How do you see yourself leaning towards AI in upcoming days?

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I am using AI to improve test coverage, speed up automation, and make QA processes more efficient.

I see myself using AI to enhance software testing by learning tools that support intelligent test automation, faster bug detection, and better test coverage

I see myself leveraging AI to enhance productivity, automate testing, and drive smarter QA solutions.
I applied zerostep on my playwright framework its love!

First of all I should learn how theses AI and LLMs are actually working, and try to figure out the aspects in which AI can add value to my testing without break the security guidelines and privacy, more importantly I should learn how to test these AI and LLMs.

As a QA, I see AI as a valuable support tool to improve the speed and accuracy of testing. I’m interested in using AI for areas like test case generation, identifying patterns in bugs, analyzing logs, and even helping prioritize what to test first.

 I've already built a POC using Copilot and Playwright MCP to explore webpages, generate test code, and self-heal locators. Moving forward, I plan to expand AI’s role in test generation, maintenance, and collaboration to drive smarter, faster, and more resilient testing.
 

I plan to lean further into AI by:

• Expanding autonomous test generation: Using GenAI to interpret requirements and user stories to create meaningful test scenarios.
• Enhancing test maintenance: Leveraging AI to predict flaky tests and proactively suggest fixes based on historical data.
• Improving collaboration: Using natural language interfaces to allow non-technical stakeholders to contribute to test creation.
• Integrating AI into CI/CD pipelines: Automating test data generation and environment setup using AI-driven tools.
• Continuously learning and adapting: Staying updated with advancements in GenAI and experimenting with new models and frameworks to push the boundaries of intelligent automation.

Unlike traditional software, LLM outputs can vary for the same input — so traditional QA methods (like checking expected vs actual) just aren’t enough. We’re living in the era of LLMs, and it's time QA evolves with it!

I see myself integrating AI throughout the QA lifecycle—using models to draft and refine test cases, uncover edge‑case scenarios, and dynamically prioritize and adjust test suites based on code changes; leveraging conversational agents in tools like Jira or TestRail for faster status updates; and feeding AI‑powered dashboards and narrative summaries to highlight trends and predict risks, so I can shift from manual tasks to strategic oversight, catch issues earlier, and communicate insights more efficiently.

I’m already using copilot in my everyday work.

In the upcoming days, I see myself increasingly leaning towards AI as both a tool and a partner in enhancing productivity, creativity, and decision-making. With AI rapidly evolving, I aim to integrate it into my daily workflow, whether it's automating repetitive tasks, gaining insights from data, or exploring new ways to solve complex problems. I’m particularly interested in learning how to collaborate with AI systems to augment my skills, stay ahead in my field, and contribute meaningfully to innovation. As AI becomes more accessible, I believe it will empower me to focus more on strategic thinking and less on routine execution.