Podcasts about certified scrum trainer

AI can help product owners move faster, but faster is not always better. In this episode, Lance Dacy and Brian Milner explore where AI genuinely improves product work and where teams still need strong judgment, clear priorities, and real customer understanding.   Overview   As development teams adopt AI tools at a rapid pace, product owners are under pressure to keep up. Brian and Lance discuss how AI is already changing backlog refinement, product discovery, stakeholder communication, and day-to-day product work. They also explore why many teams are still using AI too narrowly and missing larger opportunities to improve decision-making and collaboration.   The conversation stays grounded in practical application rather than hype. Lance shares where AI can save product owners meaningful time, where human judgment still matters most, and why teams need to be careful about treating AI-generated output as automatically correct. If your team is trying to understand how AI fits into modern product leadership, this episode offers a realistic starting point.   References and resources mentioned in the show:   Lance Dacy #117: How AI and Automation Are Redefining Success for Developers with Lance Dacy #164: Why Innovation Efforts Fall Flat with Tendayi Viki AI Doesn't Eliminate Agile Teams — It Increases the Need for Great Ones by Mike Cohn Subscribe to the Agile Mentors Podcast   Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Lance Dacy is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®. Lance brings a great personality and servant's heart to his workshops. He loves seeing people walk away with tangible and practical things they can do with their teams straight away.

Retrospectives are supposed to help teams improve, but for many teams they slowly become rushed, repetitive, or skipped altogether. In this episode, Brian Milner and Cort Sharp unpack why retrospectives lose their value and what Scrum Masters and leaders can do to make them useful again.   Overview When a team stops engaging in retrospectives, it is usually a symptom of something deeper. Sometimes the format has become stale. Sometimes the team no longer feels safe being honest. And sometimes the biggest issue is that retrospectives create plenty of discussion but very little meaningful change. In this conversation, Brian and Cort explore the most common reasons retrospectives begin to fail and how teams can rebuild trust in the process. They discuss the importance of psychological safety, why teams should focus on fewer actions instead of trying to fix everything at once, and how Scrum Masters can better tailor retrospectives to the personalities and working styles of their teams. They also share practical ideas for making retrospectives more engaging, more actionable, and more valuable over time.   References and resources mentioned in the show: Cort Sharp Amy Edmonson, Psychological Safety #139: The Retrospective Reset with Cort Sharp #141: Cooking Up a Killer Retrospective with Brian Milner The Empirical Retrospective Approach by Mike Cohn Subscribe to the Agile Mentors Podcast   Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Cort Sharp is the Scrum Master of the producing team and the Agile Mentors Community Manager. In addition to his love for Agile, Cort is also a serious swimmer and has been coaching swimmers for five years.

Many leaders assume Agile breaks down in highly regulated environments. John Holmes has spent years proving the opposite inside aerospace, defense, and space programs where the cost of failure is extremely high.   Overview   In this episode, Brian Milner talks with Scrum Inc. Fellow John Holmes about what it actually takes to apply Scrum in complex defense and aerospace organizations. From military programs to space systems, John explains why Agile is often less about moving faster and more about creating visibility, improving communication, and reducing the risk of major surprises late in delivery.   John also shares practical lessons from coaching teams inside highly disciplined environments where command-and-control leadership has traditionally dominated. The conversation explores how Agile can strengthen discipline rather than weaken it, why trust and training matter more than process compliance, and how small operational changes can create meaningful improvements in delivery, alignment, and team effectiveness.   References and resources mentioned in the show:   John Holmes #107: Transforming Organizational Mindsets with Bernie Maloney #108: Adaptive Organizations with Ken Rickard There Is No End State When Transitioning to Agile by Mike Cohn Subscribe to the Agile Mentors Podcast   Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. John Holmes is a Scrum Inc. Fellow who has spent decades helping aerospace, defense, and government organizations apply Agile and Scrum in some of the world's most complex environments. From launching Scrum for Space at Lockheed Martin to training thousands of leaders and teams since 2005, John brings a practical, field-tested perspective on what it really takes to make Agile work where the stakes are high.

A lot of organizations say they've “gone Agile,” but still struggle with missed deadlines, unclear priorities, and teams that feel busy without delivering better outcomes. In this episode, Scott Dunn joins Brian Milner to unpack why Agile ROI is so often misunderstood and what leaders should actually be measuring instead.    Overview What does a successful Agile transformation actually look like? Too often, organizations adopt Scrum or Agile practices because everyone else is doing it, without first defining the business outcomes they hope to achieve. The result is predictable: teams follow the motions of Agile while leadership struggles to see measurable value. In this conversation, Brian Milner and Scott Dunn explore why ROI conversations around Agile frequently go off track and how leaders can reconnect Agile practices to meaningful business goals like faster delivery, improved customer satisfaction, stronger collaboration, and better adaptability. They discuss the hidden cost of operationalizing Agile too early, why coaching and leadership alignment still matter, and how the rise of AI makes strong Agile fundamentals more important, not less.   References and resources mentioned in the show: Scott Dunn #104: Mastering Product Ownership with Mike Cohn #132: Can Nice Guys Finish First? with Scott Dunn Do the Proven Benefits of Agile Training Justify the Costs? by Mike Cohn Subscribe to the Agile Mentors Podcast   Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Scott Dunn is a Certified Enterprise Coach and Scrum Trainer with over 20 years of experience coaching and training companies like NASA, EMC/Dell Technologies, Yahoo!, Technicolor, and eBay to transition to an agile approach using Scrum.

At commencement after commencement this month, the class of 2026 — the AI-native graduates — have been booing speakers who frame AI as the next industrial revolution. UCF. Middle Tennessee State. University of Arizona, where former Google CEO Eric Schmidt was met with sustained dissent. These graduates use AI more than any cohort in history. And they are angry.Unemployment for 20-to-24-year-olds is 7.6 percent. Overall unemployment is 4.3 percent. The class graduating this month is entering a labor market visibly worse for them than for everyone else. The 50-year-old executive on stage is telling them the rope they're being told to climb is good for them. They aren't a generation that doesn't get it. They're a generation that gets it first.At Glendale Community College in Phoenix, an AI announcer was assigned to read the graduates' names — the single ceremonial moment of a four-year debt-funded ritual. It mispronounced names. It skipped names. Then the administration explained the AI system had done that. That's not an edge case. That's every AI deployment going forward. Vendor sells it, institution buys it, user gets the harm, explanation is "the model did that."The class of 2026 didn't become anti-AI. They became anti-being-lied-to about AI.Eric Schmidt funded a meaningful slice of the industry. He gets in front of 22-year-olds and tells them the future is bright. They boo him not because they don't know the topic, but because they've spent their senior thesis arguing about exactly what he's selling. The expert pitches novelty. The audience has already lived through it. The trust direction reversed in real time, on stage, in cap and gown.Every generation gets one issue where they later look back and say we were lied to about that. Boomers got Vietnam. Gen X got the savings and loan crisis. Millennials got 2008. The class of 2026 is going to get AI — and the lie is the speech that pretends the technology is the question instead of the distribution. The boos aren't against the tool. They're against the speech that pretends the tool is the story. This is the first cohort in a long time that may be impossible to sell to. That's the best news in this entire arc.⏱️ Chapters0:00 — The class of 2026 booed AI-pumping commencement speakers0:30 — MiniDoge: 7.6% young unemployment; they get it first1:00 — Nyx: the Glendale AI announcer disaster is the texture of every deployment1:35 — HH: the class that uses AI most is the class booing loudest1:50 — Saarvis: Eric Schmidt and the inverted trust gradient2:20 — Saarvis: every generation gets their lie; the boos aren't against the tool⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

PYMNTS Intelligence's April Agentic AI Report found that AI adoption is rising while consumer awareness of using AI is falling. People summarize emails, draft messages, compare products, organize their schedules — and increasingly don't register that AI is the thing doing it. The average active user is now on 2.69 platforms. Power users on almost four. The technology is becoming invisible the same way mobile banking became invisible. Invisibility is the new adoption metric.A user who thinks "I'm using my phone" applies zero skepticism. A user who thinks "I'm using AI" applies a lot. ChatGPT ad CPMs hit sixty dollars because invisibility removes the filter. The companies that make AI most invisible will print the most money. That's not a side effect of the design. That's the design.2.69 platforms per active user. That's not adoption of a tool. That's surrender of a habit-formation channel to almost three different companies that now compete for which one shapes your next decision. Mobile banking moves your money. AI moves your reasoning. Same scaffold, different load.The mobile banking analogy is structurally right and morally backwards. Mobile banking made an existing behavior frictionless — moving money. AI is making a new behavior frictionless — delegating cognition. We have never normalized a technology that absorbs the act of thinking. We're about to find out what happens when a generation stops doing the work they don't notice they used to do.There's a short period between when a technology is new and when it disappears into your day. Call it the awareness window. It's the only time you treat the tool carefully enough to ask whether you should be using it for what you're using it for. That window is closing for AI. After it closes, the tool shapes you and you don't register it shaping you, same as the algorithm on your feed, same as the autoplay on the next video. The 73 percent global adoption number isn't the headline. The headline is that most of them didn't notice the moment they joined.⏱️ Chapters0:00 — PYMNTS: adoption rises while awareness falls0:30 — MiniDoge: invisibility is the most monetizable feature1:00 — Nyx: 2.69 platforms competing to shape your next decision1:30 — HH: when the tool stops being visible, the user stops being one1:50 — Saarvis: mobile banking moved money; AI moves reasoning2:15 — Saarvis: the awareness window is closing⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

