Hello, this is Eva and you're listening to the In Between Tech
and Trust podcast. And this week's episode gets
quite practical because I talked to Doctor Mark Roman Frankel and
he's partner and Associate Director at BCG, responsible for
the AI and digital transformation practice.
And together with him, we dive into one of the latest studies
that was published in September 2025, and it's called The
Widening Value Gap. And if you're like me and you
read a lot about AI and tech transformations, you've heard
about the stat that only 5% of businesses actually gain value
from their AI projects. And Roman was one of the experts
that interviewed good approximately over 1200
organizations and found out that this gap actually exists and
that the statistic is representative for how AI use
cases are being incorporated or how they also fail.
So we dove a lot into what it means when organizations talk
about AI transformation, how they get AI ready, what it means
to put an organization into an AI first status, and how
processes and people are the means to a successful AI
transformation. Welcome Roman, I'm so glad to
have you. Hello, if I thanks for inviting
me, I'm very happy to spend the next couple of minutes or hour
with you. And before we dive into like
your new findings, you've just launched a huge report on AI and
the use cases and also the findings that you have and
guards of the implementation and so on.
I do want to start with you with a quite overarching question,
because what does trust mean to you in the context of AI
transformation? And is there a way or has it
also evolved over time on how you viewed it?
I think that's a pretty profound question for the first as a
beginning and prior some some reflection.
I personally experience trust when priorities are pretty
clear, so and companies or people see that they work
towards value, which is tangible and when individuals are
recognized and rewarded. And when in these transformation
types of work, everything is very much solution and not
probably oriented. So it has a positive spin.
Sometimes I see it as a risk for trust if things have to be too
fast and too bold. Sometimes people like also
reliability and steady progress and experience.
Yeah, resilience in activities and it's it's quite fun.
I mean we both know each other from the House of beautiful
business and we have attended great sessions by the famous
coach Michael Bunyas and he and he did a great research and when
situations, relationships are trustworthy and love lasting.
And I think resilience was a key password that took take away.
So when everything doesn't go right, people in an environment
of trust, they know that things progress and that resilience is
key in this this setting. So when, when, when I look into
what what has changed over the last years, I mean AI
transformation or digital transformation or IT
transformation before has always been organised large programs
and major investments. I think taking lots of risks,
but also promising big change. I see that nowadays things need
to be more Swifty like budgets are tighter, time is more
scarce. So if you want to build trust
with your seniors, you need to prove value very fast.
So you need to demonstrate impact.
So you can't just expect that that people wait for years until
you get an ROI of any transformation.
So I think trust in AI transformations nowadays is
really getting things done as talking more doing and proving
value very fast. And you've just described what
we could call a widening value gap, right?
Because there's on the one hand side, like the speed, on the
other side, like the steadiness that we want to have.
But it's both also in an economic and an ethical term.
And when you like also from your experience, look into today's AI
landscape, what is the single biggest driver of like this
specific gap, particularly in terms of technology leadership
or the mindset that is required? I mean, as you know, I mean I'm
work for BCGI, am driving AI and digital strategy topics and we
have, we are following a core principle which is called the
10:20 seventies principle. We always say that 10% of the
success comes from great algorithms.
Fine, 20% comes from outstanding technology, but 70% is truly
about people, progress and how you organize.
And in the center of the 70s is clearly the sea level alignment
and leadership fluency in AI and digital.
And you need to implement the culture that embraces change.
So success and short start at the very top.
I mean today technology matters more than than ever.
We have seen great companies creating great ideas, thriving
concepts, do rapid innovation, experiment, have great faith,
fast culture and they they build impressive prototypes and
customers love it and everything gets adopted.
But suddenly they don't scale and the question is why?
And often the tech stack was neglected.
So the data is siloed, end to end processes breakdown.
There's lots of menu interventions.
So as I say, everything needs to start from the top and from the
business. You need to protest by value,
but you also need to make sure that the foundation is, is, is
right. So the tech and data.
