What to Automate First in QA

When teams ask what to automate first in QA, they are usually asking a deeper question: where will automation actually reduce risk, save time, and improve release confidence without creating a maintenance burden? That is the right question to ask. The wrong question is, “Which tests can we automate fastest?” Speed matters, but the first tests you automate will shape how your automation suite grows, how much people trust it, and how much work it creates later.

A good starting point is not the largest regression suite, or the most annoying manual checks, but the workflows that are both important and stable enough to automate well. That usually means a small number of critical user journeys, a few high-value API checks, and some basic environment and data validation. From there, you can expand into broader regression coverage, visual checks, mobile paths, and eventually maintenance-focused automation.

The first automation wins are usually not the most numerous tests, they are the ones that protect your most valuable user flows with the least ongoing maintenance.

This guide explains how to decide what to automate first in QA, how to prioritize candidates, what to avoid, and how to structure an automated testing strategy that scales.

Start with the purpose of automation, not the tool

Before deciding what tests to automate, define why you are automating at all. In most teams, the goal is some mix of these:

• Catch regressions before release
• Reduce repetitive manual work
• Support faster CI/CD feedback
• Increase confidence in critical workflows
• Make release decisions less dependent on individual memory

This matters because the “best” first automation candidates depend on the goal. For example:

• If your goal is release confidence, prioritize the workflows customers use most.
• If your goal is developer feedback, prioritize fast API and integration checks.
• If your goal is reducing manual toil, start with repetitive smoke tests.
• If your goal is compliance or governance, prioritize checks that prove required behavior, not just happy paths.

A useful definition of test automation is any repeatable execution of checks with software support, often as part of software testing and test automation practices. But the important part is not the label, it is the feedback loop. A test that runs often and reliably has more value than a larger test that nobody trusts.

The best first automation candidates share four traits

When people ask what tests to automate, I recommend scoring candidates against four practical criteria.

1. High business impact

Automate workflows that protect revenue, account access, checkout, onboarding, login, subscriptions, password reset, order creation, or other core flows. If the workflow breaks, the business feels it quickly.

Ask:

• Does this path affect sign-up, payment, or active usage?
• Would a defect here block users or create support load?
• Is this the path the product team cares most about this quarter?

A checkout flow in an e-commerce app is a stronger first candidate than a rarely used settings page. A password reset flow is often stronger than a niche admin report.

2. Repetitive and deterministic

Good automation candidates are the tests humans keep repeating in the same way. If a person runs the same steps every day, with the same expected results, that is a strong signal.

Look for flows that are:

• Repeated often
• Easy to describe as steps
• Expected to behave the same way each time
• Not heavily dependent on subjective judgment

Examples:

• Login
• Signup
• Add item to cart
• Submit form with known input
• Create entity through an API
• Verify a confirmation email is generated

If the expected result is constantly changing because of design tweaks, dynamic content, or manual interpretation, automation is still possible, but it may not be the first thing to do.

3. Stable enough to maintain

A test can be important and repetitive, but still be a bad first automation choice if the UI or requirements change every week. High churn creates brittle tests and erodes confidence.

Prefer scenarios where:

• Selectors are stable
• The user journey is unlikely to be redesigned tomorrow
• The acceptance criteria are clear
• The test data can be controlled

A feature still in heavy discovery mode is usually not a great first candidate for deep end-to-end automation. A stable, production-like flow is better.

4. Cheap to run and easy to diagnose

The first automated tests should be easy to understand when they fail. If a failure requires a long investigation every time, the suite becomes a tax.

Better first candidates are tests that:

• Fail for clear reasons
• Use visible assertions
• Are fast to execute
• Have small setup and teardown requirements

This is why many teams begin with API checks and a handful of critical web journeys, instead of trying to automate everything in the browser at once.

What to automate first in QA, in practical order

The right order usually looks like this.

1. Critical user journeys

These are the workflows that represent the heart of the product. In most web and SaaS products, this means a small set of end-to-end paths that prove the application is usable.

