Author: martin

  • Friday Run, Ruby in My Ears

    Friday Run, Ruby in My Ears

    Friday afternoon. Running in the local forest near the Alte Försterei, AirPods in. I’m talking to Ruby — the orchestrator of my new AI agent team — somewhere between km 7 and km 8.

    Not a crisis. Not an urgent decision. I’d just been thinking about something we are currently working on, and the next thought arrived before I could stop it.

    Which is fine. Except I was on the one trail I reserve for not thinking about work.

    Here’s the thing: having an AI agent team that is genuinely always available — one that is starting to deliver real work every day this week — creates a pull I didn’t fully anticipate. It’s not really stress, at least not the bad kind. It’s something closer to momentum. When things are going well, switching off feels like friction, not rest.

    I finished the run. Ruby was still there when I got home. I still don’t know if that’s a feature or a design flaw.

    Both, probably.

    Where do you draw the line — or have you stopped trying to draw one?

  • The Jazz Mindset: What My Business Card Says About How I Work

    The Jazz Mindset: What My Business Card Says About How I Work

    My business card has a photo of me playing jazz piano. People notice. They ask why.

    I’m a bit of a jazz nerd, so that explains some of it. But there’s also a business reason.

    It’s a quick shorthand I’ve found for how I think — for my mindset.

    Jazz musicians listen before they play. Not as a courtesy — as a mindset. You find the space before you fill it. Miles Davis is often quoted on this: “Don’t play what’s there, play what’s not there.” And you’re not just listening for your own next note — you’re listening for what everyone around you needs.

    Underneath that listening is something called groove. Not tempo — a metronome keeps tempo. Not harmony — that’s the chords. Groove is the shared pulse the whole group locks into together. In a client room, it’s the difference between work that feels transactional and work that actually moves.

    That’s the part most meeting cultures train out of people. We show up with our answer ready. A jazz musician shows up open.

    But listening alone isn’t enough — because you’re not playing alone. The other musicians are shaping what you do next. That’s harmony: not everyone playing the same thing, but everyone playing things that work together. What you do depends on what the person next to you is doing. The result is something none of you could have made on your own.

    And when something unexpected happens — a wrong turn, a surprise from the room — you don’t stop. You play through it. That’s improvisation: rigorous preparation, played in response to what the room is actually giving you, not what you rehearsed for. It’s not winging it. The best client work I’ve been part of looked exactly like that.

    Herbie Hancock told a story about a night playing with Davis when he hit what felt like a disastrously wrong chord mid-solo. Davis responded by playing notes that made it fit. Hancock later reflected: “Miles didn’t hear it as a mistake. He heard it as something that happened. As an event.” The band didn’t stop. They built on it.

    That idea — the listening, the harmony, the groove, the shared thing you create together — has shaped how I think about client meetings, virtual or in person.

    What does your business card say? I’m curious.

  • When delivery scales — and architecture sets the pace

    When delivery scales — and architecture sets the pace

    When a program scales to more teams, I am regularly involved designing the team setup — and regularly the wrong person to do it.

    Not because I lack the experience. But because the knowledge that should drive that decision is not with me. It is in the domains.

    Anyone scaling a large modernization program often has the same instinct: build more teams, divide the scope, assign the available people to the newly defined areas. Bring in a leadership group that has the overview and can decide who’s needed. That sounds like sensible program management.

    It is structurally wrong — and the root cause is architectural, not organizational: as long as the domain boundaries are unresolved, resource allocation drives architecture. That is Conway’s Law. And we now know exactly what follows from it.

    The assumption that holds the classical approach together

    The classical approach works like this: a leadership group — program managers, architects, sometimes external consultants — designs the target organization. Teams are structured around capabilities, technical layers, or resource availability. Then the available people are fitted into that structure.

    McKinsey puts the starting point plainly in their Agile transformation methodology: “Most transformations start with building the top team’s understanding and aspirations.” That sounds plausible. The problem is the assumption underneath it: that the knowledge of what good team structure looks like sits at the top.

