From the start, automated testing services have been hailed as the best invention since sliced bread. Yet, in 2026, most teams struggle to get favorable ROI from them. The thing is, you can get the most exquisite loaf and still mess up your sandwich.
In this article, we’ll:
This is your guide to building automation that actually delivers.
The primary challenges faced during automation testing fall into 4 categories: technical, operational, strategic, and organizational. Let’s get to know our enemies.
Automation testing coding challenges relate to the way a team works with their scripts: creation, execution, and maintenance. Coding is a fundamental practice in test automation services. Yet, around half of organizations experience difficulties when it comes to it.
In the quest for speed, teams overlook structure more often than not. At first, this will work and even bring in good results. But as your product and codebase expand, the challenges of automated functional testing will become a staple.
Scripts will turn into a jumbled mess. Adding new tests will feel like going through a labyrinth. Making changes will be akin to exploring the Mariana Trench. And small shifts and mistakes can affect everything else in unpredictable ways. Without logical organization of tests, shared functions for repeated actions, and consistent naming, automation will be hard to manage.
Another one of the challenges of automation testing is script brittleness. Automated tests interact with your product through code instructions: click this button, fill this form… When these instructions are tightly tied to specific elements or timing, even small changes can cause tests to fail without an actual bug. For example, if you move a button or get a slightly slower page load, the script will break as these “new events” are beyond its instructions.
Such failures stop teams in their tracks, create false alarms, and slowly erode confidence in automation.
Tests exist in a “biosphere” of build systems, cloud environments, and test data. If they aren’t designed to handle variations in that biosphere, like server differences, slower responses, or local developer setups, they can fail unpredictably, run slowly, or require extra effort to execute. The result is a suite that is fragile and difficult to rely on, frustrating developers and slowing down delivery.
These software testing automation challenges aren’t anything new. In fact, they stem from a few “classic mistakes” that have been around for years.
Such issues are sort of normalized. The pressure to deliver at the speed of sound leaves little wiggle room. Finding and supporting skilled specialists is a task to behold. And keeping up with all the new frameworks, tools, and methods can overwhelm anyone. So, there’s no “blame”. Only a recommendation from us to get professional help before things get out of control for your project.
Among challenges faced in automation testing, there’s a relatively new one. The boom of artificial intelligence created a double-edged sword — AI-generated code. It substantially accelerates feature delivery. But can your QA keep up with it? Quite often, AI-generated code is too much to check thoroughly. Reviews get skipped. Tests are cut out. And the quality of that code is put on the back burner because speed is still king.
Here are some pointers on how to deal with functional test automation challenges we’ve discussed so far.
Invest in code organization. Group related tests, reuse shared functions for repeated actions, and name tests clearly. Regularly review and remove outdated or duplicate tests.
Design tests to focus on what users do, not exact UI details. Use flexible selectors (like element names instead of coordinates), add retries for slow-loading pages, and avoid hard-coded timing.
Automate the setup of test data and environments so tests run consistently on local machines, cloud servers, and CI/CD pipelines. Account for variations like network speed, server response times, or temporary data issues.
Focus testing on critical workflows rather than trying to cover every new AI-generated function immediately. Review new AI code paths, expand automation in stages, and retire tests that no longer add value.
These might take up some of your time now. But you’ll be thankful they did later.
Operational challenges in automation testing come from how automation fits into daily workflows and the systems around it. This category is about extracting day-to-day value from your scripts.
54% of companies aren’t sure what to automate. They struggle to decide what will give them the best ROI. That’s how many end up automating either too much or too little. Over-automating low-value scenarios bloats the test suite, makes runs slower, and overwhelms teams with noisy results. Under-automating leaves gaps, letting issues slip through into production.
Your test suites will expand naturally. Automation testing challenges arise when it comes to balancing velocity and scrutiny. As your product grows, more features require more coverage, and more scenarios need validation. But CI/CD pipelines operate under strict time constraints. If automated tests take too long to execute, they slow down the pipeline and delay releases. If they are shortened to keep the pipeline fast, important checks may be skipped.
Maintaining that balance becomes an ongoing battle.
Apart from setting up your environments, you also need to maintain them. Servers get updated, the cloud evolves, and test data needs regular refreshing. Making sure all these pieces are aligned with your automation means constant upkeep. And if you skip it, you get an ecosystem within which your automated tests can’t exist.
Here is how you mitigate these challenges faced in automation testing.
Prioritize high-impact automation. Focus on tests that protect critical workflows, like core user journeys or high-risk features. A good rule of thumb is to automate where failures would be costly or manual testing would be tedious.
Design layered test suites for CI/CD. Fast checks, such as unit tests and small integration tests, run on every commit and provide immediate feedback. Larger end-to-end tests run less frequently or in parallel pipelines, ensuring deeper coverage without blocking everyday development.