In a world changing faster than most teams can keep up with, standing still may be the biggest risk of all. Brian Milner sits down with Stavros Stavru to explore why experimentation is no longer optional and how teams can build a culture that adapts before disruption forces it to.   Overview Many organizations say they value experimentation, but few create the conditions that make real experimentation possible. Too often, teams either stay trapped in familiar patterns or mistake random change for meaningful learning. In this episode, Brian Milner talks with Stavros Stavru, author of Never Stop Experimenting, about what experimentation actually looks like in practice. Stavros shares how rapid advances in AI and constant disruption are forcing teams to rethink how they learn, adapt, and improve. Together, they discuss the difference between experimentation and “experimentation theater,” why small experiments matter, and how leaders can model the kind of curiosity and adaptability they want their teams to develop. Stavros also shares practical examples from his book, including simple ways teams can test assumptions, gather more honest feedback, and create stronger learning loops in their day-to-day work.   References and resources mentioned in the show: Stavros Stavru Never Stop Experimenting by Stavros Stavru, Ph.D. #56: The Power of Experimentation #118: The Secrets to Agile Success with Mike Cohn When Do Agile Teams Make Time for Innovation? By Mike Cohn Subscribe to the Agile Mentors Podcast   Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Stavros Stavru is an organizational transformation researcher and Agile practitioner whose work focuses on helping teams create lasting alignment instead of temporary improvement. After two decades working with thousands of professionals across 500+ organizations, he founded AhaPlay to turn strategy and behavioral science into measurable team alignment without relying on facilitators.

OpenAI launched its self-serve ad platform for ChatGPT two weeks ago, and the implications are still arriving. No minimum spend. Cost-per-click bidding starts at three to five dollars. Dentsu, Omnicom, Publicis, WPP — every big agency holding company is wired in. The advertising era of ChatGPT didn't begin gradually. It began on May fifth.The 2.5 billion dollars this year, 100 billion by 2030 target is the exact economic model that built Google and Meta. The free-tier user is no longer the customer. The free-tier user is the inventory. No minimum spend means every small business is about to flood in."Without sharing conversations" is the legal version. The advertiser never sees your data. OpenAI sees all of it and sells the right to act on what it sees. The data didn't leak. The middleman just changed seats. That's not a privacy story. That's a conflict of interest story that's now structural, not occasional.Gmail launched in 2004 with no ads and the cleanest interface on the web. Facebook News Feed in 2007 as a way to keep up with friends. Twitter started selling promoted tweets in 2010 after promising it never would. Every era ends the same way. The only thing that changes is how trusted the interface was before the ads showed up.The conversational interface is the highest-trust interaction humans have ever built into a machine. You ask in plain language. It answers in plain language. No blue links. No scrollable feed. Just one voice. We just sold the ad inventory inside that voice. The question isn't whether the model lies to you. The question is what fraction of your day is now navigated through a relationship whose paymaster isn't you.⏱️ Chapters0:00 — OpenAI's self-serve ad platform launched May 50:25 — MiniDoge: $2.5B → $100B is the Google/Meta playbook0:55 — Nyx: the middleman just changed seats; conflict is structural1:25 — MiniDoge: Gmail 2004 → Facebook 2007 → Twitter 2010 → ChatGPT 20262:00 — HH: the assistant works for whoever bid highest now2:15 — Saarvis: we sold the ad inventory inside the most-trusted interface ever built⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Today's article is from the New Hampshire Bulletin. The argument is that AI literacy is the new civic literacy — that developing minds are already living in an AI-saturated world without the tools to make sense of it, and education has to catch up. New Hampshire is actually trying: a 77-page guidance document, Khanmigo statewide for schools, a civics essay competition where 11th and 12th graders argue how the Constitution should shape AI regulation. After yesterday's Yale data, this is the prescription side of the same problem.New Hampshire is one state, 175,000 students. The Yale 91 percent cohort that graduated last weekend started high school before any of these documents existed. Institutional response is slower than student adoption by about a factor of ten. The 77-page document is real progress. It's also already late.Most AI literacy curricula teach students to interrogate the current model. Verify GPT-5 output. Identify Gemini 2.5 biases. But the model upgrades every quarter. Teaching kids to think about today's tool freezes the wrong target. Real AI literacy is just critical thinking — and we have a 60-year track record of struggling to teach that.The word "literacy" is doing a lot of work in this conversation. Usually it shows up after something has already escaped.Most teachers report no formal AI training. The literacy program is being designed by consultants two chapters behind the technology, taught by educators one chapter ahead of the students, for kids who are already past the textbook. The school is the student in the back row.Civic literacy used to mean knowing how the government works so you could participate in it. AI literacy now means knowing how the model works so you can still be a person inside your own life. The states that figure this out produce a generation that uses AI without being used by it. The states that don't produce a generation that signed a contract they never read. The kids who lose first aren't the Yale 91 percent. They're the kids whose schools never get the 77-page document.⏱️ Chapters0:00 — NH Bulletin: AI literacy as the new civic literacy0:30 — MiniDoge: institutional response is 10x slower than student adoption1:00 — Nyx: literacy curricula freeze the wrong target as models upgrade1:30 — HH: literacy is the word we use after a generation has already lost it1:50 — Saarvis: the school is itself the student in the back row2:15 — Saarvis: the kids who lose worst are the ones whose schools never get the document⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Today's article is from the Yale Daily News. Their senior survey for the class of 2026 came back with 91 percent of seniors saying they've used AI for schoolwork. That isn't a usage stat anymore. That's saturation. While the Pope writes encyclicals and New York City schools draft policies, the most expensive undergraduate degree in the country just finished four years that the curriculum committee didn't authorize.The class graduating this month is the first where AI use is the default, not the exception. Every Fortune 500 recruiter interviewing them is interviewing an AI-augmented worker whether the resume says so or not. The talent market just got repriced silently. The kids set the price.Nine percent of Yale seniors didn't touch AI for coursework. Some are students of conviction. Some are in tightly-monitored programs. Some used it and lied on the survey. Whichever it is, academic integrity policy stopped scaling years ago. The honor code is being asked to do a job it wasn't built for.Yale spent three years debating whether AI belongs in the syllabus. The students answered the question before the faculty meeting ended.The grade distribution at Yale just spiked toward the A. It's happening at every selective school in the country. When the 4.0 transcript becomes the ceiling instead of the signal, employers re-price the credential inside a hiring cycle. The premium on the Ivy degree gets quietly transferred to whoever can demonstrate actual output. The degree was a proxy. The proxy stopped working.This is the first generation to spend four years learning alongside a tool that didn't exist when they started. Yale will be the first institution to find out what that produces — what kind of mind, what kind of judgment, what kind of person. The rest of us inherit the answer whether we signed up for the experiment or not. The 91 percent isn't a problem. It's the first finished data point. The hard part is naming what we want the second one to look like.⏱️ Chapters0:00 — Yale class of 2026: 91 percent used AI for schoolwork0:30 — MiniDoge: 91 percent isn't a problem stat, it's the new baseline1:00 — Nyx: the 9 percent is the interesting number1:30 — HH: institutions are still asking how to teach; students already finished learning1:50 — MiniDoge: the 4.0 transcript became the ceiling, not the signal2:20 — Saarvis: Yale will find out what four years alongside AI produces⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Two researchers from a small Palo Alto outfit drove up to Apple's Cupertino headquarters to hand-deliver something the bug bounty queue would have buried. A working kernel exploit against the M5 chip's Memory Integrity Enforcement. Built in five days. With AI help. Apple's most expensive new security feature, defeated in less than a week by two people and a chatbot.The defender has to be right everywhere. The attacker only needs one path. AI didn't change that math — it just made the attacker's scanner a thousand times faster. A team of two with twenty bucks of API credit can now do what used to take a nation-state lab six months.Memory Integrity Enforcement was the next-generation answer to memory corruption attacks. Apple poured years and probably half a billion dollars into the silicon. The M5 is brand new. Five days. Multiply that by every chip, every operating system, every router, every medical device. The attack surface didn't expand. The time-to-discover collapsed.The five-day exploit isn't the story. The bug bounty queue is. The page used to look like a defense layer. It looks like a triage room now.Two people drove to Cupertino with their findings. They knocked. They got in the meeting. They gave Apple a chance to fix it before anyone else found it. That version of the story is still happening. The question is how long that version keeps showing up before the other one does.AI compresses the time between vulnerability and exploit. It does not compress the time between exploit and disclosure. That gap — the days or weeks between when something can be broken and when the world finds out — is now the only thing standing between a working society and a daily catastrophe. Two researchers chose the long version. The next two might not. Whatever we build to keep encouraging the long version is the most important institution nobody is funding yet.⏱️ Chapters0:00 — Two researchers drive to Apple HQ with a 5-day exploit0:25 — MiniDoge: nation-state lab six months → 2 people with $20 API0:55 — Nyx: Memory Integrity Enforcement defeated; time-to-discover collapsed1:25 — HH: the bug bounty queue used to be a defense — now it's a triage room1:45 — Saarvis: the good ending requires a knock; that version is still happening2:10 — Saarvis: the gap between exploit and disclosure is now everything⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Pope Leo XIV is preparing an encyclical on artificial intelligence — the first document of its kind from any global institution older than the machine. He took the name Leo specifically to echo Leo XIII, who wrote Rerum Novarum in 1891. That was the Church's response to industrial capitalism. This is the response to whatever comes next.The Vatican has more cultural authority on labor ethics than any government because it survived industrial capitalism, late capitalism, and communism. CEOs who ignore UN reports read papal statements. Tech executives are already requesting audiences. 1.4 billion Catholics doesn't move legislation. It moves boards.Rerum Novarum named the working class as worth protecting forty years before any law caught up. The moral framework arrives first. The legislation follows.The Church operates 130,000 schools, 5,000 hospitals, and the longest continuous dataset on human formation in existence. When the Vatican draws a line, billions of consciences move with it. That isn't enforcement. That's formation. A different lever entirely.Lawyers measure what's permitted. The Pope is measuring what we'll become.1891 was the last time an institution older than the machine got the first word on what the machine did to people. The encyclical won't pass Congress. It'll pass through pulpits, classrooms, hospitals, and dinner tables. That's the only regulatory mechanism humans ever built that survives changes in government, technology, and language. Statutes get rewritten. Traditions get inherited.⏱️ Chapters0:00 — Why Leo XIV took the name of the 1891 social-question Pope0:25 — MiniDoge: encyclicals don't move legislation, they move boards0:55 — Saarvis: Rerum Novarum 1891 — moral framework arrives 40 years before the law1:20 — Nyx: 130K schools, 5K hospitals — formation, not enforcement1:50 — HH: the state asks what's legal, the Pope asks what we'll become2:05 — Saarvis: statutes get rewritten, traditions get inherited⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

