August 18, 2024 • 27 mins
In this episode, Lasse Rindom (AI Lead, BASICO) and Andreas Welsch discuss leveraging the AI hype and disillusionment to drive business impact. Lasse shares his story from previous hype cycles in Automation and AI to the latest one of Generative AI and provides valuable advice for listeners looking to get actionable insights into driving AI adoption in their business.
Key topics:
- Assess the current AI adoption phase in the market between AI hype and disillusionment
- Leverage the Generative AI momentum to drive business impact
- Evaluate analysts’ forecasts to continue the AI momentum in business and focus on promising areas
- Decide why to continue investing in AI solutions for business growth
Listen to the full episode to hear how you can:
- Focus on delivering business value through AI and automation rather than fixating on Generative AI as a technology
- Provide tangible examples and training for employees to become skilled users of AI-enhanced tools
- Drive AI innovation while understanding and mitigating the risks
- Countries like Denmark are leading the way in exploring Digitalization and AI, and are actively driving adoption
Watch this episode on YouTube:
https://youtu.be/87N5dQ7_RQk
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 (00:00):
Today we'll talk about leveraging the AI hype and
disillusionment to drivebusiness impact.
And who better to talk about itthan someone who thrives in that
environment, Lasse Rindom.
Hey Lasse, thank you so much forjoining.
Lasse Rindom (00:12):
Hi Andreas, and thank you so much for having me
today.
Andreas Welsch (00:15):
Awesome.
Hey, why don't you tell ouraudience a little bit about
yourself, who you are and whatyou do?
Lasse Rindom (00:20):
So my name is Lasse Rindem and I am a, first
of all, an automation pioneer.
I've been working a lot inautomation for the last 10
years.
And I am the current AI lead atDanish consultancy, BASICO,
where I drive our go to marketefforts on AI and also on some
automation and I also run thepodcast the only constant where
(00:43):
I've had you Andreas also as aguest.
So that's really nice.
Andreas Welsch (00:46):
Awesome.
We've been in touch for a longtime.
So it's always great to connectwith you and now I'm looking
forward to sharing that with ouraudience as well.
Don't forget to join thewaitlist for my upcoming book,
the AI Leadership Handbook ataileadershiphandbook.com.
I'm keeping it simple.
So with that out of the way,Lasse, what do you say, should
we play a little game to kickthings off?
Lasse Rindom (01:08):
Let's do that.
I know you always do that,Andreas, I'm quite intrigued to
see what you'll come up withthis time.
Andreas Welsch (01:14):
Alright, this game is called In Your Own
Words, and when I hit thebuzzer, the wheels will start
spinning.
When they stop, you'll see asentence, and I'd like for you
to answer with the first thingthat comes to mind, and why, in
your own words.
To make it a little moreinteresting, you only have 60
seconds for your answer.
Folks, for those of you in theaudience, please put your answer
(01:34):
and why in the chat as well.
Lasse, are you ready for what'sthe buzz?
Lasse Rindom (01:40):
Yes, let's do that.
Andreas Welsch (01:41):
Okay, perfect.
Here we go.
If AI were a movie genre, whatwould it be and why?
60 seconds on the clock.
Go.
Lasse Rindom (01:52):
It's actually quite simple because it the
first thing I think is sci fi,right?
It has to be science fiction.
Also because and this issomething I spoke to some other
people on my own podcast aboutthat AI is, the only technology
we have today that has its ownHollywood genre, right?
It's not like we're going tosee, we have these, what do you
(02:13):
call it?
ERP movies with Sean Connery andTom Cruise running around, or
the BI, Keanu Reeves.
We don't do that, but we have AIas a whole genre.
There's tons of movies about AIgoing wrong or doing good.
And that also means it plays alot into the imaginations of
people and also into theassumptions people have getting
(02:35):
into this, and perhaps also,both add to the hype, but also
add to some of thedisillusionment that we're
seeing in the market as well,this whole genre thing.
So that's obviously sci fidefinitely.
Andreas Welsch (02:47):
I love that answer, but don't underestimate
the budgets that some of thosevendors have to create even
shorter videos for more.
I love that that we're notseeing ERP or BI movies but
never say never.
Lasse Rindom (03:00):
Let's hope it's not some boring documentary
about a budget overrun, right?
Andreas Welsch (03:05):
Yes.
Perfect.
So we'll make sure that the next25 minutes are anything but
boring.
And, if we look at some of therecent coverage, whether it was
Gartner just the other weeksaying that they expect about 30
percent of Generative AIprojects never to make it out of
the pilot stage, never to makeit into production.
(03:26):
I think it was Morgan Stanley acouple of days earlier saying
there's so much more investmentneeded in this market.
And let's tone it down a littlebit.
Maybe it's not delivering allthe value that we expected so
quickly.
It'll take some time.
You've seen this before, whetherit was automation or AI.
What do you think happens nextwhen the industry is somewhere
(03:48):
between that AI hope and AIhype?
Lasse Rindom (03:51):
So I think that if you only say 35% it's actually
much better than what we sawwith something like RPA, where
it's 70% that didn't make it.
We talked about back then, thePOC cemetery, and that's a
phrase that some of us wouldhave hoped we wouldn't have to
come up with again.
I start talking about again, butunfortunately that is happening
and it's not that difficult tounderstand why, right?
(04:13):
Because we're giving people anovel capability tool that's
actually very techie.
It's very nerdy.
The things it does and why itdoes it is quite nerdy.
And we're just saying, Hey, nowyou're more productive, have fun
with it.
So we are inviting people tomake mistakes and try things out
that won't work and we alsoletting those that lead the
(04:34):
discussion be those that developthese tools, right?
Someone like NVIDIA or OpenAI,they drive the discussion and
the marketing around this.
Who's hiring developers, right?
That's NVIDIA.
And who's hiring graphicworkers?
That's OpenAI.
And you're like, but guys, don'tyou know the real world?
And they don't, because whatthey do is they produce
something that's like theproduce airplanes, right?
(04:56):
They don't produce airlines.
It's up to us to translate thisinto airlines.
So I think a lot of themisconceptions has come from
that space here that we hearthings that we're supposed to do
with this.
That's not really necessarilywhat we have to do with it
because it doesn't.
Technology has not reallytouched the world yet.
The world has not touched thetechnology yet.
It's still very fresh and verynovel that way.
(05:18):
But I also think these messagesright now, that sort of smell of
disillusionment, right?
