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

• Code-centric automation continues to slow teams down as UI changes multiply, making stability and evidence hard to maintain.
• AI code generators don’t solve the problem because they still produce brittle test code that requires constant oversight.
• Live LLM-driven execution introduces unpredictability. Regulated teams need deterministic runs, not improvisation
• A clearer path is intent-driven authoring paired with deterministic engines and Visual AI that detects visual drift and preserves audit-ready evidence.

Request our Governance Readiness Checklist

Teams in regulated environments face a familiar strain. Applications grow in complexity, expectations for fast releases keep rising, and every update requires clarity about what changed and whether required elements still appear as intended. Traditional automation wasn’t built for that pace or level of oversight, and the recent wave of AI coding tools hasn’t solved the core challenges.

A better model is emerging—one that uses AI to reduce the workload of authoring and maintaining tests while keeping execution deterministic, reviewable, and aligned with how people evaluate digital experiences.

This post breaks down why the legacy testing model is hitting its limits and how AI can support a more stable, more trustworthy approach.

Why traditional automation keeps slowing teams down

As digital experiences expand across pages, portals, member journeys, and product flows, test code becomes difficult to scale. Even minor UI changes break locators and assertions, creating unpredictable test runs, delayed reviews, and long maintenance cycles.

Developers are often asked to take on more of the testing responsibility. While this can improve feedback loops, it does not reduce the burden of maintaining code that reacts poorly to UI changes. And when teams already lack time, context switching between product development and test diagnostics becomes expensive.

The result is a predictable bottleneck: too many tests tied directly to implementation details and not enough stability across releases.

Why AI-generated test code hasn’t fixed the problem

The last few years have produced a surge of tools that promise to generate automation code automatically. But teams report the same issues repeating in a new form. LLMs can produce code quickly, yet the resulting output still inherits all the maintenance challenges of coded automation.

AI code generators also excel more at producing new code than updating existing flows. They struggle with assertions, hallucinate element behavior, and require human supervision to validate every step. For regulated teams that must show repeatability and generate evidence for every release, inconsistency becomes a risk rather than a convenience.

If the goal is to escape brittle code, producing more of it is not the answer.

Why live LLM-driven execution creates instability

Another idea gaining attention is allowing an LLM to operate the UI directly during test execution. In theory, this removes the need to write code. In practice, teams quickly run into new risks: undefined steps, inconsistent interactions, slow decision-making, and no reliable way to debug.

Execution in regulated environments must be predictable. It must be reviewable. And it must produce evidence that can be traced, explained, and defended. Live improvisation during a test run undermines each of these requirements.

Determinism matters more than novelty. A testing approach must produce the same result today, tomorrow, and during an audit review.

A clearer path forward: intent-driven authoring with deterministic execution

A more reliable model is emerging that uses AI to simplify authoring without relying on AI to make real-time decisions during execution.

Teams describe test intent in natural language. An AI system translates that intent into structured steps during authoring, where humans can review and adjust. Execution is then handled by deterministic engines and Visual AI that observe the rendered UI and detect visual changes, required-element presence, placement consistency, and contrast.

This separation delivers two advantages:

• People write and maintain far fewer lines of test code
• Test runs become stable, repeatable, and easier to verify

Visual AI provides a complete view of the screen state and compares each run against an approved baseline. When something changes, the system surfaces the difference, captures evidence, and supports reviewer approvals. When the change is expected, one acceptance updates the baseline and applies it across browsers and devices.

The outcome is a testing layer that is easier to maintain and easier to trust.

What this looks like in practice

Teams adopting this approach typically see changes across several parts of their workflow:

• Tests are written in plain language, without selectors or framework setup
• Visual AI validates full screens for layout, presence, placement, and readability
• Changes are highlighted automatically to reduce manual inspection
• Evidence is captured through screenshots, diffs, timestamps, and logs
• Debugging takes place in an environment where runs behave the same every time
• Reusable flows and data-driven steps integrate into the same natural-language format

Instead of managing a growing volume of fragile code, teams maintain intent-level descriptions supported by deterministic execution.

What this means for oversight and compliance

For teams in financial services, healthcare, insurance, or life sciences, the benefits go beyond efficiency.

A visually grounded testing model helps confirm that required notices, disclosures, language-access elements, and other regulated UI content remain present and placed as expected. It documents what changed and preserves evidence for review. It supports consistent experiences across browsers, devices, and PDFs without checking whether values, data, or regulatory text are correct.