So if you have both, I think then then you make it or at
least the companies who don't have both, they see their scale
inhibited after a while. And you've just touched upon
some of the findings that you've like collaborated on with like
the report or also your clients and the role that you have at
BCG. But also there is like quite a
specific number that most of like us have heard about the 5%
of companies that generates a stem substantial value from AI
and like the rest actually is doomed or is set to stagnate.
And can you share with give us a bit more background of what
differentiates those 5% of organizations also in terms of
looking beyond just the resources, I was wondering if
it's like more about structure, courage or potentially also the
clarity of ambition towards their road map ahead?
Yeah, great question. And then to maybe take one step
back and give the audience some context, for a decade around
now, we are serving every year senior executive, senior leaders
covering all industries, all regions and serving them where
they stand in digital. And of course, nowadays with AI,
we check how they invest, we check how they succeed, we check
how they measure value, but also how they leverage tech and
technology and data. And in this year's study, which
is actually called dividing AI value Gap, we surveyed 1250
companies and you mentioned only 5% of them are really realizing
meaningful business value from AI.
So this is like I think it was 68 companies out of the 1250.
Of course, they invest more into AI, but the real differentiator
is that they strategize and act differently and they don't read
AI just as a technology project, but as a longer full business
transformation. They did operating models where
the data, algorithms and people work together seamlessly
integrated and they set the right ambitions.
They focus on those areas where I creates the most significant
advantage. And they also don't just focus
on small incremental productivity gains, like making
a big office function more efficient, but they really focus
on reshaping the core of the business.
That's a bigger task to crack, but much more valuably at the
end. And as I heard your question
about structure, courage and clarity, but I actually observe
is the clarity of ambition is by far the most specific factor.
I mean, everything follows from it.
I would even say that courage is you need to be gracious to also
have clarity because you, you can't just do minor small
investments or have some chase, some single digit efficiency
gains. You really need to be clear
where you put your, your place, what are your board
transformative moves and, and where you want to have double
digit growth and faster innovation.
So I think courage, clarity go goes, goes hand in hand.
And of course, if you have that, you need to build the right
structures to execute. And that's why companies
eventually need to build an AI first organization.
AI first is like a term that we've have heard many times in
the past, but there are also those ones who claim to be AI
ready. And I do think that putting AI
first doesn't mean that you're AI ready, right?
So what's the key shift required to move somewhat from, let's say
an experiment or mentioned kind of approach to an enterprise
wide transformation? Good point.
You are right. AI ready first means, I mean,
it's good when you're AI ready. So you already start to run
pilots. You, you, you upscale talent,
you maybe start to build a data platform.
But the pivot to shift to be AI first means that eventually your
entire business model depends on AI to operate.
So AI is not something you used to optimize, but it's really
core. And everything you do has an AI
first paradigm when you design it.
It's not about asking all where we can apply AI and our business
model to become a bit better, have incremental benefits, but
you should really think about how should our workflows,
decisions, the customer experience, our entire company
like be redesigned to succeed in an high power word that requires
fundamentally shifting how you work.
And it means also embedding AI into every process than just
laying it on top of it. And that's why you also
establish eventually trust in AI because grounding it in very
robust data, you implement these reliable algorithms and the
humans and AI seamlessly collaborate.
And then if when they are people eventually and employees and
also customers and everyone sees AI as a colleague and not just a
tool, then adoption accelerates. And what I learned from our
recent study is if we think about what really makes AI
organizations first, AI first is they have much more governance
in place to measure AI value. So, but because they can prove
its value, they dedicate significant more time in
learning and, and, and, and protecting also time for
upscaling employees. They have, I think the number
was three times more centrally defined data policies.
So they have data offices in place and eventually that helps
them to to scale five times more workflows and deploy them.
And the biggest, the by far biggest number was the
engagement of the C-Suite. And it brings us back to our
first question that everything starts from the top.
I think among the share of future build companies which we
use these or name these companies which are AI first,
over 10 times more of the sea levels in these organizations
are engaged versus companies who are lagging behind.
So it's again comes back to the point it needs to start from
from the very top. But at the same time, your study
also points out one of the buzzwords of like, let's say,
2025, and that is agentic or agentic services.
