It Is Crystal Clear What Winning Looks Like – and Losing Always Has Consequences

It is crystal clear what winning looks like, and losing always has consequences.

This sentence almost sounds banal, and yet it is so hard to put into practice in some organizations.

An organization that lives this will shine with AI.

Today and over the next few months, I want to describe to you what, in my opinion, an AI-ready organization looks like.

Over the last few months I’ve noticed that most people who have identified the right problems, and in some cases analyzed them well, have little to offer when it comes to an inspiring vision.

If you have no idea what a good organization should be like, you can’t make it happen either. It’s that simple.

There is a lot of catching up to do here, and in this article series I’m now going to show you my vision.

It is crystal clear what winning looks like, and losing always has consequences

Let’s go back to the restaurant example from my last article. Can you describe winning and losing for the restaurant? You can, it’s easy.

But let’s take a big, abstract initiative: building an AI Semantic Data Platform for a complex company. Many teams and managers are working on it.

Now take any manager, any engineer, a business leader, some SMEs, and ask them what winning looks like with regard to this platform and what the consequences of losing are. In some organizations you get a different answer from everyone at the detail level, or else a very generic, not very meaningful answer at a very high level.

For an engineer, the answer could be that it runs stably. For a manager, it’s maybe some slogan like “Data as a Product finally becomes reality.” For a data governance specialist, the answer is probably “Winning is finally having clean governance for once.” And the business leader maybe thinks in regard to that platform: I’ll nod along to everything, as long as nobody takes my Excel away from me.

An organization that is AI-ready can define winning like that: Through the data and the AI that uses the data, we can give our customers xxx information xxx faster, or additionally offer xxx service, or additionally offer xxx product, or sell more from xxx or learn xxx about our customers in even more detail and find xxx more customer segments or gain xxx more leads.

xxx must be specific and measurable!

This perspective naturally narrows the scope.

An organization that can say this in one, or at most two, sentences including the customer, and I mean with everyone involved saying the same thing, doesn’t need me.

On the consequences of losing

There has to be a very clear separation between the people and the initiative.

If an initiative “loses” by definition, that doesn’t automatically mean that something bad happens to the people involved. It may simply not have been worth it. This insight is a healthy one, because it keeps you from investing even more time, money and sanity in an approach that doesn’t work.

An organization that is AI-ready treats even abstract technology platform initiatives like a startup: you formulate hypotheses, get budget and lots of support, validate the hypotheses early and regularly, and kill everything that doesn’t contribute to winning.

That can mean throwing out assumptions, dissolving teams, changing the approach (how to do something) or going all the way back to square one.

Being wishy-washy, on the other hand, looks like this: increasing budgets, pushing back deadlines, changing decisions without evidence. None of that is following through.

A game is only a game if there are rules

Imagine a soccer game where one team is about to lose. So that it doesn’t lose, everyone now decides to arbitrarily extend the game, put up an additional goal and hire more expensive referees, who are also supposed to run around on the field. On top of that, there are better jerseys. Idiotic, right?!?! And yet exactly that happens every day, figuratively speaking, in technology initiatives!

That’s why winning can only be defined in relation to the customer. There are certainly different assumptions and hypotheses about how to get there.

But either you deliver a better, cheaper, or faster service or product, or provide more services or products, or you gain more insight into the needs and desires of your customer. The list is not endless. This is why this North Star gives you stability in your strategy.

If you define winning in relation to technology, you will waver and constantly have to change direction. Vendors and internal politics will exploit this to play their own games and change the rules on you, watering down your concept of winning to the point of complete ineffectiveness.

When you sell technology you can still define this in relation to the customer. So please get me right.

Think of soccer or think of a startup. And yes, there are large corporations who also put this mentality into practice.

Make losing transparent and follow through on the consequences. Do this in many fast iterations. That’s what it’s all about.

The seven disciplines in practice

Let's now go through the seven points I introduced in my last article to see whether this is a useful framework:

People at the center

If you ask in an AI-ready organization what winning looks like, the sentence will also include the customer, user or citizen.

This shared north star connects all departments with each other.

Shared values

Spelling out what winning and losing look like, and doing it in a way that makes the strategy executable, is hard work.

If you do this work, you show humility, commitment and respect. You’re also being transparent and fair.

Not backing away from the agreements and following through shows reliability, and trust builds up.

Through these actions and activities alone, values are visibly lived, and that’s worth more than just writing them down.

End-to-end responsibility

The moment winning means added value for the customer/user/citizen, I automatically take on end-to-end responsibility, even for rather abstract topics like a data platform.

Repertoire of methods

Without a repertoire of methods, I can’t do this translation, coordination and leadership work at all. Scrum isn’t enough here, and doing everything through meetings isn’t enough either. In my next article I will introduce some helpful methods.

There are many more, but I will focus on the Value Proposition Canvas, the Lean Startup method, Impact Mapping, the Balanced Scorecard, and methods where I took known elements, modified them slightly, and simplified them to the maximum: I call them the Circle of Leaders and Board Review Pitches.

People in an AI-ready organization know many methods, have reflected on them, and are able to adapt and combine these methods to their own needs.

Learning organization

Think of team sports. After a defeat, there’s analysis and learning. If you take spelling out winning and losing seriously and everyone consistently sticks to it, you automatically learn as an organization, because you check whether you won or lost and naturally do everything you can to win.

Just by consistently spelling out the rules of the game, you set the framework for learning across multiple teams.

Organizational design

When the rules of the game are clearly written down and everyone understands exactly what winning and losing look like, it’s enough to have a work chart. A work chart is a list of the people who work on the AI Semantic Data Platform, regardless of where they sit in the same org chart box.

You don’t need a change project. The only important thing is that the hierarchy in the org chart is suspended for this group. The rules of winning and losing override the hierarchy, that means, no leader is allowed to undermine that.

The sponsor of the AI Data Platform only has to make that clear once. And just like that, you’ve created the basis for improving the org design. The org chart becomes less important. That’s the first step.

Mindset

By mindset I don’t mean: let’s all hold hands and sing Kumbaya.

By mindset I mean unconscious beliefs that keep us from putting this into practice. Everyone would agree with me that the ideas in this article are good. And yet it’s so rare. Why?

You might say that incentives are the biggest problem. Yes and no.

If you have clearly defined what winning and losing look like, you can align bonuses, promotions and careers accordingly. The mechanisms are there. In most organizations, you could simply change what people are rewarded and held accountable for.

So why doesn’t it happen?

I think that’s where we need to dig much deeper into how we see the world.

I’ve been thinking about this for quite some time. We’ve discussed it far too superficially for far too long, which is why so little changes.

I want to explore that, but that’ll be a book.