Introduction 

In recent years, artificial intelligence (AI) has become one of the most influential forces shaping the software industry. It writes code, analyzes logs, generates test ideas, and even simulates user flows. With this growing level of automation, it is understandable that many QA Engineers feel anxious, wondering whether AI will eventually take over their roles entirely. 

As someone who has spent years working in QA across products, teams, and evolving technologies, I have learned that the core of quality assurance has always been more than executing test cases or automating flows. QA is fundamentally critical for thinking, understanding users, and advocating quality throughout the entire product lifecycle. AI doesn’t eliminate that responsibility. In fact, it highlights the importance of those human skills. 

The real risks: How AI challenges the QA role 

It’s important to start by acknowledging the real disruption AI brings. Many AI-powered tools can now automatically generate test cases, predict weak points in applications, and analyze massive volumes of logs within seconds. For organizations under cost pressure, this can appear to be an opportunity to reduce manual testing roles – particularly those focused on repetitive or highly structured tasks. 

Traditional entry-level QA work, such as basic regression testing or step-by-step validation, is especially vulnerable. In these areas, AI’s speed and scale are unmatched. However, these risks – while real – tell only part of the story. 

The misconceptions: What AI still cannot replace 

Despite its rapid advancement, AI is far from being a true replacement for human QA engineers. At zen8labs, we’ve seen firsthand that while AI excels at generating ideas, summarizing information, and automating repetitive checks, it consistently falls short when deeper thinking, contextual understanding, or judgment is required. These limitations become most apparent when testing features that involve ambiguity, user behavior, or have a real business impact. 

Below are the most common misconceptions that lead people to overestimate AI’s capabilities- and why QA expertise remains irreplaceable. 

1. AI cannot understand context, meaning, or nuance 

AI is exceptional at pattern recognition. Ask it to generate test cases for a login screen, and it will produce dozens of predictable scenarios. What it cannot do is understand the broader purpose, business meaning, or nuanced intent behind a feature. 

Real-world example: 

When testing a transaction summary screen that displays financial data, AI can validate formats and basic calculations. But it won’t naturally ask: 

• Does this comply with regulatory requirements? 
• Does this rounding logic align with finance expectations? 
• Would this presentation confuse a non-technical user? 

Only a human QA can connect a defect to real-world risk – something AI simply cannot grasp. 

2. AI cannot navigate ambiguity, shifting requirements, or incomplete documentation 

Modern product development is rarely clean or linear. Requirements evolve mid-sprint, assumptions shift, and insights emerge through user feedback. 

AI struggles in this environment. It relies on clear inputs and stable rules. At zen8labs – where product direction often evolves rapidly – AI suggestions can quickly become outdated or irrelevant. 

For example, if a story states, “Simplify the onboarding flow,” AI cannot interpret: 

• What “simplify” truly means 
• Which users are struggling 
• What feedback led to this change 
• What the product team defines as “friction” 

A QA engineer, however, will immediately identify these uncertainties and initiate conversations that prevent costly misalignment. 

3. AI lacks curiosity – the foundation of exploratory testing 

Exploratory testing is not just clicking through screens; it’s driven by curiosity, intuition, and creative suspicion. AI does not “wonder” about unusual behavior, nor does it experience the gut feeling that something feels off. 

Example: 

While testing a rewards redemption flow, a human tester might try: 

• Redeeming twice in quick succession 
• Leaving the browser idle for 20 minutes 
• Switching devices mid-process 
• Entering unexpected characters (emoji, Arabic script, etc.) 

AI won’t explore these scenarios unless explicitly instructed. It doesn’t challenge assumptions – because it doesn’t understand risk or consequences. 

4. AI cannot evaluate user experience, emotions, or intentions 

Quality is not purely functional – it’s experiential. AI cannot judge whether: 

• A loading spinner feels too slow 
• An error message feels discouraging 
• A flow feels unintuitive 
• A design inconsistency creates cognitive friction 

These insights come from empathy and human psychology. At zen8labs, while AI can confirm that a new UX flow works correctly, only human QA engineers can determine whether users feel more confident, whether steps feel lighter, or whether the interface truly improves usability. AI sees the flow. Humans feel the flow. 

5. AI cannot take responsibility or make judgment calls 

When production issues arise, AI doesn’t attend stand-ups or write root-cause analyses. It doesn’t explain trade-offs or help prioritize risk. 

QA engineers constantly make judgment-based decisions, such as: 

• Is this bug release blocking? 
• Is this edge case worth fixing now? 
• How much risk can we accept before launch? 

These decisions require a deep understanding of business goals, timelines, user impact, and team dynamics – areas where AI has no awareness. 

6. AI struggles with novel or ethical scenarios  

AI performs best when problems resemble past data. But innovation introduces new domains, workflows, and ethical risks. 

For example, when testing AI-driven recommendation systems, human QA engineers ask: 

• Are certain user groups unfairly deprioritized? 
• Does this behavior introduce bias or unintended harm? 

AI cannot reason about ethics or long-term consequences. Humans can. 

7. AI lacks the collaborative awareness needed in real software teams 

Quality isn’t built in isolation. QA engineers work closely with product managers, designers, developers, and clients to align expectations, clarify requirements, and define what “done” truly means. 

AI lacks awareness of team dynamics, shifting priorities, cultural context, and unspoken signals. It can’t read the room, sense misalignment, or navigate human relationships. QA engineers fill this gap by applying emotional intelligence, communication skills, and judgment – ensuring quality through collaboration, not just testing. 

Brief conclusion

It is undeniably reshaping how Quality Assurance work is performed; however, it is not redefining what quality truly means. While AI excels at speed, automation, and pattern recognition, it still lacks the context, curiosity, empathy, and judgment required to ensure software works effectively for real people in real-world situations.

As a result, the role of QA engineers is not disappearing—it is evolving. As AI increasingly takes over repetitive and mechanical tasks, human QA becomes even more critical. Instead of focusing solely on execution, QA engineers are shifting toward risk assessment, cross-team collaboration, and quality leadership throughout the entire product lifecycle.

At zen8labs, we believe the future belongs to teams that know how to combine intelligent tools with strong engineering judgment. In part 2, we examine how QA engineers can turn AI from a threat into a strategic advantage.

👉 Learn more about zen8labs’ IT consulting on our website, and follow us on LinkedIn to see how our teams work in practice and explore current QA career opportunities.

Tran Van Toan, Software Engineer