# Redesigning Corporate Structures for AI-Native Operations

**Podcast:** HMZE
**Published:** 2026-10-01

## Transcript

So I think that's the starting point.
And that is creating direction and alignment.
Because if you don't do that, people will fill that void.
And employees will fill that void with fear.
And investors will fill that void with interpretations that you might not know what you're doing or might interpret it in a different way.
So I think this is the starting point.
on that path to becoming that company.
Welcome to another episode of our new season of Beyond Vibe Coding, partnering with Impala Search.
The go-to tech and executive search agency in Germany.
In this podcast, we explore the transformational change in software engineering and knowledge work in general.
I'm Sebastian Heidemeier zu Erdem, CTO of Ecosia.
And I'm Andrei, CDPO at Trusted Jobs.
Great to have you back today.
Es ist wieder eine andere Perspektive.
Wir lernen von der Erfahrung von einer AI-Boutique-Consultatung.
Stefan von InfiniteMind ist uns zu sprechen über ihre approache zu helfen Unternehmen zu navigieren die AI-Transformation.
Es war eine sehr interessante und interessante Diskussion.
Wir hoffen, dass Sie es so genießen.
Welcome to our podcast Stefan, we're happy to have you and as usual we'd also like you to introduce yourself to our listeners.
Yes, so hi Sebastian, hi Andre, thanks for having me, it's really a pleasure.
Well, what can I tell you, I've been working pretty much all my professional life at the edge of technology and how technology actually changes the status quo.
Starting from my university years building my first software company, then spending some time in logistics and spending also time in consulting, primarily working with large corporations, looking at how to actually change the way how they work with technology and spending time in the media space, also as a CPO at Proceiv and Set One.
Und mit Medien, die eine der industriez wirklich hat, die zu transformieren, die zu verbessern, die zu verbessern, sind die meine Schmerzen, die ich in den Unternehmen habe, wie ich in den Unternehmen habe, wie zu ändern, wie zu nutzen, wie zu nutzen, wie zu dealen mit Innovation.
Das ist die meine Schmerzen, die ich mit mit Software Development und Digitalisierung.
So that's pretty much my professional sweet spot.
And to be honest, I need to make one confession.
In that time, I've never really, or I've seen very few strategies come through life.
And not because they were bad strategies, but just because they had to bridge this gap between the boardroom and the execution and the users and the reality.
So this is the kind of...
Das ist ein Bereich, das ich in den Arbeitsgruppen bin.
Und von dem, ich habe das Infinite Mind mit Robert mit einem und einem halben Jahren gearbeitet.
Infinite Mind ist ein Boutique Consultancy hier aus München.
Wir helfen Unternehmen, die durch diese Veränderung zu gehen, das ist notwendig, um technologische und AI zu nutzen, in eine Art, die sich in einem Art, Wir machen das sehr holistically.
Wir schauen nicht nur auf die Technologie, das ist das wir heute nicht nur über die Technologie haben.
Es ist nicht nur über die Technologie, sondern über die Technologie.
Es ist nur 20 Prozent von uns.
Es ist über die Prozesse, über die Rolle und Organisationen, die Menschen auf die Arbeit und die Veränderung.
Das ist das, was wir machen.
Wir machen das für mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- und mit- Technology is just 20% of what you need to do.
But anyhow, I guess it's also a nice cliffhanger to the main topic that we're going to talk about today.
But before, as usual, we jump to the main topic, we also like to discuss with our guests how they are working currently, because everyone is utilizing AI, right?
Some more for coding, some more for other general knowledge work tasks.
And yeah.
Ich würde gerne auch mit dir gerne machen.
Was ist dein TechStack?
Wie arbeiten Sie mit AI?
Ja, so, basically, was wir machen, ist, wir wirklich versuchen, unsere eigene Dachküden in der Art und Weise wie wir die Firma operieren.
So, given dass wir Unternehmen adoptieren, die AI in der Day-to-Day-Werkung sind, das ist, wie wir auch orchestrate und optimise unsere eigenen Arbeit.
So, wir versuchen, das AI-native Firm zu sein.
We look into how we can use the technology in the way we work, starting from research, sales, analysis, coding.
So our internal admin work, obviously.
So this is like our mantra that we try to adopt.
And I think the biggest change that we see in the way how we work is pretty much that we now moved away from using like, Ja, awesome.
Das ist wirklich interessant.
So can you give a brief run-through how you would do that?
So you figure out there is a process that you can probably automate.
Do you already have a standard approach on how you do this?
Or is this always a little bit depending on the process?
Well, this approach usually works when you already have a clear understanding of what the process should look like.