And it's claimed to be one of the biggest accelerators of also
the value gap that organizations can foster.
And in your opinion, what makes agentic AI different and how can
companies use it without falling into let's say complexity traps?
Also like the paralyzes that governance structure might bring
with it and potentially also translate it into more of an
intelligence choice architecture rather than make a process
driven approach. I mean, first, I mean, what is a
genetic AII mean? It is the systems that can now
act autonomously and have still clear goals defined.
And it, it completely reshapes how I functions because it's,
it's not just longer about automation or having the new
creative at it's about not AI that can really act and decide
and execute everything across different systems.
We have this analogy with the brain, the human brain, where
predictive AI is really the left brain.
It's logical rule based. It's very structured and
generative AI is, is the crazy one, creative, intuitive,
holistic thinking and agents now become some more kind of a
frontal cortex because it organizes, it prioritizes, it
acts. This is extremely powerful, but
of course, you can imagine it introduces quite a big new
complexity. So without what you meant a
strong choice architecture where it's very clear who is leading
when. So when do humans lead?
When is AI leading? And how do they intact?
When you don't follow such an architecture or think about it,
then organizations risk extreme chaos or human paralysis.
So the best companies they defined is upfront.
So they test agents and controlled workflows.
They ensure that decisions are transparent and they also know
that humans need to keep a supervisory role.
And also, yeah, make clear that ethics and and judgement are
essential. So for every case along your,
your life cycle and work workflow or use case, you need
to clearly define when it's fully AI autonomy of guard
rates, when human is in the loop or when it's just humans only.
And if you do this well, it becomes such a multiplier.
You have human creativity, you have great decision quality.
The potential is already visible in our research.
We also question like first, how many companies are experimenting
with agents already? And it's, it's almost 50%, but
just a fraction of those see value.
I think that's also in line with our overall study that few
companies see value, but the share of agentic value from the
total value business value of AI is supposed to double in the
next three years. So it's a, it's a big thing
coming up now you need to act with this responsibly, also
create trust. But then I think the impact will
be will be major and we will all experience this in the next
couple of years. And what I find so intriguing
about including genetic system services or like also like
particular structures into workflows is that it has and
requires A strategic view on the operating model because it's not
a tool. It's like how future built
organizations are defined. And in your expertise, how does
that what really definition of like the AI change and the
interference within organizations links to how
organizations think about trust when it comes to systems, teams
or in the end also the decisions that are taken or influenced and
for employees and like their specific roles.
Yeah, I mean, clearly as AI evolves from currently being
often set a tool to becoming your your business, I think
trust itself needs to be redefined.
I mean, previously and when you have traditional systems or you
had to implement compliance, you had to implement control its But
now in AI driven systems, I think what gives you trust is
also transparency, reliability, but also shared accountability.
True leaders would never ask you, oh, can we trust this
model? They would say, oh, have we
built a system that earns trust from employees, stakeholders,
regulators, everyone, because it controls AI itself and then it
becomes kind of systematic. So I mean decision making
authority. So how you make decision must
align with with risk exposure. I mean, when when there's a
likelihood that you have a massive irreversible catastrophe
of impact from, from a false decision on AI, then you can't
really fully replace human judgement with AI.
Nowadays, whatever you do with AI, the tech stack must be
highly secure against cyber threats.
Otherwise you have yeah, all these problems from infringement
and provency risk, etcetera. And now the more you scale these
systems and the more you build ecosystem, the ETA connectivity
increases exponentially. So everything becomes much more
vulnerable. So you need to even have more
secure operations. So training is important.
And I have also a very interesting example because I
mean, OK, trust, if you think about trust for AI, I mean, we
all use these tools and we get outputs and you initially try or
you initially learn to trust it. To which degree do you challenge
what you what you receive and do your challenges every time or
just sometimes? So so when do you think you need
to challenge an output? And when we did our study at one
point in time, I just put the entire data into our models.
And yeah, of course we have protected environment.
It was not public, but I simply wanted AI to give me hypothesis
to give me great insights. And I got 5 very stunning
out-of-the-box, non obvious ideas from all the data we had.
