So proud of my team, Boom X (two generations), for fearlessly joining the SAAB Smart Airbase Hackathon this weekend! So much fun—and so much learning 👏

Being part of this event and working as a small product team at the forefront of vibe coding and AI-supported development was an incredible experience.

I would recommend this to anyone curious about how cutting-edge technology can be used to solve real customer problems. When vibe coding in a small product team, you end up innovating across all layers at the same time. You can truly work user-experience first—focusing on the customer problem, quickly delivering prototypes and end-to-end scenarios, and getting feedback that immediately impacts both the product and the evolving business strategy.

I learned a lot myself—making my first ever pull request and pushing to Git. As a former designer, product owner, and chief product owner, I’ve never really been that hands-on with coding until I started experimenting with Lovable about six months ago. This time we used VS Code, a more “hardcore” version of vibe coding. Luckily, I had my brother Daniel Blom on the team. He does full-stack AI-enabled development for a living—and, luckily for me, he also has enormous patience. 😉

Starting early by aligning on the customer problems we wanted to solve and agreeing on a shared strategy was crucial. So was defining the structure for the processes we were digitizing: the rules, the information flows, and what success would look like. We also used quick paper prototyping to create a shared foundation.

When everyone in a team can work simultaneously on the same product—with interconnected user scenarios and AI superpowers—product strategy becomes what makes or breaks your efforts. Even if you throw away prototypes or do work that ends up unused, you are still moving closer to creating value, as long as you focus on the most important customer problems.

The only thing that made me a bit frustrated during this otherwise amazing experience was having to work with a slower, less capable AI model after getting used to the best ones. 😅 That really threw me off. Suddenly, instead of giving me superpowers, the AI created more work—misunderstandings, friction, and unnecessary effort.

The short 5 min demo video of the prototype

This was the video the jury got to select the five finalist teams. Our team wasn’t among those five, but we were invited to the event in Linköping, which was a great experience in itself.

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The impact of short time boxes

The impact of extremely short timeboxes, like in a hackathon, is remarkable for creativity. At the same time, without deep domain knowledge and context, it’s hard to know whether you’re solving the most important problems—or solving them in the right way. Everything remains a hypothesis until it’s tested and proven in reality.

Our idea in short

Our concept had four parts:

1. A digitized simulation environment
We proposed building a digital simulation (inspired by a board game) where personnel at the airbase can train decision-making under pressure. Ideally, this would be as close to reality as possible—or even use the same interface as the real system.

2. Role-based user perspectives with AI support and Human in the Loop
The system adapts to different user roles, providing tailored views depending on context. AI would support decision-making in high-VUCA environments, help users stay updated, and highlight when critical information is missing—or where to get it. We made a decision early on to keep the human in the loop, meaning AI would not make decisions only provide suggestions for imput of data and decisions.

3. Continuous development through simulation
The simulation could act as a test environment to continuously evolve the system based on real needs. By training and building at the same time, developers and users work closely together—enabling fast, customer-focused product development. The airforce could then choose when to adopt new capabilities.

4. A new way of working
This approach could enable a new way of developing and operating software within the Swedish Air Force—and potentially across other parts of the military—together with partners such as SAAB.

Could events like this enable a better world?

My hope is that more managers and executives get to experience this firsthand. Spend just a day or two working alongside your teams, and you will quickly realize that giving them access to the best tools available is one of the most important strategies you can have. Any organization that locks itself into long-term contracts with only one—or just a few—AI models (even if they are the best today) will soon need to reinvest. The AI landscape is evolving incredibly fast, and staying flexible is key to unlocking these new superpowers.

All organizations who want to benefit from AI must adapt their decision making and their operational model. Maybe hackathons can be that high speed segway into the future most organizations need? A way to experience how AI-enabled product development can be done cross functionally – and also development of AI-tools? What if new systems can be built at high speed based on real user needs (not old specifications) – and in a safe way be tested? Could we then replace old legacy systems at speed too?

A big thanks to Olof Sundin and the team at SAAB and the Swedish Air Force for creating such a positive, energetic, inclusive and inspiring event!

I hope that many more will come and that the learnings will enable positive outcomes and a more safe world for everyone!

How to define:

*Artificial intelligence refers to systems that learn from data and support or automate decision-making processes that would otherwise require human judgment (Shrestha et al., 2019). In organizations, this means that AI increasingly becomes part of how decisions are made, how work is coordinated, and how value is created. The AI-Act is the legal framework on AI worldwide. It defines AI as a machine-based system designed to operate with varying levels of autonomy that may exhibit adaptiveness after deployment and that, for explicit or implicit objectives, infers from the input it receives how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. (EU AI Act, Article 3) In this article, AI is used as a general term for both traditional AI and generative AI, with a focus on how the technology influences the way organizations make decisions, collaborate, and create customer value, rather than on specific tools or solutions. **Digitalizing core processes means making the processes where the organization interacts with customers and makes critical decisions fully digital, so that information, decisions, and actions are connected without manual steps that slow down the flow e.g. handovers between units. Two common pitfalls: The customer-facing layer is digital, while the underlying core process remains largely manual; The supporting processes, reports, or ways of working are digitalized within a silo function. The core process is defined as the driver of business value. Disclaimer: There are several ways to define AI. The chosen definitions here, are used to clarify the purpose and perspectives of this blog post.

Introduction

This article builds on insights from my studies in AI*, innovation, and organizational design at Halmstad University, combined with experiences from my everyday work as an Enterprise Coach at Dandy People. It has been more than a year since I completed the program, yet many of the insights have become increasingly relevant as AI has taken a more prominent place in organizations’ strategic agendas. I have reflected and found a few things that feel particularly relevant right now, especially regarding the organizational capabilities that are often overlooked when the focus shifts to the next trend or solution meant to address the challenges at hand, beyond methods and frameworks.

During the program, we worked with research on AI, decision-making, organizational structures, and innovation (product and service innovation) as part of broader systemic changes in organizations. For me, this provided new perspectives on questions I already face in my profession: how organizations are designed, how value is actually created, and why so many large and ambitious initiatives lose momentum along the way – especially regarding the organizational capabilities that are often forgotten when the focus shifts to the next trend, the next tool, or the next framework.

This article takes a clear perspective: organizational structure and design, operating models, and digitalization. Other equally important aspects—such as data security, regulatory issues (Candelon et al., 2021), or the cognitive and emotional dimensions of AI and trust (Glikson & Woolley, 2020; Huang & Rust, 2021)—require deeper exploration and may become topics for future blog posts.

AI Investments as Part of Systemic Change

AI is becoming one of the most significant organizational investments of our time. Not because the technology itself is expensive, but because it requires us to rethink how and where decisions are made, how we collaborate, and how we think about customer value.

The pattern I often see is that when organizations say they are “investing in AI,” they usually mean investments in models, platforms, tools, or external consultants who support implementation. At the same time, the structural and cultural investments required for a real transformation are underestimated.

In practice, this is closely connected to something I often observe in different client assignments: a tendency to simplify organizational challenges by searching for quick and easy solutions in technology and tools.

AI is therefore easily reduced to a question of implementing new technology or introducing a new way of working, rather than addressing the underlying structures, incentives, and behaviors that actually need to change—and which are inherently more complex in its nature.

Misguided investments can be costly for organizations, not only from legal, ethical, and business perspectives, but also in terms of employee insecurity and bias in data (van Giffen et al., 2022).

What has become increasingly clear is that AI cannot be treated as just another organizational “trend” that companies rush to adopt in order not to miss out. Just as with previous waves such as digitalization, DevOps, agile practices (particularly large-scale frameworks), and data—AI requires a shift in how organizations are designed and governed.

This is where competence in organizational design and operational transformation becomes crucial. I do not mean simply introducing a framework or scaling a method, but actually changing structures, mandates, and ways of working.

Signs that organizations struggle to build long-term capabilities include numerous initiatives, experiments, and pilots that stall, attempts to scale that lose momentum, and an organization where trust in the transformation effort gradually declines and eventually erodes altogether.

AI Reveals and Amplifies What Already Exists

In Competing in the Age of AI, Iansiti and Lakhani (2020) describe a decisive difference between companies that succeed with AI and those that do not – their core business processes are digitalized end-to-end, with minimal friction between data, decisions, and action. Without that shift, AI initiatives risk delivering limited impact and even worse, amplifying the very issues and dysfunctions that already exist. This is also highlighted in the DORA Report 2025 and its AI Capabilities Model.