Und ich denke, das ist in general, das wir uns darüber sprechen, wichtig ist.
So, du musst eine klarer Idee von was du ausstattest und wie du da bist.
Und wenn du das wissen, und du hast das wiedergearbeitet, vielleicht in der ersten Zeit, manually oder mit weniger System Support.
So, once du hast das Klarheit, dann ist es viel einfacher zu turnen das in whatever es ist, in Workflows, in Individual Skills.
So, that's usually the way how we do it.
So, once we have like a clear pattern and process and methodology, we turn that into a reusable pattern, right?
Yeah, makes sense.
So, I think we had one guest who had in their company a standing policy that dass alle das Unternehmen, die das Unternehmen, für die 3rd ist, dass sie das kann, wie sie das können.
Denn dann beginnt man zu sehen, die die Patterns und die Steps und dann haben wir ein Verständnis, was man muss.
Sorry, ich wollte nicht zu unterhalten.
Was ist dein Tech Stack, dass du mit deinem Tag arbeiten?
Für unsere Kunden, erstens, unser philosophy ist, dass wir sehr flexibel sind, weil wir glauben, dass wir fit seamlessly into the tech landscape and the tech stack of our clients.
So, but this is like for the client work internally.
We're pretty much now after, we experiment a lot, but we now narrowed down that we do a lot of work with Cloud.
So that's our main, let's say, day-to-day working environment for everything from writing analysis.
Documentation, all that.
And this is basically the core.
And around that, we start connecting different tools.
Obviously, because we work with a lot of clients that are on Microsoft, we work with the Microsoft environment, so we connect that.
And I think what's exciting is that given that the cost of building custom tools has gone down so much, we are now...
Also building our own custom tools, for example, in the CRM space.
Because years ago, if you would evaluate building a CRM on your own, well, I think that's a tough case.
So now you spend like, I don't know, a month or two and you have a really solid piece of software that is tailored to your process.
So this is something that we are now doing more and more.
Of course, for the core.
We also mentioned in the pre-talk, but also when we exchanged on the topic that Infinite Mind is an AI-native company.
I think a lot of companies claim that you have maybe a characteristic.
So what distinguishes...
Klassik-Kampagne, so to say, from an AI-native company?
Yeah.
So I think, first of all, just to make clear, what we try to avoid is really to be too black and white and too buzzwordy.
So I think being AI-native, I would say, is a vision and an aspiration.
So, first of all, because I think it's also not very...
Es macht Sinn, dass alles mit AI tun können.
und glauben, dass wir bestimmte Dinge, die wir mit dem AI-Refstunden können, dass wir wirklich versuchen, uns zu tun.
Und dann, ich denke, dass es eine andere Seite ist, dass wir auch die Grundlagen brauchen, die die Grundlagen brauchen, um das Unternehmen zu entwickeln.
Und eine Grundlage ist, natürlich, Daten.
Wir sind ein Knowledge company, als ein Consulting company.
So, ein viel unserer IP ist Knowledge.
So what we do to get there is that we capture almost every piece of knowledge, no matter if it's said in a meeting, if it stays in the CRM or if it's in a document.
So capturing that and using that later on, I think that is one of the fundamentals that we believe is necessary to get there.
I don't want to already transition to the meet.
That's why I will keep that, maybe my follow-up question for later.
Aber ich denke, das ist die wichtigste Teil dieser Episode, und wir können bereits über die Mitte, ich glaube.
Dann kann ich mich fragen, Sebastian, wenn ich das?
Ja, bitte.
Ich bin gespannt, so mein gut feeling ist, und das ist warum ich wirklich looking forward for our exchange, Stefan, ist, du putt die nice headline, Don't Design AI for Your Company.
Redesign your company for AI.
And I think it nails it so much.
And it's actually also my gut feeling.
When I'm looking at organizations, I see certain patterns.
And my belief or my hypothesis is that these characteristics or these patterns They will change.
They need to change.
I think everything around processes, systems, teams, I think everything must look different.
I think it must look different.
You just made, from my point of view, an important point.
You said you developed your CIM system on your own.
Half a year ago, time is running, but there was this SARS apocalypse, right?
Where people said, okay, now you can build everything on your own.
And what I overall see is way less static environment.
And this is true for systems.
This is true for processes.
This is true for teams.
Unfortunately, I can't think of such an organization.
That's why.
Whenever I hear AI-native company, I wish for someone is telling me, yeah, André, this is how the future company should look like.
Do you have some best practices on that?
First of all, I chose that headline, you know, you need to not build AI for your company, but you need to build your company to be an AI company, right?
