April 18, 2025 • 24 mins
It's the age-old problem: business and technology stakeholders don't speak the same language. But why do we need them to come together—and how can that happen?
In this episode of "What's the BUZZ?—AI in Business," host Andreas Welsch engages in a compelling conversation with Camila Manera, an AI expert and entrepreneur, as they explore the critical intersection of artificial intelligence and business strategy.
Camila shines a light on the pressing need for AI "translators" within organizations—individuals who bridge the gap between AI technology and business goals, empowering teams to take ownership of AI projects. Together, they address essential topics such as:
- How to cultivate a culture of AI understanding
- The importance of long-term strategies over short-term fixes
- The current state of AI adoption in South America, specifically in Argentina
Whether you're navigating the complexities of AI implementation or seeking to leverage its potential for operational efficiency, this episode is loaded with valuable insights. Discover how AI can transform not just processes but also team dynamics, enabling your organization to thrive amid the chaos of technological change.
Ready to harness the true potential of AI in your business? Tune in now to listen and learn how to turn AI hype into concrete outcomes!
Questions or suggestions? Send me a Text Message.
***********
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 (00:00):
Hey, welcome to What's the BUZZ?, where leaders
and HandsOn experts share howthey have turned hype into
outcome.
Today we'll talk about bridgingthe gap between AI and the
business, and who better to talkabout it than someone who's
actively doing that, CamilaManera.
Hey Cami, thank you so much forjoining.
Camila Manera (00:18):
No, thank you.
I'm very happy to be here.
We have the amazing timetogether in a lot of conference,
so I am very happy to share thisspace with you and all of your
audience.
Andreas Welsch (00:29):
Fantastic.
Hey, why don't you tell ouraudience a little bit about
yourself, who you are and whatyou do.
Camila Manera (00:34):
Yes.
So I am from Buenos Aires,Argentina.
If you don't know, it's verylike south of the world.
We are in that part of thecontinent.
I am a graphic designer as aprofession, but I have been
changed to work in AI for thelast eight years.
I started my career working forthe World Disney Company in the
(00:55):
creative team.
And one day I woke up and say,okay, I want to study AI before
ChatGPT BT launch.
So it was like eight years ago.
In that time it was very hard tohave courses and places to study
AI.
So I went to India to studythere and I have an idea of how
(01:16):
the Disney could use data and AIfor, so I have.
I changed from the creative teamto being a data scientist.
I was in Disney for more thanseven years.
We launched a product that wonthe best of Disney Award in
technology, in data and AIworldwide.
And after that I resigned.
(01:37):
And I have go to co-found mystartup of AI applied to sports.
To football or soccer if you arein United States.
So we have developed a productto help clubs and agents to find
their the correct player fortheir squad and their teams.
And now I also have gone fromthe startup and I am working
(02:01):
with a lot of companies aroundthe world to help them use data
and AI to.
To empower their business andalso the careers and objectives
that they have.
Andreas Welsch (02:12):
Awesome.
Thank you so much.
I'm super excited to have you onthe show today.
Like you said, it's been a whilesince we've been on some of the
conferences and panels together,so I'm excited that you.
Have made time to be with ustoday.
Camila I remember I forgot tobrief you on this.
So there's a little surprisehere.
There's a little icebreaker.
And that icebreaker is a littlegame called in your own words.
(02:33):
So I'll read you this question.
And you have 60 seconds toanswer.
I'm curious, what's the firstthing that comes to mind and
why, in your own words.
Are you ready?
Camila Manera (02:44):
Yes, I'm ready.
Andreas Welsch (02:46):
Perfect.
If AI, where are a, if it were abook, what would it be?
60 seconds on the clock go.
Camila Manera (02:57):
Oh.
I think that it has a lot ofsense with AI to be a book
because of their superintelligence and all the
information that it has.
If it would be a book, it wouldbe like a little bit limited
because it would have just thatinformation and that's all.
So I think that, I think itwould be a word that it's
(03:21):
called, like transformation.
I think I would go with that.
Good answer.
Yes.
Andreas Welsch (03:26):
We'll let that count too.
There's a lot of transformationhappening.
And sometimes even, a whole bookof words that doesn't get across
what we actually need to convey.
Which in many ways brings us totoday's topic as, as well,
right?
It seems that there are so manywords and sentences and concepts
being discussed in exchangebetween business and AI teams
(03:47):
and vice versa, but.
Even though they seem to be thisthey seem to be speaking the
same language, evidently they'renot.
A lot of times we see that, heythe technology guys, they just
want to play around with thelatest stuff.
And on the other hand, oh, thebusiness doesn't even know
what's new and what's hipping,what what's cool and what we can
do.
But it's all a matter ofbringing those two together,
(04:08):
right?
When 80 to 85% of AI projects.
Fail or don't deliver the value.
And we see AI is getting cheaperand that will create more
demand.
I also feel we'll see even morepressure on AI teams to do
something with AI to, to deliverprojects.
