Appian (NASDAQ:APPN) Chief Financial Officer Serge Tanjaga said the company is focused on automating “mission-critical” processes for large enterprise and public sector customers, particularly in highly regulated industries, during a panel discussion at the Morgan Stanley TMT conference. Tanjaga, who joined the company in mid-2025 after more than a decade at MongoDB, described Appen as a process automation platform that typically replaces manual workflows, custom-built applications, or legacy solutions that span multiple data silos.
Tanjaga cited customer examples to illustrate Appen’s use cases, including customer automation and management for a large wealth manager, credit card dispute resolution for an Australian bank, and installation ordering workflows for a medical equipment manufacturer. In the public sector, he said a large civilian agency is using the app to automate fraud detection and resolution work that previously required manual effort and data extraction from multiple systems.
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He also cited investors’ comments that Apple is simply a “low-code” tool for building relatively simple applications. In his view, the label can mask the complexity of opioid deployment. Tanjaga said that apps are typically delivered by Appen or third-party partners, with customers paying “five to eight figures” for an app. He called it difficult and “very sticky” to implement, and emphasized that Appen’s “low-code” approach is less about citizen developers and more about enabling reusable solutions without hiring large teams of expensive developers to build custom code.
Tanjaga addressed investor concerns that AI could destroy process automation platforms, arguing that customer conversations are more focused on getting their first successful AI use case into software deployment and production. He said enterprises are looking for AI that can run with high accuracy within operational workflows, which he described as challenging because AI is “potential” and needs to work within “evolved” systems to deliver reliable results in areas such as onboarding, procurement and budgeting.
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As an example, he described a North American insurance company choosing Appen’s DocCenter, an AI-enabled document extraction product, for its first production use case to manage 400,000 documents a year. He said it took several months to implement and tune for accuracy, and customers are now discussing a second use case involving 1.2 million documents a year. Tanjaga added that when customers are ready to adopt AI, Appen’s win rates are “significantly higher” than normal, which he sees as validation of Appen’s approach as a “node in the process” of using AI as an alternative to end-to-end workflows.
According to Tanjaga, about 80% of Opin’s business comes from government, financial services, insurance, and healthcare—industries he described as “demanding,” “risk averse” and highly regulated. He said that Appen’s framework is about placing the best tool at every point in the process, citing historical nodes such as business rules engines, RPA bots, and process mining, with AI as another “worker” to be used in the right context.
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He also highlighted the platform’s capabilities including security, auditability, compliance, and authentication, arguing that it is difficult for AI tools to replicate and necessary for complex workloads that require high accuracy. Looking further, he said it’s hard to imagine a world where AI is fully autonomous and self-managing, and he framed competitive threats as a long-term reality in software, emphasizing the difficulty of building enterprise-ready solutions with customer trust in App’s core vertical.
Tanjaga said Appen’s AI capabilities have evolved from earlier AI/ML integrations towards a broader GenAI roadmap. He outlined the progress of the proposals:
AI skills: Features that allow clients to call a larger language module within a process to produce specific outputs.
DocCenter: AI-enabled document extraction, starting with production implementation in industries in late 2024.
Agent Studio: A more autonomous “Agent” proposition, with the primary customer production reach.
Composer / modernizer: Early stage efforts to modernize legacy technology on a modern platform using AI.
He said many of these capabilities are available in the company’s advanced subscription tier and described Appen’s approach as clearly charging for AI in production. Tanjaga said the average realized price to move from Standard to Advanced is about a 25% increase, noting that Appen previously disclosed that a quarter of its customers pay for Advanced. He described the near-term focus as driving adoption and becoming a “trusted vendor” for customers’ first, second, and third AI use cases.
He also discussed the Premium tier, which he said has 25% to 35% extra height and currently has a limited feature set, although a few customers are already paying for it. Tanjaga said Appen plans to add more features to the premium tier as adoption advances and modernization use cases expand.
Regarding pricing, Tanjaga described a “metric” of models including per-user, per-app, enterprise agreements, and consumption options, with pricing increasing based on price. He emphasized that Appen is focused on “sales value,” citing an example discussed for the first quarter in which an aerospace manufacturer signed a seven-figure deal after Appen concluded it could help save a customer $60 million.
Tanjaga said Appen’s execution has historically been less consistent in go-to-market than product, and described a shift that began about two years ago to focus on the market, including reducing the sales organization about 18 months ago to focus on larger opportunities. He said Commercial North America saw better performance following leadership and process changes implemented beginning in 2025, and highlighted what he called North America’s best commercial growth in three years.
In federal business, Tanjaga described DOGE as a “massive positive,” saying it has increased the emphasis on efficiency, direct vendor engagement, and automation. He also pointed to a framework agreement with the US military for up to $500 million over 10 years, describing it as a “hunting license” to pursue additional use cases.
Regarding profitability, he said Appen quickly moved from a negative 8% EBITDA margin to a positive 11%, and that during his tenure the company guided the company to a 7% EBITDA margin at the midpoint but ended the year at 11% while keeping operating costs flat. Tanjaga said product-to-market improvements have earned “moderate growth right,” with planned investments in market penetration and overseas R&D while still targeting margin expansion.
He also said Appen was GAAP profitable last year, pointing to GAAP net income of $1.2 million, and highlighted its focus on limiting attrition. Stock-based compensation as a percentage of revenue is less than half of the average company of the same size, Tanjaga said. He noted that Appen authorized the $50 million buyback, establishing it as the start of a steady return on investment strategy and saying it essentially prevented a decline given the company’s low exports.
In discussing cloud growth, Tanjaga said the company’s confidence for 2026 is supported by new business timing that is “finally loaded”, currency dynamics, and pipeline strength and sales execution.
Appen Corporation is a global technology company specializing in low-code automation platforms designed to simplify business processes. Founded in 1999 by Matt Calkins, the company provides an integrated suite of tools that enable organizations to rapidly develop enterprise applications and workflows with minimal manual coding. The platform combines process management, robotic process automation (RPA), artificial intelligence (AI) capabilities and data integration into a single environment, allowing businesses to accelerate digital transformation initiatives.
The core offering, Appen’s low-code platform, empowers users—from professional developers to business analysts—to visually model, design, and implement applications that can automate complex operations, organize tasks across systems, and deliver real-time analytics.
The article “Appen’s CFO: AI Improves Win Rates as Enterprises Automate Mission-Critical Workflows at TMT Conference” was originally published by MarketBeat.
