The empirical principles at the heart of Scrum are precisely what AI-driven delivery requires. Whether your organization actually supports them — and whether the framework itself is ready — is another matter. Get insights from Alan Brown that drove him to write the new book, Making AI Work for Britain in this blog post.

Num mundo onde a Inteligência Artificial já influencia decisões, recomendações e experiências digitais, uma checklist técnica tradicional deixou de ser suficiente. Código concluído, testes executados e deploy realizado podem já não garantir qualidade no que realmente importa. Neste artigo exploramos porque a Definition of Done precisa de evoluir para responder aos novos riscos e oportunidades dos produtos com IA, protegendo utilizadores, reforçando confiança e ajudando equipas Scrum a entregar valor real de forma sustentável.

A detailed tutorial on building an AI Agent with Make.com to delegate writing release notes.

When you are using AI to refine something, run it by an AI. And then ask another AI what it thinks of the deliverable.

In diesem Artikel beschreibt PST Simon Flossmann 5 todsichere Wege, als Scrum Master miserabel zu sein

Most mistakes in Scrum aren’t because people don’t understand the framework—they come from applying reasonable thinking in the wrong context. Cognitive traps happen when decisions favor efficiency, control, or comfort over transparency, inspection, and adaptation. Here is one of ten cognitive traps: role authority trap.

While the organizational design debate appears to be the surface, the ongoing role repositioning is what the people on the ground are living through.

The 85% who skip Cost of Delay aren't untrained. Their prefrontal cortex is depleted by 10am, SCARF-threatened by quantification. Rock's neuroscience is the missing operating manual.

Scrum Masters who merely run events are easily replaceable, but is that what the Scrum Master was meant to be?

Let’s walk through the types of questions showing up in Product Owner interviews today—and what they’re really testing.

In the Scrum Guide, “state” does not mean a vague status update. It means the current condition of the Product Backlog, Sprint Backlog, and Increment at a particular moment in time.

Ihr Claude-Pro-Abo stößt schneller an Grenzen als im Januar, denn Anthropic hat die Obergrenze stillschweigend neu bepreist. Vier Prinzipien erklären, wie die Token-Ökonomie tatsächlich funktioniert.

In the last post we established transparency over the amount of work a team has in hand. We put together what was essentially a "To Do" list -- "To Do", "Doing", and "Done" -- and gained an understanding of what it means to be working on something "right now". The next step is to tighten all of this up by clearly relating "Done" to "usable", and understanding what that actually means.

In this blog, I share my straightforward perception of the changes that we will see for the Scrum Roles in the future, affected by AI.

AI is changing the game, but Professional Scrum is as relevant as it ever was. Let’s talk about why and what that means for Scrum Masters.

An AI-Augmented Product Backlog is an ordered list of work that has been explicitly formatted not just for humans, but as machine-readable technical schemas (Prompt-Ready PBIs) that autonomous agents can execute without hallucinating.

In diesem Artikel erklärt PST Simon Flossmann, warum Konflikte IT-Teams ihre besten Entwickler kosten.

Have you gotten used to your new companion? Well, brace for impact as the economic reality kicks in. We are witnessing a pattern we have seen several times before: the land-grab period comes to an end. And subsidies.

Reinertsen found 85% of product managers cannot price their time. Without Cost of Delay, prioritization is opinion. In the AI era, pricing time is survival discipline.

What AI has finally exposed about individuals and interactions

Treat people for who they truly are; not only does this change how an organization operates, but it also changes how people acknowledge their work and themselves. When people are no longer reduced to fixed metrics or roles, decisions become more long-term and less dependent on short-term pressures. And perhaps, then work is no longer a system for people to adapt to, but becomes a space where people can better understand themselves – and the reason "why we are doing what we are doing."

Most Sprint Retrospectives do not fail because people did not talk. They fail because the team avoided the real issue and left without changing a damn thing in the next Sprint.

Artificial Intelligence is transforming how teams do their work. AI assisted coding has taken teams by storm, every team now uses one of the coding agents like Claude or Copilot. All the work and changes are becoming faster, data-driven and augmented by AI. However, with all this technological advancement and if I may call it the rat-race of creating the next AI driven product, one question is getting overlooked: Is Psychological Safety still intact when AI enters the team?

Kate was a Business Analyst for 12 years in an IT company in Greece. Once she made a definite decision to jump out of her comfort zone and become a Scrum Master. Let's walk through her story.

The rise of AI-enabled Scrum Teams has fueled debate over Sprint Length, with some proposing daily Sprints due to AI's high productivity. However, shortening Sprints or broadening goals must prioritize human and organizational constraints over raw output. Key considerations include the limited Availability of stakeholders for frequent feedback and the risk of cognitive overload from reviewing massive amounts of AI-generated content. Teams should consider using the time saved by AI to improve the product's operating model, conduct research, or invest in training, rather than simply increasing the scope of the goal (Focus and Value). Furthermore, the rapid emergence of new AI tools makes historical data for Predictability less valuable, suggesting a focus on timeboxing the most valuable Sprint Goal. Ultimately, the ideal Sprint length must account for stakeholder capacity, feedback cycles, and the scope of the broader value stream, not just the speed of AI delivery.

