AI is changing software testing.
But probably not in the way many QA engineers think.
AI is not simply going to replace Selenium scripts, Playwright tests, API automation, or performance testing.
The bigger change is this:
The role of an SDET is moving from writing every test manually to designing, guiding, reviewing, and validating intelligent testing systems.
I have worked across API automation, UI testing, CI/CD, and performance testing. One thing is becoming increasingly clear: knowing a testing framework alone may not be enough in the next few years.
An SDET should understand how to work with AI coding agents, browser agents, AI-assisted API testing, visual AI, and intelligent test maintenance.
Here are seven AI tools and technologies I believe SDETs should learn in 2026.
GitHub Copilot is no longer useful only for application developers.
For an SDET, it can act as an AI pair programmer inside the automation repository.
Imagine you have a Playwright framework written in TypeScript.
Your team adds a new checkout API.
Normally, you might:
Instead, you can give the AI detailed testing context.
For example:
Create Playwright API tests for the checkout endpoint. Cover successful checkout, invalid product ID, missing authorization token, zero quantity, negative quantity, and duplicate transaction requests. Follow the existing API test structure in this repository.
The important part is not that AI writes code.
The important part is that a modern coding agent can understand the repository context.
It can inspect existing patterns and help generate code that follows the current framework.
Consider a regression suite containing 700 automated API tests.
The development team introduces a new authentication header:
X-Client-Version
Instead of manually finding every affected API helper, an AI coding agent can inspect the repository and identify request utilities that build HTTP headers.
The SDET still reviews the changes.
But the initial repository analysis can be significantly faster.
Learn how to:
The future skill is not simply prompting.
It is providing enough engineering context for AI to make useful changes safely.
This is one of the most interesting developments for test automation engineers.
Traditional test automation works like this:
Human explores application → Human identifies flow → Human writes test
AI-assisted browser testing changes the workflow.
It can become:
AI explores application → AI creates a test plan → AI generates tests → Human reviews
Playwright's AI-oriented tooling makes browser interaction available to agents.
The AI can inspect the page, interact with elements, and understand application flows.
Suppose you ask an agent:
Explore the login and account recovery flows. Identify important functional and negative test scenarios.
The agent may explore:
It can then help convert the discovered flows into Playwright tests.
Imagine your company launches a new admin portal.
There are 25 pages.
Traditionally, an automation engineer may spend several days manually exploring the application before building a regression strategy.
An AI browser agent can perform an initial exploration.
The SDET can then review the discovered flows and ask:
Which business-critical scenarios are missing?
This is where SDET knowledge remains important.
AI may understand the UI.
It may not understand that changing a jackpot configuration, financial limit, payout rule, or user permission could have a major business impact.
The SDET provides risk context.
Learn:
I believe understanding AI browser agents will become an important automation skill.
Many testers use AI like this:
Write a Selenium test.
That is the lowest level of AI usage.
A software engineering agent can work at a larger task level.
For example:
Analyse our Playwright test framework. Identify duplicated API authentication logic, propose a refactoring plan, update the affected tests, and run the relevant test suite.
This is very different from generating a code snippet.
The agent works on an engineering task.
Suppose your test repository contains:
You can ask an engineering agent to investigate the repository.
The agent can help identify repeated patterns and propose changes.
The SDET's job becomes reviewing questions such as:
Imagine 40 tests fail randomly in CI.
The failures occur only during parallel execution.
An AI agent can inspect:
It may discover that multiple tests use the same customer account.
The AI can suggest generating isolated test data per worker.
But an experienced SDET should validate the root cause.
AI accelerates investigation. Engineering judgment validates the conclusion.
Practice asking AI agents to:
Do not use AI only as a code generator.
Use it as a test engineering investigation assistant.
API testing is one area where AI can immediately improve an SDET's productivity.
Postman's AI assistant, Postbot, can help with API workflows, including test scripts and troubleshooting.
You receive the following response:
{
"transactionId": "TXN-98271",
"status": "COMPLETED",
"amount": 500,
"currency": "USD"
}
You can ask the AI to create validation tests.
It may generate checks for:
But a strong SDET should go further.
Ask:
Generate negative and boundary scenarios for this transaction API.
Now the AI may suggest:
Imagine a microservice exposes 150 REST APIs.
The API documentation exists, but automated coverage is incomplete.
An SDET can use AI to perform the first level of test design.
Input:
Output:
The SDET then reviews scenarios based on actual production risk.
Never assume AI-generated API tests are complete.
For example, AI may test:
amount = -1
But a performance or financial system may have much more interesting boundaries:
2147483647
2147483648
999999999999
Floating-point precision.
Duplicate requests.
Concurrent requests.
Idempotency behaviour.
