TL;DR

• Generative AI is highly effective for creating tests, data, and analysis, but execution has different requirements.
• Test execution demands repeatability, determinism, and explainable failures.
• Probabilistic systems, including LLMs, introduce variability that leads to flaky tests and loss of trust.
• Teams that separate where generative AI helps from where deterministic execution is required scale testing more reliably.

Generative AI has dramatically changed how teams create tests. Requirements can be translated into test cases in seconds. Automation scripts can be bootstrapped with natural language. Test data can be generated on demand.

But many teams are discovering an uncomfortable truth: faster test creation does not automatically lead to more reliable releases.

Execution is where confidence is earned or lost. And test execution demands guarantees that generative AI—including large language models (LLMs)—was never designed to provide.

Where generative AI fits well in testing

Generative AI excels in parts of the testing lifecycle that tolerate variation. These are areas where approximation is acceptable and speed matters more than precision.

Teams are successfully using AI to:

• Generate test cases from requirements
• Assist with unit and integration test authoring
• Create realistic and varied test data
• Summarize test results and surface patterns

In most of these cases, teams are relying on LLMs to generate intent, not to make final execution or release decisions.

These use cases benefit from flexibility. Minor differences in output rarely introduce risk, and human review is often part of the workflow.

The challenge emerges when that same probabilistic behavior is extended into execution.

Why test execution is fundamentally different

Test execution is not a creative task. It is a verification task.

Execution requires:

• The same test to behave the same way, run after run
• Assertions that are precise and stable
• Failures that can be reproduced and diagnosed
• Outcomes that can be explained clearly to stakeholders

Generative AI systems—particularly LLMs—are probabilistic by design. That variability is useful for exploration and generation, but it works against the repeatability and determinism execution depends on.

As AI accelerates development, repeatability becomes more important than intelligence in test execution.

How probabilistic execution creates real problems

When probabilistic systems are used to drive execution, teams often encounter the same failure modes:

• Tests that pass one run and fail the next without code changes
• Assertions that subtly change or disappear
• Longer debugging cycles because failures can’t be reproduced
• Rising compute costs from repeated executions
• Engineers losing confidence in automation results

When failures aren’t repeatable, teams stop trusting their tests—and that’s when automation becomes a bottleneck instead of a benefit.

– Shaping Your 2026 Testing Strategy

Once trust erodes, teams compensate. Manual validation creeps back in. Releases slow down. Automation becomes something teams work around rather than rely on.

Execution amplifies risk: security, governance, and explainability

Execution is also where risk concentrates.

When AI systems drive test execution, they may:

• Send application context externally
• Make decisions that can’t be fully explained
• Produce outcomes that are difficult to audit

These concerns are most visible in regulated and high-risk environments, but they apply broadly. Any team responsible for production releases needs to be able to explain why a test failed—or why a release was approved.

Reliable execution is not just a technical concern. It’s a governance concern.

Why deterministic execution matters at scale

Deterministic systems behave predictably. Given the same inputs, they produce the same outcomes.

In test execution, this enables:

• Reliable failure reproduction
• Faster root cause analysis
• Lower maintenance overhead
• Clear audit trails
• Reduced noise in pipelines

What test execution demands is not intelligence, but guarantees: the same inputs producing the same outcomes, every time.

Reliable test execution depends on determinism, not creativity.

Rethinking AI’s role in execution

The goal is not to abandon generative AI. It’s to use it where it fits.

Effective teams are separating responsibilities:

• Generative AI for creation, exploration, and analysis
• Deterministic systems for execution and verification

This separation allows teams to move quickly without sacrificing confidence.

What this means for engineering and QE teams

As AI becomes more deeply embedded in testing workflows, the key decision is no longer whether to use AI—but where.

Teams that succeed will:

• Accept variability where it’s safe
• Demand determinism where decisions are made
• Measure success by signal quality, not test count
• Optimize for trust before speed

The biggest risk in AI-driven testing isn’t lack of automation—it’s lack of trust.

Choosing confidence over convenience

Generative AI has changed how tests are created. It should not change the standards by which tests are trusted.

Execution is where reliability matters most. Teams that recognize this distinction will scale testing with confidence, even as AI continues to reshape software development.

Watch Shaping Your 2026 Testing Strategy now.

The post What Test Execution Demands That Generative AI Can’t Guarantee appeared first on AI-Powered End-to-End Testing | Applitools.

TL;DR

• AI is now foundational to software testing, but more AI often creates more noise.
• AI-assisted development increases code volume and pressure on QA teams.
• The biggest bottleneck in testing today is signal-to-noise, not execution speed.
• Successful testing strategies in 2026 prioritize trust, explainability, and reliable results.

AI has quietly moved from the edges of software testing into the center of it. For most teams, it’s no longer a question of whether AI plays a role in testing, but how deeply—and how intentionally.

Quality and Engineering leaders are feeling this shift firsthand. AI-assisted development is increasing the volume and pace of code changes. Release cycles are accelerating. At the same time, testing teams are being asked to scale confidence without scaling headcount.

