Welcome to part two. In part one, we uncovered in
our interview with Oliver, founder of ADA AI, why the
traditional back office is breaking under the weight of modern startup
operations and how AI employees are already
taking over full workflows at companies adopting
agentic AI. But today we go even deeper
in part two of this episode. Originally planners one episode,
but Oliver was. Oliver was giving so good and long
answers, we spontaneously decided to break it into two.
In this episode, Dr. Oliver Lugos reveals the
technical breakthroughs that separates simple chatbots from real
autonomous agents. The industries that are secretly becoming
agent ready. And why building an AI workforce might
be the defining strategic advantage over the next decade.
If you ever wondered how autonomous an AI should
be, what God rays matter, or how far you
can push automation without losing control, this is the episode
you cannot miss. Let's jump into part two.
Welcome to Startuprad IO,
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Welcome to part two of our conversation with Dr. Oliver
Lugosch, co founder of ADA AI and
previously co founder of the Razer Group based in Berlin, the E commerce company
that scaled beyond 700 million euros in annual
revenue. In part one, Oliver walked us through the origins
of ADACOP, the rise of AI employees and
why Agentic AI is fundamentally different from
every wave of automation that came before it.
Today we shift gears. In the second segment, Oliver
breaks down the technical and operational mechanics that
allow AI employees to execute multi step
workflows, make decisions, coordinate across tools and
operate at levels that were considered impossible just a few years
ago. We explored the real breakthroughs behind autonomous
agents. How companies move from one agent to AI
workforce, would guardrails prevent runaway autonomy,
emerging industry leading the adoption curve, and
how agentic AI will reshape high rank robos
and organizational design. In part one
showed us why AI employees matter and
part two shows us what's coming next and how fast.
Oliver, welcome back. Thank you. Great to be here.
Totally my pleasure. For everybody listening, it was a few days or even
longer for us, it has been just five minutes. So
let us talk about what's the real technical
breakthrough that makes AI employees
fundamentally different from chatbots or those
RPAs? Why we have chosen to
call them AI employees is because that
wording should show that
the usage, the application, goes beyond these very
singular purposes. What do I mean by that? A
chatbot is there to chat, Right? It's mostly
confined to the exchange of communication. Right?
You can have small LLM helpers that for
example, summarize documents. Right. And give you
quick notes around it. But the thought about AI
employees is that it can go much, much further, it can
go beyond. It can actually act on a longer process
scale, so to speak, and connect many different dots.
And what you need for that is many different
things. You need the capability of using LLMs in
exactly the right way, that they produce the outcome that you want
human like communication. You need integrations into many
different systems. You need an integration into your communication system,
into your Outlook or whatever email provider you use,
into your maybe direct messaging service. You need integration
integrations into your system of records, into your erp, your CRM,
you know, for the logistics folks out there, into the tms, the wms,
you need a human interface, you need a front
end that the team can actually use, right? And
there are all of these different elements that you need to combine in order
to really think an entire process. Because only if you
think an entire process, you can also have
impact that is really sizable, that is really substantial.
And it's more than a little helper here, a little
helper there, where yes, it might be interesting,
it might be helpful in everyday life, but it doesn't create
this measurable meaningful impact for organization
that truly, you know, gives teams the room to
breathe and feel like, wow, something has really changed.
And because you can combine all of these different things now, and
because you can tie all of these ties together, you
can think about a broader automation and more impact
that creates benefit for the organization.
As AI grows quickly,
what patterns are you noticing in the demand
of your customers? Which industries are or
which processes are agent ready? Now,
as of today, the pattern that. Emerges is
that companies often have one or
two use cases that is on top of everybody's minds because
it seems to be present, it seems to be that people discuss these
and we often start there. And they
most frequently lie in the procurement process,
sometimes in the distribution process, but often in procurement.
And then when you go deeper and you start to work on the project and
you start to build, then more and more people hear
about, you know, what is possible and what can be done nowadays
with the help of LLMs and other technology. And then
more ideas emerge and people start to see more use cases
from different parts of the organization and start to discuss
and also initiate further project, further
use cases across the org. So I would say
classically your procure to pay and your order to cash
flows are agent ready or automation
ready? Really? I think the word AI
agent is something that I find, I
don't know, difficult in how to interpret it. Right.