Most importantly, it delivers predictable results.

Regulated environments depend on clarity and traceability. When every test run yields reviewable outputs, and every change is captured with context, teams can maintain confidence and release with speed.

If you’re assessing how well your testing workflow supports stability and audit readiness, request our Governance Readiness Checklist. We’ll share the version designed for your stage—whether you’re evaluating Applitools or optimizing an existing deployment.

The post AI Testing in Regulated Environments: Smarter Testing Starts With Stability, Not More Code appeared first on AI-Powered End-to-End Testing | Applitools.

Why Test Maintenance Breaks at Scale

Test maintenance at scale slows releases. Teams that rely on coded assertions spend more time updating tests than improving coverage. Brittle locators, environment drift, and false positives all add up—turning automation into a maintenance cycle.

Neglecting maintenance is like skipping car care: small issues snowball into costly downtime. A smarter approach replaces manual review and locator-based scripts with automated, visual validation that adapts as your UI evolves.

How Visual AI Delivers Test Maintenance at Scale

Visual AI replaces dozens of coded assertions with a single checkpoint that mimics how humans see. It validates full UI states, detecting layout shifts, missing elements, and text overlaps automatically.

By consolidating validations into one Visual AI check, teams cut review time, reduce false positives, and gain faster feedback cycles.

Scale Reviews with Ultrafast Grid and Grouping

Running tests one browser at a time no longer scales. The Applitools Ultrafast Grid executes a single test once, then validates results across every browser and device combination in parallel.

Batching and grouping features make reviews equally efficient—approve or reject similar changes across entire runs in just a few clicks.

How it works

• Replace assertions with one visual checkpoint
• Run once across all browsers and devices
• Batch results for unified review
• Approve or reject in bulk
• Tune match levels for dynamic content

Together, these capabilities eliminate redundant effort and make large-scale testing faster to maintain.

Customer Results: 78% Less Maintenance

Teams that adopt this approach see measurable ROI. At Peloton, replacing a legacy visual testing tool with Applitools Visual AI produced a 78% reduction in maintenance time and saved about 130 hours per month.

With dynamic leaderboards, live data, and responsive layouts across web and native mobile, Peloton maintains quality at scale without expanding test overhead.

Three Features That Change Maintenance

“Ultrafast Grid, Visual AI match levels, and bulk grouping—those three change the game.”

Mike Millgate, Smarter Test Maintenance at Scale

These three deliver flexible validation, fast execution, and effortless maintenance. Each removes manual steps and accelerates the feedback loop that keeps releases reliable.

Smarter Maintenance for Modern Teams

Smarter test maintenance isn’t about writing more code—it’s about automating intelligently. Visual AI reduces flakiness, speeds reviews, and scales across devices and environments.

To see what’s next, explore Applitools Eyes 10.22, featuring faster review cycles, new Storybook and Figma integrations, and even shorter feedback loops for test maintenance at scale.

The post Test Maintenance at Scale: How Visual AI Cuts Review Time and Flakiness appeared first on AI-Powered End-to-End Testing | Applitools.

When testing needs to keep up with faster releases and growing complexity, the challenge isn’t just what to automate—it’s how fast you can create and validate reliable tests.

Code and no-code testing now work together to accelerate test creation, expand coverage, and deliver faster feedback across browsers and devices. By combining AI-assisted test creation with visual validation, you can go from setup to scale in hours instead of weeks.

A Smarter Way to Split Your Effort

High-performing teams balance two types of coverage:

• 20% custom flow tests: Focused, AI-assisted checks for your most critical user journeys
• 80% visual coverage: Full-page validation across browsers and devices with Visual AI

This approach ensures your key flows are verified with precision while everything else is continuously validated for layout, content, and visual consistency.

Full-Site Testing in Minutes

With Autonomous testing, you can point to any URL—or even a subfolder—and let AI do the rest. It crawls your sitemap, creates baselines, and runs cross-browser and cross-device tests automatically.

Setup takes minutes. You can schedule recurring tests daily or weekly, and catch both visual regressions and new pages as they appear.

During one large-scale migration, this approach tested more than 1,500 pages across five browsers and devices. Visual AI caught thousands of small layout changes, grouped them by pattern, and reduced the workload to just 10 unique issues after a single fix acceptance.

