future of work Archives - Scrum Master Toolbox Podcast

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

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.

BONUS: AI Won’t Just Change How You Work — It Will Reshape Your Organization

The Global Agile Summit is around the corner, and the AI in Organizations track is one you don’t want to miss. In this episode, track co-hosts Michael Dougherty and Michał Parkoła walk us through what they’ve built — from the thinking behind the track name to the sessions that stood out, and why this isn’t just another AI conference lineup.

Why “AI in Organizations” — Not Just “AI”

“AI will not only be useful to existing organizations, but it will reshape organizations in a very significant way, the same way cars reshaped cities.”

Michael and Michał drew a deliberate line with the track name. Michael points out that AI has been around for decades — it didn’t start with ChatGPT. The real shift now is AI agents scaling to enterprise level, replacing automation that used to require specialized tools.

Claude Enterprise holds about 29% of the enterprise AI market, Gemini around 15%. But Michał pushes the framing further: the first-order effect is applying AI to existing work. The second-order effect — the one he’s most interested in — is how AI will reshape organizations themselves. New species of companies will emerge, smaller teams will achieve what used to require hundreds of people, and some existing organizations won’t survive the transition. That’s the conversation this track is designed to start.

Filtering the Signal From the Slop

“There was a bit of AI slop in the submissions. There was a lot of talk that, unfortunately, was meta-talk — there was no real value that I could glean.” Continue reading BONUS: AI Won’t Just Change How You Work — It Will Reshape Your Organization

AI-Assisted Coding: Why a Distinguished Engineer Stopped Reading Code — Lights-Out Codebases and the End of the IC

Philip Su has spent two decades at the highest levels of software engineering — Microsoft, Meta (where he reached Distinguished Engineer, IC9), OpenAI, and now building his own product solo with AI. In this episode, he makes a provocative case: the individual contributor role as we know it is over, code reviews are becoming a liability, and the best engineers are already managing AI agents instead of writing code themselves.

From Dirty Fuel to Clean Fuel — Philip’s Unusual Career Arc

“When I look at those employment opportunities, I feel like maybe one theme is moving from what people are calling dirty fuel into clean fuel — meaning, what sorts of things motivate you? Dirty fuel would be something like, my parents never complimented me and so now I’m going to drive and prove them wrong. Clean fuel would be, I’ve always loved computer science and I’m here to do it.”

Philip’s path through tech is anything but linear. He became a dev manager at Microsoft before 30, then spent the next decade comfortable but stagnating — until he interviewed at Google, Amazon, and Meta and received offers three levels below his Microsoft position. That fear drove him to Facebook in 2010, when it had just 500 employees and people were still saying Friendster would come back. He scaled the London engineering office from a dozen engineers to 500+, then stepped away entirely.

Depression led him to take a warehouse floor job at Amazon during Peak 2021 — 11-hour shifts, moving six tons of packages a day with his arms. He documented the experience in his Peak Salvation podcast. The lessons were sharp: incentives shape behavior (warehouse workers got raises based on tenure, not performance, which incentivized doing the minimum), changing machines that are already operating is brutally hard, and misplaced pride is dangerous. Workers on the floor laughed when robots failed. Philip’s takeaway was the opposite: “Do you think the corporate robot designers just gave up and went home? Or do you think V2 is coming and V3 is coming until finally it takes over your job?” He later joined OpenAI as an individual contributor before leaving to build Superphonic, an AI-powered podcast player.

No More Code Reviews: The Lights-Out Codebase

“I grew up in the 80s and 90s. Today, even Stockfish, free on your phone, can beat the world’s best chess masters. What if I proposed to you today that you should take that free software, and when you’re playing some other player, have a human chess master review every move, just to make sure the AI hasn’t made a mistake? I would think that was patently ridiculous.” Continue reading AI-Assisted Coding: Why a Distinguished Engineer Stopped Reading Code — Lights-Out Codebases and the End of the IC

When Boundaries Vanish: The Tokenization of Work and the Wisdom of Burnout

We are living in an era of fragmented identities. The modern individual is a complex mosaic, simultaneously inhabiting a multitude of roles: expert, content creator, CEO, parent, and self-improver. The late visionary Esko Kilpi aptly described how work has shifted from a place you go to a series of interactions within networks. This vision is becoming reality faster than we realize. Professional life is becoming “tokenized,” and the danger is that this new freedom is leading more of us toward the path of burnout.

The Collapse of Transaction Costs and the Fragmentation of Work

The structures of working life are undergoing a radical transformation. Previously, working hours defined by a contract and the physical location of an office created boundaries that were easy to follow. These long-term employment contracts acted as protective walls; within them, employees were shielded from the constant bidding wars of the work market because purchasing work in tiny increments was expensive and cumbersome for companies. In economics, this is referred to as high transaction costs.

However, technology is breaking down these walls. It is now easier than ever to dismantle large projects into micro-tasks and find a performer instantly via the web. We are witnessing a collapse in transaction costs, which inevitably leads to the fragmentation of work.

