November 14, 2025 • 29 mins
AI agents are reshaping how enterprises innovate, organize work, and experience disruption.
In the latest episode of “What’s the BUZZ?”, Andreas Welsch speaks with Christian Muehlroth, CEO of ITONICS, about how agentic AI will redefine innovation management and why many organizations remain structurally unprepared for it.
Here are the key insights from the conversation:
- AI’s foundations were created decades ago, but recent advances in computing, interfaces, and delivery models have turned it into a scalable innovation engine.
- Each technological wave builds on prior ones, accelerating change while large organizations slow down due to processes, politics, and legacy structures.
- Agents function as tireless digital interns with expert-level capabilities in narrow domains, amplifying the output of teams that already demonstrate initiative and creativity.
- Many place AI on top of legacy processes or rely too heavily on public LLMs, resulting in misaligned outputs and “AI tourism” instead of measurable impact.
- Clean enterprise data, secure deployment setups, and redesigned processes are essential to making agentic AI operational and strategically valuable.
Key takeaways:
- Focus on real-value use cases: innovation begins with a business problem, not with experimenting for its own sake.
- Prioritize structural readiness: clean data, redesigned workflows, and enterprise-grade AI infrastructure determine whether agentic systems deliver results.
- Empower motivated teams: the highest return comes from equipping individuals who seek change with advanced tools that amplify their capacity, rather than attempting blanket adoption across the organization.
Leaders need to take disruption seriously, double down on strategic intelligence, empower the people who want change, and invest in data and platform foundations before scaling agents.
Is Agentic AI already disrupting businesses (or can we just not see it yet)?
Questions or suggestions? Send me a Text Message.
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Disclaimer: Views are the participants’ own and do not represent those of any participant’s past, present, or future employers. Participation in this event is independent of any potential business relationship (past, present, or future) between the participants or between their employers.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Andreas Welsch (00:30):
Today we'll talk about how agents are disrupting
business, and who better to talkabout it than someone who's
actively working on innovationmanagement and disruptive
innovation.
Christian Muehlroth.
Christian, thank you so much forjoining.
Christian Muehlroth (00:43):
Thanks.
It's a pleasure.
After seeing you in person acouple of weeks ago now, also
being on this digital formatthanks for having me.
It's great.
Andreas Welsch (00:50):
Wonderful.
it's been a while since we've orsince we've crossed paths
digitally.
I think it's almost two yearsago that you invited me to your
show.
So it was great seeing you inperson a couple of weeks ago and
now having you here.
But not everybody might know youalready, and if you don't know
Christian yet, you shoulddefinitely give him a follow on
(01:10):
LinkedIn.
But why don't you tell us alittle bit about yourself, who
you are and what you do.
Christian Muehlroth (01:15):
Yeah, sure.
I keep it short.
I'm Chris.
I'm the CEO of ITONICS.
We are a Software as a Servicesprovider.
We build software basically.
And what we build is a platformfor enterprise innovation and
technology management.
Which means we help companiesactually make use of new
technologies and createinventions and innovations and
(01:36):
bring them to market toaccelerate growth to be more
resilient in the time ofdisruption.
And that we do with a softwarefirst approach.
And that's why we also talkabout agents today.
Andreas Welsch (01:47):
Exactly right.
There's so much talk aboutinnovation in general.
But very few are doing it well.
So I'm sure you're getting afront row seat at what's working
and what's not working.
So I'm really excited to havethis conversation with you.
In good old fashion, why don'twe play a little game to kick
things off?
Let's see.
Sure.
(02:08):
Fantastic.
When I hit the buzzer, thewheels will start spinning and
when they stop, you'll see aquestion.
I would love for you to answerwith the first thing that comes
to mind and why, in your ownwords.
Okay.
Okay.
Are you ready for What's theBUZZ?
Let's do it.
Okay, here we go.
(02:30):
If AI were a color, what wouldit be?
60 seconds on the clock to makea little interesting.
Here you go.
Christian Muehlroth (02:38):
I say if AI were a color, it would probably
be white.
The reason is because whitecontains all colors.
So I think that resonates wellwith AI, which is more, in my
opinion, a general purposetechnology.
That probably can take on anytask in the digital, but also
(03:01):
maybe soon in the physicalrealm.
But also it needs something tochannel through like, a prism
needs a prism to be useful.
Data ontology policies.
Agency tasks, intention, ideas,whatever.
Yeah.
Unfocused, it's white.
Yeah.
It's a clear, yeah.
But then focus through laserfocus.
It becomes a color and then alsouseful in practice.
Andreas Welsch (03:24):
I love it.
What an awesome answer.
And what a great segue to talkabout innovation too, right?
You really need to channel itthrough it.
Something almost like acatalyst.
Otherwise it's just there, butit doesn't do anything.
It doesn't do anythingmeaningful for you.
Beautiful.
Just like that, right?
(03:44):
Innovation also isn't somethingthat just magically happens and
also doesn't happen momentarily.
How does innovation typicallyoccur and, what comes together
now in this wave of AI agents?
Christian Muehlroth (04:00):
Yeah, it's so interesting because if you
think of the major innovationsfirst of all, probably it's
great to distinguish betweeninvention and innovation.
I know it's been done inliterature and everywhere else,
but I just think it'sinteresting.
And important to do itspecifically in the age of AI
because AI was not invented inthe recent months or years.
(04:24):
The foundation, specifically thephysics, but also the machine
learning algorithms of themathematic and the statistics
and the algorithms they're basedin, decades ago in, in
groundwork that was being donedecades ago.
But so that was the invention.
But the innovation now came inthe recent years, which made
(04:45):
things much more useful.
That's because of the processingpower for sure, but that's also
because of the electricity needsof the data centers, the scale,
the user interface and so on.
So these innovations, sure, alsoin algorithmic, but also in
delivery of the AI actually madeit much more useful in the
recent years.
And that, that's still a littlebit so sometimes confused in
(05:07):
practice the difference betweeninvention and innovation.
And I think right nowspecifically with AI, we see it
very clearly.
What's an invention and what isactual an innovation that's
useful to the market?
Andreas Welsch (05:19):
Yeah, so when we met a couple of weeks ago, it
was part of a panel discussionand you mentioned some parts of
innovation management.
