# AI Implementation: Mindset, Data, and Leadership

**Podcast:** LEITWOLF Podcast - Leadership, Führung & Management
**Published:** 2026-10-01

## Transcript

Typically, the time-save potential is around 70-90% for a process.
Wow.
Being able to quickly adapt and iterate is something I think that should be changed or improved in many aspects of the world of work we see today.
Hello and welcome to today's Lightwolf podcast.
It's great that you, dear listener, are in with us today again.
Today, we are exploring a topic.
Das ist ein weiterer Geist.
für meine Business und für meine Rolle als Leader.
Und in order to discuss these questions, es ist mein Herz, ich bin wirklich delighted to welcome a sehr special guest this morning, someone I know und respect very much, Katja Spöhrl.
Welcome to the Lightwolf Podcast.
Thank you, Stefan.
It's a pleasure to be here.
I'm very excited to talk.
Und ich bin froh, dass du hier heute bist.
Katja ist der Managing Director von der Newnow Group.
Wunderbar zu haben Sie hier und zu shareren Ihre Erfahrung, weil Katja ist Arbeit auf AI und AI-related Projekte, day in und day out.
So, wir müssen erst einmal ein bisschen who du bist, Katja, so people verstehen dich als eine Person.
Do du still remember?
Du bist viel younger als ich, so ich denke, du bist.
Do you still remember who you were as a teenager, Katja?
Yes, I do remember myself as a teenager.
Back then at school, I would consider myself a nerd.
Not necessarily the cool kid, but very much focused on my smaller circles and totally inner world for myself.
So probably not engaged in the most hype topics, but rather...
Being focused on the things that I have to do, which is school and a lot of sports actually.
So every day I think I had something else.
So I was participating in volleyball, in tennis, in dancing, in badminton.
And that basically was occupying all my afternoons after school.
So, yeah, that was basically my teenage time.
Yeah, it went past quite quickly.
And every time if I reflect back nowadays, when I talk with my friends back at school, they would talk about people in our classes and would say, I can't actually remember that those people existed because I was so focused in my own world that sometimes if you're not in that smaller cycle, I was like, what that person really there?
Yeah.
Und hat eine Fähigkeit von deinen Sporten?
Ich habe ein Fähigkeiten.
Ich liebe Badminton die meisten.
Es ist fast.
Es ist gut für...
Du hast so lange distances.
Ich habe einen guten Sportler.
Ich bin ein guter Sportler.
All meine Trainers sagen.
Katja, du hast guter Skills.
You're very precise and so on is how you play things and strategic about where you want to play.
But you're just very lazy.
Sometimes you just don't get your butt up and run for that last point.
So at some point I realized I need to smaller the playing field for myself and for a play.
So that's why I landed at Badminton, which is one that I still pursue because this is the one that I like the most.
Because it fits my character, apparently.
What an interesting view on reducing the scope of a field to just match your preferences.
And if I may introduce your professional side and your academic achievements.
Katja, you studied in St.
Gallen.
You received your PhD from the European Business School in Östrichwinkel.
You've seen Marilyn Lynch, you've seen Morgan Stanley from inside, and you got an outstanding management experience at the Boston Consulting Group.
So it's evident that what maybe at times may have been at play in sports, so not always wanting to go the extra mile, was the exact opposite in your professional and academic career, right?
Because you must have worked hard.
Ja, ich denke, ich bin ein sehr goal-orientiertes Person.
So, wenn ich mich selbst ein goal, ich werde es, dann werde ich es.
Für all die anderen Sporten, obwohl ich eine preference für Badminton habe, ich habe es für 10 Jahre.
Und das ist, ich denke, obwohl es nicht mein erstes, ich still mag es.
So, das ist, ich denke, etwas, das ich auch.
Also, nowadays and across all dimensions, I would say, I always need a clear goal.
I think that gives me also in all the things and complexity and uncertainty in the world, for myself, a little bit of certainty to like, I know where I want to go.
And for that goal, obviously, you go through up and downs, where it takes to get there.
Yeah, awesome.
And then about two and a half years ago.
You became the managing director of a company that I co-founded with six others five years ago.
And it's amazing to work with you, Katja, managing director of the Neunau Group in Frankfurt and Vienna.
And you combine a deep understanding of data and AI on the one hand side with experience in applying them in business.