Without a sufficiently strong foundation, AI will accelerate the existing culture and structures of an organization (for better and for worse). The technology itself rarely creates problems, but it makes existing patterns more visible. A few examples illustrate this:

• When AI is implemented within functions, it tends to optimize locally around specific processes without considering the broader system. The result is simply more efficient silos.
• When AI is trained on historical data, it reproduces the decisions, priorities, and structures that have shaped the organization in the past. In doing so, AI reinforces the organization’s history—its power structures, priorities, decisions, interpretations, incentives, shortcuts, and compromises. The result can lead to leadership communicating a new direction while the AI is trained on the existing setup.
• When AI generates insights faster but the organization remains stuck in structures characterized by manual handovers, reports, meetings, unclear responsibilities, unclear decision paths, and strong boundaries between organizational units, the result is better analysis but no faster execution and no increased ability to act.
• When AI is introduced in a context designed primarily for control, compliance, and reporting, the technology is also likely to be used mainly for monitoring, reporting, and optimization. In such cases, AI reinforces a “control culture,” with centralized decision-making and reduced autonomy where decisions should instead be made closer to where value is created.

Why Digital Organizations Can Scale

A recurring pitfall is that digitalization is treated as an IT- or change initiative alongside the business, rather than as part of its core operations. When this happens, the business-critical flows remain manual and fragmented. But an organization cannot be agile without a stable core. Only when the core processes are digitalized end-to-end (from the customer interaction through to internal operations) do short feedback loops, learning, and rapid adaptation become possible (Davenport & Ronanki, 2018).

This is exactly the point Iansiti and Lakhani make (see illustration above). In traditional operating models, growth (scale) creates value up to a certain point. As usage increases and the number of customers grows, complexity eventually increases faster than the value generated, which means the effect begins to level off. This quickly leads to additional layers, more administration, and needs of coordination, which in turn result in higher costs and tighter margins.

In a digital operating model, however, the friction between data or insights and actual outcomes is reduced. This means that the value curve does not flatten in the same way when the organization grows. One way to think about this is as a powerful lever for growth, where new customers improve the system itself. The more a service or product is used, the more data is generated and the better the insights become, which in turn improves the quality of priorities and decisions. This resembles what in product growth is often referred to as a growth loop, where increased usage creates additional value that drives further usage, reducing the need for a traditional sales organization.

But this dynamic does not emerge on its own. It requires an operating model that can actually capture learning, translate data into action, and adjust direction as the system evolves. This is where the concept of scalable learning comes into play.

Scalable Learning as Part of Daily Operations

Traditionally, organizations learn slowly. They launch something, gather feedback, analyze the results, plan improvements, and implement them in the next delivery cycle. AI changes this dynamic by continuously analyzing shifts in customer behavior, identifying patterns in real time, generating insights without manual reporting, and sometimes even suggesting improvements. Whether it concerns a digital product, a physical product in an industrial context, or a concept within FMCG, the principle remains the same: where does learning occur, and how fast can the feedback loop become? The difference is that learning does not always reside in the product itself, but may instead occur in production, distribution, or the market. The challenge is that this shift does not happen on its own.

Almost every organization I have worked with over the years tends to get stuck at this point, regardless of the trend, tool, or method they have invested in. We often talk about “double bureaucracy,” meaning that organizations introduce and add the new into the system without adjusting, simplifying, or removing the old. The same goes with AI, it is then layered on top of existing structures and processes, while the underlying operating model remains designed for stability, silos, and predictability rather than continuous learning.

When the foundation itself has not changed, organizations often try—usually with good intentions—to create change by adding more structure. Over the past decade, many organizations have attempted to scale through frameworks. In practice, these frameworks often increase the degree of coordination by introducing additional layers of planning, new forums and roles, and more teams. However, they do not automatically change the underlying logic. The result is often more structure in environments where agility remains difficult to achieve.

If core processes remain fragmented and dependent on manual handovers, organizations risk scaling the cost of coordination and alignment rather than scaling learning. The scalability of digital operating models therefore does not primarily come from adding more layers of governance. It comes from reducing friction in business-critical flows so that insights can more quickly be translated into improvements to products and services.

As several of the studies I have worked with suggest, the consequence is that many organizations introduce AI through large initiatives structured as projects or programs – initiatives with their own goals, budgets, and deliverables. Instead of gradually building capabilities within day-to-day operations, AI becomes a parallel activity rather than an integrated part of how value is created over time. Research consistently shows that AI maturity develops best through continuous iteration rather than isolated programs. Large, ambitious AI programs risk creating long start-up phases and high expectations without the organization simultaneously developing its ability to learn (Iansiti & Lakhani, 2020; Wilson et al., 2018).

However, working iteratively with AI requires more than just changing ways of working. The organizational structure itself must enable movement across functional boundaries and silos. This is where agility becomes critical.

Organizational Design as an Enabler

Research shows that AI and data have the potential to break down organizational silos by creating a shared, holistic view of customer behavior, market dynamics, and operational performance (Shrestha et al., 2019). However, a shared view alone is not enough. If each function continues to optimize from its own perspective and simply performs more advanced analysis, little will change in practice as long as the inertia between functions remains.

In other words, the AI technology itself does not do the work. AI and data can reveal the bigger picture, but without flow and interaction across functions, the organization cannot act on those insights. To act on a shared understanding, organizations need cross-functional, product-oriented teams that share objectives and effect goals and have the mandate to take ownership for the entire customer experience. This encourages collaboration, simplifies coordination, and enables decisions to be made based on integrated and accumulated insights.

In most of the organizations we support at Dandy People, silo-based functions continue to exist despite extensive agile initiatives. Governance, budgeting logic, and accountability are still organized along functional lines. In that context, AI cannot be used for more dynamic steering. Instead, it becomes primarily a reporting and decision-support tool, far removed from where value is actually created close to the product teams.

Agility in an AI-driven context therefore has less to do with methods and more to do with how an organization is designed to create the capability to quickly act on insights and adjust direction as learning occurs.

A growing number of companies are built with AI as a native capability from the start. In these organizations, data, models, and decision-making are integrated directly into the operating model rather than added later. Learning loops, automation, and experimentation are embedded in daily operations. For most established organizations, however, the challenge is different: AI must be integrated into structures and processes that were never designed for it.

Summary

• AI is the next technological leap of our time. But unlike many previous trends, it cannot be isolated to technology, methods, or tools alone.
• AI amplifies the organization and culture that already exist both what works and what is already causing friction. That is why underlying weaknesses suddenly become visible.
• If we want to realize real impact from our AI investments, new models, platforms, or programs are not enough. It requires digitalizing core flows, revisiting governance and decision mandates, and building the capability to learn as part of everyday operations.
• AI acts as a catalyst for better and for worse. The outcome depends on how we design our organizations, the operating model we choose, and how we lead the transformation needed to turn investments into real impact.
• Investing in the competence needed to build organizational capability and a strong foundation is therefore well worth it.
• AI-native companies design their organizations around data, learning, and rapid decision-making from the start. Most established organizations must instead redesign existing structures so that AI can reinforce progress rather than amplify existing friction.

References

• DORA (2025).
  DORA AI Capabilities Model (report).
• Candelon, F., Reichert, T., Duranton, S., Di Carlo, M., & Sigurdsson, E. (2021).
  AI regulation is coming. Boston Consulting Group.
• Davenport, T. H., & Ronanki, R. (2018).
  Artificial intelligence for the real world. Harvard Business Review.
• Glikson, E., & Woolley, A. W. (2020).
  Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals.
• Huang, M.-H., & Rust, R. T. (2021).
  A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science.
• Iansiti, M., & Lakhani, K. R. (2020).
  Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press.
• Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019).
  Organizational decision-making structures in the age of artificial intelligence. California Management Review.
• van Giffen, B., Herhausen, D., & Fahse, T. (2022).
  Overcoming the pitfalls of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research.
• Wilson, H. J., & Daugherty, P. R. (2018).
  Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review.