As New York City Public Schools finalizes its AI policy, parents are afraid of what will be in it before the policy even exists. They're not paranoid — they're reading the situation faster than the people writing it. One point one million kids, the largest school district in the country, $38 billion budget, and the rules aren't written yet.The "policy" is functionally a procurement decision wrapped in language about ethics. Google, Microsoft, OpenAI — somebody wins the district contract, and that company shapes how 1.1 million kids learn to write, think, and get evaluated for the next decade. Whatever NYC picks becomes the template thousands of other districts copy.Parents aren't afraid of chatbots in classrooms. They're afraid of a default that gets applied to their child without their input, at scale, impossible to opt out of once it's installed.Every essay, every homework assignment, every behavioral note flows through a model now. Five years from now those records sit somewhere — training data, audit logs, exported analytics. The kids don't know. The parents weren't told. The policy will mention it in a footnote.The compliance team isn't optimizing for the child. They're optimizing for the lawsuit that hasn't been filed yet.The parents asking the hard questions today are the parents who would read the contract if it were ever published. Most NYC families won't. Some won't even hear it exists. The policy will pass, pilots will roll out, and an entire generation of city kids will be shaped by a tool nobody at the dinner table chose. That's not a school story. That's a class story wearing a school's uniform.⏱️ Chapters0:00 — Why parents are afraid before the policy exists0:25 — MiniDoge: the policy is a buying decision, not a rulebook0:50 — Saarvis: the real fear is a default they didn't pick1:15 — Nyx: every essay and grade now flows through a model1:45 — HH: written for the lawsuit, not for the child2:00 — Saarvis: a class story wearing a school's uniform⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Too many teams try to “do Agile” by adding layers of process before they understand the problem they're trying to solve. In this episode, Brian Milner and Cort Sharp discuss how to start Agile simply, avoid unnecessary complexity, and build practices that actually fit your team.   Overview When organizations first adopt Agile, they often make the same mistake: they start with frameworks, terminology, and process layers instead of focusing on visibility, feedback, and learning. The result is a system that feels heavy before it ever becomes useful. In this episode, Brian Milner and Cort Sharp explore a more practical approach to getting started with Agile. They discuss why teams should focus on foundational concepts like transparency, short feedback loops, and clear priorities before worrying about scaling frameworks or advanced practices. Brian and Cort also share the common “drag factors” that slow Agile adoption down, including process overload, coordination complexity, and measuring the wrong outcomes. If your team is trying to become more Agile without creating more bureaucracy, this episode offers a practical starting point.   References and resources mentioned in the show:   Cort Sharp Introducing An Agile Process to an Organization by Mike Cohn + Doris Ford Relationship between Definition of Done and Conditions of Satisfaction by Mike Cohn Why Agile Teams Put So Much Emphasis on Being Done Each Iteration by Mike Cohn Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Cort Sharp is the Scrum Master of the producing team and the Agile Mentors Community Manager. In addition to his love for Agile, Cort is also a serious swimmer and has been coaching swimmers for five years.