In this environment, speed alone is not the differentiator. Trust is. 

In AI-driven testing, speed without trust slows teams down.

AI is no longer optional in testing

Across the software delivery lifecycle, AI is already embedded in day-to-day workflows. Teams are using it to generate test cases from requirements, assist with automation, create test data, and analyze results. In many organizations, this adoption didn’t start with QA—it started with developers.

What’s changed is that AI is no longer experimental or isolated. It’s shaping how testing actually happens.

This matters because AI-assisted coding changes the scale of the testing problem. More code is being produced, faster than before, and not all of it is high quality. That shift pushes pressure downstream, straight onto QA and QE teams.

More AI hasn’t reduced pressure on QA—it’s increased it

For many Engineering Managers, AI has delivered productivity gains on the development side while increasing complexity on the testing side. Test suites grow larger. Pipelines generate more results. Failures are harder to interpret.

As Applitools CEO Anand Sundaram recently described, the imbalance is real:

“You have more code to be tested, sometimes not the best code, more coverage required, and fewer people.”

– Shaping Your 2026 Testing Strategy

This combination exposes a deeper issue. As tooling improves, teams don’t just get more data, they get more noise. And noise is expensive.

The real bottleneck is signal-to-noise

Most mature teams are no longer blocked by how fast they can run tests. They’re blocked by how confidently they can interpret the results. 

As AI accelerates development, signal quality matters more than test volume.

False positives, flaky tests, and inconsistent outcomes force teams into defensive behaviors: re-running pipelines, manually validating changes, and delaying releases “just to be safe.” Over time, automation stops accelerating delivery and starts slowing it down.

This is where many AI-driven testing initiatives struggle. AI can generate more tests and more output, but without reliable signals, that output doesn’t lead to better decisions.

Not all AI is suitable for testing decisions

One clear theme for 2026 is that AI is not a single, interchangeable capability. Different phases of the testing lifecycle have very different requirements.

Large language models excel at tasks that tolerate variation: generating test ideas, creating data, summarizing results, and assisting with analysis. But test execution and release decisions demand consistency, repeatability, and explainability.

This distinction becomes especially clear when you look at test execution. Unlike test generation or analysis, execution depends on consistent behavior and repeatable outcomes.

When test outcomes change run to run, teams lose trust. When failures can’t be reproduced, debugging slows down. And when decisions can’t be explained clearly, confidence erodes—both within engineering and with leadership.

Trust, explainability, and repeatability matter more than novelty

As AI adoption grows, testing teams are being forced to answer harder questions. Can we trust these results? Can we explain them? Can we confidently make release decisions based on them?

These questions matter in regulated and high-risk environments, but they’re just as relevant for any team shipping customer-facing software at speed. Reliability is not a constraint on velocity—it’s what makes velocity sustainable.

Teams operating under stricter compliance requirements have already learned that explainability and repeatability are non-negotiable for AI-driven testing decisions. (Read more—AI Testing in Regulated Environments: Smarter Testing Starts With Stability, Not More Code.)

This is why many teams are rethinking how they apply AI to testing. Deterministic approaches—systems that behave consistently and predictably—make it easier to reduce noise, identify real failures, and move faster with confidence.

What this means for testing strategy in 2026

The takeaway for Quality and Engineering leaders isn’t to slow down AI adoption. It’s to be more intentional about it.

Successful testing strategies in 2026 will share a few characteristics:

• AI is treated as foundational, not experimental
• Different phases of testing use different kinds of AI
• Reliability and explainability are prioritized where decisions are made
• Signal quality and maintenance reduction are explicit goals

Not all AI belongs everywhere. Choosing where reliability matters most is becoming a core leadership responsibility for engineering and quality teams. The biggest risk in AI-driven testing isn’t lack of automation—it’s lack of trust.

Choosing progress over noise

AI is reshaping software testing whether teams are ready or not. The challenge now is judgment. Knowing where AI accelerates quality—and where it quietly undermines it—is what separates teams that scale confidently from those that drown in noise.

The fastest teams aren’t the ones chasing the newest tools. They’re the ones that trust what their tests are telling them.

Watch Shaping Your 2026 Testing Strategy now.

The post AI Testing in 2026: Why Signal, Trust, and Intentional Choices Matter More Than Ever appeared first on AI-Powered End-to-End Testing | Applitools.

TL;DR

• Reducing test maintenance and improving result accuracy are becoming core evaluation criteria for autonomous testing platforms
• Visual validation is increasingly used to ensure UI accuracy across web, mobile, and native applications
• These capabilities help teams maintain release confidence and reduce risk in complex and dynamic, user-facing experiences at scale

Modern software teams ship faster than ever, and testing teams need tooling that keeps up. In Q4 2025, Forrester published The Forrester Wave: Autonomous Testing Platforms, Q4 2025, evaluating autonomous testing platform providers.

Applitools is named a Strong Performer in this evaluation.

The momentum behind autonomous testing

Teams now build and ship across more devices, frameworks, and release cadences. That reality pushes quality practices toward higher automation, better maintenance efficiency, and faster feedback loops.