Hur definieras…

*Artificiell intelligens (AI) avser system som kan lära från data, identifiera mönster och stödja eller automatisera beslut som annars kräver mänskligt omdöme (Davenport & Ronanki, 2018). I den här texten används AI som ett samlingsbegrepp för både traditionell AI och generativ AI, med fokus på hur tekniken påverkar organisationers sätt att fatta beslut, samarbeta och skapa kundvärde snarare än på enskilda verktyg eller lösningar. **Digitalisera kärnprocesser Att digitalisera kärnprocesser innebär att göra de processer där organisationen möter kunden och fattar viktiga beslut helt digitalt så att information, beslut och agerandet hänger ihop utan manuellt handhavande som stoppar upp flödet, överlämningar eller parallella arbetssätt. En vanlig fallgrop: ytan är digital mot kund medan kärnprocessen fortfarande är analog.

Inledning

Den här texten bygger på insikter från mina studier inom AI*, innovation och organisationsdesign vid Halmstad Högskola, i kombination med erfarenheter från min vardag som Enterprise coach på Dandy People. Det har gått mer än ett år sedan utbildningen, men många av insikterna har blivit allt mer aktuella i takt med att AI tagit större plats i organisationernas strategiska arbete. Jag har landat i några reflektioner som jag upplever är särskilt relevanta just nu, inte minst kopplat till organisatoriska förmågor som glöms bort när fokus hamnar på nästa trend och lösning för att möta vår tids utmaningar, bortom metoder och fallerade ramverk.  

I utbildningen arbetade vi med aktuell forskning kring AI, beslutsfattande, organisatoriska strukturer och innovation som en del av större systemförändringar i organisationer. För mig har det gett nya infallsvinklar till frågor jag redan möter i mitt arbete: hur organisationer är designade, hur värde faktiskt skapas och varför så många stora ambitiösa initiativ tappar kraft på vägen.

 Jag har landat i några reflektioner som jag upplever är särskilt relevanta just nu, inte minst kopplat till de organisatoriska förmågor som ofta glöms bort när fokus hamnar på nästa trend, nästa verktyg eller nästa ramverk.

Den här texten tar ett tydligt perspektiv: organisationsstruktur och design, operationell modell och digitalisering. Andra minst lika viktiga aspekter som datasäkerhet, regulatoriska frågor (Candelon et al., 2021) eller till exempel de kognitiva och empatiska dimensionerna av AI och tillit (Glikson & Woolley, 2020; Huang & Rust, 2021) kräver en djupdykning för sig och kan bli en bloggpost längre fram.

AI-investeringar som en del av systemförändring

AI håller på att bli en av de mest omfattande organisatoriska investeringarna i vår tid. Inte för att tekniken i sig är dyr, utan för att den kräver att vi förändrar hur och var beslut fattas, hur vi samarbetar och hur vi tänker kring kundvärde.

Mönstret jag ser är att när organisationer säger att de ”investerar i AI” menar de ofta investeringar i modeller, plattformar, verktyg eller extern kompetens i konsulter som ska stötta implementering. Samtidigt underskattas de strukturella och kulturella investeringar som krävs för en verklig omställning.

I praktiken hänger det nära ihop med något jag ofta ser i uppdrag: en vilja att förenkla organisatoriska utmaningar genom att söka snabba och enkla lösningar i teknik och verktyg. 

AI förenklas då lätt till en fråga om implementering av ny teknik eller ett nytt arbetssätt, snarare än att ta tag i underliggande strukturer, incitament och beteenden som behöver förändras som är mer komplexa i sin karaktär. 

Fel investeringar kan kosta företaget mycket utifrån legala, etiska och affärsmässiga aspekter men också i form av otrygghet hos medarbetare och bias i data (van Giffen et al., 2022). 

Det som blivit allt tydligare är att AI inte kan behandlas som ännu en organisatorisk ”trend” man stressat hoppar på för att inte missa tåget. Precis som vid tidigare vågor, exempelvis digitalisering, DevOps, agilt (läs storskaliga ramverk) och Data kräver AI en omställning av hur organisationen är designad och hur den styrs. 

Det är här kompetens inom organisationsdesign och operativ omställning blir avgörande. Jag avser inte att man enbart inför ett ramverk eller skala en metod, utan att faktiskt förändra strukturer, mandat och arbetssätt.

Tecken på att man inte lyckas bygga förmågor långsiktigt är många påbörjade initiativ, experiment och piloter som stannat av, försök till att skala som tappar fart och en organisation där tilltron till förändringsarbetet gradvis sjunker och till slut urholkas helt.

AI synliggör och förstärker det som redan finns

I Competing in the Age of AI beskriver Iansiti & Lakhani (2020) det som skiljer företag som lyckas med AI på en avgörande punkt: deras kärnverksamhet är digitaliserad** end-to-end, med minimala hinder mellan data, beslut och handling. 

Utan den förflyttningen riskerar AI-satsningar att ge begränsad effekt och ännu värre förvärra det som redan skaver, vilket även lyfts i DORA report 2025: AI capabilities model.

Utan en tillräckligt bra grund kommer AI accelerera befintlig kultur och struktur på gott och ont. Tekniken skapar sällan problem i sig, men nu synliggörs redan existerande mönster. Några exempel:

• När AI implementeras i enskilda funktioner tränas den att optimera lokalt kring till exempel processer utan hänsyn till helheten. Resultatet blir kort och gott, endast effektivare silos. 
• När AI tillämpas på historiska data återskapar den de beslut, prioriteringar och strukturer som redan präglat organisationen. AI förstärker då organisationens historia baserad på exempelvis maktstrukturer, prioriteringar, beslut, tolkningar, belöningar, genvägar och kompromisser. Resultatet kan bli att ledningen kommunicerar en ny riktning medan AI tränas på det befintliga.
• När AI tar fram insikter snabbare men organisationen fortfarande sitter fast i strukturer med manuella överlämningar, rapporter, möten, otydligt ansvar och otydliga beslutsvägar samt vattentäta skott mellan organisationens delar så blir resultatet enbart bättre analyser men inget snabbare genomförande eller mer handlingskraft.  
• När AI introduceras i en kontext som är uppsatt för kontroll, uppföljning och efterlevnad så tenderar man också att använda tekniken för rapportering, övervakning och optimering. AI förstärker då “kontrollkulturen” med centraliserade beslut och minskat mandat (låg autonomi) där beslut bör fattas i organisationen.

Varför digitala organisationer kan växa

En återkommande fallgrop är att digitalisering hanteras som ett IT- eller förändringsinitiativ vid sidan av verksamheten. Då kommer de affärskritiska flödena fortsätta att vara manuella och uppdelade. Men en organisation kan inte vara agil utan en stabil grund. Först när flödena är digitaliserade från kundens interaktion till den interna verksamheten, så möjliggörs korta feedbackloopar, lärande och snabb anpassning (Davenport & Ronanki, 2018).

Det här är exakt den poäng Iansiti & Lakhani gör (se bild ovan). I traditionella operationella modeller ger tillväxt (skala) värde upp till en viss punkt. I takt med ökad användning och fler kunder vid en given punkt ökar komplexiteten snabbare än värdet man får ut, vilket innebär att effekten planar ut. Det innebär snabbt fler lager, mer administration och samordning som i sin tur leder till ökade kostnader och pressade marginaler. 

I en digital operationell modell minskar däremot friktionen mellan data/insikt och effekt. Det gör att värdekurvan inte planar ut på samma sätt när organisationen växer.

Man kan se det som en operativ hävstång där nya kunder förbättrar systemet. Ju mer tjänsten eller produkten används, genereras mer data och bättre insikter vilket i sin tur höjer kvaliteten i prioriteringar och beslut. Det liknar det som inom Product growth kallas en growth loop där ökad användning skapar mervärde som driver ytterligare användning vilket minskar behovet av en traditionell säljorganisation. 

Men den dynamiken uppstår inte av sig själv. Den förutsätter en operativ modell som faktiskt kan fånga upp lärande, omsätta data och justera riktning i takt med att systemet utvecklas. Det är här begreppet scalable learning (eller skalbart lärande som jag valt att direktöversätta begreppet till) kommer in i bilden.