But if they're not alreadycommunicating well enough and
understanding what are therequirements and what we're
(04:29):
actually building, right?
That just leads to morefailures.
I assume.
What issues do you see in yourwork or have you previously seen
in your work?
Camila Manera (04:40):
Yes, so I think like the problem that most
companies are having like rightnow is that these profiles or
personalities inside a companythat I like to call that like
the AI translator.
So you have a lot of teams thathave a lot of problems and have
perhaps a lot of ideas.
But previously it was mostcommon that you call like the IT
(05:03):
team or the data team.
And when they want to takeadvance in some projects, like
they don't know where to call ifthere's a person inside the
company that carry on like theAI strategy or the projects.
I think like that, it's like aproblem like in the starting
point of any project and fromthe other side, perhaps if they
(05:25):
call to the technology team orthe data team, they have a
roadmap or a timeline.
And when they want that projectto jump in, they say, okay,
let's talk like one here.
And now we are not in a momentthat we can wait for one year.
So I think that or my vision inthis kind of moment that we are
with the AI hype and also allthe things that are going on, is
(05:49):
that like we need to havesomebody with AI skills and
knowledge in every team insidethe company.
So that would help a lot to belike more independent.
To try to carry on their ownprojects because now with all
the tools that we have we canhave that type of a structure,
so the sales team perhaps havesomebody like with the
(06:11):
understanding of AI inside theteam and they would go like much
faster.
They will have independence andalso like somebody inside the
company that carry on thestrategy.
So I think not having that inthe table of conversations and
decisions now makes like teamsgo a little bit slow.
(06:33):
And also like being a little bitafraid of what to do, what not
to do, if they can use tools ornot, if they're secure or not.
So I think that we are in thatmoment of the process.
And with, without this kind ofprofits inside the company I, I
think the adoption, it's goingto be a little bit more like
slow that we all want to dobecause we want to go do a lot
(06:56):
of things and go much faster,but.
We need to think about theculture, the teams, and the
structure and how they adapt toall the new things that are
going on.
Andreas Welsch (07:05):
I think that's a really important point that
you're making, right?
There's so much push and so muchpressure to do something with
AI.
And just seeing how companiesand leaders have e evolved over
the last three years when itcomes to AI from what is this
generative AI thing, to, we needan AI strategy to let's pilot a
bunch of things and see what wecan actually build and if
there's something meaningful anduseful out of it.
(07:27):
To now actually saying, Hey,show me the money.
But it's not just show me themoney.
It's also I need to invest intraining in that cultural
change, like you're saying, tobring people along on their
journey.
It's not just about giving thema tool or giving them a new tool
and expect them to be moreproductive the next day.
Camila Manera (07:43):
Yes I think there is like the keynote to have a
very solid idea, ambition andnot to do things all like
separate, but have a visionwhere you want to company to
take and really create a AIjourney for the company.
I think that it's my desire butit's a little bit difficult to
achieve.
So
Andreas Welsch (08:03):
I'm curious there, what are you seeing in
South America?
What are you seeing inArgentina, maybe specifically, I
know that the continent is veryinnovative in very forward
leaning when it comes to thesetechnologies that lots of
opportunities.
What are you seeing when itcomes to AI adoption?
Camila Manera (08:19):
Yes.
So I think we are a very hightalent region and also high
talent country.
I have the chance to meet likepeople, like very young people
with very strong AI and alsotechnology skills.
But I think we need to be alittle bit more potentiated and
expanded in what we do becausein the region has not much
(08:42):
resources and things are morelike difficult for us to
achieve.
So that everything is a little,like harder for us.
When I work with companies liketrying to help them achieve
their AI goals.
They all start perhaps like thelast year to with something very
small to be like on trend.
But they were not thinking likein this long vision project.
(09:06):
So I think now this year it ischanging a little bit and we are
more in the in the year ofimplementation.
But last year we were more likein, okay, I want to listen, I
want to learn, I want to go alittle bit more like slow in the
implementation or a process.
But this year it's changing alittle bit.
And now.
(09:26):
Where companies reach me out orsomething they say I want to do.
No, that's, that is the biggestchange I'm seeing.
So I think that it's very goodfor not just Argentina but the
region because we are not, we'regoing to start seeing a lot of
use cases, products and projectsmore hands-on than last year.
That was more like experimental.
(09:48):
There's a very bad statisticsthat for 10 projects of AI, just
three go into production.
So I think this year we aregoing to go like perhaps from
ten five.
That's good because we are goingto go a little bit up in numbers
but yes we are I think a verylike.
Passionate country and alsoregion, but we need to create
(10:12):
more ecosystem and also moreconnections.
I am working a lot in that, inthe country and the region to,
to have meetups, to learn fromothers, to listen other stories
in order to incentivize theregion to achieve this step.
Because I think if we make thatwe are going to have a lot of
business opportunities and wehave very good.
