Scrum Master Toolbox Podcast: Agile storytelling from the trenches

BONUS: How Scrum Masters Can Use AI Without Becoming the Human API
 
AI is already changing the practical, everyday work of Scrum Masters and Agile Coaches. In this BONUS episode, Vasco talks with Fred Deichler about moving from curiosity to real AI-supported workflows: finding team signals faster, preparing better conversations, keeping documentation in sync, and staying focused on outcomes instead of just producing more output.
From Automation to an AI Sparring Partner
"I have this partner I can work with to help me ideate things."
 
Fred's journey into AI started before the current AI wave, with automation in Jira and the practical need to surface bottlenecks without manually watching every board. The turning point came when ChatGPT stopped feeling like a search box and started acting like a thinking partner. Faced with a team being pushed toward multiple sprint goals, Fred dumped the context into AI and asked for three to five options instead of one "right answer." That shift helped him move from a deterministic mindset into a problem-solving mindset, using AI to explore options, challenge his own assumptions, and prepare a better conversation with the team.
The Monday Morning AI Context Builder
"It instantly sets that context for me. It sets that tone for the whole week."
 
Fred describes his weekly workflow as a very practical use of AI: on Monday morning, he opens Cursor and works with his own AI harness, Atlas. Atlas is built from markdown files containing persona, skills, history, meeting transcripts, notes, and Jira data. When Fred says "good morning," the system pulls the most relevant signals forward: aging work items, backlog health, sprint goals, and where the next team conversation should focus. Instead of starting the week by hunting for data, Fred starts with questions he can bring into the first stand-up: what is stuck, what needs refinement, and what risk is already visible?
Making Flow Metrics Visible Inside Jira
"If you're on a page, what do you hope you could learn without asking?"
 
Beyond using AI as a thinking partner, Fred used AI to build a Chrome extension that surfaces useful Jira insights directly where the team already works. On the active sprint board, it shows work in progress, aging items, sprint goals, and sprint changes. In the backlog, it exposes backlog health and epic health. On the sprint report page, it adds cycle time per item so the retrospective can move from generic discussion to concrete learning. The point is not to shame the team with metrics. The point is to make the right conversation easier to start: what caused this item to take seven days, what blocked it, and what do we want to learn from that?
AI as Extra Eyes and Ears for Team Conversations
"It helps make sure that we don't lose sight of these things I can bring back to the team."
 
Fred also uses AI agents to review meeting transcripts and look for patterns that are easy to miss in the flow of daily work. The system can notice hesitation, unresolved topics, or a requirement problem that was mentioned once and never followed up. For retrospectives, Fred's Atlas setup can review sprint transcripts around day nine of the sprint and suggest themes for the next retro. It can even generate prompts for a visual retrospective board in Copilot or a Miro board from a prompt. This helps Fred avoid relying only on recency bias and creates a better starting point for the conversation the team actually needs.
Keep the Human in the Loop, Especially for Outcomes
"It's not about building a faster hammer. It's about identifying the outcome you're going for."
 
A major warning in the episode is that AI can make Scrum Masters faster at producing more of the same: more notes, more summaries, more documents, more reports. Fred argues that the useful question is not "can we automate this exact process?" but "what outcome are we trying to achieve?" In a Jira-to-Azure DevOps migration, for example, the goal was not to reproduce the current time-tracking process perfectly. The goal was to provide the information accounting needed. That outcome focus keeps the human accountable for direction, judgment, and value, while AI helps explore better ways to get there.
The SPOT Framework for Finding AI Opportunities
"If it meets three or four of those, this is a great opportunity for an automation to free me up to do that more human-centric work."
 
Fred uses a simple filter, the SPOT framework, to decide what AI should help with and what should remain human-led. A task is a good candidate when it is simple, predictable, observable, and tedious. Simple means it requires low human judgment and could be explained on an index card. Predictable means it happens repeatedly, either on a schedule or triggered by an event. Observable means the needed data is available to the agent and not locked away in someone's head or behind policy. Tedious means it consumes energy without adding much human value. When a task matches most of those criteria, Fred looks for ways AI can handle the toil while he stays focused on facilitation, coaching, and decision-making.
A Small Experiment Scrum Masters Can Try Tomorrow
"If a Scrum Master finds himself operating as, like, I'm moving data from point A to point B, that is a great opportunity to say, how might we do this?"
 