What does an agent Actually do. What does an agent mean? I feel
like it's a word that everybody uses. But what is really behind
that, behind that curtain? What, what, what is an agent?
I would bet if you ask 10 people, you get 12 answers.
That always reminds me, in the, in the mid-1990s,
everybody was trying to sell multi multimedia
computers and they had experts from like five new
newspapers doing the elevator pitch and everybody talked about something
different. Yeah, I fully, fully agree. Right. And
by the way, I'm not claiming that AI employee is the best wording, right. In
any way. I'm not claiming that. I'm just saying that, you know, these AI agents
have, have popped up and I find it difficult to grasp what is really,
really meant by it. In, in any case, I think
these use cases pop up throughout the classical again, procure
to pay, order to cash cycles also on the money flow,
right. Speaking about finance, order invoice processing,
issuing of invoices, reconciliation accounting, also these
processes are ready. What you shouldn't forget
is that this human in the loop process is something that
you typically start from, right? If you want to automate a process,
do it, but still have a human confirm certain actions,
confirm certain communication that goes out. So you can already
get 80% of the efficiency of
the time saving, maybe even 90%. But you still have that
component of a human double checking and ultimately signing off
on certain actions. And that is typically the starting point. And then when you
want to move to full automation, get these last 20 to
10% of, of, you know,
capacity of efficiency. That's what you should do under certain
circumstances, right? If you really have the trust, if you really have seen
a lot of positive examples, a lot of perfect
drafts and proposals from the AI, that's when you can move to
full automation. But that's a very, very organic process.
That's a very trust driven process that just comes over
time and again. Even if you are stuck with these, you know,
human in the loop processes, you still have most of the value
that will, that you will get ultimately.
We've been always talking about the employees who get rid of very boring
stuff. We've been talking about the entrepreneurs who get more
efficient with a few employees. But where are the
customer wins? What's the most surprising outcome a
customer has seen after deploying an AI
employee? The first thing that customers think about
is the efficiency, right? And saving capacity,
saving resources. But what also comes out
of, of all of these projects is
an improved speed of processing, data processing
input, responding to customers, responding to suppliers,
reacting to any kind of signal that comes in really. So
you get rid of this backlog, right, that often piles up and it
can again slow down other parts of the company. Other
teams will notice, will notice that
processes run quicker and more reliably
with the use of these automations. And the
quality of the output data is typically much better.
If anything, it's certainly of higher consistency,
right? Because if you use an AI based automation,
there's consistency of how things are interpreted, of how things
are put into a certain place in the system, for example, and
that consistency drives usability of that data
because, you know, it's no longer, I would say a human spread
of how things are being done, but it's typically very clean and
very standardized. And that is
a benefit that many companies don't foresee, but
that they experience as they use the AI employees because
they make the data more consistent and more usable going
forward. Also for other teams.
I see you do that because you want to scale.
But what are some scaling challenges with
AI employees? What's the most
underestimated challenge that companies, your
customers face when scaling from one agent
to an AI workforce, to many agents?
When we think about these automations and changing
processes in companies, ultimately that's what we're doing, we're changing processes.
Processes. Then you should never underestimate the human
factor, right? You will always have those teams that are
responsible for the processes, that oversee the processes, that
further develop and advance the processes. And
you cannot go faster than the team can go,
right? You always have to take everybody along and make sure
that these changes happen at a pace that is digestible
for the organization. And so the limitation
in scaling is really not a technical limitation.
It is not a limitation of, you know, being able to produce these
automations. It's a limitation of how quickly can
teams and organizations adopt to these changes. And I think that's a very,
very natural limitation. And it's something that you
should always keep in mind when you think about how quickly do I want to
scale from 1 to 2 to 5 to 10 use
cases or AI employees.
I see in the founders world we will be talking about
what's the workflow. No founder realizes can be
automated, but every founder should automate using
AI employee. Let's do a little bit
outlook into the future. How do you see the balance
between human teams and AI employees
evolving over let's say the next five years? I know
it's pretty, pretty early, but givens a positive vision,
I think this. View is pretty clear.