It's a winter is coming from theGame of Thrones thing.
But the winter is only as coldas the summer was hot.
So it's all relative that way.
And, the hype we've had havebeen so massive, even my
grandmother, I know she doesn'texist, but my wife's grandmother
(05:40):
knows about this AI, right?
Everyone knows about this.
AI, everyone's talking about it.
So the hype has been massive.
And of course there'll be somedisappointments there because it
can't be like that.
Nothing changes that quickly andthank God it doesn't, right?
Because that would be incrediblyinsecure to live in that world
as well.
So I think that was a very hotsummer, I could say.
(06:02):
But I think also that mostenterprises have, and this is
just from the conversations I'vehad, kept a rather tight leash
on the AI.
Maybe they've done Copilot orthey're giving people access to
ChatGPT, but that's still been alimited investment for them,
most of them.
So I don't think that the hypeis really hurting enterprises as
much.
Most of them have been playing awaiting game.
(06:23):
We're used to, from older techwaves, to look at insurance or
banking to see where, whatthey've done with it, because
they've always been ahead.
But this time no one's beenahead.
Everyone has access to the sameChatGPT at the same cloud.
There's no better models outthere than the ones we have
access to all of us.
There's no one who's had it for20 years.
(06:43):
It's the same level we are,especially but the foundational
models, obviously I'm talkingabout here.
So that also means there was noone to look for, no one to
imitate.
So everyone's been playing thiswaiting game a little bit.
Done doing some things, but notdoing a lot.
I think the ones that are goingto maybe get a little bit burned
here the investors who've beenjust investing in airplanes when
they should have invested inairlines, that might be a real
(07:03):
issue.
Andreas Welsch (07:04):
So yeah, real quick folks, for those of you in
the audience, if you have aquestion for Lasse, feel free to
put it in the chat as well.
I'm going to take a look in aminute or two and pick them up,
but keep on going.
Lasse Rindom (07:14):
Yeah, but I think also we have to talk about what
is actually the real problemthese models are trying to
solve, right?
The productivity claim is quiteunspecific.
I used to say that it's anunfalsifiable claim to say that
you will use this AI thantomorrow, you'll be more
productive.
But the point is maybe I wake uptomorrow and I'm not more
(07:35):
productive.
I'm still just me.
I haven't changed them.
Oh so why haven't I changedthere, OpenAI?
Oh, that's because you are notusing it correctly.
And they can keep on sayingthat.
So it's a free thing to say.
It's not Karl Popper wouldprobably turn in his grave with
this kind of messaging, right?
It's like saying you have aproblem with your mother.
No, I don't.
Yes, you do.
You just don't know it yet.
(07:55):
It keeps on being on theirterms, right?
Instead of us saying that, okay,what do we actually want to do
with this?
What are the problems it'strying to solve?
It's, I think the key issue iswe need to turn these
capabilities from being just anopen model that gives us any
type of AI we could possiblywant into a platform that
(08:15):
actually gives us a specific AI.
So instead of talking aboutthis, this very broad agents
thing that can do anything.
The undetermined, nondeterministic thing that UIPath
is also promoting and many otherin automation spaces promoting
non deterministic agents justgive them your credit card and
access to SAP and it will doeverything for you.
Let's maybe talk a little bitmore about how we get them to
(08:37):
work in.
Be be specific.
We want to have consistence, or,broad, because we can't do it
all at once.
And then, once we have all thestuff that we need, we can
(08:57):
narrow it down to a few specificindividuals, but we can't point
in a role.
That's where you get all thevalue from it.
Andreas Welsch (09:09):
That's awesome there were so many good nuggets
in there.
I don't even know where tostart.
There are a number of thingsthat I heard.
One was hey, we have technologyplayers pushing the technology
saying you can do anything andeverything with it.
We're not having enough debateabout what exactly can we do
with it?
How do we want to do it?
What do we want to do?
So the old connection that'soften missing between
(09:32):
technologists and businesspeople.
How can we use it to reallydrive an impact?
And I think something else thatyou mentioned about the winter
really resonates with me aswell.
Yes, there is going to be somedisillusionment that it's not as
easy as everybody has beenhoping or has been promising,
especially those with financialinterests in it.
Doesn't mean that it's going tobe like that.
Lasse Rindom (09:53):
But it looks like a UX, right?
So everyone thinks it is a UXalready.
It's something you just interactwith.
But to be honest, none of us hasreally been used to interacting
with a system that way andtalking back to it and it's
talking back to me and I have totalk to it.
That's not how you go aboutsystems, right?
That was also why someone likeGoogle who invented the
transformer didn't win in the AIthe race for this getting it
(10:15):
out, because they thought peoplewanted accuracy and precision,
but then suddenly people werelike, oh, it's okay just to chat
with something that makes somethings up.
But I think that the key isactually a lot to look in into
the context of these models andthe training data and what I
said this.
Multiple models they have intheir stomach, because then you
get to a point where you can seeyou, this can work on any type
(10:36):
of unstructured data you have.
And there's real power in thatbecause it expands the potential
footprint of your digitaltransformation.
You can digitalize anything nowwith these models, because if
they look at something, if yougave it a picture of whatever
you do, and you say, what'sthis, put it in a tabular form,
they will actually structure itfor you and contextualize it
because they have that context.
(10:58):
In their minds, most of it, atleast, that's quite big.
I think that's reallyinteresting.
Andreas Welsch (11:04):
Indeed, yeah.
And I'm wondering as we're nowseeing on one hand the hype and
on the other hand the other sideof the slope, the
disillusionment, and figuringout where and how can we
actually use this?
What are you seeing?
How can leaders leverage thatmomentum in the market to still
drive business impact?
And are there any particularlines of business to, Michael's
point here in the chat, whereyou're seeing them leading or
(11:27):
being front and center?
Lasse Rindom (11:29):
I think first of all how can they drive the
leverage to momentum?
I think it's impossible to missthe momentum.
If you're an AI leader rightnow, you're not feeling the
momentum.
I don't know what you're doing.
You must be living under a rock.
Okay.
That's one thing out of the way.
But what you need to do quickly,it's actually to take the focus
away from just.
Just buying Copilot and notgiving a shit like most other
companies are actually doingjust buy Copilot, throw it out
(11:51):
there and then see what happens.
You need to start working onbuilding automation utilizing
these well defined, narrow AI'sthat actually understands what
they're doing, and then alsotrain people to use the
technology the right way.