Depth Where It Matters

For the 20% that need fine-grained control, AI-assisted test authoring speeds up creation. You can describe each action in plain English—“add item to cart,” “verify success message,” or “fill out this form”—and the system turns those steps into repeatable tests.

AI assists by:

• Generating realistic test data
• Creating textual and visual assertions
• Masking sensitive fields automatically

The result: fast, accurate flows that non-coders and engineers can both maintain.

Reliable Execution, Every Time

Applitools’ deterministic LLM executes steps based on visual descriptions, not fragile locators or XPath. That means if a class name or element ID changes, the test still runs correctly.

It also eliminates token costs and flaky reruns common with external LLM agents, since all logic runs natively inside the platform.

Data Validation Included

End-to-end validation doesn’t stop at the UI. Within the same test, you can call APIs, capture responses, and assert that backend data matches what appears on screen.

Visual results, API responses, and data integrity checks all happen within a single low-code environment.

Reuse More, Maintain Less

Reusable test flows—like login, cleanup, or environment switching—save time and cut duplication. You can parameterize roles or URLs, then reuse those flows across staging, integration, and production.

That modular structure lets QA, developers, and product teams collaborate without reinventing the same tests for each environment.

The Fast Track to Full Coverage

By combining AI-assisted test creation with Visual AI validation, teams achieve:

• Broader coverage with less maintenance
• Faster release confidence
• Consistent, human-readable results

Whether you write code daily or prefer a visual test builder, this blended approach keeps quality high and bottlenecks low.

Try It Yourself

See how AI-assisted testing speeds up coverage for your own apps with Applitools Autonomous, or explore how Visual AI helps teams validate every page and device in minutes.

The post Accelerate Test Creation and Coverage with Code and No-Code Speed Runs 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.

The numbers are in, and five Applitools webinars have emerged as the most-watched so far this year. From no-code test creation to AI-driven automation and real-world ROI, these sessions delivered the strategies and insights that top testing teams are putting into practice right now. Whether you missed them live or want a quick refresh, we’ve rounded up the highlights and key takeaways so you can dive straight into the content that’s driving real results.

Building No-Code Autonomous End-to-End Tests

The dream of building fully autonomous tests without writing a single line of code is now a reality. In this session, Adam Carmi, Applitools Co-Founder and CTO, demonstrates how to leverage Applitools Autonomous to create robust, end-to-end tests that execute with speed and precision—no hand-holding required.

Key Takeaways:

• How to set up and run no-code tests in minutes
• Real-world examples of scaling tests across multiple environments
• Reducing maintenance costs by up to 80%

Watch the Webinar: Building No-Code Autonomous End-to-End Tests

AI-Assisted, AI-Augmented & Autonomous Testing: Choosing the Right Approach

Not all AI is created equal. In this session, we break down the differences between Assisted, Augmented, and Autonomous testing models. Learn when to deploy each for maximum impact.

Key Takeaways:

• Clear definitions and use cases for each AI model
• How to integrate AI into existing testing pipelines
• Choosing the right strategy for different application types

Watch the Webinar: AI-Assisted, AI-Augmented & Autonomous Testing

Creating Automated Tests with AI

What if you could create fully automated tests with just a prompt? In this session, Cory House, Playwright, React and JavaScript specialist, explore how tools like GitHub Copilot, ChatGPT, and Applitools Autonomous are changing the speed and reliability of automated test creation.

Key Takeaways:

• Generating test cases from requirements and prompts
• Reducing manual authoring with AI-driven test generation
• Integrating Copilot and Autonomous for seamless test runs

Watch the Webinar: Creating Automated Tests with AI

The ROI of AI-Powered Testing

AI-driven testing is more than just hype—it’s delivering real business impact. This session dives into the hard numbers and real-world examples of how automated visual testing reduces costs and increases release velocity.

Key Takeaways:

• Measuring ROI with data-driven insights
• Reducing the need for manual testing by 70%
• Increasing deployment speed without sacrificing quality

Watch the Webinar: The ROI of AI-Powered Testing

Code or No-Code Tests? Why Top Teams Choose Both

Hybrid testing strategies are becoming the go-to for teams that want the flexibility of no-code with the depth of code-based tests. Eric Terry, Senior Director of Quality Control at EVERSANA INTOUCH, unpacks why top engineering teams are choosing both to maximize coverage and efficiency.