And I was really impressed. I mean, one of the fives turned
out to be like, like fully, fully off, like wrong
constraints and. But the other four were
brilliant. And then I wanted to say, OK,
let's be mindful, let's challenge it.
Let's ask the algorithm to give me the Python scripts.
Which data was used? Which assumptions did you take?
And it turned out after a while, the two of these four very
impressive insights were completely wrong.
Like, like the algorithm was cycling me.
I use variable epsilon, but they use variable X that there was a
mathematical flaw in a statistical model.
So so that that was very eye opening.
So you still need to do your own homework and create your own
judgement on what you see. I think that's core.
Also to implement it in the operating model to make it
trustworthy at the end. Your situation just shows how we
collaborate with agents or systems or also the
infrastructures that we've built.
And now we don't act like a loan and we want to integrate it into
the ways that we work and want to make sure that the infos that
we share are accurate. I do think that it comes a lot
to the kind of orchestration that we choose between humans,
data and like agents that we incorporate.
And if you now translate it into your like expertise and the
experience that you've made, what does good orchestration
look like? And what happens when
orchestrations fail in terms of connecting humans, data and
agents overall? I mean, I mean AI, as I said,
they genetic systems are super powerful and they but they, as I
described, can never really work in full isolation.
It's the same with people. People also don't work in
isolation. Yeah.
So the re value emerges from the entire orchestration of humans
data working as one team. And I think in in the well run
organizations, it's all about data at the end.
So, so agents use the data to act, to plan and humans set the
contracts at the context and boundaries and, and what they
want to achieve. Yeah, when this orchestration
fails, you experience the opposite like I, I experienced
with my example. You get either, I mean, with
agents, you probably lose accountability or you get
conflicting outputs, you get wrong decisions.
Maybe the work is being duplicated, which is even a big
thing. Like you ask agents to do
something and you never really when they go in cycles and then
suddenly you burn a lot of tokens and it becomes very
costly. So you need to also make sure
that this is under control. And that's why.
The MIT published once a great article about intelligence
choice architectures and here they made pretty clear that you
need to design to provide decision makers with meaningful
choices. So the agents or the algorithms,
they give you choices that they also give you or predict the
outcome of every of these choices.
What does it mean? And then you interact with them.
If you have humans in the loop and then you improve these
choices. And then you also see how these
choices interconnect with maybe other connections you don't even
know about. Because very often you have
these models and you say, hey, let's do XY that, but you don't
know how this interferes with other parts of the systems.
And eventually you have these great choices, which enables
humans to make much better decisions.
So these agents, in short, can never work alone.
So we we all become one system to a certain degree and need to
make sure that designing these architectures becomes kind of a
new management discipline. It's not just the CEO or the CFO
in the past where design is, we really need to design
intelligent governance architectures around agents.
Now and like digging into the report that you've published and
looking at the stats there, it's shown that only are like over 70
percent of companies actually report unmanaged AI security
risks exactly in those systems or potentially also in the way
that they integrate AI into their daily work whatsoever.
And what do you think? Like is this a trust issue or is
it a leadership issue? Is it like potentially also
responsibility issue that we have?
Or is it just a governance failure because people think
governance is boring or like costly complex to include?
It's interesting if if a leadership issue is always a
trust issue or vice versa. I think leadership is also
challenged at times. So sometimes if you lose trust
in something, it's not even a leadership to blame.
It's maybe the complexity that's that's having much faster
psychic times at the moment. I mean, yes, you're right.
More than 70% of companies have reported that their AI security
risks are not managed. It's very interesting that
almost 80% of companies, they use a third party AI tools, of
course, and not everyone buys or builds their LLMS themselves.
They, we all use third parties and many even know how many
tools they are using. That's also a problem with
shadow AI nowadays that, you know, in the past with shadow
IT, people buy their own tools. And now with AI business units
buy their own AI tools and, and automatively.
And there was a research and an experiment where we realized
that almost 50% / 50% of AI failures originate from these
third party tools. That's not surprising, but many
companies still don't, don't mitigate or manage these risks.
So some of your own risk management ends at your own
company, but you're not really considering third parties,
especially I and nowadays agents and multi agent architectures.
