you get the payoff is essential to catalyzing their large interest in the topic today; so that’s helpful. I also feel good about where the pipeline stands and particularly the large end of the pipeline. As you know, we're focused a little bit more on those larger opportunities now.

Yes, that makes a lot of sense. And then Matt, for you, I mean, the positive adjusted EBITDA in Q4, that was really nice to see. When we think about the balance between expense or managing margins versus growth, what's the potential for that to continue from what we saw in Q4? Is any, or said another way, would the Q4 sort of bend the benefit from any sort of shifting in expenses to get to that positive adjusted EBITDA in Q4?

Sure. Yes. No, we really didn't do anything out of the ordinary to get to a positive adjusted EBITDA in Q4. That was an artifact of our strong revenue performance. We had a really good level of linearity on the top line. And we're just on our plan here on the expense side that we've discussed in the past, and we're steady as she goes on that in terms of operational discipline. But the name of the game is still growth for us. We're just doing so with a lot of scrutiny on our expenses to make sure we're getting the ROI we need and running a tight ship. But there was nothing out of the ordinary for Q4 in that regard.

And our next question comes from the line of Steve Enders from Citi.

Thanks for taking the questions here. I guess maybe just to start, just maybe thinking more broadly about kind of the bigger demand environment and kind of what you're assuming kind of for '24. I know that for 4Q, there's extra conservatism kind of baked in for government shutdown and some other things. But I guess, what are you seeing today in kind of the deal environment and what's kind of being assumed in the outlook here for '24?

Okay. So broadly, the deal environment. I still think that there's some macro disruption, but it never rose to the level of a recession. I think that there's genuine interest across the board in what we can do for them with AI. I think that there's recognition that we're creating real value, and that sparks expansion opportunities and it propels demand for our industry, not just for our organization. I think this is a workable demand environment. I think this is a demand environment where we can succeed.

Okay, that's helpful. And then maybe just on the, I guess the kind of the net new versus customer expansion. I guess, for one, it's really good to see the net retention number pick up here. I guess maybe what drove the strength of the expansion here in the quarter? And how do you view the sustainability of that moving forward? And what's being embedded into the guidance for '24?

All right. Now we don't make any guide on NRR. I am also pleased to see it tick up. However, it hasn't ticked up that substantially. It's a couple of points. And I don't want to dwell on that. That's a blip for now. Maybe we can make it a trend, but it's a blip for now. I do think that it's something we want to excel in. We want to see more expansion. And we are focusing more on the techniques that lead to expansion, deepening the relationship that we have with our clients, having more touch points, having more exposure, emphasizing our humanity in contrast with the big tech substitutes, sort of what they might conceive to be a substitute for Appian technology. I think we want to shine in the ways that we are naturally advantaged against our larger competition. And we do that by having the sort of pervasive human connection. And that's the sort of thing that will lead to more expansion if it works. So this is an important number to me, but I don’t want to make any implications about where it’s going.

And our next question comes from the line of Jake Roberge from William Blair.

Congrats on the solid results. Matt, I know it's early, but can you talk about how you see monetization shaping up for some of the new Gen AI solutions and data fabric? Could those initiatives start to drive any growth heading into 2024? Or is it still too early for that?

We have a monetization strategy for both of those features. We have a stratified pricing system whereby you pay more for data fabric if it's accessing multiple data sources, and more for AI, or specifically for Private AI. So we are absolutely expecting that these features will drive revenue differentiation. Not just volume, not just retention, not just a competitive advantage, but also tagging them with revenue.

Okay, helpful. And then, you've made some changes to your go-to-market organization over the past year or so, between leadership changes, a small restructuring, and then also just the deeper focus on the partner organization. How do you feel like the go-to-market motion is positioned as you head into this year?

I feel like we're a lot stronger than we were a year ago. That's how I'd read it. I think that we've been careful with the changes that we made last year, but they've been changes for the better.

And our next question comes from the line of Derrick Wood from TD Cowen.

This is Cole on for Derrick. You flagged good strength in the TCV for top 10 net new customers. Could you just unpack that a little bit and talk about what drove that strength?