Forrester frames this market shift directly:

“This is why we changed this Forrester Wave category from ‘continuous automation testing platforms’ to ‘autonomous testing platforms.’”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

What buyers should look for in autonomous testing platforms

When you evaluate autonomous testing platforms in 2025, three practical questions usually help teams make sense of the space:

• Platform fit: Can the platform support your mix of apps and test types, plus your workflows across engineering and QA?
• AI-infused automation: Does the platform reduce authoring and maintenance effort in a way you can trust and govern?
• Testing AI-enabled experiences: As more teams ship AI-enabled features, can your testing approach keep pace with new failure modes and higher variability?

These questions help teams connect product capabilities to real delivery constraints: speed, coverage, confidence, and operating cost.

How the report characterizes Applitools

This report describes Applitools’ approach through Visual AI and ML-resilience oriented toward UI accuracy and maintenance reduction:

“(Applitools) It features Visual AI to validate UI accuracy across web, mobile, and native apps and support modern digital experiences at scale.”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

It also cites a strategy emphasis on reducing maintenance and improving accuracy:

“Applitools stands out for innovation, gaining an above-par score due to its Visual AI and ML-driven resilience that reduce test maintenance and improve accuracy.”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

What this can mean for engineering, QA, and design teams in 2025

Engineering teams can treat autonomous testing as a way to protect delivery speed. When teams reduce flaky failures and avoid constant test repairs, they shorten the path from code change to deployable signal.

QA teams can prioritize scalability and governance. As test suites grow, teams need tools and workflows that improve coverage without creating unsustainable maintenance load.

Design teams can connect UI intent to release confidence. When teams validate UI accuracy consistently across browsers, devices, and releases, they reduce risk in UX-heavy, customer-facing journeys.

Across all three groups, teams can get more value when they align on what “quality” means for the product and then choose automation approaches that enforce that definition consistently.

Read the report

While you’re evaluating autonomous testing priorities for 2025, read the full report to understand the evaluation criteria, methodology, and vendor profiles in context.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here.

The post Applitools Named a Strong Performer in The Forrester Wave™: Autonomous Testing Platforms Report, Q4 2025 appeared first on AI-Powered End-to-End Testing | Applitools.

TL;DR

• Autonomous testing is maturing quickly, but regulated organizations must evaluate platforms through the lens of traceability, auditability, and control.
• Forrester’s Autonomous Testing Platforms Landscape, Q3 2025 shows that the real differentiators now are explainability, risk-based orchestration, and AI governance—not just automation speed.
• Use this checklist to choose a platform that accelerates delivery while protecting oversight.

Download Forrester’s full report for detailed market insights

Rethinking Autonomy for Regulated Teams

With hundreds of tools now promising “AI-driven automation,” sorting true autonomy from clever scripting has become increasingly difficult. This matters even more for regulated teams planning their 2026 quality strategy. Speed is no longer the only concern. Proof, traceability, and controlled execution are now essential.

Forrester’s recent analysis highlights a market shifting from test automation to AI-augmented and agentic systems that generate, maintain, and execute tests under human supervision. The key question for regulated buyers is not whether autonomy will help, but whether the platform provides clear governance around how that autonomy operates.

Use this checklist to evaluate solutions with the guardrails required for safety-critical or compliance-heavy environments.

Core Capabilities Every Autonomous Testing Platform Should Provide

These capabilities form the baseline for operating safely and efficiently in regulated sectors.

Plain-language test authoring and execution
Non-technical reviewers should contribute without adding risk. Natural-language authoring and guardrails make collaboration safe and auditable.

Transparent AI actions
Every generated or changed step must be reviewable. No black-box maintenance. No silent updates.

Evidence management and auditability
Exportable logs, change histories, and evidence packs should support internal and external audits without manual rework.

Role-based control and gated approvals
Automation should accelerate work, but never bypass required compliance workflows.

Adaptive, governed maintenance
Self-healing is useful only when changes are traceable and reversible. Regulated teams need adaptive maintenance under human oversight.

If a platform lacks any of these essentials, it’s not built for environments where documentation and control are mandatory.

Where Advanced Platforms Differentiate

Once the fundamentals are covered, regulated organizations should look at the capabilities that separate mature autonomous solutions from those still catching up.

Intent-based visual and experience validation
Pixel comparison is brittle. Intent-driven validation ensures the interface appears correct, accessible, and compliant across devices and browsers.

Governance dashboards
AI actions, risk coverage, and test triggers should be visible and easy to trace for auditors and managers.

Actionable analytics and reporting
Evidence should turn into insights that support risk management, release approvals, and executive reporting.

Risk-based orchestration
Platforms should prioritize tests based on business criticality, change impact, and historical issues—not just run everything in bulk.

Applying Autonomous Testing in Regulated Workflows

Organizations across healthcare, life sciences, financial services, and other regulated industries are already adopting autonomous testing—but always with governance in place.

