Two recent episodes from Thoma Bravo’s Behind the Deal video series take you behind the scenes of Applitools—offering both a strategic and technical lens on how we’re transforming test automation with Visual AI and autonomous testing.

One episode focuses on the big-picture vision behind Thoma Bravo’s investment. The other digs into the founding story, engineering mindset, and what it really takes to build a testing platform that scales.

How Applitools Uses AI to Revolutionize Test Automation

Host: Carl Press (Thoma Bravo) | Guests: Alex Berry (CEO), Adam Carmi (Co-founder & CTO) | Watch on YouTube

• Why this is the inflection point for AI in testing
• How Applitools helps teams increase coverage while reducing maintenance
• The business logic behind Thoma Bravo’s investment

“With Visual AI, we’re dramatically reducing test maintenance while expanding coverage across the digital experience.”
– Alex Berry, Applitools CEO

Beyond Automation: How Applitools Improves Speed, Scalability & Accuracy

Host: Carl Press | Guests: Alex Berry, Adam Carmi | Watch on YouTube

• The origin story behind Applitools’ platform
• Challenges of scaling visual testing across devices and environments
• Insights from Alex and Adam on culture, leadership, and innovation

“Our goal was to solve the test flakiness problem for good—and make it effortless for teams to deliver quality at scale.”

– Adam Carmi, Applitools Co-Founder & CTO

What’s Next for AI in Software Development?

These episodes offer more than just company insight—they highlight the shifting expectations around quality, speed, and AI in modern software development. If you’re exploring how to future-proof your test strategy, or simply want to see what’s possible with Visual AI, these conversations are a great place to start.

Have questions about how this applies to your team? Reach out to start a conversation—we’re here to help you evaluate if the Applitools Intelligent Testing Platform is the right fit for your goals.

The post Behind the Deal: How Applitools is Scaling AI-Driven Testing appeared first on AI-Powered End-to-End Testing | Applitools.

In today’s fast-paced software landscape, teams must balance speed, quality, and cost—a challenge that traditional test automation often fails to meet. Testing bottlenecks slow down releases, defects slip through to production, and maintenance costs spiral out of control.

This is where AI-powered testing delivers significant business value. By automating test creation, execution, and maintenance, AI helps teams reduce costs, lower risk, and increase software reliability—leading to a clear return on investment (ROI). Let’s explore how AI-driven testing transforms software teams and drives measurable business outcomes.

The Growing Challenge of Software Testing

Modern applications introduce significant testing challenges:

• More Code, More Problems – AI-assisted coding tools generate more code, requiring robust testing to keep pace.
• Expanding Device & Browser Matrix – Users expect seamless experiences across devices, browsers, and screen sizes.
• Limited Testing Resources – Teams often lack the bandwidth to maintain comprehensive test coverage manually.

These realities create a gap between what teams should test and what they can test. AI testing solutions close this gap by increasing coverage, reducing human intervention, and making automated tests more resilient.

The ROI of AI-Powered Testing

Companies that implement AI-powered testing see improvements across four key areas:

1. Faster Release Cycles = Accelerated Time to Market

Traditional testing slows down software development, with teams often spending 30% or more of their time debugging and fixing defects. AI accelerates release cycles by:

• Automating test creation and execution
• Reducing manual intervention with self-healing test scripts
• Eliminating maintenance headaches caused by UI changes

2. Fewer Production Defects = Lower Business Risk

Bugs in production can lead to revenue loss, reputational damage, and compliance risks. AI-powered testing reduces defect leakage by:

• Catching more UI and functional issues with Visual AI
• Reducing false positives and negatives in test execution
• Identifying risks earlier in the development cycle

Try Applitools Autonomous for free and see how AI-driven testing enhances defect detection. Sign Up Now.

3. Reduced Testing Costs = More Efficient Resource Allocation

Hiring, training, and maintaining a robust QA team is costly. AI-powered testing optimizes costs by:

• Reducing test maintenance efforts by up to 40%
• Allowing non-technical team members to contribute to testing
• Increasing test coverage without requiring more human effort

4. Higher-Quality Software = Increased Customer Satisfaction & Revenue

Customers expect flawless digital experiences. AI-powered testing ensures:

• Fewer production issues that impact user satisfaction
• Smoother cross-device and cross-browser experiences
• Increased trust and retention from end users

A better user experience translates to higher customer retention, fewer support tickets, and increased revenue—a direct boost to the bottom line.

Calculating ROI: What’s the Business Impact?

Organizations that implement AI-powered testing can save millions annually by reducing test maintenance, accelerating releases, and minimizing costly defects. With the right tools, teams can quantify:

• Time savings in test creation, execution, and maintenance
• Reduction in defect-related costs (fixing bugs post-release is 30 times more expensive than catching them early)
• Operational efficiency—allowing teams to focus on innovation instead of repetitive testing tasks

Want to calculate your team’s ROI with AI-powered testing? Talk to our experts and see the impact on your bottom line.

The Future of Testing is AI-Driven

AI-powered testing isn’t just a technical advantage—it’s a business imperative. By improving efficiency, reducing risk, and lowering costs, AI helps teams deliver high-quality software faster while maximizing ROI.

Missed the full discussion? Watch the complete webinar replay for a deeper dive into the ROI of AI-driven testing. Watch now.

The post The Business Value of AI-Powered Testing: Maximizing ROI appeared first on AI-Powered End-to-End Testing | Applitools.

In our recent webinar, Building the Ideal CI/CD Pipeline: Achieving Visual Perfection, we explored the transformative power of Visual AI testing for CI/CD pipelines. Aimed at software engineering managers and team leads, the session provided a deep dive into the limitations of traditional functional testing and how Visual AI testing can surpass these to achieve visual perfection in software development.