Yes, all right. Well, first of all, I think part of it is driven by our strategic focus. We believe we belong in the big organizations doing mission-critical things at relatively higher price points. And that strategy, I think, has something to do with the fact that we're seeing higher TCVs on our top 10 deals and, for that matter, higher on our median deal. We're just trying to raise the target sizes a little bit and we're seeing that that's happening. So yes, I'll just say it's strategically aligned. It's not unintended. And I don't want to make any promises about where it's going, just to say that it was gratifying to see it come in where it did because that's what we intended.

And our next question comes from the line of Kevin Kumar from Goldman Sachs.

I wanted to ask about the international public sector and the traction you're seeing there. Maybe talk a little bit about the go-to-market investments you're making there. And higher level, I guess, how early are these public sector organizations in terms of thinking about AI and kind of implementing more intelligence into their workflows?

As you know, we're a Washington company and I'm looking at the beltway out my window right now as I take this call, and we've done a lot of business here in Washington, D.C. with the U.S. federal government. The international public sector has always represented a big opportunity for us. And for that matter, so has state government in the U.S., and it is a largely untapped opportunity. I did mention in the prepared remarks one substantial organization in the state government level that works with us and does hundreds of millions of dollars of procurement every year on the Appian Platform. That's great, but that's the beginning. This is tip-of-the-iceberg stuff. And even though we have notable wins in other international or non-federal public sector opportunities, I still feel like the penetration is so minimal. We've done just enough to prove we can do it and not enough to show what we can do, like how much we can do. So that's an opportunity. We look forward to moving into it. We're making an effort to move into it, and it's largely unsaturated right now.

Our next question comes from Frederick Havemeyer from Macquarie Capital.

I wanted to ask about data fabric in a little bit more depth here about, generally speaking, it seems like being in the enterprise data space and data integration space right now is a fantastic bit of positioning considering what enterprises are trying to do with their data and trying to make it useful. And of course, everyone is trying to have a Gen AI strategy. So I'm curious with data fabric, when you're helping customers implement this, what have been the most significant challenges that you're helping them to address? And also around that, what are the most significant challenges that you or your partners face when implementing and onboarding customers to data fabric?

Yes, first of all, it's important to expand their thinking. Typically, organizations do not envision the possibility of merging data silos and reaping combined benefits from them. We are accustomed to a software environment filled with divisions. You must first inform them that this integration is achievable. Then, there's the integration process itself. Sometimes it's straightforward, working seamlessly with APIs, while other times, it involves dealing with custom-built or outdated systems, presenting more challenges. However, once the benefits are clear, overcoming these challenges becomes manageable; we can effectively integrate these data silos. It's simpler than one might expect and is feature-rich. Users can read, write, and filter based on individual permissions. It's a very robust solution. I also anticipate that this year, more organizations will begin to mention "data fabric," likely claiming to have something similar. However, I believe what they possess may not be as comprehensive as what we have developed over the years. This difference stems from our distinct data strategy. Many competitors aim to unify and control the data within an enterprise, leveraging their size to compel customer aggregation under their brand. We do not pursue that approach. Instead, we have consistently adopted a customer-centric and open data strategy that respects and enhances the existing data architecture of our customers. This is the reason we ventured into the data fabric concept initially: to enable customers to manage their data in their preferred manner, rather than forcing everything into our database. We developed this technology because we decided to support a distributed data strategy. Since our competitors generally have not made this choice, they also have not created similar technology. I believe this reflects fundamental differences in market philosophy, resulting in a more enduring technology divide than it may first seem.

Matt, I wanted to ask also, on both renewal rates and net retention rates, understanding also, like you said earlier, that a couple of data points does not yet a trends make. But I wanted to ask, it looks like your total gross renewal rate ticked down slightly in 2023 by quarters, while your cloud subscription revenue retention rate ticked up. So I wanted to ask, is there anything happening between the total company business and cloud that would be worth calling out at this point that could be attributable to that?

Yes. First of all, I want to address that downtick. Our gross revenue retention rate did indeed downtick from 99% to 98%, bottoming at 97% and it's now risen back to 98%. And I just want to clarify that though that may have been a downtick, it’s still best in class. It's still remarkable numbers. And then secondly, I want to say there has been, I would say, just a little bit of migration, just a very small amount, from on-premise to cloud, at a point when I thought there wouldn't be any more, but there was just a little bit. And so that may be impacting the numbers a small amount.