And in innovation theory, westarted talking about these
waves, these
innovation waves or KondratievWaves based on the
Soviet economist.
(05:41):
And I don't hear a lot of peopletalking about this, by the way.
It's also in the book becausewhen I took innovation
management at university, I wasquite excited to learn about it.
But maybe you can share a littlebit about what's up with
Kondratiev Waves and how theynow fit into this innovation
cycle.
Christian Muehlroth (05:59):
Yeah.
I think it's so interesting, andI love that you have it in your
book.
I'll totally recommend the book,by the way.
It's, really great.
And the, pure fact that youalready have conative waves in
there I know.
Makes it a great book.
Yeah.
And I think it's interestingbecause ev the, theory is that
everything.
Really everything in life, notonly technology, but everything
(06:21):
goes in waves.
Yeah.
Things go up, things go downthen, they go sideways for some
time, but ultimately they go inwaves.
And that's if you look back inhistory and the history of
technology and then alsosociety, that actually has been
the case.
You always know in retrospectwhat these waves are.
(06:42):
For example the steam economy,electricity, then the internet,
these are all considered bigwaves, big technological waves
of change typically.
And then after technology,typically big societal and also
work transformation and businesstransformation follows.
(07:03):
But of course it takes sometime.
And by the way, many of theseactually stem from military and
defense applications like theinternet.
So it's, pretty interesting toalso see what those folks are up
to.
But that's the theory and theinteresting thing about it that
I think time is gettingcompressed right now.
(07:25):
So the length.
Of each wave, maybe back in the18 hundreds, something, it was
recognized to be about 60 years,70 years maybe.
But these waves are gettingshorter and the reason is pretty
obvious is because if you canstack the technologies on top of
each other, so the previous waveor the previous waves even
(07:46):
support.
The next one we need electricityfor the internet.
We need the internet now forscaling AI and so on.
So they built upon each otherand that's why we feel that
everything is accelerating thesedays.
Andreas Welsch (08:00):
To me that is fascinating, right?
Especially the layering ofthings.
And, if you've been in theindustry for a while, you have
seen this, right?
Whether it was your dial upmodem and cable now on your
phone and whatnot and, all theservices that that enables.
Now, to your point, add data,add AI on top and, whatever is
next.
So it also, it feels like thingsare moving indeed faster, right?
(08:23):
We've been talking about thisfor, a long time and you observe
this probably in your own livestoo, but it's sometimes getting
to the point where it's not onlyoverwhelming, but where you feel
disrupted and maybe where yourbusiness feels disrupted.
And what are you seeing when youwork with companies who are
looking at innovation managementand, who are, for example,
(08:44):
looking at software to managethat process.
Why do companies fear this, kindof disruption?
And what do you think now withagents, this disruption really
drives?
Christian Muehlroth (08:55):
Yeah it's, very interesting because you
have if, you consider this ontwo axes, right?
So you have the acceleratingtechnological change.
That's very true.
And every, as you say, I 1%agree, everybody feels it.
Everybody can observe it rightnow.
But there is also second aspectto it which is that specifically
(09:16):
large enterprises and bigcorporations, they know very
well that the larger they get,the slower they actually get,
and that's a problem.
Because then you have basicallya, a gap between the
accelerating technologic change.
Things get faster and fasterOver the years, we've
specifically seen it with thefoundation models over and over
(09:40):
again improving at a, impressiverate.
But then at the same time, wesee organizations always running
behind because they haveprocesses, they have politics,
they have, some policy stuff andwhich basically leads them to be
slower in the adoption rate.
(10:00):
So the bigger they get, the moreprocesses, the more politics,
the more policies they have,which means ultimately that they
actually slowed down.
So you have an exponentialacceleration in tech.
You have actually an algorithmicrate of change of those large
organizations.
They're getting slower andslower.
So that means that the risk ofdisruption is pretty real
(10:21):
because it's even for, thoseorganizations, it's even a super
exponential, it's more than anexponential rate of change
because they get slower andthat's where the risk is.
Andreas Welsch (10:31):
So we've seen these examples time and again,
right?
Kodak who dismissed digitalcameras and we believe people
still want to take pictures inanalog and film and things.
We've seen Blockbuster whodidn't see Netflix coming.
We had who, Nokia, who didn'tsee the iPhone coming.
(10:53):
All of these.
Are those the kind of examplesthat we should be looking for in
our own organizations, or arethere other examples?
For me it's not quite thatobvious.
And by the way, it's onlyobvious in hindsight what they,
yeah.
Christian Muehlroth (11:08):
And that's the main point.
It's always, unfortunately, it'salways obvious in hindsight.
But on the other hand, I'msurprised that we, as with me
the collective of organizationsthat are on the market.
we we don't, learn too much fromhistory because there is tools
(11:30):
and methodologies and ways forexample, there is corporate
foresight.
And I'm, not, saying thatorganizations should engage in
year long scenario creationtechniques because that's
probably an outdated way exceptfor your public agency.
But specifically privatecompanies who cannot look
forward right now, 3, 5, 7years.
It's, rare.
(11:50):
To be able to do that.
But there is systems,algorithms, and ways how to
understand signals of contin ofchange.
How you capture them, how youinterpret them, how you bring
them in the organization, andhow you also use them for
decision making.
And I'm, always surprisedbecause in, in, specifically.
(12:13):
Turbulent econom e economictimes, which we in 2025 might
have right now, depending on theregion where we're in right now.
But then specifically companiesdeinvest in those capabilities
and they go back and focus onthe core business.
They actually should be theexact opposite thing.
I know it's hard in these times.
But specifically in those times,you should watch out for the
(12:34):
next thing, for the next moveand have a very rigid strategic
intelligence.
But that's not what they do.
So they deinvest in stages whereuncertainty is the highest.
Where actually in those stagesthey should invest a lot in
exploring potential futurepathways.
Andreas Welsch (12:49):
So how does that then play out with Agentic AI
and I, feel we're still early bythe way.
I saw Gartner's generative AIhype cycle and generative AI
moving into the trap ofdisillusionment.
Great.
That means.
Relief is near.
People are figuring out whatthis can really do beyond shiny
objects and throw away proofs ofconcept.