So the point is, as I saidbefore, UX, but it's not just
the UX and it's not simple forthat reason.
People don't know how to use it.
The best use cases have beenstill with IT developers because
(12:12):
they know how, what they'resupposed to do with it, narrow
it down, make sure it works onthat code they know, and that's
just intuitive for them.
But it's not intuitive foreveryone else.
It's like with the low coderevolution some years back,
right?
I've spoken to so many.
I've said this for years aswell, right?
It might be low code or no code,but you still need to understand
loops and if then and else andvariables, you need to
(12:33):
understand these things to workwith it, and to some extent,
it's not the same with the LLMs,but there are some things you
need to understand on how toprompt engineer it.
How many people have you spokento outside of IT who says, it
doesn't give me the answer Iwant, right?
Tons of people, right?
It happens all the time but I'mfrom IT.
I understand.
Oh, I need to ask directly into,I need to make sure it answers
(12:55):
correctly.
And you also asked me andMichael asked that in the chat,
right?
What line of business presentsthe strongest case?
That's a really difficult one toanswer right now, I think.
My focus is very much on how wecan lift the business support
functions into something better.
Cause that's what we do in mycurrent company.
We transform business supportfunctions like payroll, HR,
(13:16):
finance, the facilitymanagement, legal, those kinds
of functions.
But the obvious one is obviouslymarketing all the time.
It's so simple to start doingthings there.
But I actually think that, whatI said, the interpretation of
more data, the controlling thatwe can do the if we ask any
business leader today if youcould get an army of interns
(13:38):
tomorrow or 20 interns, whatwould you have them do?
I would have them control allthese invoices for errors and
blah, blah, blah, blah, blah.
And then we start gettingsomething where it actually gets
extremely productive, butbecause you can get these
interns tomorrow using thesemodels, right?
Just define the role, have themlook at your data and get some
responses back.
Controlling is immense in this,right?
(13:59):
Also, on the other side, we'reall going to be controllers as
well.
We're definitely not going toget out of a job.
There's tons of things tocontrol in an AI world as well,
right?
Andreas Welsch (14:07):
Very true, right?
It shifts from creating tocontrolling or to reviewing and
guiding.
What you said having a differentinteraction with these systems
and making sure that they createoutput that you actually want
and that you need, and that'sbetter than me.
Lasse Rindom (14:23):
Yeah, exactly.
And also Andreas, the unpleasantquestion in all this really,
right?
And the existential question foran AI leader or someone working
in AI like you is if everyonegets more productive, is anyone
really more productive?
Andreas Welsch (14:38):
On the other hand, what's that what's that
expression, a rising tide liftsall boats.
So there's some optimism in thatas well.
Now, again, with those correctedprojections of, Hey, we're
solving each and every problemwith AI, AI will outsmart us by
the end of the year.
All those exaggerated claims.
(14:59):
And now being corrected.
Maybe not this year, but nextyear, five years, 10 years, who
knows?
How can leaders use still thatmomentum when the outside
market, when the analysts, aresaying, let's tone it down a
little bit.
Are they all just going to sitback and wait for the storm to
(15:20):
pass?
Can they even do that?
What are you seeing there?
Lasse Rindom (15:24):
I think the honest answer is let's just take the
assumptions you're saying, atthey're toning down their
analyst forecast and theirprojections, but where on earth
did they get those projectionsfrom, Andreas?
To me, most of them seems likethey did 30 trillion.
And someone else said, no, I'mgoing a bit more 15 trillion.
(15:45):
And it just kept on going likethat.
And then you had, I thinkQualcomm said that the potential
for Generative AI is unlimited,and then you can't really beat
it anymore.
And that also led to theserumors of Sam Altman asking for
6 trillion in an investment.
It's what guys, what are we eventalking about?
So I always had this feelingthat, okay, everything they're
saying is over the moon.
And now it seems like they'retoning it down and that makes it
(16:09):
everything look negative.
But we're still talking about atechnology with immense
potential, right?
If you follow along on LinkedIn,someone like Dr.
Jeffrey Funk, I follow him a lotand he's always very critical of
these models.
There was this news out a coupleof weeks ago that 80 percent of
workers found that working withwith LLMs made them less
productive.
He was actually surprised and hesaid, that doesn't hold onto
(16:32):
what I usually see, right?
I'm critical, but there aredefinitely value and
productivity in this, so this istoo much, right?
But we just given everyone thistool and giving everyone the
pressure of having to work witha tool we didn't train them on
and said this looks like an easyto use UX and I had it to make a
poem for my grandma so now Iwill fire one of your co workers
and you just have to be moreproductive.
(16:53):
See you guys.
And the world doesn't work thatway.
So of course, people arestressed out about this.
They need to know it's likegiving them we gave them Excel.
You didn't just go around thenext day and said, now
everyone's more productive.
You have to train them.
You have to learn what they door say, okay, so someone
invented the steam engine.
So let's just put that in a loomfactory.
(17:15):
But shouldn't it do somethingwith it?
No, just, we have a steam enginenow.
Let's have fun with it.
You have to make it, you have tointegrate it into the way you
work, into your production.
This is really simple.
It's about we've said this foryears.
You don't automate things youdon't understand.
And that was my first thing whenthis came out.
I was like, this still holdstrue.
This will be an eternal truth.
(17:35):
Of course I had my moments ofdoubt because this seems so
aggressively.
Everything, everywhere, all atonce, right?
This new tool, right?
But, really right now, whatwe're seeing is, yeah, you still
need to know what you'reautomating.
End of story.
Andreas Welsch (17:50):
I think especially around the part of
training, adequately preparingpeople for it, telling them and
letting them know, Hey, this iswhat it can do.
This is what it is not reallygood at.
This is how you get betterresults from it.
And I think that kind of goesback to a community of practice,
community of multipliers typeideas that I think many
businesses, especially largerones, would be well advised to
(18:13):
establish again.
And, have some experts have somemultipliers that then take that
knowledge again back into theirlines of business and say, Hey
this is what we found.
What are you seeing?
How can we help you?
I think it needs a lot morecollaboration and a lot more
hand holding to get thoseexcellent results than just to
your point, dropping it on themand say here's whatever tool.
Lasse Rindom (18:36):
Training, but also a fair bit of patience, right?
That's the boring thing to say.
I'm not going to sell anyonepatience as a consultant
because, but because training isobviously something we can
provide.
