How do we strengthen our foundational knowledge amid rapid AI growth?

How should we report on quality?

Analyse the risks of stagnant pull requests and adopt a Quality Engineering mindset to reduce technical debt and accelerate value delivery through developer-led testing and faster merge cycles.

Leverage AI as a personalised "code coach" to bridge the gap between manual testing and automation by translating plain English into executable scripts and providing line-by-line logic explanations.

Prioritise high-impact user journeys and critical risks over exhaustive checklists to ensure essential product functions remain reliable and user-focused through a product-minded testing approach.

How do AI guardrails deal with millions of possibilities?

Shift from treating AI as a "single source of truth" to using it as a "clarity booster" for human-led ideas.

Did things get worse when QA stopped being the quality gatekeeper?

Modernise your design strategy with mobile-first and keyboard-centric approaches that create more resilient, accessible, and user-friendly software.

Transform your team’s approach to quality from a late-stage gatekeeper process into a proactive, shared responsibility that identifies risks early.

Why showing up helps quality professionals acknowledge their continuous learning journey