(13:12):
But with agen AI, do you thinkthis is really going to be that
big of a disruption as some ofthe industry players and
marketing departments are hypingit up to be?
Christian Muehlroth (13:22):
Yeah, I think there is two, two things.
One is the agentic AI is goingto move us closer to an
abundance economy because of thecollapsing cost that's behind
it.
So right now agents are ki likeinterns that never sleep.
(13:44):
They can be super smart like PhDlevel in certain domains in
general they consider them asinterns because they need
oversight.
But because the, labor costtrends towards zero or whatever,
you pay to these AI specificallyfor some bridged tasks or some,
mundane tasks.
(14:06):
You don't have a headcountconstraint anymore.
You actually have a throughputconstraint.
And sometimes maybe even a lackof ideas.
Okay, what should we do next?
Very practical.
For example, in.
You can do much faster.
Market scan preparations evencoding of course.
(14:31):
These models are so great atproducing software code.
Yes.
They might not know all yourcode base yet, but it's
brilliant.
Yeah.
So that's in the dig digitalspace and also in the physical
space.
When we have less boundaries onland.
You can create, I don't know,maybe even cheaper physical
production, higher buildingsfaster production of facilities
(14:52):
or whatsoever if you now do thiswith robots or machines,
whatever this is.
So that's the first thing.
It moves us through toabundance, specifically for
labor.
And that is in combination withsome, something that we call.
Amplified intelligence or AI isamplified intelligence because
(15:13):
humans still set the intent.
We are, we still have theagency.
But the AI agents, they expandyour capacity, sometimes even
your capability to execute stuffspeed, precision and maybe even
latency.
So two things are interestingbecause they amplify human
intention, and if you're reallygood at something, you can use
(15:35):
AI to really 10 XA hundred x.
What you're doing.
And at the same time, it givesus specifically for mundane
tasks, basically abundance andlabor.
And that, that's reallydisruptive.
Andreas Welsch (15:47):
I've been thinking about this quite a bit
and that's a lot of theconversation right now.
Also if you look at media andwhat CEOs are publicly saying,
especially those that areleading or want to be perceived
as leading.
A lot of this conversation isabout we are reducing the number
of people.
We're not hiring people becausewe want to look at AI is this
(16:11):
the right, and the only way tolook at this or, why is nobody
saying it saying about we'regiving you agents so you can 10x
or 100x what you're doing.
What's your perspective there?
Christian Muehlroth (16:21):
I think you're spot on.
You're really spot on becausethe truth is probably both.
Yeah.
The truth on one hand is if youconsider AI and agents as a way
to automate specific.
Labor specific tasks then yesthere will be people being laid
(16:42):
off.
We, already see it specifically.
For the tasks where a text-basedAI we, currently just think
about lms.
There are different ways ofartificial intelligence, but
right now we focus a lot oftext-based AI, so large language
models and the like.
And there is just so much youcan automate with this sort of
(17:20):
intelligence.
So it's the truth.
But at the same time everythingthat requires human agency and
also human creativity, humaningenuity.
And these things will remain andprobably the people who already
are pretty creative, pretty havea great level of ingenuity
agency.
(17:41):
They now have tremendousopportunity to make a career, to
make a business and to bringthis to life.
So it's, both sides.
Andreas Welsch (17:51):
So, then where do organizations fall short?
We know they should embrace thisover short or long, they will
eventually look at this even ifit takes a couple years like
we've seen with cloud or withmobile and other trends.
But where are they failing inadopting this AI right now?
And what's your recommendation?
(18:11):
What are you seeing?
Christian Muehlroth (18:13):
Yeah so, we are seeing, first of all, that
there is no redesign of legacyprocesses.
So that means you just takeinefficient processes and you
put AI on top of it.
But if you, I don't know, if youautomate a shitty process, you
get an automate shitty process.
Basically.
Yeah.
(18:33):
So that, that's that, that'snot.
So you need to design yourprocesses to work in this new
area.
And if they really don't makesense then, you shouldn't be
automating it.
For example the, in, in Germanywe are digitizing our public
processes to a large extentdispute.
Trying since a couple of years.
So what we do is basically we,had all these forms that you
(18:56):
have to hand into some publicagency to get some stuff and you
have to write physically writeon those forms.
What we did is when we digitizedthis process, so we just put the
forms on the screen.
But we never we, never thinkabout does it actually make
sense to have a form?
For example, if you, I don'tknow if you if, you have a
newborn, child.
(19:17):
Yeah.
You still have to hand in aspecific a specific document to
request some things from, thegovernment.
But the government already knowsanyways that you have a child
because there are otherprocesses registering your
child.
So why should they actually haveto fill the form?
Why couldn't the government justdo this automatically as they
(19:37):
know I have not your child.
So we, just took the sameprocess.
Digitized it and made them formon the screen.
And if organizations do the samenow with AI, have legacy
processes and just try toautomate them, it's probably not
a good idea.
So it's very expensive.
Yeah.
If you do this that's one of thethings.
(19:58):
So just reconsider what makesactually sense and it's a great
opportunity right now to do it.
And the leading organizations doit.
Andreas Welsch (20:05):
Yeah.
But it costs money and it takestime, and we don't know if it
works.
And I still want a paycheck in abonus and a raise.
Yeah.
Yes, innovation, but please, nottoo much and not too much at the
same time.
Is that something you encounteror you perceive?
Christian Muehlroth (20:24):
Yeah.
It's, the truth.
100% because.
It's comfortable.
Yeah.
Specifically, being in anorganization may, maybe you're
in there for 10, 10, 15, 20years.
It you have a comfortable job.
Why change?
If there isn't pressure?
Unfortunately, and there can bepositive and negative pressure
(20:44):
for sure.
So it, it goes both ways.
But if there is a certaintendency and an aversion to
change.
But there is also people whoreally want it.
We, again, we see both in, inbig corporate, we see people
that say, ah go away with thisstuff, or maybe I can set it out
or wait it out, or, I don'tknow, just see what happens.
(21:05):
We see this a lot.
But to be, to be honest, we alsosee the opposite.
We see great people who haveagency, who have ideas, who
wanna push forward, who alsowanna make a career.
And for them that's a perfecttime to grab these opportunities
and really deliver impact inthose organizations.