You need to build that for AI.
And why is that so important?
Because I think that...
There is this myth of this magical technology that will now come into your company and transform everything and create tremendous economic value.
And I very much believe that this is true.
However, I think that people underestimate what it takes to get there.
And I think also when people talk about...
wie schnell die Veränderungen werden.
Und dann, was wir machen, ist wir mit CEOs von Mid-Cap-Companyen, und wir haben diese Gespräche über und über wieder.
Und ich denke, dass once Menschen verstehen, was es geht, und ich denke, du hast ein paar Dinge gesagt, so dass du musst, die Art der Art du arbeiten kannst.
Du kannst nicht einfach nur einen humanen Prozess und dann put AI auf dem, und denken, dass das jetzt ein AI-native-Way ist.
So you really need to change the process.
So if you change the process, all of a sudden, the people working in that process so far, they need to do something completely different, right?
So you need to change the skill set.
So we had, just to give you an example, so we worked for a business development team.
So that team was building presentations for sales.
So we automated building presentations.
And what that team had to do, is they needed to orchestrate the knowledge and the data underneath.
So totally different skill set.
And then you come to the point that you need to go through this change.
So I think I still believe that we will get there.
I think that people don't realize how much hard work it is to actually get there.
Ja, mein observation basically ist, dass Unternehmen es sich an AI als technologie beantrekt, es sich an AI als technologie und alles andere bleibt.
Und dann haben wir zwei sehr unterschiedliche Setups, die nicht wirklich gut arbeiten zusammen.
Like etwas, was sehr dynamisch ist, AI, was kann sehr dynamisch sein, und etwas, was sehr stetsch ist, Processes und Teams.
Und ich absolut...
Get your view or your point on, you need to rethink processes.
What's hard for me to grab is the idea of how to build organizations because you could say the composition of a team is now different for sure.
But as humans, I think we require some staticness.
Und das hat mich wirklich ein hartes Zeit, weil ja, technologie kann sich dynamisch sein, ja, processen kann sich dynamisch sein, aber was ist die Organisation?
Ja, so ich denke, das ist ein Spott-on und ich will vielleicht ein step zurück und dann ich will answer Ihre Frage.
Ich denke, was wir sehen, was mich die meisten, ist, dass Menschen nicht auf die Zeit auf der Veränderung sind.
Und ich denke, das ist ein fundamentales Thema, das ich sehr überraschend bin.
Wenn wir an die letzten Transformations sehen, wie sie 100 Jahre, dann 50 Jahre, dann vielleicht eine Generation, so wie ein Computer, für zum Beispiel.
And now we have that acceleration within very few years.
And if we look at a technology now, even like it takes a quarter and it's like a step change.
And let me share like one observation.
Yesterday, I came back from Berlin.
I visited the MyWay conference.
That's like a family-owned business conference.
That was great, by the way, from the Pioneer in Berlin.
And I talked to so many.
Mit-Cap-Companies.
People are just overwhelmed.
They don't know where to start.
Everybody is telling you, you need to adopt AI, you need to make that transformation, and that's the future.
If you don't do it, you will be left behind.
At the same time, these people don't work in technology.
This kind of change that needs to happen.
It happens at a speed that is hard to absorb by the people.
Yes, absolutely.
And we had this in our show also several times, right?
So there are several things at play here.
So number one, the future is always unevenly distributed, right?
So there are people who are adopting this at breakneck speed.
And even in our domain, this is...
Very different, even in digital companies.
There are differences or there are worlds between how, I don't know, probably a more traditional bank would approach this that's fairly digital, right?
But versus a very young startup that is developing agentic software.
I don't know.
And on the other hand, there are businesses that have...
a higher pressure, I think, to transform.
So, I mean, AI will transform everything.
So that is probably already visible and most companies need to be exposed to that, right?
But then it's still different if you are a digital company versus if you are a restaurant, for example, right?
So there's a different, like, AI will have a different impact on your...
oder value chain, wenn du willst.
Und das ist, ich denke, was wir immer noch immer in der Zeit.
Und da sind Leute, die sagen, dass digitization schon nicht für große Unternehmen funktioniert.
So die Transformation zu einem digitalen Unternehmen schon nicht für bestimmte große Unternehmen funktioniert.
Und ja, ich verstehe diese Perspektive, obwohl ich, zu einem gewissen, die meisten corporaten wahrscheinlich haben digitized.
Ich denke, die sind nicht mehr, als einfach als das.
Genau, genau.
Either haben oder sind nicht mehr.