In the pharmaceutical sector, EVERSANA INTOUCH takes a hybrid approach, combining Applitools Eyes for Visual AI validation with Applitools Autonomous for intelligent test generation. This end-to-end strategy ensures quality products, supports compliance-ready evidence, reduces maintenance, and provides end-to-end coverage across complex workflows—all while keeping human reviewers in charge. Read the EVERSANA INTOUCH case study.

These hybrid models show how autonomy can increase coverage and speed without loosening control.

Applying the Checklist to Your Evaluation Process

Use this framework when comparing platforms side by side:

• Map your highest-risk business journeys. Focus on areas tied to compliance, customer safety, or financial impact.
• Prioritize transparency. Ensure the platform shows why AI takes each action and allows review before changes go live.
• Assess evidence and governance. Exportable results, audit-ready logs, and approval gates are non-negotiable.
• Evaluate adaptability. Autonomous maintenance should reduce manual effort but still operate inside defined boundaries.

• Reassess regularly. The market is moving fast. Capabilities that seem advanced today will become baseline expectations.

Choosing with Confidence

Autonomous testing is reaching maturity, but regulated organizations need more than speed—they need governance, visibility, and trust. Forrester’s research confirms that platforms built with explainability and risk alignment at the center are the ones best suited for compliance-driven teams.

Use Forrester’s analysis and this checklist to guide your next evaluation and choose an autonomous testing solution that accelerates both delivery and confidence. Download the Autonomous Testing Platforms Landscape, Q3 2025 report.

The post Buyer’s Checklist for Autonomous Testing in Regulated Environments appeared first on AI-Powered End-to-End Testing | Applitools.

Update & TL;DR

This post was written while Forrester’s research on agentic and autonomous testing was still emerging. Since publication, Applitools has been included in The Forrester Wave: Autonomous Testing Platforms, Q4 2025. The perspective outlined below reflects how this shift has since been validated and formalized by independent industry analysts.

• Agentic automation shifts testing from brittle, script-driven execution to intelligent systems that adapt based on change, risk, and context.
• AI augments human intent rather than replacing QA teams, enabling people to focus on quality strategy, governance, and risk decisions.
• This model is increasingly shaping how autonomous testing platforms are evaluated in the market.

Forrester, a leading global research and advisory firm, identified a major turning point in software testing in its Autonomous Testing Platforms Landscape, Q3 2025. The research describes a shift from traditional scripted automation to AI-augmented systems that can learn, adapt, and act under human guidance. This shift signals the rise of agentic automation: intelligent systems that create, run, and optimize tests within defined boundaries.

As delivery cycles compress and complexity grows, quality and engineering leaders are redefining what effective testing means in practice. Agentic automation bridges human intent with machine-driven precision—transforming testing from a reactive maintenance task into a proactive engine for reliability, speed, and continuous improvement.

From Automation to Intelligence

Traditional automation accelerated execution but left teams managing brittle scripts and endless maintenance. AI-augmented testing changes that dynamic. These systems:

• Learn continuously from results and application change.
• Adapt test scope and prioritization based on business risk.
• Optimize coverage while maintaining human oversight.

The result is testing that behaves less like a checklist and more like a self-improving quality partner, one that scales reliability across every release.

The Three Business Values Driving This Shift

Forrester highlights three outcomes motivating investment in more intelligent testing systems:

1. Accelerate Time to Value – AI-driven generation and self-healing shorten feedback loops and reduce maintenance.
2. Reduce Strategic Risk – Risk-based orchestration and built-in governance connect quality metrics directly to business priorities.
3. Democratize Testing – Low-code authoring and natural-language interaction let non-developers participate in quality, closing skill gaps.

Agentic automation brings these together: human-directed intent, machine-driven efficiency, and transparent oversight.

How AI-Augmented Systems Complement Human Expertise

AI in testing works best as augmentation, not replacement. By handling repetitive execution and maintenance, intelligent systems free QA professionals to focus on:

• Defining risk and coverage strategy.
• Establishing governance frameworks that maintain trust.
• Collaborating earlier with product and development teams.

Agentic automation shifts QA leadership from running tests to steering quality outcomes.

The Role of Visual and Experience Validation

Intelligent automation depends on reliable validation signals. Traditional assertions can’t always capture what matters to real users: layout, accessibility, and experience consistency. 

Visual and experience validation fill that gap, giving AI-augmented systems context they can trust. When machines validate what users actually experience, teams gain both speed and confidence—without rigid pixel-level comparison.

Building Toward AI-Augmented Readiness

Forrester describes this as a maturing market: organizations are blending traditional automation with AI capabilities to move toward greater autonomy over time. QA leaders can start by:

1. Stabilizing automation foundations and addressing flakiness.
2. Adopting AI-assisted detection of UI and data changes.
3. Integrating experience-level validation for richer feedback.
4. Connecting quality analytics to business metrics for continuous improvement.

Each step builds the trust and data maturity required for agentic automation to succeed under human orchestration. As adoption increases, these maturity steps align with how leaders in the market are being evaluated on autonomous capabilities.

What QA Leaders Can Do Next

Forward-looking teams are already experimenting with:

• Adaptive execution that prioritizes tests dynamically.
• Governance dashboards linking coverage, risk, and compliance.
• Visual AI that helps systems understand real user impact.