Andreas Welsch (21:24):
So you have super progressive and excited
and tech savvy.
Folks on your team or in yourorganization, they want to try
this out.
They say, yes, I want change.
We need to do this differently.
And then you have the other sideof the spectrum who say please
leave me alone.
I just want to collect thepaycheck and be here my nine to
five, and then have a lifeoutside of work.
(21:46):
And by the way I'm retiringsoon, or Yeah, to your point,
and I like change.
How do you bring these twotogether?
And is that maybe even thesolution to making progress but
still taking everybody along onthis journey?
Christian Muehlroth (22:01):
Yeah it's a great question and I think to
some extent the decision makerswho actually decide which way to
go, they just need to be honestwith themselves and with the
organization.
I think it is a very noble goalto say, let's.
Get as many people as we can onboard.
I think it's probably the rightway to do.
But that's theory.
(22:22):
And, then there is practice andyou, always have people who just
do not want for specificreasons.
Maybe some can, some don't wantit doesn't really matter.
But that's just normal.
And I think while this is maybea ground truth, specifically
senior executives and decisionmakers, they should.
(22:43):
Then rather go with an 80%solution and say, we need to
move on.
This is the path.
Everybody who wants gets onboard and the others.
We need to see what we shouldcan do for them and how we can
help them basically get along.
But, as much inclusion as, isneeded.
Perfect.
Do it.
But you also need to think interms of the enterprise.
(23:04):
And as we discussed in thebeginning, we have this
disruption risk and it'simminent.
It's really here.
So maybe go with the 80%solution and do whatever it
takes.
Andreas Welsch (23:16):
See, I'm wondering if, some of that is
also more culturally relevant.
I know Germany has a verydemocratic process in companies
and we should talk to manypeople and make sure everybody's
heard and included and so on.
And to your earlier point, Ithink that can a lot of times
slow your business down as well.
Yeah.
I feel over here in the US, itmight not be that long of a
(23:39):
deliberation.
We see the opportunity, we seethe potential.
We also see the pressure.
What happens if we don't do thisif we don't adopt this?
And I get the sense people aremoving, businesses are moving
relatively fast on many of thesetopics.
Certainly not all.
But maybe culture plays in intothat too, I don't know.
Christian Muehlroth (23:58):
Yeah, for sure.
There is times where a moredemocratic approach might work a
little bit better and there arejust times where a little bit of
more straightforward approachmight work better.
It's like a pendulum thatswings, it depends on the
situation of the industry or thecompany of the enterprise.
And for both, have a reason toexist.
But you just need to know whento deploy deploy, which approach
(24:20):
and at which time.
Andreas Welsch (24:21):
Yeah.
Good, point.
Yes.
So definitely your leadershipskills in figuring out when do I
invite a dialogue?
When is this a decision thatmove forward with or to your
point, an 80% solution thathelps us move forward.
So before we wrap up, I'mcurious here, what do you
(24:42):
recommend leaders do in additionto being sensible?
What approach they should followwhen it comes to leadership, but
what should leaders do as theywant to arrive this wave of
disruption?
And instead of being disrupted,what's the one thing they need
to do right now?
Christian Muehlroth (24:59):
Yeah, so the one thing that is often
overlooked and that leads to AITourism typically is as boring
as it may sound.
Yeah.
Laying the platform and the datafoundation for it because Mo,
most of the AI applicationswe've seen, they all are based
on dirty data which means wrongdata sources.
(25:24):
Just an example we've seen manycompanies try and use an
experiment with public largelanguage models.
For any sort of innovation orresearch and development tasks.
But there's a couple of problemswith it.
For example, public LMScurrently lean a lot towards
open web sources like Wikipedia,R and, Reddit.
(25:45):
There is some statistics thatkind of try to prove it, but
for, let's say a concise r and dstrategy, our new product
development strategy need cleanenterprise.
Data, that's customer insights,that's patents your own patents
to see where you havetechnological capability, but
(26:06):
then also competitorinformation.
You need to take your current rand d pipeline into account.
You need to take your currentproduct and services offerings
into account, and so on.
Otherwise you lean too much intothe public LLM data.
So we are actually in a veryclean enterprise pool to make
this usable.
Maybe even a shared taxonomythat's, for example, what we
(26:27):
enable in atix.
So we, we make sure, so we,don't use public lms.
We have a different approach.
We didn't train our own, but wedeploy one specifically in our
infrastructure.
Data never leaves cynics, itdoesn't go to open eye, it
doesn't go to tropic, it doesn'tgo to complexity.
It's all within the existingdata privacy contract you
already have with Atix in thatcase.
So it's much more safe andsecure and we can prove that.
(26:49):
So that's the thing later.
Foundations specifically withthe data, otherwise you have
journey data and you have justopen web source data, which is
not really useful in, in thespecific r and d and innovation
context.
Andreas Welsch (27:01):
Wonderful.
Thank you for sharing.
Chris, we're getting close tothe end of the show and I was
wondering if you can summarizethe key three takeaways for our
audience today.
Christian Muehlroth (27:10):
Yeah, sure.
Let's go to the beginning andlet's, so the first one is the
abundance AI, the abundanceeconomy.
So because AI means amplifiedintelligence, I would look.
Specifically towards use casesand empowering people that want
the change and amplify theirhuman intelligence with AI,
(27:33):
amplify their creativity, givethem the most expensive tools.
Don't I, understand the approachof a.
Rolling out AI to everybodybecause you want to leverage or
leverage everybody and raise thebar for everyone.
Okay?
But that's a lot ofexperimentation and leads to AI
tourism because everybody triesout some things actually wasted
(27:53):
potential and wasted time andmoney.
So my first suggestion,recommendation would be.
Think of AI as amplifiedintelligence and provide the
tools to the people who reallywant the change and give them
the best tools, the mostexpensive ones, because that's
10 Xing a hundred Xing,sometimes their capabilities.
And I think this is, one key,takeaway.
(28:16):
The second thing is.
Take disruption seriouslybecause we've seen it a lot.
You've mentioned so manyexamples.
Yeah.
We're tired of the Kodak and,Nokia examples.
Yeah.
'cause they've been decades ago.
But I'm pretty sure we'll seemany of those examples in the,
future.
