Appian Corporation

Good day and welcome to Appian's fourth quarter 2024 earnings conference call. At this time, all participants are in listen-only mode. After the speaker's presentation, there will be a question and answer session. Instructions will be given at that time. As a reminder, this call may be recorded. I would like to turn the call over to Jack Andrews, Vice President of Investor Relations. Please go ahead.

Good morning and thank you for joining us. Today, we'll review Appian's fourth quarter 2024 financial results. With me are Matt Calkins, Chairman and Chief Executive Officer, and Mark Lynch, Interim Chief Financial Officer. After prepared remarks, we'll open the call for questions. During this call, we may make statements related to our business that are considered forward-looking. These include comments related to our financial results, trends and guidance for the first quarter and full year 2025, 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. These statements reflect our views only as of today and don't represent our views as of any subsequent date. We won't update these statements as a result of new information unless required by law. Actual results may differ materially from expectations due to the risks and uncertainties described in our SEC filings. Additionally, Non-GAAP financial measures will be discussed on this conference call. Reconciliations of GAAP to non-GAAP financial measures are provided in our earnings release. With that, I'd like to turn the call over to our CEO, Matt Calkins. Matt?

Thanks, Jack, and thanks, everyone, for joining us today. In the fourth quarter of 2024, Appian's cloud subscription revenue grew 19% to $98.9 million. Subscription's revenue grew by 18% to $136.8 million. total revenue grew 15% to $166.7 million. Our adjusted EBITDA was positive $21.2 million, and our cloud subscription revenue retention rate was 116%. Our non-GAAP gross margin was 80% in Q4, our best performance since the IPO. For the full year, Appian's cloud subscription revenue grew 21% to $368 million, Subscriptions revenue grew 19% to 490.6 million. Total revenue grew 13% to 617 million. Our adjusted EBITDA was positive 20.3 million. The world of AI is very exciting, but it contains an unsustainable imbalance, and all of you know what it is. There's a monstrous amount of investment in AI. The largest American tech firms spent nearly a quarter of a trillion dollars of capex last year without a proportional return. AI doesn't generate enough revenue because AI doesn't generate enough value. And this is where Appian can help. Appian creates real value with AI by putting it where it can do the most good. While other firms bring work to AI, we bring AI to work. I mean we go where work happens and that's where we deploy AI. We equip AI to make an impact directly in the places where the heaviest and most valuable work already occurs. Work happens inside of a process. A process is a high volume flow of tasks handled individually and procedurally inside a corporation. These tasks are carefully orchestrated to serve an important goal. Process is how an insurer manages claims and a bank validates money. It's how the government operates procurement cycles and pharma companies run clinical trials. Process is how organizations spend their money, serve their customers, comply with regulation, and build their reputations. Appian is called the process company, and we do a lot of processes. Appian runs 10 to 20 billion transactions per day on AWS alone. a lot of those transactions will run better when we apply AI. We used to staff our processes with digital workers like RPA and business rules. Now that we have AI, I believe that the value of process automation technology has roughly doubled. It will take years for that value to reach our accessible market, but it's real. And the value of AI can also double. when it's used in a process. That is a bold statement, but if it sounds like an exaggeration that the value of AI could double when used in a process, then hear me out as I make the case for AI process synergy. Here are six ways AI is better when deployed in an Appian process. I'm starting on slide five in the earnings deck. First, It's easy to instantiate AI within an Appian process. To launch an AI agent in any node of a process model takes only a few clicks. Customers can use AI to make suggestions, generate content, parse documents, or take action. We're agnostic about the AI model. Customers can use ours, usually Claude, or bring their own. We're making it easier to access AI. And if DeepSeq commoditizes AI or makes it cheaper, We and our customers stand only to gain. Second, on slide six, our process gives AI structure. AI to make value in high volume workflows, it must have a structured role. Process gives AI a job within a coordinated effort, working toward an important goal. AI gets a team of coworkers, an inbox and an outbox, an escalation path, exception handling, and human oversight. I want to emphasize this human to AI coordination Many processes that can benefit from AI require humans as well, as overseers, exception handlers, or final results checkers. For example, one of our customers, an international health technology company, uses AI in a process to allocate human attention to incoming work. They sell medical devices and supplies to healthcare providers, and they're working with us to automate order fulfillment. A third of their orders are submitted through email, and those emails will be processed by Appian AI agents whose job is to parse, appraise, and route correspondence to the right actor within the organization. Many of these requests require a human response, so the AI will frequently assign the next stage of the work to a person. The third reason why Appian process is good for AI is data. AI needs data, and different implementations require different provisioning strategies. It's