Answer Chris and Sanjay's Question - The Cost of Quality

I have used AI for 1 year in daily QA work like analyzing requirement, test case creation, test plan preparation, writing test design, implementation, review… Increasing about 30% productivity. 

We’re in the early stages of adopting AI within our QE practice. Right now, we’re exploring use cases, evaluating tools, and building internal understanding of where AI can add the most value particularly in areas like test case generation, defect prediction, and accelerating root-cause analysis.

Just starting: We are exploring AI tools in our QE practice, evaluating opportunities to automate repetitive tasks like test generation, defect analysis, and reporting. At present using tools like TestRigor and Applitools.

As a team of manual QAs, we’re only at the beginning of our journey in integrating AI into our workflow. We’ve started using it to summarise and simplify complex Acceptance criteria. We also utilise it to help us structure our test cases so we don’t miss anything.

We’re already seeing significant benefits in terms of speed and test coverage.

We currently use Testim, and we are exploring autonomous test generation tools.

I have been using AI in our regular testing for the past year. I use it to create test cases after finalizing requirements, verify and refine them, and also support our overall testing environment.

Just starting

We’ve used AI for test case creation, test data creation, test automation development, and fixing test automation bugs. Each case has improved productivity, test coverage, and test effectiveness. Our largest opportunities are defect prediction and accelerating Root Cause analysis.

​@Mustafa 

I’m already using AI actively in my QA workflow. Not at the “just exploring” stage anymore. I’d say I’m between pilot and clear ROI.

How I’m using it

• Generating and refining test cases

• Writing and debugging automation scripts faster

• Using self-healing locators to cut maintenance

• Creating synthetic test data

• Getting early risk insights from AI-based analysis

What ROI looks like

• Faster test cycles

• Better coverage

• Fewer flaky tests

• Less manual effort

• Earlier defect detection

Tech I follow
GenAI automation tools, self-healing frameworks, AI-driven API testing, autonomous testing agents, and AI-powered dashboards.

Just starting