Maturity is an underlying problem with AI adoption strategies, many teams just aren't ready for using AI and moving at pace. AI lets you go faster, but if your engineering standards and quality practices are already immature and bad this means you’ll be doing bad things faster! Teams need to prove out that they can do things properly before they add in AI or risk real stability issues in their quest to go faster and do more.
In this talk I’ll use my experience as a seasoned tester and a QE that’s working to strategise and help teams to safely implement AI to go through my observations and show why many teams should hold back on its use. I’ll give some tough love and honest feedback on where many teams are at and why going faster through AI just wouldn’t be helpful.
Following this, based on discussions with CTOs, Architects and Software Engineers as well as review of the DORA state of AI assisted software delivery report, I’ll explain what is needed in order to build and prove out maturity in engineering (and quality engineering) including where software testers can really help.
Attendees will be challenged to change their thinking around how they think about adopting AI and preparing their teams to get ready for it. Focusing more on becoming a trusted reviewer, codifying standards and even considering turning the testing pyramid on its head.