Artificial intelligence isn't just changing what we do as business leaders – it's transforming who we can become when we leverage these tools strategically. In this provocative conversation with Patrick, a leading AI expert from BCG, we explore how professionals across industries can build their personal AI organization to dramatically increase their impact.
Most discussions about AI focus on efficiency, but Patrick reveals a deeper opportunity: the chance to reshape your entire role around what you do best. By thoughtfully distributing tasks between yourself and AI assistants, you can focus on the aspects of your work that truly energize you while automating the rest. This isn't about replacing yourself – it's about becoming an entrepreneur within your organization who builds and manages a team of digital collaborators.
The path to this transformation begins with structure. Since AI systems originate from the highly organized environments of software development, creating similar organization in your work makes these tools dramatically more effective. Simple practices like documenting your thought processes, maintaining structured folders, and recording your lessons learned create the foundation for AI to understand your needs and preferences over time.
Perhaps most revolutionary is how AI changes the distribution of responsibility within organizations. Patrick advocates moving from assigning KPIs to distributing genuine ownership of outcomes. When team members have stakes in results rather than just tasks, they're motivated to build AI systems that create true value. This shift requires leaders to become coaches who help their teams navigate expanded responsibilities and leverage new capabilities effectively.
For industries with high quality standards like German automotive manufacturing or management consulting, the challenge becomes balancing the speed of "80% solutions" with the need for excellence. Creating processes that support rapid iteration after initial AI output represents a new paradigm that many organizations are still learning to navigate.
Whether you're in marketing, R&D, or finance, the future belongs to those who can leverage abundant intelligence to pursue their goals faster. Start building your AI team today, and discover what's possible when you focus on what truly matters in your work.
Listen to the Leadership Espresso Podcast:
https://open.spotify.com/show/4OT3BYzDHMafETOMgFEor3
View the Leadership Espresso Podcast:
https://www.youtube.com/@Stefangoetz_Global_Leadership/videos
Connect with Stefan Götz on LinkedIn:
https://www.linkedin.com/in/stefangoetz/
Check out Stefan's Executive and Team Coaching
https://www.stefan-goetz.com/
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Speaker 1 (00:00):
Hi Patrick, it's great having you on the show,
and today it's all about AI.
You are one of the mastermindsin AI at BCG, based in Denver,
colorado.
Happy to have you on the show.
Speaker 2 (00:14):
Thank you so much, Stefan.
Speaker 1 (00:15):
My pleasure to be here we are allowed today to
talk about the future of AIactually, and what kind of
impact that makes on business,on organizations and on
leadership.
Now, what do you believe in IsAI going to make?
Speaker 2 (00:38):
us faster or better?
You see, that's a good question, and I mean to take a brief
step back right.
With AI, there's almost onlyfuture to talk about.
Ai history is so far fairlylimited, so we have an exciting
time ahead of us and definitelyfew historic precedents, which
makes it even more interestingto talk about it.
And in terms of speed versusquality, right, I think we can,
(01:00):
at the end of the day, probablyget both.
But the question is what youpersonally need and what you're
trying to get out of it.
And with AI, there is currentlya focus on how do we broadly
apply it, how do we bring it toorganizations?
And to me, the much moreinteresting discussion is how do
we make it work for you as anindividual, right, and what can
(01:22):
you do to make it work foryourself?
And then it comes down to whatis your optimization function
right and what can you do tomake it work for yourself?
And then it comes down to whatis your optimization function
right?
Are you planning to be a lotfaster at what you're doing, in
which case it might just be amatter of building the right
outsourcing functions with AIaround you, or are you trying to
become much better at whatyou're doing when you are
perhaps in a highly qualityfocused role, right?
(01:43):
I've spoken to doctors who nowuse AI tools to go much deeper
into their assessments andpatient history and so on, so
both is possible.
I think there's a lot ofpotential either way, and it
comes down to what makes sensefor you.
Speaker 1 (01:57):
Yeah, but now the question of what makes sense for
me.
You know you spoke aboutoutsourcing.
It feels like I'm outsourcingmyself, myself.
So what do you think?
Speaker 2 (02:09):
about this I?
I think it's an opportunity tooutsource all the things about
yourself that you're notnecessarily thrilled to do every
day or of which you would liketo do even more right?
Um?
So, naturally, coming from bG,something I spend a lot of time
on is preparing presentationsand preparing documents, and I
wish I could outsource more ofthat so I can focus on the
(02:32):
actual content, on interactingwith the people I care about, on
leading and working with myteams.
Right, and AI gives me thatunique opportunity to build an
ecosystem for myself, almostlike building my own little
organization within my jobdescription to allow me to shape
what my everyday looks like.
(02:52):
Right, in a way, you can takeyour job description, ensure you
get everything done within that, perhaps even more beyond the
job description, but you can nowdecide what you want to get
done by yourself and what youwant to push to one of your new
AI colleagues, and it all comesdown to creating an environment
that makes it work for you.
Right?
That means having structure inplace, having files in place.
(03:16):
Yesterday, I had an interestingdiscussion with female leaders
across leading German companiesand they asked so what can I do
day-to-day now to ensure thatonce the genius AI breakthrough
comes in two years withuniversal general artificial
intelligence or something likethat.
What can I do so that it'll beready to push me to 100X?
(03:40):
Right, and I genuinely believeit starts with the basics right
Ensuring I have the structure inmy life so that if a summer
intern came in tomorrow and hadto take over parts of what I'm
doing, they would be ready to go, because, at the end of the day
, that's what AI is going toface.
Speaker 1 (03:56):
Absolutely.
Now let's be more explicit.
Now let's take an example.
You are a R&D leader in thecompany, you are a marketing
leader in the company, You're acontroller.
So how does this apply yourstructuring to these maybe three
functions?
Speaker 2 (04:14):
Yeah, great examples, Look.
The first thing I believepeople need to understand is
that all AI is being produced bysoftware developers and these
frontier AI labs.
And foremost, they developsystems that work for them right
, Because that's the environmentthey know.
They are used to working withtext files in this very specific
(04:35):
environment that softwaredevelopers are in.
Speaker 1 (04:37):
We call them nerds.
You know, we don't thinkthey're somewhere on the
Philippines or somewhere andthey produce software.
But how does that relate to me?
Speaker 2 (04:49):
Well, I think they're increasingly in SF and not in
the Philippines.
But, yeah, well, the question ishow can you ensure that you
create parallels between yourstructures and your work and the
way these teams are set up?
