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.

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.

Test automation is essential but often time-consuming. Writing and maintaining tests, generating reliable data, switching tools for API calls, and keeping everything aligned across environments can slow down even the best teams.

The latest updates to Applitools Autonomous, part of the broader Applitools Intelligent Testing Platform, introduce features that significantly reduce this overhead. From natural language test authoring to integrated API testing and deterministic execution, these additions help teams move faster with fewer manual steps. Alongside ongoing improvements to Applitools Eyes, the platform continues to evolve to support modern testing workflows at scale.

New Applitools Autonomous Highlights at a Glance:

• Natural language test creation powered by LLMs
• On-the-fly test data generation
• Enhanced API testing with visual builder
• Deterministic test execution (no LLMs at runtime)
• Upcoming support for mobile apps, IDE integration, and Storybook workflows

Natural Language Test Creation, Powered by LLMs

Instead of writing test steps manually or wrestling with locators, you can now describe your intent in plain English. Applitools Autonomous converts your input into executable test steps.

Autonomous interprets the instruction and adapts to your application’s context. Tests can be created by typing, recording interactions, or letting the system generate steps automatically. This approach makes test authoring more accessible, easier to maintain, and more readable across teams.

On-the-Fly Test Data Generation in Context

Need a specific persona, value range, or edge case? Autonomous now includes built-in test data generation. No external tools required.

Just describe what you need, a French fashion designer or a prime number over 1,000, and the platform generates valid, realistic data at runtime. Datasets are generated ahead of time, so test execution remains fast and predictable.

Enhanced API Testing in the Same Flow

You can now send and validate API requests directly within your test flow, using a Postman-style interface.

Author steps in several ways:

• Describe them in plain English
• Use raw HTTP or cURL
• Use the interactive UI builder

Once executed, responses can be inspected, variables extracted, and values asserted. UI, API, and visual checks all operate in a single environment—no tool switching needed.

Deterministic Execution Model for Reliable AI-Powered Tests

A standout feature of this release is the deterministic execution engine behind every test.

“You don’t need to be a prompt engineer or even a developer to scale automation. But when your test runs, it executes with the speed and reliability of code.”
— Adam Carmi, CTO and Co-founder of Applitools

Unlike some platforms that rely on live LLMs during execution—an approach that can be slow or unpredictable—Applitools separates test creation from test execution.

• LLMs assist during authoring and data generation.
• Test runs are powered by a proprietary deterministic model that ensures speed, stability, and consistent behavior.

This offers the flexibility of AI and the dependability of code, without trade-offs.

What’s Coming Next

Applitools continues to invest in both Autonomous and Eyes, with upcoming features focused on deepening cross-functional collaboration, improving performance, and expanding platform coverage.

For Applitools Autonomous:

• Native mobile app testing: Author and execute tests across devices and operating systems.
• Autonomous MCP server: Translate high-level test cases or BDD scenarios into full test flows.

For Applitools Eyes:

• Eyes MCP server: Move Visual AI directly into your workflow. Maintain, review, and run tests directly from your preferred IDE.
• Visual testing in Storybook: Approve changes directly where components are built.
• Performance improvements for component tests: Shorter pipelines and faster feedback loops.
• Figma collaboration enhancements: Sync designs and visual testing for consistent results.

Where Things Stand Now

Whether you’re building automation for the first time or looking to reduce the overhead of test maintenance, this release meets teams where they are. With natural language authoring, integrated data and API support, and a deterministic execution engine, Applitools helps teams reduce manual effort and work more confidently.

If you’re already using Applitools, now’s a great time to explore the latest features. If you’re just getting started, we invite you to see what’s possible with a free trial for you and your team.

The post Applitools Autonomous and Eyes: New AI Features, Better Execution, and What’s Next appeared first on AI-Powered End-to-End Testing | Applitools.

Test maintenance is one of the most persistent bottlenecks in software quality engineering. From flaky tests and brittle locators to scattered tools and time-consuming debugging, teams often find themselves fixing instead of progressing.

With the right combination of AI-powered automation, no-code tools, and efficient test execution strategies, teams can reduce maintenance effort by up to 75% while improving reliability and accelerating feedback cycles.

Watch the full session now on-demand.

Top Techniques to Cut Maintenance Costs and Improve Test Stability

Use AI-Powered Self-Healing

When UI elements shift, traditional tests often break. AI-powered tools like Applitools Visual AI detect these changes and automatically adjust, reducing dependency on DOM locators.