You and I went to...
Ich habe jetzt ein Jahr ein Jahr in Vienna und habe es mit einem neuen Kunden gearbeitet und es ist mir klar, dass man, day in und day out, wo organisations sind auf der Weg zu gehen, um über experimentation zu gehen und zu nutzen, um bessere Entscheidungen zu machen, um für ihre Kunden und ihre Shareholder zu machen.
to listen to you when you share your experience.
Let's start with a couple of topics regarding artificial intelligence.
If you look at what you see in the world, Katja, if you look at NewNow's client portfolio now and where the projects stand and you have complete oversight as our MD, where do you see the biggest challenge of our business clients regarding implementation of AI?
I think there are definitely several ones that different businesses, depending on their size, depending on their industry, probably face.
And I think those challenges actually have changed since two, three years ago versus now.
When I reflect on two, three years ago, I would say many of them are still at the start.
They didn't know where to start.
Some of them might have not even got on the train in terms of how important AI is and can be.
And some might even say this is some new technology that is not reliable and will not be important in the future.
And that probably today I hear very, very rarely.
So attention and focus on a management level is the first step as a challenge, I think, for implementation, obviously, at the start.
The second one is the mindset.
So we're in Germany and as Madoff Germany is a brand that is basically based on precision of German engineering.
It's a mindset in those companies that is basically completely different to how AI works.
Because AI is all about approximation.
It's about iteration.
It's about being wrong at the beginning and getting better and better in the fastest way possible.
And that is in contrary to the mindset of those organizations.
So when they start off...
Implementing AI, the businesses typically do better when they have people that obviously already understand that point and know how to start dealing with AI.
So when they come with the right mindset at the beginning, implementation works a little bit better because otherwise it's a lot of, yeah, you need a lot of culture change in order for AI to really stick.
Was ist eigentlich weniger Problem ist eigentlich die Technologie, weil Technologie ist besser und besser.
So die Qualität der Ausbildung von AI wird sich entwickeln, mit LLM-s zu werden, und mehr intelligenter und intelligenter.
Die Dinge, die es der Herausforderung ist, sind alles um die Prozesse.
the data that you feed into AI and the people basically that should use it.
And that's all above that mindset topic, right?
Because it's a mindset that decides how you do your processes.
Are you willing to change it when you adopt that new technology?
Knowing that it probably is quite big of a change, knowing it probably is going to lead to resistance and fearful responses within the organization.
Ist das etwas, was du bereitst zu machen, wenn du es bereitstest, als ein change ist?
Oder ist es zu viel für die Organisation für die Organisation?
So das natürlich auch decides auf wie du eigentlich willst, wie du über die Implementation hast.
Dann, der zweite, natürlich, die Daten, die ich glaube, ist ein Greates.
Great topic.
I mean, I think everyone, it's quite leveraged actually in the media as well, that data obviously is something that AI is built on, meaning good data leads to better results.
And none of the organizations did actually their data strategy 10, 20 years ago with in mind of the purpose of feeding it someday to an AI.
So the data, unfortunately, is not in a place where it is suitable for AI.
And that probably coming together with the data topics, the structural topics, the process topic, and of course, lastly, leadership and mindset are the things that decides whether implementation works better or less.
Yeah, very helpful.
So based on your perception, technology is no longer a barrier.
The tech works.
The barriers you see are much more in mindset and in data strategy, maybe data readiness.
Now on that first one regarding mindset, what you described, and I am German, so I can acknowledge this somewhat stereotypical German mindset.
Das ist in den nächsten Jahren, in der sich in den Augen zu sehen, und in der sich für Precision, von Anfang zu Ende, ist in contrast zu dem du musst, um die AI zu machen.
Weil es ist experimentation at the beginning, es ist oftmals failing, a lot of times early on, und dann nur in iteration.
Finding the right spaces in the right way.
So it's a completely different mindset.
Now, looking at the many projects you've managed, led or supervised in NewNow, Katja, have you had cases of successfully helping C-level leaders who are often the decision makers?
their mindset from a not helpful one to a helpful one regarding AI?
Yes, I think it takes time.
And it oftentimes takes explanation to open up to say how AI is composed, so the basic components of AI and how AI works.
And the one case where they see the process of things getting throughout weeks, throughout iteration cycles.
