How QA Teams Scale Test Automation Without Increasing Maintenance

Do you love your to-do list being clogged up by routine maintenance tasks? Do you live for the hours spent fixing flaky tests after a small UI change? 

No? Join the club. 

The good news is that you don’t have to do that anymore. Here’s how smart QA teams scale their test automation without an associated increase in maintenance (and the slowdowns that come with it). 

How Does Test Automation Maintenance Create Bottlenecks?

1. Fragile Tests

Traditionally-automated tests break easily because they rely on brittle locators; a slight change in CSS classes, element position, or text can cause failure. This means that QA teams sink time into constant script repair tasks, which slows everything down. 

2. Lack of Manpower

QA teams are getting smaller. More features = more time scripting = more maintenance. This might be manageable for an MVP, but as your app expands, the numbers don’t add up. 

3. Accumulation of Technical Debt

Technical debt isn’t inherently bad, as long as you can pay it back. Hard-coded test data, fragile locators, and a lack of reuse strategies make this difficult, so over time, teams spend more time refactoring old tests than building new ones.

4. Added Mobile Complexity

Mobile teams need to ensure their app works across a variety of devices and platforms.

Inaccurate simulators lead to inconsistent execution, as well as endless triage and fixes.

Smart Teams Use AI For Test Automation Maintenance

It’s 2026, and smaller teams with tighter budgets are the norm. This means you’ve got to be smart about test automation maintenance if you want to scale effectively. 

AI-driven tools can take much of the repetitive work off your plate, making test automation easier to scale and shifting your QA engineers from an overwhelmed test scripting team to a useful strategic resource. 

Here’s the AI functionality you should be looking out for

1. Natural Language Test Creation

Traditional automation typically requires programming knowledge; there are low-code tools, but these tend to be of limited use for anything remotely complex. As well as slowing things down, this restricts who can test. 

Natural language test creation solves this issue by making it easier and faster than ever to create tests for programmers and business teams alike. 

What Natural Language Testing Does

Natural language testing allows QA teams to write test cases in plain English. The AI then builds this into a test; the whole process takes minutes, not hours, and you can update the test in seconds if you need to.  

For example:

• When I log in as an admin
• And I navigate to the user dashboard
• Then I should see exactly 10 active users listed

A system like Momentic can interpret this intent and generate automation directly, reducing both the creation and maintenance burden.

How Natural Language Testing Reduces Test Automation Maintenance

• Easier to update: When your app changes, updating a natural language test is faster and less error-prone than rewriting scripts
• Bridges communication gaps: Tests are written in human language, so they are easier for non-technical teams to interpret and maintain collaboratively

2. Agentic AI

Agentic AI tools can act on behalf of users, making decisions, generating new artifacts, and modifying existing ones without explicit programming. For your QA team, they’re a handy autonomous coworker that saves time on a range of tasks. 

What Agentic AI Can Do

AI agents can automatically generate tests based on application behavior and user flows, suggest where to expand coverage after intelligently exploring your app, and update broken tests autonomously when minor changes occur.

Momentic’s agentic AI, for example, can observe an application, propose test suites, and proactively maintain tests. This reduces the time your QA team has to spend on manual test automation maintenance and makes it easier to scale. 

How Agentic AI Reduces Test Automation Maintenance

• Proactive fixes: AI can detect patterns of failure and adjust tests automatically
• Adaptive learning: As the app evolves, AI adapts tests based on real changes rather than static definitions
• Reduced human input: QA teams spend less time troubleshooting broken scripts and more time building strategy

3. Self-Healing Tests and Intent-Based Locators

One of the most significant (and irritating) sources of test automation slowdown is having to manually fix flaky tests caused by fragile UI locators.

Intent-based locators update tests with changes in the DOM, so you don’t have to do that anymore. 

What Intent-Based Locators Do

Self-healing mechanisms use AI to detect changes in the UI hierarchy and compare new structures to known patterns. They can then automatically update selectors or find alternative paths to the element under test.

This allows tests to ‘self-heal’; they update with UI changes, so you don’t need to worry about doing it yourself. 

