If you're a founder, operator or executive, here's the
uncomfortable truth. Your back office is stuck in
2015 if you're drowning in repetitive
work, operational inefficiencies and processes that
break every time you try to scale Today's guest
believes the future of work will not be AI
assistants, but AI employees,
autonomous agents that think, plan and
execute real workflows. Dr. Oliver
Lugosch built Razor Group to more than 700 million in
annual revenue, managing global operations at
massive scale. He's now building ADA
AI, or ADA AI as you can say in German, where
AI employees are already replacing entire back office
workflows. In this episode we we break
down what AI employees can actually do today,
where they fail, and how founders can employ their first
agents in under 30 days. Get ready.
This conversation will fundamentally change how you think about
operations.
Welcome to Startuprad IO,
your podcast and YouTube blog covering the German
startup scene with news, interviews and
live events.
Today we're joined by Dr. Oliver Glugosz, a founder
who has lived through one of the most operationally intense
journeys in Germany's startup ecosystem. Oliver co
founded the Razer Group, an e commerce powerhouse that
scaled to over 700 million euros in annual revenue,
navigated global supply chains and estimated executed M and A
at the pace that would break most companies. That
experience gave him a firsthand look into the limits of human
only operations. The arrows, the repetitions, the
scalability ceiling. And it led him to a bold
idea. What if startups didn't hire more humans
for repetitive work, but deploy
AI employees instead? Today all Oliver is a co
founder of ADA AI, one of
Europe's emerging leaders in agentic AI, a
new category where autonomous agents don't just
assist, but act, plan and operate
independently. These AI employees handle full fledged
workflows from data processing to back office tasks,
customer operations, and complex decision paths that
previously required entire teams. In this
interview we'll explore the real limits of traditional automation,
how AI employees differ from chatbots and LLM
assistants, where autonomous agents shine and
where they break, what founders must know before deploying their first
AI employee and how AgentIC AI will reshape the
startup workforce over the next five years. They
that was a long introduction. Oliver, welcome. Thank you very
much. Thanks for having me. It is totally my pleasure.
I'm very, very curious about those AI employees,
especially having a company where I still do
maybe more work than needed manually. It is always something
I had an eye on but could never really get the time
to do it. That is something of real interest and I
do believe A lot of founders here will, will really,
really listen carefully. If they can do something like their
monthly tax filings, that will be just amazing. But
first, let us start. You scaled Razor Group into one of
Germany's largest E commerce operations. What was the moment where
you realized the traditional human only back office model simply
wouldn't scale anymore? Wow. Very
good question. Very good question. Let me think back to the time when we founded
Razor Group. I did this with three other founders
back in summer of 2020. It was
just a few months after the pandemic hit. So everything
was changing, everything was in a turmoil. Things
were changing at a rapid pace.
And so we started our journey
based out of Berlin, getting in touch with e
commerce companies, small e commerce companies that we first
had to explain to, you know, that you can actually sell
your business, right? You can find a buyer and sell your business
to somebody else and have a, you know, life changing
exit event. That was what we started with
and that was, was more, that is what most of Razer was
focused on, right? Getting these deals, finding these
companies that you could acquire. My role as
CEO and co founder was to think about the operations before we
had any operations, right? Everybody was chasing these deals and these
companies. And I was thinking, okay, once we close those deals, once we buy
these companies, what do we do? How do we operate, right?
Starting obviously with a small team, having a few people on board,
the first deals started to come in. I think we acquired our first
company in October of 2020, like two, three months after founding
the, the company. And it was all,
you know, setting up the absolute basics, setting up structures,
setting up responsibilities, setting up
processes. And I very clearly remember this first company
that we acquired. Everything was hands on, right? Everything
was Excel files, very, very manual. Everything
was double checked by a, by a colleague, human, human
interventions. Nothing was, was really system
based. We did know that we would grow very rapidly.
