People shape the journey while AI sharpens the tools.

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A few months ago, I started experimenting with AI in my testing workflow. What began as curiosity quickly became a powerful companion in my day-to-day testing activities.

When a new feature or requirement comes in, I often use AI to brainstorm test scenarios. It helps me expand my thinking beyond the obvious happy paths and identify edge cases and negative scenarios that could easily be missed during manual analysis.

In one of my recent projects, I was testing a mobile application that uses the same APIs as its web platform. AI helped me quickly map out cross-platform risks, API validation ideas, and unusual user behavior scenarios that could break the flow between mobile and web.

I’ve also used AI while reviewing automation frameworks and test data setups. For example, when setting up data-driven tests in an Automation Tool, AI helped me troubleshoot issues with CSV test data binding and quickly identify the root cause.

Another interesting use case was during bug investigation. While analyzing screenshots from a medical device display, I used AI prompts to ensure the orientation of the device was interpreted correctly before extracting values. This helped avoid reporting incorrect readings due to rotated images.

AI has also been useful when reviewing security scan results (like Snyk reports). Instead of just seeing a list of vulnerabilities, AI helped summarize the risks and suggest how they could be structured into actionable tickets for the team.

For me, AI doesn’t replace a tester. It acts like a thinking partner — helping me explore deeper scenarios, analyze problems faster, and approach testing with a broader perspective.

The most exciting part is that every week I discover a new way AI can improve how I test.