(10:34):
Professionals here in, inArgentina and also in, I think
Andreas Welsch (10:38):
That's really exciting, right?
And I think that speaks also tothe broader community and how
you can p how you can bringpeople along on their journey,
not just within your company,but to your point, within your
region, within your country toget them excited about this too.
Now, look I come from a techbackground myself, and it's
super exciting to see what a newkind of technology can do for
(10:58):
you and where its limitationsare.
I'm wondering how do yourecommend then also in your
conversations that leaders andtheir peers work together with
the business on these AIprojects?
Camila Manera (11:11):
Yes.
So I think we developing a veryold school ability that is
listening.
I think that we are like era ofbeing very good listeners.
Because it's very very human,like the first part of
implementing an A project thatit's like seeding.
(11:32):
For example, my experience whenI create the first AI project in
Disney was I sit with a lot ofpeople and just listen and
listen to problems.
Okay.
And I make good questions and Imake good questions and over
that you start likeunderstanding and going like
very deep in the problem.
(11:52):
The other like team or personhas.
And that is not any tech, it'sjust human tech to really have
that ability.
Because once you, you understandin all the layers that are when
you have a first conversationwith something with somebody and
they talk, okay, this is myproblem.
But you make a lot of differentquestions and you start like
(12:14):
understanding that the problemis other problem that they have,
but they didn't realize thatthey have it.
So I think that it's like thestarting point of anything.
And also regarding like businessproblems and also leadership
strategies.
And once you have that, thenlike now understanding how's the
pipeline, the roadmap, theprojects, it's the easiest part.
(12:36):
No, but you have to work alsolike in the culture, in the
communication, in like how youare going to communicate the
changes that these perhapsprojects it's going to take.
And all the more like the humanside of implementation.
I think there, it's now thebiggest challenge that we are
having because we are likesaying a lot of people that
(13:00):
forget your 20 years job andwhat you have been doing and now
you have to do things completelydifferent.
So I think that we have to bevery careful in that part of
transformation because like forthis like last, it is chaotic.
No, we receive a lot ofinformation.
(13:21):
The models based the everything.
And for users, final users, it'svery confusing.
No.
So I think now leaders andbusiness have to come a little
bit, create the vision.
Think of the culture and afterthat, the implementation, I
think it's going to go verysmooth in the process.
Andreas Welsch (13:42):
That sounds great.
And again, emphasizes you needto work together.
You really need to start tolisten in and understand what is
it that my stakeholders areactually looking for?
How can we help them improvetheir business, improve their
process and help them workbetter and more efficiently?
And I think, sometimes we tendto forget that in, in, in all of
that mix of technology and theexcitement on one side or the
(14:05):
fear and concern on the other.
And, hey we're actually justpeople working together and
trying to figure this out.
It, it also wouldn't be.
An episode in 2025.
If we didn't talk about theindustry's favorite buzzword of
the year, Agentic AI.
How do you see that makingthings better or worse in that
collaboration?
Camila Manera (14:26):
I think it's going to be very good for
businesses because I think forthe last years, like AI, it's
evolving very fast because firstwe talk about AI, then we talk
about Gen AI.
Then we have the Shiptperplexity deep and all the
language.
For a battle.
(14:46):
And now we have another thingthat is agent AI.
If we think now, like goingbackwards in the line, it all
makes sense.
No.
And previously we talk aboutdata now because we need the
data.
Now that we have the data, wecan develop models.
Now that we develop models, wecan create new stuff and now we
can make them operate forourself.
(15:06):
I think that if for example, wehave a lot of different, like
needs in the day asprofessionals, if we have, can
have for example, differentagents to resolve different kind
of problems.
We all wanted to love that.
No, I think because for example,and also because now we have to
be very like completeprofessionals because we are
(15:28):
professionals.
We have to study a.
We, things are changing all thetime.
We have to develop content.
We are here doing that also.
So I think like the professionalscope has changed a lot and we
do so much stuff than before.
Thanks for all this technologythat it's evolving very fast.
(15:49):
So I think that if every humancan have their own like circle
of agents.
Resolving things that we don'twant.
It's going to be great there.
It's where we start like puttingthe limits, no.
Okay.
These things, yes, this thing.
No I want to go faster in thispoint because I don't like it,
(16:12):
but perhaps other people like todo that.
So that's why I think it is goodbecause we are going to put our
limits, develop depending whatwe enjoy.
Of our day by day and what wenot.
That is my vision because I'mvery optimistic about AI and
things.
(16:32):
But other people's going to say,okay, no, this is going to take
a lot of jobs and that it's notmy point of view because I think
that we drive the tech and notthe tech drivers.
So I think although I don't seea lot of implementation here in
the region of agents, but Ithink like for the next month,
(16:54):
it's going to be definitely likethe game changer of this year.
Yeah.
Andreas Welsch (17:00):
By the way I must say I share your sentiment
around adoption, right?
There's a lot of talk in themarket.