So at that time we already checked what
ERP system do we want to use, right? We already thought about scalability,
had that process running and ultimately even went live with
our ERP at the turn of the, of the year, just
like five, six months after founding the company, we were live with our
erp. We, we chose Oracle netsuite back then. And in
that fall of after the first acquisition,
the second came a third came right, and there was still a pace
that you could manage, right? You hire another colleague, another
specialist for logistics, another one maybe for E commerce
operations. But when this wave of
acquisitions came around, where we heard,
okay, in January, we might do four to six
acquisitions that was my oh shit
moment, pardon my French. That was
exactly. That was the moment where I thought, okay, you can never
hire at high quality at that pace, right? How do you do
this? And so it was the beginning of
thinking even more in terms of scalability, even more
in terms of automation. Standardization. The first step for us
was to standardize the integration process, right?
Understand what are the basic steps that you need to do, in
what order, how do you standardize the entire process so that you can
handle four to six integrations per month.
And that was really the first piece that we had to get in line
to. Then also in second step, think about the operations, right? Once
you integrate that company, it is part of your landscape, it's part of your
IT infrastructure, of your system, process infrastructure, team
infrastructure, then how do you operate it? And so we
always thought in these waves, starting with the integration all the way to
operations, and then improvement, continuous improvement,
implementing more and more standardization, operations,
automation and all of these things. I was
thinking about a company that acquires a lot of other company,
aggregates them. I would instantly have in mind
like one big machine. And you take. Get
rid of as many individual processes as possible and
add all those processes to as
efficient working central machine as possible.
Absolutely. Try to standardize as much as possible, right? Because if
you standardize, you can repeat. It's a blueprint, right? This is the
plan, go execute. You can use some basic
automation. We are not talking about LLMs yet, right? LLMs have not been
anywhere in mainstream at that time, 2020. But it was
more of if then connections, right? If there is an email coming
in, then execute that. Or if somebody pulls a project from a certain
stage to the next one, you automatically send that set of
documents to a certain person, right? So yeah. And this
IFTT logic was already around in the 90s in
Excel. Exactly. Why do you think the back office and
operational processes remain so repetitive,
manually error prone and resistant to automation in.
Even in tech companies, there's a certain degree
to which they need to be in certain companies. But
why even in tech companies, you do everything in front
of us to apply LLMs, to apply agents, to make it
as easy as possible. And then in the back office
you start working paper basement stamps. Yeah,
very good question. I think it has two main components,
really. One component is still of
technical nature, right? Before LLMs,
you could not automate the process of understanding communication,
right? How do you automate an email communication process? Right.
There was no tool that could understand, for example, a
supplier's message. And then reason think
through what the appropriate answer is, and then put that answer into an
email and send it. There was just no technology. Right. And that technology, these
large language models, have now only been around really for a couple of
quarters. Right. So it's still a novelty in terms of technology, but
it is available. It is feasible nowadays, so
that problem is solved. I think the other side of the coin is the
topic of trust. Trust and accountability. Right.
Because when it comes to decisions and to actions, even if
it's just as small as a communication to a customer, to
a supplier, companies tend to still trust
humans more than machines. Right. And if there is a decision
that needs to be taken, for example, on changing delivery dates, on
accepting a certain change, then structures and
organizations rather trust a human to take that call because that human
would be accountable for the outcome. And how do you hold a machine accountable to
something? Right. I think that's the second element that. I
think that, that, that's an important question everybody needs to
answer in the future. It's, it's, I do believe the same question. How do you
hold a company accountable? How can you hold
an AI agent accountable? It will more or
less end up to. To be the accountability
of some human either in charge of those agents or the person
who coded those agents. What do you think?
I agree. I think it's a process that we'll go through
finding exactly how these things are settled. But we do have examples
nowadays. Right. So who is responsible ever? I'd say
classical automation fails. Right? It's the
party who set up that automation or who
agreed to maintain it, to oversee it.
Right. You clearly define this upfront. And I think we're moving into a
world where you need to define it between the parties.
And it's typically something that needs to develop over
time. Kind of what is the standard that everybody agrees to
and how is that all figured out? I think that's a process that
we are in the middle of seeing the development
of. I'm very sure a lot of people who
are deploying or thinking about deploying
AI agents are now thinking, huh, that's a good
question. Never thought of that. What was
like the spark moment that led you to the idea of
an AI employee autonomous
agent that can run a full workflow?
It really was a thought
that came up during the entire journey of Razor Group. Right.
That journey for me was roughly five years long. I just explained
what happened in the first few months. And over the next
quarters, we built that organization. It grew and grew and grew.