Because if something works forthem, the question is how can I
set up my workspace and what Ido to make sure it also works
(05:12):
for me?
So one basic example I gave tothose ladies yesterday is look,
in software engineering, it'sbest practice that in every
folder I have a little textdocument that describes what
this folder is about and whatdocuments you can find in this
folder.
If you have massive folderswith many documents in your
organization, it would be agreat idea to add a little text
(05:33):
document to each folder thatsays hey, this is what you can
find in this folder, and itmight be helpful to you if
you're trying to do X, y, zright.
So, really starting to addthese little snippets of
infrastructure.
That again would make it easierfor a summer intern to find
their way around the project aswell, because the same thing is
going to work for an AI agent,and AI nowadays might be able to
(05:54):
navigate your messy emails andfolder structures even without
these little helpers, but it'sjust a question of whether you
wanted to spend the effort onnavigating all your file
structure or whether you want toput it in a place where it can
help you get your best work done.
Speaker 1 (06:11):
Now this sounds a little bit incremental.
I mean I would expect more forit.
But let's be it Again.
Take marketing leader, r&dleader, controller.
Let's apply your wisdom tothese functions.
Speaker 2 (06:28):
Yeah, so one thing I do myself and which I think, for
example, in marketing, would bevery helpful, is start to think
about how can you put more ofyour thoughts and your ideas and
vision and so on on paper.
Because one problem you're goingto have when working with any
new coworker and this applies toAI just as much as it would to
(06:49):
a human is they come in and youhave all this history of things
you've tried that didn't work,and experiments and ideas and
graphic layouts in mind and soon, and you've never really
sketched them out.
Or maybe you sketched them outon a piece of paper, but it's
(07:10):
lying somewhere at home and yourcoworker will never see it
right.
And so how can you put that onpaper so that, on day one, your
coworker which in the future,very well might be your own AI
assistant right is able to findall these things right?
So put your thoughts andlearnings and so on on paper,
take a note.
So I know a lot of peoplewho've been very successful at
making AI work for themselveshave had a great digital diary
(07:31):
the last few years and they wereable to read in that diary and
AI knows them very well and canhelp them pursue their own
personal goals.
Speaker 1 (07:40):
It sounds like creating, you know, putting my
genius to paper and make itavailable to AI.
Speaker 2 (07:51):
To some extent right, at least your learnings.
So maybe it's not a personaldiary necessarily.
Maybe it's something like a jobdiary.
I already request all my teammembers to every day write down
two things they've done andthree things they've learned or
would do differently next time.
If you could do the same thingevery day in your job, then that
(08:11):
would be an amazing tool setfor someone else, like an AI, to
learn from right.
Speaker 1 (08:18):
So this sounds more I invest into the future.
Actually, it's taken meprobably more time.
I'm already working 60 hours.
Actually, I talked to a COO inthe Chinese automotive market.
He said, Stefan, if you canfind me a way, a strategy, a
leadership lesson that I canreduce from 100 to 60, that
would be great.
(08:38):
So you're asking me to do evenmore.
Speaker 2 (08:43):
Well, I will admit there might be a bit of upfront
investment here and there.
Well, I will admit there mightbe a bit of upfront investment
here and there, but at the sametime, I think if you start using
AI today, there are veryimmediate time gains that should
just make up for these sort ofthings, right, Whether it's
(09:09):
scanning documents more quicklygetting a quick overview of what
happened in a meeting doingmuch more rapid online research
drafting the for you,summarizing the emails for you,
right, you know we are familiarwith all these uh already.
Speaker 1 (09:17):
I think many work with co-pilot or chat, gpt or
whatever.
To summarize emails, right, oryou know faster.
Speaker 2 (09:24):
But what is it?
Speaker 1 (09:25):
you claimed it's like uh, you become more an
entrepreneur within theorganization, so how could ai
shape that and create arevolution?
Speaker 2 (09:41):
well.
Look at the end of the day,it's about building your own
team, and in the past, a wellperforming employee might have
started staffing interns orbuilding up people who can
support them, who learn fromthem, and in the future, that
will still be part of success,but it will also include
(10:05):
building up a little AI org thatworks for you right.
So, if you are that marketingleader, I think it would be a
great investment to sit down ona Friday afternoon or Saturday
morning or whatever and writedown.
These are all the things Icurrently spent my time on and I
think, very quickly you willfind things where it would be
(10:25):
great if you could, you know,start handing them to someone
else.
And the great opportunity withAI is that it's incredible at
learning from your notes and soon.
So my encouragement to everyonewould be just get started right
.
If you spend a lot of timereading and drafting emails, put
Copilot or a custom gpt orsomething in place today, and it
(10:49):
won't do the job perfect jobperfectly yet, but you will keep
giving it feedback and you willkeep iterating on instructions
and so on.
It'll come better and betterover time, and so, the same way
as you would build up a humanorg where you don't expect
everyone to come and be perfecton the first day.
You have to go about buildingup an AI org that works for you,
and what's really fascinatingto me is that people with humans
(11:11):
are entirely ready foronboarding someone new and
giving them six months todevelop, or something With AI,
if it doesn't do the jobperfectly on day two.
You're like well, what am Ipaying for?
Patrick?
Speaker 1 (11:23):
that's the normal case, everybody's patient you
talked about kind of you knowyou check your case.
Is it about, you know, beingfaster or higher quality?
Now I think we're pretty clearabout faster.
This is what you elaborated.
But it's much more interesting.
(11:47):
What you are provocativelysaying is like AI is going to
give you 10 times scale or evenmore.
So let's apply it again tomarketing, to R&D.
Maybe R&D would be a good, nicecase now.
So how could that tenfold orx-fold my quality or
(12:08):
breakthroughs?
Speaker 2 (12:11):
yeah, and look the reason I've been uh trying to
stay away from from your r&dexample a little bit is that r&d
functions are so different inmy experience that they're
incredibly hard to advise.
Speaker 1 (12:22):
I want to challenge you.
Speaker 2 (12:25):
That's great.
I I appreciate it.
So let me indulge in yourexample of R&D right, and I'm
totally with you.
The exciting thing about AI isthat you can 10x or even 100x if
you manage to build out theright freedom for yourself right
, and if you can work withleadership to give you that
freedom.
And I do genuinely believe thatone of the biggest
(12:47):
opportunities now in companiesis going to come from leaders
giving their teams theinfrastructure and the liberties
to experiment and push theenvelope much more than they
would have done in the pastabsolutely yeah.