Create Tests Without Code

With interactive browser recording and LLM-assisted test creation, teams can skip manual scripting entirely. Typing, “Fill out the form as a Disney character,” becomes a self-maintaining test with generated steps and realistic data.

Run Tests in Parallel Across Devices

Applitools’ Ultrafast Grid lets teams execute a test across dozens of browsers and devices in parallel. This helps identify platform-specific issues quickly without slowing down delivery.

Approve Changes in Bulk

AI detects patterns like currency updates or copy changes and groups them for bulk approval. You can accept or reject across multiple screens in a single click.

Consolidate Your Tool Stack

Instead of juggling five tools to cover visual checks, API tests, and accessibility, Applitools offers a unified platform. Less context switching means faster results and fewer points of failure.

Real-World Results: Peloton’s 78% Reduction in Maintenance

Peloton replaced a legacy testing solution with Applitools and saw a 78% drop in test maintenance. That’s over 130 hours saved per month. They automated more than 3,000 tests across web and mobile—without adding headcount.

Where Things Stand Now

Automated test maintenance can help reduce the overall cost of software testing by minimizing the time and resources required to update tests when application changes occur. Whether you’re building new tests or maintaining legacy suites, smart tools can shift the balance from rework to progress.

To see more of how Applitools leverages AI-powered automation, test grouping, and visual intelligence to reduce effort while increasing test coverage and confidence, speak with a testing specialist today.

The post Slash Test Maintenance Time by 75% with These Proven Strategies appeared first on AI-Powered End-to-End Testing | Applitools.

You’ve got your unit tests. Your end-to-end flows. Maybe even 100% code coverage. But bugs still slip through.

That’s because full code coverage doesn’t guarantee full test coverage. Visual glitches, browser inconsistencies, and content drift often escape traditional automation — and they’re exactly the kinds of issues your users notice first.

In The Coverage Overlook, the kickoff session of our Testing Your Way: Code & No-Code Journeys webinar series, we explored five critical coverage gaps most teams miss — and how to close them with AI-powered visual testing and no-code tools.

1. Visual and Layout Bugs

Code-based assertions won’t catch when an element shifts, disappears, or overlaps. That’s where Visual AI steps in.

By analyzing the rendered UI — not just the DOM — Visual AI identifies layout issues, missing images, overlapping text, and subtle visual defects with a single line of code (or none at all).

“Visual AI can instantly catch layout shifts, missing elements, and new text that coded assertions would miss — all without the maintenance burden of custom locators.”
— Tim Hinds, Applitools

2. Cross-Browser and Device Inconsistencies

Most test suites default to Chrome. But real users span dozens of devices and browsers.

Visual AI tools like Applitools Eyes can validate your app across multiple browsers and screen sizes in parallel — using a single test run. No custom scripting required.

3. Dynamic Content Variations

Personalized content, A/B tests, and location-based content are tough to verify with scripted tests alone.

Visual AI combined with flexible match algorithms can confirm layout structure while ignoring safe visual differences — helping your team catch what matters, without writing exceptions for every variant.

4. Lower-Priority Flows and Pages

Teams tend to focus their test coverage on critical flows — like checkout or login — and leave lower-traffic pages untested.

No-code tools like Applitools Autonomous make it easy to cover the rest. A built-in crawler can scan your site and establish visual baselines across dozens (or hundreds) of pages — all without writing a single test script.

5. Accessibility Gaps

Code coverage can’t catch color contrast failures, missing labels, or overlapping elements that make your UI inaccessible.

Visual AI can. And with upcoming enforcement of the European Accessibility Act, now is the time to start catching these issues early.

Watch the Full Session On-Demand: Code & No-Code Journeys: The Coverage Overlook

Closing the Gap

Code coverage still has value — but modern teams are shifting toward user-centered test coverage.

As shared in the session, teams like Eversana are combining code-based, no-code, and visual testing strategies to expand coverage, accelerate feedback, and reduce risk. With this blended approach, they’ve achieved:

• 65% reduction in regression testing time
• 750+ hours saved per month
• 90% test stability
• A unified testing culture across manual testers, developers, and QA

What’s Next in the Series?

The journey continues with The Maintenance Shortcut, where we explore how teams are reducing flaky tests, eliminating brittle locators, and cutting test maintenance with Visual AI and Autonomous.

The post Think You Have Full Test Coverage? Here Are 5 Gaps Most Teams Miss 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.

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.