AI has become a central topic in today’s QA and IT industries. Both companies and employees are exploring how it can transform our daily work. There are many ways to use AI in our daily tasks — and, as with any new technology, opinions range from overly optimistic to deeply skeptical, with most people landing somewhere in between.
In this article, let’s explore a few extreme viewpoints and discuss why staying in any one of them can limit growth, create unnecessary fear, or prevent us from taking advantage of what AI truly offers.
“AI will replace my job” employee
This is the panicked employee that believes AI is here not to help, but replace their job or do it better hence endangering their job security and current financial stability.
While it’s true that AI can automate some tasks, it cannot replace creativity, context understanding, or the nuanced judgment that experienced testers bring. Instead of resisting, learning how to use AI tools can make you more valuable within your team — not replaceable.
How? you may ask — be that person that has a proactive mindset and seeks to improve processes, delivery times, productivity within the team, making excellent use of such tools.
“Let’s use AI to deliver more with less resources” management
This mindset often comes from non-technical leadership who view AI as a magic solution to productivity and cost. They might assume AI can “replace an employee” or that “AI will do 250% more work.”
However, all AI tools have limitations. They can make mistakes, hallucinate, or produce inaccurate results. Even the most advanced models include disclaimers — “This AI may make errors. Please verify important information.”
Rather than replacing human expertise, managers should focus on using AI to empower teams: automate repetitive tasks, accelerate test data generation, or assist in root cause analysis — but always with human oversight.
“I’m better than AI, so I won’t use it” employee
Confidence in your skills is great, but refusing to adapt is not.
If an AI tool can automate a one-hour manual task in ten minutes, why not take advantage of it? Using AI doesn’t make you less skilled — it makes you more efficient. Work smarter, not harder 😉.
“I’ll Just Ask the AI to Do It for Me” Colleague
On the other extreme, some rely too heavily on AI for every task. They skip the thinking process entirely, copying and pasting results without review.
That’s dangerous — because AI can generate incorrect or misleading outputs. Always validate what the AI produces, and use your own judgment before trusting or acting on it. Also, according to a study published in 2025 by Michael Gerlich, reliance on AI tools will negatively impact critical thinking.
“I’ll never use AI” employee
This is the colleague who refuses to even try AI tools. Often, it comes from fear or skepticism.
Refusing to explore new technology is similar to being afraid of it. The industry is evolving rapidly, and those who learn to integrate AI into their workflows will naturally move ahead.
Why Extremes Don’t Work
Each of these mindsets represents an extreme — either resistance, overconfidence, or overdependence. All of them limit growth.
The balanced approach is to understand what AI can and cannot do, then use it to enhance your daily tasks. Whether it’s generating test cases, summarizing bug reports, or speeding up regression analysis, AI can be a valuable assistant — if used wisely.
A Balanced Path Forward
For QA professionals, AI is not the enemy; it’s an accelerator.
Use it for:
- Test case generation – AI tools can analyze user stories, requirements, or existing test scripts to suggest new or missing test cases, ensuring broader coverage with less manual effort.
- Test data creation – Generating realistic, diverse datasets (especially for edge cases or negative testing) can be time-consuming; AI can automate that process efficiently.
- Visual regression testing – AI-powered image comparison tools can identify subtle UI differences that traditional pixel-based comparison might miss.
- Defect prediction – By analyzing commit history and previous bugs, AI can predict high-risk areas in the code, helping teams prioritize testing.
- Intelligent test maintenance – AI-driven tools can automatically update locators or scripts when UI elements change, reducing flaky test failures and manual upkeep.
- Natural language test automation – Some tools allow testers to write test steps in plain English, which AI then translates into executable scripts — lowering the entry barrier for manual testers.
- Test result analysis – AI can cluster failed tests, identify common causes, and even suggest possible fixes or areas for investigation.
But always review, verify, and think critically. AI amplifies both strengths and weaknesses — your expertise is what keeps it reliable.
Final Thoughts
AI isn’t here to replace quality assurance professionals — it’s here to amplify their efficiency.
The balanced approach is to understand what AI can and cannot do, then use it strategically to strengthen your testing process rather than replace it.
The future belongs to those who know when to rely on AI and when to rely on human judgment.
Use it wisely, stay curious, and let it make your work faster, smarter, and more rewarding. 🚀