This connects to something i keep noticing - org design conversations often stay at structure level, but the real friction shows up in day-to-day decisions.
who gets to decide, how fast, and with what context.
you can redraw teams on paper, but if those decision paths don’t change, not much really shifts. that’s where most redesigns quietly stall.
You are right, that is an important part. I partly address it in the next article. Somehow there are a lot of posts about decision architecture at the moment. I am trying to incorporate that into the context of team and organizational design, because I fear that if you just layer the new decision architecture on top of the old structures, it will not help anyone either. So it needs to be combined in a meaningful way. But how?
I look at part of that in my next article, but not all of it. I also first need to work through how to abstract the current examples with agentic AI and derive principles from them. I need a few more practical examples for that.
I also always see the danger that a new framework comes to market and then it does not fit every industry and every type of company. I have experienced that firsthand too many times. That is why I am trying to convert practice into principles and then explain that again using practical examples.
This connects to something i keep noticing - org design conversations often stay at structure level, but the real friction shows up in day-to-day decisions.
who gets to decide, how fast, and with what context.
you can redraw teams on paper, but if those decision paths don’t change, not much really shifts. that’s where most redesigns quietly stall.
You are right, that is an important part. I partly address it in the next article. Somehow there are a lot of posts about decision architecture at the moment. I am trying to incorporate that into the context of team and organizational design, because I fear that if you just layer the new decision architecture on top of the old structures, it will not help anyone either. So it needs to be combined in a meaningful way. But how?
I look at part of that in my next article, but not all of it. I also first need to work through how to abstract the current examples with agentic AI and derive principles from them. I need a few more practical examples for that.
I also always see the danger that a new framework comes to market and then it does not fit every industry and every type of company. I have experienced that firsthand too many times. That is why I am trying to convert practice into principles and then explain that again using practical examples.
My brain is almost tied in a knot :-D
i think that “layering on top” risk is exactly where things break.
new decision models sound right, but once they hit existing incentives and habits, they get diluted or ignored.
maybe it’s less about a perfect combined model, and more about fixing where decisions are actually getting stuck today.
also relate to the framework fatigue. teams don’t reject ideas, they reject things that don’t translate.
100%