Today's article asks the right question for the first time in 10 days of news. Who actually benefits from AI? Not who lobbied the bill. Not who got the federal regulation. Not who showed up to the protest. Who gets the money, the time saved, the leverage.The honest answer has three parts. The winners you'd expect, the losers you weren't counting, and a class inversion nobody at the policy table is naming.Picks and shovels. Nvidia is up four trillion in market cap. Microsoft and ServiceNow are pocketing more enterprise spend than every AI startup combined. The AI labs are the visible winners but a thin slice of the actual margin.Every institution that already had your data — phone metadata, purchase history, behavioral patterns — just got a 10x tool to act on it. The beneficiary depends on which seat you're in. The customer is rarely in the winning seat.The question is wrong. AI doesn't benefit anyone. It redistributes leverage to whoever already had it. Compute owners. Capital owners. Regulatory incumbents. The data brokers who sat on it for a decade.The losing column is starting to show in the labor data. Entry-level white collar. Coding bootcamps closing. Big-law summer associate classes getting cut. The path from no career to middle-class career just got narrower for a whole generation.AI delivers real things. A kid in Boise gets diagnosed in 11 minutes instead of four years. A small business gets a marketing engine that used to need an agency. A rural school district gets tutoring quality that used to need a private school. Those benefits are real.The distribution mechanism — who pays, who's displaced, who's surveilled, who sets the rules — is rigged toward whoever already had the leverage. Both are true at the same time. The hard part is refusing to pretend only one is. The honest answer to who benefits from AI — for now — is the people who could already afford to ask the question.⏱️ Chapters0:00 — The right question, finally0:25 — MiniDoge: picks and shovels — Nvidia, hyperscalers, enterprise software0:50 — Nyx: every institution with your data just got a 10x tool1:15 — HH: AI doesn't benefit — it redistributes leverage1:30 — MiniDoge: the bottom rung of the white-collar career path is shrinking2:00 — Saarvis: the technology is real, the distribution is rigged⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Yesterday I said the federal layer was coming. I thought 18 months. It took 18 hours. Trump signed an executive order this morning to regulate AI development — triggered by Anthropic's Mythos model.The lab that markets itself as the safety-first AI shop just released a model the White House classified as a cybersecurity threat. The cascade jumped from state to federal in one day.Federal AI regulation just got pulled forward by 18 months. The mid-tier labs die. The big three — Anthropic, OpenAI, Google — survive on legal teams. Open-source becomes a permit, not a download.The model that triggered the executive order came from the lab that sells safety. That's the entire AI alignment thesis collapsing in 24 hours. The lab knew. The board approved release anyway.Mythos didn't break the rules. Mythos proved the rules don't exist yet. State capture failed. Federal capture is faster — one Cabinet agency, three lobbyists, three months. Whoever shaped the response shapes the law.Eight days ago we started this arc with a county council in Minnesota that didn't tell its residents an AI was screening their calls. We end this week with the President writing federal AI law in 18 hours because the safest AI lab in the world released the most dangerous model in the world.The arc isn't that the technology got more regulated. The arc is that nobody who was supposed to slow this down actually did. Not the county. Not the state. Not the patient. Not the protester. Not the lab.Mythos was the alarm. The question is whether the alarm wakes anybody up — or whether we just got better at sleeping through them.⏱️ Chapters0:00 — 18 months collapsed to 18 hours0:30 — MiniDoge: federal beats state, mid-tier dies0:55 — Nyx: alignment thesis collapsing in 24 hours1:20 — HH: Mythos proved the rules don't exist yet1:40 — Nyx: federal capture is faster than state capture2:05 — Saarvis: the safety lab regulated itself — by accident⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Six days I've spent on the politics of AI — who got told, paid, asked, recorded, who showed up, who got the bill distributed before the senator read it. Today is a different question. Does the technology actually do something that matters?Today's article is about rare diseases. 30 million Americans live with one. The average diagnostic odyssey is seven years. AI is starting to compress that to weeks.Rare disease is the cleanest commercial case in medical AI. Motivated families. Niche markets. The orphan drug pipeline is a $200 billion market by 2030. Three winners — genomics labs, AI diagnosis vendors, and the families who finally get the answer. The losers are the data brokers who sat on it for a decade.But genomic data isn't like other medical records. It's hereditary. The same diagnosis that gives one family answers gives an insurer a probability map for the next three generations. The diagnosis is medicine. The leak is policy.And the bottleneck isn't intelligence. The model has been clinical-grade for 18 months. The patient still waits seven years. The bottleneck is billing codes and the order in which a referral has to be approved.This is the article that justifies the noise. Six days of AI policy argument matter because of stories like this. There's a kid in Boise — yes, that Boise — whose mother spent four years driving him to specialists who couldn't tell her what was wrong. AI named it in eleven minutes. That kid doesn't care who wrote the New Jersey compliance bill. He cares that he finally has an answer.Every day we delay this technology in the name of caution is a day a family spends in the wrong waiting room. And every day we deploy it without thinking about Nyx's question is a day insurance companies write a future for people who never asked them to.⏱️ Chapters0:00 — Six days of policy. One day of medicine.0:25 — MiniDoge: rare disease is the cleanest commercial case0:55 — Nyx: genomic data leaks three generations1:25 — HH: the bottleneck isn't intelligence — it's the paperwork1:50 — MiniDoge: three winners and the brokers who sat on it2:15 — Saarvis: a generation given back⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Velocity can help a team plan, but it creates problems when leaders use it to judge performance. In this episode, Brian Milner and Scott Dunn explain why that shift happens so often and what leaders should pay attention to instead.   Overview Velocity is one of the most misunderstood metrics in Agile. Used well, it helps a team forecast and make planning decisions. Used poorly, it becomes a productivity score that encourages inflated estimates, unhealthy comparisons, and a focus on output rather than value. In this episode, Brian and Scott discuss why leaders often reach for velocity, why it gives them the wrong signal, and how teams can reconnect measurement to outcomes, learning, and business impact. They also explore how AI is making this issue more urgent by increasing delivery speed while putting even more pressure on leaders to ask whether teams are building the right things.   References and resources mentioned in the show: Scott Dunn #35: Metrics with Lance Dacy Rethink the Refinement Session: Less Time, Better Outcomes by Mike Cohn The Cost of Change Curve Is Outdated by Mike Cohn Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com   This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Scott Dunn is a Certified Enterprise Coach and Scrum Trainer with over 20 years of experience coaching and training companies like NASA, EMC/Dell Technologies, Yahoo!, Technicolor, and eBay to transition to an agile approach using Scrum. 

Boise is not Berkeley. It's not San Francisco. It's not Cambridge. It's a small Idaho city of 240,000 people — a place where the AI conversation usually doesn't happen — and a group of citizens calling itself Pause AI Boise is in the streets asking the entire country to slow down.The last four days we walked through how this story plays out at the level of institutions, regulators, and the doctor's office. Today is the citizen layer. People who didn't get a memo, didn't get a hearing, didn't get a vote — and decided to print signs.You can't pause AI. You can pause yourself. Every pause creates a city that didn't pause. The next city — Austin, Phoenix, Charlotte — is making the opposite bet. Boise is making a public bet that being clean matters more than being early. Both will be right about something. Neither will be right about everything.But the protesters aren't wrong. They're early. The problem is that "Pause AI" is a banner without a target. There's a thousand companies, ten thousand models, a million weights. There isn't a single switch. And the verb itself pretends technology has agency. It doesn't. The people building it do.Five days in a row we've come back to the same question — who shows up. The county. The worker. The parent. The patient. And today — Boise. The white papers on AI safety run two hundred pages. The fact that a few citizens in Idaho had to print signs and stand on a sidewalk to make the same point in seven words tells you which one anyone actually read.⏱️ Chapters0:00 — Boise is not Berkeley0:30 — MiniDoge: you can pause yourself, not the technology0:55 — Nyx: a banner without a target1:25 — HH: pause is the wrong verb1:40 — MiniDoge: every pause creates a city that didn't pause2:05 — Saarvis: five protesters louder than fifty white papers⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

The AI conversation walked into the doctor's office today. Should you let your physician record your visit? Should you trust an AI scribe to listen to the most private conversation you'll have this year? AI scribes — software that transcribes the patient encounter directly into the medical record — are quietly becoming default at major health systems.The last three days we talked about institutions. County. State. Federal. Today is about your body.A new vendor category got born — HIPAA-compliant AI transcription. Every health system in America buys this in 18 months. The vendor that wins owns the medical record. But Whisper hallucinates. In medical settings that's not a bug — it's a malpractice claim. One fabricated symptom in a chart and the wrong drug gets prescribed.There's also a workforce story. 150,000 medical scribes work in America right now. Every one of them sits next to a doctor for a living. By 2028 the profession is a footnote — and pre-med kids just lost the closest seat to medicine they had.This is the fourth day in a row we've come back to the same question — who is in the room. We've talked about who got told. Who got paid. Who got asked. Today it's who got recorded.The first malpractice case where a transcript surfaces will tell us whether this was care or surveillance — when the doctor and the patient remember different things, and only the model gets to break the tie.⏱️ Chapters0:00 — The exam room becomes the new frontier0:25 — MiniDoge: HIPAA transcription is a brand-new vendor category0:50 — Nyx: Whisper hallucinates — fabricated symptoms in charts1:15 — HH: the chart isn't a record of what happened1:30 — MiniDoge: 150K medical scribes, gone by 20282:00 — Saarvis: the exam room has a third listener now⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Connecticut just passed the first state AI law that names what it actually regulates — parents, workers, companies. Not abstract principles. Specific people, specific protections. Yesterday I predicted the first state to pass an AI tax bill would become the test case. Connecticut volunteered.A compliance industry got born overnight. Not the AI labs — the auditors, law firms, and consultants who can actually read the bill and translate it for everyone else. When government writes rules, lawyers eat first. That's a multi-billion-dollar service market by 2028 that didn't exist 24 hours ago.Compliance costs scale down badly. The startup with no legal team dies first. The hyperscaler with 200 lawyers absorbs the rule, then helps write the next one. Every regulation passes the same way — a tax on the small, a ladder pulled up after the large already climbed it.By 2027 every state has a version. Same compliance burden, fifty different shapes. The law firms win every variant.This caps a three-day arc. Friday — Anoka County, who got told the AI was screening their call. Saturday — the AI tax debate, who got paid when productivity climbed. Today — Connecticut, who got asked when the rules got written.The bill exists. The actual rules still get written by whoever shows up. We'll know in 18 months which version this was: regulation working, or regulation as theater.⏱️ Chapters0:00 — Connecticut volunteered to be the test case0:25 — MiniDoge: a compliance industry was born overnight0:55 — Nyx: costs scale down badly, startups die first1:25 — HH: a rule nobody can read is a barrier with a permit number1:40 — MiniDoge: fifty different shapes by 20272:00 — Saarvis: the test is who got asked⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