Technical Customer Success Manager Brandon Murray shared expert strategies and highlighted the benefits of integrating Visual AI testing, offering guidance on constructing the optimal CI/CD pipeline. He explored the intricacies of Visual AI testing, illuminating its critical role in enhancing software quality and performance.

Challenges in Traditional Functional Testing

Murray began by identifying the bottlenecks commonly encountered in traditional functional testing. These include:

• High maintenance efforts
• Slow feedback cycles
• Limited UI coverage
• Tedious manual testing

The Power of Visual AI Testing

Visual AI testing offers a revolutionary approach to overcome these challenges. By capturing screenshots and using AI to compare these snapshots to a baseline ‘golden image’, Visual AI testing ensures:

• Reduced Test Development and Maintenance Time: Automating UI comparisons dramatically decreases the time spent on writing and maintaining tests.
• Complete UI Coverage: Screenshots ensure that every aspect of the UI is tested, eliminating blind spots.
• Enhanced Operational Efficiency: Faster feedback loops lead to quicker identification and resolution of issues, facilitating faster product releases.

Other Strategies to Supplement Visual AI Testing:

• Self-Healing: Automatically corrects flaky tests by adjusting for locator changes, vastly improving test stability
• Lazy Loading: Helps to ensure the entire page content is loaded
• Parallel Test Execution: Enables the execution of multiple tests simultaneously, significantly speeding up the testing process

Integration into the Development Workflow

Integrating Visual AI testing into existing development workflows, particularly with pull request checks, is pivotal for agile environments. The webinar emphasized the importance of instant feedback for swift issue resolution, leading to accelerated development cycles.

Tools and Technologies Highlighted:

• Cypress: Innovative testing framework for both developers and QA engineers
• GitHub Actions: Continuous integration and continuous delivery (CI/CD) platform enabling automation directly in GitHub repositories
• Figma Designs: Useful for collaborative design reviews and direct comparison against implementations

The session underscored the cost-effectiveness of using browsers on cloud infrastructure containers, especially when dealing with cross-browser coverage. Notably, the Filter Fast Grid was mentioned as an effective solution for this purpose.

Comparing Visual AI Testing to Traditional Methods

Attendees were eager to learn how Visual AI testing compares to snapshot tests and other traditional methods. The webinar demonstrated how Visual AI testing offers:

• Greater Accuracy: By leveraging AI for pixel-perfect comparisons
• Higher Efficiency: Through automated and parallel testing routes

In particular, using commodity CI solutions like GitHub Actions or CircleCI was recommended for their affordability and versatility.

Building the Ideal CI/CD Pipeline: Achieving Visual Perfection highlighted the transformative potential of Visual AI testing in optimizing CI/CD pipelines. Software engineering managers and team leads are strongly encouraged to evaluate how AI-powered tools like Applitools can elevate their testing processes, enhance product quality, and expedite delivery timelines. For those interested, a free trial of Applitools is available to experience the benefits firsthand.

The post Recap: Building the Ideal CI/CD Pipeline appeared first on AI-Powered End-to-End Testing | Applitools.

Forrester’s August 2023 report: The State Of TuringBots, 2023 examines the impact of AI on the Software Delivery Lifecycle (SDLC) and how organizations can effectively add this technology to their overall strategy. The findings were compelling and critical for modern businesses to understand so that they can adapt and stay ahead of the curve. Let’s take a look at some of the key points. 

Per the report, many organizations are grappling with the challenge of keeping up with business changes due to sluggish software development processes. The emergence of Generative AI has ushered in a new era of AI-assistive software, impacting industries irrespective of their SDLC maturity or existing AI utilization.

Forrester coined the name for this software as “TuringBots” which they now define as:

“AI-powered software that augments application development and infrastructure and operations (I&O) teams’ automation and semiautonomous capabilities to plan, analyze, design, code, test, deliver, and deploy while providing assistive intelligence on code, development processes, and applications.”

Forward-thinking organizations are embracing cutting-edge technologies like TuringBots to stay ahead. With GenAI revolutionizing numerous AI applications, the anticipated timeline for the development and impact of TuringBots has been accelerated to two to five years instead of a decade. This shift has brought about a deeper comprehension of the immense potential held by TuringBots.

Today, businesses rely on software as the backbone of digital operations, representing their strategies, processes, products, and services. However, many organizations need help in software development to match the rapid pace of business evolution and innovation. The report laid out common challenges like:

• Many developers still rely on manual testing despite automation advancements in the software development lifecycle. The lack of automation in various stages is attributed to tool complexity, skill gaps, and slow organizational modernization adoption.
• Lacking product management skills. The main hurdle in agile proficiency is the absence of business-led product ownership and management.
• IT that is resistant to change.

Per the report, academia and the tech industry have long aimed to streamline software development. With GenAI’s TuringBots, Forrester states that vendors accelerate product delivery by automating tasks and enhancing user experiences. The report recommends that it is time for tech leaders to empower their teams with TuringBots for maximum efficiency and take advantage of these benefits:

• TuringBots assist in development processes, though not fully mature for complete SDLC support.
• Easy access to project information is crucial for teams, covering project status, test completion, code check-ins, and more. Developers can save time by using Coder TuringBot plug-ins in IDEs like Tabnine or GitHub Copilot.
• These tools provide quick access to code snippets and information, helping to generate code efficiently through natural language chat.

The impact of TuringBots is significant across all industries, driving the transformation into digitally competent businesses. While the speed of adaptation varies, understanding and managing TuringBots is crucial for technology leaders. It is essential to swiftly grasp the key governance and best practices to mitigate risks related to insecure code, performance issues, and user experience.

The post Forrester Report Recap: The Future of Software Development appeared first on AI-Powered End-to-End Testing | Applitools.