So how can, how can I ensurethat my team feels comfortable
(13:08):
using these new AI tools andexperimenting with them and
trying something out and sendingit to me, even if it might only
be an 80% version?
Because the exciting thing withAI is that we're now extremely
good at creating 80% versionsvery quickly and most orgs just
aren't accommodating of 80%output.
(13:29):
And I think this is especiallysomething I felt when working
with German clients and Germanteams.
Speaker 1 (13:34):
Right, you mean Chinese?
Go with 75%.
Speaker 2 (13:43):
I think Chinese might be more similar to Germans than
many other countries.
I think Chinese might be moresimilar to Germans than many
other countries.
But so you know, how can wecreate an environment where an
80% starting point is acceptableand even encouraged, so that we
can move much faster and theexciting thing, right?
Let me just briefly finish onthis 100x point.
(14:05):
Right, the 100x speed willoften come at some trade-off,
and what's fascinating to me isthat there's this dual function
of a.
Can we create an environmentwhere we start accepting these
trade-offs?
Right, because we are no longerbogged down on one very
specific metric.
And b where do we reach thetipping point where a hundred x
(14:32):
speed gain and or quality gainin one area makes up for these
small disadvantages in otherareas?
Right, and I'm just going touse one more consulting example,
because that's that's where Ispend a lot of my time at the
end of the day.
Right, powerpoint, slides andpresentations.
I'm a big advocate that thereis going to be a big leap
forward in presentations and Ipersonally believe it will not
(14:54):
be PowerPoint for varioustechnical reasons, but naturally
there will be trade-offs.
Right, we now have AI toolsthat can start generating slides
100x faster than the best VCGor code, but you might not be
able to change the color of thefootnote as easily.
And is that the tipping pointright?
(15:14):
Is it enough to be 100x fasteroverall but to have this
limitation that you can't changethe font color or something, or
do we need to push further andenable some other feature?
And I think you're going tofind the same thing everywhere,
whether it's R&D marketing.
Speaker 1 (15:30):
I mean, your example is a little bit hingy, as you
know.
Changing the color of thefootnote may be important for
some people, but overall doesn'tgive an extra value.
Speaker 2 (15:45):
That's a hot take, Stefan.
Yeah, I know, I know extravalue.
Speaker 1 (15:53):
But if that's a hot take, stefan, yeah, yeah, I know
, I know I rest my case in inyour case, but again, as I'm,
you know, like kind of many ofmy clients on the automotive
market and now we're facing ingermany at vol, audi, porsche,
mercedes, bmw, everywhere, weneed to reduce our development
cycles, we need to take morerisks, because we can't squeeze
(16:15):
in what we've done before faster, just faster, or even leave our
test cycles and say, okay, aChinese car doesn't need to run
150,000 because it would neverrun 150.
It just goes.
You know, it's a bill forinfotainment, not for running
driving.
So there the you know thetrade-off having an 80 solution
(16:38):
with all the risks combined, howwould ai work in that case?
Speaker 2 (16:45):
I mean, look, I'm not an automotive expert, um, but
I've, uh, I've had the pleasureof being a passenger in some
chinese taxis abroad, where thedrivers claim that they're now
heading towards 200 000 milesand they're very happy with
their car.
So I don't want to talk downtheir, their abilities and I you
know.
I think that it's aroundcreating processes and
(17:07):
structures that support thisexperimentation and 80% work
right.
So, for example, I think areason that a lot of car
startups have been verysuccessful in recent years,
despite often launching with an80% product, is that they've
been able to implementstructures where they can
iterate on their product pastshipping date.
(17:29):
So if you have a test slide,it's going to come with all
sorts of software faults, if indoubt, in the very beginning
when a new model is released,but they can keep releasing
those updates that make the carbetter and they've created a
service model where it's veryconvenient for users to have
changes made to their car orimprovements made to their car,
(17:51):
and these sort of structures areprobably what you'll need to
accommodate the 80% and movefast and break things approach.
That is becoming more prevalentwith AI and maybe it's stacking
different AI solutions on topIn engineering.
You now have all theseengineers building AI solutions
(18:12):
very fast and then having botsthat go over it and keep
iterating past the initialdevelopment date.
Speaker 1 (18:22):
I see that point and I want to go further.
I want to go now.
What impact would that have?
Organ of an organization and onleadership?
As if we apply AI system, wetrain them taking notes every
Friday sitting, drafting andcreating a digital library.
(18:43):
This, of course, will make ourwork probably, over time, faster
.
As a, I will kind of bring outthe best of all things.
So you have a overtime of gaintimes of coming up with 80%
ideas, results whatever.
So the time gained looks likeyou can now apply to make things
(19:10):
even better or come up withanother solution, because part
of your time is not wasted onrepetitive or other coming up
with something any longer.
But how will we deal inorganizations with that
(19:32):
trade-off, with that risk, withthat?
You say liberty in leadership.
You know now, if you allow thatkind of 80%, you know, to a
German automotive company, thatis hilarious, that's insanity.
(19:52):
So what do you see?
How can we handle that kind?
What does it take?
What is your idea?
Speaker 2 (20:04):
So, look, almost everyone I've worked with in a
role that has been faced withmore AI support or interaction
has shifted their focus andtheir activities from task
execution to A task planning andstructuring and B task
(20:25):
reviewing, right.
And so, as a leader, thequestion is how do you support a
team and build the incentivestructure where people are
focused on structuring andreviewing, and that probably
takes a different structure, adifferent reward system.
I think you need to pass downownership in many ways, because
(20:48):
reviews are always going to behalf-hearted if you're not the
one responsible for the outcome.
So you need to see your teammembers less as contributors and
more as their own littlebusiness owners, right, maybe
even P&L owners, if we go backto the marketing example.
Really give everyone a piece ofvery specific but tangible
(21:16):
ownership and responsibilitythat they can hold on to so that
they can build their own littleAI work and business around
fulfilling that target andowning that little stake of the
business.
And so, as a leader, my biggestquestion would be how can I
(21:37):
start chunking up my business,not into KPIs and abstract
metrics, but into pieces ofownership that I can assign to
individuals and tell them dowhatever you think is the best
thing to do to get this not justdone, but to own this and what
would be pieces of ownership?
(21:59):
like comparing to kpis yeah, andso I think, for example, if we
go back to the marketing example, how can you branch out from
where someone has an abstractKPI to optimize search engine
(22:21):
performance to optimizing salesthrough search, things like that
?