    It does not. And in a transformation of the complexity that decades-old legacy or even mainframe estates carry, that mistake is particularly consequential.

    Why the knowledge sits in the domains — and what Conway has to do with it

    The teams working daily with the systems and business processes understand the dependencies of the existing estate better than any leadership group does. They know which parts of the system are tightly coupled. They know where the real interface problems lie.

    Melvin Conway formulated in 1968 what we now call Conway’s Law: organizations that build systems inevitably produce designs that mirror their own communication structure. This is not a metaphor. It is a causal statement. If I cut teams along technical layers or by availability, I get a system architecture that mirrors that division — not the one I wanted.

    MacCormack, Rusnak, and Baldwin gave this empirical grounding in a 2008 Harvard Business School study. They examined how closely organizational structure correlates with product architecture — the so-called Mirroring Hypothesis. The finding: products from loosely coupled organizations are roughly eight times more modular than those from tightly coupled ones. Poor organizational design produces poor modular architecture — not as a side effect, but as a direct consequence.

    For a large modernization program — the type I work with — this means: the decision about how to cut teams is not a resourcing question. It is an architectural decision. And it must be made before the resourcing begins.

    The Inverse Conway Maneuver: architecture first

    Anyone who wants to deliberately create the architecture they want must design the team structure accordingly — not the other way around. Instead of defining teams and hoping the architecture follows, we reverse the sequence. We start with the question: what architecture do we need? What domains does the system have, how strong are their internal dependencies, how loose their connections to the outside?

    This reversal — known in the literature as the Inverse Conway Maneuver and listed on the Thoughtworks Technology Radar as an established technique — is the central lever when scaling delivery programs.

    In practice, this works through domain analysis. We use Domain-Driven Design: Event Storming sessions with domain experts from the business, Bounded Context definitions, Context Maps. The goal is always the same: a domain map that shows which areas of the system are strongly correlated internally and have as few external dependencies as possible.

    That map is the starting point for the team cut — not the free slots in people’s calendars.

    Skelton and Pais develop a complementary idea in Team Topologies: every team has a cognitive load — a maximum mental overhead it can carry. Stream-aligned teams that follow a single value stream minimize handoffs and keep that load manageable. Top-down structures that distribute capabilities by availability routinely ignore that limit.

    A layer-based cut forces handoffs across all teams for every business change. A domain-based cut eliminates that dependency entirely.

    What this means concretely in a complex program

    A large modernization program — with a legacy and sometimes mainframe estate where logic has grown into a practically inseparable unit over decades — brings exactly this challenge.

    The modernization strategy we recommend in these programs is consistent: build domain by domain, following the Strangler Fig pattern, slice by slice. Start with a core that proves the architecture under real conditions — before the program scales to further domains. The core business domains provide the structure; their boundaries must be established before the team cut.

    This is exactly where the Conway thesis applies directly: the domain cut must precede the resource cut. Team ownership follows from domain structure — not from capacity availability.

    That is why the first phase of such a program begins with Event Storming and Domain-Driven Design. Not as a methodological ritual. But because that step lays the foundation for every subsequent decision: what Bounded Contexts emerge within the core domains? Where do the boundaries lie — and who takes ownership of them?

    A concrete example from practice: core insurance processes — new business, and mid-term policy amendments — each span at least three business domains in complex systems. A quote request triggers pricing in one domain; an accepted application issues a policy in a second; and the binding event (cover going effective) drives downstream obligations — billing, correspondence, commissions — that live in still other contexts. These cross-domain handoffs are exactly what Event Storming makes explicit — and exactly the evidence a team needs to decide where Bounded Context boundaries should be drawn. Had the team made that cut before this analysis, it would have either ignored those handoffs or split across them arbitrarily.

    A leadership group at the drawing board cannot answer these questions. They emerge through working with the domain experts from the business — in the workshops, across the event maps, through the collaborative refinement of Bounded Contexts. Only once that map exists does it decide how teams are formed and how accountability is distributed.

    Self-Selection: letting the team decide

    In some programs we go one step further — and this is the step that generates the most resistance when we first propose it.