And we always saw that there is a lot of manual work
required to really keep things running. Right? You have these,
I would say that API of anything which is human
led. Humans are the API of anything because they understand things,
they can verify things, you can teach them things.
And so they are bridging what classical
systems like ERPs cannot solve, right? For
example, the interaction. And over these five years, as the,
as the organization grew, every, every now and then I was thinking
about, wow, this, this, there must be a different way, there must be a better
way, there must be a way to automate also these processes that are
highly repetitive that you can put into an
sop, a standard operating procedure, but the technology
just wasn't there. And then in 2024 when,
you know, 01 and 4O from ChatGPT, from OpenAI
came out, that was really a pivotal moment for
me and I think also for a lot of other people,
seeing the potential that comes
with these models of understanding, communication, of understanding
context, of understanding, documents that had this
aha moment, wow, now we have the technology that is
reliable enough to do these things, to do these
previously exclusively human processes now
in a machine. And that was the point where we said, let's go ahead,
let's see what we can do with that technology. And that was
really the spark for us to embark on the journey.
And that actually leads us to the next question. What did you do
with those AI employees from Eraser Group?
Experience which operational bottleneck
with the biggest cost sync or scalability
barrier? And how would an AI
employee have solved it? Razer is an
E commerce business and Razer sells
everyday goods, right? And so the
procurement of these goods is very
fragmented. We had a lot of different suppliers from
all around the globe, most of which in fact came from, from
Asia. And the coordination with all
of these suppliers always was a very, very human
driven task, a very manual task. Because a lot of suppliers weren't that
large, right? They didn't have massive, massive factories. They
may not have had, you know, large teams and
also IT teams that work on integrations,
standardized interfaces, etc. But they, they had no
API. You just could not put your order in your ERP and
went over, you had to put it somewhere in your erp, then double
it up, send an email, getting confirmation and so on and so forth.
A lot of back and forth, right? Absolutely, 100%,
a very, very manual process. And that was
the field where we figured, wow, we have a lot of volume, right?
Every single product that we buy and then sell to
customers goes through that process. We had a lot of fragmentation,
right? I think about 500 suppliers or even more. A lot of
different languages at play, right? Sourcing from China, from Vietnam,
from India, from all around the globe and all of that variety.
And we figured, well, this is something that we want to take up first
and set up a trial. And that trial, taking
care of that supplier communication and coordination, right? The
exchange of, update of dates, update of quantities,
etc. Tackling that that was our first use case that we
approached and we just saw the impact that you can
create and that was, that was a real eye opener for us.
Before we get into the question about your assumption about
starting ADA AI, how did you get the name?
Very good question. Sreshta, who is
a co founder of mine, she was also
a co founder at Razor Group CTO and is now also co
founder at ADA AI. She's, she
studied computer science in Stanford. So she's a techie through and through.
And I remember it was her idea that we name
ADA ADA because of ADA Loveless, which I
learned was the first computer programmer.
And you know, sounds good,
it has a good ring to it. And so I think we went on board
with it and like the name so far. When you then started
ada, what was your assumption about
agentic AI? And what assumption turned out to be
completely wrong? If you go through,
you know, LinkedIn, the media, any business related
social media out there, you always get the
statement, do vertical AI, do highly
specialized AI just become the best AI for
customs declarations, become the best AI for
indirect procurement? And that was something that
we heard, that we listened to, but that ultimately
for us turned out to not be the right
approach. Because when we started to speak to
customers, basically asking them, hey, where do you have
repetitive manual work? Where do you have processes
that have not been automated yet? Mostly because they include a lot of
communication, a lot of language, a lot of
unstructuredness in the data. The responses that we got
were all over the place, all over the entire supply chain from
procurement to distribution,
logistics, invoice processing,
sales from all parts of the business. And we started to look
into these use cases and started to look, okay, what does it take
to now build an automation that works end to end? And that
really takes that burden of manual work off
of the team's shoulders. And it turns out that all of
these are similar in terms of what
you need to make them work in the front end. When you look at
it at face value, these use cases are very, very different, right?
