Is “AI in Quality Engineering” just automation with a new label, or is it fundamentally reshaping how enterprises build trust in software?
How do you sell it to the senior managers who have tight purse strings and don’t have AI in QA in the roadmap or budget?
How much could we rely on AI while UAT and use case Testing , as most of it involves human behaviour?
What top 3 pain points are you seeing at enterprises currently preventing selling these magic tools?
As AI transforms test requirements, scenarios, and automation, how do we measure its success beyond productivity—ensuring compliance, ethical integrity, bias reduction, security, and long-term trust?
A recent study from MIT indicates 95% of GenAI projects fail at enterprise not due to the quality of the models, but lack of understanding and integration with the workflows. how can an enterprise choose the right use case of GenAI in QE projects?
How AI initiatives tied to measurable business outcomes and to define the success metrics upfront and track them?
What is the tipping point at which one should scale AI in testing, how long does it usually take to get there?
What is the single most critical, non-obvious ‘play’ from your new playbook for scaling AI that most companies get wrong when they try to move beyond the successful pilot stage?
What is the right balance between human testers and AI-driven testing?
Which skills are becoming most critical for data scientists as AI and automation expand ?
What are the challenges faced by you in adopting AI in SDLC/ STLC and how you have overcome those?
Do you think AI will eventually replace our current automation testing tools entirely?
How do we maintain AI solutons, for eg: the solution develop today can quickly get outdated in a month. So what is your suggestion to stay
What are the signs or metrics to monitor in order to declare a successful scaling of AI in QE?
Implementing AI is resulting in more work for the associates (review) in the project which is an alarming. How to mitigate this?
How do you validate or ensure the quality and reliability of AI-generated test cases?
What lessons have we learned from failed or stalled AI initiatives in testing?