The goal isn’t full autonomy—it’s AI-augmented confidence: testing that’s faster, smarter, and more inclusive across roles. Read the full report now.

The post Agentic Automation: Preparing QA Leaders for the Next Leap in Testing appeared first on AI-Powered End-to-End Testing | Applitools.

By Anand Sundaram, CEO of Applitools

I’m thrilled to share that I’ve joined Applitools as CEO.

For over a decade, I’ve admired this company for pioneering Visual AI and transforming how teams think about software quality. Applitools Eyes didn’t just make testing faster—it created an entirely new category that fundamentally changed how organizations approach quality at scale. Today, with the Applitools Intelligent Testing Platform and our Autonomous product evolving rapidly, we’re once again reshaping what’s possible.

Why I Joined Applitools

What drew me here is the rare combination of groundbreaking technology and exceptional people. Applitools has built a platform that sits at the heart of modern software delivery, helping teams validate quality at every stage—from design to deployment—and enabling them to ship with confidence.

We’re at another major inflection point. AI is transforming how software is built, tested, and delivered—at unprecedented speed and scale. Code is being generated faster than ever by both humans and machines, and quality can’t become the bottleneck. Applitools is uniquely positioned to help organizations navigate this transformation, delivering applications and services with speed, confidence, and uncompromising quality.

Looking Ahead

Over the next few weeks, I’ll be focused on listening and learning. I want to hear from our employees, customers, and partners—what excites you about Applitools, where you see opportunities, and what bold ideas we should explore together. Your insights will shape our path forward.

This is an incredible moment for Applitools. Together, we’ll build on our momentum, deepen our impact with customers, and define the future of AI-driven software quality.

Thank you to everyone who has already extended such a warm welcome. I’m honored to lead this team and excited about what we’ll accomplish together.

— Anand

Anand Sundaram

Chief Executive Officer, Applitools

Anand Sundaram is a seasoned product and technology executive with more than two decades of experience in software quality. He has held multiple senior leadership roles and founded three startups, including RSW Software, which was acquired by Teradyne and became the foundation of the Oracle Application Testing Suite. Read more.

The post A New Chapter for Applitools: CEO Anand Sundaram on Why He Joined appeared first on AI-Powered End-to-End Testing | Applitools.

Software leaders often face a false choice: should testing be code-driven or no-code? The truth is, the strongest strategies use code and no-code test automation together. By letting each approach play to its strengths, teams cut bottlenecks, empower more contributors, and deliver quality software faster.

The Pitfalls of Choosing One Approach

When organizations lean too heavily on one side—whether code or no-code—the same challenges show up again and again:

• Skill gaps: Engineers and testers bring different levels of coding expertise, which creates dependencies and slows progress.
• Silos: Developers, QA, and manual testers often work separately, with little shared visibility.
• Maintenance overhead: Purely coded frameworks can be fragile and time-consuming to update, while a no-code-only strategy can limit flexibility for advanced scenarios.

Instead of streamlining releases, testing becomes another obstacle—especially when teams frame it as code versus no-code instead of embracing code and no-code test automation as a unified strategy.

The Strengths of Code-Based Automation

Code-based frameworks like Selenium, Cypress, and Playwright remain essential for complex cases. They provide:

• Flexibility and customization to test virtually any scenario.
• Fine-grained control over selectors, browser behavior, and environments.
• Precision that’s critical when working with complex workflows.

For engineering teams, code is still the best tool for edge cases and advanced automation.

The Strengths of No-Code Automation

No-code testing platforms such as Applitools Autonomous thrive on speed and accessibility. With plain-language test authoring and visual interfaces, they allow non-technical testers to contribute directly. This makes them ideal for:

• Regression and smoke tests that repeat across releases.
• Routine workflows that don’t require custom code.
• Broad participation across QA and business testers.

The benefit: engineers aren’t pulled into repetitive work, freeing them to focus on higher-value challenges.

Code + No-Code in Action

The difference becomes clear when comparing the two side by side. In one demo, a Selenium test for a simple e-commerce checkout flow took nearly an hour to script. Using Autonomous, the same flow—with assertions—was built in just two minutes.

The takeaway isn’t that one should replace the other. No-code handles what’s fast and repeatable; code handles the complex and custom. Together, they balance speed and depth.

Watch Code & No-Code Journeys: The Collaboration Campground now on-demand.

Real-World Proof: EVERSANA

EVERSANA INTOUCH, a global life sciences agency, illustrates what this balance looks like in practice. Faced with strict compliance requirements and fragmented workflows, they needed to unify testing across teams worldwide.

• First step: Adopted Applitools Eyes (code-based visual testing).
• Next step: Expanded to Autonomous, allowing global manual testers to build end-to-end tests in the browser.

Result: A 65%+ reduction in regression testing time, faster validation across browsers and environments, and a new “Autonomous-first” policy before assigning engineering resources.

The biggest change wasn’t only speed—it was collaboration. Developers, testers, and compliance began working from shared results, cutting duplicate effort and improving trust across the organization.