One use case may be supplier communication. A supplier
notifies you of delays, or a supplier Notifies you of
change of quantity, right? It's completely different when you compare it
to, I'm processing order in a, in a food
business, right? I'm processing small individual orders from
small retailers, right? I get emails, I need to interpret the email, need to
pull out the items from the email and put them into the erp. Two
very different use cases. But if you look under the hood,
what you need to make them work is really comparable because
what are the elements that you need? You need to be able to understand
communication, number one. Number two, you need to
match certain data in an unstructured way to another
set of data, right? Because people don't use exact
namings of products, people don't use exact phrases, but
you have to do what we call fuzzy match. You need to be integrated
into these different systems. You need to have an
engine that understands context. You need to
have a user interface, right? Because you need to think, what does the team do?
The team needs to interact with the AI. And there are a couple more elements
and modules you could call them. And we identified that
these by themselves are quite repeatable.
They are quite, you know, you just need to arrange them a little
bit, you need to readjust them a little bit, but you can reuse them. And
that was something that came out through the interaction with other
customers, listening to them, going through the process of
figuring out how do you build these automations? That then told us, well,
actually just being vertical, being extremely narrow
is not what yields the best result for the customer.
But being able to think horizontally across the entire
supply chain, across the entire, entire process landscape that you
have in a company is really what, what turns
out to create the most value for the customer.
A lot of companies out there really love the idea of
automation, but feel the real autonomy.
Where do you see the biggest misconceptions about those
autonomous agents? And from my experience, I used to be
a management consultant a couple of markets and what I always have in mind
is night trading. Like, like the Knights in
Armor Night Trading group there was 2012,
bankrupted by an algorithm going, going crazy. Within a
day, they lost almost half a billion US dollars. And
I'm just waiting for the headline news that an autonomous agency
has also done this for a company making the bankrupt. But
before we get into that, because you should not simply say,
okay, let's work, it'll all be fine. You need to manage
that. Do you see as the biggest
misconception about autonomous agents
in general? I think the biggest misconception is
probably around the fact that we are already There
to have true and 100%
autonomy in its purest sense. When we
speak about autonomy, I think there are certain degrees of
autonomy. There's a spectrum of autonomy in
executing certain tasks, right? I think on the very
far end of the spectrum there's this human, human autonomy, right? If you have
a worker and they get the task to get the best deal
on supplying a certain material, then they would go ahead.
They first create a plan, okay? I need to find out who are all of
the relevant suppliers. Then I make a plan for the negotiation. I
have them sent over test samples, right? I do the quality check.
I in parallel need to check with accounting to set up the vendors in the
ERP, etc. It's a multifaceted approach, right?
With different systems interacting with different scenarios that
you have to go through. This is the far end of autonomy, right? But if
you start to cut it down and take away some of these
dimensions and put it into one process, which is, for
example, there are orders incoming, right? There's communication
incoming. That communication has your products,
it has a delivery date, it has an address, then this can also
be automation, sorry, it can also be automation, autonomy, but
in a far less complex way, right? And I think the
misconception that is, that is very dominant still out there is
we are at this far end that AI is possible
to do what a human can do in all its facets and think about
all of the possibilities. But what we see when you
really apply AI and LLMs in a company
in production scale, then what works right now is
this, what I mentioned at the other end of the autonomy spectrum, right? You
have to give the AI AI guardrails. You have to
ensure that whatever should be deterministic, if
then remains deterministic and is not overruled by an
LLM because of its probabilistic nature, right?
And I think that this misconception of we are already there,
I think that's very dominant and it's a little bit dangerous because the
expectations for LLMs and what they can do are completely
overhyped, right? If you really want to have LLMs and
AI work at scale, start with this beginning. Start with these
processes that are well defined. Start with these processes
where also humans exactly know what to do. And it's repetitive
and it's clear we are not there yet at this
very, very far end of, you know, full and true and 100%
autonomy in all of its facets.
I totally believe so. There are still a lot of
jobs. You think, oh, the employees know what to do, but Then
you realize at one point, well there's this aspect
that it's not totally defined and we need to do decision. Well, there's
this one, there's that one. You need to know X, you need to know Y.
So even simple tasks are
difficult sometimes, especially if you need to teach
it through to machine.