This is why domain knowledge still matters.
Traditional visual testing can create too many false failures.
A tiny rendering difference may fail a pixel comparison.
For example:
Visual AI attempts to identify meaningful visual differences instead of relying only on basic pixel comparison.
Imagine an e-commerce checkout page.
A functional automation test checks:
await expect(page.locator('#pay-button')).toBeVisible();
The test passes.
But the actual page has a serious UI issue.
The Pay button is visible but overlaps the order total.
The functional test still passes.
A visual test may detect the layout change.
Consider an application supporting:
You have 50 important pages.
Manually validating every visual combination becomes expensive.
Visual AI can help identify meaningful UI differences across these environments.
Visual AI is particularly useful for:
Understand:
Do not replace functional assertions with screenshots.
Use visual testing as an additional quality layer.
One of the biggest problems in test automation is not creating tests.
It is maintaining them.
Imagine having 2,000 UI tests.
The development team changes the DOM structure.
Suddenly:
300 tests fail.
Did the product break?
Maybe not.
The selectors changed.
This is where AI-assisted test maintenance becomes interesting.
Tools such as mabl focus on AI-powered test creation, execution, failure analysis, and recovery when applications change.
Consider a SaaS product releasing changes every day.
The application team frequently updates the frontend.
A traditional automation suite may generate significant maintenance work.
An AI-native testing platform can help reduce some repetitive maintenance.
But there is an important question.
Should every broken test automatically heal itself?
My answer is no.
Suppose the original button says:
Transfer Money
After a product change, the application displays:
Delete Account
If a self-healing system blindly finds another button and continues, the test may execute the wrong action.
SDETs need to define governance.
For example:
Understand:
The technology is useful.
But governance is equally important.
UI automation engineers know this pain:
//*[@id="app"]/div[2]/div[4]/button
Someone changes the page layout.
The test fails.
Traditional automation often depends heavily on selectors.
AI-assisted automation platforms such as Testim use intelligent locator strategies designed to improve test stability when applications change.
A traditional test may identify a login button using one locator.
An AI-assisted system can use multiple element characteristics to understand the target element.
If one attribute changes, the system may still identify the correct element.
Imagine a large enterprise application with 3,000 automated UI tests.
The UI team regularly changes:
The application behaviour remains the same.
Traditional selectors may create hundreds of failures.
AI-assisted locator strategies can potentially reduce unnecessary maintenance.
However, an SDET should still analyse healed tests.
Ask:
A test that passes incorrectly is more dangerous than a test that fails.
The Real AI Skill SDETs Need in 2026
Learning seven AI tools is useful.
But there is a bigger skill.
Learn how to test AI-generated work.
If an AI generates 100 Playwright tests, your job is not finished.
Ask:
AI can generate automation very quickly.
It can also generate bad automation very quickly.
The ability to review AI-generated test systems may become one of the most valuable SDET skills.
A Practical AI Learning Roadmap for SDETs
If I were starting today, I would learn in this order:
Month 1: AI-Assisted Coding
Learn GitHub Copilot or an AI software engineering agent.
Use it with your existing Cypress, Playwright, REST Assured, or API automation framework.
Do not create demo projects only.
Use a real automation repository.
Month 2: Playwright and AI Agents
Learn Playwright.
Understand MCP.
Experiment with browser agents.
Ask an agent to explore an application and create test scenarios.
Compare the AI-generated scenarios with your own test design.
Month 3: AI for API Testing
Use Postman AI workflows.
Generate API tests.
Generate negative scenarios.
Compare AI test coverage against business requirements.
Month 4: Visual AI
Learn visual regression testing.
Understand the difference between functional assertions and visual validation.
Month 5: AI Test Maintenance
Explore self-healing and AI-native automation platforms.
Study where automated healing is safe and where human approval is required.
Month 6: Build an AI-Assisted Testing Workflow
Create a complete workflow:
Requirement
↓
AI-generated test ideas
↓
SDET risk review
↓
AI-assisted test implementation
↓
Automated execution
↓
AI failure analysis
↓
Human validation
This is where I believe modern SDET engineering is heading.
Final Thoughts
AI will not remove the need for skilled SDETs.
But it may reduce the value of engineers whose only skill is converting manual test cases into automation scripts.
The valuable SDET will understand:
The question is no longer:
“Will AI replace testers?”
A better question is:
“Can I use AI to test a complex system better than I could before?”
That is the skill worth learning in 2026.
What AI tools are you currently using in your testing workflow?
I would be interested to hear real SDET use cases — especially around Playwright, API automation, performance testing, and AI agents.
Let me know in you suggestion and feedback in the comment section.
If you want to connect with me then connect me at: www.apps.ganvwale.com