Join Subba Lakshmi Ramaswamy as she shares how to adopt AI & GenAI in Quality Engineering with a practical Crawl, Walk, Run approach.
Discover how to start with AI foundations and pilots, scale models into existing strategies, and ultimately achieve AI-powered orchestration and continuous optimization.
Learn real-world strategies, avoid common pitfalls, and explore tools that accelerate AI adoption in testing while maximizing value at each stage.
Don’t miss out, book your free spot now
What’s the best first step for teams starting their GenAI journey in quality engineering?
In the early adoption phase of AI in testing, which core principles should QEs grasp first, and what myths or false assumptions typically slow them down?
During the “Crawl” phase, what are the key foundational AI concepts for QEs to understand, and what are common misconceptions hindering initial adoption?
Do you see testers becoming copilots for AI, or AI becoming copilots for testers? What skills do you think will matter most in QA five years from now?
If AI takes over repetitive QA tasks what will YOU focus on instead? How GenAI will evolve QA role?
What KPIs should be tracked to evaluate whether AI-driven automation is improving test coverage and defect detection?
What’s stopping you from using GenAI in your QA process today?
How do you ensure AI in testing stays ethical, explainable, and bias-free at scale?
With agentic AI, what are your thoughts on a team-based agent for consistency versus each team member having their own agent/style? Or a hybrid of a team-managed agent file referencing each individual’s style file?
What are the key considerations when evaluating and selecting AI/GenAI testing tools that will align with a team’s existing tech stack and long-term strategic goals (while remaining future-proofed)?
What should companies be doing today to reskill their QA teams for AI?
What mechanisms can be put in place to make sure that AI-driven test suites are constantly learning and adapting to changes in the app under test, without requiring significant human intervention?
What are some frequently encountered issues when integrating GenAI specifically into existing testing strategies, and what proactive measures can teams implement to mitigate those risks?
Looking beyond the “Run” stage, what do you see as the next frontier for GenAI in quality engineering? Is there a “Fly” stage?
At what point does GenAI become a core team member, not just a tool?
Looking ahead to the future of AI, GenAI, and agentic AI in quality engineering, what emerging trends or innovations, beyond those mentioned in the presentation are particularly exciting or impactful for the field?
What is the fastest way/best strategy to transform from QA to QE?
For teams struggling with the cultural shift required for AI/GenAI adoption, what strategies have been most effective in growing a culture of innovation, collaboration, and continuous learning amongst QE and dev teams?
How do you see the classic Build vs Buy options with dramatically fast changing AI landscape?