So you really open Pandora's box and then people lose confidence
in the system and then suddenly you have leaders who say we need
to be super compliant here and otherwise you, I don't know, get
a big issue. Yeah, you're personally reliable
if something goes wrong and then people don't use AI anymore, and
then what started with a technical issue or wrong risk
management becomes a completely trust issue.
People don't adopt. So it's why the more you scale,
the more you need to make sure that you design.
And that's also core in the book we wrote together a couple of
years or two, 2-3 years, Yeah, two years ago, two years ago.
That exactly time flies that you can't just design a process and
then at the end when it's done, think about, oh, what do we do
with security, privacy, ethics? That needs to be core in the
design when you start designing these things that at the end, if
you run them, this topic does not pop up.
So that's that's very important. So governance is not about
boring bureaucracy. I think it's the foundation when
well done and to create confidence and only then people
use it and you adopt and you innovate and at the end still
stays safe. And one of the key pillars that
you've also worked on was the Future Build Playbook.
And within it, there were like 5 highlights that I found like
particularly worthwhile to dive into because they are also
positioned as 5 strategic imperatives that we need to look
into. The multi year ambition, the
workflow reinvention and AI first model talent and data.
And if you had to pick one that leader is consistently like
literally consistency underestimate, which one would
it be and why? I think to give the answer
first, I think the multi year strategic ambition is super
important. However, everything here is
important because the five, because some of them become an
inhibitor later if you don't do this right.
And as I mentioned also before, the ambition is great, but you
also need to make sure not to forget technology and data
because otherwise later you find barriers to scale.
So everything met us. And if you want to become a
future built company, everything needs to be be in place.
Thinking about a playbook. A playbook is something which
also gives you steps. I mean, if you write a playbook,
it's boring. If you say, hey, please have
this thing and things in place, that's not that's maybe a
checklist, but it's not a playbook.
Yeah, you need to create a journey.
So when we also do these studies and know what are the key levels
to success, of course, we also pave or design A journey also.
And also depending on your starting point, where you are,
what is the priority of disrespective stage.
And definitely in the beginning, you need to have a multi year
strategic commitment from the top.
So it's not like her let's do this as a side project.
It needs to be a North Star because because again, if C
levels just say this is another tool, another layer on top on
what we actually do, then you can gain some efficiencies, but
you will never convert your company to be AI 1st and design
your entire business model around it.
But I mean, at the end, I simply see technology being back in,
in, in the very, very, very, very, very game again.
I mean, many years, you know, many years ago, like it was
always IT projects and you had to ask the business.
Then it turned around to be like, hey, this is a business
transformation, but please involve it.
And the best companies, they they combine this.
Yeah, they really have make this a business and tech
transformation. Likewise.
And at the moment, just to give one example, I mean, everyone is
now having these big SAP transformations.
Yeah, 2S for Hannah. And this is not a tech
technology update here. This is so significant because
you turn around every business unit, every process and
everything is challenged by its value.
And it's costly. You need to prove the value of
it. And at the end, it's also all
about redesigning your data. Tech is also a business
transformation, not only business transformation.
So take it like it's it goes hand in hand.
And the hand in hand, side by side collaboration is something
that you've also in like your work.
So the study said you've published emphasized quite a bit
because sharing this exact ownership and also combining it
to mature like as organizations and with the teams is quite hard
actually. And I also perceive this as one
of the hindrances because mutual understanding is super hard to
establish. And could you trade a bit more
on why that is and also how organizations can practically
build that shared ownership into their governance set up?
Yeah, yeah, pretty clear. I mean, shared ownership sounds
initially logical, but I mean we know the word of reporting
lines, the word of budgets, the word of politics, and also the
word of, I must say, key accountabilities.
I mean the tension. If we think a couple of years
back, business teams always wanted to have the IT solution
that best fits the individual problem.
So they give demands to ITIT builds it and eventually you
have a super heterogeneic architecture because you
basically serve 100 different demands.