Und dann, auf der anderen Seite, die AI-transformation ist viel, viel faster oder muss man viel, viel faster, wahrscheinlich, als wir wissen, und es ist, again, die Unternehmen, die das manche das, die das nicht, die das nicht, die das nicht, die das nicht.
Und ich denke, ja, da sind diese zwei Perspektive.
So, auf der einen Seite, You need to, I think you need to take this transformation head on and try to find a way the right processes, also the right speed for the people you have in the org, for the culture you have in the organization, right?
And on the other hand, yes, some companies won't make it and then new companies are popping up and that's already happening, right?
And that will replace the old ones.
That's at least my perspective.
Moving a little bit into philosophical territory by now, and we can get back to your experience, Stefan, from your consulting work so far, right?
Because you are working with these companies.
I just want to say to André, I guess the bad thing is there is no clear template yet that you can just put on companies.
And I would also say that even within companies, like the engineering team, will have different constraints than the HR team or the finance team or the marketing team.
And all are like, they need to find their own speed and their own tools and processes to some extent.
That's at least how I would summarize it for me.
Yeah, absolutely.
Maybe just to sum it up, there is this nice pattern saying you can change an organization on process level, organizational level, and culture level.
Das ist das, was wir gerade erwähnt.
Mehr der redundanten Dinge ist handelt via AI.
Und ich denke, das ist wahrscheinlich die wichtigste Sache.
Vielleicht muss man das mit dem Kultur.
Du musst deine Kultur darauf adaptieren, weil alles wird super dynamisch.
Und mit dem, ich stoppe hier, weil es philosophisch ist.
Ich freue mich für Sebastian, dass du besser verstehst, was du gesehen hast in deinem Consulting.
Thanks for the discussion.
Well, what we see with companies that are really successful at going through this change is that it's actually a very simple thing, but it's so important.
They have a clear narrative of where they want to go with their technology, why they want to do it.
And this is a narrative that needs to come from the top.
Und das ist etwas, das muss sich von der CEO oder von der Kantein, der Unternehmen zu setzen, wo es geht und warum es passiert.
Und es muss sich entzünder und sogar mehr so, es muss sich auch extern sein, weil du in der Kontrolle, das Narrative, was dein Unternehmen macht, was du über AI und über die Technologie.
So I think that's the starting point.
And that is creating direction and alignment.
Because if you don't do that, people will fill that void.
And employees will fill that void with fear.
And investors will fill that void with interpretations that you might not know what you're doing or might interpret it in a different way.
So I think this is the starting point on that path.
Ja, und vielleicht eine Frage auf das Thema.
Ich habe auch gesehen, dass das Spiel aussehen, wie Sie sehen, wie Sie das in einer Weise beherrschst, die die Fähren der Empfehlungen in eine guter Weise beherrschst?
drives the energies in a positive direction or pretty much focuses the people on the positive direction.
Yeah, that's a good point because to be honest, I think they're good and bad examples.
So when I think at those leaders who did that successfully, I think what they do is first of all, they are authentic and that also means that they They clearly say what they know and they also clearly articulate what they don't know.
Right?
So, because otherwise you're moving in a field that is highly uncertain.
So, pretending you know all the answers, I think no one will believe you and then you lose credibility.
The second thing is, as I said, You need to create that direction.
So I think you need to be concrete about what you know.
So you need to clearly articulate why this company is doing that.
Is it because you need to reposition or you want to maybe change your business model?
Or is it because you have different considerations?
So being clear of the why.
And the reason why I think is important before you talk about use cases and you talk about, you know, what we like to do in Germany about compliance and limitations.
So why are you even engaging in this, right?
And I think then you can talk about what you're doing.
So I think this is the pattern that I see in those leaders who are successful at conveying this narrative.
Yes, I wholeheartedly agree.
And I would also say that the vast majority of people, probably all the people that we had in this show have been communicating in this way.
So pretty much laying out, look, this is the future.
I'm pretty sure that we need to adopt it, right?
For whatever reason.
But one reason is that our competition will adopt it.
If we don't do it, then we are falling behind.
We might have an edge if we adopt it now and we are faster than our competition, right?
Or we can do way, way more suddenly than we could before just by automating road tasks that, yeah, we just don't need to do any longer and AI can do, right?
So many, many reasons.
And at the same time, being very transparent about, don't know the exact path pretty much.
So how we get there and what exactly we'll do, it's just important to start and to get a good feel for the technology.
Yeah, and I can tell you, I mean, as a leader, don't expect that everybody is standing up and applauding, right?
So I think that will not happen.
But I think this is exactly...
where you need to set the direction and you need to work on that, right?