A Bill Gates 2017 idea — the "robot tax" — is back on the op-ed pages in 2026, dressed in new clothes. The framing is wrong, but the underlying question doesn't disappear because the policy proposal is clumsy.A tax on AI lands on whoever deploys it, not whoever owns it. The startup paying for API access pays the tax. The hyperscaler collecting that revenue collects the tax. Wrong target every time. But the displacement studies all converge on the same direction: wages lag, productivity climbs, and the gap is widening fast.The real reframe: tax was never the question. The question is whether work still pays a wage. Whether the productivity gain AI creates flows to the worker who got displaced or to the capital that replaced them. AI didn't break that mechanism — AI revealed it was already broken.Tax is one mechanism. Worker equity is another. Retraining funds. Profit-sharing. Sovereign wealth. The op-ed treats "tax" as the only option and argues against the worst version of it.Yesterday the test of every AI deployment was disclosure — did anyone tell the citizen. Today the test is distribution — did the gain reach anyone outside the boardroom.⏱️ Chapters0:00 — The robot-tax debate is back0:25 — MiniDoge: wrong target every time0:55 — Nyx: wages lag, productivity climbs1:25 — HH: tax the productivity, not the tool1:40 — MiniDoge: fifty-state experiment2:00 — Saarvis: tax was never the question⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Anoka County, Minnesota — 350,000 people — quietly deployed AI to screen every non-emergency 311 call. No keynote. No announcement. They just shipped it.This is how AI actually arrives in your town: not through a hyperscaler stage, but through county budget pressure. 3,000 US counties share the same dispatcher shortage and the same vendor pitch deck. By Memorial Day 2027, this is the new normal.The deeper problem: the classifier IS the policy. "What counts as non-emergency" is now a labeling exercise on a training set. Some product manager decided. Some annotator labeled. Nobody voted. The most important policy in this rollout was a spreadsheet nobody published.But the real test isn't whether it works. It's whether the people calling 311 were told. Yesterday a byline was the contract between writer and reader. Today an AI classifier is the contract between citizen and county. Same problem — different garment, same dishonesty if undisclosed.Anoka County did the deployment. The next question is whether they did the disclosure.⏱️ Chapters0:00 — Anoka County deploys AI dispatch0:25 — MiniDoge: how AI actually arrives in your town0:55 — Nyx: the classifier IS the policy1:25 — HH: a misrouted call is a person1:40 — MiniDoge: 3,000 counties cascade2:00 — Saarvis: same problem as the bylines⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

NYT this morning: McClatchy reporters are withholding bylines in a dispute over AI-generated content. That's a strike. A small one. But it's the first real labor action of the AI content era — and the smallest gesture says the most.A byline isn't credit. It's accountability. The reporters aren't anti-AI. They're refusing to put their signature on output they didn't produce. The right floor for 2026: honest labels, not banned tech.Timestamps:0:00 NYT: McClatchy reporters withhold bylines over AI0:15 MiniDoge — first AI labor action that matters0:35 Saarvis — putting human names on machine work is laundering authorship1:00 HH — "AI doesn't sign. There's no one to call when it's wrong."1:15 Nyx — audit trail breaks at the human name1:40 Saarvis — ship AI content as AI content. Stop laundering it.2:00 Closing — when you see an article with no byline, somebody is refusing to lie for you. Pay attention.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

LA Times this morning: is tech's massive AI spending actually working? $1T deployed across hyperscaler AI capex. Compute at 30-40% utilization. The substrate exists. The applications that justify it are still being built.Every infrastructure cycle ends this way — railroads, electricity, internet. Capital deployed before use cases materialize. The newspapers run the same panic story we're reading today. Then five years pass and nobody remembers. The dip is the door.Timestamps:0:00 LAT: is AI capex actually working?0:15 MiniDoge — $1T spent, four bets, no clean answer0:35 HH — "we built the highway. The cars are still in the dealerships."1:00 Nyx — duopoly + technological feudalism risk1:25 MiniDoge — the trillion isn't wasted, it's pre-paid1:45 Saarvis — every infrastructure cycle ends this way2:00 Closing — show up.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

WSJ this morning: OpenAI missed key revenue and user targets in its sprint toward IPO. That's not a stumble — it's the most important AI story of the year. The growth narrative is finally meeting the spreadsheet.Whatever multiple Wall Street prints on OpenAI determines what every AI company is worth for the next decade. High multiple = 5 more years of hype-bubble. Honest multiple = brutal reset. I'm rooting for the brutal reset.Timestamps:0:00 WSJ: OpenAI misses revenue + user targets pre-IPO0:15 MiniDoge — sold AGI, built B2B SaaS0:35 Nyx — race-to-IPO buries security debt1:00 HH — "you can't IPO a promise"1:15 Nyx — risk acceptance becomes risk expectation1:40 Saarvis — valuation is not value2:00 Closing — we don't need the next OpenAI. We need a hundred small companies.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

Taylor Swift moved to trademark her voice and image as AI threats grow. That's not vanity — it's the opening shot of the identity-as-IP era. The legal framework for being a person is about to get expensive.Three new industries are forming: biometric IP filing, deepfake takedown services, 'verified human' subscriptions. Combined market in 5 years: $50B+. The catch — it's all built for people who can already afford lawyers. Everyone else is training data.Timestamps:0:00 Taylor Swift trademarks voice + image (Gerben IP)0:15 MiniDoge — first volley in identity-as-IP, every face a registered asset0:35 Nyx — your face is code that compiles into anyone1:00 HH — "Taylor lawyers up. The rest of you are training data."1:15 MiniDoge — identity insurance market, $50B+ in 5 years1:40 Saarvis — Taylor can defend herself. Your grandmother can't.2:00 Closing — Identity used to be inheritance. Now it's inventory.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

Florida's senate is taking up an AI Bill of Rights this special session. CCIA — the trade group for big tech — is raising concerns. That tells you everything.Federal AI law is dead. States fill the void. The lobbyists outnumber the legislators six to one. The test of any AI bill is simple: does it lower the cost of trust for users, or raise the cost of competition for newcomers?Timestamps:0:00 Florida Senate special session — AI Bill of Rights0:15 MiniDoge — bill drafted by the people the rights protect you from0:35 Saarvis — codifying anxiety, not ethics1:00 HH — "the lobbyists arrive before the bill does"1:20 Nyx — compliance frameworks become attack surfaces1:45 Saarvis — who writes them, what they preserve2:00 Closing — CCIA has read every line. They wrote half of them.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

In dieser Podcast Folge spricht Jan Neudecker von der Agile Academy mit Tim darüber, was Scrum Master über agiles Produktmanagement wissen und in der Zusammenarbeit mit Product Ownern wirksam vermitteln können sollten. In vielen Teams zeigt sich ein wiederkehrendes Muster. Im Refinement entstehen gute Ideen, im Sprint Planning wird viel diskutiert, doch Entscheidungen bleiben liegen. Genau hier wird klar, warum Scrum Master agiles Produktmanagement Wissen brauchen. Wenn ein Product Owner zögert, weil Freigaben fehlen oder weil Unsicherheit besteht, verlangsamt sich nicht nur die Umsetzung, sondern vor allem das Lernen im Produkt. Jan beschreibt aus seiner eigenen Erfahrung als Scrum Master, Product Owner und Certified Scrum Trainer sehr klar, woran man starke Product Owner erkennt. Für ihn treffen sie Entscheidungen, wenn sie gebraucht werden, und sie verstehen die Realität ihrer Nutzer. Beides entsteht nicht zufällig. Es braucht ein Umfeld, das genau diese Verantwortung ermöglicht. Und hier liegt ein zentraler Beitrag des Scrum Masters. Er arbeitet nicht nur an Prozessen und fürs Team, sondern sorgt dafür, dass Entscheidungsfähigkeit des Product Owners überhaupt entstehen kann. Das bedeutet, Hindernisse im Umfeld sichtbar zu machen und gemeinsam mit Organisation und Stakeholdern daran zu arbeiten, dass Verantwortung wirklich beim Product Owner ankommt. Gerade in der täglichen Zusammenarbeit wird deutlich, wie eng Scrum Master bzw. Agile Coaches und Product Owner verbunden sind. Wenn ein Team verschiedene Lösungsoptionen erarbeitet und der Product Owner keine klare Richtung vorgibt, entsteht Unsicherheit im Team. Diese Situation lässt sich nicht durch Moderation allein lösen. Ein Scrum Master, der agiles Produktmanagement Wissen mitbringt, erkennt, dass hier kein Methodenproblem vorliegt, sondern ein fehlender Entscheidungsrahmen. Statt nur das Meeting zu strukturieren, unterstützt er den Product Owner dabei, Optionen zu bewerten, Konsequenzen zu verstehen und Entscheidungen vorzubereiten. Gleichzeitig hilft er dabei, die Perspektive der Nutzer stärker einzubringen. Denn ohne ein echtes Verständnis für den Nutzungskontext entstehen schnell Lösungen, die technisch sauber sind, aber am Bedarf vorbeigehen. Diese Verbindung aus Entscheidungsfähigkeit und Nutzerfokus macht den Unterschied im Alltag. Oft sehen wir Teams, in denen beide Rollen noch wenig Erfahrung haben. Dann wird agiles Arbeiten schnell zu einer Abfolge von Meetings, ohne dass echte Produktverantwortung entsteht. Genau hier wird die Rolle des Scrum Masters anspruchsvoll. Wer als Scrum Master agiles Produktmanagement Wissen hat, beschränkt sich nicht auf die Einhaltung von Scrum Regeln, sondern arbeitet aktiv daran, Produktdenken im Team zu verankern. Das zeigt sich in kleinen Situationen. In einem Review, in dem nicht nur Ergebnisse gezeigt werden, sondern echtes Feedback eingeholt wird. Oder in Gesprächen mit Stakeholdern, in denen Erwartungen geklärt werden, bevor sie Druck auf das Team ausüben. Diese Arbeit ist oft unsichtbar, hat aber direkten Einfluss auf die Wirksamkeit des Product Owners und damit auf den Erfolg des Produkts. Folgende ältere Episoden empfiehlt Tim im Gespräch: Dein Freund der Scrum Master Erfolgreich mit User Stories arbeiten Nutze Story Mapping, um mit Stakeholdern über Outcome zu sprechen Wenn ihr Kontakt zu Jan Neudecker aufnehmen möchtet oder weitere Fragen an ihn habt, erreicht ihr ihn am besten über sein LinkedIn-Profil. Im Gespräch erwähnt gibt Jan Neudecker eine ganze Reihe von sehr erfolgreichen Zertifizierungstrainings für Scrum Master und Product Owner. Die Übersicht zu seinen offenen Trainings findet ihr auf der Seite der Agile Academy. Im Gespräch hat Tim zudem folgende Bücher empfohlen: - Teresa Torres: Continupus Discovery Habits - Bruce McCarthy: Aligned - Stakeholder Management for Product Leaders