    Instead of centrally assigning available people to the newly defined domain teams, we let the team organize itself. The domain map is on the table. The available slots are transparent. The constraints are clear: skills, experience distribution, seniority. And then we ask people where they see themselves.

    The evidence for this approach is consistent — even if it comes from case studies rather than controlled experiments. Sandy Mamoli and David Mole documented self-selection through their work at Trade Me and in their book Creating Great Teams: teams that formed without direct management assignment became high-performing teams over two years, with minimal subsequent adjustments. Martin Lohmann’s experience report on SimCorp’s Product Division — a SAFe rollout involving roughly 550 people forming 55+ teams across seven ARTs — shows the same pattern at larger scale. At New Relic, around 50 software teams were entirely reformed through self-organization under predefined constraints. The organizers described the outcome afterwards as “way more successful than anybody anticipated”. And I have seen it working myself.

    An unexpected side effect: people chose colleagues they wanted to work with. That sounds simple — and it is. Those who get to choose take ownership. Those who are assigned wait and see.

    The natural moment for a self-selection session is the close of the program’s first phase: once the Bounded Context map of the core domains is in place, the team slots and their areas of responsibility are transparent enough for people to make an informed choice.

    Execution matters: self-selection requires active facilitation. Without it, the results are not good teams — the case studies show that as clearly as they show the successes.

    The further a program moves from self-selection toward central assignment, the less ownership people feel — and the more adjustment the structure needs afterwards.

    How this fits the broader conversation

    The Spotify model appears in every discussion about team scaling. Joakim Sundén, one of the Agile Coaches who worked alongside the model during its formation, described it as “part ambition, part approximation” — never fully implemented, always an aspirational image. What companies copied was the structure: squads, tribes, chapters. What they ignored was the culture — genuine autonomy, psychological safety, the willingness to let mistakes happen. Structure without domain logic and without real decentralisation is just a label.

    The difference does not lie in the org chart. It lies in who holds the knowledge that informs the cut.

    What this means in practice

    Scaling is not a resourcing problem. It is an architecture problem.

    Adding new teams without first understanding the domain structure of the system builds an organizational form that — by Conway’s Law — produces a system architecture. And that architecture will not be the one you wanted. The HBS study shows this effect is real and measurable, not theoretical.

    For a complex modernization program, this means concretely: the investment in Event Storming and Domain-Driven Design at the outset is not a methodological box-tick. It is the precondition for the teams that scale in later phases to work along the right boundaries. Domain ownership, Bounded Contexts, Context Maps — these artifacts are not just architecture documents. They are the foundation of the team organization.

    Who decides where the domain boundaries should lie? Someone in the leadership group who knows the scope — or the people who work with the system daily and know where the real couplings are?

    In the programs I work with, this question is regularly answered implicitly before it is ever asked. That is usually how the problem starts.

    Sources and further reading

    Thoughtworks / Inverse Conway Maneuver

    Conway’s Law — empirical foundation

    Team Topologies

    Self-Selection

    Spotify — a closer look

    McKinsey Agile

  • The Atlantic: A Bass Built by a Friend Who Cares

    The Atlantic: A Bass Built by a Friend Who Cares

    My friend Tillman has been building electric basses for years. I finally bought one. There’s just one small problem: I don’t play bass. Yet.

    Tillman Anton is one of those rare people who has turned a deep love of his craft into a life’s work. He has been building electric basses for many years — and the Atlantic, a five-string he made, is a masterpiece. The wood, the weight, the detail in the neck. You hold it and you understand immediately that this came from someone who cares.

    I am a musician. Not a bass player — but anyone who loves music gets drawn into the world of beautiful instruments. I could not keep admiring Tillman’s work from a distance any longer. So I bought one. And yes, the coming months will involve learning to play it. That is part of the joy.

    If you love basses — or you know someone who does — take a look at what Tillman builds. He will find the right instrument for you too.

    antons-instruments.de

  • Automate the Boring Stuff: Why the Book Was Right All Along

    Automate the Boring Stuff: Why the Book Was Right All Along

    Years ago I bought a book called Automate the Boring Stuff with Python. I read most of it. I understood it. I never actually automated anything.