Really seeing that it's working is something that obviously leads to changes in how people think.
And then it also requires their trusted people inside their own organization to be there.
So the person responsible for AI is oftentimes very critical in guiding the top management.
und die Organisation und die Organisationen und die Organisationen um AI, die Organisationen, um die Transformation and Change zu verändern und die Kontinuously informieren, wie AI ist, weil, am Ende der Ende des Tages, AI ist eine Technologie, die neu ist.
Wir adoptieren AI, alle sind adoptieren AI, während die Technologie noch weiterzukehren.
Meaning at this point in time, actually what you want to have is actually to encourage exchange and to really being able and take the time to talk about it, what everybody learned.
Because through the technology improvement, obviously there are other risks, there are other challenges that evolve.
I think as I said two years ago, it was more about can the technology actually deliver?
Because the quality of it was still not.
As of today, usable, as easy and as good as today.
And I think what then changed is obviously the speed of technological improvement that basically erased that worry or concern.
And then when things get better with technology and LLM are implemented into AI systems that can work.
It obviously triggers other concerns in terms of security, in terms of roles, permissions inside organizations, how processes need to change, and how you actually start, need to rethink about your organizational structure, how you make decisions, and all that is something that is evolving and that needs to be talked about, thought about.
Try it out and to learn from it.
And that's probably something that is not very comfortable for many organizations that have been very successful in the things they do.
But now it's basically an open playing field where everybody starts fresh and the fast learners will probably succeed in it.
Great.
And if you now look across our project and client portfolio, Katja, and if you...
Distinguish and segment those projects that are moving slowly, laggers, where things move slowly, and those others where projects are already pretty advanced and starting to show tangible value.
Maybe two questions.
What symptoms do you see in terms of results or the lack of results in the slow ones?
And what first evidence of real business value can you see in the advanced ones?
So in terms of symptoms of the laggers, typically it's, I would say, an unclarity of the goals that AI should bring.
So basically at the beginning, at the setup of things, the experiment was not.
Set was a clear goal.
The experiment was there, but there is either a clarity of ownership who would own the goal missing or an unclarity of the goal itself.
And the ones that have that ROI goal and want to have also a process how they want to achieve that, so how AI is actually developed, implemented, adopted, scaled.
Those typically are the ones that can move faster because they have thought about it in terms of the timeline, how they want to go about it.
And the reason for that is actually at some point you ask about how much, at some point the budget question would impact the speed of implementation because of regular...
Regular planning cycles inside organizations.
And if you don't have that ROI number at the start that is already very clear to all stakeholders, at some point somebody will ask, where are we actually?
How can you verify the value that you already created?
And what are we getting out of this?
And then that can trigger some questions.
And then it ultimately then...
kann es hinder oder slow down die Implementation, weil wir das Unclarity haben, wie viel wir wollen, für welche Outcome sind und wo wir auf der Reise sind.
Und diejenigen, die das haben, die das gemacht haben, die eigentlich wissen, was sie wollen, was sie aus dem Wert.
Ja, so Sie highlighten die Importanz der Beziehung einer clearen Beziehung auf der Fronten.
Ja.
Und das ist eine Beziehung, die ROI-related ist.
So that you define up front what return on your investment you want from artificial intelligence.
It sounds so simple, but it is so powerful.
And it is at times forgotten to just set the goal before you start, right?
And maybe it's also because people want to experiment with AI.
Why don't we just first try a bit?
But you just...
Das ist mir wieder für die wichtigste, dass man eine clear goal early auf, so dass man klar ist, was man versucht, was man versucht.
Jetzt, eine Frage, Katja, anonyms, aber real.
Du und ich, über ein Jahr ago, met einem Now client von NewNow für den ersten Mal.
Ich erinnere mich, die high number, die double-digit number, der Service Providers, die waren gezwungen, die erinwerte, die present waren.
Und ich habe mich gefragt, ob wir wirklich gehen können, wenn es so viele gibt, dass sie wirklich wissen, was sie wollen.
Und Sie haben mich gefragt, Stefan, wir sollten gehen.
Ich habe gesagt, okay, fine, ich werde noch einen Flugzeug.
Ich komme zum Platz und wir presentieren uns zusammen.
