Join Daniel Roe, Karan M V , Maaret Pyhäjärvi, and Ram Iyengar for AI and Community: Shifting Roles, Rising Impact. This panel explores how AI is transforming the role of communities, reshaping collaboration, knowledge sharing, and the way developers, testers, and leaders engage.
Hear diverse perspectives from industry leaders on the opportunities AI unlocks for community growth, the challenges it brings, and how it’s redefining influence in the tech ecosystem.
Save your free spot now and be part of the conversation on the future of AI and communities!
How AI will impact different QA roles like manual, automation engineer etc?
Will AI replace software engineers in the next 10 years
What are the risks of over-reliance on AI in quality assurance communities?
AI handling more of the repetitive scripting and regression testing, what new responsibilities and skills should QA engineers prioritize
Where AI can be implemented - apart from Testdata and Testcase generation in testing?
Can AI-driven insights help communities identify and fix flaky tests faster?
While a decade back, “Automation” was seen as a potential threat to the craft of Testing. Now its AI! How do you see the “Testing Craft” still claiming its value in the age of AI? Especially Exploratory Testing
What are the ethical considerations of using AI for test automation in open communities?
How can AI Testing ensure more quality ?
How can AI and community-driven platforms together redefine the future of continuous testing?
Can anyone answer how does one measure the accuracy of the AI outcomes?
How does AI reduce repetitive manual checks, freeing testers for higher-value work?
How can AI-powered analytics foster collaboration among distributed QA teams?
Looking ahead, I see AI evolving as a true companion to engineers, potentially taking on around 30% of the workload. My question is: how do you envision engineers utilizing the remaining 70% of their bandwidth in such a future?
There are new AI tools everyday, how can the QA Team navigate through them to define which are worth implementing?
As AI takes over repetitive tasks, what new skillsets should community members focus on to stay relevant and valuable?
What are the top AI-powered tools or technologies in software testing that are most in-demand right now, and which ones should I prioritize learning to stay relevant in the market?
What are the ethical considerations and potential biases that testers and their management need to be aware of when implementing AI-based testing solutions, and how can they mitigate these risks to maintain or build trust in the testing process?
How should QAs approach testing vibe coded projects?