But patience, it also takes thatbecause luckily, as I said
earlier, the world doesn'tchange from one night to the
other, right?
It doesn't do that.
People have their habits, theway they're working, and that's
(18:57):
what gives stability to thingsthat was give stability to our
organizations, creates ourorganizations in the end.
So this, having this realisticview of things, that's actually
quite important.
Andreas Welsch (19:08):
Now, I'm curious if we assume we are only at the
very beginning of this, and Ithink Michael made a good
comment earlier when we talkedabout movies and genres.
It's like the like silent movieswere at the very beginning.
And we can see where it's going20, 50 years from now.
(19:30):
But also, again, in light ofthose projections, on one hand,
the hey, this will create aneconomic impact or a value the
size of France's GDP orGermany's GDP, I think those
were the McKinsey projectionslast year, or 300 million jobs
will be impacted over the next10 years.
I think it was in the US alone,Morgan Stanley type quotes.
(19:50):
Why do you think we'll seebusinesses and leaders still
continue investing in AI, eventhough we've reached the peak or
so it seems and it's going downthe other end of the slope?
Lasse Rindom (20:02):
The technology is really great, right?
This is something that's quiteastonishing.
And I think still it isastonishing what it can do if
you just, sometimes it feelslike you're saying something
when I say this can workuntrained, because it's pre
trained on unstructured data.
It sounds like I'm narrowing itdown and making this a small
thing, but it's actually a hugething.
If you've given this to me threeyears ago and said, I have a
(20:24):
tool that can do this, I stillthink this is a novelty in all
of it, like ever, right?
We've never seen a technologythat can do this out of the box
with no training.
That's absolutely crazy.
And the sheer potential in usingthat to expand the digital
transformation footprint, as Isaid, for companies is immense.
(20:47):
Not even mentioning the chatbotfeature, because I think that's
still there's some hallucinationthings that needs to be weeded
out.
It's a little bit tricky withthat.
I actually think that's tricky.
That's what people have beentrying to do initially.
I don't think that's where youshould look initially.
I think you should look atassisting, helping you write
things and being a personalproductivity assistant and
working with unstructured in anautomation setup, that's where
(21:08):
you can get value right now.
But a lot of people have beentrying bigger things and then
gotten a little bit disappointedwith that.
But I think the value is just soimmense in that, that I see
obvious reasons for people tokeep working with it.
And also they see, they start tosee other companies getting some
value.
I have a lot of companies comingto me saying that, okay, now
we've heard real stories aboutother companies getting
(21:31):
something from this, and that'swhy we want to move on it as
well.
But you also see companieswhere, you know companies of a
couple of thousand people sayingthat, Hey, we saved 100,000 last
year using AI.
And they're writing storiesabout it.
And you're like, nah, that'sjust very little.
But that's why it's crawl, walk,run, right?
(21:54):
And we still need to do that.
We get the tech, but we didn'tget the change.
Tech's not change, right?
Technology doesn't just changethe world.
The world needs to change thetechnology.
I think we need to understandthat.
Andreas Welsch (22:06):
That's a very powerful quote.
That's awesome.
Now, I'm wondering, in theNordics specifically, where I
believe you do a lot of yourwork, what are you seeing there?
Is there anything specific,anything that stands out, how
people are approaching it, howthey're thinking about it?
Is it more collaborative, we canuse this automation and it frees
(22:29):
up people so they can do highervalue work?
Is it more of the we need fewerpeople where's the discussion
going?
What are you seeing?
Lasse Rindom (22:38):
I don't see anyone saying we need fewer people
cause the economic growth isstill on.
And I think, most of the Westernworld have.
I'm 40 years old, so I'm in thesmallest generation in Denmark
in 150 years, and I don't seeany reduced demand for anyone in
my generation, to be honest.
So I don't see any layoffs thatway, but I see a lot of people
in Denmark adopting it to theextent they can.
(22:59):
Whenever you talk to someone,they're saying like, okay we
tried it.
We using it for some things we'dlike to get more productive with
it.
We had to know what we can useit for more specifically, learn
how to prompt it, get someguidance on how we integrate it
into our processes.
That's the discussions I'mhaving right now.
Obviously every developer hasembraced it immediately.
So this is happening and it'scoming.
(23:21):
And Denmark has for years beenthe most digital country in the
world in the public sector.
That mirrors the rest of the ofthe country as well.
So, our adoption rate issomething that people are
looking at as well.
And I think it was a survey thatsaid they surveyed 300,000 Danes
where they almost all of themhave been tried ChatGPT and I
(23:42):
think 30 percent or 40 percentof something that was working
with it.
I'm still curious where they got300,000 Danes from, because
we're only like 6 million, so300,000 Danes is actually quite
a lot of people to survey.
I don't think I've ever seenthat big a survey in Denmark.
So I have my doubts about thatsurvey a little bit, to be
honest, but it is true.
People have worked with it.
People know what it is andthey're not afraid of it.
Andreas Welsch (24:03):
That's awesome.
That's great to hear.
And I love a good success storyand seeing what is working well
in different parts of the world,in different countries even.
Lasse Rindom (24:13):
I see a lot of comms about risk and everything.
It's okay.
I share a quote from from mylast my last episode on my
podcast as well, because I spoketo someone called Bohan Blili
Hamelin.
And he said, cause I said to himhe works with AI risk.
And I said, aren't you afraidthat everyone wants to talk
about AI innovation?
You're talking about AI risk aswell.
This seems like you're talkingthe wrong, barking up the wrong
tree.
Those things are the two sidesof the same coin you can't have
(24:37):
innovation.
You can't take a chance if youdon't understand the risks
associated with that chance.
And I think that's something wereally need to get to grips with
in this market.
We need all the AI influence tostop either being yay sayers and
nay sayers and understand thatwe need to have the common
conversation saying that, okay,in order for this to actually
change some things, we need totalk about what could go wrong.
(24:58):
You're right.
That's, important.
And I think people need to getto that point very quickly.
Andreas Welsch (25:03):
Nothing else to add.
It's perfect.
The, only thing that I would,say though, is that we're coming
close to the end of the show.
And I was wondering if you cansummarize the key three
takeaways for our audience fromour conversation today.
Lasse Rindom (25:16):
Yeah the thing I just said let's get innovation
and risk to be a part of theconversation at the same time.
I think also let's focus alittle bit less on the airplane
and focus more on building theairline.
