By now, AI is already becoming mainstream in software development (right?), but are we focusing on the right areas? Developers now write code faster than ever with AI-powered tools like Copilot, yet meetings, requirements engineering, and testing remain time-consuming bottlenecks. What if AI could optimize these less exciting aspects of the Software Development Lifecycle (SDLC) instead?

Take a nurse as an example. She wants to spend more time with patients, yet much of her day is spent on administrative tasks behind a computer. Yet we focus on optimizing her time with patients, while we should focus on reducing the digital overhead instead? The same applies to software teams: developers want to build great software, product owners want to shape the vision, and testers want to ensure quality. All without being bogged down by tedious work.

In this session, we’ll explore the AI-Powered SDLC, moving beyond just coding assistance. We do a quick round through all the steps of the SDLC and how AI can help us to be better, faster and ... less bored by tedious tasks.

You will learn:

• Explain the limitations of focusing AI adoption solely on coding
• Identify opportunities to apply AI across the entire Software Development Lifecycle
• Reframe AI as an enabler of impact rather than speed