We go later into our founders world and they you'll talk about
the one operational failure that kept you up at night and
an AI employee would have helped to prevent.
Let's talk a little bit about obstacles and how to overcome
them. So we already talked a little bit about challenge,
how far AI is. But what was the hardest technical
operational challenge in building AI
employees and how did you solve it? The biggest
challenge really is to grasp
what I would call context. Because
if a process stays within the defined boundaries and
everything is plain vanilla, then it
is not as challenging to, you know, build the
automation, run the automation. But as you mentioned, these exceptions are
what makes it really, really interesting. And behind
these exceptions still there can be rules. But these
rules are typically just in the heads of those people that execute the
process, right? So if I go back to the example of receiving
orders, let me say this is a company that produces some food
product and there are smaller companies that order from them, retailers
or other small companies, then
the context that may be in the head of the operators who previously
received those orders and put them into, into the system, they
may know, you know, a certain customer has certain
conditions for the order, certain delivery days. You can only deliver on
Mondays, Tuesdays and Wednesdays. These kinds of things typically are
not encoded in the erp. They are not encoded in some file,
they are just in the hands of these operators, right? Or for
a certain customer, you have to interpret their language slightly differently,
they refer to a product differently, right? And that context,
that is really what drives complexity and also
what makes it hard for LLMs and AI
if you don't teach it properly to work at that scale.
Because it becomes very frustrating for the team member, right. If it always have
to correct the AI for the same exact thing, right? The
AI over time needs to understand that context,
these very, very special things that again only sit in the head of the
operators. And that is something that, that we came across very early in our
journey and that we solved with what we call our context
engine. It's a part of the product that is absolutely critical
to drive the efficiency and drive the
accuracy of ADA over time because it
captures that context that once again sits in the head
of the operators exclusively in the head of the operators.
And that was a real unlock for us to make sure that this
context gets enriched, codified and used
for the, for the operations going forward.
Yes, that, that knowledge that is locked up
in the mind of an employee, worst, worst case of
a former employee, it's good when they retired because they like, like
to come in again and talk about their job. But if they got
fired, you won't get that knowledge back. So I
totally understand what you're going for. You
also bump if you do process optimization and some stuff so
that there's always a lot of stuff that's not written down.
Without naming confidential clients. What type of
workflows are your employees already replacing
today? The use cases
really come from all parts of the organization. A lot of
use cases, of course are in procurement, in the procure to pay
cycle, but also in order to cash on the distribution
side, in invoice
processing, booking of invoices, you
can think about applications even in other
areas. So what we build
is again customized. It fits to
the specific process at the customer and it can be as
easy as processing order confirmations
from your suppliers, checking any differences and
putting that into the ip. It can be part of your
sales process. Right. If you think about a company that has
large B2B customers that do an annual review of the
contracts and discount negotiations, there's
also a use case that we are, that we've built where
the sales team interacts with ADA
requesting the confirmation for certain discounts they
want to offer. ADA does the calculation of whether this is within
range and then gives the green light to the sales reps to go ahead and
and interact with the customers and offer them these terms. Right. That is also
a use case that we've built. There is a use
case of matching incoming goods
against the purchase order and against the invoice that was
issued. Right. Which would be a three way match or you can even extend to
a four way match. Right. Also, a previously human process
of combining all of these data points, validating
all of these documents and then acting in case of a
discrepancy. That's also a use case that we've built. So there are many, many different
use cases across the entire supply chain.
And beyond that you can automate nowadays.
What always comes to mind for me, keep in mind, I have a finance
background, I work there a lot, is accounting processes.
Because the funny thing is I once talked to
a friend, if you're creative and you're creative in finance,
you'll be rich. If you're creative and you are creative in
accounting you'll go into jail. Right. So
there are very certain limits that you, very
strict rules that you have adhered to. But I do believe
the potential that something goes wrong. There is also one of the
reasons why we'll see this likely later on.
Talking about tax filings. Yeah. If you have a
mistake in there and usually you need to pay
like a thousand euros a month in taxes and then it
ends up 10 billion, you do have a problem.
Yeah. Let's go a little bit into your playbook.