I think there will be less and less and less of these
painful Manual things that you have to do, the repetitive ones.
I think we will spend more time on really figuring out stuff,
figuring out how to do things and then having your AI
employee, your assistant, your agent, whatever you want to call it, take
over what you have defined. It
will also help us become more creative.
You can use LLMs to get new ideas, get new
impulses and then turn them into great ideas, turn them into great
execution and really move your business,
your startup, to the next level. So I think you will
have less of this boring, painful piece and you will have
more of the creativity. Do things differently
and, you know, come into a state where it's, it's, it's
exciting to advance your business because you no longer have to take
care of the very, very basic stuff.
I was just wondering about AI employees. The
very, very boring stuff. I was wondering, would it be a dream
for you to automate or start
automation? German tax authorities but
because I do believe there will be quite a lot of potential
and I also do believe there are a lot of people who
have jobs that are very repetitive and I'm sure
they would love to get rid of at least those steps
of the work. I'm not a
tax expert, but what I can say as, you know,
being subject to tax, I fully agree with you. I think
there are probably many, many process steps in, you know,
filing taxes, anything around taxes that
could be at least simplified or
accelerated with AI and LLMs. And
I think there is a huge space that is to be tackled in that area.
I fully agree now around the regulation
aspect of it. I cannot speak to it, but I'm sure
we'll figure this out and make sure that, you know,
we can also use AI in these, in these topics
and these areas of businesses agree.
Going back into the, from government into the
real industries. We've been talking about
AI agents helping employees to
get rid of boring work. But I was
wondering, how do you imagine
agentic AI reshaping the org
charts of larger companies? I think
AI will start to become part of the org charts.
Right. Ultimately, that's where we're headed.
And what does it resemble? Org charts basically resemble
or mirror, you know, certain resources
capacities. I'm sorry, I'm not trying to be, you know, inhumane or anything.
I'm just trying to abstract it. Right. What is mirrored in an
org chart, it is, you know, human resources
executing certain processes, certain tasks, taking over certain
responsibilities. As we see AI applications take
over that role, they move into that capacity. Right. And
you will see that a person will be completely
occupied with running many, many, many
operational or a large volume of operations through
the help of AI, AI employees, agents, whatever you want to call them,
and being that person that keeps an eye on all of
these processes, right, keeps an eye on quality, further
advances the process, further advances the model, the
application. And really being able to
have such a huge leverage, right? One person, imagine one
person handling, I don't know, thousands of customer requests every day.
Why? Because they have LLMs and AI at their fingertips,
right? And they can do this at a speed and a
quality that was unprecedented, that you could never have had with
just humans. Because you can further improve
these algorithms and these automations more and more and more. And
I think that operational leverage that you can have will result in org
charts looking very different from today. Do you know
when you've been talking about the difference in org
charts, what do you say triggered one idea in
my mind because when I was working in a lot
of different companies as a consultant, if you're a good consultant, you get
invited to the Christmas party of the company and
then you do have the retirees, the people who come in, they
retired and they talk about what was work like 5, 10,
20 years ago. And I was wondering if at one point
you'll have like one room in a company. I
had in mind a lot of old screens where you can basically talk to old
AI agents and, and pick their brains
on knowledge from the past or how it was done in the past.
Why not? I think it would be enriching, right, if you could tap into
that knowledge. It's still fresh because it will always be fresh, right? It
will remember and we'll be able to help us and learn, right.
Obviously you will never be able to fully take over
that human element, right? You will always have the core
team, will be humans at your Christmas party and you will probably
not celebrate with AI employees. I don't know, maybe we do,
I assume we will not. But having that,
you know, this, these capacities of remembering stuff very
clearly giving you insights, you can ask any questions it will
hopefully answer in the correct way, in a true way.
I think this is a value add that we are just starting
to understand, just starting to grasp really
what I. Had in mind when I was talking about this was having a digital
room, not necessarily to be entered during Christmas party. We all know that
can get out of hands. A digital room where can
basically look into different screens and have
all the old AI agent archived there, which
of course will be a big job for knowledge management. I'm sure you're working on
IDs for that as well.
