The Evolution of the Quality Role

If you ask someone what a QA engineer does, the answer used to be simple: write test cases, find bugs, and block releases when quality looks risky. In 2026, that definition already feels outdated.

Quality today is no longer owned by a single team, a phase in the SDLC, or even a set of test cases. It’s becoming continuous, intelligent, and shared and that raises a big question:

Will we still call it QA in five years?

 

From Gatekeepers to Enablers of Quality

Traditionally, QA teams acted as gatekeepers. Code was “thrown over the wall,” tested, and either approved or rejected.

That model started breaking down when:

1. Releases moved from quarterly to weekly (and then daily)
2. Microservices and micro-frontends exploded
3. CI/CD pipelines made waiting for QA impractical

Real-world example:
A fintech company running daily deployments realized QA approvals were slowing releases. Instead of expanding the QA team, they embedded quality checks into pipelines and empowered developers with automation, while QA focused on risk analysis and edge cases.

The QA role didn’t disappear, it evolved.

 

Automation Is No Longer a Differentiator

A few years ago, “knows automation” was a strong differentiator for QA engineers. In 2026, it’s a baseline expectation.

But automation itself has changed:

1. Self-healing locators fix broken tests automatically
2. AI prioritizes which tests to run
3. Test generation is driven by real user behaviour

Real-world example:
An e-commerce platform using AI-driven automation stopped maintaining thousands of brittle UI tests. Instead, the system learned from session replays and updated locators automatically cutting test maintenance by over 50%.

QA engineers now review automation outcomes, not babysit scripts.

 

The Rise of the Quality Engineer (QE)

The title “QA” is slowly giving way to Quality Engineer, Quality Advocate, or Quality Architect. Why? Because modern quality work involves:

1. Designing quality strategies, not just tests
2. Defining risk models
3. Observability, monitoring, and production validation
4. Collaboration across product, DevOps, and design

Real-world example:
In a SaaS company, QEs helped define SLIs and SLOs alongside DevOps. When performance degraded in production, alerts triggered automated tests and session analysis, before users complained. Quality moved closer to the business.

 

AI Didn’t Replace QA, It Changed What Matters

There’s a common fear that AI will replace QA roles. In reality, AI is replacing low-value, repetitive tasks.

What remains deeply human:

1. Exploratory testing
2. Asking “what if?”
3. Understanding user intent
4. Ethical and accessibility concerns

Real-world example:
An AI-generated test suite missed a usability issue affecting elderly users. It was caught during exploratory testing by a QA engineer reviewing session replays. AI is powerful but context still belongs to humans.

 

Tailored View by Experience Level

For Junior QA Professionals 

1. Learn fundamentals: testing principles, risk-based thinking
2. Don’t fear automation, understand what it solves
3. Focus on curiosity and product understanding

For Mid-Level QA Engineers

1. Move beyond script writing
2. Learn how AI-driven tools work under the hood
3. Start influencing test strategy and coverage decisions

For QA Leads & Managers

1. Redefine success metrics (less bugs ≠ more quality)
2. Invest in observability and production quality
3. Coach teams to think in terms of risk, not checklists

 

So… Will We Still Call It QA?

Maybe not. But whatever the title becomes, quality won’t disappear. It will:

1. Start earlier
2. Run continuously
3. Be powered by AI
4. And guided by humans

The future isn’t about defending the QA role. It’s about owning quality, wherever it lives.