Key Takeaways:

• Combining code and no-code for better test coverage
• Reducing maintenance through smarter orchestration
• Scaling tests across browsers and devices seamlessly

Watch the Webinar: Code or No-Code Tests? Why Top Teams Choose Both

Ready to Elevate Your Testing Strategy?

Don’t miss out on the insights that are transforming how teams build, maintain, and scale tests. Dive into the full sessions and see how Applitools is pushing the boundaries of what’s possible in test automation. See all our webinars.

The post Top 5 Webinars of 2025: AI-Driven Testing, No-Code Strategies, and Real ROI appeared first on AI-Powered End-to-End Testing | Applitools.

The excuse “we don’t have time to write tests” doesn’t hold up anymore. AI has reshaped the way teams approach software testing, making it faster, smarter, and more accessible than ever. Tools like GitHub Copilot, ChatGPT, and Applitools Autonomous can generate reliable automated tests without slowing down your development flow.

If you’ve ever struggled with limited testing resources or hesitated to adopt AI-enhanced workflows, now is the perfect time to embrace AI-powered testing.

How GitHub Copilot Helps Accelerate Unit Test Creation

GitHub Copilot can dramatically speed up unit test creation. It can generate unit tests directly in your editor with a single prompt. For example, typing “create unit tests for Hello.tsx” in VS Code can instantly produce functional test cases using React Testing Library.

While Copilot’s first drafts were impressive—correctly using accessible locators and matching key UI elements—it’s important to note that AI-generated tests often require slight refinements.

Expecting a one-shot from AI is probably unrealistic—but in my experience, it gets you pretty darn close.

Copilot typically picks up on your dependencies, infers structure, and outputs readable, executable tests. If the results aren’t perfect, for instance, using fragile selectors or inconsistent naming, you can quickly iterate. Adjusting your prompt often resolves these issues. In many cases, reprompting is faster than manual edits.

Accessible locators and consistent naming can be enforced through clearer prompting or by storing preferences in a centralized configuration file

The key? Good prompts make a big difference. Prompting Copilot to use best practices, like favoring accessible selectors, resulted in much cleaner and more reliable output.

Taking Testing Further with Playwright and Copilot

Beyond unit tests, AI can support end-to-end testing for full user flows. Using Copilot with a framework like Playwright, you can prompt test generation by simply referencing a live URL and desired interactions.

For example, pointing Copilot to a public demo app like TodoMVC and requesting end-to-end tests will often result in tests for adding, completing, deleting, and filtering tasks—all without writing code manually.

To further improve coverage, ChatGPT can help by generating a requirements document for the app. This doc acts as a guide to ensure tests align with expected behaviors.

The better the input we provide the LLM, the better output we’re likely to get. A requirements doc is a really important piece of input.

Once the requirements are defined, you can direct the AI to use them when generating tests, producing more complete and targeted coverage. Just remember to include your preferences for things like locator strategy and naming conventions in your prompt or project config.

The message is clear: Combining ChatGPT and Copilot creates a powerful AI-assisted workflow for test generation. This approach cuts down on manual scripting while improving test depth.

Boosting End-to-End Testing with Applitools Autonomous

Applitools Autonomous handles creating automated tests with AI differently. Instead of writing code or interacting with the DOM, you provide a URL, and the system automatically scans the app. It generates visual and functional tests and organizes results into a centralized dashboard.

Highlights of what Autonomous can do include:

• Crawl an entire application from just a URL and automatically generate visual and functional tests
  
• Use plain English commands to create, edit, and validate tests (no coding needed)
  
• Validate UI, behavior, and API responses in one workflow
  
• Capture dynamic data like confirmation IDs, verify API responses, and support parameterization without code

Unlike traditional recording tools, Autonomous intelligently builds stable, scalable tests while seamlessly validating across browsers. It even flags hidden 404 errors—showcasing the tool’s ability to catch issues early.

Another key point is that anyone, regardless of technical background, can create sophisticated tests using natural language. At the same time, it maintains the depth and flexibility senior developers demand.

Key Takeaways for Modern Testing Workflows

Today’s AI software testing tools are designed for real-world developer needs:

• Copilot accelerates unit and E2E test creation with natural language prompts.
• ChatGPT fills documentation gaps by drafting requirements for better test coverage.
• Applitools Autonomous redefines E2E testing, combining visual validation and functional flows—from UI to visual to API—and plain-English test authoring. It integrates these into a single, no-install SaaS platform.