Und ich habe es nicht mehr, aber ich habe es nicht mehr, weil sie hat angefangen, weil sie angefangen hat, weil sie angefangen hat, sie hat angefangen, auf AI zu arbeiten.
But they had not yet seen significant or any value coming out of it.
Because at least partially the solutions they designed were never used by employees.
Now, if you reflect on the progress this client made since, how far have they come now in the first year of our collaboration?
What do you see has changed in the way they work before working with us?
Yeah, it's a very interesting example.
Because as you said, before we actually joined, they already started with the topic of AI a year ago.
And basically in the end, all that year of investment, of implementation to tools is sunk cost now.
Und wenn wir angefangen haben, die erste Sache wir gemacht haben war, die Strukturierung, ein AI-Ambition zu haben, um an Ibit-level, an company-weit profitability-level zu sagen, was die Ibit-improvement ist, dass wir mit AI erreichen wollen in drei, fünf und sieben Jahren haben.
Natürlich, fünf bis sieben Jahren ist es nicht möglich, wir werden aber...
Ich denke, Sie müssen mit der Ambition Level beginnen, um die Sinklage zu machen, die Sie wollen, mit einem Portfolio der Use Cases zu implementieren.
Und zu sagen, okay, sind wir eigentlich da?
Sind wir mehr?
Was ist eigentlich needed?
Denn dann wird die Implementation viel mehr konkret werden.
Und du kannst du sehen, ob die Plan und die Rollout ist eigentlich in die Überalltart.
Und basically, was wir haben, ist es, alle Strukturen und Prozesse in place mit der Ambition, der Portfolio von Use Cases, der Strukturen, wie du von Use Case zu einem Rollout hast, und die HRI von den Use Cases zu den Linken in der Bag zu den Finanzien numbers der Firma.
Und dann in parallel, starting mit fünf QuickWin-Use-Cases, das sind bereits jetzt in use, die in den letzten Jahren, die in den letzten Jahren, die in den letzten Jahren, die in den letzten Jahren, die in den letzten Jahren, in den letzten Jahren, in den letzten Jahren, in den letzten Jahren, in den letzten Jahren, So, and they are now from the first year with a single digit of use case implementation.
They're also building up their AI capabilities internally.
Because of that structure, when you have an ambition, you have a plan, you know how much you want to invest.
And then you can also decide in terms of what is the capabilities internally you need.
With that plan in place, you can actually accelerate the implementation.
So from that single-digit use case implementation, they are now moving in a second year to a double-digit use case implementation.
And next year, it's going to be a medium-sized double-digit use case implementation as well.
So you will see that really getting the foundations right will accelerate your implementation adoption fast.
What time, Katja, did you see or do you expect to see business value resulting from this use case adoption in terms of saving costs, saving time, increasing revenue, creating real ROI?
As of when do you see signals and as of when do you see results?
I think the first results you can already have in the piloting phase where you obviously track down the RRIs that you want to measure.
And typically at the beginning, we were talking about NVPs of use cases.
The focus is very much about efficiency gains.
And there you typically track the time saved.
Und dann natürlich die nächste Sache, was du machen, ist, dass es in Use ist, es muss mit deinem Business Planung sein.
Die nächste Schritt ist eigentlich, um die Finanzierung und Value aus es zu machen, geht in deinem Planning Cycle.
Das ist eigentlich, das ist wie du mit oder mit dem Potenzial und den Quantifizierung von KPIs in eine Real Business Value.
Und das wird wahrscheinlich ein bisschen Zeit.
So die Use Cases, die wir in der ersten Jahre starten, implementieren und in der ersten Jahre, sind die ersten Jahre, die wir in der Planning Cycle gehen.
Ja.
Und was ist die Realistik Dimension und Size von Business Value zu erwarten?
Kind of eine Realistik Ballpark von Commercial Value generated?
Das ist eine Sache, das ist eine Beispiel, das ist nicht zu begeneralisiert, aber es gibt einen Ballpark der Wert der Wert der kann.
Wenn du die Zeit-savungen jetzt sehen und wenn du das die größte von einem Tag einstieg wird, wird das Potenzial?
So typically the time save potential is around 70-90% for a process.
Wow.
Yes, it is quite a lot.
Wow, I'm sitting up in my chair right now.
Yeah, we're talking about days or weeks of work that can be then now generated in an hour or less than an hour.
