Join Ayoola Ogunsola, Bhupesh Mittal, Navneet Goyal, Trincy Thomas, and Zeba Khan as they discuss how AI, automation, and speed are reshaping Quality Engineering strategies.
Discover how teams are adapting to continuous delivery, the real impact of AI on test engineering, and the evolving role of automation in modern pipelines, all while keeping quality at the center.
Don’t miss out, book your free spot now
How can organizations strategically leverage AI to elevate the role of Quality Engineers, enabling them to focus on higher-value activities and innovation?
How can organizations measure the effectiveness and ROI of their AI-driven QE strategies, weighing both quantitative metrics and qualitative impacts on team productivity and software quality?
Beyond hype, where have you seen AI deliver tangible improvements in testing efficiency?
How do we futre-proof our automation and testing architectures for quantum computing, more centralized agentic AI, and ongoing regulatory change?
How can orgs overcome the challenges of integrating AI and automation with legacy systems and traditional QA practices to make sure of a smooth transition to a more intelligent and balanced QE approach?
How will the shift to AI-powered QE influence the traditional balance between speed and quality in software development, and how can organizations strike the right balance for optimal outcomes?
Are we feeding our AI test models with real-world edge cases or just happy paths and documentation dreams?
How do you ensure your ETL processes are accurate so the rest of your testing has correct data to work with?
AI is highly effective for web and API test automation. But how well can it address challenges like knowledge graph validity or data lake validity?
What qualities make human testers irreplaceable in an AI-driven QA process?
Will AI ever truly replace exploratory testers?
As test automation platforms become more “intelligent,” how do architects ensure modularity, auditability, and reusability across microservices and event-driven architectures?
What architectural considerations are needed to ensure AI-enhanced QE can adapt in real time to changing business priorities and regulatory constraints
How do we measure QE effectiveness when speed, automation, and AI are all changing the traditional definitions of coverage and quality?
What new skills or mindsets should QE professionals develop to stay relevant in an AI-driven, automation-first world?
What’s the biggest shift QE teams need to make right now to keep up with AI and automation?
How are forward-looking enterprises aligning their architectural roadmaps to both deliver today’s outcomes and anticipate tomorrow’s demands around autonomous testing and adaptive quality engineering?
With the increasing complexity of software ecosystems, how can AI be used to optimize test coverage and prioritize testing efforts to maximize efficiency and minimize risks (to achieve both trust and quality)?
At an individual level, what can SDET/ QA Automation engineers do to upskill in the AI-space in order to prepare ourselves for future?