AI doesn’t replace the tester’s critical thinking — it augments your workflow, helping you focus on improving test quality, not just checking boxes.

In Summary…

The landscape of automated testing is still evolving. With tools like Copilot, ChatGPT, and Applitools Autonomous, building and maintaining high-quality automated tests no longer has to be a slow, painful process. Whether you’re a front-end engineer, QA lead, or tech manager, adopting AI-powered workflows will free up your team’s time. It will increase your confidence in releases and bring better quality to every sprint.

Want to learn more about how to create automated tests with AI? Watch the full session on demand to see in-depth demos.

The post Creating Automated Tests with AI: How to Use Copilot, Playwright, and Applitools Autonomous 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.

For many QA teams, automation presents a challenge: how do you scale efficiently when team members have different levels of coding expertise?

Eric Terry, Senior Director of Quality Control at EVERSANA INTOUCH, shared how his team successfully adopted a hybrid automation approach—leveraging both coded and no-code automation to bridge skill gaps, improve test efficiency, and enhance collaboration.

For teams considering Applitools Autonomous, Eric’s journey offers a real-world example of how AI-powered, no-code automation can accelerate testing while making automation accessible to a broader team.

The Challenge: Skill Gaps in Testing Teams

QA teams often include a mix of experienced developers and manual testers with limited coding experience. This gap can create inefficiencies and limit test coverage.

Common challenges QA managers face:

• Manual testers want to contribute to automation but lack programming skills.
• Automation engineers spend too much time maintaining scripts rather than innovating test strategies.

Inconsistent automation practices lead to knowledge silos and increased maintenance overhead.

The Solution: An AI-Powered Hybrid Approach to Coded and No-Code Automation

Eric’s team adopted a hybrid strategy that leverages both coded and no-code automation tools, ensuring that:

• AI-powered no-code tools (like Applitools Autonomous) allow manual testers to create automated tests with minimal coding.
• Coded automation remains essential for complex test scenarios requiring deep customization.
• Teams focus on collaboration, mentorship, and upskilling rather than forcing a single approach.

Eric’s team reduced test maintenance by 40% by integrating AI-driven no-code automation while keeping developers focused on high-value coding tasks.

The Benefits of an Autonomous-First Approach

One of the biggest breakthroughs for Eric’s team was prioritizing Autonomous-first testing for repetitive and UI-driven test cases. This led to:

• Faster onboarding for manual testers wanting to contribute to automation.
• Significant reduction in test maintenance, as AI-driven automation adapted to UI changes.
• More streamlined workflows, with non-developers actively participating in automation.

Key benefits of AI-powered no-code tools:

• Faster test creation for repetitive workflows.
• Reduction in script maintenance by up to 60%.
• Empowering manual testers to contribute without coding expertise.
• Accelerated test cycles by running automated tests in parallel.

Real-World Example: EVERSANA’s No-Code Automation Success

• Challenge: The team had a mix of highly technical engineers and manual testers who wanted to contribute to automation but lacked coding skills.
• Solution: They implemented a no-code-first strategy, using Applitools Autonomous to allow non-coders to automate repetitive UI tests.
• Results: Faster test execution, reduced manual effort, and a more collaborative approach to QA.

Want to see how Applitools Autonomous can help your team bridge your skill gaps to scale test automation? Try a free trial today.

How to Get Started: Lessons from Eric Terry’s Team

For QA teams considering Applitools Autonomous, Eric’s experience provides a clear roadmap for success in scaling coded and no-code automation to boost test efficiency. His key recommendations include:

1. Encourage cross-functional collaboration – Build mentorship programs where automation engineers support manual testers.
2. Adopt an Autonomous-first mindset – Automate simple workflows first before investing time in complex scripting.
3. Leverage AI-powered tools – Use visual testing and self-healing automation to minimize maintenance effort.
4. Align automation efforts with business goals – Ensure test automation supports faster releases and higher product quality.

Learn more by watching Code or No-Code Tests? Why Top Teams Choose Both

The post How an Applitools Customer Scaled QA Automation by Bridging the Skill Gap appeared first on AI-Powered End-to-End Testing | Applitools.