Blog
Blog
It Is Crystal Clear What Winning Looks Like – and Losing Always Has Consequences
An AI-ready organization knows what winning means for its customers and what happens when an initiative loses. A look at platforms, football, and seven disciplines.
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Let's start exploring how we can build AI-ready organizations
My mission is to make complex organizations AI-ready. A restaurant metaphor shows why that takes more than AI tools – and which seven disciplines belong together.
Read article →Can you do simple maths?
Why limiting work in progress is basic mathematics – and why data and AI organizations should start doing less at the same time.
Read article →AI Transformation Happens in Phases, AI Operating Models Will Change
Why AI transformation unfolds in phases, why new companies may overtake the old ones and why hierarchical org charts are of little use along the way.
Read article →McKinsey Doesn't Know Either
McKinsey's new article on the AI operating model contains nothing truly new – AI forces you instead to finally implement insights that have been known for ages.
Read article →How Can It Be Fun Again?
Seven criteria for organizations where technology, business, responsibility, and professional craft come together again.
Read article →Muda – the 8 types of waste
The eight types of waste in Lean philosophy – applied to software, data and AI teams.
Read article →Customer Orientation and AI
Why customer centricity gets lost despite all the familiar concepts — and which new questions AI brings to the table.
Read article →Transformation is not a product
Isolated AI optimizations weaken the overall system when roots, vision and scaling are not considered from the start.
Read article →"AI transformation" is really the wrong word
It is always a transformation of the company — AI is merely the occasion, the enabler, the accelerator. What it looks like to start with the customer.
Read article →AI Transformation As I See It
What AI transformation means to me - coping with bureaucracy, customer orientation and personal AI agents navigating a codified world.
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Local AI — for individuals and organizations
A hands-on report on running local AI on a MacBook, and the three operating models for organizations — API, managed deployment, self-hosting.
Read article →Organizational Debt
Definition and mechanics of organizational debt.
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Shape Up vs. Scrum — an improvement from my perspective
Where Shape Up moves further than Scrum — bets instead of backlogs, cycles instead of sprints, outcome instead of process theatre.
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The AI Operating Model Starts with Mindset
…and with how we think about people.
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AI Models — An Overview
Model types, hosting, calls — and why this is actually the simplest problem when building agentic AI.
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The Agentic AI Stack
Six architectural layers, six stack types—and why your choice of agentic AI stack determines your operating model.
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The Method Maze
Guest article in the Practical Data Community by Joe Reis.
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Methods for Innovation
An innovation journey through a chocolate manufactory — six methods for problems where the path to the solution isn't clear.
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The Black Peter Principle
What has this to do with Data and AI??
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End-2-End Teams and a Leadership Team Along the Value Stream
End-2-end teams, leadership team rethought, career paths along the value stream
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