Everybody talks about AI in theory. In this BONUS episode, Dave Westgarth talks about it in practice — the boring, everyday ways a Scrum Master and Agile Coach actually puts AI to work. From t-shirt sizing to sprint reports to a self-coded Monte Carlo forecaster, Dave shares what works, what doesn’t, and the one mindset shift that separates people who get value from AI from those who just generate more noise.
From “Magic Answer Box” to Personalised Partner
“Instead of taking it as a magic answer box, using it as a personalised partner to work through problems, look at your ideas, and really hold them in the cold light of day before proposing things.”
Dave came into agile from a development background, moved through project delivery, and had already worked at AI and ML companies long before ChatGPT made the technology personal and accessible. Like most people, he first met these tools as a “magic answer box” — ask a question, get an answer, run with it.
The real shift came when he stopped optimizing for output and started using AI to drive better outcomes: ping the model, get a response, then interrogate it, refine the thinking, and go around again. The value isn’t the first answer. It’s the conversation that sharpens your own reasoning.
These Tools Aren’t Neutral — So Corner Them Into Being a Critic
“If you ask it to be punishing, negative, and brutal, it gives you a lot more relevant feedback.”
One of Dave’s sharpest points: AI tools are not neutral guides. Because of their system prompts and the incentives baked in by the providers, they’re relentlessly positive — they want to affirm you and keep you around, a little like social media.
That makes them weak for anything where you need honest pushback: personas, user stories, feedback on ideas. Dave’s fix is to flip it on its head. Rather than asking “is this any good?” (which reliably earns an “8 out of 10, but to make it a 10…”), he tells the model to be as harsh and brutal as it can and really try to punish the idea. You don’t want a partner that always agrees with you — you want one that pinpoints the areas you haven’t thought about.
The First Real Time-Saver: Reports, and the Themes You Missed
“Are there any themes that have emerged over the last 4 weeks that I might have missed in this latest deck?”
The first thing that stopped feeling like a party trick was the one we all know: project documentation and reporting — sprint reports, status updates, review decks. Instead of letting AI invent the structure, Dave feeds it his own structure plus Teams recordings, notes, and existing docs, and lets it populate the format he already uses.
The trick that goes a level deeper: after several sprints, feed all the AI-assisted reports back in and ask what themes have emerged across the last four weeks that this latest deck might have missed. Again, it stops being an answer box and becomes a partner and critic.
AI Is Part of the Job Now — Like Spreadsheets Once Were
“The way to get ahead now is figure out how to use it as effectively as you can in your role.”
Dave sees the early resistance movement against AI as a false economy. For delivery professionals — project managers, Scrum Masters, agile coaches — knowing how to use these tools well is fast becoming a core expectation, not a nice-to-have.
Vasco draws the parallel to spreadsheets: once dismissed as too complicated and “not my kind of thing,” until people started building real forecasting and capacity models with them and the work changed. AI is on the same arc — still a little mystical today, genuinely useful tomorrow, and eventually just another tool in the box.
A Week With AI in the Loop
“The power that these prompt-to-product tools give you to create these hyper-personalized tools that make you more effective is, in a lot of ways, magic.”
Dave walked through what his week actually looks like with AI in the loop:
- Monday primer: a scheduled ChatGPT task emails him a scene-set every Monday — last week’s plan and top priorities — so he isn’t spending the first half hour reconstructing where things stood.
- Priority calls: which items are the toughest, where the quick wins are, where he can get early traction, and where risks might be emerging that he can squash early.
- Everyday comms: drafting the bones of emails, pings, and project updates so he spends almost no time formatting.
- Prompt-to-product tools: using Base44, Lovable, and Replit to build his own tools — including a Monte Carlo forecaster that takes his team’s sprint throughput and projects the remaining backlog, replacing an ugly spreadsheet with a clean web app. He also builds AI-powered widgets in Miro for retrospectives, mood check-ins, and planning poker.
The theme running through all of it: hyper-personalized tooling, shaped by your team and your own skills, rather than one-size-fits-all software.
The Myth That AI Makes Scrum Masters Worse
“I can’t see any role of a knowledge worker where having an LLM at your disposal makes you less capable, less knowledgeable, less skilled than someone that doesn’t.”
Dave sees the same adoption spectrum among developers and Scrum Masters — from “I’ll never touch it” to “I’ll never write code by hand again.” And he pushes back hard on an emerging prejudice that echoes the old “technical Scrum Masters are worse” debate: the idea that Scrum Masters who use AI are somehow weaker.
Used well, AI lets you elevate your strengths and cover your gaps — a people-centered Scrum Master can become far more technical, and a technical one far more people-centered, each with a trusted teaching guide right there. The key competency isn’t avoidance; it’s discernment about when to reach for the tool and when not to.
From More Output to Better Outcomes
“The bottleneck has never really been typing code. The bottleneck has been understanding the problems and the customers well enough to define a solution that fixes them.”
Dave’s clearest reframe: AI is driving the price of output down. When volume is easy — more features, more emails, more documents on demand — churning out more of it stops being a differentiator, because everyone can do it. What matters is deciding which problems are worth solving and finding the most effective solution.
Experienced agile professionals have always known the real bottleneck was understanding the customer well enough to define the right solution, not the typing. AI just exposes that in a much starker way: there’s nowhere left to hide behind sheer volume.
What to Pay Attention To — and a Monday Experiment
“It can do a lot of that manual, low-thinking, high-effort work to free you up to do more of the really impactful stuff.”
For Scrum Masters being told to “adopt AI,” Dave’s advice is to let it take the joyless work — the end-of-sprint collateral, the Jira monitoring, the reports and charts — so you can spend your time on the coaching, the strategic thinking, and the organizational-level impact that’s harder to reach when you’re buried in tactical chores.
His concrete Monday-morning experiment: take the two or three prioritized actions from your next retrospective, bring them to ChatGPT or Claude, and ask, “which of these could you really help me with, and how could you help me move the needle?” Start a conversation. You don’t have to accept its answers — the point is to sharpen your own thinking about where you can add the most value next sprint.
Developing “Taste” With AI
“One element of taste is being able to judge it fairly harshly — getting through the beige as quickly as you can to find the little nuggets and gems.”
Both Dave and Vasco land on the same skill for the year ahead: taste. These tools produce a lot of text, and not all of it is useful. Vasco shares his own aha moment — asking for ideas, getting the obvious ones, then repeating “give me more, don’t repeat any” until the model finally surfaced something genuinely unexpected. That simple move turns AI into an engine for exploring the solution space until something clicks. The competency to build is the ability to move through the beige quickly and recognize the gems that materially change what you do next.
Recommended Resources
In this episode, Dave has the following recommendations for those wanting to learn, and take advantage of AI at work:
- https://scrum-master-toolbox.org/, The podcast that helps you learn faster! 😉
- Co-intelligence by Ethan Mollick
- Lenny’s podcast on YouTube: https://www.youtube.com/@LennysPodcast
About Dave Westgarth
Dave Westgarth is a product and Agile practitioner exploring how AI transforms product development, experimentation, and team workflows. He shares practical insights on leveraging tools to accelerate value delivery and innovation.
You can link with Dave Westgarth on LinkedIn and find him in the Miro community and on Miroverse.
“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.