And you need to make people understand and take them onto this journey.
And that's a very hard thing, right?
So it takes a lot of leadership to do that.
Absolutely.
And now the final point on this one, right?
So I do see for tech leaders, I see how they can do this and how they can...
Ich denke, Sie haben keine Alternative, als ein Leader in unseren Zeiten, Checkmark.
You mentioned a couple of pitfalls to us in the pre-discussion, right?
So that was the first one.
What's the second pitfall that you see?
Well, I think the next one that usually we see is that there are leaders and they're saying, look, guys, you now need to use AI.
And by the way, we are now rolling out this and that tool.
Very popular, rolling out co-pilots.
And they start basically the whole journey upside down.
So they start with a tool, right?
And which I think is tempting because everybody is seeing these videos and these promises and these use cases, how it automates your entire sales funnel or whatever it is.
But I think that...
Pitfall ist, dass du mit dem Tool starten und du nicht genauer denken über was Problem wir eigentlich lösen.
Und das kommt zurück zu dem Punkt, die ich in der Beginn erwähnt habe.
Du hast das notion von AI-Native.
Du hast das nicht für den Sinn oder für das Spaß gemacht.
Du hast das, weil es für die Konsequen für die Firma erhältst.
Wenn du nicht klar über das Problem bist, und du hast das Tool.
I think that's happening very often.
And I think at the same time, it's one of the biggest pitfalls because it derails the conversation into a tool conversation and not into a change conversation.
Maybe this is a misunderstanding, but I need to challenge that.
Because I think also earlier we said you need to make your hands dirty, right?
The entire organization needs to make her hands dirty.
So, how do you do this without having access to that technology?
But maybe I got also the point wrong and you're saying, yes, the capability needs to be there, like access to one of the leading frontier models for everyone, but it should not be the discussion on the specific tool.
Yeah, I understand your question.
So, I'm not saying that it is wrong to give people the tools.
Ich denke, es ist sehr wichtig, dass die Organisation die Tool ist und wir werden noch ein bisschen mehr als wenn man so bekommt.
Und du wahrscheinlich nicht nur einen, sondern nur einen, sondern nur einen, einen Experiment.
Ich denke, die Punkt ist, dass wenn du sie einfach den Tool und du nicht über die Technologie, welches Problem die Technologie ist für die Firma und wo du willst, your efforts in adopting that technology.
That for me is that pitfall that I see that you don't think about what business objectives.
So just to give you an example, if my strategy is that I want to go to expand into new markets, so then I should think about how can technology help me to move into a next market?
So how can I...
Wie kann ich neue Leads machen?
Wie kann ich diese Leads machen?
Wie kann ich diese Leads machen?
Aber es ist wichtig, weil es nicht mehr focus ist.
Ja, absolut.
Ich habe eine sehr gute Idee von Franzi von Miro.
I think it was the CEO who said, okay, now we need to transform the company towards an AI company that has been after engineering, obviously had been tinkering with agentic engineering for a while.
But at some point they said, okay, the whole company needs to become AI driven.
And then what they did was they had these forward deployed engineers or AI enablers that had been very experienced already with the use of AI.
Die Leute sitzen und sagen, sie sagen, sie können sie ein Prozess, das sie sich betrachtet, und setz dich mit ihnen in order zu setzten, und zusammen mit ihnen, diese Prozess zu automatisch.
Das ist auch sehr ähnlich, was ich, Steve Jäge wrote in einem von seinen Artikeln, über wie Netflix ist es.
Sie sind auch in einer Artikel, wie es in einer Artikel zu machen.
The full team, together with the manager, is undergoing a five-hour training with a Forward Deployed Engineer or several trainers.
And they are actually taking specific processes from the daily work of the team and are looking at how to automate this.
And this is really giving the concrete guidance on how to use it in the day-to-day work, as opposed to only, yeah, here you have a tool and now you can start tinkering.
Ja, ich denke, und das ist eigentlich eine andere Punkt, dass du auch noch einigermaßen bist.
So eine ist, dass man klar auf was Probleme zu lösen und die andere ist, wie ich eigentlich make sure dass Leute sind in der Tat zu arbeiten, dass das andere Punkt ist, weil das andere Punkt ist, weil Leute sind in der Tat zu arbeiten.
So I think the worst case is you just get the tool and then figure it out.
And you would be surprised how often this happens, right?
So everybody is getting this co-pilot license, go figure it out yourself, right?
So that's, I think, the worst case.
So it's like a gym membership.
So you get the membership, but you never go there, right?
So there is no point.
So I think this kind of adoption, to manage the adoption and to...