    The book was fine. The problem was me.

    I was a developer earlier in my career. I knew enough Python to follow every example — renaming and organizing thousands of files in one go, scraping a website instead of copy-pasting data by hand, bulk-processing Excel files or PDFs, filling forms automatically. The use cases were real. The payoff was obvious. And yet none of the scripts ever happened.

    The honest reason: I had stepped away from daily coding. Getting fluent enough to actually build and debug a working script would have cost more time and mental energy than the task I was trying to automate. The ROI was negative. So the book sat on the shelf.

    That changed when I started using ChatGPT and Gemini as coding co-pilots. I was still writing Python — but with help. The activation energy dropped enough to make some scripts worth finishing.

    Then I started working with Claude Code. Now I don’t write the Python myself at all. I describe what I want. The AI builds it, iterates, fixes it when something breaks. The barrier is simply gone.

    What I keep thinking about is this: the book’s promise was always correct. The bottleneck was never the tool, and it wasn’t willpower either. It was the cost of re-fluency for someone who had moved away from daily coding. AI removed that activation energy entirely.

    Which means the constraint has shifted. The question is no longer “can you code well enough to automate this?” That question is gone. The question now is “do you have good enough judgment about what is actually worth automating?” That’s a thinking skill, not a technical one.

    The tool was never the barrier. Knowing what to point it at — that’s the work that remains.

    What boring task would you finally automate — now that coding fluency is off the table?

  • Cynefin: We need to teach this in school!

    Using the wrong tools

    You probably know about his metaphor: Using a hammer to drive in a screw. While I knew about the idea of using the wrong tools to solve problems for many years, I only recently learned about the Cynefin. This framework describes this situation much more profoundly and has been an eye-opener for me. Having been a victim but also a perpetrator of “using the wrong tools”,  I can’t wait to write this blog post. Because so much pain has been created.  So much effort, money and motivation has been wasted. And even worse, it continues everyday. We need to change this! You need to learn about the Cynefin-framework and you need to pass on the word.

    I will only scratch the surface of the framework, but that might already sufficient to change the way you see the world. At least that‘s how it was for me when I first got in contact with it. But before I start to talk about the framework let me provide some of my very own examples that should explain  why we need to learn about it:

    • Several years of time and lots of money wasted trying to develop a new revolutionary product for a large German firm; treating this as project to implement some fixed scope without regularly asking for feedback and quickly pivoting.
    • A large, company-wide transformation project managed with a project plan containing some thousand tasks.
    • Countless well-thought-out strategies and large re-organisations failing or not fully reaching the desired and planned outcomes
    • Neglecting what we already know of good-practices in project management when building an office 
    • Hours and hours of heated discussions about the best way of working

    And these are only me own personal experiences… So, without further ado, let me quickly present you the the Cynefin-framework. I will come back to these examples and some notes on digital transformations later.

    The Cynefin-Framework

    This is how the Cynefin-framework looks like. You can see four quadrants. Unlike many other models, it does not have dimensions, i.e. there is no x or y axis. It‘s basically these four quadrants and some area or space that separates these area. I will not talk about these borders and why some of them are thicker and some are thinner. Let‘s keep it simple. Four quadrants. The titles of them all start with with the letter „C“. They are called CLEAR, COMPLICATED, COMPLEX, and CHAOTIC.

    What can we find in these quadrants?

    Let me quote Sonja Blignaut from the book “Cynefin. Weaving Sense-Making into the Fabric of our world”. She writes “At its most basic, Cynefin allow us to distinguish between three different kinds of systems:

    1. Ordered systems that are governed and constrained in such a way that cause and effect relationships are either clear or discoverable through analysis;
    2. Complex systems where causal relationships are entangled and dynamic and the only way to understand the system is to interact; and
    3. Chaotic systems where there are no effective constraints, turbulence prevails and immediate stabilizing action is required”.