And then, if I give them thatpiece of ownership sales through
search I start to move theminto a P&L position where they
have profit and they can investinto AI tools or whatever else
they need.
(22:41):
But they now start to own theirown little P&L.
Speaker 1 (22:44):
So you would chunk them up, you would come with the
overall target, rather thangive them, you know, pieces,
elements yeah, and and I thinkit's it's about giving them
pieces of a bigger target,giving them stakes in that
target.
Speaker 2 (23:02):
Because, as we established, right, making this
ai org work for you takesinvestment, and people can only
make good investments if theyhave some sort of return
function as well.
I mean, um, so I I hate it whenI see organizations where
people are given specificbudgets for ai investments or
(23:22):
something that doesn't makesense.
Right, the goal is here's whatyou need to do and this is how
much money it's going to makefor the organization or whatever
your metric is, and then, basedon that, you can invest, and
obviously you shouldn't investmore than whatever you think you
can bring in with yourimprovements now, patrick, I'm
gonna put you down on this one.
Speaker 1 (23:41):
Um, how does ai work for bcg?
If you're allowed to talk aboutit, but maybe you can talk
about how does it change yourlife within BCG and your
teamwork?
Speaker 2 (23:51):
so we get a sense and a feel from real daily practice
well, look, I'm very lucky andproud to be at the forefront of
much of the AI work we do, sofull disclaimer.
My experience isn't yetrepresentative for the broader
company or let alone industry,but for myself it has totally
(24:13):
upended my work.
Yesterday I held a keynotewhich was entirely ai generated.
Instead of creating the slidedeck page by page, I sat down
for 20 minutes.
I wrote down everything Iwanted to say and the points I
wanted to make in a presentation, and then I handed it over to
an AI tool and said now buildthe slides with these talking
(24:34):
points, and I did zeroiterations.
I took the slides and went topresent.
This is not yet your everydayBCG and it's going to be a while
.
Maybe it will never happen formany of our projects, right?
Speaker 1 (24:47):
MDPs don't listen.
Speaker 2 (24:49):
Right, but this is the case study for myself as to
what it could be like, at leastfor very specific types of work
and for your consulting work asa team leader.
Speaker 1 (25:04):
how does the daily setup look different now with
the pieces of ownership and soon?
Speaker 2 (25:12):
Indeed this shift that I mentioned from execution
to structuring and review.
This has already happened inmany of our teams, I dare say I
think for a long time, thebiggest challenge for project
leaders was all of a sudden, myteam is generating things and
(25:34):
having AI do research.
How do I get them to review theoutcomes resulted in the
follow-on responsibilities of,for example, presenting that
work or working with the team toimplement that work that you
come up with being passed downto more junior team members.
(25:55):
So that's something I'm a bigfan of and I think it's an exact
example of this ownership shift.
Right, because as a projectleader, it's becoming harder for
me to review some of the workthe team is doing.
So I tell them look, guys, Iyou know I'm happy to give
advice and provide the overallstructure and so on, but if
you're doing a lot of ai workhere, then you need to be
(26:17):
comfortable and present this andand I think it's your role
entirely.
Speaker 1 (26:24):
Uh, you know, first you give ownership, you give to
liberty, you need to give spaceand you need to become a, a real
great um, a person who iscapable of finding the next
question, of finding it.
Could this be true of reviewing, of asking good questions to
(26:49):
your juniors?
So how does that work?
Speaker 2 (26:54):
Absolutely.
And look, I think it would befair to say everyone has to
become a bit more of a coach.
Speaker 1 (27:03):
Yeah, I love this one .
I train coaches.
Speaker 2 (27:05):
There you go.
And so yeah, absolutely.
I personally believe that thereis an exciting opportunity to
shift responsibility downwardsand to have people own things
earlier and more thoroughly.
And then your responsibility asa leader starts to become A how
(27:28):
do I help them get used to thisresponsibility that people face
early on?
And b you know how do I act ifthings go wrong?
Because things will go wrong.
Right, we talked about the 80approach.
Ecg is not a fan of the 80approach, as you can, as you can
imagine, right we're definitelya 100% company.
That is our value proposition,and so if we are a 100% company
(27:53):
and we have tools that are beingused by people, whether we want
or not an associate is going touse ShedGPT, there's no point
trying to stop them, and werealized that early on, luckily
and if we have people in our orgthat are going to use tools
that produce 80% outputs, butour target is to have 100%
service, then how do wedistribute the responsibility
(28:17):
and the tasks needed to take the80% that goes in somewhere and
convert it into the 100% that weneed to bring to our clients?
Speaker 1 (28:27):
Absolutely, so you gain time.
Obviously you know people arefaster in producing first time
results 80% results.
So you have some time to review, to challenge, to whatever.
So how does work?
How is it in your daily workLike how?
Speaker 2 (28:46):
You know it's a lot about deep reviews, discussing,
digging deeper.
I think it's an opportunity todive deeper into topics than you
ever would have before.
That's entirely true inconsulting and outside
consulting and ensuring that youbecome an upscaling a quality
(29:11):
upscaling function right andthat you again build structures
that work for you.
Speaker 1 (29:21):
Okay, let's check out what I learned about it.
We have new opportunities.
We have the choice between oractually both maybe to become
faster and to have a leveragefor more quality.
(29:42):
But our role as leaders is goingto change and it's going to
help the company actually becomenot just faster with 80%
solutions, but using the timegained to build up a digital
library that makes you betterover time, makes your team
(30:03):
better, and your role is more ofgiving space and of allowing
your team members to dwelldeeper, to ask more provocative
questions, even to turn thingsupside down, and this is going
(30:25):
to create value, as youmentioned, because in the end
not just in consulting, but Icome back to my R&D If I can
come up with something thatdrives mobility in a smarter way
, this is value creation.
So I see what I learned is it'sa coexistence between AI and
(30:49):
myself as a leader, but also myjuniors.
What I learned is not aboutfirst an 80% version and be
(31:12):
creative with it and not judgeit, not blame it, but allow the
team the time to take ownership,give them greater pieces of
ownership that will create valuealready and not just pieces of
KPIs.
Did I miss anything?
Speaker 2 (31:31):
I think those are all great points.
The last one I would love toadd is my favorite talking point
about leverage, and I'm sureyou're waiting for it already,
because I can never end an AIconversation without mentioning
leverage.