It seems Everybody that's beendoing some kind of machine
learning all of a sudden did AI,then they did gen AI.
Now they're doing agent AI andthey've been doing all of this
for a very long time.
If you believe them, but I thinkthe proof is still in the
pudding.
We still need to see morecompanies adopt this beyond the
(17:22):
lighthouses, beyond the onesthat say, Hey, we've been
experimenting with this for awhile.
We've rolled this out.
And I think it's the classic ininnovation management,
innovation adoption dilemma.
You have a few very earlyadopters, then there's a big
gap, and then the rest willeventually follow.
And I think once that happensit'll be really interesting to
see how that progress.
(17:43):
I think there's a lot ofopportunity and, as I'm thinking
about this, honestly I'm split alittle bit.
How big is the opportunity orhow big is the promise to what
is actually real?
We've seen this withtechnologies like RPA Robotic
Process Automation.
A couple years ago, we're goingto automate everything in a
business, and people startedlooking at this and they said
it's actually not that easy.
No, we won't be able to doeverything part of me thinks
(18:06):
well are we going to see thesame thing with agents as well?
Is there just so much optimismand so much hype that we're not
seeing beyond that yet?
Have we not seen enoughimplementations yet?
And maybe the truth is somewherein the middle.
But also here in North America.
I think companies are slowlystarting to look at this and
figure out what is this actuallyand how do I want to use this?
Camila Manera (18:30):
Yes.
And also I think that we arelike putting like boundaries to
everything.
No, I think we are also like inthat moment, like deciding what
it's okay and what no.
So we are like finding like theright way to do things with
agents also.
Andreas Welsch (18:48):
Yeah.
And I was thinking about thisthe other day.
On one hand we see so muchinnovation come out on a regular
basis.
You mentioned a few of thethings that were in, in the news
just in January and Februaryfrom we're headed for AGI and we
know exactly how to build it to,we are running out of public
data to train on to, hey, here'sthis thing called deep seek and
(19:08):
it's way cheaper than theWestern models and incumbents
two I dunno what the next thingwas.
Grok, GPT-4.5 and so on and soon.
So there's a whole range of newsif you want to give in into
that.
Yet I feel the adoption incompanies will be a lot slower
because there are risks, thereare concerns, there are skills
you need to build, you want totry this out.
(19:28):
So on, on some on, on, on somelevel there's more technology
that then you can adopt fastenough to keep up.
Which is an interesting time to,to live in.
I think in many.
Now you mentioned some of theheadlines.
And I'm curious to me it feelslike the last four weeks yes,
there was an Nvidia GTC Yes.
There were a couple otherthings, but were there any big
(19:50):
headlines that, that stand outto you, maybe even from this
first quarter that are relevantfor business and AI teams?
Camila Manera (19:59):
Yes.
I think that from different likeaspects, like all the companies
want to show us how good theyare or how important they are or
how they are the best.
And I think that it's not likethe scope or the vision that we
have to do.
We have to center more like inpeople.
Than companies.
(20:19):
And also this is mainly becausepeople are going a little bit
like slow in the adoption as wewere talking about, and having
all the time like this, like bigtech like battle to say I'm the
best, I'm the best.
It doesn't matter who is thebest, what does matter is what
best for people.
So I think that also companieshas to be a little bit more like
(20:43):
humanized than they are now.
Like not trying to like in thatbattle for who is the stronger
or biggest or that I think thatfrom that aspect, my, my
favorite like news of theseweeks, it's like all the
launches that OpenAI has givenmainly because I think they're
the ones that are going a littlebit more like far away in the
(21:07):
capabilities of the model in themore like deep search and making
also agents to operate.
Because what is the best answeror the best model now?
I think it's I'm not for now.
And also I think that all the aproduct that I have been using a
(21:27):
lot, it's like n eight, nine foralso like making some
automatization and I thinkthey're like.
Making a very good userexperience interface for non
code I think are like the mostimportant launches of this
month.
I don't want to go much deeperlike in the group or deep seek
(21:51):
or that file because I think.
What they have been launched areall like, quite the same and not
very innovative on that aspect.
But I think that open AI withthe agents and also NH nine I
think they are the mostinnovative right now.
Although I know that in April wewere having like Google next, so
(22:12):
they're going to launch very,Google is going to launch, I
think very important stuff forthat event.
So perhaps that so surprise us a
Andreas Welsch (22:21):
bit.
Who knows?
Seems like there's surprises allaround us in this day and age
with AI.
Now, hey we're getting close tothe end of the show and I was
wondering if you can summarizethe key three takeaways for our
audience today.
Camila Manera (22:34):
Yes.
So first of all, for allbusinesses that want to apply
AI, you need a translator.
You need a business AItranslator in your company.
You have to think of independentstructures so everybody can
create and develop AI inside theteams.
Don't get confused with all theinformation that it's going up
(22:54):
there.
You have to center in what yourcompany needs and what your
culture needs, and develop along-term vision plans and know
just one month AI implementationbecause if not, you are gonna
have to get frustrated on theroad.