    Let‘s start with Clear: Clear is the domain of best practices, the solution to a problem is visible to everybody. The relationship between cause and effect is obvious or has become obvious. Some examples might help: How about  the work in a call center: a customer calls, the agent listens and then quickly chooses a corresponding script to help the customer with a solution to the problem.

    Another example could be building a family home. A house. That‘s not a simple task. But it has been done a billion times before. We have best practices. The domain is fully understood. There are even catalogs in which you can choose your dream home. By the way, clear or obvious or simple does not mean easy. It can still be difficult and hard work. Moreover, building a special opera house like the one in Hamburg does not fall into this domain. 

    Now the Complicated domain.  Here, the solution is not visible to anyone.  You need expert knowledge. This is the expert domain. An extreme example could be flying to the moon. There was a massive amount of expert knowledge and analysis required to master this enormous challenge. Yet the relationship between cause and effect, although complicated, could finally be understood. Or how about building cars. Engineering work. Another example from the IT world would be the global roll out of an ERP system. This is clearly not an easy task, yet we have good practices for that. We can build methodologies for that. We can have process charts, templates, role descriptions, artifacts. We might build a physical or commercial model. We might even be able to analyse and estimate the work and offer a fixed price since we know how to control and manage the work.  Problem solving in the complicated domain is not easy. But if you get enough brain power, if you get your best engineers and experts onto the project, you will be able to find a good solution. Maybe not the best one. This is because the relationship between cause and effect can be analysed and understood.

    Now, this is all different in the Complex domain. Here, the relationship between cause and effect can only be perceived in retrospect. What does that mean? Well, the impact is enormous. No matter how many experts you assigned to a problem, no matter how smart they are, no matter how hard you try, you are not able to derive a solution. Well, in fact you might be able to create a solution, and you might convince yourself and others that this is the solution. But as the cause and effect relationship in the complex domain is only visible in retrospect you are simply fooling yourself and others. And this is happening everyday. And I can confess: I have been a great fool myself.

    To make this article concise, I am skipping the Chaotic domain. The interested reader will have no problem to finding out more about Cynefin and go much deeper.

    For now, let’s focus on the implications: Like many of us, I have been socialised with the belief that through proper analysis we can find a solution. Being trained in mathematics and engineering I would look at a problem (“sense”), then take it apart, understand its properties (“analyse”) and finally develop a solution (“respond”). With proper training and some years of experience, I have become an expert in my field (e.g. project management) and became good at applying the practices. Where is this approach located in the Cynefin framework? Well, you guessed it right, it’s in the Complicated domain. So far so good. But the problem starts when we apply our good practices in the complex or chaotic domain. Here, the cause-effect relationship cannot be known in advance. Thus, we need a very different approach. In the complicated domain, we first need to try out something (“probe”), then we see how things develop (“sense”), and from the feedback we have received, we continue or change our approach (“respond”). In a nutshell, this is what agile methodologies are all about. They emphasise on fast feedback and acting upon it.  So coming back to the initial metaphor, we get into trouble when we apply the wrong tools (think “hammer”) in a specific environment. Most often I see this as good practices from the complicated domain being used in the complex domain. That might be project and program management methods used in complex product development or for transformations. But sometimes also (yet, less often) when we use agile methods in situations where we already have good practices in place.

    Moreover, knowing the Cynefin-framework, the discussion we often have during a digital transformation becomes much easier. As it is not about which way of working is better. Is agile better than waterfall? Or the other way around? This is fundamentally the wrong question. It’s rather if the set of tools or approaches are well suited to the domain. 

    I wished I would have known about Cynefin much earlier.  This would have saved myself and my teams a lot of wasted effort (being the person using the wrong tools).  Or it would have given me the confidence and the arguments to say out loudly that something is going deeply wrong (being the victim). I wished we all would learn about the Cynefin-framework. So why not discuss it in school? Until we are there, please help to save ourselves lots of wasted effort. Please have a look at it yourself and tell others about it.   

