The evolving role of QA: from test executor to quality strategist 

As AI handles more repetitive tasks, QA responsibility shifts toward strategy, insight, communication, and risk management. The role becomes less about “clicking through steps” and more about thinking – connecting quality with product vision, user experience, system behavior, and organizational outcomes. 

In this new landscape, QA Engineers become: 

• Quality architects, designing smarter test approaches 
• Strategic collaborators, clarifying requirements and risks 
• Exploratory thinkers, uncovering surprising behaviors AI cannot predict 
• User advocates, ensuring technology aligns with real human needs 

And this evolution opens the door to a new opportunity: using AI not as a replacement, but as a powerful partner. 

How QA Engineers can apply AI in real life: practical examples at zen8labs 

To better illustrate what this partnership looks like in practice, let’s explore how QA teams at zen8labs – a global technology consulting firm – apply AI in everyday workflows. This approach is not about deep technical automation. Instead, it reflects a mindset of shift: using AI to enhance reasoning, communication, and creativity, while keeping human judgment at the center of quality. 

1. Using AI as a “requirement clarifier” before testing begins 

Requirements are often vague or incomplete. Instead of diving straight into testing, QA engineers can use AI to sharpen their understanding. At zen8labs, a QA might take a loosely defined user story like: “Improve onboarding flow to reduce friction.” 

AI can help by: 

• Highlighting ambiguities 
• Suggesting missing acceptance criteria 
• Generating clarifying questions for the Product Owner 
• Listing potential risks or edge cases 

This transforms AI into a thinking partner that helps QA uncover hidden assumptions before testing even starts. 

2. Using AI as a “scenario brainstorm partner” during exploratory testing 

Exploratory testing thrives on curiosity and creativity. AI can amplify this by offering alternative perspectives and prompting new lines of thinking. 

When testing a feature such as a payment redirection flow, a QA engineer at zen8labs might ask AI: 

• What unexpected user behaviors could break this flow? 
• What edge cases do similar applications commonly face? 

AI may surface scenarios such as: 

• Slow or delayed bank responses 
• Browser back-button behavior 
• Unexpected currency or locale formats 
• Interrupted or unstable network connections 

These prompts expand the tester’s mental model and help uncover paths that might otherwise be overlooked. 

3. Using AI as a fast feedback loop during testing 

During manual testing, QA engineers often encounter questions that require quick analysis or domain insight. AI can act as a rapid feedback loop by: 

• Summarizing complex logs 
• Suggesting hypotheses for inconsistent behavior 
• Explaining potential user impact 
• Referencing comparable patterns from similar systems 

For example, when dealing with intermittent empty API responses, a QA engineer might ask: “What could cause this behavior, and how might it affect real users?”. This accelerates decision-making and helps prioritize issues more effectively. 

4. Using AI to create clear, impactful bug reports 

Clear communication is one of QA’s most critical responsibilities. AI can help refine and structure information without replacing analytical thinking. 

At zen8labs, QA engineers may use AI to: 

• Rewrite the reproduction steps for clarity 
• Add business or user-impact context 
• Suggest more human-friendly explanations 

The result is not automated reporting, but better communication – allowing cross-functional teams to align and respond faster. 

5. Using AI to support communication across teams 

QA engineers at zen8labs collaborate closely with clients, designers, and product managers. AI can help tailor communication for different audiences by: 

• Rewriting findings in business-friendly language 
• Preparing concise sprint summaries 
• Translating technical risk into plain terms 
• Creating clear talking points for demos or reviews 

For example: “Explain the current testing risks in a way suitable for a client presentation”. This enables QA to influence decisions more effectively and bridge the gap between technical detail and business understanding. 

6. Using AI as a personal learning and mentorship tool 

Rather than waiting for formal training, QA engineers can use AI to continuously upskill. Common prompts include: 

• Act as a Product Owner and challenge my test approach 
• What are the most common pitfalls in this domain? 
• What does this term typically mean in similar systems? 
• Generate practice scenarios based on this product context 

In this role, AI becomes a 24/7 learning companion – supporting growth, reflection, and deeper domain expertise. 

Final thought: Co-existing with AI, not competing with it 

AI will transform QA. It will automate repetitive work, surface insights faster, and reshape workflows. But it will not replace the human ability to reason, empathize, prioritize, and think holistically about quality. 

At Zen8Labs, we see AI not as a replacement for QA engineers, but as a force multiplier – one that elevates the role from execution to strategy. Our QA teams work alongside AI to design smarter testing approaches, strengthen collaboration across product teams, and ensure quality aligns with real user and business outcomes. We believe the future belongs to QA engineers who: 

• Use AI as a partner, not a threat 
• Strengthen critical thinking and communication 
• Evolve into quality strategists 

The future is not QA or AI. It is QA powered by AI – working together to build better, more thoughtful, and more reliable products. 

👉 Explore how Zen8Labs applies AI-driven QA and product consulting to deliver real-world impact. 

Tran Van Toan, Software Engineer