Examples:

• New user sign-up
• Login and session persistence
• Password reset
• Core purchase or subscription flow
• Create, edit, and save a primary object
• Search and retrieve a critical record

These are valuable because they reflect real customer behavior and provide a clear release signal. Keep the number small. Many teams do better with 5 to 10 high-value journeys than with 50 shallow browser tests.

A practical way to start is to define the top three workflows that would cause the most damage if broken, then automate those first.

2. API and service-level checks

If your application has a backend API, a lot of useful validation belongs here. API tests are often a stronger first automation layer than UI tests because they are faster, more stable, and easier to diagnose.

Prioritize API checks for:

• Authentication and authorization
• CRUD operations on core resources
• Validation rules
• Error handling
• Contract-like response shape checks

These tests help you catch issues before UI layers are involved. They are especially valuable for teams practicing continuous integration, where fast feedback is essential.

Example of a small Playwright API check:

import { test, expect } from '@playwright/test';

test('creates a customer record', async ({ request }) => {
  const response = await request.post('/api/customers', {
    data: { email: 'qa@example.com', name: 'QA User' }
  });

expect(response.ok()).toBeTruthy(); const body = await response.json(); expect(body.email).toBe(‘qa@example.com’); });

The same principle applies if you use a different framework, the test should prove something important, with minimal setup, and fail for a specific reason.

3. Smoke tests for deployment confidence

Once the main user journeys and APIs are covered, automate a small smoke suite. Smoke tests answer a simple question: did the build deploy successfully enough that we can continue testing?

Typical smoke checks include:

• App loads
• User can log in
• Core API responds
• Primary page renders without errors
• A basic transaction can complete

These tests are ideal for CI/CD gates because they are short and useful. They do not need to be exhaustive. They just need to be trustworthy.

Example GitHub Actions smoke job:

name: smoke-tests

on: pull_request: push: branches: [main]

jobs: test: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - uses: actions/setup-node@v4 with: node-version: 20 - run: npm ci - run: npm run test:smoke

4. High-risk edge cases

After the happy paths are covered, add the edge cases that would be expensive to miss. These are usually not the first automation targets, but they are high value once your foundation exists.

Examples:

• Invalid form input
• Expired session
• Permission denied flows
• Empty state handling
• Payment failure
• Network timeout recovery
• Duplicate submission prevention

These cases are important because they exercise system resilience. They are often easier to automate at the API or component level than through the UI.

5. Regression-prone areas

If a particular area keeps breaking, automate it. Recurring bugs are a strong signal that a workflow deserves protection. This is especially true when a fix is hard to verify manually or when the same defects come back after every release.

Do not confuse this with automating every bug report. Pick the recurring patterns, the ones that would otherwise keep costing time.

What not to automate first

Knowing what to leave out is just as important.

Do not start with flaky flows

If a scenario depends on unstable third-party systems, unpredictable timing, or highly dynamic UI behavior, it may create more noise than value. Flaky tests quickly lose trust.

Do not start with frequently changing features

If the design is still changing, an automated test will likely need repeated rewrites. That is fine later, but not a great first investment.

Do not start with purely visual judgment calls

If the expected outcome is, “Does this look good?” you may need human review or visual testing later. That should not be your initial automation target unless it is clearly defined. Visual testing is valuable, but only once your baseline workflows are stable.

Do not start with huge end-to-end suites

Long browser chains are expensive to maintain. They can still be useful, but if you try to automate everything through the UI first, you will often create slow, brittle feedback and unclear failures.

A good automated testing strategy usually layers fast API and component checks under a smaller set of end-to-end journeys, instead of making the browser do all the work.

A simple prioritization model you can actually use

If you need a concrete way to decide what to automate first in QA, score each candidate from 1 to 5 in these categories:

• Business impact
• Frequency of use
• Stability of requirements
• Ease of automation
• Ease of diagnosis

Then add a penalty for:

• External dependencies
• Data complexity
• Flaky setup
• Subjective verification

A test with high impact, high frequency, and low maintenance cost should rise to the top.

Example decision matrix:

This kind of scoring is not perfect, but it helps teams align on priorities instead of arguing from gut feel.