Read more about how EVERSANA INTOUCH cut regression testing time by 65% in the customer case study.

Takeaway for QA and Engineering Leaders

The question isn’t “code or no-code.” It’s how best to integrate both. For many teams, this means adopting code and no-code test automation to scale testing with confidence. By using no-code for regression and repeatable flows, and code for complex scenarios, teams reduce bottlenecks, shorten feedback cycles, and scale their testing with confidence.

For mid-size to enterprise teams, this balanced approach delivers:

• Faster test creation and execution.
• Greater collaboration across roles and skill levels.
• A testing strategy that keeps pace with modern release cycles.

Next Steps

Identify where no-code can relieve your engineers, and where code provides the precision you need. The future of testing isn’t about choosing sides—it’s about working smarter with both. Start your own code and no-code journey with Applitools Autonomous.

The post Why the Future of Test Automation is Code AND No-Code appeared first on AI-Powered End-to-End Testing | Applitools.

Testing has always been about more than just catching bugs. For QA and engineering leaders, it’s about enabling collaboration across teams, keeping pace with rapid release cycles, and maintaining confidence in quality. But traditional approaches often break down when skill gaps, silos, and tool fragmentation get in the way.

Modern testing platforms are changing that—not by replacing testers, but by using AI to bridge technical and non-technical team members, giving everyone a way to contribute to test creation and maintenance.

AI as the “Trail Guide” for Testing

Think of AI as an experienced trail guide: it understands the terrain, spots shortcuts, and helps both experts and first-timers reach their destination faster.

For testing teams, this means:

• Non-technical testers can describe flows in plain language and see them converted into robust test steps.
• Engineers save time on repetitive tasks and focus on complex automation.
• Teams build trust by working from the same results.

Key Capabilities of Modern Testing Tools

AI-powered platforms don’t just make testing easier, they expand what teams can accomplish together. Some of the most impactful capabilities include:

• Plain-language test authoring: Write test steps in English, not code.
• Interactive recording: Capture actions directly in the browser, instantly translating clicks into test steps.
• LLM-assisted authoring: Automatically generate test steps and validations.
• Data-driven testing: Parameterize values, generate contextual test data, and run variations without rewriting scripts.
• JavaScript injections for advanced logic: Give power users the ability to add complexity when needed.
• Self-maintaining suites: Tools can crawl a site, adapt to changes, and keep tests stable over time.

Deterministic LLMs: Reliable Execution at Scale

Not all AI is created equal. General-purpose models can hallucinate or create inconsistent results — exactly what teams don’t want in testing. Purpose-built, deterministic LLMs address this by focusing on consistency, speed, cost, and security:

• Consistency: Predictable execution without variance.
• Speed: Optimized models built specifically for test authoring and execution.
• Cost control: More efficient to run at scale.
• Security: Use of synthetic data ensures sensitive information is never exposed.

Visual AI for Complete Coverage

AI doesn’t just streamline test authoring. Visual AI extends coverage across devices, browsers, and operating systems with far fewer steps to maintain.

• Visual assertions reduce the need for brittle, locator-based checks.
• Multi-device coverage comes with less authoring overhead.
• Group maintenance lets teams accept or reject changes across multiple screens with a single action.

This creates both broader coverage and long-term scalability.

The Impact on Team Collaboration

The real value isn’t just in new features — it’s in how teams work together. AI-powered tools let QA, developers, and business testers all contribute to the same automated workflows. That reduces bottlenecks, speeds up release cycles, and shifts attention to what matters most: quality insights and critical thinking.

Takeaway for QA and Engineering Leaders

AI isn’t here to replace testers — it’s here to elevate them. By bridging skill levels, reducing repetitive work, and maintaining tests automatically, modern platforms create a more collaborative, efficient testing culture.

For mid-size to enterprise organizations, the benefits are clear:

• Faster test authoring and maintenance.
• Broader participation across roles.
• Reliable execution with reduced risk.

Next step: Watch Code & No-Code Journeys: The Collaboration Campground now on-demand, or speak with a testing specialist to explore how AI-powered testing can unify your team and simplify your QA strategy.

The post How Modern Testing Tools Use AI to Bridge Teams and Simplify QA appeared first on AI-Powered End-to-End Testing | Applitools.

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.

AI is transforming software testing—but without clear context, even the smartest models can fall short. The new Model Context Protocol (MCP) aims to solve that problem, and it’s picking up momentum fast. Here’s what QA and development teams need to know—and why it matters right now. If you have questions about how we’re building for the future or how this fits into your testing strategy, let us know—we’d love to talk.

What Is MCP?

MCP, or Model Context Protocol, is an open standard designed to help applications provide AI models with structured context. Think of it as a standardized way for tools and systems to tell an AI assistant what’s going on—who the user is, what they’re doing, and what resources are available.

Anthropic introduced MCP in late 2024, and it’s already being adopted by major players like OpenAI, Microsoft, and testing leaders building next-generation AI workflows. Addy Osmani, an engineering leader at Google, calls MCP “the USB-C of AI integrations,” highlighting its potential to standardize the connection between tools and intelligent agents.