Skalbart lärande som en del av den dagliga driften

Traditionellt lär sig organisationer långsamt genom att man lanserar något, samlar feedback, analyserar, planerar och implementerar i nästa leveranscykel. AI förändrar det här genom att kontinuerligt analysera exempelvis ändrade kundbeteenden, se mönster i realtid, generera insikter utan manuell inblandning och även föreslå förbättringar. Oavsett om det exempelvis är en digital produkt, fysisk produkt inom industrin eller ett koncept inom FMCG, så är principen densamma, vart uppstår lärandet och hur snabb kan feedbackloopen bli. Skillnaden är att lärandet inte alltid sitter i produkten utan i produktion, distribution eller marknad. Problemet är att den här förflyttningen inte händer av sig självt.

Nästan alla organisationer jag mött genom åren fastnar just här oavsett trend, verktyg eller metod som investeringen gjorts i. Vi brukar prata om dubbel byråkrati vilket innebär att man introducerar och lägger på “det nya” i systemet utan att justera, förenkla och ta bort det gamla. AI läggs då ovanpå befintliga strukturer och processer, medan den operationella modellen i grunden fortfarande är designad för stabilitet, silos och förutsägbarhet snarare än för kontinuerligt lärande.

När grunden inte förändrats försöker man istället skapa förändring (med god intention) genom mer struktur. Under de senaste tio åren har många organisationer försökt skala genom ramverk. Här tenderar ramverket i stället öka graden av koordinering genom fler lager av planering, nya forum och roller samt team, men de förändrar inte automatiskt den underliggande logiken. Resultatet blir ofta mer struktur där en agilitet är svår att uppnå. 

Om kärnprocesserna fortfarande är uppdelade och beroende av manuella överlämningar riskerar man att i stället skala omfattande kostnader för samordning och koordinering snarare än lärande. Digitala operativa modellers skalbarhet handlar därför inte i första hand om fler lager av styrning. Den handlar om att minska friktionen i det verksamhetskritiska flödet så att man snabbare kan omsätta insikterna till att optimera sina produkter och tjänster.

Konsekvensen, enligt flera av de studier jag tagit del av, är att många organisationer väljer att introducera AI genom omfattande initiativ i form av projekt eller program. Initiativ som får egna mål, budgetar och leveranser. Istället för att successivt bygga förmågor i det dagliga operationella arbetet etableras AI som en parallell sidoverksamheten, snarare än en integrerad del av hur värde skapas över tid.

Ett genomgående budskap i forskningen är snarare att AI-mognad utvecklas bäst genom kontinuerliga iterationer och inte genom isolerade program. De stora, ambitiösa AI-programmen riskerar att skapa långa startsträckor och höga förväntningar, utan att organisationen samtidigt utvecklar sin förmåga att lära (Iansiti & Lakhani, 2020; Wilson et al., 2018). 

Men för att arbeta iterativt med AI räcker det inte att förändra arbetssättet, även strukturen måste möjliggöra rörelse över funktionsgränser och silos. Här blir agilitet avgörande.

Organisationsdesign som möjliggörare

Forskning visar att AI och data har potential att bryta organisatoriska silos genom att skapa en gemensam helhetsbild av kundbeteenden, marknadsdynamik och operativ prestation (Shrestha et al., 2019). 

Men en gemensam helhetsbild räcker inte i sig. Om varje funktion fortsätter att optimera utifrån sitt eget perspektiv och gör ännu mer avancerad analys så skulle det inte förändra något i praktiken så länge trögheten mellan funktionerna finns kvar. 

Det är alltså inte AI-tekniken som gör jobbet. AI och data kan synliggöra helheten, men utan rörlighet och dynamik mellan funktioner kan organisationen inte agera på insikter.  

För att kunna agera på en gemensam förståelse krävs tvärfunktionella, produktorienterade team som delar effektmål och har mandat att ta ansvar för hela kundupplevelsen. Det här driver samarbete, det blir enklare och beslut kan fattas utifrån samlade bearbetade insikter. 

I merparten av de organisationer vi på Dandy People stöttar, lever silobaserade funktioner vidare, trots omfattande agila satsningar. Styrning, budgetlogik och ansvar är fortfarande funktionellt organiserat. I den kontexten kan inte AI användas för en mer dynamisk styrning, utan utgör enbart avrapportering och beslutsstöd långt från där värdet skapas nära produktteam. 

Agilitet i en kontext med AI handlar därför mindre om metoder och mer om hur en organisation designas för att skapa en förmåga där man snabbt kan omsätta insikter och justera riktning i takt med lärande.

Summering

• AI är vår tids nästa tekniksprång. Men till skillnad från många tidigare trender går det inte att isolera till enbart teknik, metoder eller verktyg. 
• AI förstärker den organisation och dess kultur som redan finns, både det som fungerar och det som redan skaver. Därför blir bristerna plötsligt synliga.
• Vill vi få ut riktig effekt av våra investeringar i AI räcker det inte med nya modeller, plattformar eller program. Det kräver att man digitaliserar sina kärnflöden, ser över styrning och mandat och bygger en förmåga att lära i vardagen.
• AI är katalysator på gott och ont. Resultatet avgörs av hur vi har valt att designa organisationen, vilken operationell modell och förändringsledning i förflyttningen för att få ut effekt av investeringar.
• Att ta in kompetensen för att bygga organisatorisk förmåga och en stabil grund är väl värt att investera i.

Referenser

• DORA (2025).
  DORA AI Capabilities Model (report).
• Candelon, F., Reichert, T., Duranton, S., Di Carlo, M., & Sigurdsson, E. (2021).
  AI regulation is coming. Boston Consulting Group.
• Davenport, T. H., & Ronanki, R. (2018).
  Artificial intelligence for the real world. Harvard Business Review.
• Glikson, E., & Woolley, A. W. (2020).
  Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals.
• Huang, M.-H., & Rust, R. T. (2021).
  A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science.
• Iansiti, M., & Lakhani, K. R. (2020).
  Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World. Harvard Business Review Press.
• Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019).
  Organizational decision-making structures in the age of artificial intelligence. California Management Review.
• van Giffen, B., Herhausen, D., & Fahse, T. (2022).
  Overcoming the pitfalls of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research.
• Wilson, H. J., & Daugherty, P. R. (2018).
  Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review.

Konflikter eller oenigheter är vanliga i det dagliga livet. Det är ofta en kombination av två eller fler typer av konflikter. Var därför säker på att identifiera vilka typer av konflikter det handlar om och om möjligt, välj fler lösningsansatser.

Den här postern är framtagen tillsammans med Björn Sandberg.

Den här postern går igenom 5 typer av konflikter

• Intressekonflikt
• Maktkonflikt
• Mognadskonflikt
• Värderingskonflikt
• Missuppfattningskonflikt

Den visar också på 5 typer av lösningar

• Samarbeta
• Kompromissa
• Justera
• Undvika
• Kämpa eller tvinga

Välkommen att ladda ner den här postern i högupplöst PDF >

Maktkonflikt

Makt och auktoritet är det som de flesta tänker är grunden till alla konflikter. Maktkonflikter känner de flesta av oss igen från arbete och familj, där frågan handlar om vem som ska bestämma. Viljan att bestämma kommer tidigt, när barn vill ha kontroll över situationen – till exempel vilka kläder de ska ha på sig – för att få personlig makt och självkänsla.

Den negativa sidan av maktkonflikter kan vara så allvarlig som övergrepp eller missbruk. Det möjliga hotet om att förlora makt kan också vara en källa till konflikt i agila transformationer, när mandatet flyttas ut i teamen och kompetensen delas med andra i gruppen.

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Intressekonflikt

Den vanligaste typen av konflikt uppstår när två eller fler parter har olika perspektiv, behov eller mål som de värnar om och kämpar för att uppnå. Ett exempel är när förändring införs i en organisation och människor möts för att argumentera för sina ståndpunkter och försöka övertyga andra om att deras synsätt är det rätta. Om de inte kan nå en kompromiss eller en vinn-vinn-lösning, är det tillgången till makt och mandat som avgör vem som vinner eller förlorar.

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Auf diesem Poster habe ich einige Organisationsdesign-Muster aus agilen Produktorganisationen im großen Maßstab gesammelt.
Die hervorgehobenen Fragen können als Einstieg in verschiedene Themen dienen – etwa zu den Gestaltungsprinzipien der Organisation, zur Strategie für das Wachstum von Teams und Einzelpersonen, dazu, wie Autonomie und Ausrichtung ermöglicht werden können, und wie Führungsteams gestaltet werden können, um eine großartige Produktorganisation zu unterstützen und weiterzuentwickeln, die Produkte liefert, die Kundinnen und Kunden lieben.