Und das natürlich hat sich ein viel Potenzial.
Weil, du hast, die wir sehen, die wir mit den Karten starten mit sind, die Leute sagen, ich kann nicht mehr machen, dass ich alles, was ich tun, in order zu machen, um unsere growth zu machen, in der Zeit, dass ich es.
Und bei der Using AI, dann kann man sich auf die Potenzial verbessern.
Man kann manierere Marketsignal und servieren mehr Sektionen.
Man kann man nicht mehr reduzieren, weil sie ein Lacken von Ressourcen, aber man kann man viel schneller.
Und das ist das, was wir sehen, in particular mit den Efficiency Gains.
Ja, das ist amazing.
Das ist ein Benschen von Business Benefit.
Das zeigt die große Potenzial.
Das ist behind all of das.
Und listening zu Ihnen, ich wusste es all along.
Aber es macht transparent, wie systematisch du work.
Ja, zu getrennt, dass du das Wert visible, definiert, identifiziert, unleashed und dann realisiert.
Brilliant.
Now, das ist ein Leadership Podcast.
Many people are used to dialing into the Lightwolf podcast with something related to leadership with AI absolutely is.
So based on your experience, Katja, how does AI change the way leaders should think, decide and act?
Ja, es ist definitiv, es ist definitiv, es hat einen impact, wie du leadst.
Und ich denke, wenn du management und dein Team ist, ist es, du will eigentlich sehen und fühlst es, ganz schnell.
Ich denke, du will realize, dass du, was wir hören, ist, dass plötzlich, da ist viel mehr output und viel mehr...
Das ist ein paar Dinge, die ich zu Reviewen habe.
Es wird ein paar Dinge, auch von einem Liederns-Perspektive.
Und einige Liedern, wie ein Anecdote, die Antwort ist, dass sie eine andere AI benutzen.
Aber ich denke, was ist wichtig hier ist, dass du, wie viel, so du, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, wie viel, of the things and you know when to trust the AI and when not.
And that's not always simple because some things it's visible in terms of where you say, okay, I know the AI is wrong.
Oftentimes you wouldn't know whether it is wrong or not because it's logical.
The structure, how the AI came to the result is The difficult part is that sometimes you don't know what is the hidden assumptions that is in the AI itself.
If that is not transparent to you, judgment becomes very difficult sometimes because the AI results can be very reasonable and logical in itself and how it became to the output.
If you don't know the underlying data and the underlying assumptions to it, sometimes it's a little bit difficult to keep your judgment.
So what I think important is judgment, actually.
The second one, I would think, is how you interact with your team.
As I said at the beginning, I think adopting AI for an organization is somewhat a huge transformation.
That means change management, guiding the team of using AI, but also changing to a whole new organization is something very important as a leader to do.
And in that aspect, I think in itself become und sich als ein AI-Learner als ein guter sign, wie du auf den Team auf den Weg kannst.
Ich denke, am Ende der Ende des Tages, AI ist eine Technologie, das wird die Welt verändern.
Jeder sieht es.
Das gibt ein vieles Uncertainty und ich denke, dass Angst ist ein naturales Reaktion.
And that's basically something as a leader that you should also address.
So being human and also guiding the team is something that is very critical in that phase.
Yeah.
What resonates a lot with me, Katja, is this many, many more decisions than before, dear leader, and much faster in much shorter periods of time, right?
I just learned it yesterday when I had the pleasure to have a conversation with Elia Werner from Sixth, who shared that the number of assets in communication has increased by up to a factor of a million.
Right?
So when I started my career, Katja, I grew up in marketing.
I grew up in Procter & Gamble in a time before.
Even the Internet was available.
At the time, the model was, you have a great, you work on a great idea, you do a TV advertising, you do some print and some visualizations and pump money behind it, right?
Compared to this one, nowadays, you go into social media, you look for shareability, and with the help of AI, you create one million more assets at a fraction of the cost.
Das Dimensionalized es zu mir.
Und listening zu Ihnen ist es, es ist, es ist, es ist, es ist, es ist, es ist, es ist, es ist, und ich höre, viele mehr Entscheidungen in viel weniger Zeit, consciously change the way you work with your team, und be human.
Ja, let's not forget this.
Und, um, in, on this last aspect of being human, ja, acknowledging fears, as this change is, At a pace that probably is unprecedented in mankind's history.