Don't undervalue and don'toverstate the two big to fail
(28:38):
Disruptions really is reallyimminent right now.
Andreas Welsch (28:41):
And make sure that your company doesn't become
the next Kodak or Blockbusterthat will tell for generations.
Christian Muehlroth (28:47):
Yeah you, don't wanna be on that list,
really.
You just don't want, yeah.
Yeah.
And then the third thing is getthe foundations and the data.
It is, it takes some time.
But it's not a hard, it's not ahard thing to do, but it's an
important thing to do.
And if you are doing it and ifyou have really clean data
(29:07):
specifically and everything rand d and innovation and over
time you stack more and moretechnology use cases on top of
it and add AI and agents to it,this is maybe one of the most
competitive advantages you canget to make most out of your
data in the next years to come.
Andreas Welsch (29:25):
Chris, thank you so much for joining and for
sharing your expertise with us.
It's always a pleasure talkingto you and thanks much for the
invitation.
Today we'll talk about how agents are disrupting
business, and who better to talkabout it than someone who's
actively working on innovationmanagement and disruptive
innovation.
Christian Muehlroth.
Christian, thank you so much forjoining.
Christian Muehlroth (00:43):
Thanks.
It's a pleasure.
After seeing you in person acouple of weeks ago now, also
being on this digital formatthanks for having me.
It's great.
Andreas Welsch (00:50):
Wonderful.
it's been a while since we've orsince we've crossed paths
digitally.
I think it's almost two yearsago that you invited me to your
show.
So it was great seeing you inperson a couple of weeks ago and
now having you here.
But not everybody might know youalready, and if you don't know
Christian yet, you shoulddefinitely give him a follow on
(01:10):
LinkedIn.
But why don't you tell us alittle bit about yourself, who
you are and what you do.
Christian Muehlroth (01:15):
Yeah, sure.
I keep it short.
I'm Chris.
I'm the CEO of ITONICS.
We are a Software as a Servicesprovider.
We build software basically.
And what we build is a platformfor enterprise innovation and
technology management.
Which means we help companiesactually make use of new
technologies and createinventions and innovations and
(01:36):
bring them to market toaccelerate growth to be more
resilient in the time ofdisruption.
And that we do with a softwarefirst approach.
And that's why we also talkabout agents today.
Andreas Welsch (01:47):
Exactly right.
There's so much talk aboutinnovation in general.
But very few are doing it well.
So I'm sure you're getting afront row seat at what's working
and what's not working.
So I'm really excited to havethis conversation with you.
In good old fashion, why don'twe play a little game to kick
things off?
Let's see.
Sure.
(02:08):
Fantastic.
When I hit the buzzer, thewheels will start spinning and
when they stop, you'll see aquestion.
I would love for you to answerwith the first thing that comes
to mind and why, in your ownwords.
Okay.
Okay.
Are you ready for What's theBUZZ?
Let's do it.
Okay, here we go.
(02:30):
If AI were a color, what wouldit be?
60 seconds on the clock to makea little interesting.
Here you go.
Christian Muehlroth (02:38):
I say if AI were a color, it would probably
be white.
The reason is because whitecontains all colors.
So I think that resonates wellwith AI, which is more, in my
opinion, a general purposetechnology.
That probably can take on anytask in the digital, but also
(03:01):
maybe soon in the physicalrealm.
But also it needs something tochannel through like, a prism
needs a prism to be useful.
Data ontology policies.
Agency tasks, intention, ideas,whatever.
Yeah.
Unfocused, it's white.
Yeah.
It's a clear, yeah.
But then focus through laserfocus.
It becomes a color and then alsouseful in practice.
Andreas Welsch (03:24):
I love it.
What an awesome answer.
And what a great segue to talkabout innovation too, right?
You really need to channel itthrough it.
Something almost like acatalyst.
Otherwise it's just there, butit doesn't do anything.
It doesn't do anythingmeaningful for you.
Beautiful.
Just like that, right?
(03:44):
Innovation also isn't somethingthat just magically happens and
also doesn't happen momentarily.
How does innovation typicallyoccur and, what comes together
now in this wave of AI agents?
Christian Muehlroth (04:00):
Yeah, it's so interesting because if you
think of the major innovationsfirst of all, probably it's
great to distinguish betweeninvention and innovation.
I know it's been done inliterature and everywhere else,
but I just think it'sinteresting.
And important to do itspecifically in the age of AI
because AI was not invented inthe recent months or years.
(04:24):
The foundation, specifically thephysics, but also the machine
learning algorithms of themathematic and the statistics
and the algorithms they're basedin, decades ago in, in
groundwork that was being donedecades ago.
But so that was the invention.
But the innovation now came inthe recent years, which made
(04:45):
things much more useful.
That's because of the processingpower for sure, but that's also
because of the electricity needsof the data centers, the scale,
the user interface and so on.
So these innovations, sure, alsoin algorithmic, but also in
delivery of the AI actually madeit much more useful in the
recent years.
And that, that's still a littlebit so sometimes confused in
(05:07):
practice the difference betweeninvention and innovation.
And I think right nowspecifically with AI, we see it
very clearly.
What's an invention and what isactual an innovation that's
useful to the market?
Andreas Welsch (05:19):
Yeah, so when we met a couple of weeks ago, it
was part of a panel discussionand you mentioned some parts of
innovation management.
And in innovation theory, westarted talking about these
waves, these
innovation waves or KondratievWaves based on the
Soviet economist.
(05:41):
And I don't hear a lot of peopletalking about this, by the way.
It's also in the book becausewhen I took innovation
management at university, I wasquite excited to learn about it.
But maybe you can share a littlebit about what's up with
Kondratiev Waves and how theynow fit into this innovation
cycle.
Christian Muehlroth (05:59):
Yeah.
I think it's so interesting, andI love that you have it in your
book.
I'll totally recommend the book,by the way.
It's, really great.
And the, pure fact that youalready have conative waves in
there I know.
Makes it a great book.
Yeah.
And I think it's interestingbecause ev the, theory is that
everything.
Really everything in life, notonly technology, but everything
(06:21):
goes in waves.
Yeah.
Things go up, things go downthen, they go sideways for some
time, but ultimately they go inwaves.