Fred's concrete advice is to start by noticing where you have become the "human API": moving data between tools, updating spreadsheets, copying information into reports, or keeping documents manually aligned with reality. Write down one tedious workflow and ask your approved work AI, "How might we automatically populate this, while keeping me in the loop?" Vasco adds a useful ideation tip: if the first five ideas are not good enough, ask for five more without repetition, then five more again. The goal is not to hand over accountability. The goal is to discover what might be possible and choose one safe, small experiment.
Resources for Scrum Masters Exploring AI
"It's all about building up your own idea of what's possible to start those how-might-we conversations."
 
For listeners who want to keep learning, Fred recommends The AI Daily Brief as a way to stay current with what is happening in AI, and Jack Roberts' YouTube channel for practical ideas around personal AI and agent-based workflows. Fred also points listeners to his own work at Triforce Agility, where he shares blog posts, resources, and updates about his talks. He will be speaking at the Kansas City Developers Conference in early September about AI and career progression, and at Scrum Day in Madison, Wisconsin in October about how AI impacts Scrum Master roles.
 
About Fred Deichler
 
Scrum Master, Agile Coach
 
You can link with Fred Deichler on LinkedIn and connect with Fred Deichler at Triforce Agility.

BONUS: How AI Took the Boring Out of Agile Coaching—Where Scrum Masters Should Actually Start, With Michael Dougherty
Today we speak with Michael Dougherty — "Agile Mike" — about how a 30-year veteran of solution development and product leadership made AI a working part of his Agile coaching practice. Michael walks us through the journey from early ChatGPT curiosity, to documentation as the first real win, to the personal-passion project that built his comfort with the tools, and finally to where every Scrum Master should start: the retrospective.
From Curiosity to Toolbox: A Slow Burn That Started With User Stories
"I tried, and I thought it sucked at that. I'd rather write this by hand."
 
Michael's AI journey started in late 2022 with ChatGPT — like most coaches, he tried to make it write user stories and acceptance criteria. It didn't work. The output wasn't usable, and he went back to writing them by hand. What looks like a failure was actually the first lesson: not every coaching task is a good fit for AI, and the way to find out is to try. He kept experimenting in the background through 2023 and 2024 — Claude, Grok, Perplexity — while keeping personal use and business use cleanly separated. The real shift came at the Department of Homeland Security, where he sat alongside ex-Googlers, ex-OpenAI people, and an Anthropic alumnus on the team building DHS's internal chatbot. Watching what AI could do inside a heavily-protected enterprise environment changed his frame: this wasn't a productivity hack anymore, it was infrastructure.
The Boring, Tedious, Now Quicker: Documentation as the First Real Win
"AI has taken all those boring, tedious tasks and made them quicker, more valuable, and more fun to boot."
 
The first AI practice that stuck was documentation — the part of Agile coaching nobody enjoys but every client demands. Michael had spent years writing SDLC docs from blank pages, and his co-author on Shift: From Product to People used to look at empty docs and ask for help getting started. AI removed that blank-page problem. He'd give the model the team context, the client's documentation requirements, and a template — and get a draft to edit. He stayed in the loop as the human, bringing context, judgment, and final shape, but he no longer started from zero. The same shift happened with presentations: instead of fighting PowerPoint spacing, he tells the AI to drop content into a template and gets back a formatted deck. Small tasks, but multiplied across a coaching week, they bought back real time.
The Personal Project Trick: Find Something You Love First
"Find something you have a passion with, something you really enjoy. That made me feel comfortable with AI so I could do more."
 
Michael's strongest advice for coaches who feel intimidated by AI isn't to start at work — it's to build something for yourself, in a domain you care about. His own example: the Nordic Metal Tour Tracker, a personal AI agent for tracking heavy metal bands across Northern Europe (and Spain and Portugal). It plays music. It maps tours by country. It exists purely because he loves heavy metal. The trick isn't the tool — it's that working on something he actually cared about removed the fear and built the muscle memory. Once the comfort was there, transferring it to work became a small step instead of a leap. He references the rundown AI as a daily input that keeps him aware of how others — even people in their 80s — are using AI for everything from gardening to balanced school lunches.
A Week With Preppy Paul: Agents, Connectors, and the End of Inbox Drudgery
"Preppy Paul gives me a list of all my meetings today and the top three items I should have on each one — based on my notes, my email, my calendar."
 
Michael's current weekly routine looks nothing like his Agile coaching week from three years ago. He works at Zion Cloud Solutions as a Tier 1 Google AI partner, splitting time across three roles: about 20–30% traditional Agile project management (coaching a Scrum Master on a state of Illinois project), and the rest as an AI Growth Engineer and AI delivery lead on short 4–6 week projects with 3–5 person teams. The backbone is Google Gemini Enterprise — a platform that lets him run Claude, ChatGPT, or any major model behind enterprise protection (Model Armor, the SAIF framework). Inside it, connectors pull in Gmail, Outlook, Calendar, Confluence, Jira, Slack, Teams, monday.com, and thousands of other tools — each agent inherits only the permissions the user already has. A few practical patterns he runs every week:
 
Preppy Paul — an agent that prepares him for the day's meetings, pulling top topics per meeting from his notes, email, and calendar.