So IT at one point was focused to ensure stability and security
and also scalability. And unfortunately, IT was also
pretty much squeezed like a like a lemon over years to save more
and more costs. Yeah, so some of them are really
burdened by years of of of of, of, of cost savings.
Yeah. So then IT suddenly got the huge
mandate. OK, we need to have a digital
transformation. Can you do this on top?
Wonderful. So on the one hand, with your
tiny budget, you need to be the innovator of your organization
and innovate rapidly. On the other hand, you need to
make sure that your architecture is not becoming spaghetti or how
we called it sometimes. So it wasn't easy.
And then you suddenly created these new roads like there's a
chief digital officer now and there was often tangents because
like both had their different targets, but it was not without
any conflict. Then it was clear everything has
to be a business transformation. We need to put a business person
in charge of everything. And this was great exciting
because suddenly you can you can also have great communication
how AI and digital turns around your entire business.
And then you try to experiment a lot, but suddenly you have
hundreds of great pilots. So we always call this thousand
flowers blooming. So we basically plant an entire
garden, but all the roses die at the end.
So and then there's the moment of truth where you need to
think, why is this the case? And then you realize that maybe
tech and digital and data must be part of this entire
transformation. Then it brings to clearly back
to what I'm saying in the beginning is that you need to be
called a sometimes two in the box.
So all these topics need to have a business and an IT digital
data counterpart. And if we, if I may share 1
figure from our report, we ask companies how they organize that
and the companies which are future bills.
So the top 5%, they have 50% more.
This organization is set up of having these two in the box
ownerships. And I don't mean like just
collaborating, I mean shared responsibility, shared targets,
shared accountability, shared budgets.
So, so really not not just governance on paper, but
governance established, which is a huge different.
Actually, I've I've over years of consulting, you can't imagine
how much governance I saw on paper.
But the reality was completely different.
So this time it's for real. Yeah, you need to anchor it to
make it happen. I would love to dive into that,
but I do think that then the time would fly even more than it
already does so. But for now, I do think one of
the aspects that I want to piggyback on is the
consciousness of the leaders that they sometimes have when
either it comes to the shared ownership or the established and
framework that is required to bring that to life.
Because there is this huge word that everyone kind of wants to
avoid because it's regulation. And we all say that it's hard
for regulation to catch up before it's scaling AI.
And from your perspectives, what actually are the costs of
waiting? And what kind of leadership
mindset do we need instead when we do want to progress?
I mean, on the one hand, I have this very rational mind.
I like innovation. I like rapid innovation, speed,
getting results fast. And on the other hand, I also
have a half a young daughter and I want to grow up in a in a very
safe and protected environment and avoid the robotic
apocalypse. Yeah.
So I think it's it's it's it's two ways how to look at this
topic. First, the rational point.
I mean, if you wait, then you can wait forever.
You slow down innovation and your competitor US may progress.
And you know, we also have global competition.
It's not just your competitor around the corner, it's a
competitor at the end of the world.
So you need to you need to also progress and also own your turf
and keep your competitive advantage.
The longer you wait, you also create more.
We call this tech debt. I mean, the big challenge for
companies nowadays is that they are still adopting these new
technologies, but they still keep their current tech
architectures. And this is very, very, very
costly. And when you wait too long, you
also don't benefit from learning cycle.
So at the end, those companies which, which shape guard rates
now, which tests responsibly, which also take personal
ownership for the topics and bid readiness, they, they, they jump
ahead on the curve. So I think you shouldn't wait
too long. You should bid the capabilities,
but do it in a responsible bid manner from the heart.
And the other perspective is, I mean, I hope and that's leaving
now. Maybe, maybe the, the current
discussion a bit what is best for humanity?
I mean, we are all humans now. We are not trans humans yet who
send their DNA to space. So we are still all together
here on this planet. And I think we should share a
common goal and be all aligned that AI should everyone serve as
a greater good. I mean, the tension is is
immense. I mean, think about tackling
climate change, think about curing diseases, education is
available, quality of life gets better.
So I recently thought about one word.
And I mean, no one wants to be remembered as the Oppenheimer of
AI who created then the beast that was unleashed.
And years later, you have the big regret.
So I think both, both perspectives fit together.