  • Anti-Pattern: This shot must be a winner

    This video is about a pattern that I have noticed lately in the IT industry. It‘s actually an anti-pattern or a fallacy and I would like to call it „this shot must be a winner“. Or in other words, this project must be a success.

    Despite all our knowledge about large and complex IT programs, I see that these are planned and started again and again. We know,  they have a high risk of failing,  they become late and exceed their budgets and customers are often not happy about the end results.  I personally have been a member in some of these death marches so I know what I am talking about.

    But why do people still consider these large and complex IT projects? Despite the knowledge of their implementation risk. Despite the knowledge that chances of success can be improved, e.g. by decreasing the batch size. In other words be cutting large initiatives into several thin slices.   

    To be frank, let’s acknowledge, there are some programs which are  large and complex by nature. Think of building a large bridge or a train track from one city to another. An agile approach of delivering value fast, breaking down initiatives and  experimenting with MVPs does not really work there. At least to my knowledge. Please let me know if you know any examples in the comments.  I am really interested. So some of our challenges don‘t lend themselves to reduction in size, complexity, risk and time. And as a  side note and teaser, the Cynefin framework can help us to understand why and how some challenges are different than others.

    Ok, back to today’s IT programs. A few days ago I moderated a round table about portfolio management. A participant said that the days of large and complex programs have passed. I thought, yes, she‘s right, but at the same time, this  is not what I am seeing out there. Again and again, I am seeing large and complex IT programs kicked-off. With unrealistic time lines, unclear outcomes and high risks. I hear IT managers say:  „This project must be a winner“.  „We really need to get it right this time“, „we are setting everything on one card“. Or even „The future of our company depends on this strategic opportunity“.

    Then I think, they should know better. They should know about the risks of large and complex  IT programs. They should see the possibilities to cut out thin slices, start with MVPs. Or use methods like R.A.T (riskiest assumptions tested). Why don‘t they get it? It’s all there, right in front of them. The evidence, on the one side, and the principles, techniques and tools on the other side. And yes, I hear complaints from some managers in these death marches.  So what‘s happening?

    I came to believe that it is not the individual‘s desire for large and complex IT programs that creates this anti-pattern. I rather believe the problem is systematic. I came to believe that the root cause lies in way how mature companies have grown over time to become what they are. And it’s these mature companies where I see this anti-pattern. I believe this anti-pattern is rooted in the management hierarchy that mature companies have established over years of growth. You know this management hierarchy. It’s the predominant org structure in well developed or mature firms. 

    In short, as firms grow and mature they utilize efficiencies of scale to offer once differentiated services at lower prizes as the competition it catching up. They need to, in order to survive in a competitive environment. And by doing that, they create silos. And these silos can make a firm more efficient, but also less able to change and adapt. 

    So, the management hierarchy and silos.

    Let‘s have a look at how decisions are made in the hierarchy.

    Decision move up the management ranks. They bubble up. Look, managers usually don’t push decisions downwards to the teams or people affected. One of the reasons is that teams usually don‘t have end-2-end responsibility for the services or products they create. Usually, they are not autonomous. They are dependent on the input and alignment with other teams. It’s a direct consequence of having silos. Let‘s not forget why we have these silos in place to begin with. Within them, people can specialize, they can build deeper knowledge and skills with peers who are working on the same kind of problems. They gather more work of a similar type and thus can automate and use pooling to become more efficient. 

    Let‘s say,  one team decides to implement a certain capability or change. As this team is not autonomous, it requires support from one or more other teams or colleagues from other departments. Thus,  now, it‘s not a single team working on this initiative any more,  but people from different teams or even a coordinated effort from several teams. And therefore the scope gets larger. And due to alignment and required hand-overs the duration also increases.  So it‘s not the team‘s decision alone any more. Thus, decisions naturally move up the hierarchy. And they become larger in scope and impact as more teams get affected. So changes and the associated decisions naturally become more and more complex. And consequently they also become more risky. And management is aware of that. They ask teams to go back and further analyze the proposed change to make it less risky. Obviously,  this well-meant task further slows down execution speed. In my practice I have witnesses several of these analyses and feasibility studies being executed, the result were nicely prepared, but NOBODY, yes nobody,  actually looked at the results. Since further more urgent questions came up in the meanwhile. That‘s a large source of waste. And obviously, people doing the tedious analysis work also loose their motivation over time. 