To me, it's about taking whoyou are, who you want to be, and
leveraging all the resourcesyou have to get to your own
(31:53):
goals and ideals faster.
In the past, you would haveperhaps raised money to get the
job done, hire more people, buya more expensive caterpillar or
whatever it is.
Today, you have a wealth ofabundant intelligence and it is
up to you to leverage thatintelligence and build not on
(32:14):
capital, but build on smarts,ideas and the very fabric of
innovation, if you will padre.
Speaker 1 (32:23):
it's uh very inspiring for today and I take
we have to talk maybe in oneyear's time and check out.
Speaker 2 (32:34):
At the rate of AI, we can talk every other month.
Speaker 1 (32:38):
We make a series.
You know the AI exploding and Iwant to hear and see more
examples, real daily examples,so people can figure out on how
to apply that stuff to their ownbusiness.
But for now, thank you for yourtime, thank you for your
(32:59):
experience.
Thank, you so much, stefan, andlet's keep on rocking.
Speaker 2 (33:04):
My pleasure.
Thanks for having me have agreat day.
Speaker 1 (33:07):
Bye, bye.
Hi Patrick, it's great having you on the show,
and today it's all about AI.
You are one of the mastermindsin AI at BCG, based in Denver,
colorado.
Happy to have you on the show.
Speaker 2 (00:14):
Thank you so much, Stefan.
Speaker 1 (00:15):
My pleasure to be here we are allowed today to
talk about the future of AIactually, and what kind of
impact that makes on business,on organizations and on
leadership.
Now, what do you believe in IsAI going to make?
Speaker 2 (00:38):
us faster or better?
You see, that's a good question, and I mean to take a brief
step back right.
With AI, there's almost onlyfuture to talk about.
Ai history is so far fairlylimited, so we have an exciting
time ahead of us and definitelyfew historic precedents, which
makes it even more interestingto talk about it.
And in terms of speed versusquality, right, I think we can,
(01:00):
at the end of the day, probablyget both.
But the question is what youpersonally need and what you're
trying to get out of it.
And with AI, there is currentlya focus on how do we broadly
apply it, how do we bring it toorganizations?
And to me, the much moreinteresting discussion is how do
we make it work for you as anindividual, right, and what can
(01:22):
you do to make it work foryourself?
And then it comes down to whatis your optimization function
right and what can you do tomake it work for yourself?
And then it comes down to whatis your optimization function
right?
Are you planning to be a lotfaster at what you're doing, in
which case it might just be amatter of building the right
outsourcing functions with AIaround you, or are you trying to
become much better at whatyou're doing when you are
perhaps in a highly qualityfocused role, right?
(01:43):
I've spoken to doctors who nowuse AI tools to go much deeper
into their assessments andpatient history and so on, so
both is possible.
I think there's a lot ofpotential either way, and it
comes down to what makes sensefor you.
Speaker 1 (01:57):
Yeah, but now the question of what makes sense for
me.
You know you spoke aboutoutsourcing.
It feels like I'm outsourcingmyself, myself.
So what do you think?
Speaker 2 (02:09):
about this I?
I think it's an opportunity tooutsource all the things about
yourself that you're notnecessarily thrilled to do every
day or of which you would liketo do even more right?
Um?
So, naturally, coming from bG,something I spend a lot of time
on is preparing presentationsand preparing documents, and I
wish I could outsource more ofthat so I can focus on the
(02:32):
actual content, on interactingwith the people I care about, on
leading and working with myteams.
Right, and AI gives me thatunique opportunity to build an
ecosystem for myself, almostlike building my own little
organization within my jobdescription to allow me to shape
what my everyday looks like.
(02:52):
Right, in a way, you can takeyour job description, ensure you
get everything done within that, perhaps even more beyond the
job description, but you can nowdecide what you want to get
done by yourself and what youwant to push to one of your new
AI colleagues, and it all comesdown to creating an environment
that makes it work for you.
Right?
That means having structure inplace, having files in place.
(03:16):
Yesterday, I had an interestingdiscussion with female leaders
across leading German companiesand they asked so what can I do
day-to-day now to ensure thatonce the genius AI breakthrough
comes in two years withuniversal general artificial
intelligence or something likethat.
What can I do so that it'll beready to push me to 100X?
(03:40):
Right, and I genuinely believeit starts with the basics right
Ensuring I have the structure inmy life so that if a summer
intern came in tomorrow and hadto take over parts of what I'm
doing, they would be ready to go, because, at the end of the day
, that's what AI is going toface.
Speaker 1 (03:56):
Absolutely.
Now let's be more explicit.
Now let's take an example.
You are a R&D leader in thecompany, you are a marketing
leader in the company, You're acontroller.
So how does this apply yourstructuring to these maybe three
functions?
Speaker 2 (04:14):
Yeah, great examples, Look.
The first thing I believepeople need to understand is
that all AI is being produced bysoftware developers and these
frontier AI labs.
And foremost, they developsystems that work for them right
, Because that's the environmentthey know.
They are used to working withtext files in this very specific
(04:35):
environment that softwaredevelopers are in.
Speaker 1 (04:37):
We call them nerds.
You know, we don't thinkthey're somewhere on the
Philippines or somewhere andthey produce software.
But how does that relate to me?
Speaker 2 (04:49):
Well, I think they're increasingly in SF and not in
the Philippines.
But, yeah, well, the question ishow can you ensure that you
create parallels between yourstructures and your work and the
way these teams are set up?
Because if something works forthem, the question is how can I
set up my workspace and what Ido to make sure it also works
(05:12):
for me?
So one basic example I gave tothose ladies yesterday is look,
in software engineering, it'sbest practice that in every
folder I have a little textdocument that describes what
this folder is about and whatdocuments you can find in this
folder.
If you have massive folderswith many documents in your
organization, it would be agreat idea to add a little text
(05:33):
document to each folder thatsays hey, this is what you can
find in this folder, and itmight be helpful to you if
you're trying to do X, y, zright.
So, really starting to addthese little snippets of
infrastructure.
That again would make it easierfor a summer intern to find
their way around the project aswell, because the same thing is
going to work for an AI agent,and AI nowadays might be able to
(05:54):
navigate your messy emails andfolder structures even without
these little helpers, but it'sjust a question of whether you
wanted to spend the effort onnavigating all your file
structure or whether you want toput it in a place where it can
help you get your best work done.
Speaker 1 (06:11):
Now this sounds a little bit incremental.
I mean I would expect more forit.
But let's be it Again.