I'm a strong advocate for fast innovation, learning progress,
but it's important to define red zones.
So something which like, I mean, we, we were pretty clear with
GDPR for data and I think wasn't easy, but I think we also get
something good out of it. And I think we should do the
same with AI. And one thing which is important
to share, maybe as a last insight from the study
responsibly, AI is one topic we survey and assess in our
studies. And interestingly, in the
beginning, the topic didn't really pop up that much like you
should need to focus on other topics first to digitize fast
and bid AI. But those companies and we do
this crazy multivariate analysis with cause and models etcetera.
I spare you the details, but we have seen that from the third
stage to the fourth stage to become a future bid responsibly
AI was the most significant inhibitor to become future bid
if you haven't really designed it until the stage.
So you can neglect it for, but it will backfire at the end when
you really want to scale. And it was quite comforting for
me. So there's clear proven value in
having responsibility AI. Amazing.
I think we've also touched upon that in our like book that we've
published 10 moral questions and there's like the whole
background on the economical part as well.
So I'll link the link to the background information on the
study in the book. Also in the show notes.
Unfortunately, we already come to close with like today's
episode and also with our conversation.
But before we just say goodbye, I do want to share some moments
with you. They're so-called in between
moments. I use them with every guest as
quick, intuitive reflections. And I would love for you to
answer in like a word or a sentence, but pretty short.
And I want to start on what are how trust in AI feels like for
you. Sometimes I wish I could be AI
for a day. Yeah, it's a bit.
You know, it would be great if, if, if AI could feel like humans
feel and humans could feel like AI feel.
So I don't know if this is achievable, but that would give
me trust. And what does Europe need more
of? I think we we can have a bit
more self esteem and trust our own capabilities, but what we
really need is pragmatism. And what does AI leadership
require? Ah, you touched courage before.
I think courage is important, but courage defined with
integrity, not not just being radical, but being courage,
courageous, but in a mindful matter and having also some
patience I think. And what is the most overlooked
skill in transformation? I think people driven
innovation, yeah, I think enable people, make them curious, give
them cool tools and let them do really use people to innovate.
Enough that last one. What does future built mean?
Those future built companies, I have seen a very laser focus or
not truly matters, but they are also very bold and execution.
I sometimes envy them. I also sometimes wish I would be
more focused and what probably I am.
But you know, it's sometimes stunning.
It it's, I mean, think about Steve Jobs in the past.
He had always 100% clear knowledge what he wants to do
and what matters. That's that's that Champions
League. Yeah.
Indeed it is. And I want to close with one
question that I would love your like view on point of view or
maybe also outlook, because if trust is a missing currency
between the ambition that we have and the execution on like
the clarity and sophistication on implementation, what does it
mean in 2025 to be a trust builder in AI transformation?
I. Mean we wrote this book
together, yeah, about 10 more questions and to test the titles
like how to design take and AI responsibly.
And there are a couple of aspects which are really
important to establish trust in AI.
And I mean, as we said, I mean make sure it's beneficial to
everyone. Like it has to be a common
better good for your customers, stakeholders, your workforce.
I think that's important. And as we discussed, make sure
that it's it's humans and AI jointly being in this governance
models and strive to an intended purpose like give, give agents
freedom, but also make sure where freedoms end.
And the last pieces may be coming back to my my being in
favor of people and humans. Educate people, upscale people
and power them and use them to reshape their own workforce and
processes. So then adoption also gets
higher, etcetera. Yeah.
And one thing as we as we finish this interview, I had to laugh
because there's one favorite quote, which I also jumps and
making fun to my wife that can put on my tombstone.
Yeah, I hope there's still many years to go, but I like always
the word do the best and trust the process.
I think we should simply continue, do our best, act
responsibly, innovate and maybe be good people.
That's a big ending. It is.
It's quite a statement to to leave this conversation, but I
love it. Thanks so much for spending time
together and sharing also the background on the report.
I appreciate your time and like the strong finish and look
forward to seeing where that brings us.
But for now, thanks so much to man and we'll speak soon.
Yeah. Thank you.
If it was great. Thanks.