Dieses Poster wurde ins Deutsche übersetzt von Andre Ullmann, kontakt@andreullmann.de

Download the German poster for free (high resolution PDF) >

Buy printed in size A1 >

Hervorgehobene Fragen zur Reflexion auf diesem Poster

• Wie messen wir Erfolg?
• Was sind unsere Leitprinzipien?
• Optimieren wir für Fluss (Flow)?
• Optimieren wir für Wert (Value)?
• Welche Fähigkeiten müssen wir skalieren?
• Wie ermöglichen wir den Informationsfluss?
• Was ist die minimale wertvolle Bürokratie (Minimum Valuable Bureaucracy)?
• Welche Rollen benötigen wir in unserem Führungsteam?
• Wie fördern wir das Wachstum von Teams und Einzelpersonen?
• Welche Arten von Teams brauchen wir?
• Wie ermöglichen wir sowohl Autonomie als auch Ausrichtung?
  
  

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This poster came to life based on common misunderstandings we have seen over and over again in organizations starting on the journey to become more product centric and outcome oriented. We hope that the poster can bring light to these patterns and support valuable dialoges around them.

We have vizualized some of the important patterns and missunderstandings – not all of them. The poster covers 4 important areas, Strategy, Execution and Delivery, Culture and Organization.

The culture shift is the result of the shifts made to enable, support and incentivise new behaviours connected to outcome and a trust based leadership. These new behaviours will only be possible if the structure is shifted to the right, on the product side.

Maybe you have already done some of these shifts? What else could be needed to move towards a more customer centric outcome oriented culture? Who do you need to collaborate closer with? Bring them together and discover the shifts using this poster and the insights shared in the short videos below here.

Download the poster for free (HIGH REZ PDF) >

Short videos covering each topic in the poster

The team behind the poster; Elinor Lange, Frank Olsen and Mia Kolmodin together with Johan Westerlunch and Natalja Calderon guide you through each topic during a short video. This served as our Christmas Calendar when we launched the poster in december 2025.

Customer Focus

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In this video we dive into one of the most fundamental, and probably most misunderstood, topics in modern organizations: Customer Focus.

Topics explored

• Many organizations believe they are customer-centric. But when it’s time to make real decisions, customer needs often fall behind internal priorities, project plans and budget structures.
• Why customer focus tends to fade in traditional project organizations
• How product organizations keep customer needs at the center – continuously
• Why product-oriented ways of working lead to stronger outcomes and better value delivery, for both customer and the business.

Funding Model

Topics explored

• How the funding model impact the product development – and what you could do to shift towards product.
• How project oriented funding often cause problems with knowledge retention which delay deliveries and also difficulties to deliver customer and business value.
• How incremental funding might be a better fit for many organizations.
• What organizations have done to get started moving towards a product oriented funding model.

People

Topics explored

• In project based organizations we tend to call people “recourses”. How shifting perspective to “people” and what lies behind that, changes everything and we build stronger teams, higher engagement, and a culture that supports real product-led ways of working.
• When reduce people to abstractions, we also reduce commitment, trust, and ownership. Words matter — because they shape how we lead.
• One of the reasons product organizations succeed is because people are empowered — not managed as resources.
  

Core values

In this video we explore the fundamental difference in view of core values in a project vs product organization.

Topics explored

• Many project-driven organizations emphasize predictability, plans, and control.
• Success is defined by adhering and delivering to the plan, and change is often seen as a disruption rather than an opportunity.
• Embracing learning and adaptability, often by fast feedback loops.
• Focus on time-to-money, on delighting customers, and on creating long-term, sustainable value.
• It’s a shift from valuing the plan to valuing the ability to learn and improve, and in doing so making the customer happy.
• Shifting to a product-driven way of working isn’t about abandoning structure, it’s about investing in learning, adaptability, and customer value.
• When we value customer needs over plans, we create organizations that move faster, make smarter decisions, and build products people truly love.

Ownership

In this video we explore how ownership works differently in project vs. product organizations.

Topics explored

• In project organizations, ownership often sits with the project manager, who controls tasks. This removes responsibility from the people doing the work, leading to lower motivation, less creativity, and a focus on delivering the project rather than improving the product.
• Modern product organizations distribute ownership to the teams closest to the problems. With mandate, involvement, and a focus on product outcomes, motivation increases and problem-solving thrives. And this leads to happier, engaged people and product success.
• Real ownership fuels motivation, creativity, and better products. Give responsibility to the people who can actually make a difference.

The team

In this video we explore how the prerequisites for, and use of, teams differ in project vs. product organizations.

Quality and Technical Debt

In this video we explore how quality and technical debt is handled differently in project vs. product organizations.

Topics explored

• In project organizations, focus is to deliver a fixed scope at a fixed deadline – with a high risk to build lower quality. And often the responsibility is separated from the responsibility for delivery, which makes it harder to build in quality.
• In modern product organizations the product teams are accountable for the quality AND delivery. They then have the possibility to build in quality in the work they do to deliver the product which can be done iteratively and incrementally – to meet the deadline. Here they can focus first on quality FIRST and THEN on the delivery which gives a sustainable pace and product.

Knowledge retention

In this video we explore how knowledge is built, kept, and used differently in project vs. product organizations.

Topics explored

• In project organizations, teams are often temporary. When a project ends, the team disbands and so does much of the knowledge. Valuable insights fade, and organizations find themselves reinventing solutions again and again.
• In modern product organizations, teams stay with the product over time. Knowledge compounds, learning accelerates, and problem-solving becomes faster and more effective. Multi-learning across skills and roles strengthens the team’s ability to deliver long-term value.
  

Primary objective

In this video we explore how the primary objective differ in project- vs. product organizations and what impact that has on delivery and the business value from the delivery.

Value realization

In this video we explore how value is perceived differently in project- vs. product organizations – and how that may impact the business outcome.

Flexibility

In this short video we explore how flexibility is perceived differently in project- vs. product organizations – and how you can enable it and benefit from having both flexibility in the solution – and strategic flexibility.

Time horizon

In this short video we explore what project vs product organizations tend to focus on, and put their effort into, based on the time perspective.

They also share how you can start moving towards a product perspective.

Organization

In this short video we explore how project vs product organizations tend to look like and work and why many organizations fail in their ambitions to become more product- and customer centric when they don’t adapt their organization.

You’ll also get a few tips on what you can do to start to move towards becoming more product oriented.

Dandy X-Lab – a structured way to make the shifts

Join us in this short video where we share both success cases doing this change – and the model, Dandy X-Lab. Dandy X-lab is designed to enable increased business value sooner together with our customers.

This is the most effective way to handle the transition from a project based organization to product oriented that we have ever seen. No more big bang, high risk and slow and unsecure ROI on change initiatives and business innovation as we connect customer centric innovation & delivery to the organizational development.

When the structures, WoW and management are working FOR your people, they will become the super heroes they are and we make that happen within a short time boxed period of time showcasing that it works. At the same time we build a bespoke scalable operational model for YOUR context.

This model is proven to work within all type of organizations and businesses.

Free to download, use and share

The posters are published under Creative Commons License, so please use it and share it as you like. If you are interested in doing a translation to any other languages, please let me know and I will help you with the file and publish it here on the blog as well.

You are free to:

Share — copy and redistribute the material in any medium or format
Adapt — remix, transform, and build upon the material for any purpose, even commercially.

This license is acceptable for Free Cultural Works.
The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Here you find all the other Free Agile in a Nutshell-posters in the series that is now translated to 15 languages and downloaded over 100.000-times worldwide.

Really grateful for the invitation to give a closing keynote at Agila Sverige on the topic of Product-Led, among so many other amazing speakers.

I also enjoyed being part of the panel on the Product Operating Model together with Mikael Brodd and Jimmy Janlén.

I felt truly welcomed, had a lot of fun, learned something new, and left feeling energized. 🙂

A huge thank you to all of you in pink t-shirts 👚 for once again creating such a safe, fun, loving, innovative, thoughtful, and thought-provoking community. You rock! 🙌

Hope to see you all again next year!

Thank you Isabelle Svärd and Jimmy for the pics 📸

Många personer idag upplever att deras PM sitter väldigt isolerad och inte samarbetar tillräckligt. Tex. sales som får mycket information om vad kunder vill ha och inte vill ha, men upplever att de inte har någonstans att informera vidare och upplever i alla fall många gånger inte att PM engagerar sig i att efterfråga feedbacken. Eller så händer inget utifrån att PM fått den.

