Applitools, known for its AI-powered test automation toolset, has announced the appointment of Anand Sundaram as Chief Executive Officer, effective immediately.
The move comes at a pivotal time, as the company, which is a major player in visual testing and AI-assisted automation, seeks to accelerate adoption of AI-native, deterministic testing in regulated environments such as banking and financial services.
Sundaram joins with more than twenty years of experience in software quality, product, and technology. Before this role, he served as Senior Vice President of Product at CloudZero and earlier as Chief Technology Officer at SmartBear, among other senior roles.
Over his career, Sundaram has been involved in scaling platform products, leading global teams, and navigating the intersection of AI, data science, and cloud infrastructure.
The company’s co-founder and Chief Technology Officer, Adam Carmi, explained the rationale behind the appointment.
“Anand is simply the right leader for this moment … He combines unmatched expertise in software testing and product leadership with a true passion for our mission.”
Commenting on his new role, Sundaram said that “I’ve admired Applitools for over a decade, and it’s a privilege to now lead this category-defining company.”
He added: “In an AI-transformed landscape where code is being generated faster than ever, by both humans and machines, Applitools’ autonomous testing platform ensures that software quality accelerates innovation rather than impeding it.”
Carl Press, partner at Applitools’ investor Thoma Bravo and co-head of its Explore platform, pointed out that “his track record of scaling platforms coupled with deep roots in the software quality ecosystem will further enable the company’s ability to deliver greater innovation and value for customers worldwide.”
Broader strategy
The leadership change, however, is only one piece of a broader narrative. In a recent QA Financial feature, Applitools’ Chief Customer Officer, Kate Cato, laid out how the firm is rethinking enterprise testing around “purpose-built models.”
She observed that “AI is a catch-all term, but the models that matter in QA are purpose-built, secure, and efficient. And that makes all the difference in environments like financial services.”
According to Cato, who has been in her role since May 2024, many competitors wrap general-purpose large language models (LLMs) with thin interfaces, but those are not always suited to the demands of regulated sectors. Applitools, she argued, is taking a different path: building deterministic, domain-specific models that are tightly scoped.
“We don’t need our testing tool to know if Hungary had a president 50 years ago. We need it to write the right command steps, validate workflows, and ensure accuracy at every layer of the stack,” Cato stated.
For banks, where data sensitivity, predictability, and auditability are non-negotiable, she emphasised that “our clients know their information won’t leak into the public sphere. That’s a critical differentiator for banks. Everything we build, from our computer vision engine to our deterministic LLM, is developed with that in mind.”
This philosophical and technical stance is evident in how Applitools is evolving its ‘Autonomous’ test generation tool. Rather than relying on brittle DOM selectors (which often break when UIs evolve), Autonomous “interacts with the page using language, not just code.”
As Cato put it: “When a test fails, it’s not because a CSS selector changed, it’s because something meaningful happened.”
Early usage metrics suggest that flakiness due to UI element changes is “almost nonexistent” under their system; failures now tend to reflect substantive changes in the application.
One of the advantages that appeals strongly to QA teams in finance is the ability to write a test once and run it across environments, such as pre-production, staging, and production, with consistency. Using parameterised data, Applitools allows testers to design flows without needing direct access to sensitive backend systems.
“We don’t need our testing tool to know if Hungary had a president 50 years ago.”
– Kate Cato
In one example, a user might simply type, ‘Log into the banking app,’ and the system will expand that into steps such as ‘enter email, enter password, press submit.’ According to Cato, “It’s clear, it’s readable, and anyone can understand it.”
Within that context, Sundaram’s arrival is more than leadership renewal, it signals an intensification of bets on AI-driven autonomy and enterprise-grade trust.
Applitools was built on visual validation and computer vision engines and has long promoted that “the Applitools Intelligent Testing Platform empowers every team member, from design and development to QA and operations, to contribute to quality assurance, regardless of their coding expertise.”
The company positions its platform as a way to accelerate testing, reduce maintenance burden, and scale coverage.
As banks and financial firms push forward with digital transformation, the tension grows between speed, compliance, stability, and observability. Applitools’ direction suggests that it sees opportunity in offering tools that bridge those demands.
Cato, speaking of the path ahead, pointed to two key areas of investment: expansion of Autonomous to mobile, and deeper component-level testing embedded at the moment of code creation, especially in workflows augmented by tools like GitHub Copilot.
She cautioned that the AI landscape is rife with uncertainty, especially around regulation and security, but affirmed: “Our mission is clear: to build trustworthy, efficient testing systems that work at enterprise scale, especially for the industries that can’t afford to get it wrong.”
For QA leaders inside banks and financial services firms, the Applitools transition is one to monitor. The combination of new executive leadership and a sharpened technical strategy in AI-based, deterministic testing may influence how the next generation of enterprise testing tools evolve, especially in sectors where the cost of error is too high to ignore.
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