And then let's get specific andlet's make them all specific,
make them productive, talk moreabout roles than agents.
I think that's where wedefinitely need to go.
And we need to start talkingabout that too, to make it work
(25:37):
right now.
Andreas Welsch (25:38):
Wonderful.
Lasse, thank you so much forjoining us and for sharing your
expertise with us.
It was great having you on.
Lasse Rindom (25:46):
Thank you so much, Andreas.
Thank you for having me.
Andreas Welsch (25:48):
Perfect.
And thanks for those of you inthe audience for joining us as
well and for learning with us.
Today we'll talk about leveraging the AI hype and
disillusionment to drivebusiness impact.
And who better to talk about itthan someone who thrives in that
environment, Lasse Rindom.
Hey Lasse, thank you so much forjoining.
Lasse Rindom (00:12):
Hi Andreas, and thank you so much for having me
today.
Andreas Welsch (00:15):
Awesome.
Hey, why don't you tell ouraudience a little bit about
yourself, who you are and whatyou do?
Lasse Rindom (00:20):
So my name is Lasse Rindem and I am a, first
of all, an automation pioneer.
I've been working a lot inautomation for the last 10
years.
And I am the current AI lead atDanish consultancy, BASICO,
where I drive our go to marketefforts on AI and also on some
automation and I also run thepodcast the only constant where
(00:43):
I've had you Andreas also as aguest.
So that's really nice.
Andreas Welsch (00:46):
Awesome.
We've been in touch for a longtime.
So it's always great to connectwith you and now I'm looking
forward to sharing that with ouraudience as well.
Don't forget to join thewaitlist for my upcoming book,
the AI Leadership Handbook ataileadershiphandbook.com.
I'm keeping it simple.
So with that out of the way,Lasse, what do you say, should
we play a little game to kickthings off?
Lasse Rindom (01:08):
Let's do that.
I know you always do that,Andreas, I'm quite intrigued to
see what you'll come up withthis time.
Andreas Welsch (01:14):
Alright, this game is called In Your Own
Words, and when I hit thebuzzer, the wheels will start
spinning.
When they stop, you'll see asentence, and I'd like for you
to answer with the first thingthat comes to mind, and why, in
your own words.
To make it a little moreinteresting, you only have 60
seconds for your answer.
Folks, for those of you in theaudience, please put your answer
(01:34):
and why in the chat as well.
Lasse, are you ready for what'sthe buzz?
Lasse Rindom (01:40):
Yes, let's do that.
Andreas Welsch (01:41):
Okay, perfect.
Here we go.
If AI were a movie genre, whatwould it be and why?
60 seconds on the clock.
Go.
Lasse Rindom (01:52):
It's actually quite simple because it the
first thing I think is sci fi,right?
It has to be science fiction.
Also because and this issomething I spoke to some other
people on my own podcast aboutthat AI is, the only technology
we have today that has its ownHollywood genre, right?
It's not like we're going tosee, we have these, what do you
(02:13):
call it?
ERP movies with Sean Connery andTom Cruise running around, or
the BI, Keanu Reeves.
We don't do that, but we have AIas a whole genre.
There's tons of movies about AIgoing wrong or doing good.
And that also means it plays alot into the imaginations of
people and also into theassumptions people have getting
(02:35):
into this, and perhaps also,both add to the hype, but also
add to some of thedisillusionment that we're
seeing in the market as well,this whole genre thing.
So that's obviously sci fidefinitely.
Andreas Welsch (02:47):
I love that answer, but don't underestimate
the budgets that some of thosevendors have to create even
shorter videos for more.
I love that that we're notseeing ERP or BI movies but
never say never.
Lasse Rindom (03:00):
Let's hope it's not some boring documentary
about a budget overrun, right?
Andreas Welsch (03:05):
Yes.
Perfect.
So we'll make sure that the next25 minutes are anything but
boring.
And, if we look at some of therecent coverage, whether it was
Gartner just the other weeksaying that they expect about 30
percent of Generative AIprojects never to make it out of
the pilot stage, never to makeit into production.
(03:26):
I think it was Morgan Stanley acouple of days earlier saying
there's so much more investmentneeded in this market.
And let's tone it down a littlebit.
Maybe it's not delivering allthe value that we expected so
quickly.
It'll take some time.
You've seen this before, whetherit was automation or AI.
What do you think happens nextwhen the industry is somewhere
(03:48):
between that AI hope and AIhype?
Lasse Rindom (03:51):
So I think that if you only say 35% it's actually
much better than what we sawwith something like RPA, where
it's 70% that didn't make it.
We talked about back then, thePOC cemetery, and that's a
phrase that some of us wouldhave hoped we wouldn't have to
come up with again.
I start talking about again, butunfortunately that is happening
and it's not that difficult tounderstand why, right?
(04:13):
Because we're giving people anovel capability tool that's
actually very techie.
It's very nerdy.
The things it does and why itdoes it is quite nerdy.
And we're just saying, Hey, nowyou're more productive, have fun
with it.
So we are inviting people tomake mistakes and try things out
that won't work and we alsoletting those that lead the
(04:34):
discussion be those that developthese tools, right?
Someone like NVIDIA or OpenAI,they drive the discussion and
the marketing around this.
Who's hiring developers, right?
That's NVIDIA.
And who's hiring graphicworkers?
That's OpenAI.
And you're like, but guys, don'tyou know the real world?
And they don't, because whatthey do is they produce
something that's like theproduce airplanes, right?
(04:56):
They don't produce airlines.
It's up to us to translate thisinto airlines.
So I think a lot of themisconceptions has come from
that space here that we hearthings that we're supposed to do
with this.
That's not really necessarilywhat we have to do with it
because it doesn't.
Technology has not reallytouched the world yet.
The world has not touched thetechnology yet.
It's still very fresh and verynovel that way.
(05:18):
But I also think these messagesright now, that sort of smell of
disillusionment, right?
It's a winter is coming from theGame of Thrones thing.
But the winter is only as coldas the summer was hot.
So it's all relative that way.
And, the hype we've had havebeen so massive, even my
grandmother, I know she doesn'texist, but my wife's grandmother
(05:40):
knows about this AI, right?
Everyone knows about this.
AI, everyone's talking about it.
So the hype has been massive.
And of course there'll be somedisappointments there because it
can't be like that.
Nothing changes that quickly andthank God it doesn't, right?
Because that would be incrediblyinsecure to live in that world
as well.