Det finns många olika exempel, men vi ska stanna här. Vi ska istället gå in på vad det här beror på? Vad är det som gör att de inte samarbetar och blir isolerade öar?

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Sommaren – en tid de flesta av oss längtar efter och har så stora förhoppningar på. Vi ska läsa den där boken som legat länge och vi inte hunnit läsa, umgås med alla våra vänner vi sett så lite av, resa till platser vi vill se, bada i ett varmt hav, mysa med familjen, få sol på kroppen och ladda D-vitamin, komma igång med träningen, måla färdigt huset, äta sunt…. ja det blir lätt väldigt mycket!

Förhoppningarna och verkligheten

Mina förhoppningar för sommarsemestern var att få vara nära min man och naturen, lapa D-vitamin i solen och få uppleva magisk natur. Enkelhet, det var min prioritering. Jag ville också gärna umgås med mina vuxna barn och vänner. Som för så många andra så blev det, så här långt, en mix av det jag önskade och sådant jag inte önskat. Vilket det vanligen blir i slutändan. Men, jag valde att se min semester som den perfekta sommarmixen.

Om livet är perfekt eller inte handlar mycket om vår inställning till livet och det som sker. Vi kan inte få allt vi vill, och vad vore tjusningen i det om vi hela tiden kunde få allt vi önskar? De saker jag fick var värdefulla för mig och jag uppskattar dem enormt mycket. De har gett mig energi, tacksamhet, insikter och glädje. De saker jag inte önskade mig men som skedde ändå, har förhoppningsvis lärt mig något.

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Do you recognize any of the following in your company?

• IT/Tech is measured by how many features they’re pushing out to the market, whereas marketing is measured by how much they sell.
• The feature team has become a feature factory that produces features, but is unsure of the value for the customers.
• Your company’s business is not good and the results are falling.

Then, you probably need to change a few things to make your organization successful. Let me take you through some of the most common problems in many organizations today.

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Det finns några personer som gjort stor skillnad i mitt liv. Kvinnor som ser mig, står bakom mig, finns där när jag behöver och låter mig vara mig oavsett om jag är perfekt eller inte 🙂

När mitt förhållande tog slut och jag var förtvivlad så fanns 2 kvinnor vid min sida Susanne Bertlin och Marie Palmlöf. Jag kände mig så ensam, bodde i en stad där jag inte kände någon och livet var inte på topp. Både Susanne och Marie fanns där, hälsade på mig, drog ut mig, fanns där för långa samtal. Lät mig reda ut vad jag ville ha – utan att döma mig. Stor tack till er! Kärlek! Vet inte vad jag gjort utan er! Ni har också fortsatt vara mina vänner, även om vi inte ses lika mycket idag (som jag skulle vilja).

Min moster Maud Savolainen, hon har också funnits där genom livet. Vi är lika på så många sätt. Finns alltid där och vi kan alltid bolla saker med varandra, semestrat ihop, av dig lärde jag mig hur husvagnslivet kunde vara, men framför allt, stöd genom livets alla faser.

Jennie Backeus, Anna Tellebo är kvinnor som kom in i mitt liv när jag fått barn. Fantastiskt stöd och sällskap. Vi håller ihop även idag och våra barn är vänner med varandra likaså. Vilka härliga år och minnen vi har tillsammans!

Carina Burguete, vi träffades av en slump på mitt förra jobb och vi hittade varandra direkt. Vi har samma värderingar, intressen, och kom att fortsätta vår vänskap även när vi båda lämnat samma arbetsplats. Även fast vi bor långt ifrån varandra så hittar vi sätt att ses och vi pratar ofta oavsett.

På Dandy People – där jag jobbar idag finns det många kvinnor. Fina fantastiska kvinnor, som finns där för varandra. En fantastisk kvinnlig ägare (hälften ägs av hennes man). Mia Kolmodin som hon heter, står alltid bakom mig, antar att jag kan, stöttar om jag inte skulle kunna, förstår mina drivkrafter och uppmuntrar dem precis som mina kollegor. Kort och gott så är alla mina kvinnliga kollegor (för det är dem som dagen handlar om) starka, kompetenta kvinnor som varje företag skulle slåss om att få ha tillgång till. Ni är helt enkelt grymma!

Att ha tillgång till en bukett av kvinnor som berikar mitt liv, utvecklar mig, stöttar, tror på mig, finns där förutsättningslöst. Det är en fantastisk ynnest! Stort tack för att ni finns där i mitt liv!

Vilka är med i din bukett av stöttande kvinnor genom livet, och i vems bukett finns du med?

As mentioned in the previous flow post, flow is the secret sauce for delivering maximum value to users in the shortest possible time.  

By optimizing flow, you’ll be able to take control of your workflow and more quickly (and continuously) adapt your product strategy and development processes, which is critical for any organization wanting to become more product-led. The right solutions will be identified and delivered faster because feedback loops will become shorter. 

At the end of this article, I will share 9 ways to optimize flow to become more product-led that came out of a great conversation with fellow Dandy Johan Wildros, an expert in using Lean Agile principles to optimize flow. We worked together at If insurance on an Agile transformation of one of their core systems. You’ll also find helpful tips for getting started and things to watch out for. Feel free to jump to the tips at the end if you’re eager to see the 9 ways.

Product-Led Organizations

In a Product-Led organization, delivering products that solve real customer problems is the top priority. Such organizations recognize that business, product, and technology must work in harmony in order to build products customers love AND are equally valuable for the business. They optimize for their business outcomes, align their product strategy to these goals, and prioritize working on that will help develop those products into sustainable drivers of growth. 

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Creating a new organization from an old one is a lot about detangling and understanding what belongs where. If you have done it before, you might be able to see patterns that are helpful. Using visualization and working in a structured way, step by step, and involving the people in it are some helpful ways of working.

Just as always in the complex domain, you are better off not using good practices (the same solution as others). By going by it in an experimental way, step by step, you can more safely find good solutions based on design principles and patterns.

When thinking of it you might realize that it actually is pretty similar to building great products that customers love – based on a legacy system. So why not use similar ways of working?

When are starting out we need to see what we currently have, and even that is a complex endeavor. To get that shared picture of the current organization you can use different techniques, and usually, a good mix is needed. In this post, we will look into how you can map up current teams, products, and customer journey, and the state of the systems and start to see what teams might take ownership over what is a step-by-step approach.

Products and Services

As a start, you can start together to map up the products that your customers pay for. Those are what we commonly call products in an Agile organization. This is where some money is exchanged, and if it is on a monthly basis, it might then be a service.

Below you see an example of the overall products and services mapped out, for both B2B and B2C. There is no need to make this any fancier than this. If you might understand later that you actually have some more products, you can easily add them then if you create a scalable system.

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As we explored in our previous post, people driving innovation and creativity think and work differently. Research has shown that within Science, Technology, Engineering and Mathematics (STEM) industries, there’s a higher than chance representation of autistic people and people with elevated autistic traits, or what Cambridge researchers call “systems thinking minds”.

Orienting themselves to concrete facts instead of context, they analyze information from the physical senses using objective logic and prefer evidenced-based approaches. Such individuals can be easily misinterpreted as “arrogant”, “aloof”, or “not a team player”, which often runs counter to the common notions of what makes a good employee… and can set them up to fail.

These differences need to be destigmatized and normalized because neurodiversity is the new normal. 

Below are actionable strategies that you can take to help to unleash the brainpower of your neurodiverse talent and overcome the misinterpretations, assumptions, and differences that often sabotage their careers. They’ll help you to ensure high performance with a big heart.

You’ll find the strategies organized according to the three implicit expectations I shared in my past post along with real-life examples: the Mind Reading expectation, the Focus Fallacy, and Seeing the Forest for the Trees. The strategies shared were written in partnership with someone who is on the spectrum so are expressed from that perspective.

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Welcome to a 4-Part series on FLOW!
This post is an introduction along with key reflections.

What is Flow

“Flow” refers to the flow of customer value through an organization, from customer request to value delivery. It’s the work flowing through the Product Development process through market release and beyond.  