not always enough to make a heap of data and train your AI model once. Sometimes you need data from disparate systems, or you need fresh data in real time from remote sources of record, or you may wish to retrain periodically on a freshly assembled data set. If you're privacy-minded, you may wish not to train at all, but instead want data provided in the moment. Our process platform sends AI the right data at the right time. The fourth reason AI is better in a process is that we give agents what they need to be successful. Agents seek data, then act. First, they need to query and learn, and for that our data fabric is ideal. Then they need to launch powerful actions. These actions should be far-reaching and coordinated and predictable and comprehensive and fail-safe. In short, they should be processes. Only a process provides the power and the guardrails agents need when taking action. Appian had agents before the term was invented. We called them skills, but we did it the right way with more structure and stronger actions. Those who let agentic AI improvise without guardrails are making a mistake. Here's an example from a top U.S. mortgage lender that became a new Appian customer earlier this year. It uses Appian to automate audit processes for over 10,000 loans annually. Our AI agents analyze hundreds of different forms and cross-validate data with the company's origination system. With 98% accuracy, the agents flag discrepancies so human auditors can review and correct the data before publishing loans to the secondary market. The company now runs this previously manual process more than four times faster using Appian. The fifth reason why AI is better inside a process platform is visibility. Everything that happens in a process platform like ours is tracked. Every node, every action, every delay, every read and write, every outcome. AI is typically a black box, but to understand it better, put it in a process. Then you can track what impact it has, what it does well or poorly, where it makes mistakes, where are its blind spots. And whatever you learn from this exercise, you can apply in the process platform from rerouting certain jobs to gathering a new training data set. The data rich environment of a process is great for validating your investment in AI and justifying your next project. And if you work in a highly regulated industry, this visibility might be a prerequisite to making any use of AI at all. Finally, my sixth reason, Appian makes AI scalable. A process platform isn't just a workflow, it's an application environment. Appian gives AI up-to-date security certifications. An interface usable on any mobile device. Hot, hot failover. DDIL capability and auto scale for usage spikes. That's why top global organizations run their most critical and complex processes on Appian. Our customers include over half the top life sciences, asset managers and insurance companies, and every U.S. government cabinet-level agency. Speaking of the federal government, there's a lot of change in that market right now. We are cautiously optimistic about the opportunity these changes may create. For 25 years, Appian has been a vehicle for efficiency and modernization in the U.S. government. Our Army Knowledge Online deployment was, in its day, the world's largest intranet. We rescued a portion of the Affordable Care Act from a faulty technology implementation. We've become an acquisition management standard for civilian defense and state agencies. We advance efficiency from optimizing how money is spent to maximizing the efficacy of each action. For example, a US military branch and longtime customer expanded its use of Appian into a new mission area this quarter. This group runs core operations like logistics, finances, and supply chain management on a series of legacy enterprise resource planning systems. The organization will consolidate these systems into a single Appian View for hundreds of thousands of users and expects to save tens of millions of dollars annually. Appian continues to upsell our existing customer base by launching new products and identifying new use cases. Two-thirds of our customers at the beginning of 2024 purchased more software during the year. We launched a tiered pricing structure last February to monetize advanced functionality like AI. Almost half of our new customers bought in above the base tier. Going forward, we hope to increase that share and to convert more existing customers. A leading US insurance provider is one of our existing customers that upgraded its licenses in Q4. The group has been using Appian for more than a decade to manage risk and compliance and automate processes across its enterprise. The customer used Appian to expand its reinsurance business practice and generate an additional $2 billion in revenue annually. This quarter, the customer purchased a seven-figure upgrade and plans to widely deploy new capabilities like Appian AI Agents. My last customer example is a medical transportation and emergency response company. I mentioned them in Q2 when they became a new Appian customer. The group has since reduced its appeal disputes processing times by 88% using Appian. Now the firm signed a seven-figure software deal in Q4 to integrate siloed systems and orchestrate its full claims lifecycle. And now I'll conclude my prepared remarks. This year Appian's focus is on the basics, our core principles, our market identity, our financial goals, our strategic priorities, and our operational rigor. We believe we have an opportunity for growth, being a leader in a growing and changing market. Growth remains our priority. If you'd like to learn more about Appian, the best place to go is our annual conference, Appian World. This year, we're meeting in Denver from April 27 to 30. You're all invited, and I hope to see you there. Now let's talk about the financials with Mark. Welcome back, Mark.