Take marketing leader, r&dleader, controller.
Let's apply your wisdom tothese functions.
Speaker 2 (06:28):
Yeah, so one thing I do myself and which I think, for
example, in marketing, would bevery helpful, is start to think
about how can you put more ofyour thoughts and your ideas and
vision and so on on paper.
Because one problem you're goingto have when working with any
new coworker and this applies toAI just as much as it would to
(06:49):
a human is they come in and youhave all this history of things
you've tried that didn't work,and experiments and ideas and
graphic layouts in mind and soon, and you've never really
sketched them out.
Or maybe you sketched them outon a piece of paper, but it's
(07:10):
lying somewhere at home and yourcoworker will never see it
right.
And so how can you put that onpaper so that, on day one, your
coworker which in the future,very well might be your own AI
assistant right is able to findall these things right?
So put your thoughts andlearnings and so on on paper,
take a note.
So I know a lot of peoplewho've been very successful at
making AI work for themselveshave had a great digital diary
(07:31):
the last few years and they wereable to read in that diary and
AI knows them very well and canhelp them pursue their own
personal goals.
Speaker 1 (07:40):
It sounds like creating, you know, putting my
genius to paper and make itavailable to AI.
Speaker 2 (07:51):
To some extent right, at least your learnings.
So maybe it's not a personaldiary necessarily.
Maybe it's something like a jobdiary.
I already request all my teammembers to every day write down
two things they've done andthree things they've learned or
would do differently next time.
If you could do the same thingevery day in your job, then that
(08:11):
would be an amazing tool setfor someone else, like an AI, to
learn from right.
Speaker 1 (08:18):
So this sounds more I invest into the future.
Actually, it's taken meprobably more time.
I'm already working 60 hours.
Actually, I talked to a COO inthe Chinese automotive market.
He said, Stefan, if you canfind me a way, a strategy, a
leadership lesson that I canreduce from 100 to 60, that
would be great.
(08:38):
So you're asking me to do evenmore.
Speaker 2 (08:43):
Well, I will admit there might be a bit of upfront
investment here and there.
Well, I will admit there mightbe a bit of upfront investment
here and there, but at the sametime, I think if you start using
AI today, there are veryimmediate time gains that should
just make up for these sort ofthings, right, Whether it's
(09:09):
scanning documents more quicklygetting a quick overview of what
happened in a meeting doingmuch more rapid online research
drafting the for you,summarizing the emails for you,
right, you know we are familiarwith all these uh already.
Speaker 1 (09:17):
I think many work with co-pilot or chat, gpt or
whatever.
To summarize emails, right, oryou know faster.
Speaker 2 (09:24):
But what is it?
Speaker 1 (09:25):
you claimed it's like uh, you become more an
entrepreneur within theorganization, so how could ai
shape that and create arevolution?
Speaker 2 (09:41):
well.
Look at the end of the day,it's about building your own
team, and in the past, a wellperforming employee might have
started staffing interns orbuilding up people who can
support them, who learn fromthem, and in the future, that
will still be part of success,but it will also include
(10:05):
building up a little AI org thatworks for you right.
So, if you are that marketingleader, I think it would be a
great investment to sit down ona Friday afternoon or Saturday
morning or whatever and writedown.
These are all the things Icurrently spent my time on and I
think, very quickly you willfind things where it would be
(10:25):
great if you could, you know,start handing them to someone
else.
And the great opportunity withAI is that it's incredible at
learning from your notes and soon.
So my encouragement to everyonewould be just get started right
.
If you spend a lot of timereading and drafting emails, put
Copilot or a custom gpt orsomething in place today, and it
(10:49):
won't do the job perfect jobperfectly yet, but you will keep
giving it feedback and you willkeep iterating on instructions
and so on.
It'll come better and betterover time, and so, the same way
as you would build up a humanorg where you don't expect
everyone to come and be perfecton the first day.
You have to go about buildingup an AI org that works for you,
and what's really fascinatingto me is that people with humans
(11:11):
are entirely ready foronboarding someone new and
giving them six months todevelop, or something With AI,
if it doesn't do the jobperfectly on day two.
You're like well, what am Ipaying for?
Patrick?
Speaker 1 (11:23):
that's the normal case, everybody's patient you
talked about kind of you knowyou check your case.
Is it about, you know, beingfaster or higher quality?
Now I think we're pretty clearabout faster.
This is what you elaborated.
But it's much more interesting.
(11:47):
What you are provocativelysaying is like AI is going to
give you 10 times scale or evenmore.
So let's apply it again tomarketing, to R&D.
Maybe R&D would be a good, nicecase now.
So how could that tenfold orx-fold my quality or
(12:08):
breakthroughs?
Speaker 2 (12:11):
yeah, and look the reason I've been uh trying to
stay away from from your r&dexample a little bit is that r&d
functions are so different inmy experience that they're
incredibly hard to advise.
Speaker 1 (12:22):
I want to challenge you.
Speaker 2 (12:25):
That's great.
I I appreciate it.
So let me indulge in yourexample of R&D right, and I'm
totally with you.
The exciting thing about AI isthat you can 10x or even 100x if
you manage to build out theright freedom for yourself right
, and if you can work withleadership to give you that
freedom.
And I do genuinely believe thatone of the biggest
(12:47):
opportunities now in companiesis going to come from leaders
giving their teams theinfrastructure and the liberties
to experiment and push theenvelope much more than they
would have done in the pastabsolutely yeah.
So how can, how can I ensurethat my team feels comfortable
(13:08):
using these new AI tools andexperimenting with them and
trying something out and sendingit to me, even if it might only
be an 80% version?
Because the exciting thing withAI is that we're now extremely
good at creating 80% versionsvery quickly and most orgs just
aren't accommodating of 80%output.
(13:29):
And I think this is especiallysomething I felt when working
with German clients and Germanteams.
Speaker 1 (13:34):
Right, you mean Chinese?
Go with 75%.
Speaker 2 (13:43):
I think Chinese might be more similar to Germans than
many other countries.
I think Chinese might be moresimilar to Germans than many
other countries.
But so you know, how can wecreate an environment where an
80% starting point is acceptableand even encouraged, so that we
can move much faster and theexciting thing, right?
Let me just briefly finish onthis 100x point.
(14:05):
Right, the 100x speed willoften come at some trade-off,
and what's fascinating to me isthat there's this dual function
of a.
Can we create an environmentwhere we start accepting these
trade-offs?