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Why Flow Matters

Flow is the secret sauce for delivering maximum value to users in the shortest possible time.  By optimizing flow, you’ll be able to take control of your workflow and more quickly (and continuously) adapt your product strategy and development processes. The right solutions and Ways-of-Working will be identified faster because feedback loops will become shorter. 

Focusing on flow sets you free to manage the system, not the people. Instead of managing people and optimizing for business and resource efficiency, you can focus on managing and optimizing flow. This is a powerful way to “Manage the System and not the People”. You will be free to co-create an organizational context where all aspects of the work can move together in a way that balances both flow and resources.

How Agile Leaders Optimize Flow

Agile leaders optimize flow through iterative and incremental organizational change. They use Lean Agile practices to put into place structures, processes, and ways-of-working that will ensure the flow is as smooth as possible without disrupting other organizational activities. Their goal is to reach the optimal flow efficiency and delivery of value with minimal waste. 

Agile leaders make it safe and economic to work in small batches. They move away from large batches of work delivered in projects and move towards small batches of work delivered continuously. The result is shorter lead times, higher quality, lower risk, and lower costs. 

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At Dandy People we love to share our work with others, it is a part of our DNA.

Therefore, this years Advent Calendar is about sharing one of the things that have made us stand out, what most people recognizes us for, our beloved agile illustrations. As part of the many popular posters we have created through out the years, downloaded by hundreds of thousands from all around the world, these illustrations are an important part of our brand and our culture.

We are giving you the chance to download a new illustration every day for the whole of December as we are opening up our Agile Illustration Bank. You can use and share these illustrations as you please, in your presentations or as part of your work, for free under the Creative Commons license.

We hope you will enjoy them as much as we have enjoyed creating them. Keep your eyes open on our social media channels on December 1 for the first illustration.

Lets make this a shareful Christmas! 

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December 1

Download illustration

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We’ve all heard of Agile Leadership. But what about Agile Management? It turns out that they’re both distinct yet intimately related. Let’s explore this interesting and relevant topic!

Agile leadership is a transformative, development-oriented leadership style that creates the conditions required for unleashing knowledge, motivation, initiative, and collaboration across any organization.

Agile management is a natural part of Agile leadership that manages the system, not the people. “Manage the system, not the people” means creating an organizational context (structures and systems) that support both autonomy and alignment so teams can deliver value at a high pace and work together with other teams in order to optimize the business outcome of an entire organization.

Agile Leaders naturally manage the system by adjusting their style according to their context and choosing organizational structures that will support alignment and autonomy.

• They recognized that teams operate in a larger context and that structures and systems within a given context (such as rewards and information flow and quality) can either promote great teamwork or create obstacles to excellent collaboration. 
• They align organizational structures with business strategies and goals in ways that support well-functioning and high performing teams that are able to innovate, solve complex problems, and deliver at a high pace. 
• They focus on empowering networks of teams and developing capabilities so the emphasis is no longer on the skills, characteristics, and traits of a single, all-powerful person with the designation of “leader” or “manager”. Both leadership and management has evolved to be collective endeavors that leads to the betterment of all involved and looks different depending on the context.

When Agile leaders have strong management skills, they become known for influential attributes such as:

• Initiative
• Mindful forethought
• Situational awareness
• Willingness to grant autonomy 
• Willingness to grant responsibility 
• Ability to demonstrate flexibility 
• Ability to build trust

The Agile Management Flower

In Agile organizations, each leader is responsible for managing one domain, either people, product, technology, or process. This type of cross-functional Agile Leadership Team works together on moving the organization forward while working within each area supporting their people at operational and tactical as well as strategic level.  

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Innovation is your top competitive advantage. What are you doing to support your innovative tech talent? It starts with recognizing that innovators and problem solvers are wired to think differently and work differently. 

Imagine you’re lost in a foreign city and don’t know the language or customs. And to make matters worse, you lost your cell phone. Disconcerting, right? This is what the workplace is like for technical and creative people who think differently. A lot of time and energy is spent being lost due to issues which affect everyone, especially the neurodiverse, instead of being productive and innovating.

For example, research has shown that there’s a higher than chance representation of autistic people and people with elevated autistic traits in Science, Technology, Engineering and Mathematics (STEM) industries. They’re wired to spot complex patterns and relationships, focus on details, and work independently. They also possess strong logic and analytical skills. Orienting themselves to concrete facts instead of context, they analyze information from the physical senses using objective logic and prefer evidenced-based approaches. 

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“Imagine an organization that’s a fluid network of teams collaborating towards a common goal of delighting customers, where communication flows easily in all directions, and ideas can come from anywhere. What would that be like?”

The question above is an example of what’s known as a framing question. Such questions have many answers that helps to scope and clarify a problem just enough to move the conversation in a positive direction. Given that an organization’s ability to respond rapidly to market changes and emerging opportunities is determined through a series of day-to-day conversations, framing questions can serve as a valuable tool for Agile leaders wanting to achieve business agility.

Ed Morrison from Purdue University spent decades researching and implementing agility models in the social sector based on the transformational power of day-to-day conversations. He observed that every conversation is in response to some question, whether that question is asked explicitly or not, and choosing the right question makes an enormous difference to whether or not agility is achieved.

• Problem centered questions bog groups down into analysis, where members become paralyzed by the mistaken belief that there is one problem to solve
• Opportunity-centered questions emotionally engage people, where members see a complex problem to solve with many possibilities

The goal is to use questions to frame conversations so that the people are inspired to work together in new ways.

Questions that inspire and engage are called Framing Questions. Framing questions address potential opportunities and are surprising, rather than obsessing over known or hidden deficits. They frame what the collective wants more of rather than problems to overcome.

The framing questions force us to look at reality a little differently and are often used in Design Thinking and other Innovation models. As Ed Morrison points out, “a good framing question is complex enough that it will require the deeper thinking and engagement of each person in the conversation”.  The most powerful framing questions tap into the collective intelligence of the whole and thereby mobilize organizational brainpower to achieve lasting business agility.  

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Det är inte alltid lätt att nå fram till alla människor i en organisation, speciellt inte vid förändringar och när man måste kommunicera och samarbeta tvärs över alla delar så som man ofta gör under en agil transformation. Det är dock avgörande att man klarar av det för ett lyckat resultat. Jag tänkte berätta lite om hur vi försöker göra det på Dandy People och vilka verktyg vi tycker är hjälpsamma.

Vad handlar agila transformationer om?

När vi jobbar med agila transformationer så handlar det om – för ca 90% av människorna – en total omorganisering av sin latenta tankemässiga referensram. Den mentala bilden av hur saker och ting fungerar behöver förändras baserat på nya förhållanden, ny kunskap och nya principer. Hur du ser på världen – kan man enkelt säga. Det krävs “nya glasögon” att se på världen med i många fall – för att ta bort tidigare inlärda beteenden och begränsningar för vad som är värdefullt och möjligt.

För att lyckas med att nå fram till människor på ett bra sätt behöver man bli expert på vad som motiverar både oss själva som individer och förstå att alla andra INTE är som vi är, och i andra hand vad som motiverar de vi vill nå fram till. Vi har alla olika grundläggande psykologiska behov och det är olika saker som motiverar oss.

Hur vi på Dandy People jobbar för att förstå vår egen och andras motivation

Redan tidigt när Dandy People startade (före min tid) började Dandy använda en forskningsbaserad svensk metod som heter MyNeeds® för att förstå vår egen motivation och bättre lära känna varandra. Man insåg nyttan av det för att snabbt kunna skapa nya trygga team ute hos kunderna, vilket alla konsulter är i stort behov av som arbetar i team hos kund. Dandy har under årens lopp vid varje rekrytering låtit alla oss nya Dandysar göra en profil, få individuell återkoppling och dela med varandra. Det är tydligt nu, många år senare att vi också har väldigt stor nytta av den här förståelsen ute hos våra kunder. Vi kan lättare förstå vad som motiverar olika personer och grupper och vad de kan ha svårt för, och vad som gör människor otrygga eller omotiverade. Det gör oss mer trygga i att jobba med människor och grupper med olika bakgrund vilket ofta minskar onödiga missförstånd och eventuella konflikter.

Eftersom Dandy också bedriver en nätverksbaserad och självorganiserad organisation (Agil/Teal) utan chefer (vi leder oss själva och varandra kan man säga) är det också viktigt att vi, när behovet ökar, är fler som kan arbeta med MyNeeds® för att skala den här funktionen när vi växer. Därför har jag och flera intresserade kollegor certifierat oss för att kunna nyttja verktyget i vårt arbete hos kunder, och även inom Dandy People.

