What Is the Easiest Way to Automate Tests?

Liviu Lupei

Founder & Solutions Architect, Endtest · July 27, 2026

The easiest way to automate tests is not necessarily the method that produces the first test fastest.

That distinction matters more than ever in 2026.

Today, almost anyone can open an AI coding tool, ask it to generate a Playwright test, and get something that looks convincing a few minutes later. The test may even pass. That creates a powerful first impression: test automation has finally become easy.

But producing the first test was never the hard part.

The hard part is building a test suite that your team still understands, trusts, uses, and maintains six months later.

After working in test automation for nearly a decade, I have seen the same pattern repeat across companies of different sizes. Teams rarely fail because they cannot create an automated test. They fail because the system around those tests becomes too expensive, too specialised, or too opaque for the rest of the company to adopt.

So the real question is:

What is the easiest way to own a reliable test suite over time?

For most growing software teams, the answer is an intelligent test automation platform where AI helps create and maintain tests, but the tests themselves remain visible, structured, and human-readable.

You may not need automated tests yet

There is a point in the life of a small software business where not having functional end-to-end tests can be a rational decision.

Imagine a small team with:

• A relatively simple product
• A limited number of user journeys
• A small customer base
• Infrequent releases
• Direct access to users
• A founder or developer who still knows the entire product

At that stage, someone can manually check the registration flow, log in, create a record, process a payment, and verify the most important screens before a release.

You may already have unit tests. Those are useful, but they usually do not provide enough confidence that the complete product still works from a user's perspective. A passing unit test does not prove that a user can sign up, receive a verification email, enter a payment method, and reach the correct dashboard.

Still, when the product is small enough, manual checking may be good enough.

Test automation is an investment. It consumes money, attention, and implementation time. Early-stage companies have many competing uses for those resources: talking to customers, improving onboarding, closing sales, reducing churn, or shipping the feature that makes the product viable.

Automating everything too early can be as wasteful as automating nothing for too long.

The economics change as the company grows

The point where test automation begins to make sense is determined by the growing cost of uncertainty, more than by team size.

As the company grows, several things happen at once:

• The product develops more features, roles, integrations, and edge cases.
• More developers change different parts of the system.
• Releases become more frequent.
• Manual regression takes longer.
• The number of customers affected by a defect increases.
• The team becomes less able to keep the entire product in one person's head.

A defect that affected three early adopters may have been an embarrassing support ticket. The same defect affecting thousands of paying users can cause a flood of complaints, lost renewals, emergency engineering work, and damage to the company's reputation.

The cost goes beyond the time required to fix the bug.

It can include:

• A poor user experience
• Customer churn
• Refunds or service credits
• Lost deals
• Support time
• Interrupted roadmap work
• Developer context switching
• Delayed releases
• Reduced confidence inside the team

A simple way to think about the decision is:

Automated testing starts to make economic sense when the recurring cost of manual regression and escaped defects becomes greater than the cost of creating and maintaining reliable automated tests.

The calculation does not have to be perfect. You can use the framework in our guide on calculating test automation ROI to estimate it more formally.

In practice, the transition often becomes obvious. Releases feel increasingly stressful. Manual regression becomes a checklist no one has time to finish. The same bugs return. Developers avoid changing fragile areas. Customers discover important defects before the team does.

That is usually the moment when automated end-to-end testing stops being an optional engineering improvement and becomes part of running the business.

The most difficult way to automate tests

Around ten years ago, the default answer was to build an internal automation framework.

A company would choose Selenium, Cypress, or another code-based library. One or two engineers would create a repository, establish conventions, build helper functions, configure environments, connect a browser cloud, and write the first group of tests.

The early demo usually looked promising.

Then reality arrived.

The framework needed:

• Authentication helpers
• Test data management
• Reporting
• Screenshots and videos
• Parallel execution
• Retry logic
• Environment configuration
• Browser and driver updates
• CI/CD integration
• Secrets management
• Email and SMS testing
• File handling
• Debugging tools
• Cross-browser infrastructure

At some point, writing automated tests had turned into building and operating an internal test automation product.

This is where the ROI frequently collapsed.

The framework remained "almost ready" for months or years. The team could run some tests, but not consistently enough to trust them. Flaky failures trained developers to ignore the results. Maintenance work competed with product development. Documentation lagged behind the code.

When the original approach became difficult to extend, the proposed solution was often to rebuild it.

Maybe Selenium was the problem. Maybe the programming language was the problem. Maybe the architecture was the problem. Perhaps Cypress, Playwright, a new page-object pattern, or a new set of AI-generated helpers would finally solve it.

The rewrite created another burst of activity, followed by another framework that was almost ready.

Low adoption is the clearest warning sign

The best indicator of a failing internal test framework is the number of people who regularly use and contribute to it, not the number of tests in the repository.

When only one or two people understand the framework (and those are usually the people who built it), the company does not have a shared testing system. It has a specialised internal project with concentrated knowledge.

