Let's remember. A few years ago, the work in QA (Quality Assurance) was pure discipline and method: writing manual test cases, reviewing requirements over and over again, conducting endless regressions every time someone touched a line of code. Automation came like a balm, yes, but also with its learning curve. It wasn’t all Selenium and laughter.

Now, in recent years, Artificial Intelligence has begun to infiltrate our tools, processes, and decisions. And it’s not just a technical change; it’s a true paradigm shift.

Where and how does AI impact QA?

1. Optimization of test time

One of the most direct advantages: AI allows tests to be executed more quickly and intelligently. How?

• Test prioritization: Algorithms that analyze code changes and automatically determine which tests are critical and should be run first. No more monolithic suites running all night!

• Impact analysis: If a function is modified, AI can infer which parts of the system might break (even if they are not directly connected). This used to require experience and extensive knowledge of the system. Now, the machine can anticipate it.

• Test data generation: Generating realistic test data was an art. Today, models can learn from the real system and generate cases much closer to actual user usage.
  
  

2. Improvement of test quality

• Assisted test case design: Tools like Testim, Functionize, or even GPT plugins for testing can suggest or generate test cases directly from user stories or technical documentation. We no longer always start from scratch.

• Early bug detection: Thanks to predictive analysis, AI can identify patterns of common failures, areas prone to bugs, and even suggest improvements in the source code.

• Enhanced visual testing: Today we can use AI to detect significant visual differences, skipping over what humans would normally miss.
  
  

What do large companies gain?

Large companies, with complex products and millions of users, see a clear return:

• Reduction of operational costs: By reducing the need for repetitive manual testing and optimizing execution time, QA is no longer seen as a bottleneck.

• Shorter Time to Market: With automated CI/CD pipelines and intelligence that prioritizes risk, releases are safer and faster.

• Continuous improvement based on data: AI learns from the actual use of the system. It detects when tests no longer add value, when to update scenarios, or when a part of the system hasn’t been touched for months (and maybe we could reduce testing there).
  
  

But, is everything rosy? No, not by far. This is where our skeptical and traditionalist side comes into play.

Real challenges we face

1. Trust in a system we do not fully understand

   • Sometimes AI makes decisions that seem magical: “Why does it recommend I run this test and not that one?”. If we do not understand the logic behind it, we risk relying on an "oracle" without control.

2. Insufficient or biased training data

   • If AI is fed poorly documented historical data or poorly designed tests, it may learn the wrong things. And that can lead to production errors. Or worse: a false sense of security.

3. Technological overload

   • Adopting AI tools requires new skills. Not all teams are ready. QA Engineers must now understand concepts of Machine Learning, model training, algorithm validation... it’s no small feat.

4. Dependence on third-party tools

   • Many AI solutions are closed and proprietary. This creates a significant technological dependency. What happens if the provider disappears or changes the terms?

     
     

The balanced vision: humans + AI

AI does not come to replace us. It comes to enhance our capabilities, as testers, as engineers, as quality leaders.

• Human judgment remains irreplaceable. Empathy with the user, the intuition that “something smells wrong” even if there aren’t any evident bugs, the ability to imagine chaotic scenarios... no model learns that.

• But if we let AI do the mechanical work, we gain time to think. QA can finally become a strategic area and not just operational. One that helps make decisions, prioritizes risks, and anticipates failures.
  
  

QA of the future is written today

We are at a turning point. AI can transform software quality, yes, but only if we use it with intelligence, critical spirit, and respect for what has been learned.

Large companies have the opportunity to lead this change, investing not only in tools but in training, culture, and human processes that synergize with the machine.

Because in the end, as always, quality does not depend on how much you automate, but on how much you understand your users, your system, and your own processes.

And in that, there is still no algorithm that can beat us. 😉

- Roberto Arce (CTO Beryon)