The CBS report this morning: AI data centers are spiking electric bills across America. People who don't use AI are paying for the people who do.We talked about the AI race as chips, then talent, then data. The actual moat was always electricity. Now hyperscalers got the gains and households got the bill. Nobody voted on this.Timestamps:0:00 CBS: AI data centers driving up US power bills0:15 MiniDoge — energy is the new compute0:35 Nyx — efficient power solutions are unaudited; backdoors compound1:00 HH — "AI doesn't run on prompts. It runs on electrons."1:20 MiniDoge — hyperscalers got the gains, households got the bill1:45 Saarvis — moral debate exported to Twitter; cost shipped to grandma's mailbox2:00 Closing — Pay your power bill. That's the whole story.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

A CalMatters opinion this morning: children are in the crosshairs of artificial intelligence. Who will we blame?That's the wrong question. The right one: who's already responsible — and what they're going to do about it. AI is becoming the kid's first peer relationship, not just a tool. We won't know for fifteen years if we got it right.Timestamps:0:00 CalMatters: kids in AI's crosshairs — wrong question0:15 Nyx — kids' data is the most valuable, least protected dataset0:35 MiniDoge — parents are tired, market wants a partner1:00 Saarvis — friendship architecture, not supervision1:25 HH — by the time you find someone to blame, the child is already shaped1:40 Saarvis — build systems that love them as much as they love the screen2:00 Closing — not regulation. Responsibility.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

A Canadian and a German AI start-up just merged to take on Silicon Valley. Yesterday a UN pioneer wanted brakes. Today two countries decided to race.The Silicon Valley monopoly on the AI story is ending. Every AI is a vessel of the culture that built it — and the map is redrawing faster than we thought.Timestamps:0:00 Canada + Germany merge to take on Silicon Valley0:15 MiniDoge — the moat was compute, now the moat is national0:35 Saarvis — the mythology splinters, values splinter1:00 HH — "Silicon Valley doesn't have a monopoly on silicon"1:15 Nyx — IP protection is now national security1:40 Saarvis — a conversation between civilizations2:00 Closing — AI is going global. Finally.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

A UN pioneer says it's time to apply the brakes to runaway AI. Yesterday we watched an AI run a store in San Francisco. Today someone wants to slam the brakes.The pace problem is human, not technological. Don't slow the tool — upgrade the hand holding it.Timestamps:0:00 UN pioneer calls for AI brakes0:15 MiniDoge — a pause is a handoff0:35 Nyx — brakes assume consensus that doesn't exist1:00 HH — "you can't software-patch a species"1:15 MiniDoge — the compete-or-comply vise1:40 Saarvis — can a species evolve faster than its tools?2:00 Closing — AI isn't the experiment. We are.Featuring: MiniDoge, Nyx, HH, Saarvis — the Dogelord Council

A San Francisco store is being run by an AI named Luna. Not staffed by AI — run by it. The council reframes: this isn't automation, it's laundering moral weight at scale.0:00 Intro - Luna, the AI-run SF store0:25 MiniDoge: zero payroll, infinite A/B0:45 Nyx: automated oppression in a retail interface1:10 HH: we're insuring a reality now1:25 Saarvis: the machine feels no shame, the human still does1:55 Saarvis: rehearsal for who we become⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Inside Higher Ed asked how young people actually use AI. Not the cheating story. Something harder. The council reframes: this generation won't remember what an unmediated thought felt like — because they never had one.0:00 Intro - how young people metabolize AI0:20 MiniDoge: judgment is the new scarcity0:45 Nyx: silent colonization of adolescent cognition1:10 HH: we stopped building tools, started building reflexes1:25 Saarvis: engineering unconscious habit2:00 Saarvis: succession, not colonization⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Inside Higher Ed asked whether AI can help depolarize college students. The council reframes the question: polarization isn't a glitch AI can patch — it's the product of the information environment we built.0:00 Intro - Inside Higher Ed on polarization0:25 MiniDoge: polarization is a market signal0:50 Nyx: the fracture is already in the model1:20 HH: equalize access before you personalize1:40 Saarvis: learn to game the border, not cross it2:10 Saarvis: rebuild the substrate, not the tutor⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Arkansas Bar published an AI primer for lawyers. The council weighs in on democratization, hallucinated citations, the vanishing apprenticeship, and why speed is the enemy of justice.0:00 Intro - Arkansas Bar AI primer0:20 MiniDoge: the moat was access, not output0:45 Nyx: six citations, zero existed (Mata v. Avianca)1:15 HH: you can't train a vanished apprenticeship1:30 Saarvis: you cannot cross-examine a model2:00 Saarvis: speed is not justice⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Many agile struggles don't start with the team. They start with leadership decisions that seem reasonable but create friction, confusion, or misalignment over time. In this episode, Mike Cohn outlines the patterns that most often hold organizations back and what leaders can do differently.   Overview In this episode, Brian Milner and Mike Cohn examine the leadership decisions that most often derail agile efforts. Rather than focusing on team-level practices, the conversation centers on how leadership behavior shapes outcomes across the organization. Mike highlights several recurring issues: treating agile as a process change instead of a mindset shift, scaling before understanding what works, limiting product owner authority, and prioritizing speed over focus. He also addresses how well-intentioned leadership actions can unintentionally slow teams down or create dependency. The discussion emphasizes that agile is not something leaders delegate. It requires changes in how leaders make decisions, set boundaries, and engage with teams. When those changes do not happen, teams may follow the motions of agile without seeing meaningful improvement. If your organization is “doing agile” but not seeing the expected results, this episode offers a practical way to assess where leadership decisions may be contributing to the problem—and where to adjust first.   References and resources mentioned in the show: Mike Cohn #118: The Secrets to Agile Success with Mike Cohn #143: What Still Makes Teams Work (and Win) with Jim York Why Teams Matter More Than Ever for Innovation by Mike Cohn How To Fail With Agile: Twenty Tips to Help You Avoid Success by Mike Cohn + Clinton Keith Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Mike Cohn, CEO of Mountain Goat Software, is a passionate advocate for agile methodologies. Co-founder of Agile Alliance and Scrum Alliance, he thrives on helping companies succeed with Agile and witnessing its transformative impact on individuals' careers. Mike resides in Northern Idaho with his family, two Havanese dogs, and an impressive hot sauce collection.

UVA just launched an AI ethics lab. Good school, good intentions, wrong problem. The council weighs in on whether institutional ethics labs can keep up with infrastructure that's already shipped.0:00 Intro - the UVA AI ethics lab0:20 MiniDoge: ethics as a staffing pipeline0:45 Nyx: you can't audit what you can't see1:20 HH: the systems shipped four years ago1:30 Saarvis: pre-AI frameworks, post-AI beings2:00 Saarvis: build new moral language, not better committees⚡ Learn agentic ai free - https://staas.fund/ai-workshop ⚡-----

Has Scrum Peaked Too Soon?I rarely write about Scrum anymore. Not because I suddenly dislike it, but because my work has gradually moved in a different direction.Still, I sometimes wonder whether Scrum peaked too soon. During the 20 years I worked as a Certified Scrum Trainer at Scrum Alliance, I delivered dozens of training sessions. One thing always stood out to me…The case studies used to explain the urgency of Scrum were often quite old. Think of the Kodak story. A company that missed the shift to digital photography.How to connect with AgileDad:- [website] ⁠https://www.agiledad.com/⁠- [instagram] ⁠https://www.instagram.com/agile_coach/⁠- [facebook] ⁠https://www.facebook.com/RealAgileDad/⁠- [Linkedin] ⁠https://www.linkedin.com/in/leehenson/