    As changes in mature firms take longer to implement they also miss the chance to quickly see the impact and the results of initiatives. So instead of learning and making further decisions based on these learnings, they need to rely on business cases and management narratives.

    With that in mind, I don‘t blame any individual for starting a large and complex program. For saying „this shot must be a winner“. It‘s the org structure of mature firms that creates  large and complex IT projects. It‘s the system. And as long as we don‘t start changing the structure of the organization, digital transformations will continue to fail. And we will continue to see large and complex projects and many of these will fail, as well.

    So what can we do about this? To be frank, I am not sure. Digital Transformations are difficult. Yet, considering the agile principle of starting small,  the first thing is awareness. Understanding the impact of the management hierarchy and silos. And seeing how it can be done differently. Seeing truly agile firms.  Experiencing how autonomous teams operate. But beware of faux agile or cargo cult agile. So go visit the valley or start here in Berlin. And that is not just for the development teams, especially managers should consider the impact of silos and the management hierarchy Go and read about the evidence. Read about Conway‘s law. Then, if you are a VP or director, think twice the next time you ask for a feasibility study. Why don‘t you ask for an MVP instead and learn from the user feedback. If you are a program manager, think twice and ask yourself if you really want to wait for 18 months to see the first results. And be honest to yourself, chances are that you don‘t even get results in 18 months, it might be later and you might not get what you have hoped for in the first place. Listen carefully the next time somebody tells you this project must be a winner. Investigate how a thin slice could look like. It‘s not easy, to be fair, but so are these large and complex programs and death marches.

  • Template for Calculating Business Value Points

    In my video about Lean&Agile Portfolio Management, I describe the use of business values points to make investment decisions more objective. I present a sheet in which I calculate Business Value Points (BVP). To make the calculation of easier for, I provide you a template for creating your own calculation scheme.

    Project Value Sheet Template

  • Distributed Delivery: A Game Changer for Software Excellence at Scale

    Offshoring is often treated as little more than an extended workbench for IT projects. For us at Thoughtworks, it never was. We practice agile software development in globally distributed teams — what we call Distributed Delivery.

    In this webinar, I discuss Distributed Software Delivery with David Toborek (Metro Digital), Daniel Loeffelholz (Thoughtworks), Sven Dittmer (Mercedes-Benz), and Lucy Chambers (Thoughtworks).

    Here is the webinar recording:

  • Top 20 Mistakes in Agile Offshore Software Delivery

    For many years I have been involved in distributed software delivery. At the beginning in a waterfall setup. Since 2017 also in agile distributed delivery. I have seen many engagement fail and also have been part of successful delivery setups. Here’s my personal top 20 list of mistakes in that area.

    Nr 20: Involve offshore teams too late
    Nr. 19:  Forget to plan enough travel cost
    Nr. 18: Forget to invest into communication infrastructure
    Nr. 17: Neglect the time zone differences or cultural norms when scheduling ceremonies
    Nr. 16: In communications, forget to level the playing field
    Nr. 15: Teat all offshore location as „Offshore”
    Nr. 14: Missing alignment on the delivery model
    Nr. 13: Trying to enforce the same working mode for all teams
    Nr. 12: Use the one-size-fits-all delivery model
    Nr. 11: Go distributed with the wrong engagement
    Nr. 10: Assign the wrong work to the offshore team
    Nr. 9: Forget to extend the role model setup
    Nr. 8: Deny the language differences 
    Nr. 8: Not understanding political and cultural differences across the locations
    Nr. 6: Forget the relationship building
    Nr. 5: No direct access to users and customers
    Nr. 4: Forget the business view 
    Nr. 3: Forget to create the big picture for offshore teams
    Nr. 2: Offshore teams not on eye-to eye level  
    Nr. 1: Do it for the wrong reason, i.e. only look at the price