So I think that was a very hotsummer, I could say.
(06:02):
But I think also that mostenterprises have, and this is
just from the conversations I'vehad, kept a rather tight leash
on the AI.
Maybe they've done Copilot orthey're giving people access to
ChatGPT, but that's still been alimited investment for them,
most of them.
So I don't think that the hypeis really hurting enterprises as
much.
Most of them have been playing awaiting game.
(06:23):
We're used to, from older techwaves, to look at insurance or
banking to see where, whatthey've done with it, because
they've always been ahead.
But this time no one's beenahead.
Everyone has access to the sameChatGPT at the same cloud.
There's no better models outthere than the ones we have
access to all of us.
There's no one who's had it for20 years.
(06:43):
It's the same level we are,especially but the foundational
models, obviously I'm talkingabout here.
So that also means there was noone to look for, no one to
imitate.
So everyone's been playing thiswaiting game a little bit.
Done doing some things, but notdoing a lot.
I think the ones that are goingto maybe get a little bit burned
here the investors who've beenjust investing in airplanes when
they should have invested inairlines, that might be a real
(07:03):
issue.
Andreas Welsch (07:04):
So yeah, real quick folks, for those of you in
the audience, if you have aquestion for Lasse, feel free to
put it in the chat as well.
I'm going to take a look in aminute or two and pick them up,
but keep on going.
Lasse Rindom (07:14):
Yeah, but I think also we have to talk about what
is actually the real problemthese models are trying to
solve, right?
The productivity claim is quiteunspecific.
I used to say that it's anunfalsifiable claim to say that
you will use this AI thantomorrow, you'll be more
productive.
But the point is maybe I wake uptomorrow and I'm not more
(07:35):
productive.
I'm still just me.
I haven't changed them.
Oh so why haven't I changedthere, OpenAI?
Oh, that's because you are notusing it correctly.
And they can keep on sayingthat.
So it's a free thing to say.
It's not Karl Popper wouldprobably turn in his grave with
this kind of messaging, right?
It's like saying you have aproblem with your mother.
No, I don't.
Yes, you do.
You just don't know it yet.
(07:55):
It keeps on being on theirterms, right?
Instead of us saying that, okay,what do we actually want to do
with this?
What are the problems it'strying to solve?
It's, I think the key issue iswe need to turn these
capabilities from being just anopen model that gives us any
type of AI we could possiblywant into a platform that
(08:15):
actually gives us a specific AI.
So instead of talking aboutthis, this very broad agents
thing that can do anything.
The undetermined, nondeterministic thing that UIPath
is also promoting and many otherin automation spaces promoting
non deterministic agents justgive them your credit card and
access to SAP and it will doeverything for you.
Let's maybe talk a little bitmore about how we get them to
(08:37):
work in.
Be be specific.
We want to have consistence, or,broad, because we can't do it
all at once.
And then, once we have all thestuff that we need, we can
(08:57):
narrow it down to a few specificindividuals, but we can't point
in a role.
That's where you get all thevalue from it.
Andreas Welsch (09:09):
That's awesome there were so many good nuggets
in there.
I don't even know where tostart.
There are a number of thingsthat I heard.
One was hey, we have technologyplayers pushing the technology
saying you can do anything andeverything with it.
We're not having enough debateabout what exactly can we do
with it?
How do we want to do it?
What do we want to do?
So the old connection that'soften missing between
(09:32):
technologists and businesspeople.
How can we use it to reallydrive an impact?
And I think something else thatyou mentioned about the winter
really resonates with me aswell.
Yes, there is going to be somedisillusionment that it's not as
easy as everybody has beenhoping or has been promising,
especially those with financialinterests in it.
Doesn't mean that it's going tobe like that.
Lasse Rindom (09:53):
But it looks like a UX, right?
So everyone thinks it is a UXalready.
It's something you just interactwith.
But to be honest, none of us hasreally been used to interacting
with a system that way andtalking back to it and it's
talking back to me and I have totalk to it.
That's not how you go aboutsystems, right?
That was also why someone likeGoogle who invented the
transformer didn't win in the AIthe race for this getting it
(10:15):
out, because they thought peoplewanted accuracy and precision,
but then suddenly people werelike, oh, it's okay just to chat
with something that makes somethings up.
But I think that the key isactually a lot to look in into
the context of these models andthe training data and what I
said this.
Multiple models they have intheir stomach, because then you
get to a point where you can seeyou, this can work on any type
(10:36):
of unstructured data you have.
And there's real power in thatbecause it expands the potential
footprint of your digitaltransformation.
You can digitalize anything nowwith these models, because if
they look at something, if yougave it a picture of whatever
you do, and you say, what'sthis, put it in a tabular form,
they will actually structure itfor you and contextualize it
because they have that context.
(10:58):
In their minds, most of it, atleast, that's quite big.
I think that's reallyinteresting.
Andreas Welsch (11:04):
Indeed, yeah.
And I'm wondering as we're nowseeing on one hand the hype and
on the other hand the other sideof the slope, the
disillusionment, and figuringout where and how can we
actually use this?
What are you seeing?
How can leaders leverage thatmomentum in the market to still
drive business impact?
And are there any particularlines of business to, Michael's
point here in the chat, whereyou're seeing them leading or
(11:27):
being front and center?
Lasse Rindom (11:29):
I think first of all how can they drive the
leverage to momentum?
I think it's impossible to missthe momentum.
If you're an AI leader rightnow, you're not feeling the
momentum.
I don't know what you're doing.
You must be living under a rock.
Okay.
That's one thing out of the way.
But what you need to do quickly,it's actually to take the focus
away from just.
Just buying Copilot and notgiving a shit like most other
companies are actually doingjust buy Copilot, throw it out
(11:51):
there and then see what happens.
You need to start working onbuilding automation utilizing
these well defined, narrow AI'sthat actually understands what
they're doing, and then alsotrain people to use the
technology the right way.
So the point is, as I saidbefore, UX, but it's not just
the UX and it's not simple forthat reason.
People don't know how to use it.
The best use cases have beenstill with IT developers because
(12:12):
they know how, what they'resupposed to do with it, narrow
it down, make sure it works onthat code they know, and that's
just intuitive for them.
But it's not intuitive foreveryone else.
It's like with the low coderevolution some years back,
right?
I've spoken to so many.
I've said this for years aswell, right?