This creates several problems:

1. Test coverage grows only when those specific people have time.
2. Every failure needs to be routed through them.
3. Product managers, business analysts, and manual testers cannot easily review what is covered.
4. Developers see the test repository as another maintenance obligation.
5. The framework becomes vulnerable when its creators change roles or leave.

Developers generally do not want another repository to maintain. They already have the product, infrastructure, deployment pipelines, internal tools, integrations, and production incidents competing for their attention.

An automation framework may be technically impressive and still be a business failure.

If the broader team cannot use it, understand it, or trust its output, the framework is moving the cost of quality into a less visible part of the engineering organisation instead of reducing it.

Intelligent test automation changed the trade-off

Test automation became much easier when intelligent platforms such as Endtest began handling the framework and infrastructure layer.

These products have been described as no-code testing tools, codeless testing tools, low-code testing tools, and more recently, AI test automation tools. The labels have changed, but the central idea has remained consistent:

Your team should spend its time defining and validating product behaviour, not rebuilding browser automation infrastructure.

That shift accelerated and democratised test creation.

A manual tester could create a meaningful regression suite without first learning a programming language, a browser automation library, a design pattern, and a CI configuration format. In the right product, a focused tester could build a substantial set of tests in a day.

When a question came up, the team could ask the vendor's support team through chat or email instead of spending hours searching old Stack Overflow threads and debugging differences between package versions.

That support is easy to underestimate when comparing a paid platform with a free open-source library.

The library may be free, but every hour spent diagnosing browser drivers, CI containers, authentication state, flaky waits, or reporting infrastructure is paid for with someone's salary and attention.

An easier tool removes entire categories of work.

Human-readable tests change who can participate

One of the most important improvements had nothing to do with AI, self-healing, or cloud execution. It was storing tests in a human-readable format instead of hiding their intent inside application code.

A test should make it obvious that the user:

1. Opens the registration page.
2. Creates an account.
3. Confirms the verification email.
4. Selects a subscription.
5. Completes the payment.
6. Reaches the dashboard.

A developer can understand that. So can a manual tester, product manager, business analyst, support specialist, or founder.

When tests are readable, more people can answer important questions:

• Are we testing the correct behaviour?
• Is this scenario still relevant?
• Which business rule is being validated?
• What changed after the latest release?
• Can this flow be reused elsewhere?
• Is a failure caused by the product or the test?

Readable tests create shared ownership.

They also make test coverage easier to review. A code repository may contain tens of thousands of lines while still leaving the team unsure about which customer journeys are protected. A structured test suite makes the relationship between business behaviour and automation much clearer.

This is one reason no-code testing helps more than non-programmers. It can improve communication across the entire product team.

Cross-browser testing should not be a future project

Internal frameworks also tend to treat cross-browser coverage as a "nice to have."

The Chrome tests come first. Firefox is planned for later. Edge appears on the roadmap. Safari requires different infrastructure and becomes something the team will address when there is more time.

There is rarely more time.

A complete cross-browser setup requires more than changing a browser name in a configuration file. Teams need access to the operating systems, browser versions, parallel capacity, debugging artifacts, and reliable execution environments.

Safari is the clearest example. Testing real Safari means running on macOS rather than a WebKit-based browser inside a Linux container.

Intelligent platforms make this infrastructure part of the product. The same test can run across Chrome, Firefox, Edge, and real Safari in a managed cross-browser cloud without turning browser coverage into another internal engineering project.

That matters because "easy test automation" should include executing the tests where your users actually are.

Creating a test quickly but running it only in one convenient browser is not the same as having useful coverage.

AI made the first five minutes misleading

Over the last two years, AI has made coding dramatically faster.

You can now describe a login flow to an AI coding agent and receive a Playwright or Selenium test almost immediately. The generated code may include selectors, assertions, fixtures, helper functions, and comments.

That is useful.

But it mainly improves the cheapest phase of the test's lifecycle: initial creation.

Things often become more difficult from there.

The application changes. A locator stops working. Authentication is redesigned. Test data becomes inconsistent. A helper function affects thirty files. The generated architecture no longer matches how the product evolved.

Now the team asks the AI to fix the tests.

That means providing the coding agent with more files, more context, more execution logs, and more instructions. Each small change becomes another AI session. Tokens go into modifying the test and into repeatedly rediscovering how the generated framework works.

Model usage is only part of the cost.

Someone still has to:

• Explain the problem
• Select the correct context
• Review the generated changes
• Run the suite
• Investigate new failures
• Confirm that the AI did not weaken an assertion
• Check that unrelated tests were not changed

AI can write code quickly, but it does not automatically create organisational understanding.

Our article on AI-generated Playwright tests explores this maintenance trap in more detail.

AI-generated test code can become a black box

A code-based test suite maintained primarily through AI can create an unusual situation: the repository contains a huge amount of executable detail, but very little shared understanding.

No one deliberately designed every part of it. No one has time to read all of it. The team sees passing or failing tests, but cannot always explain exactly what each test validates or why the framework behaves as it does.

That is effectively a black box.

This matters because a test that passes for the wrong reason is more dangerous than a test that fails loudly.

