BONUS How Scrum Masters Turn AI Into a Thinking Partner With Dave Westgarth

BONUS: How Scrum Masters Turn AI Into a Thinking Partner, Not a Magic Answer Box

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."