What would your QA team do with an extra 40 hours every month?
According to a 2024 Tricentis survey of more than 500 DevOps practitioners, managers, and executives, almost one-third of respondents (32%) estimated that AI-augmented DevOps tools could save their teams more than 40 hours per month. That is roughly an entire working week.
But the more interesting question isn't whether AI can save 40 hours.
It's where those 40 hours are being spent in the first place.
The real cost of automated testing isn't always running the tests
Automated testing is supposed to reduce repetitive work. Yet anyone who has managed a growing test suite knows that running the test is only one part of the process.
Tests need to be maintained when applications change. Locators break. Elements load differently. Network conditions vary. And when a test fails, someone still has to determine whether the failure points to a real product defect, an environment problem, a timing issue, or the test itself.
That investigation can take longer than the test execution.
This is the part of testing that is rarely captured by metrics such as the number of automated tests or percentage of automation coverage. A team may have thousands of automated tests and still spend a significant amount of engineering time keeping those tests reliable and understanding their failures.
The question, then, isn't simply how many tests you can automate. It's how much human effort your automation still requires.
Testing is already a major AI opportunity
The industry appears to be moving in that direction. In the same Tricentis research, 60% of respondents ranked testing as the most valuable area for AI investment across the software development lifecycle.
Teams are already applying AI to several testing activities. The survey found that 47.5% were using AI for test planning and deciding what to test, 44% for test-case generation, and 32% for analyzing test results. Another 42% of respondents said AI had made them more productive in testing and QA.
That tells us something important: the opportunity isn't limited to generating more test cases.
AI can potentially reduce the amount of manual effort required before, during, and after execution.
More tests don't automatically mean better testing
It is tempting to measure progress by test count. A larger suite appears to mean greater coverage and more confidence.
But a test suite is only valuable when its results can be trusted.
If every failure requires someone to investigate a broken locator, wait for an element that hasn't loaded, rerun an inconsistent test, search through logs, and collect evidence before anyone can understand what happened, the automation itself becomes another source of overhead.
This is why intelligent automation matters.
The goal isn't simply to execute more tests. It's to make the tests you already have easier to run, maintain, understand, and trust.
Development is getting faster. Testing cannot remain the bottleneck.
This becomes even more important as AI accelerates software development.
DORA's 2024 research found that AI adoption significantly increased individual productivity, flow, and job satisfaction. At the same time, it found negative effects on software delivery stability and throughput, reinforcing the importance of fundamentals such as small batch sizes and robust testing.
The lesson isn't that AI makes software development worse. It's that making one part of the development process faster doesn't automatically make the entire delivery process faster.
If developers can produce changes faster but QA teams still have to manually investigate every ambiguous failure, the bottleneck simply moves.
Testing has to become more efficient alongside development.
So where could those 40 hours come from?
They may not come from making test execution itself dramatically faster.
They could come from reducing everything that happens around execution: maintaining broken locators, waiting for application conditions, rerunning unreliable tests, investigating failures, and collecting the evidence needed to understand what happened.
That's where intelligent test automation can make a meaningful difference.
QAlity is built with this problem in mind. Instead of treating test execution as the entire testing workflow, QAlity brings intelligent waiting, locator healing, and execution evidence together to reduce the effort required to operate and understand automated tests.
The objective isn't to give teams another dashboard full of test results.
It's to reduce the work required to get from "the test failed" to "we know why."
The real goal of test automation
The 40-hour figure is an estimate, not a promise. But it gives teams a useful way to think about the value of AI-assisted testing.
If automation gives your team more time, what should that time be worth?
More exploratory testing. Better coverage. Stronger test strategies. More attention to performance and security. Or simply more time spent solving product problems instead of maintaining the machinery used to find them.
That's the opportunity.
The future of test automation isn't about running more tests. It's about spending less time dealing with them.
And if your QA team could get a week back every month, the better question might be:
What would you finally have time to test?
Sources
Tricentis, AI-augmented DevOps: Trends Shaping the Future, 2024. Survey of 500+ DevOps practitioners, managers, and executives.
Google Cloud DORA, Accelerate State of DevOps Report 2024.