Right, because we are no longerbogged down on one very
specific metric.
And b where do we reach thetipping point where a hundred x
(14:32):
speed gain and or quality gainin one area makes up for these
small disadvantages in otherareas?
Right, and I'm just going touse one more consulting example,
because that's that's where Ispend a lot of my time at the
end of the day.
Right, powerpoint, slides andpresentations.
I'm a big advocate that thereis going to be a big leap
forward in presentations and Ipersonally believe it will not
(14:54):
be PowerPoint for varioustechnical reasons, but naturally
there will be trade-offs.
Right, we now have AI toolsthat can start generating slides
100x faster than the best VCGor code, but you might not be
able to change the color of thefootnote as easily.
And is that the tipping pointright?
(15:14):
Is it enough to be 100x fasteroverall but to have this
limitation that you can't changethe font color or something, or
do we need to push further andenable some other feature?
And I think you're going tofind the same thing everywhere,
whether it's R&D marketing.
Speaker 1 (15:30):
I mean, your example is a little bit hingy, as you
know.
Changing the color of thefootnote may be important for
some people, but overall doesn'tgive an extra value.
Speaker 2 (15:45):
That's a hot take, Stefan.
Yeah, I know, I know extravalue.
Speaker 1 (15:53):
But if that's a hot take, stefan, yeah, yeah, I know
, I know I rest my case in inyour case, but again, as I'm,
you know, like kind of many ofmy clients on the automotive
market and now we're facing ingermany at vol, audi, porsche,
mercedes, bmw, everywhere, weneed to reduce our development
cycles, we need to take morerisks, because we can't squeeze
(16:15):
in what we've done before faster, just faster, or even leave our
test cycles and say, okay, aChinese car doesn't need to run
150,000 because it would neverrun 150.
It just goes.
You know, it's a bill forinfotainment, not for running
driving.
So there the you know thetrade-off having an 80 solution
(16:38):
with all the risks combined, howwould ai work in that case?
Speaker 2 (16:45):
I mean, look, I'm not an automotive expert, um, but
I've, uh, I've had the pleasureof being a passenger in some
chinese taxis abroad, where thedrivers claim that they're now
heading towards 200 000 milesand they're very happy with
their car.
So I don't want to talk downtheir, their abilities and I you
know.
I think that it's aroundcreating processes and
(17:07):
structures that support thisexperimentation and 80% work
right.
So, for example, I think areason that a lot of car
startups have been verysuccessful in recent years,
despite often launching with an80% product, is that they've
been able to implementstructures where they can
iterate on their product pastshipping date.
(17:29):
So if you have a test slide,it's going to come with all
sorts of software faults, if indoubt, in the very beginning
when a new model is released,but they can keep releasing
those updates that make the carbetter and they've created a
service model where it's veryconvenient for users to have
changes made to their car orimprovements made to their car,
(17:51):
and these sort of structures areprobably what you'll need to
accommodate the 80% and movefast and break things approach.
That is becoming more prevalentwith AI and maybe it's stacking
different AI solutions on topIn engineering.
You now have all theseengineers building AI solutions
(18:12):
very fast and then having botsthat go over it and keep
iterating past the initialdevelopment date.
Speaker 1 (18:22):
I see that point and I want to go further.
I want to go now.
What impact would that have?
Organ of an organization and onleadership?
As if we apply AI system, wetrain them taking notes every
Friday sitting, drafting andcreating a digital library.
(18:43):
This, of course, will make ourwork probably, over time, faster
.
As a, I will kind of bring outthe best of all things.
So you have a overtime of gaintimes of coming up with 80%
ideas, results whatever.
So the time gained looks likeyou can now apply to make things
(19:10):
even better or come up withanother solution, because part
of your time is not wasted onrepetitive or other coming up
with something any longer.
But how will we deal inorganizations with that
(19:32):
trade-off, with that risk, withthat?
You say liberty in leadership.
You know now, if you allow thatkind of 80%, you know, to a
German automotive company, thatis hilarious, that's insanity.
(19:52):
So what do you see?
How can we handle that kind?
What does it take?
What is your idea?
Speaker 2 (20:04):
So, look, almost everyone I've worked with in a
role that has been faced withmore AI support or interaction
has shifted their focus andtheir activities from task
execution to A task planning andstructuring and B task
(20:25):
reviewing, right.
And so, as a leader, thequestion is how do you support a
team and build the incentivestructure where people are
focused on structuring andreviewing, and that probably
takes a different structure, adifferent reward system.
I think you need to pass downownership in many ways, because
(20:48):
reviews are always going to behalf-hearted if you're not the
one responsible for the outcome.
So you need to see your teammembers less as contributors and
more as their own littlebusiness owners, right, maybe
even P&L owners, if we go backto the marketing example.
Really give everyone a piece ofvery specific but tangible
(21:16):
ownership and responsibilitythat they can hold on to so that
they can build their own littleAI work and business around
fulfilling that target andowning that little stake of the
business.
And so, as a leader, my biggestquestion would be how can I
(21:37):
start chunking up my business,not into KPIs and abstract
metrics, but into pieces ofownership that I can assign to
individuals and tell them dowhatever you think is the best
thing to do to get this not justdone, but to own this and what
would be pieces of ownership?
(21:59):
like comparing to kpis yeah, andso I think, for example, if we
go back to the marketing example, how can you branch out from
where someone has an abstractKPI to optimize search engine
(22:21):
performance to optimizing salesthrough search, things like that
?
And then, if I give them thatpiece of ownership sales through
search I start to move theminto a P&L position where they
have profit and they can investinto AI tools or whatever else
they need.
(22:41):
But they now start to own theirown little P&L.
Speaker 1 (22:44):
So you would chunk them up, you would come with the
overall target, rather thangive them, you know, pieces,
elements yeah, and and I thinkit's it's about giving them
pieces of a bigger target,giving them stakes in that
target.
Speaker 2 (23:02):
Because, as we established, right, making this
ai org work for you takesinvestment, and people can only
make good investments if theyhave some sort of return
function as well.
I mean, um, so I I hate it whenI see organizations where
people are given specificbudgets for ai investments or
(23:22):
something that doesn't makesense.
Right, the goal is here's whatyou need to do and this is how
much money it's going to makefor the organization or whatever
your metric is, and then, basedon that, you can invest, and
obviously you shouldn't investmore than whatever you think you
can bring in with yourimprovements now, patrick, I'm
gonna put you down on this one.