And once you want to get realbig changes, you need to have
very good long-term strategies.
(23:16):
Wonderful.
Andreas Welsch (23:17):
Camila, it was a pleasure having you on the show.
Thank you so much for sharingyour insights and your learnings
and how business and AI leaderscan work better together.
Camila Manera (23:27):
No, thank you.
It was a pleasure to be here.
To tell you a little bit abouthow South America and Argentina
it's developing and strategiesaround AI.
So it was.
A pleasure for me to share thisspace with you.
Hey, welcome to What's the BUZZ?, where leaders
and HandsOn experts share howthey have turned hype into
outcome.
Today we'll talk about bridgingthe gap between AI and the
business, and who better to talkabout it than someone who's
actively doing that, CamilaManera.
Hey Cami, thank you so much forjoining.
Camila Manera (00:18):
No, thank you.
I'm very happy to be here.
We have the amazing timetogether in a lot of conference,
so I am very happy to share thisspace with you and all of your
audience.
Andreas Welsch (00:29):
Fantastic.
Hey, why don't you tell ouraudience a little bit about
yourself, who you are and whatyou do.
Camila Manera (00:34):
Yes.
So I am from Buenos Aires,Argentina.
If you don't know, it's verylike south of the world.
We are in that part of thecontinent.
I am a graphic designer as aprofession, but I have been
changed to work in AI for thelast eight years.
I started my career working forthe World Disney Company in the
(00:55):
creative team.
And one day I woke up and say,okay, I want to study AI before
ChatGPT BT launch.
So it was like eight years ago.
In that time it was very hard tohave courses and places to study
AI.
So I went to India to studythere and I have an idea of how
(01:16):
the Disney could use data and AIfor, so I have.
I changed from the creative teamto being a data scientist.
I was in Disney for more thanseven years.
We launched a product that wonthe best of Disney Award in
technology, in data and AIworldwide.
And after that I resigned.
(01:37):
And I have go to co-found mystartup of AI applied to sports.
To football or soccer if you arein United States.
So we have developed a productto help clubs and agents to find
their the correct player fortheir squad and their teams.
And now I also have gone fromthe startup and I am working
(02:01):
with a lot of companies aroundthe world to help them use data
and AI to.
To empower their business andalso the careers and objectives
that they have.
Andreas Welsch (02:12):
Awesome.
Thank you so much.
I'm super excited to have you onthe show today.
Like you said, it's been a whilesince we've been on some of the
conferences and panels together,so I'm excited that you.
Have made time to be with ustoday.
Camila I remember I forgot tobrief you on this.
So there's a little surprisehere.
There's a little icebreaker.
And that icebreaker is a littlegame called in your own words.
(02:33):
So I'll read you this question.
And you have 60 seconds toanswer.
I'm curious, what's the firstthing that comes to mind and
why, in your own words.
Are you ready?
Camila Manera (02:44):
Yes, I'm ready.
Andreas Welsch (02:46):
Perfect.
If AI, where are a, if it were abook, what would it be?
60 seconds on the clock go.
Camila Manera (02:57):
Oh.
I think that it has a lot ofsense with AI to be a book
because of their superintelligence and all the
information that it has.
If it would be a book, it wouldbe like a little bit limited
because it would have just thatinformation and that's all.
So I think that, I think itwould be a word that it's
(03:21):
called, like transformation.
I think I would go with that.
Good answer.
Yes.
Andreas Welsch (03:26):
We'll let that count too.
There's a lot of transformationhappening.
And sometimes even, a whole bookof words that doesn't get across
what we actually need to convey.
Which in many ways brings us totoday's topic as, as well,
right?
It seems that there are so manywords and sentences and concepts
being discussed in exchangebetween business and AI teams
(03:47):
and vice versa, but.
Even though they seem to be thisthey seem to be speaking the
same language, evidently they'renot.
A lot of times we see that, heythe technology guys, they just
want to play around with thelatest stuff.
And on the other hand, oh, thebusiness doesn't even know
what's new and what's hipping,what what's cool and what we can
do.
But it's all a matter ofbringing those two together,
(04:08):
right?
When 80 to 85% of AI projects.
Fail or don't deliver the value.
And we see AI is getting cheaperand that will create more
demand.
I also feel we'll see even morepressure on AI teams to do
something with AI to, to deliverprojects.
But if they're not alreadycommunicating well enough and
understanding what are therequirements and what we're
(04:29):
actually building, right?
That just leads to morefailures.
I assume.
What issues do you see in yourwork or have you previously seen
in your work?
Camila Manera (04:40):
Yes, so I think like the problem that most
companies are having like rightnow is that these profiles or
personalities inside a companythat I like to call that like
the AI translator.
So you have a lot of teams thathave a lot of problems and have
perhaps a lot of ideas.
But previously it was mostcommon that you call like the IT
(05:03):
team or the data team.