How Intent-Based Locators Reduce Test Automation Maintenance

• Fewer broken tests: Tests continue running even if the UI changes slightly
• Automated recovery: AI remaps elements, so fixes are applied instantly and reliably
• Fewer engineer hours: Teams no longer fix every locator break manually, saving time

4. Accurate Mobile Simulators

Traditionally, the choice for mobile QA teams was between more physical device testing (slow, expensive, and difficult to scale) and reliance on imprecise mobile simulators. 

AI has improved simulator options significantly, so that teams can rely on them more for accurate results and reduce reliance on expensive real device testing. 

What Better Mobile Simulators Do

Today’s AI-enhanced mobile simulators use AI to replicate real device behavior more closely. This means touch events behave more realistically, better simulation of network conditions and sensory inputs, and UI rendering that’s closer to real device performance.

How Better Mobile Simulators Reduce Test Automation Maintenance

• Consistent results: Fewer false failures and false positives mean less investigation
• Broader coverage, earlier: Tests can be confidently run across many environments without waiting for device availability
• Reduced costs: Fewer reruns on real devices means mobile testing is cheaper and quicker to scale

So, What Happens to Your QA Team? 

Traditional QA is dying, and your team will need to move away from it to remain competitive. There’s no getting around it; the time savings AI offers for test automation maintenance are too great to be ignored. 

You could let half of your QA team go and maximize on short-term cost savings. There’s an advantage to that, particularly if you’re a startup that’s working on a super lean budget. 

We think, however, that the companies that win in the AI era won't be the ones that got 30% more efficient. They'll be the ones who used that 30% to do things that were previously impossible.

What does that look like for your QA team? It means more free hours to delve into the (vastly more accurate) results of the tests that AI maintains for you. Rather than being stuck doing never-ending routine maintenance tasks, your QA team can take a more strategic, analytical role, which could be 

Best Practices for Low-Maintenance Test Automation

1. Use Low-Code Tools

Less code means fewer breakpoints. It also means tests are much more accessible for non-technical team members, so more people can help maintain your test cases. 

2. Leverage AI Tools To Cut Routine Test Maintenance

Self-healing tests with intent-based locators, insights from AI agents, and mobile emulators that actually work, all useful in making test automation maintenance more scalable.

3. Run Early and Often

Integrate automated tests into CI/CD pipelines to help ‘shift left’ and catch issues early. AI-assisted test generation ensures new coverage is always created with releases.

4. Refocus Your Team on Analytical

Human-centered analysis adds value to your business; trawling through endless test fixes does not. Look at the potential of your QA team, and, if your circumstances permit, give them the right tools and training to provide these insights.

What is the Future of Test Automation Maintenance?

With the right tools, test automation maintenance will be easier. It is easier now, with a range of AI test automation tools on the market; it will be even easier in the future as these tools evolve. 

In other words, the concept of test automation maintenance will shift from a pain point to a managed, intelligent lifecycle:

• Tests will auto-adapt, self-heal, and regenerate
• QA teams will function more like product quality strategists than script repair techs
• Collaboration across developers, QA, and product teams will improve via natural language tests that are more accessible to organization-wide stakeholders

FAQs

1. What is test automation maintenance?
   Test automation maintenance is the ongoing work to keep automated tests stable and accurate as the product changes.
2. Why does test automation maintenance become a bottleneck as you scale?
   Brittle locators, shrinking QA bandwidth, and growing test debt create frequent breakages and slow delivery.
3. How do AI tools reduce test automation maintenance?
   Natural-language tests, agentic AI, and self-healing locators reduce script rewrites and cut flaky failures.
4. What are the best practices for low-maintenance automation?
   Use low-code tools, isolate test data, run tests early in CI/CD, and fix root causes instead of patching symptoms.
5. How does Momentic help reduce automation maintenance?
   Momentic supports natural-language test creation, agentic AI, and self-healing locators to keep tests resilient as the UI evolves.

Momentic: Test Automation Maintenance, With AI 

After implementing Momentic for end-to-end tests, our customers, GPTZero, experienced: 

• 80% faster release cycles
• 75% reduction in test generation times
• 89% decrease in defect escape rate

Want to see your team hitting numbers like that? Get in touch to see how Momentic could help. 

Schedule a demo