How do organizations avoid AI hype traps and ensure real business value in QE?
Can AI models be trained to detect usability and accessibility flaws, or will that remain a human strength?
Should there be global standards for AI in quality engineering (like ISO/ISTQB for testing)?
How to overcome the FEAR “No AI → No Jobs” as became a buzz — how do we identify our strengths?
Do you see AI becoming co-pilots for testers?
How do you measure ROI and success metrics for AI in testing?
How to use AI and GenAI at workplace for fruitful outcome in Testing the Product?
Biggest failure point you’ve seen when teams move from Crawl to Walk? What steps or processes did you follow to enable GenAI in QA teams?
What are the best & efficient strategy/steps to transform from QA to QE?
What are the top AI tools you use to improve productivity and reliability?
How do you handle bias in AI-driven risk-based testing decisions?
What’s the biggest cultural shift teams need to embrace as AI becomes part of QE?
Looking ahead, do you see AI becoming a co-pilot or autopilot for testers?
What’s the quickest small pilot that shows ROI for leadership buy-in?
Want to know a real world example with respective to AI , GenAI and Agentic AI in simple terms.
Can GenAI truly achieve shift-left testing at scale, or will human oversight always be required?
Is there a dictionary of the verbiage used for Automated Testing and AI?
How can testers leverage Agentic AI for exploratory testing and defect discovery in ways traditional automation can’t?
AI learning is needed OR AI featured tools learning is needed?
Could agentic AI democratize testing so that non-testers can spin up quality checks via natural language?