It might be low code or no code,but you still need to understand
loops and if then and else andvariables, you need to
(12:33):
understand these things to workwith it, and to some extent,
it's not the same with the LLMs,but there are some things you
need to understand on how toprompt engineer it.
How many people have you spokento outside of IT who says, it
doesn't give me the answer Iwant, right?
Tons of people, right?
It happens all the time but I'mfrom IT.
I understand.
Oh, I need to ask directly into,I need to make sure it answers
(12:55):
correctly.
And you also asked me andMichael asked that in the chat,
right?
What line of business presentsthe strongest case?
That's a really difficult one toanswer right now, I think.
My focus is very much on how wecan lift the business support
functions into something better.
Cause that's what we do in mycurrent company.
We transform business supportfunctions like payroll, HR,
(13:16):
finance, the facilitymanagement, legal, those kinds
of functions.
But the obvious one is obviouslymarketing all the time.
It's so simple to start doingthings there.
But I actually think that, whatI said, the interpretation of
more data, the controlling thatwe can do the if we ask any
business leader today if youcould get an army of interns
(13:38):
tomorrow or 20 interns, whatwould you have them do?
I would have them control allthese invoices for errors and
blah, blah, blah, blah, blah.
And then we start gettingsomething where it actually gets
extremely productive, butbecause you can get these
interns tomorrow using thesemodels, right?
Just define the role, have themlook at your data and get some
responses back.
Controlling is immense in this,right?
(13:59):
Also, on the other side, we'reall going to be controllers as
well.
We're definitely not going toget out of a job.
There's tons of things tocontrol in an AI world as well,
right?
Andreas Welsch (14:07):
Very true, right?
It shifts from creating tocontrolling or to reviewing and
guiding.
What you said having a differentinteraction with these systems
and making sure that they createoutput that you actually want
and that you need, and that'sbetter than me.
Lasse Rindom (14:23):
Yeah, exactly.
And also Andreas, the unpleasantquestion in all this really,
right?
And the existential question foran AI leader or someone working
in AI like you is if everyonegets more productive, is anyone
really more productive?
Andreas Welsch (14:38):
On the other hand, what's that what's that
expression, a rising tide liftsall boats.
So there's some optimism in thatas well.
Now, again, with those correctedprojections of, Hey, we're
solving each and every problemwith AI, AI will outsmart us by
the end of the year.
All those exaggerated claims.
(14:59):
And now being corrected.
Maybe not this year, but nextyear, five years, 10 years, who
knows?
How can leaders use still thatmomentum when the outside
market, when the analysts, aresaying, let's tone it down a
little bit.
Are they all just going to sitback and wait for the storm to
(15:20):
pass?
Can they even do that?
What are you seeing there?
Lasse Rindom (15:24):
I think the honest answer is let's just take the
assumptions you're saying, atthey're toning down their
analyst forecast and theirprojections, but where on earth
did they get those projectionsfrom, Andreas?
To me, most of them seems likethey did 30 trillion.
And someone else said, no, I'mgoing a bit more 15 trillion.
(15:45):
And it just kept on going likethat.
And then you had, I thinkQualcomm said that the potential
for Generative AI is unlimited,and then you can't really beat
it anymore.
And that also led to theserumors of Sam Altman asking for
6 trillion in an investment.
It's what guys, what are we eventalking about?
So I always had this feelingthat, okay, everything they're
saying is over the moon.
And now it seems like they'retoning it down and that makes it
(16:09):
everything look negative.
But we're still talking about atechnology with immense
potential, right?
If you follow along on LinkedIn,someone like Dr.
Jeffrey Funk, I follow him a lotand he's always very critical of
these models.
There was this news out a coupleof weeks ago that 80 percent of
workers found that working withwith LLMs made them less
productive.
He was actually surprised and hesaid, that doesn't hold onto
(16:32):
what I usually see, right?
I'm critical, but there aredefinitely value and
productivity in this, so this istoo much, right?
But we just given everyone thistool and giving everyone the
pressure of having to work witha tool we didn't train them on
and said this looks like an easyto use UX and I had it to make a
poem for my grandma so now Iwill fire one of your co workers
and you just have to be moreproductive.
(16:53):
See you guys.
And the world doesn't work thatway.
So of course, people arestressed out about this.
They need to know it's likegiving them we gave them Excel.
You didn't just go around thenext day and said, now
everyone's more productive.
You have to train them.
You have to learn what they door say, okay, so someone
invented the steam engine.
So let's just put that in a loomfactory.
(17:15):
But shouldn't it do somethingwith it?
No, just, we have a steam enginenow.
Let's have fun with it.
You have to make it, you have tointegrate it into the way you
work, into your production.
This is really simple.
It's about we've said this foryears.
You don't automate things youdon't understand.
And that was my first thing whenthis came out.
I was like, this still holdstrue.
This will be an eternal truth.
(17:35):
Of course I had my moments ofdoubt because this seems so
aggressively.
Everything, everywhere, all atonce, right?
This new tool, right?
But, really right now, whatwe're seeing is, yeah, you still
need to know what you'reautomating.
End of story.
Andreas Welsch (17:50):
I think especially around the part of
training, adequately preparingpeople for it, telling them and
letting them know, Hey, this iswhat it can do.
This is what it is not reallygood at.
This is how you get betterresults from it.
And I think that kind of goesback to a community of practice,
community of multipliers typeideas that I think many
businesses, especially largerones, would be well advised to
(18:13):
establish again.
And, have some experts have somemultipliers that then take that
knowledge again back into theirlines of business and say, Hey
this is what we found.
What are you seeing?
How can we help you?
I think it needs a lot morecollaboration and a lot more
hand holding to get thoseexcellent results than just to
your point, dropping it on themand say here's whatever tool.
Lasse Rindom (18:36):
Training, but also a fair bit of patience, right?
That's the boring thing to say.
I'm not going to sell anyonepatience as a consultant
because, but because training isobviously something we can
provide.
But patience, it also takes thatbecause luckily, as I said
earlier, the world doesn'tchange from one night to the
other, right?
It doesn't do that.
People have their habits, theway they're working, and that's
(18:57):
what gives stability to thingsthat was give stability to our
organizations, creates ourorganizations in the end.
So this, having this realisticview of things, that's actually
quite important.
Andreas Welsch (19:08):
Now, I'm curious if we assume we are only at the
very beginning of this, and Ithink Michael made a good
comment earlier when we talkedabout movies and genres.
It's like the like silent movieswere at the very beginning.