And that's if you look back inhistory and the history of
technology and then alsosociety, that actually has been
the case.
You always know in retrospectwhat these waves are.
(06:42):
For example the steam economy,electricity, then the internet,
these are all considered bigwaves, big technological waves
of change typically.
And then after technology,typically big societal and also
work transformation and businesstransformation follows.
(07:03):
But of course it takes sometime.
And by the way, many of theseactually stem from military and
defense applications like theinternet.
So it's, pretty interesting toalso see what those folks are up
to.
But that's the theory and theinteresting thing about it that
I think time is gettingcompressed right now.
(07:25):
So the length.
Of each wave, maybe back in the18 hundreds, something, it was
recognized to be about 60 years,70 years maybe.
But these waves are gettingshorter and the reason is pretty
obvious is because if you canstack the technologies on top of
each other, so the previous waveor the previous waves even
(07:46):
support.
The next one we need electricityfor the internet.
We need the internet now forscaling AI and so on.
So they built upon each otherand that's why we feel that
everything is accelerating thesedays.
Andreas Welsch (08:00):
To me that is fascinating, right?
Especially the layering ofthings.
And, if you've been in theindustry for a while, you have
seen this, right?
Whether it was your dial upmodem and cable now on your
phone and whatnot and, all theservices that that enables.
Now, to your point, add data,add AI on top and, whatever is
next.
So it also, it feels like thingsare moving indeed faster, right?
(08:23):
We've been talking about thisfor, a long time and you observe
this probably in your own livestoo, but it's sometimes getting
to the point where it's not onlyoverwhelming, but where you feel
disrupted and maybe where yourbusiness feels disrupted.
And what are you seeing when youwork with companies who are
looking at innovation managementand, who are, for example,
(08:44):
looking at software to managethat process.
Why do companies fear this, kindof disruption?
And what do you think now withagents, this disruption really
drives?
Christian Muehlroth (08:55):
Yeah it's, very interesting because you
have if, you consider this ontwo axes, right?
So you have the acceleratingtechnological change.
That's very true.
And every, as you say, I 1%agree, everybody feels it.
Everybody can observe it rightnow.
But there is also second aspectto it which is that specifically
(09:16):
large enterprises and bigcorporations, they know very
well that the larger they get,the slower they actually get,
and that's a problem.
Because then you have basicallya, a gap between the
accelerating technologic change.
Things get faster and fasterOver the years, we've
specifically seen it with thefoundation models over and over
(09:40):
again improving at a, impressiverate.
But then at the same time, wesee organizations always running
behind because they haveprocesses, they have politics,
they have, some policy stuff andwhich basically leads them to be
slower in the adoption rate.
(10:00):
So the bigger they get, the moreprocesses, the more politics,
the more policies they have,which means ultimately that they
actually slowed down.
So you have an exponentialacceleration in tech.
You have actually an algorithmicrate of change of those large
organizations.
They're getting slower andslower.
So that means that the risk ofdisruption is pretty real
(10:21):
because it's even for, thoseorganizations, it's even a super
exponential, it's more than anexponential rate of change
because they get slower andthat's where the risk is.
Andreas Welsch (10:31):
So we've seen these examples time and again,
right?
Kodak who dismissed digitalcameras and we believe people
still want to take pictures inanalog and film and things.
We've seen Blockbuster whodidn't see Netflix coming.
We had who, Nokia, who didn'tsee the iPhone coming.
(10:53):
All of these.
Are those the kind of examplesthat we should be looking for in
our own organizations, or arethere other examples?
For me it's not quite thatobvious.
And by the way, it's onlyobvious in hindsight what they,
yeah.
Christian Muehlroth (11:08):
And that's the main point.
It's always, unfortunately, it'salways obvious in hindsight.
But on the other hand, I'msurprised that we, as with me
the collective of organizationsthat are on the market.
we we don't, learn too much fromhistory because there is tools
(11:30):
and methodologies and ways forexample, there is corporate
foresight.
And I'm, not, saying thatorganizations should engage in
year long scenario creationtechniques because that's
probably an outdated way exceptfor your public agency.
But specifically privatecompanies who cannot look
forward right now, 3, 5, 7years.
It's, rare.
(11:50):
To be able to do that.
But there is systems,algorithms, and ways how to
understand signals of contin ofchange.
How you capture them, how youinterpret them, how you bring
them in the organization, andhow you also use them for
decision making.
And I'm, always surprisedbecause in, in, specifically.
(12:13):
Turbulent econom e economictimes, which we in 2025 might
have right now, depending on theregion where we're in right now.
But then specifically companiesdeinvest in those capabilities
and they go back and focus onthe core business.
They actually should be theexact opposite thing.
I know it's hard in these times.
But specifically in those times,you should watch out for the
(12:34):
next thing, for the next moveand have a very rigid strategic
intelligence.
But that's not what they do.
So they deinvest in stages whereuncertainty is the highest.
Where actually in those stagesthey should invest a lot in
exploring potential futurepathways.
Andreas Welsch (12:49):
So how does that then play out with Agentic AI
and I, feel we're still early bythe way.
I saw Gartner's generative AIhype cycle and generative AI
moving into the trap ofdisillusionment.
Great.
That means.
Relief is near.
People are figuring out whatthis can really do beyond shiny
objects and throw away proofs ofconcept.
(13:12):
But with agen AI, do you thinkthis is really going to be that
big of a disruption as some ofthe industry players and
marketing departments are hypingit up to be?
Christian Muehlroth (13:22):
Yeah, I think there is two, two things.
One is the agentic AI is goingto move us closer to an
abundance economy because of thecollapsing cost that's behind
it.
So right now agents are ki likeinterns that never sleep.
(13:44):
They can be super smart like PhDlevel in certain domains in
general they consider them asinterns because they need
oversight.
But because the, labor costtrends towards zero or whatever,
you pay to these AI specificallyfor some bridged tasks or some,
mundane tasks.
(14:06):
You don't have a headcountconstraint anymore.
You actually have a throughputconstraint.
And sometimes maybe even a lackof ideas.
Okay, what should we do next?
Very practical.
For example, in.
You can do much faster.
Market scan preparations evencoding of course.
(14:31):
These models are so great atproducing software code.
Yes.