Standardize and review environments and data. Go for infrastructure containers, infrastructure-as-code, or reproducible test setups. Refresh your datasets, track configuration changes, and schedule environment updates.
Strategic challenges in automation testing are those that prevent you from extracting value long-term.
Users love personalization. Yet, apps’ capability to adapt their interfaces to how people interact with them creates key challenges in UI automation testing. A dashboard might hide rarely used options until a user hovers over a menu. A form may reveal additional fields only after certain selections are made, etc. Scripts that expect elements to always be visible or in fixed positions can break, even when the software itself is functioning perfectly.
Because interfaces change depending on context, maintaining reliable test coverage becomes tricky. Tests that once ran smoothly can start failing unexpectedly, creating extra maintenance work and slowing down feedback.
Some older automation scripts are built to interact with elements in very specific spots on the screen. That worked fine when layouts were static. But today’s UX demands give preference to products that rearrange content depending on screen size or user context. A button that appears in one place on a laptop might be moved, resized, or hidden on a tablet or phone.
When tests expect everything to stay in the same place, they fail every time there’s the tiniest shift. So, among the challenges in automation testing is building scripts that focus on the elements themselves rather than where they happen to appear. Transitioning to this approach requires careful planning: deciding which legacy tests to replace, which to adapt, and how to ensure coverage without slowing down development.
Small visual issues, like overlapping text or misaligned fonts, don’t break functionality. But they matter to your clients. Especially for premium products, even minor regressions can signal a lack of polish and professionalism. And that’s a very deep cut to your user trust.
Deciding which visual issues to automate and which to review manually is a persistent strategic question. But you need to figure it out. Teams might either waste effort chasing every minor pixel difference or leave important issues unnoticed.
Here’s what helps handle these automated testing challenges.
Focus on how users reveal elements, not just what’s visible on the screen. Simulate the actions that trigger UI changes – hovering menus, expanding sections, or making selections that reveal new fields. Work with developers on stable selectors or dedicated test IDs for dynamic components. Even if the UI shifts or hides elements temporarily, tests can still target them reliably.
Target what an element is, not where it appears, using stable attributes, accessibility tags, or test IDs instead of coordinates or fragile DOM paths. For teams with older scripts, the practical approach is gradual modernization. Start by refactoring the most brittle tests and critical user flows. Introduce abstractions like page objects to reduce the impact of layout changes.
Treat visual quality as a separate testing concern. Automate visual checks for key screens where polish matters most. And allow some tolerance elsewhere. Pairing visual automation with targeted manual review helps teams catch meaningful UI issues without chasing every minor pixel difference.
Organizational challenges in automation testing are created by team structure, roles, or collaboration. They’re the most prevalent by far.
Automation blends testing instincts with software development skills. Teams often need time and support to grow these capabilities, learning how to write maintainable tests, design automation frameworks, and integrate testing into development. Yet, less than 30% of organizations plan to invest in upskilling in the next 3 years.
On the other hand, there’s a practical constraint. Experienced automation engineers and SDETs are in short supply. Building a mature automation strategy requires people who understand testing, development, and system design — a combination that’s difficult to hire for quickly. For many organizations, this talent gap slows down automation adoption or leaves internal teams stretched too thin to maintain a reliable test suite.
These challenges of automation testing are the hardest to tackle as they require deep changes within your company. And, naturally, they take a long time to set in.
The solution with fast and long-term results is QA outsourcing. A dedicated QA partner is a strategic accelerator. They bring experienced automation engineers, proven testing frameworks, and established practices without the long hiring cycle. More importantly, they help you establish improvements that last.
Challenges faced in mobile automation testing deserve their own section due to their uniqueness.
The “3rd wave” of devices — foldables, dual-screen devices, and high-refresh-rate displays — creates major automation headaches. Emulators can’t capture real-world performance differences, especially on regional, carrier-locked firmware. Layouts shift dynamically, refresh rates vary, and traditional scripts often fail. And legacy scripts or narrow device labs quickly become obsolete.
Without broad, real-device coverage, teams risk false positives, crashes, and missed defects.
Some of the biggest challenges in mobile automation testing come from the influence of environmental and interaction factors.
These factors make mobile automation inherently less predictable and more brittle.
Here’s what helps overcome these automation testing challenges.
Use real devices strategically. Instead of testing every scenario on every device, prioritize devices that represent the largest portion of your users. Rotate coverage weekly for lower-volume devices to balance cost and coverage.
Automate gestures and sensor events. Use frameworks that allow scripted multi-touch gestures, rotations, and sensor input. For example, test pinch-to-zoom, device tilt, or screen rotation during critical flows like login or checkout.
Simulate network and power conditions. Run automated flows under throttled networks, intermittent connectivity, and low battery modes. Include scenarios like Wi-Fi to 5G switching, airplane mode toggle, and backgrounding apps mid-flow.
Monitor app health during tests. Capture memory, CPU, and battery metrics alongside functional results. Set thresholds for automated failure if the app exceeds expected limits, so performance regressions don’t slip through.