Meeting-to-email — AI records and summarises every meeting, and he asks the agent to draft updates to specific team members directly from the summary.

LinkedIn drafts in his voice — hashtags, formatting, and tone all match his style; he edits lightly and posts.

 
The pattern across all of these: the agent isn't replacing the work. It's removing the friction between the meeting and the next conversation.
The Retrospective: The Best Place for Every Scrum Master to Start
"Don't have it be the same darn Jira spreadsheet of the same darn three questions every time. Go to AI and eat your heart out with it."
 
When pushed for one thing a Scrum Master could try tomorrow morning, Michael was clear: the retrospective. It's the one Scrum event where creativity is usually welcome, where format experimentation has a low cost, and where teams are already expecting something new. Ask AI for retrospective formats that fit the team's current situation — review five or ten options, pick one, run it. The signal you watch for within a week is double: how many improvements the team generated, and how many of them actually got done. The simpler signal is the team's reaction. If at the end someone says "that was fun" — and then you tell them you used AI to design the format — you've shown the team what AI is good for without a single slide or framework. It's the smallest possible experiment with the largest possible upside.
Experiment Like a Scientist, Not a Spectator
"Don't be afraid of it. Try whatever you can with it. Have the viewpoint of a scientist."
 
The trap Michael sees most coaches falling into is curiosity without commitment — reading about AI, watching demos, talking about it, but never building anything. His framing is the opposite: be a scientist. Run experiments. Some will fail (his user-story attempt did). Some will quietly become essential (documentation did). The way to find out which is which is to keep trying, in low-stakes contexts, with both personal projects and small work experiments. The coaches who'll be useful to their teams in 18 months aren't the ones who can describe AI accurately — they're the ones who've built enough small things to know what AI is bad at, what it's surprisingly good at, and where the human in the loop has to stay.
 
About Michael Dougherty
 
"Agile Mike" has over 30 years of experience with solution development and product leadership, working in nearly every IT role that exists and literally hundreds of companies during his career. Michael has taught multiple Agile courses to over 1000 people, spoken at multiple events and podcasts, written dozens of blogs, and has been recently serving the US Government. He is the other co-author of Shift: From Product to People.
 
You can link with Michael Dougherty on LinkedIn and find more about his work at shiftingpeople.com and AP8XGlobal.com.

Arun Parameswaran: Product Owners Who Create Better Product Conversations in Scrum
In this episode, we refer to Arun's AI prompt library for Scrum Masters and project managers.
The Great Product Owner: Curiosity, Clear Direction, and Outcome Focus
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"A great PO doesn't only ask, did we deliver? They ask, do people communicate openly?" - Arun Parameswaran
 
Arun describes great Product Owners as collaborative, curious, and close enough to the team to understand how delivery really works. The best POs he worked with did more than check whether items were delivered. They asked whether knowledge was distributed, whether people communicated openly, and whether everyone had a chance to contribute. They gave the team clear product direction, supported prioritization, managed stakeholders, and stayed available when the team needed context. Arun emphasizes outcome focus: value for the customer expressed in a way the team can understand. A strong PO becomes the bridge in both directions, helping stakeholders understand the team and helping the team understand the customer.
 
Self-reflection Question: Does your Product Owner help the team understand customer value, or only the next ticket?
The Bad Product Owner: Turning the Team Into a Ticket Factory
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"The Scrum Master shouldn't just help the PO write better tickets. We should help create better product conversations." - Arun Parameswaran
 
The anti-pattern Arun warns about is the Product Owner as ticket distributor. Stakeholders ask for something, the PO turns it into a ticket, and the team delivers without understanding the problem, the outcome, or why the work matters. Over time, the team becomes a delivery factory, and Scrum starts to look like waterfall with smaller batches. Arun's coaching move is to shift the conversation away from "better tickets" and toward better product thinking. What problem are we solving for customers? What outcome do we expect? What should we prioritize? What can we say no to? For Scrum Masters, this means coaching the PO and the team to bring customer context, stakeholder feedback, and prioritization into the same conversation.
 
Self-reflection Question: What product conversation is your team avoiding by hiding behind ticket writing?
 
[The Scrum Master Toolbox Podcast Recommends]
🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.
 
🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
 
Buy Now on Amazon
 
[The Scrum Master Toolbox Podcast Recommends]
 
About Arun Parameswaran
 
Arun is an Agile delivery leader at Bosch Global Software Technologies who builds high-performing, data-driven ecosystems. He blends flow metrics, Jira analytics, Power BI, and applied AI to turn delivery into a transparent, predictable system.
 
You can link with Arun Parameswaran on LinkedIn.
 
You can also explore Arun's AI prompt library for Scrum Masters and project managers.

Arun Parameswaran: Success for Scrum Masters Means Teams Ask Better Questions Without You
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"My goal isn't to become indispensable. My goal is to create a team that doesn't depend on me." - Arun Parameswaran
 
For Arun, Scrum Master success is not measured by how many meetings he runs or how often the team asks him for help. It is measured by the team's ability to solve problems without depending on him. If he has to remind everyone to update Jira, facilitate every difficult conversation, or resolve every obstacle, he is creating dependency. A healthier signal is when team members talk directly to each other: "This is blocked. Who can help me?" or "Let's finish this before starting something else." Arun compares the Scrum Master to a football coach watching the match from the side. The team needs space to play, make decisions, and learn. The Scrum Master still observes and coaches, but the goal is to intervene less often and more intentionally. As Vasco summarizes, this requires learning to wait before stepping in.
 
Self-reflection Question: What would your team do differently if you waited one more minute before intervening?
Featured Retrospective Format for the Week: What? So What? Now What?
Arun's favorite retrospective format is "What? So What? Now What?" because it moves the team from facts to meaning to action. "What happened?" helps the team describe reality. "So what?" asks why it matters. "Now what?" turns the discussion into a small experiment for the next sprint. Arun also avoids using the same format every time. He often shares the board two or three days before the retrospective so people can reflect before the meeting, add observations, and appreciate teammates. That preparation saves time in the session and makes space for a short fun activity before the team works through the real topics. His goal is simple: every retrospective should produce learning and at least one concrete experiment.
 
[The Scrum Master Toolbox Podcast Recommends]
🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.
 
🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
 
Buy Now on Amazon
 
[The Scrum Master Toolbox Podcast Recommends]
 
About Arun Parameswaran
 
Arun is an Agile delivery leader at Bosch Global Software Technologies who builds high-performing, data-driven ecosystems. He blends flow metrics, Jira analytics, Power BI, and applied AI to turn delivery into a transparent, predictable system.
 
You can link with Arun Parameswaran on LinkedIn.

Arun Parameswaran: Using Scrumban and WIP Limits to Help Agile Teams Find Focus
Read the full Show Notes and search through the world's largest audio library on Agile and Scrum directly on the Scrum Master Toolbox Podcast website: http://bit.ly/SMTP_ShowNotes.
 
"The WIP limit helped the developers not context switch." - Arun Parameswaran
 
Arun brought a practical challenge to the Wednesday coaching conversation: a team doing both operations and development work needed more visibility and focus than their Scrum board was giving them. The trigger was context switching. Work stayed "in progress" even when blocked, people picked up new topics before finishing old ones, and stakeholders kept asking what was actually happening. Arun helped the team experiment with Scrumban, keeping useful Scrum events while adding a Kanban-style board and explicit WIP limits. The breakthrough was not just visualizing the work. It was helping the team own the limits. They created an "operator of the week," rotating the responsibility for watching WIP and calling out when the team was taking on too much. That small practice made focus a team responsibility instead of a Scrum Master lecture.
 
Self-reflection Question: Who owns your team's WIP limits in practice, not just on the board?
 
[The Scrum Master Toolbox Podcast Recommends]
🔥In the ruthless world of fintech, success isn't just about innovation—it's about coaching!🔥
Angela thought she was just there to coach a team. But now, she's caught in the middle of a corporate espionage drama that could make or break the future of digital banking. Can she help the team regain their mojo and outwit their rivals, or will the competition crush their ambitions? As alliances shift and the pressure builds, one thing becomes clear: this isn't just about the product—it's about the people.
 
🚨 Will Angela's coaching be enough? Find out in Shift: From Product to People—the gripping story of high-stakes innovation and corporate intrigue.
 
Buy Now on Amazon
 
[The Scrum Master Toolbox Podcast Recommends]
 
About Arun Parameswaran
 
Arun is an Agile delivery leader at Bosch Global Software Technologies who builds high-performing, data-driven ecosystems. He blends flow metrics, Jira analytics, Power BI, and applied AI to turn delivery into a transparent, predictable system.
 
You can link with Arun Parameswaran on LinkedIn.