“We have twelve scrum masters. Why is nothing working?” If you have ever asked some version of that question, this episode is for you.

Kate Megaw, Anu Smalley and Ryan Smith dig into the challenge they hear in almost every class they teach, a real lack of role clarity. Companies send a strong project manager to a two day course, hand them a new title, and then wonder why nothing changes. The honest answer is that nobody ever defined what the role is actually accountable for. This episode unpacks the difference between roles and accountabilities, why a scrum master is not optional, and how to define seats around outcomes instead of titles. Along the way Kate and Anu have a friendly fight over RACI, whether it hardens into swim lanes or serves as a living guiding light, and what responsible really means at a startup versus a large enterprise.

What we cover:

• “We have twelve scrum masters, why is nothing working?”
• Roles vs. accountabilities, and why the org chart lies
• The RACI debate: swim lanes vs. guiding light
• The kitchen sink job description with 45 impossible duties
• Start with outcomes, not titles, and revisit at every kickoff

Everything is a fire. Everything is priority number one. And by tomorrow, the number one priority has changed again. Sound familiar?

In this episode, Kate Megaw, Anu Smalley, and Ryan Smith dig into the challenge they hear at almost every client and leadership class: a real lack of prioritization. Not just inside the sprint, but across the whole organization, where teams get handed a brand new top priority every single day.

When everything is important, nothing is important. Constant reprioritizing whipsaws teams, burns people out, and leaves a trail of half-finished work and rising tech debt. Jerry Weinberg's research found you can lose 20 to 40 percent of productivity every single time you switch context, so three projects can leave you down 60 to 80 percent.

In this episode, we discuss:

• Why a lack of prioritization is really a sign that your stakeholders are not aligned
• The real cost: burnout, rework, tech debt, lost innovation, and the context-switching tax
• Why this is a leadership problem, not a team problem, and why the team always gets blamed
• Using the sprint to hold the line and protect work the team has committed to
• Emergent requests as a better signal than velocity for how often the team gets interrupted
• MoSCoW for sorting the must-haves from the nice-to-haves
• The 20/20 approach from Innovation Games for a truly ordered backlog
• The impact-effort matrix for spotting quick wins and killing low-value work
• Buy-a-feature with stakeholders and a limited budget
• The wins on the board debate: put easy wins up first, or dig into why the big thing is big

Every time someone says yes, it consumes time, money, and attention. Prioritization is the discipline of protecting all three.

Referenced in this episode: Jerry Weinberg's research on the cost of context switching, the 20/20 prioritization method from Innovation Games, the MoSCoW method, and the Eisenhower impact-effort matrix.

AI can generate an answer in seconds. The harder question is whether it is the right answer to the right question, and what you actually do with it.

In this episode, Kate Megaw, Anu Smalley, and Ryan Smith dig into what “human in the loop” really means, and why so many AI transformations are failing. Forbes puts enterprise generative AI failure near 95%, and RAND says more than 80% of AI projects miss. The pattern echoes the early Agile years: chasing a shiny tool without knowing what problem it solves.

AI sees the data. Humans see the story behind it. The human brings context, ethics, and judgment, and stays the ethical guardian who catches the hallucination and the answer that is right for the wrong reasons.

In this episode, we discuss:

• The human algorithm - turning AI outputs into real outcomes through context, ethics, and judgment
• Why AI sees the data but only humans see the story behind it
• Anu’s five workflow principles for human-led AI, including protecting the retro and naming a human decision owner for every recommendation
• Why so many AI transformations fail, and how it mirrors the early Agile years
• AI-enabled vs. AI-native organizations, and why native wins
• Using AI as a tool versus trusting it to run the business
• Choosing the right tool for the job instead of defaulting to one model for everything
• The ethical guardian role - catching not just what AI gets wrong, but what it gets right for the wrong reasons
• Knowing when to trust AI, when to challenge it, and when to override it

Just because AI can do something does not mean it should. That is where humans come in. We are not using AI to replace thinking. We are creating more space for higher quality thinking for the human in the loop.

Referenced in this episode: the documentary How I Became an Apocalyptimist (Daniel Rohrer), the Conan O’Brien podcast on how tools change but the task doesn’t, the New York Times feature on Box adding AI roles, and the AI-native shift discussed at the Miro Canvas conference.