Speaker 1 (23:41):
Um, how does ai work for bcg?
If you're allowed to talk aboutit, but maybe you can talk
about how does it change yourlife within BCG and your
teamwork?
Speaker 2 (23:51):
so we get a sense and a feel from real daily practice
well, look, I'm very lucky andproud to be at the forefront of
much of the AI work we do, sofull disclaimer.
My experience isn't yetrepresentative for the broader
company or let alone industry,but for myself it has totally
(24:13):
upended my work.
Yesterday I held a keynotewhich was entirely ai generated.
Instead of creating the slidedeck page by page, I sat down
for 20 minutes.
I wrote down everything Iwanted to say and the points I
wanted to make in a presentation, and then I handed it over to
an AI tool and said now buildthe slides with these talking
(24:34):
points, and I did zeroiterations.
I took the slides and went topresent.
This is not yet your everydayBCG and it's going to be a while
.
Maybe it will never happen formany of our projects, right?
Speaker 1 (24:47):
MDPs don't listen.
Speaker 2 (24:49):
Right, but this is the case study for myself as to
what it could be like, at leastfor very specific types of work
and for your consulting work asa team leader.
Speaker 1 (25:04):
how does the daily setup look different now with
the pieces of ownership and soon?
Speaker 2 (25:12):
Indeed this shift that I mentioned from execution
to structuring and review.
This has already happened inmany of our teams, I dare say I
think for a long time, thebiggest challenge for project
leaders was all of a sudden, myteam is generating things and
(25:34):
having AI do research.
How do I get them to review theoutcomes resulted in the
follow-on responsibilities of,for example, presenting that
work or working with the team toimplement that work that you
come up with being passed downto more junior team members.
(25:55):
So that's something I'm a bigfan of and I think it's an exact
example of this ownership shift.
Right, because as a projectleader, it's becoming harder for
me to review some of the workthe team is doing.
So I tell them look, guys, Iyou know I'm happy to give
advice and provide the overallstructure and so on, but if
you're doing a lot of ai workhere, then you need to be
(26:17):
comfortable and present this andand I think it's your role
entirely.
Speaker 1 (26:24):
Uh, you know, first you give ownership, you give to
liberty, you need to give spaceand you need to become a, a real
great um, a person who iscapable of finding the next
question, of finding it.
Could this be true of reviewing, of asking good questions to
(26:49):
your juniors?
So how does that work?
Speaker 2 (26:54):
Absolutely.
And look, I think it would befair to say everyone has to
become a bit more of a coach.
Speaker 1 (27:03):
Yeah, I love this one .
I train coaches.
Speaker 2 (27:05):
There you go.
And so yeah, absolutely.
I personally believe that thereis an exciting opportunity to
shift responsibility downwardsand to have people own things
earlier and more thoroughly.
And then your responsibility asa leader starts to become A how
(27:28):
do I help them get used to thisresponsibility that people face
early on?
And b you know how do I act ifthings go wrong?
Because things will go wrong.
Right, we talked about the 80approach.
Ecg is not a fan of the 80approach, as you can, as you can
imagine, right we're definitelya 100% company.
That is our value proposition,and so if we are a 100% company
(27:53):
and we have tools that are beingused by people, whether we want
or not an associate is going touse ShedGPT, there's no point
trying to stop them, and werealized that early on, luckily
and if we have people in our orgthat are going to use tools
that produce 80% outputs, butour target is to have 100%
service, then how do wedistribute the responsibility
(28:17):
and the tasks needed to take the80% that goes in somewhere and
convert it into the 100% that weneed to bring to our clients?
Speaker 1 (28:27):
Absolutely, so you gain time.
Obviously you know people arefaster in producing first time
results 80% results.
So you have some time to review, to challenge, to whatever.
So how does work?
How is it in your daily workLike how?
Speaker 2 (28:46):
You know it's a lot about deep reviews, discussing,
digging deeper.
I think it's an opportunity todive deeper into topics than you
ever would have before.
That's entirely true inconsulting and outside
consulting and ensuring that youbecome an upscaling a quality
(29:11):
upscaling function right andthat you again build structures
that work for you.
Speaker 1 (29:21):
Okay, let's check out what I learned about it.
We have new opportunities.
We have the choice between oractually both maybe to become
faster and to have a leveragefor more quality.
(29:42):
But our role as leaders is goingto change and it's going to
help the company actually becomenot just faster with 80%
solutions, but using the timegained to build up a digital
library that makes you betterover time, makes your team
(30:03):
better, and your role is more ofgiving space and of allowing
your team members to dwelldeeper, to ask more provocative
questions, even to turn thingsupside down, and this is going
(30:25):
to create value, as youmentioned, because in the end
not just in consulting, but Icome back to my R&D If I can
come up with something thatdrives mobility in a smarter way
, this is value creation.
So I see what I learned is it'sa coexistence between AI and
(30:49):
myself as a leader, but also myjuniors.
What I learned is not aboutfirst an 80% version and be
(31:12):
creative with it and not judgeit, not blame it, but allow the
team the time to take ownership,give them greater pieces of
ownership that will create valuealready and not just pieces of
KPIs.
Did I miss anything?
Speaker 2 (31:31):
I think those are all great points.
The last one I would love toadd is my favorite talking point
about leverage, and I'm sureyou're waiting for it already,
because I can never end an AIconversation without mentioning
leverage.
To me, it's about taking whoyou are, who you want to be, and
leveraging all the resourcesyou have to get to your own
(31:53):
goals and ideals faster.
In the past, you would haveperhaps raised money to get the
job done, hire more people, buya more expensive caterpillar or
whatever it is.
Today, you have a wealth ofabundant intelligence and it is
up to you to leverage thatintelligence and build not on
(32:14):
capital, but build on smarts,ideas and the very fabric of
innovation, if you will padre.
Speaker 1 (32:23):
it's uh very inspiring for today and I take
we have to talk maybe in oneyear's time and check out.
Speaker 2 (32:34):
At the rate of AI, we can talk every other month.
Speaker 1 (32:38):
We make a series.
You know the AI exploding and Iwant to hear and see more
examples, real daily examples,so people can figure out on how
to apply that stuff to their ownbusiness.
But for now, thank you for yourtime, thank you for your
(32:59):
experience.
Thank, you so much, stefan, andlet's keep on rocking.
Speaker 2 (33:04):
My pleasure.
Thanks for having me have agreat day.
Speaker 1 (33:07):
Bye, bye.
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