They might not know all yourcode base yet, but it's
brilliant.
Yeah.
So that's in the dig digitalspace and also in the physical
space.
When we have less boundaries onland.
You can create, I don't know,maybe even cheaper physical
production, higher buildingsfaster production of facilities
(14:52):
or whatsoever if you now do thiswith robots or machines,
whatever this is.
So that's the first thing.
It moves us through toabundance, specifically for
labor.
And that is in combination withsome, something that we call.
Amplified intelligence or AI isamplified intelligence because
(15:13):
humans still set the intent.
We are, we still have theagency.
But the AI agents, they expandyour capacity, sometimes even
your capability to execute stuffspeed, precision and maybe even
latency.
So two things are interestingbecause they amplify human
intention, and if you're reallygood at something, you can use
(15:35):
AI to really 10 XA hundred x.
What you're doing.
And at the same time, it givesus specifically for mundane
tasks, basically abundance andlabor.
And that, that's reallydisruptive.
Andreas Welsch (15:47):
I've been thinking about this quite a bit
and that's a lot of theconversation right now.
Also if you look at media andwhat CEOs are publicly saying,
especially those that areleading or want to be perceived
as leading.
A lot of this conversation isabout we are reducing the number
of people.
We're not hiring people becausewe want to look at AI is this
(16:11):
the right, and the only way tolook at this or, why is nobody
saying it saying about we'regiving you agents so you can 10x
or 100x what you're doing.
What's your perspective there?
Christian Muehlroth (16:21):
I think you're spot on.
You're really spot on becausethe truth is probably both.
Yeah.
The truth on one hand is if youconsider AI and agents as a way
to automate specific.
Labor specific tasks then yesthere will be people being laid
(16:42):
off.
We, already see it specifically.
For the tasks where a text-basedAI we, currently just think
about lms.
There are different ways ofartificial intelligence, but
right now we focus a lot oftext-based AI, so large language
models and the like.
And there is just so much youcan automate with this sort of
(17:20):
intelligence.
So it's the truth.
But at the same time everythingthat requires human agency and
also human creativity, humaningenuity.
And these things will remain andprobably the people who already
are pretty creative, pretty havea great level of ingenuity
agency.
(17:41):
They now have tremendousopportunity to make a career, to
make a business and to bringthis to life.
So it's, both sides.
Andreas Welsch (17:51):
So, then where do organizations fall short?
We know they should embrace thisover short or long, they will
eventually look at this even ifit takes a couple years like
we've seen with cloud or withmobile and other trends.
But where are they failing inadopting this AI right now?
And what's your recommendation?
(18:11):
What are you seeing?
Christian Muehlroth (18:13):
Yeah so, we are seeing, first of all, that
there is no redesign of legacyprocesses.
So that means you just takeinefficient processes and you
put AI on top of it.
But if you, I don't know, if youautomate a shitty process, you
get an automate shitty process.
Basically.
Yeah.
(18:33):
So that, that's that, that'snot.
So you need to design yourprocesses to work in this new
area.
And if they really don't makesense then, you shouldn't be
automating it.
For example the, in, in Germanywe are digitizing our public
processes to a large extentdispute.
Trying since a couple of years.
So what we do is basically we,had all these forms that you
(18:56):
have to hand into some publicagency to get some stuff and you
have to write physically writeon those forms.
What we did is when we digitizedthis process, so we just put the
forms on the screen.
But we never we, never thinkabout does it actually make
sense to have a form?
For example, if you, I don'tknow if you if, you have a
newborn, child.
(19:17):
Yeah.
You still have to hand in aspecific a specific document to
request some things from, thegovernment.
But the government already knowsanyways that you have a child
because there are otherprocesses registering your
child.
So why should they actually haveto fill the form?
Why couldn't the government justdo this automatically as they
(19:37):
know I have not your child.
So we, just took the sameprocess.
Digitized it and made them formon the screen.
And if organizations do the samenow with AI, have legacy
processes and just try toautomate them, it's probably not
a good idea.
So it's very expensive.
Yeah.
If you do this that's one of thethings.
(19:58):
So just reconsider what makesactually sense and it's a great
opportunity right now to do it.
And the leading organizations doit.
Andreas Welsch (20:05):
Yeah.
But it costs money and it takestime, and we don't know if it
works.
And I still want a paycheck in abonus and a raise.
Yeah.
Yes, innovation, but please, nottoo much and not too much at the
same time.
Is that something you encounteror you perceive?
Christian Muehlroth (20:24):
Yeah.
It's, the truth.
100% because.
It's comfortable.
Yeah.
Specifically, being in anorganization may, maybe you're
in there for 10, 10, 15, 20years.
It you have a comfortable job.
Why change?
If there isn't pressure?
Unfortunately, and there can bepositive and negative pressure
(20:44):
for sure.
So it, it goes both ways.
But if there is a certaintendency and an aversion to
change.
But there is also people whoreally want it.
We, again, we see both in, inbig corporate, we see people
that say, ah go away with thisstuff, or maybe I can set it out
or wait it out, or, I don'tknow, just see what happens.
(21:05):
We see this a lot.
But to be, to be honest, we alsosee the opposite.
We see great people who haveagency, who have ideas, who
wanna push forward, who alsowanna make a career.
And for them that's a perfecttime to grab these opportunities
and really deliver impact inthose organizations.
Andreas Welsch (21:24):
So you have super progressive and excited
and tech savvy.
Folks on your team or in yourorganization, they want to try
this out.
They say, yes, I want change.
We need to do this differently.
And then you have the other sideof the spectrum who say please
leave me alone.
I just want to collect thepaycheck and be here my nine to
five, and then have a lifeoutside of work.
(21:46):
And by the way I'm retiringsoon, or Yeah, to your point,
and I like change.
How do you bring these twotogether?
And is that maybe even thesolution to making progress but
still taking everybody along onthis journey?
Christian Muehlroth (22:01):
Yeah it's a great question and I think to
some extent the decision makerswho actually decide which way to
go, they just need to be honestwith themselves and with the
organization.
I think it is a very noble goalto say, let's.
Get as many people as we can onboard.
I think it's probably the rightway to do.
But that's theory.
(22:22):
And, then there is practice andyou, always have people who just
do not want for specificreasons.