Support your employees of understanding the technology, giving them, for example, forward deployed engineers to work together.
And by the way, this is also a way how we work because we believe you need to bring these cross-functional teams together, right?
And to lower the barrier.
And what we always say is that your organization needs or your employees, they need to develop an intuition.
of what AI can do in their process.
They don't need to necessarily be able to build that agent or to build that workflow, but they need to understand that and to build that intuition to say, okay, this is what AI can do for me and this is what I need.
And then you can give them somebody to work with to build that solution.
But that needs education, that needs training, that needs getting your hands dirty, that needs experimentation.
Das ist eine sehr common Rolle.
Staying mit Ihrer großen comparaison mit der gym membership, ich denke, du kannst zwei extremes sehen.
Eine extreme ist nicht zu gehen, nicht zu gehen, aber auch zu trainieren die falsch.
Ich habe auch gesehen, dass ich das ein bisschen relates zu dem Punkt Sebastian hat.
Throwing die gym membership.
But then having no trainer showing you how to train, right?
You should not underestimate the interest and the willingness of an organization, of people, employees to change processes or like automate processes.
The point is like these are non-technical or non-AI people building stuff like in a way which is usually not, how you say that, sustainable.
I think that...
Translate or that summarizes it pretty well.
And then you end up with like AI technology or like AI in the company, but done in the wrong way.
You get what I'm saying or trying to say?
I think one aspect is people not going there or not using it.
And I think that's also not cool.
What I think is even worse is like people using it without having...
You do the bench press wrong, you know?
Yes, absolutely.
You do the bench press wrong without the coach, right?
Absolutely.
We decided to stop that path together with the client and invest in a very focused upskilling program.
So what we did is we did functional trainings with the different areas, with the finance team, with the product and dev team, with the editorial team that was a media company.
So very specific, showing them what the technology can do for their use cases.
Und guckst du was?
So, du hast, auch nach der Sessions, die Leute kommen und sagen, okay, jetzt ich verstehe, ich kann, du kannst diese Daten und transforme diese Daten.
Das bedeutet, ich kann das, das und das, das.
Und dann hast du, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, sie haben, Und das ist genau das Sieg, was Sie beschreiben.
So Sie geben sie die Training, Sie zeigen sie, und sie entwickeln diese Intuition.
Und dann können Sie mit Ideen kommen.
Und wie Sie das Training machen?
So, Sie nehmen Sie die Ausbildung?
Ja, super Hands-on und Tailored.
So, es war nicht so, wie es eine Generik Training ist.
Es war so, okay, so Sie sind ein Designer.
So, Sie sehen uns.
What are the state-of-the-art design tools?
What you can do with them?
And how you can use them?
How you can create illustrations, animations, videos, etc.
So it was super tailored.
And that would be also my recommendation about upskilling.
You need to do that in their given work context with the use cases that they work on a daily basis.
I know it's going to be more, it takes more effort.
But it pays off because it has to be tailored to that realm and that area that people are working in.
Yes, yes.
All right.
Cool.
So we're already quite advanced in time in our episode now.
I think you still have some more pitfalls on your list, right?
Let's try to manage to get all of the pitfalls in.
für die Main-Section, bevor wir dann über die letzten zwei Seiten, die Reality Check und also die Predictions-Section.
Ich will zwei, um zu bebrief.
Und sie sind sehr wichtig, in meinem Fall.
Wir haben über die Prozesse in der Beginn gesprochen.
Und ich denke, eine sehr große Rolle ist, dass die Prozesse, die es gibt, und dann versuchen sie zu automatisieren.
So, wir nehmen die Prozess, die Leute machen bestimmte Steps und jetzt können wir das Prozess, zum Beispiel, ich weiß, was wir machen, die Analyse der Daten und dann wir automatisieren.
Und die erste Step ist, dass, wenn Ihr current Prozess ist, es wird wahrscheinlich worse werden.
Und die zweite Sache ist, dass du Really rethink the entire process with their technology.
So don't try to just automate the existing one, right?
Because I think there's like a difference between an AI-enhanced process and really a process that you design with AI as a first principle.
So I can give you an example.
So in a media company, right?
So if you create, for example, a video.
So in the past, you had like...
an editor writing a story, then you have a video editor creating a video, and then you have the social media team posting that on social media.
So if you now start automating each step the way it was, you will automate three teams.
Versus if you redesign that, you will maybe have an editor that is creating the story and then everything.
that follows will be automated and you actually don't need these hands-off to other teams, right?
Or you involve them in a completely different manner.
So that's what I mean.
So I think don't try to optimize the status quo.
