The Nevada Agile and Scrum User Group

Everyone with a LinkedIn account has declared agile dead because of AI. They're confusing an industry that collapsed with the discipline that didn't. This session takes the "agile is dead" argument seriously, concedes what's true, and then shows why the teams getting real speed from AI are the ones that kept the discipline and sped up the loop.

Takeaways:

• why more code hasn't meant more shipped software;
• the five things AI needs that agile teams already do;
• the one mistake in every "agile is dead" post.

For: Scrum Masters, developers, POs, and anyone who's been told their skills are obsolete.

The gap between an average team and an exceptional one isn't 20%. It's often a factor of five, and it isn't talent. Jim Schiel walks through the argument of his new book: what actually separates 5X teams from the rest, and what leaders can change to close the gap.

Takeaways:

• the TeamOS framework in one session;
• why most "high-performing team" advice targets the wrong variables;
• what to change first.

For: team leads, engineering managers, and Scrum Masters who want a model, not a pep talk.

For managers. Your organization spent years and real money on agile. Then AI arrived, the coaches got laid off, and someone told you it was all sunk cost. It wasn't. This session is for the people who signed the checks: what the layoffs actually measured, why the companies that got slower with AI are the ones that dropped the discipline, and what to do with the capability you already built.

Takeaways:

• the difference between the agile industry and the agile discipline;
• why AI moves the how-we-work decisions to the team;
• what to keep, what to drop.

For: engineering managers, directors, and VPs. Members are encouraged to bring theirs.

McKinsey's 2026 numbers: AI tools raised coding activity by 180% and shipped releases by 30%, and in nearly a third of companies delivery got slower. This session is about the 150% that vanished: where it goes, why it isn't the tool's fault, and what teams that don't lose it do differently.

Takeaways:

• where AI-generated work piles up;
• why review, testing, and decisions are now the constraint;
• the operating changes that turn code into releases.

For: developers, tech leads, and anyone who adopted an AI coding tool and can't explain why nothing got faster.