After more than 20 years coaching Scrum Teams, I’ve learned the interview isn’t just about them judging me: I’m judging them right back. I’m looking for clues about whether this is a place where I can actually help teams improve, or if it's just not a good fit for me.

In this blog, David Spinks discusses the importance of true stakeholder collaboration, and why using tools like a stakeholder map may not be enough to achieve it.

En este artículo el PST Alex Ballarin explica como descomponer bien las épicas en historias de usuario. Además recuerda el origen y verdadera utilidad de este formato de paquete de trabajo, e incluye ejemplos prácticos de como escribir buenas épicas, features e historias de usuario.

Real questions hiring managers might actually ask

The landscape of software development has permanently changed. You should also adapt to the AI era and refine your events to induct AI agents in your Scrum Team

Scrum is no longer a specialist competency, but a fundamental literacy for anyone who wants to work in a collaborative, modern workplace. Let's review 8 powerful triggers that force people to learn Scrum.

In diesem Artikel teilt PST Simon Flossmann 3 KI-Prompts mit denen typische Probleme in Scrum Teams im Handumdrehen lösen lassen.

A new edition of the FREE "Scrum Master Interview Questions Guide" is available: 97 Questions, including guidance on answers and red flags, covering all relevant fields.

When AI commoditizes execution, competitive advantage shifts to organizational context and learning speed, operating model capabilities most organizations have broken or never built.

En este artículo el PST Alex Ballarin reflexiona sobre qué debe saber el Scrum Master acerca de los aspectos éticos y legales de la IA. Estas responsabilidades suelen estar en los tejados de los departamentos legal y otros, pero los equipos también deben conocerlas y co-responsabilizarse de los efectos de su trabajo.

It is said that Developers are committed to the Sprint Goal and flexible about the scope of the Sprint. But what does it mean?!

Too many people throw around terms like 10x developer or 100x team. I want to talk with you about the -100x team.

Most mistakes in Scrum aren’t because people don’t understand the framework—they come from applying reasonable thinking in the wrong context. Cognitive traps happen when decisions favor efficiency, control, or comfort over transparency, inspection, and adaptation. Here is one of ten cognitive traps: efficiency over empiricism.

Many teams blame the Daily Scrum when it starts feeling repetitive, pointless, or performative. But the meeting is usually not the real issue. The real issue is weak transparency: the team cannot clearly see what is true, what threatens the Sprint Goal, and what should change today.

Most teams “do Scrum.” Few see real ROI. Here’s how Scrum drives growth by cutting costs, accelerating delivery, and aligning work with customer value, plus practical tactics you can apply immediately by PST Robb Pieper.

Seu produto está morrendo na lama operacional? Entenda por que POs e empreendedores falham ao ignorar a conexão entre o negócio e o backlog. Usando o exemplo prático de uma padaria artesanal, aprenda como transcrever o Business Model Canvas em uma estratégia de produto matadora e alcance o Product-Market Fit de uma vez por todas

In Scrum, there are three artifacts helping us keep transparency high. But what are their practical shapes?

Most problems in Scrum don’t come from misunderstanding the framework. They come from applying reasonable thinking in the wrong context. These cognitive traps show up as decisions that feel right—more efficient, more controlled, more predictable—but quietly undermine transparency, inspection, and adaptation. This overview outlines ten of the most common traps and how they distort delivery in practice.

In this blog, I want to discuss a powerful phrase that has come up in my interviews and research: AI is the next printing press. The analogy is powerful because, just as the printing press turned knowledge from a scarce luxury into an abundant product, AI and automation are acting as a "cognitive printing press" by democratizing capabilities. This lowers the barriers for knowledge workers, even for someone like me who is not a front-end developer, to rapidly create and adapt products, greatly accelerating the delivery pipeline. While I am ultimately a techno-optimist, I recognize that the printing press analogy also offers a word of caution, highlighting potential risks such as quality issues (like "hallucinations"), job displacement, and the pressing need for new regulation and ethical frameworks. To navigate AI's impact, I believe agile product developers must focus on three critical things: improving and changing the innovation pipeline, ensuring governance is robust through transparency and separation of controls, and doubling down on discipline and professionalism.

En este post el PST Alex Ballarín explica como potenciar la efectividad de las retrospectivas de Scrum usando la IA: variar los formatos, preparar mejores preguntas, detectar lo que el equipo no dice, crear compromisos concretos y factibles y mejorar la propia retro.

Many Scrum Masters think they should update the Sprint Backlog, but ...

スクラムは、スクラムのフレームワークに従うことで効果を発揮できるようになっており、そこからチームと組織の自発性、自律性、複雑な問題へ取り組むことに対して主体的になれるような仕組みが備わっています。また、意図的に不完全にもしています。この利点を活かしたスクラムへの取り組み方を紹介します。

In this article PST Simon Flossmann shares 3 levers for better team performance.

In diesem Artikel beschreibt PST Simon Flossmann seine Erfahrung nach 10 Jahren IT-Beratung: Diese 5 „Feinde“ verbrennen das Budget – noch bevor Features live gehen

Many change efforts hit a wall - not because the idea is bad, but because organizations work like tensegrity structures: highly interconnected, under tension, systemic. Understand that, and you'll approach change differently. As a Scrum Master, this might be the missing piece you've been looking for.