Why Context Matters in AI-Assisted Testing

Large language models (LLMs) are only as good as the context they receive. Without proper inputs, you get generic outputs—or worse, hallucinations. For QA teams using AI to generate tests, interpret failures, or automate user flows, missing context leads to fragile results and wasted time.

MCP helps solve this by passing structured information to the model: which test framework is in use, what files are open, what code just changed, and more. That means faster, more relevant AI assistance—and more accurate automation.

What MCP Enables in Testing Workflows

MCP makes it easier for tools and AI assistants to share structured context—like which framework is active, what code changed, or what the user is trying to do. That unlocks more accurate test generation, better debugging, and scalable, reusable automation.

It also supports dynamic discovery, so AI systems can find and connect with available tools at runtime—no brittle configs or manual setup required.

As testers ourselves, we take a measured approach to adopting new AI standards like MCP. That means vetting integrations for stability and reliability, so our customers can move fast without sacrificing trust.

Why It’s a Big Deal Now

There are two key reasons to pay attention to MCP today:

First, the standard is taking off. Thought leaders like Angie Jones, Filip Hric, Tariq King, and Addy Osmani are publishing real-world MCP demos and contributing open-source tools. It’s not theoretical anymore—it’s happening.

Second, the stakes are high. As more testing platforms integrate AI (including Applitools Autonomous), the ability to connect tools through open standards like MCP is becoming a competitive differentiator.

How Applitools Fits In

Applitools has long focused on intelligent automation—delivering AI-powered test creation, visual validation, and self-healing across platforms. As open standards like MCP emerge, we’re building on that foundation to extend context-sharing across tools, so teams can:

• Automatically create or update visual and functional tests based on code changes
• Route test context through the pipeline for faster root cause analysis
• Improve AI-generated tests with better accuracy and explainability
  

Security is also critical. As MCP evolves, host-mediated permissions and encrypted communication protocols are being considered by contributors to ensure context is shared safely and responsibly.

At Applitools, we’re building these principles directly into the future of Autonomous and Eyes—and we’d love to walk you through what’s on our roadmap. If you’re already an Applitools customer, reach out to your account team to schedule a preview conversation. If you’re not already using Applitools, schedule time with one of our testing specialists—we’re here to help.

The post MCP: What It Is and Why It Matters for AI in Software Testing appeared first on AI-Powered End-to-End Testing | Applitools.

If you’ve already explored how AI-powered, no-code test automation tools can expand who contributes to testing, the next question is: how do you choose the right AI approach for your broader strategy?

Teams today face more pressure than ever to deliver faster without compromising quality. Traditional test automation can’t keep pace—it’s often brittle, siloed, and difficult to scale across teams.

AI-powered testing offers new ways to accelerate coverage, improve stability, and reduce manual effort. But not all AI is created equal. Understanding the differences between AI-assisted, AI-augmented, and autonomous testing models can help you adopt the right tools at the right time—with the right expectations.

Understanding the AI Testing Landscape

AI is showing up everywhere in the testing conversation, but it’s not always clear what type of AI is in play—or how much human involvement is still required. Here’s a breakdown:

AI-assisted testing

These tools support engineers during test creation. Think: autocomplete, code suggestions, or debugging help. They speed up test authoring but still rely on someone writing the test manually.

AI-augmented testing

These systems go further by analyzing existing test repositories, usage data, or logs to identify missing coverage or redundant cases. The AI assists strategically, but the tester still has the final say.

Autonomous testing

This model allows AI to execute test scenarios based on higher-level inputs—like a test goal or an intent. With access to the application, past test data, and usage patterns, it can decide what to test and how. Human oversight is still essential, but the AI drives more of the process.

Each model – assisted, augmented, or autonomous – shapes who can contribute to testing and how much oversight is needed. Choosing the right mix ensures your entire team can move faster without sacrificing quality.

Solving for Coverage, Speed, and Stability

As testing shifts left—and right—teams need solutions that can handle growing complexity without adding manual effort. AI helps in several key areas.

Reducing Flaky Tests

Flaky tests are a drain on time and confidence. They often result from brittle locators, timing issues, or inconsistent environments.

AI-powered self-healing automatically updates broken selectors when the UI changes, helping teams avoid rework and unnecessary test failures.

Authoring Tests Without Code

AI can also simplify how tests are created. NLP-based test creation, for example, allows users to define actions in plain English or record workflows that are translated into readable steps.

This approach has become one of the most accessible and impactful uses of AI in testing, enabling broader participation—from QA to product to manual testers.

Visual Validation for Real-World UI Testing

Functional scripts may confirm that a button exists—but they can’t always tell if it’s visible, clickable, or correctly placed. Visual AI ensures that tests validate what a user actually sees, not just what’s in the DOM.

This level of intelligence is especially critical for responsive design testing and dynamic layouts.

Choosing an Approach That Fits Your Team

The right AI testing strategy depends on where your team is in its automation journey.

