Let's start exploring how we can build AI-ready organizations

My mission is to make complex organizations AI-ready. Often it starts with software, data, or AI teams doing something for the organization, now with AI.

However, we need to broaden our perspective and look at the entire organization.

Let's imagine a restaurant:

The guests are your stakeholders, service stands for leadership and coordination, and the chefs are your engineers.

The restaurant guests order a 4-course meal and are irritated due to construction noise, as a sunroom is being built at the same time the restaurant is operating.

After the waiter had to ask several times because of the noise, the chefs first have to go grocery shopping because there was a dish on the menu that wasn't discussed beforehand, so the ingredients are missing.

Two chefs head out, meanwhile, the other chefs are bored and therefore have to help out on the construction site.

As soon as the ingredients arrive, total chaos breaks out and nobody knows exactly where they are assigned.

In the end, there is too much meat and no sauce (no one was responsible for it, and it wasn't explicitly on the menu either). The vegetables are now overcooked.

The restaurant guests are horrified, the food was not delicious, and the construction site was unbearable. They leave quickly, won't come back, and prefer to order fast food instead.

The construction site keeps getting bigger, and conflicts arise in the kitchen and between the kitchen and service.

New high-tech kitchen machines are introduced. They can invent recipes and cook at the same time. You just have to say what you want.

The long-time chefs are fired, inexperienced, cheaper chefs are hired. The machines are used. The dishes that are cooked are boring and unimaginative.

The people working in service are desperate because they actually want to be a 5-star restaurant.

The restaurant manager is overwhelmed and doesn't know which dishes to put on the menu. Nobody agrees on where the journey is going.

Those responsible for the sunroom keep making the construction site bigger and say they aren't responsible for the concept of the restaurant.

The junior chefs say they are only responsible for operating the machine. There is no shared, coordinated way of working. The guests stay away.

You think I'm exaggerating? I swear, I was on a project exactly like that (it had something to do with a data platform).

And now let's dream of the perfect 5-star restaurant

Potential restaurant guests find the new restaurant through descriptions that make them curious and make their mouths water.

It's a new concept. There is nothing comparable in town.

You are greeted warmly. The service recommends a wonderful four-course menu.

You sit down, and before you know it, an amuse-bouche arrives from the kitchen.

The service recommends the best wines and can offer detailed advice. The dishes are exquisite. The atmosphere is pleasant. The sommelier drops by between courses to chat briefly.

The head chef brings the dessert personally. The guests feel like kings, in German we would say "like God in France".

Upon leaving, the service offers recommendations regarding the guests' questions, whether it's cooking tips, good wines, or activities in the city.

Full, happy, and enriched by this unique experience, they recommend this restaurant to others. It is constantly fully booked.

This restaurant uses high-tech kitchen machines, too.

The senior and junior chefs, the service team, and the restaurant manager experimented with them, and now they can offer meals that weren't possible before. They now have a unique concept.

Young chefs in training still learn the craft by hand to gain experience and build their creativity.

The restaurant management, service, and kitchen regularly discuss the concepts, all methods and workflows are practiced regularly, errors and optimization needs are analyzed and continuously improved.

Everyone must engage in continuous learning.

Motivation is extremely high, the guests are happy, and a visit to this restaurant is a highlight.

Soon it is known nationwide.

Organizations have to learn — a lot!

A large organization is usually much more complex than a restaurant, and yet even with this simple metaphor, it becomes crystal clear that you have to learn and master many different things!

Everyone must understand their craft, and on top of that, you have to offer more than others, move with the times, engage with AI, and make the customer happy.

We must look at a wide variety of disciplines together, and so that it doesn't look like these topics are unrelated, I have prepared the following overview.

The new article series will cover:

• Mindset – Mindset is the beginning of everything. Our attitude determines how we shape the working world, and we often have internal core beliefs that we aren't even aware of.
• Putting People First – People must be at the center – the customer, the user, or the citizen. No matter which software, data, or AI initiative I undertake: losing sight of the customer is the beginning of the end.
• Shared Values – An organization needs shared values. Better to have a few, but lived daily in the culture.
• End-to-End Responsibility – End-to-end responsibility is the basis for good workflows. As soon as I choose a structure that is not mapped end-to-end, structures gradually and steadily develop over time that cost time, money, and nerves without delivering added value.
• Method Repertoire – Everyone who works in software, data, or AI should have a repertoire of methods, ways of working, and schools of thought available to choose from. Choosing the right tool at the right time is the prerequisite for effectiveness and efficiency.
• Learning Organization – The goal is to become a fast-reacting and learning organization. The outside world is unstable and complex and constantly changing. If an organization can no longer react to the outside world in time, it will eventually go under.
• Organizational Design – Organizational design must enable and map all of this. Software, data, and AI will soon be indispensable in every area. Any org chart that does not consciously map points 1 through 6 will eventually become a real obstacle.

In the coming months, we will look at various aspects of these 7 points, always using concrete examples from organizations that are AI-ready and those that are still at the very beginning.

Yesterday's news…

You might think that you've heard all this before. Some have, yes! But not everyone has heard it yet. And then some have forgotten it again. And even more don't know how to apply that knowledge in practice.

…is organizational debt

The more of it you accumulate, the higher the total cost to the organization, because this often creates structures that prevent more than they enable, that are cumbersome, that allow waste to pass, friction losses occur, wrong decisions are made, it becomes "political", employees get frustrated – you all know it.

Throw AI into the mix, voilà! Here we are now.

The system as a whole emerges from the interplay of multiple disciplines

That is why we need to systematically learn once again how to build good organizations and how to build them differently so that AI can be used meaningfully in the first place and people and customers are happy.

The challenge is that there are many different disciplines, and they all need to work together seamlessly to create a functioning system. If you don't get that right, the different elements will still interact and form a system — just not the system you intended to build.