It's the fastest period of technology-led change I can look back to in my life.
There are fears.
There is laziness.
I still can't believe you had it in sport, Katja, but I learned it from you.
There is laziness.
There is comfort.
People prefer not to change because it costs energy.
I think you elaborated very well on how this will change the work of leaders.
Get familiar with many more decisions in much less time.
There is one more aspect, and what you share resonates with me a lot and evokes an old case out of the times before AI.
When I took a business decision that mistrusted the data I saw.
The data was reported by the market-leading market research company in the world.
So the data was accurate.
Still, the conclusion drawn from the data was inaccurate.
I just couldn't prove it yet.
But I could smell it.
And I then went back, challenging the assumptions under the data, spending time with a great person like you.
It was not my idea.
It took us two times two hours to find the one wrong assumption in the data that led to the wrong conclusion.
Right?
So at the time there was two calendar years being compared and the assumption was that the universe is defined the same way in year one and year two.
Which it wasn't.
Because acquisitions in year two Die comparability, aber nicht in hindsight war.
Und mein Partner fand es.
Und ich dann zurückgekehrt, habe ich 20,000 Euro für die Universen gekalculate.
Und was a surprise, dass ich da ein völlig anderes conclusierter.
So, du hast es auf den sidelines.
Regarding der Interpretation von AI.
What would you advise leaders like me and those thousands on the call?
What do leaders need to learn new in terms of really understanding the composition of data to inform the right conclusions and interpretations?
I think one thing is obviously critical thinking.
So taking much more time to To go through probably the complete different scenario to say, what if, so go through really to say, okay, what if everything here is not true, what would be, must be the case?
So basically reversing what I'm seeing and to say what I'm seeing is not right, what would be then the conclusion out of that?
Das ist wahrscheinlich etwas, was wir in der Zeit, als Management, nicht unbedingt so oft machen, als längst, als wenn wir so viel AI output kommen würden, weil AI output werden besser und besser.
Und das führt zu der zweiten Punkt, dass wir uns auch brauchen AI, die uns helfen.
to decipher those assumptions and decisions.
And because of the speed of those things coming up, it will require us to also use AI in itself to probably challenge what another AI has done.
And I think the third is to monitor AI also in its underlying assumptions.
What I observe is...
Organizations have not really started to systematically thinking about the new data sources that the interaction with AI actually creates.
As we have learning systems, we say AI is getting better.
They remember, they can learn from your feedback.
I'm not seeing that organizations are systematically tracking those feedbacks because in those feedback, those are the assumptions.
You're distilling basically assumptions into it when you say, okay, this is good, this is not good, because you're giving a preference into the AI system.
And I think this is not being done yet, but I do see that this will become very, very important in actually being able to explain every decision that AI has made implicit.
Throughout its activities actually going forward.
Yeah, very good.
Very helpful.
On this note, I have a suspicion and also partly I would say supported by some evidences I've seen with my clients, with leadership and strategy clients and working on team productivity.
I see slightly significantly increasing time pressure in clients.
Ich sehe die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, in 2015 war 2.
In 2025 war 10.
So, da war ein Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränderung, die Veränder Whether we are still taking enough time to do exactly what you suggested Katja, which was take time to track your assumptions, take time to understand the assumptions in the first place, and then to monitor the assumptions afterwards.
To take the time to ask insightful questions and truly listen.
So that's one of the skills I think is potentially at risk in this ever faster time.
And the other one is, in terms of capabilities, I sometimes wonder whether the broad presence now of LLMs in our lives risks to make us lazy in thinking.
So that people just get used to delegating the hard work of condensing a huge amount of data to the essence, to the core, which I think Was done by humans and only by humans until 10 years ago.
Has been done by data more and more in the last 10 years.
But is now delegated more and more to AI.
Do you see a risk that we become lazy in thinking?
I think there is definitely a risk.
I think there are many research about it.
I think there is one research that I am...
I've seen that said that if you use AI just by really just replacing the things that you're doing and using the output getting out of it, it will definitely make your, you will become lazy in terms of your brain capacities.
But if you don't do it, but use AI to challenge your thinking, to make yourself more productive.
Then it's actually expanding your brain activities.
And it's the same for actually children, because in San Francisco, they open up an AI schooling where children are actually only taught two hours a day instead of five, six, I think, which is a normal curriculum.