An AI agent may replace a precise assertion with a broader one to make the test pass. It may introduce a fallback that hides a product defect. It may duplicate helpers, misunderstand state, or change behaviour outside the requested scope.

The larger the generated codebase becomes, the more context the AI needs to process. More context can mean more irrelevant information, more stale assumptions, higher token usage, and more opportunities for hallucinated changes.

You can reduce some of this risk through careful prompting, smaller context windows, and disciplined review. We cover those tactics in our guide to reducing AI hallucinations in test automation and our article on reducing AI token usage.

But the deeper question remains:

Why should AI repeatedly read and rewrite thousands of lines of framework code when the test could be represented as a concise sequence of human-readable actions?

The better model: AI creates, humans can inspect

"AI writes all the test code" is not the ideal scenario. The ideal is:

AI accelerates test creation while the resulting test remains understandable and editable by humans.

This is the model used by intelligent test automation platforms such as Endtest.

You can describe a scenario in plain English. The AI Test Creation Agent inspects the application and creates the test. But the result does not disappear into an opaque code repository.

The generated test is stored as regular, visible steps.

You can inspect the actions, locators, values, waits, and assertions. You can modify them directly. You can reuse existing test cases, parameterise values, add new checks, or hand the test to another member of the team.

AI handles the acceleration. The structured test remains the source of truth.

This changes the maintenance model.

When the product changes, the team does not need to ask a general-purpose coding agent to rediscover an entire custom framework. A person can open the test, understand the affected flow, and make a targeted change. AI can assist without owning the only understandable representation of the system.

There is no conflict between AI and human-readable testing. They work better together.

What is actually the easiest approach in 2026?

There are now three common approaches:

The correct choice still depends on the company.

A highly specialised engineering organisation may have strong reasons to own every line of its automation infrastructure. A small product with very little regression risk may continue with manual testing for a while.

But most growing software companies do not gain a competitive advantage from building a custom test framework.

Their advantage comes from shipping a reliable product, learning from customers, and moving quickly without repeatedly breaking important workflows.

For those teams, the easiest path is usually:

1. Identify the user journeys whose failure would cost the business the most.
2. Automate those journeys first.
3. Store the tests in a format the wider team can understand.
4. Use AI to accelerate creation and maintenance, not to hide complexity.
5. Run the same tests across the browsers and environments that matter.
6. Measure whether the suite reduces regression time and escaped defects.
7. Expand coverage only when the economics justify it.

This approach avoids both extremes: automating everything too early and building an internal framework that never becomes dependable.

Start with the business risk, not the tool

The best first automated test is the workflow where a failure would hurt the business, not necessarily the easiest screen to automate.

That might be:

• Account creation
• Login
• Checkout
• Subscription upgrades
• Password reset
• Email verification
• A critical dashboard
• A customer-facing report
• A core integration
• A recurring production monitor

Start with one of those.

Build a small group of tests that protect real customer value. Run them consistently. Make sure the team pays attention when they fail. Then expand based on observed risk rather than an abstract target such as "80% automated coverage."

The easiest test suite to maintain is usually the one where every test has a clear reason to exist, and it is rarely the largest one.

The real measure of ease

An impressive demo does not make a tool easy.

It is easy when:

• A new team member can understand the tests.
• A manual tester can contribute without waiting for an automation engineer.
• A product manager can review what behaviour is covered.
• A failed test provides enough information to act.
• Browser infrastructure does not become an internal project.
• Small product changes do not require large AI coding sessions.
• The suite is still trusted after hundreds of releases.
• The cost remains lower than the defects and manual work it prevents.

That is the standard teams should use in 2026.

AI-generated Playwright and Selenium code can be useful. Open-source frameworks can be the right choice for teams that want to own and maintain them. Manual testing can remain rational while a product is small.

But for a growing business that wants reliable end-to-end coverage without creating another software product inside the company, an intelligent platform such as Endtest is typically the fastest, easiest, and most affordable way to create and maintain automated tests.

You can start a free trial and create your first human-readable test without setting up a framework, installing browser drivers, or generating thousands of lines of code.

Frequently asked questions

What is the easiest way to automate website tests?

For most teams, the easiest approach is to use an intelligent test automation platform that can create tests from plain-English instructions and store the result as editable, human-readable steps. This removes much of the framework, infrastructure, and maintenance work associated with Selenium or Playwright.

When should a small company start test automation?

A small company should consider test automation when manual regression begins delaying releases, recurring defects consume engineering time, or failures in important customer journeys become materially expensive. Start with a few high-risk end-to-end flows rather than trying to automate everything.

Is AI-generated Playwright testing easy to maintain?

AI can generate Playwright tests quickly, but maintenance may become difficult as the codebase grows. The AI needs increasing amounts of context, engineers must review the generated changes, and the wider team may struggle to understand the test coverage.

Are no-code tests a black box?

They do not have to be. In a transparent platform, each action, locator, value, and assertion is visible and editable. AI may help generate or maintain the test, but the team can still inspect exactly what the test does.