Build the first suite as a thin, useful layer

Your first suite should not try to prove everything. It should prove the most important things with the least friction.

A practical starter shape is:

• 3 to 10 critical web journeys
• 5 to 20 API checks for core behavior
• 1 to 5 smoke tests for deployment confidence
• A handful of key negative cases

That gives you immediate value without creating a giant maintenance burden. It also gives you a structure you can expand later.

Keep the suite focused on stable, important user outcomes. For example, if the product is a subscription app, automate:

• Sign up
• Verify email or onboarding entry point
• Start trial or choose plan
• Add payment method
• Reach the dashboard
• Cancel subscription, if that is a critical account path

Those flows are more valuable than dozens of low-signal checks on internal settings.

How to choose between UI, API, and mobile for the first tests

The best first layer depends on your product.

Web apps

Start with browser automation for the main customer journey, but keep the scope narrow. Browser tests are ideal for proving that the UI, routing, state, and backend all work together.

APIs

Start here when business logic is more important than presentation, or when the frontend is not yet stable. API tests are often the easiest place to achieve early signal.

Mobile apps

Start with the top user path that justifies the app’s existence, usually login, onboarding, and the core task. Because mobile suites can be slower and more device-dependent, focus on the most valuable flows first.

Visual checks

Use these after you have a stable reference flow. They are most useful for layout-sensitive components, branding, and cross-browser presentation changes. Pair them with functional checks rather than replacing them.

Maintenance is part of the strategy, not an afterthought

The first tests you automate should be chosen with maintenance in mind. A strategy that ignores upkeep will fail, even if the first week looks productive.

To reduce maintenance:

• Prefer stable locators, not brittle XPath chains
• Centralize test data setup
• Keep assertions specific but not overly exact
• Avoid over-mocking in end-to-end tests
• Split high-level journeys from low-level validation
• Review failing tests quickly so the suite stays trusted

When a test fails, ask whether the failure means the product is broken or the test is poorly designed. If you cannot answer that quickly, the test needs improvement.

For teams that want help keeping the suite healthy, automated maintenance tooling can reduce some of the repetitive work around selector changes and test upkeep. Use that kind of capability to support a thoughtful strategy, not replace one.

Where Endtest fits for teams starting small

If your team wants a practical way to begin with critical user journeys, Endtest’s AI Test Creation Agent is worth a look. It uses agentic AI to turn a plain-English scenario into an editable, runnable test inside the platform, which can help QA managers and product teams get a first set of high-value flows covered without starting from a blank framework.

That does not change the strategy. You still need to decide which workflows matter most. What it can change is the friction of turning those decisions into working automation, especially if your team wants to move quickly on a small set of critical paths first.

Other teams may prefer to bring existing tests with them, and that is fine too. The important part is starting with the right flows, not forcing a rewrite before you have value.

A practical first-automation roadmap

Here is a simple way to roll this out in a real team.

Week 1, identify the shortlist

Gather QA, product, and engineering. List the top workflows that matter to users and the business. Then score them for impact, frequency, stability, and maintenance cost.

Week 2, define the first suite

Pick a narrow set of checks, often fewer than ten. Write down what each test should prove, what data it needs, and what environment dependencies it has.

Week 3, implement the core paths

Start with the most stable and highest-value tests. Prefer API checks where possible, then add the smallest set of UI journeys needed to cover the true customer experience.

Week 4, add CI and review failures

Run the suite in CI/CD, watch for flaky behavior, and refine locators, waits, and setup. The first month is about trust as much as coverage.

The rule of thumb to remember

If you are deciding what to automate first in QA, automate the workflows that are:

• Important to users and the business
• Repeated often
• Stable enough to maintain
• Easy to diagnose when they fail
• Cheap enough to run regularly

For most teams, that means critical user journeys first, API checks next, smoke tests soon after, and broader regression coverage later. If you start with that shape, your QA automation strategy is more likely to support the team instead of creating noise.

That is the real goal of an automated testing strategy, not maximum test count, but maximum useful signal.