• If you’re accelerating test writing with existing frameworks, AI-assisted tools may be the quickest win.
• If you’re optimizing test coverage and reducing redundancy, AI-augmented systems can help prioritize the right areas to test.
• If you’re expanding test ownership across roles, autonomous testing—especially when paired with no-code NLP creation—offers the scale and accessibility to match.

Many teams benefit from a layered approach, combining all three models across workflows.

And behind the technology, delivery matters. Tools powered by in-house AI models offer faster, more consistent results with greater control over privacy and cost—key factors for scaling in enterprise environments.

What’s Next

AI in testing isn’t about replacing people—it’s about enabling them to do more with less. Whether you’re automating UI tests with NLP, analyzing risk with augmented AI, or building autonomous test flows, the goal is the same: faster releases, better coverage, and fewer late-cycle surprises.

Want to explore how different AI models can work together across your test strategy? Watch the full session on demand and see how teams are applying AI-powered testing models to scale quality without increasing complexity.

The post AI-Powered Testing Strategy: Choosing the Right Approach appeared first on AI-Powered End-to-End Testing | Applitools.

In today’s hyper-competitive environment, efficiency is king. Ensuring quality without slowing down development cycles is a critical priority for organizations looking to stay ahead. Hybrid test automation—combining both coded and no-code tools—has emerged as a game-changer. The smartest organizations are adopting this approach to reduce maintenance, accelerate releases, and empower cross-functional teams.

Applitools customer, Eric Terry, Senior Director of Quality Control at EVERSANA INTOUCH, underscored how a hybrid approach to test automation bridges skill gaps, enhances collaboration, and accelerates time-to-market. This article explores why a dual automation strategy isn’t just an IT initiative—it’s a business imperative.

The Business Risks of Choosing Just One Approach

When organizations lean too heavily on either coded or no-code automation, inefficiencies emerge. Coded automation offers flexibility and customization but demands highly skilled engineers, creating bottlenecks. No-code automation empowers non-developers but may lack depth for complex scenarios.

A hybrid strategy aligns technical capabilities with business needs, ensuring that:

• Routine tasks and UI-driven tests are handled by AI-powered no-code tools like Applitools Autonomous.
• Complex scenarios requiring deep customization leverage coded automation.
• Testing scales across diverse skill levels, unlocking greater efficiency.

Faster Releases, Higher Quality: A Competitive Advantage

Accelerating time-to-market while maintaining quality is a strategic advantage. Companies that integrate both coded and no-code automation realize efficiency gains, including:

• Reduced test maintenance: “We cut test maintenance by 40% by integrating AI-driven no-code automation,” Eric shared.
• Parallel execution: Running tests simultaneously across environments accelerates feedback loops.
• Smarter test selection: AI-powered tools identify the most critical tests, reducing regression cycles by up to 70%.

Collaboration as a Business Driver

Siloed workflows kill efficiency. When manual testers, automation engineers, and developers operate in isolation, knowledge gaps and redundancies increase risk.

Successful hybrid test automation programs:

• Encourage mentorship, where automation engineers guide manual testers.
• Align automated testing efforts with broader business goals.
• Leverage collaborative tools like Azure DevOps and Microsoft Teams for transparency.

Cost Savings: The Overlooked Benefit of Hybrid Automation

Cost efficiency isn’t just about reducing headcount; it’s about maximizing team output. Organizations that embrace a hybrid test automation approach realize:

• Lower hiring costs by enabling manual testers to contribute to automation efforts.
• Higher productivity by freeing developers from routine scripting.
• Broader adoption as business teams leverage no-code tools for non-QA applications, such as UI validation.

“Anytime that you can save some time, it has the potential to that into revenue,” Eric emphasized.

The No-Code Mindset Shift: A Leadership Imperative

Historically, tech leaders viewed no-code solutions as limited. But AI-driven platforms like Applitools are changing the game, allowing teams to scale automation without specialized expertise.

“I think we’ll start to see the uptick,” Eric predicted. “Tools are getting better, and they’re making automation more accessible than ever.”

See first-hand how Applitools can help your teams bridge skill gaps and scale test automation with a free trial.

Next Steps: Implementing a Hybrid Approach in Your Organization

For leaders looking to integrate both coded and no-code automation, consider these steps:

1. Assess your skill gaps – Identify where no-code solutions can bridge inefficiencies.
2. Start small, then scale – Pilot no-code automation for repetitive workflows.
3. Foster a whole-team quality mindset – Align teams around a shared automation vision.
4. Leverage AI-powered tools – Reduce maintenance while increasing test accuracy.

Future-Proof Your Testing Strategy

In the words of W. Edwards Deming, “It is not necessary to change. Survival is not mandatory.” Organizations that resist automation evolution risk falling behind. By strategically integrating both coded and no-code automation, businesses position themselves for faster innovation, higher quality, and stronger collaboration.

Hear more of EVERSANA’s story by watching Code or No-Code Tests? Why Top Teams Choose Both.

The post Bridging the Gap: Why Businesses Thrive with Hybrid Test Automation appeared first on AI-Powered End-to-End Testing | Applitools.