And they're basically seeing the same demographics in children.
They're teaching children with AI and they're seeing the ones that are using it.
by pure replacement, that is actually having less advantage of using AI and the other ones that are actually becoming more productive and actually expanding their intelligence.
I'm not sure how I should think about using AI in childhood education, if that is a result of it.
But I think as an adult, I think that...
Ich habe schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon schon Whether we use it, but it's about how we use it.
Because like you say, the research seems to suggest depending on how you use it, it can even widen and increase your brain capacity and your ability to think depending on how you use it.
So let's be careful and be choiceful and be smart in learning how to use it, which this is all about.
And this conversation is terrific.
Let me zoom into another part of your career so far and into a related topic, Katja, which is the topic of culture.
You, with your career path, have had so far business experience as a leader in two different kinds and sizes of companies.
BCG is one of the most successful and most reputed consulting companies on the planet.
Based on my experience working with BCG, being a client of BCG, very deserved, very rightly so.
It's an excellent company.
Now, you joined us, the NewNow Group, more than two years ago, and you were our managing director in a more senior role in a smaller company.
If you compare your work experience between the two, and if you compare the culture you experience in those two companies, BCG and NuNow.
How would you compare your experience?
As you said, I think one thing that stood out at BCG is obviously the people that you encounter there.
The standard of thinking, the speed of thinking, the abilities to structure, the people that you work with all come with the very standard of all of those.
Und das hat die Erfahrung gemacht, hat mich sehr viel, aber hat die Erfahrung gemacht.
Und Poins ist natürlich ein large organisation.
Es hat hierarchies, es hat Structure, es hat Processes.
Das ist gut.
Ich denke, es ist gut.
Leute, die Leute, die Structure, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, die Leute, It's definitely something that will serve that preference of people and wish that people have for certainty and predictability.
At NewNow, what excites me is that I don't have that.
And at the same time, it also is probably one of the key learning and growth areas that I experience at NewNow.
Obviously, being at a more senior role in a smaller company, as a startup, Facing quite a lot of uncertainty is definitely the opposite to what was given actually at BCG.
But I find that exciting and it's an opportunity for a growth of myself.
In terms of culture as a team, we are a much smaller team and it's a remote setup.
I think both taken together, it's another great experience.
Small team means we experience each other in a much closer way.
So we have two, three weeks in a year where we are on retreat and we all live under one roof.
I think you get to know each other in a much intimate way.
On the other hand, not seeing in a remote setup, not seeing each other, not being able to do coffee chats, not being able just to talk to each other across the hallway is obviously also something that impacts the culture in itself as a team.
So you really need those anchor points in those weeks to come together to formulate a culture.
Culture is something that is made out of groups.
You need people to come together to live that culture.
And in that remote setup, sometimes it's difficult.
At some point, I think throughout time in New Now, you basically become befriended with everyone in the group.
Und manchmal ist es auch relativ zurück zu BCG times.
Ist es?
Es ist, weil bei BCG, obwohl du in ein large Organisation bist, du bist immer mit dem Team, dem Projekt Team.
Und das ist ein Grupp von fünf, sechs Leuten, manchmal zehn.
Und das Grupp eigentlich spendet ein paar Zeit zusammen.
when you're on a project, working alongside each other.
And sometimes that reminds me a little bit of that new now spirit, where you get to know each other in a very short period of time, very, very closely.
And that's actually where you find friends.
Yeah, cool.
Thank you so much for this very inspiring comparison between the two.
Thank you.
Now it's obvious that you develop yourself by the path you do.
I can see it and hear it and feel it by the way you communicate, Katja.
But more specifically, would you mind sharing with the group here, what do you do to develop yourself?
Development can obviously happen across different layers.
I think the best school for content development, like actual content knowledge and Das ist auch gut, dass du dich mit dem Sachen, die du willst, lernen.
Und dann, natürlich, ich denke, als ein Leadership Person, es ist auch gut, dass du Zeit für dich und jemanden, dass du mit und wirklich über die Situation, die du erleben kannst, das kann ein freshes Perspektiv und herausfinden, die du mit den Entscheidungen machst, weil du mit den Entscheidungen machst, weil du all ein bisschen...