What's like the biggest lesson for founders when
identifying which workflows of theirs are
ripe for agentic automation? And keep in mind, I think with the
counter. Great guys. Very good
point. No, when I think about
founders, you
should start thinking about LLM based or AI based
automation when you have understood a process. I
think that is a very good starting point because if you're still figuring out,
if you're still testing, then you know, what are you going to
automate? How do you going to tell the AI or the LLM
or the system that you want to build what to do? Right. I think
first comes the clarity of what's supposed to happen. How is it supposed to
happen? How will it help me? And does it have scale? That really makes sense
Because I tell you, building something that works
reliably across, you know, all of the
incoming signals that you can have, the variety of, you know, day
to day operations takes time and takes effort. It
may be reduced by now because there are all of these different tools that you
can use, like you know, these Copilot Studios and N8N
and all of these self serve environments which are certainly
helpful, which you can certainly use. But you still need to have a
good understanding of where you want to get to. Right. And that can only happen
if you are clear about the process that you want to, that you want to
automate. But if you have that, then I can encourage you to already start
when you're building a company to already start very early on. Right. Because
it certainly helps you to scale yourself and scale your time
because you can move from doing these things to monitoring these
flows and get more things done in a
shorter time period.
For our audience and the founders are listening, I was
wondering think one repetitive process in your startup
that breaks every week. Got it. Great. Keep that in mind as we
move to the next section. Talking a little bit about
your tactical framework here. Could you walk us through
like 30 day deployment blueprint for onboarding
a company's first AI employee. And
when I think about onboarding and all the stuff you need to add in. Have
you ever heard a politician talked about taxing AI
employees? A very good
question, taxing AI employees.
No, I have not heard about that so far. But it's a very interesting
thought, Very interesting thought.
What's the playbook or what's the approach? So first,
similar to my answer about, you know, when founders should think about
AI based automation, you first need to have the use case clear,
right? What is the process that currently creates a lot of pain, a lot
of headache and ties up very valuable
human resources, human capacities. And if you have that clear,
then it's all about understanding that process in depth, really. Going
through, going through the systems, going through the decision trees. Some of those
may be obvious, some of those may be hidden. And you really only find
out those rules when you go in, when you ask
questions, and when you understand it in more depth. And
we've really encountered quite a few times that having
both a manager and the operator in the same meeting going through that
process, some things emerge where
both had discrepancies. Manager thought, it's handled this way.
The operator says, no, no, no, we have to do it that way because
of reason. Xyz, that happens quite often
and that's a good thing because it creates clarity, it creates visibility.
And very, very often we also have an element of
changing business processes as we uncover them, as we go through
them, the teams that are involved often agree to
change certain things about the process because it makes the
process better. We are not talking about automation, we're just talking about a
shit process being a shit process. Sorry for my language. And a good process
being a good process. And if you go into depth on these processes, you often
find these shortcomings that you can fix right
then and there, just changing how it's supposed to work and
how the sequence should be. Now, if you have done that, if you have
really understood what the process is, then we go ahead and we develop, right?
We develop custom, we develop this bespoke for our customers. So we
need a little bit of time, a few weeks to do that. But when we
are done, we hand over the
ready product. ADA is doing what ADA is supposed to do. And
the customer can test from top to bottom. The customer can
test on all different, you know, scenarios with
live data. And we really appreciate feedback
during that time because we can incorporate the feedback very, very quickly from one day
to the next. And so over a short period of time, a week or two,
we iterate up to a point where ADA creates a lot
of value for the team and the team sees the value because the team has
interacted with ADA a lot and sees that it can take over a lot
of these processes and can help them really meaningfully in the day to day work.
They really feel that they have less burden on their
shoulders and then you're ready to go live. And that is
typically a phase of 30 days. Maybe a little more, maybe
a little less.
I was, I was also putting myself in the shoes of those
employees. And what do you know what's really cool? Because you can
outsource the most boring Nerf and
will breaking repetitive tasks to
an AI agent that you have in your job
and that usually helps. So I think there are a lot of people
who are really interested in helping you there.
Oliver, Normally we would go into an ad break, but we're
recording for already more than 40 minutes. So
I would suggest we make this part one and say
goodbye and then have a part two. What do you say? Love
it. Let's do it. Okay, guys. Oliver, thank
you very much. We will be back with part two with
Oliver and ADA AI.
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