Let's pick your brain a little bit about a contrarian view.
What is one unpopular truth about autonomous
agents that nobody in the AI industry wants to
admit? I think it comes down to this
topic of autonomy that you mentioned before. I
think the unpopular truth is that we are
still far away from this true, true fundamental
autonomy of really running human processes.
People sometimes claim and continue to claim that we are there
already. I don't see it. I have not seen it in
application and I think it's still a little bit of
a way to go to really have that live
and ready and in production.
For our founders out there, for our audience. DM us on
LinkedIn, me, your manager.
What would be the one process you'd
automate first? We'll share the most creative ones
anonymously.
Looking forward to ideas there. Let's get a
little bit towards the end and this pencil a little bit at end. The
advice, what's
your advice to early stage founders who want to build
AI ready operational structures when they're
starting out like today or.
Well, Today is late November 2025, but this will
air in January 2026. A lot of activity is going on in
January. Everybody wants to start something new. What advice would you give
to entrepreneurs now thinking about setting up the company?
Great question. I think yes, it should be
AI native, I think because not starting something
AI native or with AI in mind would be
foolish given all of the technology that we have and the
capabilities the technology brings. But I won't, I wouldn't
overdo it. I wouldn't say, you know, first day you start
thinking about AI. No, first day think what is your business? What do you want
to do? What's the value that you will bring to your
customers, to, you know, society, to the world. And since post
corona, how do you make money? What's your unit?
Economics, your mix of scale. And then think about
how I could raise that. That would
be something I will be thinking about. How about you, Oliver? Exactly 100%.
So first start with, you know, what are you doing? How do you create app
value? How do you monetize? All of these are very, very important questions. And
once you figure that out, or once you have an hypothesis and you want to
test it, use AI as a tool, right? Use LLMs to then
scale yourself, scale your time. But
if you're not a business, you know that, that,
that sells AI where AI is really the core product.
Don't start with AI the very first day. Start with the fundamentals of the
business. I think this is true. This will always be true. And once you've started
to figure out things and you have your hypotheses or something that's even
proven, use AI to scale. Use AI to
create that leverage that you want to have.
I'm curious for one thing, because we go
into the last standard question, and then we have the two mandatory. But
before we go into that, can you see a future
where the AI employees are targeted
by hackers and used for nefarious purposes?
I think that time is already here. I'm very sure that
hackers are already trying to attack LLM systems,
AI systems. And it's something that you have to stay on
top of and that you have to build
countermeasures against. So I think that's absolutely critical.
Whenever you design any kind of AI system that you have that in mind,
people will always try to get to your system. They will
always try to hack you. So security is
number one priority, particularly if you think in broad applications.
Right. For a small, small company, small startup, they might not
be the first target, but when you move to larger scales, then you're
certainly targeted day in, day out. And that must be very core
to how you approach the entire thing. I
see, I see. And the
last question I prepared for this interview, if every company
in the world would deploy AI employees tomorrow,
what do you guess would be the one that breaks first?
Like what function? What capability?
What would break first? Great question,
great question. I mean, looking at what LLMs are great
at, I would assume some of the numerical stuff would
probably break. I don't know, maybe the forecasting piece or
the optimization piece might be something that is
most fragile because, you know, LLMs,
large language models, are not designed for
numerical analytical approaches. But that's just my guess.
I hope we will not see that happen. But if I had to guess that,
that would be my take.
Our standard questions are pretty, pretty normal. Are you open to talk
to new investors? We are always open to talk to new
investors. We are not actively fundraising, but we are always
open to talk. And
this is a funny question for you, because are you also open
to look for talented employees that you cannot yet,
that you cannot completely replace with AI employees?
We are also looking for great people. Absolutely. At any point in
time. So, yes, we are hiring. So
basically, we'll link down here your career website and your personal
LinkedIn profile so investors can reach out to you. Perfect. Let's do that.
Oliver, thank you very much. Was such a pleasure to have you here twice.
Thank you. Thank you, Johan. It was a real pleasure. Was great talking to
you. Same here. Have a good day. Bye bye. Thanks.
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