And when they want to takeadvance in some projects, like
they don't know where to call ifthere's a person inside the
company that carry on like theAI strategy or the projects.
I think like that, it's like aproblem like in the starting
point of any project and fromthe other side, perhaps if they
(05:25):
call to the technology team orthe data team, they have a
roadmap or a timeline.
And when they want that projectto jump in, they say, okay,
let's talk like one here.
And now we are not in a momentthat we can wait for one year.
So I think that or my vision inthis kind of moment that we are
with the AI hype and also allthe things that are going on, is
(05:49):
that like we need to havesomebody with AI skills and
knowledge in every team insidethe company.
So that would help a lot to belike more independent.
To try to carry on their ownprojects because now with all
the tools that we have we canhave that type of a structure,
so the sales team perhaps havesomebody like with the
(06:11):
understanding of AI inside theteam and they would go like much
faster.
They will have independence andalso like somebody inside the
company that carry on thestrategy.
So I think not having that inthe table of conversations and
decisions now makes like teamsgo a little bit slow.
(06:33):
And also like being a little bitafraid of what to do, what not
to do, if they can use tools ornot, if they're secure or not.
So I think that we are in thatmoment of the process.
And with, without this kind ofprofits inside the company I, I
think the adoption, it's goingto be a little bit more like
slow that we all want to dobecause we want to go do a lot
(06:56):
of things and go much faster,but.
We need to think about theculture, the teams, and the
structure and how they adapt toall the new things that are
going on.
Andreas Welsch (07:05):
I think that's a really important point that
you're making, right?
There's so much push and so muchpressure to do something with
AI.
And just seeing how companiesand leaders have e evolved over
the last three years when itcomes to AI from what is this
generative AI thing, to, we needan AI strategy to let's pilot a
bunch of things and see what wecan actually build and if
there's something meaningful anduseful out of it.
(07:27):
To now actually saying, Hey,show me the money.
But it's not just show me themoney.
It's also I need to invest intraining in that cultural
change, like you're saying, tobring people along on their
journey.
It's not just about giving thema tool or giving them a new tool
and expect them to be moreproductive the next day.
Camila Manera (07:43):
Yes I think there is like the keynote to have a
very solid idea, ambition andnot to do things all like
separate, but have a visionwhere you want to company to
take and really create a AIjourney for the company.
I think that it's my desire butit's a little bit difficult to
achieve.
So
Andreas Welsch (08:03):
I'm curious there, what are you seeing in
South America?
What are you seeing inArgentina, maybe specifically, I
know that the continent is veryinnovative in very forward
leaning when it comes to thesetechnologies that lots of
opportunities.
What are you seeing when itcomes to AI adoption?
Camila Manera (08:19):
Yes.
So I think we are a very hightalent region and also high
talent country.
I have the chance to meet likepeople, like very young people
with very strong AI and alsotechnology skills.
But I think we need to be alittle bit more potentiated and
expanded in what we do becausein the region has not much
(08:42):
resources and things are morelike difficult for us to
achieve.
So that everything is a little,like harder for us.
When I work with companies liketrying to help them achieve
their AI goals.
They all start perhaps like thelast year to with something very
small to be like on trend.
But they were not thinking likein this long vision project.
(09:06):
So I think now this year it ischanging a little bit and we are
more in the in the year ofimplementation.
But last year we were more likein, okay, I want to listen, I
want to learn, I want to go alittle bit more like slow in the
implementation or a process.
But this year it's changing alittle bit.
And now.
(09:26):
Where companies reach me out orsomething they say I want to do.
No, that's, that is the biggestchange I'm seeing.
So I think that it's very goodfor not just Argentina but the
region because we are not, we'regoing to start seeing a lot of
use cases, products and projectsmore hands-on than last year.
That was more like experimental.
(09:48):
There's a very bad statisticsthat for 10 projects of AI, just
three go into production.
So I think this year we aregoing to go like perhaps from
ten five.
That's good because we are goingto go a little bit up in numbers
but yes we are I think a verylike.
Passionate country and alsoregion, but we need to create
(10:12):
more ecosystem and also moreconnections.
I am working a lot in that, inthe country and the region to,
to have meetups, to learn fromothers, to listen other stories
in order to incentivize theregion to achieve this step.
Because I think if we make thatwe are going to have a lot of
business opportunities and wehave very good.
(10:34):
Professionals here in, inArgentina and also in, I think
Andreas Welsch (10:38):
That's really exciting, right?
And I think that speaks also tothe broader community and how
you can p how you can bringpeople along on their journey,
not just within your company,but to your point, within your
region, within your country toget them excited about this too.
Now, look I come from a techbackground myself, and it's
super exciting to see what a newkind of technology can do for
(10:58):
you and where its limitationsare.
I'm wondering how do yourecommend then also in your
conversations that leaders andtheir peers work together with
the business on these AIprojects?
Camila Manera (11:11):
Yes.
So I think we developing a veryold school ability that is
listening.