Automate platform-specific workflows. Include OS permission prompts, biometric logins, and notification handling in automation scripts. Use device profiles to mimic different regional firmware or carrier behaviors to catch hidden bugs early.
The very thing that helps you work with automated scripts can make it harder. And that’s a crucial point. You can have the absolute best tools in your project. But if you don’t have the skills to utilize them fully, you’re losing a big chunk of potential speed, quality, and convenience.
Selenium is a widely used tool. But its popularity doesn’t stop teams from struggling with test stability.
One of the biggest issues is timing. Tests often try to interact with elements before they’ve fully loaded. For example, a script might try to click a button that hasn’t appeared yet, causing the test to fail. This leads to false failures that are frustrating to debug.
Another one of the challenges in automation testing with Selenium is fragile element targeting. Many tests rely on detailed paths (like XPaths) to find elements on the page. These paths depend on the exact structure of the UI. So when a developer makes even a small change — like wrapping a button in a new container or renaming a class — the path changes, and the test can no longer find the element.
Selenium also requires extra setup work for common scenarios. Handling things like pop-ups, embedded frames (iframes), or generating useful test reports isn’t built in by default. Teams often need to write additional code or integrate third-party tools just to cover these basics.
Modern tools like Playwright and Cypress improve speed and reliability in many areas. But they come with their own limitations.
Cypress, for example, runs directly inside the browser. This makes it very good at interacting with page elements — tests can “see” and react to the UI more naturally, which reduces some timing issues.
At the same time, Cypress doesn’t handle multiple browser tabs or switching between different websites (domains) very well. In real apps, this matters — for example, when a user logs in through a third-party provider or is redirected to another domain during a payment flow. Testing these scenarios often requires workarounds, like restructuring tests or mocking parts of the flow instead of testing them end-to-end.
Playwright handles these complex scenarios much better. It supports multiple tabs, different browsers, and cross-domain flows. But that flexibility comes at a cost: it requires strong coding skills to use effectively. Teams need to write more structured, programmatic tests, which can slow things down if those skills aren’t already in place.
Both tools also support modern UI patterns, like shadow DOM (where elements are intentionally hidden inside components) and strict browser security rules. These can make elements harder to locate or interact with.
To subdue these automated testing challenges, we recommend the following.
You can see why challenges of automation testing persist. Automation is an objectively mature practice. Yet, rather few have mastered it. That’s because there are so many factors to consider that building a reliable and lasting framework requires far more work than most anticipate. Or have time to carry out.
So, we’d also like to highlight a few practices that should be a part of your test automation strategy. These will help you establish a solid base and make improving it easier.
Always remember the 80/20 rule. A small portion of your tests (around 20%) will deliver the majority (around 80%) of the value.
In automation, that 20% typically includes:
These tests run frequently, catch real issues, and save the most time — so they’re worth automating and maintaining well.
The remaining 80% (edge cases, rarely used features, or highly unstable UI areas) often come with automation testing challenges. They tend to:
So, leave them for manual or exploratory testing, where human judgment is more effective.
The goal isn’t maximum coverage — it’s maximum impact for the effort you invest.
At some point, every team needs to step back and ask a simple question: is our automation actually helping us, or just creating more work?
A helpful audit doesn’t need to be considered another one of the challenges of automation testing. In most cases, it comes down to three signals:
Looking at these three together gives a clear picture: fast, stable, low-maintenance tests add value. Everything else is friction.
Automation isn’t something you set and forget. As your product grows, test suites naturally become larger, slower, and harder to maintain.
That’s why you should treat automation as a living system. Regularly review what’s worth keeping, what needs refactoring, and what can be removed. Without this, even a good framework can slowly turn into a burden.
The challenges in automation testing aren’t something to be afraid of. In fact, we suggest treating them as checkpoints: you learn to deal with one = you get better. With every checkpoint you complete, you advance your automation and refine your product.
We can help you get there faster.
Our approach balances advanced automated testing with strategic manual checks. Everything is aligned with ISTQB standards. And we provide full documentation and audit-ready reporting to meet European and US legal and technical requirements.
By tapping into our pool of certified SDETs, teams avoid local hiring delays. We also reduce the burden of QA process management. Internal leadership can focus on innovation while quality is handled reliably.
We stabilize existing test suites and optimize repetitive regression testing. Automation is integrated into the definition of done, so QA adds value from the earliest stages of development. CI/CD integration delivers feedback in minutes, release cycles become predictable, and test coverage can scale dramatically — often 10x more coverage without 10x the cost.
Finally, our automation audits pinpoint exactly why tests aren’t scaling. We provide a high-velocity roadmap to fix them, giving teams confidence that their automation is stable, effective, and future-ready.If you’re ready to stop thinking about how to solve challenges faced during automation testing and start actually solving them — we’re just a click of a button away.
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