Fewer of us now sell our time in a single lump sum to one employer in exchange for a monthly salary and a title. Instead, we distribute our expertise in small pieces across various channels. One piece goes to a primary job, another to a side project, a third to maintaining a social media presence, and a fourth to managing the logistics of a modern blended family. We have become portfolio managers of our own energy. Our psychological and physical strength is a currency that we invest in different activities throughout the day. This fragmentation of work into small, independent tasks can be called the tokenization of work.

In the era of AI, this development is only accelerating. When AI agents can execute individual tasks in seconds, the friction between delegating and performing work almost entirely disappears. Work threatens to become a series of lightning-fast assignments, which reflects directly onto us as humans. Life turns into an endless stream of small performances, all competing for our attention every hour of the day.

Limited Credits – Threat or Opportunity?

Recently, I have been building applications using new no-code AI tools. The experience is addictive. You fall into a state of flow, and ideas soar as the application takes shape before your eyes with astonishing speed. Rapid progress opens the dopamine taps, and the hunger to do more grows.

Then, something unexpected happens. A notification appears on the screen: “You’ve hit today’s free limit of tokens.”

Most of these tools allow for a limited number of actions per day. Once they are used, you are forced to stop. I cannot continue, even if I want to. Initially, this is frustrating, but I soon realize that running out of tokens is a blessing in disguise. Because the “chips” run out, I don’t have time to get exhausted or bored. The activity remains meaningful because it has a clear endpoint.

This experience made me reflect on how such boundaries are missing from our fragmented professional lives. When tokenized work allows us to be “on” 24/7, we lose the natural sense of when it is time to rest. Quick hits of dopamine encourage us to keep going, leading us to inadvertently spend a month’s worth of energy in a single week. In the long run, this “overconsumption” leads straight to burnout.

The Wise Compass of the Psyche

At this juncture, the thoughts of Swiss psychiatrist C.G. Jung offer a valuable perspective. We tend to view burnout merely as a sign that the battery is empty and the tokens have been spent. From a Jungian perspective, however, burnout is a compensatory function of the psyche. The mind naturally strives for balance.

If our conscious mind blindly pushes in one direction—toward performance, efficiency, and omnipresence—the unconscious mind begins to generate a counterforce.

Burnout is the psyche’s intelligent way of pulling the emergency brake. It doesn’t just tell us about the quantity of our effort, but also its direction. An AI tool forces you to stop when the credits run out. Burnout forces us to stop when we have invested too much energy into things that are not aligned with our true purpose.

The danger is that we scatter our energy in tiny flecks: a message here, a like there, a quick meeting, a workout in between, and a LinkedIn post in the evening. At the end of the day, the balance is zero, even if nothing truly meaningful has been achieved.

Invest in a Deeper Purpose

If we think of our energy as finite credits, every choice is an investment. The constraints set by free tools teach us prioritization: when you know you only have a certain number of tokens per day, you want to invest them wisely.

From the perspective of the psyche, tokens should be invested in a way that builds a cohesive and meaningful whole. All living beings naturally orient themselves toward broader possibilities and the fulfillment of their own potential. For humans, belonging to a larger community or whole is also essential. Realizing these things, however, requires the courage to invest energy into stopping and building a broader perspective—even if, in the eyes of a world that worships efficiency, it looks like idleness.

A quiet moment without stimulation, a walk in the forest, a deep conversation with a friend, or simply being are investments in well-being. During these moments, the mind naturally shifts toward the “big picture,” revealing what is truly important.

In a tokenized working world, one of the most vital future skills is the ability to set those boundaries yourself—the ones that technology or modern work structures no longer provide.

Stop for a moment. Check your balance. Where do you want to invest the rest of today’s precious energy? Perhaps into something that keeps you well and helps you move toward your deeper purpose?

About the Author: Ari-Pekka Skarp is a psychologist, psychotherapist, and non-fiction author conducting doctoral research on nondualism. His work, “Mindfulness, mielenselkeys ja myötätunto” (Basam Books, 2023), is a profound survival guide for people in today’s digital society. He also hosts the popular “Mielen laboratorio” (Laboratory of the Mind) podcast. www.nondual.fi

BONUS: The Future of Agility, Insights from Industry Research with Simon Powers

Is Agile really dead? What does this wide research from industry reveal?

In this detailed, and insightful episode, we explore the current state and future of agility with Simon Powers, founder of The Deeper Change Academy. Simon shares insights from his comprehensive survey conducted between October and December 2024, which included 311 survey responses, 15 case studies, and 8 in-depth interviews with senior leaders.

The Current State of Agility

The research reveals that while there’s been a decline in traditional agile roles and certifications since October 2023, approximately 70% of organizations continue to invest in agile practices and transformations. However, these initiatives are often being rebranded and restructured, moving away from traditional “agile transformation” terminology.

“Organizations are still embracing agility and moving forward with change, but what they’re doing is that the names they’re using are changing. People aren’t necessarily being employed as agile coaches, but the agile coaching responsibilities are still required within organizations.”

Leadership Development and Organizational Structure

Continue reading BONUS: The Future of Agility, Insights from Industry Research with Simon Powers