Maybe some can, some don't wantit doesn't really matter.
But that's just normal.
And I think while this is maybea ground truth, specifically
senior executives and decisionmakers, they should.
(22:43):
Then rather go with an 80%solution and say, we need to
move on.
This is the path.
Everybody who wants gets onboard and the others.
We need to see what we shouldcan do for them and how we can
help them basically get along.
But, as much inclusion as, isneeded.
Perfect.
Do it.
But you also need to think interms of the enterprise.
(23:04):
And as we discussed in thebeginning, we have this
disruption risk and it'simminent.
It's really here.
So maybe go with the 80%solution and do whatever it
takes.
Andreas Welsch (23:16):
See, I'm wondering if, some of that is
also more culturally relevant.
I know Germany has a verydemocratic process in companies
and we should talk to manypeople and make sure everybody's
heard and included and so on.
And to your earlier point, Ithink that can a lot of times
slow your business down as well.
Yeah.
I feel over here in the US, itmight not be that long of a
(23:39):
deliberation.
We see the opportunity, we seethe potential.
We also see the pressure.
What happens if we don't do thisif we don't adopt this?
And I get the sense people aremoving, businesses are moving
relatively fast on many of thesetopics.
Certainly not all.
But maybe culture plays in intothat too, I don't know.
Christian Muehlroth (23:58):
Yeah, for sure.
There is times where a moredemocratic approach might work a
little bit better and there arejust times where a little bit of
more straightforward approachmight work better.
It's like a pendulum thatswings, it depends on the
situation of the industry or thecompany of the enterprise.
And for both, have a reason toexist.
But you just need to know whento deploy deploy, which approach
(24:20):
and at which time.
Andreas Welsch (24:21):
Yeah.
Good, point.
Yes.
So definitely your leadershipskills in figuring out when do I
invite a dialogue?
When is this a decision thatmove forward with or to your
point, an 80% solution thathelps us move forward.
So before we wrap up, I'mcurious here, what do you
(24:42):
recommend leaders do in additionto being sensible?
What approach they should followwhen it comes to leadership, but
what should leaders do as theywant to arrive this wave of
disruption?
And instead of being disrupted,what's the one thing they need
to do right now?
Christian Muehlroth (24:59):
Yeah, so the one thing that is often
overlooked and that leads to AITourism typically is as boring
as it may sound.
Yeah.
Laying the platform and the datafoundation for it because Mo,
most of the AI applicationswe've seen, they all are based
on dirty data which means wrongdata sources.
(25:24):
Just an example we've seen manycompanies try and use an
experiment with public largelanguage models.
For any sort of innovation orresearch and development tasks.
But there's a couple of problemswith it.
For example, public LMScurrently lean a lot towards
open web sources like Wikipedia,R and, Reddit.
(25:45):
There is some statistics thatkind of try to prove it, but
for, let's say a concise r and dstrategy, our new product
development strategy need cleanenterprise.
Data, that's customer insights,that's patents your own patents
to see where you havetechnological capability, but
(26:06):
then also competitorinformation.
You need to take your current rand d pipeline into account.
You need to take your currentproduct and services offerings
into account, and so on.
Otherwise you lean too much intothe public LLM data.
So we are actually in a veryclean enterprise pool to make
this usable.
Maybe even a shared taxonomythat's, for example, what we
(26:27):
enable in atix.
So we, we make sure, so we,don't use public lms.
We have a different approach.
We didn't train our own, but wedeploy one specifically in our
infrastructure.
Data never leaves cynics, itdoesn't go to open eye, it
doesn't go to tropic, it doesn'tgo to complexity.
It's all within the existingdata privacy contract you
already have with Atix in thatcase.
So it's much more safe andsecure and we can prove that.
(26:49):
So that's the thing later.
Foundations specifically withthe data, otherwise you have
journey data and you have justopen web source data, which is
not really useful in, in thespecific r and d and innovation
context.
Andreas Welsch (27:01):
Wonderful.
Thank you for sharing.
Chris, we're getting close tothe end of the show and I was
wondering if you can summarizethe key three takeaways for our
audience today.
Christian Muehlroth (27:10):
Yeah, sure.
Let's go to the beginning andlet's, so the first one is the
abundance AI, the abundanceeconomy.
So because AI means amplifiedintelligence, I would look.
Specifically towards use casesand empowering people that want
the change and amplify theirhuman intelligence with AI,
(27:33):
amplify their creativity, givethem the most expensive tools.
Don't I, understand the approachof a.
Rolling out AI to everybodybecause you want to leverage or
leverage everybody and raise thebar for everyone.
Okay?
But that's a lot ofexperimentation and leads to AI
tourism because everybody triesout some things actually wasted
(27:53):
potential and wasted time andmoney.
So my first suggestion,recommendation would be.
Think of AI as amplifiedintelligence and provide the
tools to the people who reallywant the change and give them
the best tools, the mostexpensive ones, because that's
10 Xing a hundred Xing,sometimes their capabilities.
And I think this is, one key,takeaway.
(28:16):
The second thing is.
Take disruption seriouslybecause we've seen it a lot.
You've mentioned so manyexamples.
Yeah.
We're tired of the Kodak and,Nokia examples.
Yeah.
'cause they've been decades ago.
But I'm pretty sure we'll seemany of those examples in the,
future.
Don't undervalue and don'toverstate the two big to fail
(28:38):
Disruptions really is reallyimminent right now.
Andreas Welsch (28:41):
And make sure that your company doesn't become
the next Kodak or Blockbusterthat will tell for generations.
Christian Muehlroth (28:47):
Yeah you, don't wanna be on that list,
really.
You just don't want, yeah.
Yeah.
And then the third thing is getthe foundations and the data.
It is, it takes some time.
But it's not a hard, it's not ahard thing to do, but it's an
important thing to do.
And if you are doing it and ifyou have really clean data
(29:07):
specifically and everything rand d and innovation and over
time you stack more and moretechnology use cases on top of
it and add AI and agents to it,this is maybe one of the most
competitive advantages you canget to make most out of your
data in the next years to come.
Andreas Welsch (29:25):
Chris, thank you so much for joining and for
sharing your expertise with us.
It's always a pleasure talkingto you and thanks much for the
invitation.
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