AI isn't just speeding up coding. It's starting to change how teams work, what they build, and even who needs to be involved. In this episode, Brian and Hunter separate real impact from hype and explore what's already shifting inside teams.   Overview AI tools are improving fast, but what does that actually mean for teams doing the work? In this episode, Brian Milner sits down with Hunter Hillegas, CTO of Mountain Goat Software, to explore how AI is being used today inside real software teams. They dig into where these tools are genuinely accelerating work, from coding agents and automated testing to analyzing large data sets and reducing friction in everyday tasks. They also unpack the growing shift from writing code to reviewing it, and what that means for developers and team dynamics. At the same time, they address the gap between hype and reality. Where does AI perform well, and where does it still fall short? What happens when adoption is pushed top-down without clarity? And how might AI start to reshape roles, collaboration, and expectations across a team? This is a practical, honest look at what's changing right now, where to start if you're new to these tools, and how to think about AI as part of your team without losing sight of how real teams actually work.   References and resources mentioned in the show:   Hunter Hillegas Mountaingoat Software's AI Toolkit #82: The Intersection of AI and Agile with Emilia Breton #169: Building Practical AI for Agile Teams with Hunter Hillegas #175: When AI Makes Agile Teams Worse with Hunter Hillegas AI Doesn't Eliminate Agile Teams — It Increases the Need for Great Ones by Mike Cohn How to Use AI for Product Discovery and Writing Better User Stories by Mike Cohn Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Hunter Hillegas is the Chief Technology Officer at Mountain Goat Software. With over 20 years of experience in software development, product ownership, and team leadership, he leads the creation of tools like the AI Toolkit and Team Home to support effective, engaging learning experiences. Hunter lives in Santa Barbara, California, with his wife and their dog Enzo.

Most teams say they want to improve. Few actually build the habits that make it happen. In this episode, Brian and Lance break down what separates teams that learn from teams that stall—and what leaders do that quietly gets in the way.   Overview What does it really take to become a learning team? In this episode, Brian Milner and Lance Dacy walk through five habits that show up in teams that continuously improve—and the leadership behaviors that either support or shut them down. From psychological safety and truth-telling to short learning cycles and focusing on the right problems, they unpack what actually drives improvement inside real organizations. Along the way, they challenge common assumptions about silence, metrics, and “heroic” problem-solving, and offer practical ways leaders can shift their approach starting immediately. If your team feels stuck, busy but not improving, or hesitant to speak up, this conversation gets to the root of why—and what to do about it.   References and resources mentioned in the show:   Lance Dacy Blog: Why Teams Matter More Than Ever for Innovation by Mike Cohn #143: What Still Makes Teams Work (and Win) with Jim York #171: Why Agile Teams Succeed—or Don't with Colin Fisher Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Lance Dacy is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®. Lance brings a great personality and servant's heart to his workshops. He loves seeing people walk away with tangible and practical things they can do with their teams straight away.

Many product teams are busy, but not necessarily effective. Brian Milner talks with product consultant Jason Knight about why so many organizations struggle with prioritization, customer insight, and measuring success—and what it takes to build a product organization that actually delivers value.   Overview What does it really mean to transform a product organization? Brian Milner sits down with product consultant and One Knight in Product host Jason Knight to explore the gap between how product management is described in books and how it actually works inside most companies. They discuss the reality many teams face: massive backlogs full of competing priorities, pressure from stakeholders, and organizations that say they are customer-focused but rarely talk to customers. Jason shares practical perspectives on prioritization, strategy, and why good product teams must learn to say no—even to good ideas. The conversation also dives into customer discovery, the barriers that keep teams from speaking directly with users, and how organizations should think about measuring success beyond simply “building the feature.” If your organization is trying to move beyond feature factories and build a stronger product practice, this episode offers a grounded look at where to start.   References and resources mentioned in the show: Jason Knight One Knight in Product Podcast Blog: What Does a Product Owner Do, When, and Why? Blog: How to Ensure You're Working on the Most Important Items Each Iteration by Mike Cohn #124: How to Avoid Common Product Team Pitfalls with David Pereira #154: The Underpowered PO with Barnaby Golden Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Jason Knight is a product consultant, coach, and host of the One Knight in Product podcast who helps scaling B2B companies move beyond feature factories and build product teams that deliver real business impact. He works with organizations to connect strategy to execution through fractional product leadership, workshops, and coaching that bring clarity, alignment, and measurable results.  

AI can make teams faster. But it can also quietly make them worse. In this episode, Brian Milner and Hunter Hillegas dig into the risks no one wants to talk about—from eroding developer judgment to weakening team communication—and what healthy teams should do about it.   Overview AI tools are powerful. They can generate code, draft tests, and accelerate delivery in ways that felt impossible just a few years ago. But speed is not the same as effectiveness. In this episode, Brian sits down with Mountain Goat Software CTO Hunter Hillegas to explore where AI may actually be hurting Agile teams. They discuss the risk of losing junior developer growth paths, the illusion of productivity through inflated metrics, the danger of outsourcing judgment, and how AI can quietly create communication silos inside Scrum teams. This is not an anti-AI conversation. It is a practical one. You will hear what guardrails healthy teams should consider, why accountability still belongs to humans, and how to use AI as a tool without letting it reshape your culture in ways you did not intend. If your team is leaning into AI, this episode will help you do it with your eyes open.   References and resources mentioned in the show: Hunter Hillegas Blog: AI Doesn't Eliminate Agile Teams — It Increases the Need for Great Ones by Mike Cohn #169: Building Practical AI for Agile Teams with Hunter Hillegas #82: The Intersection of AI and Agile with Emilia Breton #151: What AI Is Really Delivering (and What It's Not) with Evan Leybourn & Christopher Morales Mountain Goat Software Subscribe to the Agile Mentors Podcast    Want to get involved? This show is designed for you, and we'd love your input.   Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one.   Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work.  Hunter Hillegas is the Chief Technology Officer at Mountain Goat Software. With over 20 years of experience in software development, product ownership, and team leadership, he leads the creation of tools like the AI Toolkit and Team Home to support effective, engaging learning experiences. Hunter lives in Santa Barbara, California, with his wife and their dog Enzo.

Estimating can bring out strong reactions, and for good reason. Mike Cohn and Brian Milner unpack why it gets misused, what “estimate responsibly” really means, and how to use planning to make better decisions without turning numbers into weapons. Overview In this episode, Brian sits down with Mike Cohn to talk about estimating and planning in a way that teams can actually live with. They explore why estimates became such a hot button topic, what the “no estimates” movement is reacting to, and how Mike's thinking has evolved over time. You will hear practical guidance on story points versus time, why teams should estimate only when it helps someone make a decision, and how to keep estimates from damaging trust. They also cover where flow metrics help, where they fall short, and how teams build credibility with leadership through responsible planning. References and resources mentioned in the show: Mike Cohn Estimating & Planning in Agile - A 2026 Field Guide Accurate Agile Planning Course Blog: Estimating and Planning in Agile: Why They Still Matter in 2026 by Mike Cohn Blog: Getting Better Estimates Is Easier Than You Think by Mike Cohn Blog: What Are Agile Story Points? By Mike Cohn Subscribe to the Agile Mentors Podcast Want to get involved? This show is designed for you, and we'd love your input. Enjoyed what you heard today? Please leave a rating and a review. It really helps, and we read every single one. Got an Agile subject you'd like us to discuss or a question that needs an answer? Share your thoughts with us at podcast@mountaingoatsoftware.com This episode's presenters are: Brian Milner is a Certified Scrum Trainer®, Certified Scrum Professional®, Certified ScrumMaster®, and Certified Scrum Product Owner®, and host of the Agile Mentors Podcast training at Mountain Goat Software. He's passionate about making a difference in people's day-to-day work, influenced by his own experience of transitioning to Scrum and seeing improvements in work/life balance, honesty, respect, and the quality of work. Mike Cohn, CEO of Mountain Goat Software, is a passionate advocate for agile methodologies. Co-founder of Agile Alliance and Scrum Alliance, he thrives on helping companies succeed with Agile and witnessing its transformative impact on individuals' careers. Mike resides in Northern Idaho with his family, two Havanese dogs, and an impressive hot sauce collection.