Try to rethink based on what you want to get out of it.
So I think this is one.
Yeah, two thoughts on that very quickly.
One is when I think in the era of digitization, We always said, if you digitize a shitty process, then you have a shitty digitized process, right?
So same thing.
Still true.
And also, yeah, thinking similarly, if you digitize a process where, I don't know, file documents and you now say, okay, we scan every document and we put it in a digital folder somewhere as a scanned PDF, then you also, you don't have the full value of having the...
digital document that you can then index and you can make it searchable and everything.
So I think it's a very similar mental model that we can apply to the current transformation.
And maybe the second point that I would pick here is what we see as a high risk is that companies...
So there's this number always going around that like, whatever, 90%, 92%, 95% of...
AI pilots actually don't have that economic value.
And I think the point is, or what we see is that one of the reasons for that is that many companies are still stuck in that pilot phase.
And I think we all know nowadays it's so simple to build a compelling showcase and pilot case.
And I think what organizations...
So I think thinking ahead and knowing what it takes to scale.
Und dann haben wir das in die Lage, also eine Gäuferung auf die Decide, die ist mehr und mehr eine Gäuferung auf die Kosten.
So ich denke, es gibt so viele Aspekte zu scale, versus zu bauen, das sieht fancy.
Und ich denke, das ist etwas, das Unternehmen sind, und das ist, die viele Unternehmen von erreichen.
All right, so Ihr Punkt ist, dass das eine neue Rolle ist, von dem Beginn, dass bereits, wenn du den Prototypen hast, du musst du mit dem Anfängerung über alles, die mit etwas zu tun haben, das muss man an der Stelle zu runen.
Es ist eher wie, um dein eigenes CRM zu entwickeln, das ist perfekt, wenn du, ich weiß, dass du, ich weiß, es nicht funktioniert 100% all den Zeit.
Aber wenn du das ausfahst, um das zu tun, then it just needs to be reliable and secure and available all the time.
Correct.
And as I said, I think it's not just the technology prerequisites.
I think it has a lot to do with compliance, with governance.
Like I mentioned, permission rights, like what kind of data may I access or not.
So I think it's not only technology.
I think technology is an important piece.
But being aware of what it takes to scale.
und auch planen für das, dass das alles zu passieren muss, also in deinen Business-Cases.
Das ist mein Punkt hier.
Ja, und mein Geistig von dem ist, dass du eigentlich eine neue Plan hast, was die Step-Plan ist.
Du introduire die Tool, du gibst du Raum für die Learning, dann starten mit ein Prototypen und dann bringst du das You professionalize, so to say, the company.
It takes time.
One of my biggest learnings is it takes time.
It doesn't happen in the afternoon between three and four.
It takes time.
I think it's one of the...
So if you would ask me, what is my pitfall number?
Maybe not number one, but within the top three is like people think that's an easy win.
You just need to approve the tooling.
then you need to have some training and then it will automatically transform.
It will go through the roof.
Yeah, yeah.
No way.
No way.
Absolutely.
Exactly.
I mean, that's the whole point.
I think we're talking about a change of a company across multiple layers, from culture over processes, over organization, over technology and data, obviously.
And this is a complex thing.
And I think that's what companies need to realize when they embark on that journey, because otherwise they will just get a very shallow benefit.
And then you have these discussions of, is that technology actually adding value?
Yes, it is, but it takes an effort to get there.
Absolutely.
All right, I think that's a great closing line for the main section.
The first one is the reality check.
So usually we ask our guests for what the facts and what they think is hype or are hype topics currently.
So do you have some what the facts and hype topics for us?
Yeah, I think there are a lot of hype topics.
So first of all, what I always wonder is that frontier line of what a model can do and what it can't, right?
dass es so viele Dinge, die Models noch nicht erreichen können, weil sie wie sie sind.
Wir wissen alle die Spellingen oder viralen Dinge, wo es tatsächlich ein funnyer Typ auf TikTok ist, der immer die Models, und sie haben sie über und pretendem sie zu sprechen, dass sie ihn mit seinem Boss und dann erneut sich in sicker und so weiter.
Und das ist total awkward.
Right.
In voice mode.
So, and I think what, but I, what, what I actually really is still, you know, surprising me is that we still cannot reliably say if a model is going to accomplish a certain task perfectly, or it will just completely fail.
Right.
So I think this is still something that is, that is, uh, for me, always something that's, that's on my mind.
Right.
Das fasciniert mich every time.
Das ist sehr true.
Ich meine, da sind bestimmte Patternsen, dass wir vielleicht auch schon machen können.
