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BONUS: AI Makes Starting Work Easy, But Flow Still Finishes It
AI is changing software teams, but Liz Rettig brings the conversation back to flow, strategy, and the discipline of finishing. In this BONUS episode, Vasco and Liz explore how Scrum Masters can use AI to make policies visible, help teams manage WIP, support Product Owners, and keep the organization focused on valuable slices instead of faster output alone.
From Anthropology To Kanban And AI
"As a Scrum Master, you're an anthropologist because you're watching people and you're recording how they are working."
Liz starts with her path from anthropology to Scrum Mastering, agile coaching, and professional Kanban training. That background matters because her work has always been about observing how people interact with systems. AI did not replace that skill. It made the system easier to see. In one client engagement, a team adopting an agentic framework also needed agile ways of working around that new capability. Liz saw that AI could help teams reach forecasting, strategy, and board-reading conversations much faster than before, as long as the human system was still designed with intent.
AI Helps Teams See Flow Earlier
"This is a tool that can help me get teams further quicker than ever before."
Liz describes AI as a practical accelerator for visibility. Instead of spending months collecting and cleaning data by hand, teams can use AI-enabled tools to surface the information already living in Jira, code repositories, customer feedback, and flow metrics systems. That changes the coaching conversation. Scrum Masters and agile coaches no longer need to persuade teams that the data could exist. They can bring the data into the room and ask what it means for the way work is moving.
Starting Is Free, Finishing Is Still The Discipline
"Starting is free with AI, and I get into that pickle all the time too."
The central warning from Liz is simple: AI makes it easier to start work, but it does not automatically make teams finish the right work. She connects this directly to Kanban practices: limit work in progress, watch aging work, make policies explicit, and help the team focus on moving existing items before pulling more into the system. Liz has used AI hooks, skills, commands, and team rules to remind people when they are about to break their own policies. The point is not to block people blindly. The point is to make the tradeoff visible, capture why the team chose to break a WIP limit, and bring that evidence into retrospectives.
Scrum Masters Can Put Team Policies Into The AI System
"Everybody has the ability to run it, and I expect everybody on the team to run it."
Liz does not keep the AI-enabled flow tools as a coaching black box. She works with teams to define their policies, then embeds those policies into the tools the team uses every day. A daily board walk can highlight aging work, blocked items, WIP issues, and the topics worth discussing in the daily review of flow or daily Scrum. This shifts the Scrum Master's work away from repeating reminders and toward helping the team interpret what they see. AI becomes a way to make the team's agreements visible and repeatable.
Leaders Still Need Outcome Conversations
"Everybody wants more faster."
Liz is clear that many organizations begin AI adoption with an output goal: more stories, more work items, more projects, less time. One client moved from roughly 75 items per month to around 400. That created a new and more important question: are those 400 items connected to the value the organization actually wants? Liz explains how AI helped reorganize work around objectives, key results, epics, and delivery data so leadership could see how throughput was being spent. The useful conversation was not "how much more did we do?" It was "how much of our capacity went to strategy, bugs, maintenance, and the work we said mattered?"
The Constraint Moves Left
"The constraint that we're finding is having the right work that aligns to the strategy ready in our backlog."
When delivery gets faster, the bottleneck often moves toward product strategy, customer discovery, and backlog readiness. Liz saw teams capable of shipping far more work than before, but the business was not always ready with the strategic work it wanted delivered. That makes Product Owners more important, not less. AI can help with the administrative side: checking whether a ticket already exists, turning customer conversations into well-structured work items, and keeping Jira cleaner. But Liz argues that Product Owners still need time to talk with customers, shape direction, and decide what is worth building.
Smaller Slices Matter More With AI
"Now we've just built the wrong thing faster."
Liz pushes back on the idea that AI makes big upfront specifications or waterfall thinking attractive again. If teams use AI to build large batches faster, they can also waste budget faster. Her answer is smaller valuable slices, delivered incrementally, with feedback built into the way the team works. She has used AI to help break epics into customer-facing slices rather than functional layers. That matters because the team can validate learning earlier, change direction cheaply, and keep the work connected to customer value.
About Liz Rettig
Liz Rettig is founder of Lil Agile Monster and a Professional Kanban Trainer. She helps teams use Kanban, flow metrics, and real data to improve predictability. Her work spans healthcare, insurance, marketing, portfolio Kanban, and AI-assisted delivery. LinkedIn: lizrettig-agilecoach.
You can link with Liz Rettig on LinkedIn, visit Lil Agile Monster, and explore Liz's Kanban Playbook.
Download audio: https://traffic.libsyn.com/secure/scrummastertoolbox/20261105_Liz_Rettig_Thu.mp3?dest-id=246429