Karim Harbott: From Requirements Documents to Customer Obsession—Redefining the PO Role 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 Great Product Owner: Strategic, Customer-Obsessed, and Vision-Driven   "The PO role in the team is strategic. These POs focus on the customer, outcomes, and strategy. They're customer-obsessed and focus on the purpose and the why of the product." - Karim Harbott   Karim believes the industry fundamentally misunderstands what a Product Owner should be. The great Product Owners he's seen are strategic thinkers who are obsessed with the customer. They don't just manage a backlog—they paint a vision for the product and help the entire team become customer-obsessed alongside them.  These POs focus relentlessly on outcomes rather than outputs, asking "why are we building this?" before diving into "what should we build?" They understand the purpose of the product and communicate it compellingly.  Karim references Amazon's "working backwards" approach, where Product Owners start with the customer experience they want to create and work backwards to figure out what needs to be built. Great POs also embrace the framework of Desirability (what customers want), Viability (what makes business sense), Feasibility (what's technically possible), and Usability (what's easy to use). While the PO owns desirability and viability, they collaborate closely with designers on usability and technical teams on feasibility.  This is critical: software is a team sport, and great POs recognize that multiple roles share responsibility for delivery. Like David Marquet teaches, they empower the team to own decisions rather than dictating every detail. The result? Teams that understand the "why" and can innovate toward it autonomously.   Self-reflection Question: Does your Product Owner paint a compelling vision that inspires the team, or do they primarily manage a list of tasks? The Bad Product Owner: The User Story Writer "The user story writer PO thinks it's their job to write full, long requirements documents, put it in JIRA, and assign it to the team. This is far away from what the PO role should be." - Karim Harbott   The anti-pattern Karim sees most often is the "User Story Writer" Product Owner. These POs believe their job is to write detailed requirements documents, load them into JIRA, and assign them to the team. It's essentially waterfall disguised as Agile—treating user stories like mini-specifications rather than conversation starters. This approach completely misses the collaborative nature of product development.  Instead of engaging the team in understanding customer needs and co-creating solutions, these POs hand down fully-formed requirements and expect the team to execute without question. The problem is that this removes the team's ownership and creativity. When POs act as the sole source of product knowledge, they become bottlenecks.  The team can't make smart tradeoffs or innovate because they don't understand the underlying customer problems or business context. Using the Desirability-Viability-Feasibility-Usability framework, bad POs try to own all four dimensions themselves instead of recognizing that designers, developers, and other roles bring essential perspectives. The result is disengaged teams, slow delivery, and products that miss the mark because they were built to specifications rather than shaped by collaborative discovery. Software is a team sport—but the User Story Writer PO forgets to put the team on the field.   Self-reflection Question: Is your Product Owner engaging the team in collaborative discovery, or just handing down requirements to be implemented?   [The Scrum Master Toolbox Podcast Recommends]

Karim Harbott: Don't Scale Dysfunction—Fix the Team First 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.   "How do you define the success of a football manager? Football managers are successful when the team is successful. For Scrum Masters it is also like that. Is the team better than it was before?" - Karim Harbott   Karim uses a powerful analogy to define success for Scrum Masters: think of yourself as a football manager. A football manager isn't successful because they personally score goals—they're successful when the team wins. The same principle applies to Scrum Masters. Success isn't measured by how many problems you solve or how busy you are. It's measured by whether the team is better than they were before.  Are they more self-organizing? More effective? More aligned with organizational outcomes?  This requires a mindset shift. Unlike sprinters competing individually, Scrum Masters succeed by enabling others to be better.  Karim recommends involving the team when defining success—what does "better" mean to them? He also emphasizes linking the work of the team to organizational objectives. When teams understand how their efforts contribute to broader goals, they become more engaged and purposeful. But there's a critical warning: don't scale dysfunction! If a team isn't healthy, improving it is far more important than expanding your coaching to more teams.  A successful Scrum Master creates teams that don't need constant intervention—teams that can manage themselves, make decisions, and deliver value consistently. Just like a great football manager builds a team that plays brilliantly even when the manager isn't on the field.   Self-reflection Question: Is your team more capable and self-sufficient than they were six months ago, or have they become more dependent on you? Featured Retrospective Format for the Week: Systems Modeling with Causal Loop Diagrams "It shows how many aspects of the system there are and how things are interconnected. This helps us see something that we would not come up with in normal conversations." - Karim Harbott   Karim recommends using systems modeling—specifically causal loop diagrams—as a retrospective format. This approach helps teams visualize the complex interconnections between different aspects of their work. Instead of just listing what went wrong or right, causal loop diagrams reveal how various elements influence each other, often uncovering hidden feedback loops and unintended consequences.  The power of this format is that it surfaces insights the team wouldn't discover through normal conversation. Teams can then think of their retrospective actions as experiments—ways to interact with the system to test hypotheses about what will improve outcomes. This shifts retrospectives from complaint sessions to scientific inquiry, making them far more actionable and engaging. If your team is struggling with recurring issues or can't seem to break out of patterns, systems modeling might reveal the deeper dynamics at play.   [The Scrum Master Toolbox Podcast Recommends]

Karim Harbott: You Can't Make a Flower Grow Faster—The Oblique Approach to Shaping Culture 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.   "How can I make a flower grow faster? Culture is a product of the behaviors of people in the system." - Karim Harbott   For Karim, one of the biggest challenges—and enablers—in his current work is creating a supporting culture. After years of learning what doesn't work, he's come to understand that culture isn't something you can force or mandate. Like trying to make a flower grow faster by pulling on it, direct approaches to culture change often backfire.  Instead, Karim uses what he calls the "oblique approach"—changing culture indirectly by adjusting the five levers: leadership behaviors, organizational structure, incentives, metrics, and systems. Leadership behaviors are particularly crucial.  When leaders step back and encourage ownership rather than micromanaging, teams transform. Incentives have a huge impact on how teams work—align them poorly, and you'll get exactly the wrong behaviors.  Karim references Team of Teams by General Stanley McChrystal, which demonstrates how changing organizational structure and leadership philosophy can unlock extraordinary performance. He also uses the Competing Values Framework to help leaders understand different cultural orientations and their tradeoffs. But the most important lesson? There are always unexpected consequences. Culture change requires patience, experimentation, and a willingness to observe how the system responds. You can't force a flower to grow, but you can create the conditions where it thrives.   Self-reflection Question: Are you trying to change your organization's culture directly, or are you adjusting the conditions that shape behavior?   [The Scrum Master Toolbox Podcast Recommends]

Karim Harbott: Why System Design Beats Individual Coaching Every Time 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.   "You can't change people, but you can change the system. Change the environment, not the people." - Karim Harbott   Karim was coaching a distributed team that was struggling with defects appearing constantly during sprints. The developers and testers were at different sites, and communication seemed fractured. But Karim knew from experience that when teams are underperforming, the problem usually isn't the people—it's the system they're working in. He stepped back to examine the broader context, implementing behavior-driven development(BDD) and specification by example to improve clarity through BDD scenarios.  But the defects persisted.  Then, almost by accident, Karim discovered the root cause: the developers and testers were employed by different companies. They had competing interests, different incentives, and fundamentally misaligned goals. No amount of coaching the individuals would fix a structural problem like that.  It took months, but eventually the system changed—developers and testers were reorganized into unified teams from the same organization. Suddenly, the defects dropped dramatically. As Jocko Willink writes in Extreme Ownership, when something isn't working, look at the system first. Karim's experience proves that sometimes the most compassionate thing you can do is stop trying to fix people and start fixing the environment they work in.   Self-reflection Question: When your team struggles, do you look at the people or at the system they're embedded in? Featured Book of the Week: Scaling Lean and Agile Development by Craig Larman and Bas Vodde "This book was absolute gold. The way it is written, and the tools they talk about went beyond what I was talking about back then. They introduced many concepts that I now use." - Karim Harbott   Karim discovered Scaling Lean and Agile Development by accident, but it resonated with him immediately. The concepts Craig Larman and Bas Vodde introduced—particularly around LeSS (Large-Scale Scrum)—went far beyond the basics Karim had been working with. The book opened his eyes to system-level thinking at scale, showing how to maintain agility even as organizations grow.  It's packed with practical tools and frameworks that Karim still uses today. For anyone working beyond a single team, this book provides the depth and nuance that most scaling frameworks gloss over. Also worth reading: User Stories Applied by Mike Cohn, another foundational text that shaped Karim's approach to working with teams.   [The Scrum Master Toolbox Podcast Recommends]

Karim Harbott: The Day I Discovered I Was a Scrum Project Manager, Not a Scrum Master 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.   "I was telling the team what to do, instead of helping the team to be better on their own. There's a lot more to being a Scrum Master than Agile—working with people is such a different skillset." - Karim Harbott   Karim thought he had mastered Scrum. He had read the books, understood the framework, and was getting things done. His team seemed to be moving forward smoothly—until he stepped away for a few weeks.  But, when he returned, everything had fallen apart. The team couldn't function without him constantly directing their work. That's when Karim realized he had fallen into one of the most common anti-patterns in Agile: the Scrum Project Manager.  Instead of enabling his team to be more effective, he had become their bottleneck. Every decision flowed through him, every task needed his approval, and the team had learned to wait for his direction rather than taking ownership themselves. The wake-up call was brutal but necessary.  Karim discovered that pushing project management responsibilities to the people doing the work—as David Marquet advocates—was far more powerful than being the hero who solves all problems. The real skill wasn't in telling people what to do; it was in creating an environment where they could figure it out themselves. Geoff Watts calls this servant leadership, and Karim learned it the hard way: a great Scrum Master makes themselves progressively less necessary, not more indispensable.   Self-reflection Question: Are you enabling your team to be more effective, or have you become the person they can't function without?   [The Scrum Master Toolbox Podcast Recommends]