Join Amanda, QA leader and practitioner, as she shares a 5-step framework for practical automation adoption, from strategy and tool choices to building sustainable processes that fit real-world constraints.
Gain insights on staffing models, test selection, and lessons learned from actual implementations to drive quality and efficiency with both code-based and AI-driven solutions.
Don’t miss out, book your free spot now
Can you share examples where AI-driven no-code solutions have measurably improved test coverage?
With AI, is there any benefit to having a team AI agent versus each team member having their own personal agent geared towards their abilities?
What are the key bottlenecks or inefficiencies introduced by relying solely on a team AI agent versus empowering individuals with their own agents?
What metrics or KPIs should be tracked to measure success?
Are you measuring the effectiveness of your tests or just their execution speed?
In the playbook context, how does AI help prioritize which tests to automate first versus which to leave manual?
How will AI change the very definition of what we consider ‘automation best practices’ in the next 3–5 years?
Between speed/simplicity (No-code/Low-code) and control/customization, what technical criteria would you prioritize to evaluate and select the “best approach”?
When using AI for automated test case generation, what technical mechanisms would be crucial to make sure of adequate test coverage?
How would you design and technically implement an automated visual testing solution, potentially leveraging AI, to ensure UI consistency and catch subtle visual regressions across different browsers and devices?
What’s the most common mistake teams make when they try to scale automation?
When a team is choosing between a traditional code-based and an AI-driven no-code solution, what is the single most important factor they should consider that is often
You mentioned navigating ‘real organizational constraints vs. textbook ideals.’ Could you share an example of a time when a ‘textbook perfect’ automation strategy failed because of a real-world constraint, and what the more practical, successful sol?
What is the key process or habit a team needs to establish to ensure their automation suite is sustainable and doesn’t get abandoned after the initial enthusiasm fades?
How do you decide between traditional code-based automation and AI-driven no-code tools?
How much time does automation testing take to identify a functional problem compared to manual testing?
What kinds of Project Management approaches might be necessary to encourage team members new and old to switch to these more efficient Development processes?
What key metrics does a tool like PromptFoo allow an organization to have insight into?
From your perspective, how do you make the transition from manual automation testing? What should you learn first and then thereafter?