And we can see where it's going20, 50 years from now.
(19:30):
But also, again, in light ofthose projections, on one hand,
the hey, this will create aneconomic impact or a value the
size of France's GDP orGermany's GDP, I think those
were the McKinsey projectionslast year, or 300 million jobs
will be impacted over the next10 years.
I think it was in the US alone,Morgan Stanley type quotes.
(19:50):
Why do you think we'll seebusinesses and leaders still
continue investing in AI, eventhough we've reached the peak or
so it seems and it's going downthe other end of the slope?
Lasse Rindom (20:02):
The technology is really great, right?
This is something that's quiteastonishing.
And I think still it isastonishing what it can do if
you just, sometimes it feelslike you're saying something
when I say this can workuntrained, because it's pre
trained on unstructured data.
It sounds like I'm narrowing itdown and making this a small
thing, but it's actually a hugething.
If you've given this to me threeyears ago and said, I have a
(20:24):
tool that can do this, I stillthink this is a novelty in all
of it, like ever, right?
We've never seen a technologythat can do this out of the box
with no training.
That's absolutely crazy.
And the sheer potential in usingthat to expand the digital
transformation footprint, as Isaid, for companies is immense.
(20:47):
Not even mentioning the chatbotfeature, because I think that's
still there's some hallucinationthings that needs to be weeded
out.
It's a little bit tricky withthat.
I actually think that's tricky.
That's what people have beentrying to do initially.
I don't think that's where youshould look initially.
I think you should look atassisting, helping you write
things and being a personalproductivity assistant and
working with unstructured in anautomation setup, that's where
(21:08):
you can get value right now.
But a lot of people have beentrying bigger things and then
gotten a little bit disappointedwith that.
But I think the value is just soimmense in that, that I see
obvious reasons for people tokeep working with it.
And also they see, they start tosee other companies getting some
value.
I have a lot of companies comingto me saying that, okay, now
we've heard real stories aboutother companies getting
(21:31):
something from this, and that'swhy we want to move on it as
well.
But you also see companieswhere, you know companies of a
couple of thousand people sayingthat, Hey, we saved 100,000 last
year using AI.
And they're writing storiesabout it.
And you're like, nah, that'sjust very little.
But that's why it's crawl, walk,run, right?
(21:54):
And we still need to do that.
We get the tech, but we didn'tget the change.
Tech's not change, right?
Technology doesn't just changethe world.
The world needs to change thetechnology.
I think we need to understandthat.
Andreas Welsch (22:06):
That's a very powerful quote.
That's awesome.
Now, I'm wondering, in theNordics specifically, where I
believe you do a lot of yourwork, what are you seeing there?
Is there anything specific,anything that stands out, how
people are approaching it, howthey're thinking about it?
Is it more collaborative, we canuse this automation and it frees
(22:29):
up people so they can do highervalue work?
Is it more of the we need fewerpeople where's the discussion
going?
What are you seeing?
Lasse Rindom (22:38):
I don't see anyone saying we need fewer people
cause the economic growth isstill on.
And I think, most of the Westernworld have.
I'm 40 years old, so I'm in thesmallest generation in Denmark
in 150 years, and I don't seeany reduced demand for anyone in
my generation, to be honest.
So I don't see any layoffs thatway, but I see a lot of people
in Denmark adopting it to theextent they can.
(22:59):
Whenever you talk to someone,they're saying like, okay we
tried it.
We using it for some things we'dlike to get more productive with
it.
We had to know what we can useit for more specifically, learn
how to prompt it, get someguidance on how we integrate it
into our processes.
That's the discussions I'mhaving right now.
Obviously every developer hasembraced it immediately.
So this is happening and it'scoming.
(23:21):
And Denmark has for years beenthe most digital country in the
world in the public sector.
That mirrors the rest of the ofthe country as well.
So, our adoption rate issomething that people are
looking at as well.
And I think it was a survey thatsaid they surveyed 300,000 Danes
where they almost all of themhave been tried ChatGPT and I
(23:42):
think 30 percent or 40 percentof something that was working
with it.
I'm still curious where they got300,000 Danes from, because
we're only like 6 million, so300,000 Danes is actually quite
a lot of people to survey.
I don't think I've ever seenthat big a survey in Denmark.
So I have my doubts about thatsurvey a little bit, to be
honest, but it is true.
People have worked with it.
People know what it is andthey're not afraid of it.
Andreas Welsch (24:03):
That's awesome.
That's great to hear.
And I love a good success storyand seeing what is working well
in different parts of the world,in different countries even.
Lasse Rindom (24:13):
I see a lot of comms about risk and everything.
It's okay.
I share a quote from from mylast my last episode on my
podcast as well, because I spoketo someone called Bohan Blili
Hamelin.
And he said, cause I said to himhe works with AI risk.
And I said, aren't you afraidthat everyone wants to talk
about AI innovation?
You're talking about AI risk aswell.
This seems like you're talkingthe wrong, barking up the wrong
tree.
Those things are the two sidesof the same coin you can't have
(24:37):
innovation.
You can't take a chance if youdon't understand the risks
associated with that chance.
And I think that's something wereally need to get to grips with
in this market.
We need all the AI influence tostop either being yay sayers and
nay sayers and understand thatwe need to have the common
conversation saying that, okay,in order for this to actually
change some things, we need totalk about what could go wrong.
(24:58):
You're right.
That's, important.
And I think people need to getto that point very quickly.
Andreas Welsch (25:03):
Nothing else to add.
It's perfect.
The, only thing that I would,say though, is that we're coming
close to the end of the show.
And I was wondering if you cansummarize the key three
takeaways for our audience fromour conversation today.
Lasse Rindom (25:16):
Yeah the thing I just said let's get innovation
and risk to be a part of theconversation at the same time.
I think also let's focus alittle bit less on the airplane
and focus more on building theairline.
And then let's get specific andlet's make them all specific,
make them productive, talk moreabout roles than agents.
I think that's where wedefinitely need to go.
And we need to start talkingabout that too, to make it work
(25:37):
right now.
Andreas Welsch (25:38):
Wonderful.
Lasse, thank you so much forjoining us and for sharing your
expertise with us.
It was great having you on.
Lasse Rindom (25:46):
Thank you so much, Andreas.
Thank you for having me.
Andreas Welsch (25:48):
Perfect.
And thanks for those of you inthe audience for joining us as
well and for learning with us.
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