AI was supposed to take work off our plates. Instead, many teams feel busier, more fragmented, and more exhausted than before. This session starts with the tension a lot of leaders are already feeling: if AI is driving productivity gains, why does work still feel heavier, and why are so many teams closer to burnout, not further from it? We'll open up a guided group exploration into the key drivers and root causes of team exhaustion, giving participants space to share real-world examples from their own teams as we go. Together, we'll unpack where that pressure is actually coming from - not just the usual causes of burnout, but the newer forms AI is introducing, such as rapid prototyping sprawl, constant context-switching, decision fatigue and the quiet pressure to always be “AI-enabled”. We’ll look at how these dynamics impact architectural health and team cognitive load are showing up across teams, placing special emphasis on how these friction points make it harder for team members to do their best work, even when they don’t fully burn out. You’ll leave with a clearer way to spot the difference between useful AI adoption and burnout by automation. Expect a practical framework for identifying where AI is helping, where it’s adding invisible workload, and what to change so your team gets the gains without paying for them in exhaustion. Key Takeaways: 1) Identify hidden AI-driven workloads, architectural friction, and systemic drivers of team exhaustion. 2) Diagnose how tool sprawl and rapid AI adoption impact cognitive load and team health across your codebase and organisation. 3) Walk away with concrete, practical, actionable strategies to protect engineering focus, guide sustainable AI usage, and improve team sustainability without sacrificing velocity.