Wir sind alle Menschen, wir haben blinden Spots, einige wir wissen, einige wir nicht wissen.
Es ist immer gut zu haben, dass jemand, be es ein Mentor, be es ein Coach, dass Sie mit Ihnen auf diese Themen gehen, und vielleicht nur für Sie, für Sie, für Sie zu erklären, wie Dinge eigentlich passiert, und zu rationalisieren, auf die Themen, das will eigentlich machen Sie lernen.
The situations you are experiencing.
So I think that is probably one of those things that I've come to notice just probably also not so long ago.
That is a very essential part in leadership to actually talk about those situations and dissect in terms of what has happened and how to go about it going forward.
Yeah, got it.
Thank you.
Thank you so much.
Coming towards the conclusion of this exciting interview, I have two more things I'd like to ask, two sets of questions.
One is five quick questions, also asking you for a quick response, short response.
And then I'd like to hand over to you for the closing part, for the closing section of this call.
So five quick questions to Katja.
What is the best advice you ever received?
Probably, someone once told me that it's not about to avoid mistakes, but to actually make original ones.
So you should always look out for mistakes that others have already made and learn from it.
But you should be the ones making the new ones, basically, and learn from it and serve it as learning for others.
Yeah, I like it a lot.
You lead leaders, also in YouNow.
What quality do you value most in leaders?
I think one thing that I really appreciate is telling the tough truth clearly.
I've many experiences in situations where I've seen that leaders are very articulate, can say a lot.
But if you think about it, there was also not a lot of things said.
And sometimes it's uncomfortable or the content that you want to convey is very uncomfortable.
I think in particular those moments you have to be clear.
And that's something that I value quite a lot.
Okay, very good.
Thank you.
What is your personal go-to tool when things get stressful?
I'm not sure if it's a tool, but what I do is I probably zoom out.
And zoom out, I mean, for example, I would just spend time playing with my son and go to an indoor playground and just be a chart again.
Yeah.
What book, podcast or quote has impressed you recently?
Probably many ones.
But I think that one that comes into mind is probably...
Das ist auch zu unserer Topik heute.
Ich denke, wir haben nicht zu Angst, dass AI eine uns zu verändern, sondern eher jemanden, der sehr gut an den AI benutzt.
Und wenn du ein Thema über die Welt der Arbeit verändern würdest, was würde es sein?
Ein Thema über die Welt der Arbeit.
I think there are two things that actually come to mind.
One thing would be how we actually define competence.
I think going forward with AI, it's less about what you actually know.
It's more about whether you understand how you get to the results and ask the right questions.
And the second is the mindset that we talked about at the beginning.
The really agile mindset or failure mindset of being able to quickly adapt and iterate is something I think that should be changed or improved in many aspects of the world of work we see today.
Awesome.
Thank you so much.
So thank you for sharing this.
I really thoroughly enjoyed this, Katja.
Thank you for spending time with us and sharing all your experience.
In closing, I would like to invite you to either leave the audience with a key message that you would like to share or a question you would like to ask the audience to reflect about.
So what would you like to share or ask?
How everyone or every leader is using AI.
Is it using AI to become a better version as a leader?
oder um es zu avoidigen, die es zu avoidigen, die es zu tun ist.
Weil ich denke, AI kann ein paar Dinge tun.
Aber es kann, ich denke, es kann, ich denke, es zu tun, und zu nehmen, die Entscheidung.
So, zu gewinnen und zu denken, es ist, ich denke, es ist Teil der wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, die wichtigsten, Ja, ich denke, das ist die perfekte conclusion von dieser Gespräche.
Ich absolut bin.
Es ist sehr, sehr inspirierend zu haben, Sie auf der call.
Vielen Dank, dear Katja Spöhr.
Vielen Dank, Stefan.
Es war ein Wunder, zu sprechen.
Es war ein sehr schönes Gespräch.
Und zu Ihnen, unser lieber listener, wenn Sie interessiert in Bezug auf und zu haben, mit jemanden mit jemanden, wie Sie arbeiten, auf AI zu arbeiten, oder zu verbessern Ihre journey, bitte nicht zu helfen, zu kommen mit Katja.
Sie finden die direktes Link zu ihr und zu Nuenau in den Show Notes.
Vielen Dank.
Take care.
Und see you soon.
Bye-bye.
Bye-bye.