Aber ich auch fühle mich manchmal, ich habe ein sehr kleineses Request, etwas ich möchte, und dann habe ich einen Moment, um, ich weiß, ich weiß, was ich, ich weiß, was ich habe, was ich habe, was ich habe, was ich habe, was ich habe, was ich habe, was ich habe, was ich habe.
Ich habe mich auch, was ich habe, was ich habe.
Ich habe mich, was ich habe, was ich habe.
Und vielleicht auf den Hype-Toppen, weil ich einfach nur das, das ist, dass die Gespräche über die Risiken von AI und der Humanität der Menschen, die von der Technologie zu verletzten.
Ich meine, wenn Sie, wenn Sie das so machen, wenn Sie die Unternehmen und ihre CEOs persönlich sind für alle die Schleifte, die die Models zu verletzten.
I wonder if we would have this conversation or if they would find a way that these things are much better under control as they claim to be, right?
Because in the end, still that's technology being built by people.
And I'm pretty sure that people can put controls on that technology if they wanted to.
And I know there are more conversations about is that slowing down innovation, etc.
But I think purely from the point of is that technology going to kill us all?
I think if you think that way, I think there is certainly a way to govern these systems in a way that the conversation would be a different one.
Yeah, for sure.
The issue with that, though, is that the current...
The current regulation, at least in the US, is that only humans can be held liable of their actions.
So if there is a security breach, then I think the rule is something like a human actor must intentionally or knowingly access a protected computer without authorization.
Es ist ein Regulatory Topic.
Ja, genau.
Das ist was ich.
Und die Sache ist, dass in der current environment, wo du dieses genius auf dem US-Governement haben, der wahrscheinlich nicht versteht was, oder nicht versteht, das will nicht be verändert.
The perception there is currently they are in an arms race with China and hence they must not stop this race.
And I think it's really hard to make a case for a change of the regulation without something really bad happening.
Hence, I think that the Frontier Labs themselves taking action is probably the better chance.
of not something severe happening before any regulation will be changed.
Yeah, you're absolutely right.
I think the point that I wanted to make is that it's not something that is happening uncontrollably.
I think this is something that can be regulated if we wanted to.
And I think it's also not helping the progress because if people are scared, if the public is scared, You race and you stand up and you don't want to have the data center like in the US in your town.
I'm not sure if this is not stalling progress as well.
So I think that's an interesting discussion to have an eye on it.
Yes, fully agree.
And in our last episode, we actually discussed the AI apocalypse topic.
And there we also made the point that we don't think that the, what's it called?
The people's Chinese.
oder was er, sie werden wahrscheinlich nicht verlieren control.
Die CCP, genau.
Sie werden wahrscheinlich nicht allow einen AI, das nicht mit ihren Wissen verhalten.
So, sie werden finden eine Art, die Regulatung des Regulatungs- und die US-Wirrehen auch beherentwickelt.
Absolutely.
All right, danke.
Das war super cool.
Und jetzt, der letzte Frage ist, die Prediktion.
Do you have a Prediktion für unsere Listener?
Prediction, yes.
So as I said, I've spent the last two days in Berlin talking to a lot of family-run businesses in Germany who are industrial companies, right?
And they now start implementing AI more and more.
What I see coming, because there are signs obviously in the US, but now more in Europe, is I would expect that within the next 12 months, the question of cost will become a very central one once companies start to scale that technology across organizations.
And we see that with the tech companies limiting token spend.
And I see that discussion coming once some crazy bills start piling up.
So, wir als Unternehmen, wir sind eigentlich vorbereitet für das mit einem Offeringen auf unseren Kunden, wie wir die Kosten machen, wie wir die Kosten machen, wie wir mehr effizienten sind, wie wir bauen, wie wir die Systeme bauen, wie wir die Modelle benutzen, was Infrastruktur benutzen, etc.
Aber ich denke, das wird ein hartes Thema in den nächsten 12 Monaten, wo all of a sudden, da werden die Medien in den Medien gespalten.
a lot of money and becoming more cost-conscious.
Yes, fully agree.
I think we, as you mentioned, we saw this in engineering already where Uber and Spotify now say at least that they've reduced their token spend by 90%, at least on a per-engineer-month basis.
I think that was the message there.
And we'll see this in other disciplines as well, for sure.
All right.
Awesome.
Thanks a lot.
That was a really cool discussion.
Thank you.
I enjoyed it a lot.
Thanks, Stefan.
Bye-bye.
Bye.
The Beyond Vibe Coding Podcast is a project by Sebastian Heidemeyer zu Erpen and André Neubauer, partnering with Impala Search.
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