I think that we are like era ofbeing very good listeners.
Because it's very very human,like the first part of
implementing an A project thatit's like seeding.
(11:32):
For example, my experience whenI create the first AI project in
Disney was I sit with a lot ofpeople and just listen and
listen to problems.
Okay.
And I make good questions and Imake good questions and over
that you start likeunderstanding and going like
very deep in the problem.
(11:52):
The other like team or personhas.
And that is not any tech, it'sjust human tech to really have
that ability.
Because once you, you understandin all the layers that are when
you have a first conversationwith something with somebody and
they talk, okay, this is myproblem.
But you make a lot of differentquestions and you start like
(12:14):
understanding that the problemis other problem that they have,
but they didn't realize thatthey have it.
So I think that it's like thestarting point of anything.
And also regarding like businessproblems and also leadership
strategies.
And once you have that, thenlike now understanding how's the
pipeline, the roadmap, theprojects, it's the easiest part.
(12:36):
No, but you have to work alsolike in the culture, in the
communication, in like how youare going to communicate the
changes that these perhapsprojects it's going to take.
And all the more like the humanside of implementation.
I think there, it's now thebiggest challenge that we are
having because we are likesaying a lot of people that
(13:00):
forget your 20 years job andwhat you have been doing and now
you have to do things completelydifferent.
So I think that we have to bevery careful in that part of
transformation because like forthis like last, it is chaotic.
No, we receive a lot ofinformation.
(13:21):
The models based the everything.
And for users, final users, it'svery confusing.
No.
So I think now leaders andbusiness have to come a little
bit, create the vision.
Think of the culture and afterthat, the implementation, I
think it's going to go verysmooth in the process.
Andreas Welsch (13:42):
That sounds great.
And again, emphasizes you needto work together.
You really need to start tolisten in and understand what is
it that my stakeholders areactually looking for?
How can we help them improvetheir business, improve their
process and help them workbetter and more efficiently?
And I think, sometimes we tendto forget that in, in, in all of
that mix of technology and theexcitement on one side or the
(14:05):
fear and concern on the other.
And, hey we're actually justpeople working together and
trying to figure this out.
It, it also wouldn't be.
An episode in 2025.
If we didn't talk about theindustry's favorite buzzword of
the year, Agentic AI.
How do you see that makingthings better or worse in that
collaboration?
Camila Manera (14:26):
I think it's going to be very good for
businesses because I think forthe last years, like AI, it's
evolving very fast because firstwe talk about AI, then we talk
about Gen AI.
Then we have the Shiptperplexity deep and all the
language.
For a battle.
(14:46):
And now we have another thingthat is agent AI.
If we think now, like goingbackwards in the line, it all
makes sense.
No.
And previously we talk aboutdata now because we need the
data.
Now that we have the data, wecan develop models.
Now that we develop models, wecan create new stuff and now we
can make them operate forourself.
(15:06):
I think that if for example, wehave a lot of different, like
needs in the day asprofessionals, if we have, can
have for example, differentagents to resolve different kind
of problems.
We all wanted to love that.
No, I think because for example,and also because now we have to
be very like completeprofessionals because we are
(15:28):
professionals.
We have to study a.
We, things are changing all thetime.
We have to develop content.
We are here doing that also.
So I think like the professionalscope has changed a lot and we
do so much stuff than before.
Thanks for all this technologythat it's evolving very fast.
(15:49):
So I think that if every humancan have their own like circle
of agents.
Resolving things that we don'twant.
It's going to be great there.
It's where we start like puttingthe limits, no.
Okay.
These things, yes, this thing.
No I want to go faster in thispoint because I don't like it,
(16:12):
but perhaps other people like todo that.
So that's why I think it is goodbecause we are going to put our
limits, develop depending whatwe enjoy.
Of our day by day and what wenot.
That is my vision because I'mvery optimistic about AI and
things.
(16:32):
But other people's going to say,okay, no, this is going to take
a lot of jobs and that it's notmy point of view because I think
that we drive the tech and notthe tech drivers.
So I think although I don't seea lot of implementation here in
the region of agents, but Ithink like for the next month,
(16:54):
it's going to be definitely likethe game changer of this year.
Yeah.
Andreas Welsch (17:00):
By the way I must say I share your sentiment
around adoption, right?
There's a lot of talk in themarket.
It seems Everybody that's beendoing some kind of machine
learning all of a sudden did AI,then they did gen AI.
Now they're doing agent AI andthey've been doing all of this
for a very long time.
If you believe them, but I thinkthe proof is still in the
pudding.
We still need to see morecompanies adopt this beyond the
(17:22):
lighthouses, beyond the onesthat say, Hey, we've been
experimenting with this for awhile.
We've rolled this out.
And I think it's the classic ininnovation management,
innovation adoption dilemma.
You have a few very earlyadopters, then there's a big
gap, and then the rest willeventually follow.
And I think once that happensit'll be really interesting to
see how that progress.