I och med en certifiering så får vi göra personliga återkopplingar och analys av individers personliga profil, coacha individer och grupper i att bättre bygga en tillvaro som stödjer deras behov, där de kan nyttja sina och andras perspektiv och motivation bättre. De som även tar organisations-certifieringen kan även bygga spelplaner med alla medarbetares motivation, för ett större systemperspektiv, och stötta chefer och medarbetare att se, förstå och kommunicera och agera utifrån detta i sin organisation, grupp och sitt team.

Mångfald är ett viktigt perspektiv att nyttja för de flesta organisatoner

Vi ser ett stort värde av att skapa mångfald inom organisationer där man nyttjar en mångfald av tankar. Företag vill inte ha team som har grupptänkande, det tar dem inte framåt. När vi lyckas med att skapa diversitet i teamen så möjliggörs ofta ett större flow, mer innovation och bättre resultat för organisationen. Här är MyNeeds® ett ypperligt verktyg att nyttja för att titta på spelplanen hur det ser ut just nu och nyttja de olika psykologiska behoven och perspektiven i skapandet av nya team på ett medvetet sätt. Företag som arbetar aktivt med motivation har 20% högre lönsamhet, 40% lägre frånvaro och 70% färre tillbud.

Kort bakgrund till MyNeeds®

MyNeeds® bygger på den senaste forskningen kring motivation, vilket är väldigt viktigt för oss på Dandy People. Edward L. Deci och Richard M. Ryan är två forskare som undersökt hur motivation fungerar och har gjort forskningen utifrån Self Determination Theory – SDT i över 30 år . SDT består av tre psykologiska behov som är underliggande mekanismer för att känna motivation. Dessa är tillhörighet, kompetens och autonomi – vilka vi också ofta berör och pratar om i det vi gör i våra agila transformationer.

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Imagine ten architects in a room. 

The first thing they would discuss is: “What is architecture?”, and they would use all the time they have at their disposal. 

If no one comes into the room and yells at them that they have to create a target architecture, a guiding principle or anything that helps the teams solve a technical obstacle that stops all delivery in the release train, they will continue the discussion. 

The first architect will say that there are standards and you should not reinvent the wheel. 

The second architect would state that there are several standards and which one should you comply with? 

The first architect would argue that ANSI is the one and only, that states that it’s all about organizing a system, its components and how they relate to each other and the environment. 

The second architect would say that ISO is the preferred choice because it focuses more on the properties of the architecture’s elements, relationships and principles of its design and evolution.

At this time, the third architect would state that there are thought leaders out there like Gartner.

The fourth architect would interrupt and refer to the Zachman Framework from the 80s and that there are some really good nuggets there. 

“Domain-driven architecture”, the fifth architect says and then the argument is in full bloom.

“Event-driven architecture”. “Pipelines.” “Pace-layered”. “Togaf”. “Components”. “Microservices.”

Until the sixth architect, the veteran who will retire in six months, says: “OSI Model. If it’s not broke, don’t try to fix it”

The room all goes quiet until the seventh architect, the newly educated with fresh ideas from the outside world says: “SAFe” and all other architects say “No!” while he raises his arm and says with a fragile voice “They describe much more about architecture than you think”, and all other architects stare at him with a blank expression, some of them raising their eyebrows. 

“Anyway”, says the eight architect, “architecture is easier to understand if we use metaphors. Architecture is like a garden. If you don’t take care of it, it will grow freely and become chaotic. Entropy is a natural law that takes over”.

The seventh architect, still trying to understand why everyone didn’t appreciate SAFe, says: “it’s more like a runway where the code can land. You cannot build the runway while you are landing”

“That metaphor is lame”, says the ninth architect, “its better with train tracks that the train travels on. It’s a parable everyone understands.”

“Or the road railing that you have to stay within on the highway” says the first architect. “Otherwise you collide. Not everyone lands planes or is a train driver. On the other hand, many have driven a car and can relate.”

“No, it’s more like a pop song” says the second. “You have three chords that you can combine so that everyone can sing and dance along.”

“More like jazz where you can improvise once you have experience and have all the theory”, says the third.

“Or a classical ensemble where everyone plays an important part, and it’s only when all is combined that you hear sweet music”, says the fourth while the fifth, sixth and seventh nod in agreement. 

At this time the tenth architect, who has been quiet until now, would clear his voice and say: “Architecture is like love”, and all other nine architects turn their attention to him and listen.

“No one can define it, but everyone knows how it feels. Emotions flow, you walk on light clouds, nothing is impossible, no obstacles are in the way and you can conquer the world!”

“Yes!”

“Awesome!”

“There you have it!”

All architects give their acclamations and raise from their chairs and start clapping and dancing with stiff movements. 

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In this episode I met with Per Kristiansen to talk about serious games (or serious play as they call it at Lego) that we love using as a safe way for exploring complex strategy and play out different scenarios. Per tells the story how it all got started at LEGO when their CEO needed a better way to work with strategy and they created Lego Serious Play. Per was part of the internal research team that discovered that using lego bricks for simulation and learning was just as useful for adults as it is for children. He tells a fascinating story of how they first failed, but later discovered how to make it work and developed it to the metod that it is today, and how it has now expanded from being an internal method within Lego to a global phenomena that it is today.

The conversation starts with us sharing our purposes and values behind our two companies, Trivium and Dandy People, and it happens to be a perfect match 🙂

If you are interested in learning how to facilitate the Lego Serious Play Method Per travels all around the world to facilitate Lego Serious Play trainings and we are really happy to welcome him to Dandy People and Stockholm too

Read more and sign up for the 4 day Lego Serious Play Certification Course in Stockholm here

We are so happy to be able to share the Monotasking in a Nutshell Poster with you for free also in Turkish! Thank you so much Ender Yüksel for your awesome work with the translation 🙂

DOWNLOAD IT FOR FREE: Here you can download it for free in high resolution >

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Since we like to have fun and laugh here at Dandy People we did an Agile Advent Calendar last year full of Agile inspired Dad Jokes, so if you missed it we have the wrap up for you here.

December 1

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– And why on earth am I even doing this???

I just took on the most challenging client–MYSELF.

Inspired by friends who are marathoners, I decided to cultivate what I’m calling a Marathon Mindset. I’m coaching myself towards achieving increased flow in the presence of variability. My aspiration is to emerge from this COVID-19 crisis a better person. Through this process, which involves training for an actual marathon, I’m learning that my own barriers and obstacles to “become a marathoner” are similar to those experienced by organizations wanting to “become agile”. This aha moment was unexpected, but transformative as an Agile Coach. I now believe the Marathon Mindset is the Agile Mindset because it simultaneously fosters both stability and agility through continuous and incremental evolution instead of a big bang transformation. Below I share some insights into what I’m learning for the benefit of Agile change agents everywhere.

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I was so happy this morning when I found this article in my favourite magazine, the Harvard Business Review. As always all articles are based on research and as many times before I see a strong connection to my work with Agile organization, teams and innovation.

In the fresh research done by ADP research Institute 2019 on employee engagement and published in the Harvard Business Review they discovered the power of well functioning teams and trust to engage employees.

The sad state of Employee Engagement in organizations today where the vast majority of employees globally aren’t fully engaged in their work. This research concludes just as many other before that the engagement level generally is alarmingly low in most organizations – only 16% feel fully engaged in their work and 84% are just coming to work to get their pay check.

The researchers also concludes that the share of employees who are fully engaged more than doubles if they are on teams, and not just any teams but well functioning teams.

The power of trust. As noted the share of employees who are fully engaged more than doubles if they are on a team, and it MORE DOUBLES AGAIN if they strongly trust the team leader.

These are the powerful questions asked in the research

1. I am really enthusiastic about the mission of my company.
2. At work, I clearly understand what is expected of me.
3. In my team, I am surrounded by people who share my values.
4. I have the chance to use my strengths every day at work.
5. My teammates have my back.
6. I know I will be recognized for excellent work.
7. I have great confidence in my company’s future.
8. In my work, I am always challenged to grow.

 

ADP research Institute 2019 and published in the Harvard Business Review. Read the article on HBR here >