Our next question comes from the line of Thomas Blakey from KeyBanc Capital Markets.

I have a couple of questions. To begin with Fred's great question about the data fabric, he also inquired about the specific use cases. Matt, could you elaborate on that? After you address that, I've noted an increase in interest regarding Gen AI, particularly within the enterprise sector. If customers choose not to utilize your data fabric, what alternative architectural solutions will these organizations pursue to eliminate silos and integrate their data? Will they be looking at something similar to Appian solutions or a cloud-based data warehouse like Snowflake? I'm interested in understanding the advantages they would miss if they don't choose your solution, especially in the context of deploying Gen AI enterprise applications based on the examples you've mentioned. Thank you.

No, that's a great question. Like, what are they going to do without data fabric? Well, Snowflake is one obvious example. Snowflake is asking, give us all your data. It's like a modern data warehouse. Just pile everything you can into this one data source. And when you do, we've already got a partnership lined up for Gen AI on top of it. That's fine, if you can move all your data there, if you can move all of it. But, boy, I talk to a lot of CIOs and I can't remember any of them saying that they could move all of their data or even all of their pertinent data into a central repository, Snowflake or anyone else's. So typically, today, AI either runs on one giant silo, like Snowflake, or all you can train, which I'll address in a moment, or a data fabric. If it's all you can train, then essentially you're saying that AI isn't going to run on a source; it’s just everything you can upload, right? So you can upload one source after another if you want, but you've got data loading costs, you've got data freshness issues, you've got variable levels of personal security access to that data issues. There's a lot of flaws with that strategy. And I think also just the idea of training at great length an algorithm that the CIO does not own is problematic for a lot of tech decision-makers. So I think that even though there is the data lake with Snowflake strategy and there is the train an external algorithm on everything pertinent strategy, these are not plausible strategies. And what I see happening, in the absence of data fabric, most of the time, is AI is too limited on the data it knows. AI runs on one silo and just one. And I think that is, unfortunately, the typical fallback in the absence of data fabric.

That's interesting. Any use cases that you've seen maybe sprout out early in the evolution or planning to in '24?

Well, you mean use cases for data fabric? Yes, most of our customers actually use data fabric. We've got a terrific usage rate, somewhere 80%, 90%, which is good for participation in a feature. Because it's so beneficial, it makes it easy to connect to data sources; even if you're using just one, it makes it intuitive and simple. But if you're using multiple, it's a huge step forward over what was possible in the past. And it also makes it far easier for a user to develop new applications because we objectize all of the data that's been touched by the data fabric so that a creator of a new report or process can just grab and drag and drop that object. All of these objects of data across the enterprise are now sort of draggable objects within the development environment. It just makes the creation of new artifacts really intuitive. And as for use cases, it's really the challenge is more thinking of cases where you don't need more than one data source. I mentioned one in my prepared remarks about the hypothetical students in need of rescue, right? And how it would be great to be able to know whether they've attended their classes or missed a tuition payment or had friends who have dropped out or had bad grades, or any of that, like all of those things are going to exist in different systems. So even a simple application like how can we help the student to do well is something that's a natural use case for data fabric.

Excellent. As a follow-up to that, my final question would be about last year's Appian World, where you significantly expanded your partner programs and reach. Where do you think Appian's infrastructure stands today regarding outreach to enterprises in terms of the sales motion? Do you believe you have the right go-to-market infrastructure with the necessary touch points in large enterprises to sell this type of Gen AI solution related to the data fabric? That would be my last question.

No. Well, we definitely did a strategic pivot on partners last year. We had 700 registered partners coming into the year. And we still do have a ton of partners. But we decided that we wanted to focus, really focus down and make big investments in partners that were willing to make big investments in us. And that beneficial reciprocity is the pattern that we have set going into 2024. I think it will be more motivational and it will allow for a level of commitment in our partner that leads to greater expansion, because it will be greater implementation quality as well.

This does conclude the question-and-answer session of today's program. I'd like to hand the program back to Sri Anantha for any further remarks.

Thank you, Jonathan, and thank you all for being here today. We look forward to seeing many of you at upcoming investor events and during our next earnings call. Thank you, and talk to you soon.

Thank you, ladies and gentlemen, for your participation in today's conference. This does conclude the program. You may now disconnect. Good day.