(17:43):
I think there's a lot ofopportunity and, as I'm thinking
about this, honestly I'm split alittle bit.
How big is the opportunity orhow big is the promise to what
is actually real?
We've seen this withtechnologies like RPA Robotic
Process Automation.
A couple years ago, we're goingto automate everything in a
business, and people startedlooking at this and they said
it's actually not that easy.
No, we won't be able to doeverything part of me thinks
(18:06):
well are we going to see thesame thing with agents as well?
Is there just so much optimismand so much hype that we're not
seeing beyond that yet?
Have we not seen enoughimplementations yet?
And maybe the truth is somewherein the middle.
But also here in North America.
I think companies are slowlystarting to look at this and
figure out what is this actuallyand how do I want to use this?
Camila Manera (18:30):
Yes.
And also I think that we arelike putting like boundaries to
everything.
No, I think we are also like inthat moment, like deciding what
it's okay and what no.
So we are like finding like theright way to do things with
agents also.
Andreas Welsch (18:48):
Yeah.
And I was thinking about thisthe other day.
On one hand we see so muchinnovation come out on a regular
basis.
You mentioned a few of thethings that were in, in the news
just in January and Februaryfrom we're headed for AGI and we
know exactly how to build it to,we are running out of public
data to train on to, hey, here'sthis thing called deep seek and
(19:08):
it's way cheaper than theWestern models and incumbents
two I dunno what the next thingwas.
Grok, GPT-4.5 and so on and soon.
So there's a whole range of newsif you want to give in into
that.
Yet I feel the adoption incompanies will be a lot slower
because there are risks, thereare concerns, there are skills
you need to build, you want totry this out.
(19:28):
So on, on some on, on, on somelevel there's more technology
that then you can adopt fastenough to keep up.
Which is an interesting time to,to live in.
I think in many.
Now you mentioned some of theheadlines.
And I'm curious to me it feelslike the last four weeks yes,
there was an Nvidia GTC Yes.
There were a couple otherthings, but were there any big
(19:50):
headlines that, that stand outto you, maybe even from this
first quarter that are relevantfor business and AI teams?
Camila Manera (19:59):
Yes.
I think that from different likeaspects, like all the companies
want to show us how good theyare or how important they are or
how they are the best.
And I think that it's not likethe scope or the vision that we
have to do.
We have to center more like inpeople.
Than companies.
(20:19):
And also this is mainly becausepeople are going a little bit
like slow in the adoption as wewere talking about, and having
all the time like this, like bigtech like battle to say I'm the
best, I'm the best.
It doesn't matter who is thebest, what does matter is what
best for people.
So I think that also companieshas to be a little bit more like
(20:43):
humanized than they are now.
Like not trying to like in thatbattle for who is the stronger
or biggest or that I think thatfrom that aspect, my, my
favorite like news of theseweeks, it's like all the
launches that OpenAI has givenmainly because I think they're
the ones that are going a littlebit more like far away in the
(21:07):
capabilities of the model in themore like deep search and making
also agents to operate.
Because what is the best answeror the best model now?
I think it's I'm not for now.
And also I think that all the aproduct that I have been using a
(21:27):
lot, it's like n eight, nine foralso like making some
automatization and I thinkthey're like.
Making a very good userexperience interface for non
code I think are like the mostimportant launches of this
month.
I don't want to go much deeperlike in the group or deep seek
(21:51):
or that file because I think.
What they have been launched areall like, quite the same and not
very innovative on that aspect.
But I think that open AI withthe agents and also NH nine I
think they are the mostinnovative right now.
Although I know that in April wewere having like Google next, so
(22:12):
they're going to launch very,Google is going to launch, I
think very important stuff forthat event.
So perhaps that so surprise us a
Andreas Welsch (22:21):
bit.
Who knows?
Seems like there's surprises allaround us in this day and age
with AI.
Now, hey we're getting close tothe end of the show and I was
wondering if you can summarizethe key three takeaways for our
audience today.
Camila Manera (22:34):
Yes.
So first of all, for allbusinesses that want to apply
AI, you need a translator.
You need a business AItranslator in your company.
You have to think of independentstructures so everybody can
create and develop AI inside theteams.
Don't get confused with all theinformation that it's going up
(22:54):
there.
You have to center in what yourcompany needs and what your
culture needs, and develop along-term vision plans and know
just one month AI implementationbecause if not, you are gonna
have to get frustrated on theroad.
And once you want to get realbig changes, you need to have
very good long-term strategies.
(23:16):
Wonderful.
Andreas Welsch (23:17):
Camila, it was a pleasure having you on the show.
Thank you so much for sharingyour insights and your learnings
and how business and AI leaderscan work better together.
Camila Manera (23:27):
No, thank you.
It was a pleasure to be here.
To tell you a little bit abouthow South America and Argentina
it's developing and strategiesaround AI.
So it was.
A pleasure for me to share thisspace with you.
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