So think of them really as an extension of your team who can build and
own a workflow end to end instead of just helping out.
So founders aren't just using agents for conversation or Q and
A. So they're running entire financial validation,
multi agent support workflows, supply chain orchestration
and content management without needing humans for every single
trigger point. So for example, startups like Quanto
use multi agent systems to process, validate and
reconcile split payments in minutes, a job that
used to take hours or even days. Also with agentic
AI, when a startup lands that big customer or their user
base grows overnight, they agents can scale to match
demand without doubling headcount or losing efficiency. And
that's really a new fundamental way to accelerate growth.
Also, agents are equipped with feedback loops that improve
themselves in production, learning from data, success and
failure. So companies don't just need to manually
update processes all the time.
Welcome to startup Rad IO,
your podcast and YouTube blog covering the German
startup scene with news, interviews and
live events.
Hey guys, welcome back to our second piece in
cooperation with aws. Together with Jennifer
SAS is being rewritten by agents if you're building software
today, your business model might not survive the next wave. Jennifer
Kroon, senior Gen AI Specialist at AWS and former
startup Country Manager and Account Manager, has
years of experience in the startup segment, now helping
founders to rethink SaaS from the ground up with
agentic AI workflows. Today we'll explore how
agents transform SaaS, what new monetization
models will dominate, and why startups need to adapt
fast. Jennifer Kruen is a
Senior Specialist for Generative AI and Machine Learning at
aws, which she drives adoption of Gen AI across Europe.
She brings a unique mix of consulting, business development
and startup leadership experience, having herself
scaled as country Manager in an international
environment and working with SaaS and B2C
startups at AWS. This gives her firsthand
understanding of the challenges founders face, from
monetizing struggles to to customer adoption
barriers. At aws, Jennifer helps startups and
enterprises alike embed agentic AI
workflows, prioritize high impact use cases and
navigate change management. In this episode
she'll share how SaaS is being disrupted by agents
and what founders must know to build the next generation of
business models. Jennifer, welcome back. Thank you
Joe. Really enjoy being back on the show. It's my
pleasure. Let's dive directly into context and
trends here. Gartner lists agentic
AI as a top strategic trend for 2025 and
very likely much further down the road. From your perspective
Working with startups why are agents more than just the hype?
So first off, for me it's important to recognize that agentic AI
is not just another round of automation. So traditional
automation, as you might know, follows scripts and rules. So
things get done, but only within tightly defined boundaries.
Agents on the other hand, set goals, make independent
decisions, adapt strategies, and also take action across
complex multi step processes or with minimal human
input. So think of them really as an extension of your team who
can build and own a workflow end to end instead of just
helping out. So founders aren't just using agents
for conversation or Q and A. So they're running entire
financial validation, multi agent support workflows,
supply chain orchestration and content management without needing
humans for every single trigger point. So for
example, startups like Quanto use multi agent
systems to process, validate and reconcile split
payments in minutes, a job that used to take hours or even days.
Also with AgentIQ AI, when a startup lands that big
customer or their user base grows overnight, the AI agents
can scale to match demand without doubling headcount or losing
efficiency. And that's really a new fundamental way to
accelerate growth. Also, agents are equipped with
feedback loops that improve themselves in production, learning
from data, success and failure to. So companies don't just
need to manually update processes all the time. Also,
agents don't just wait for instructions. So they spot issues,
escalate, adapt campaigns, reorder inventory,
or shift customer interactions on their own before problems really
take a bigger impact. And also this
aligns with what research shows. So
organizations deploying agentic AI are starting to see already
measurable business outcomes. So for instance, think about a jump in
customer satisfaction, boosted employee productivity and
ROI typically achieved within a short time frame.
And gentic startups draw investor attention not just for the tech,
but because autonomous systems deliver compounding value
as the business scales. So this means improving margins, resilience
and speed to market. Of course, in all of the
light there's also shade shadow. So definitely there's
security, governance and robust guardrails that are really
a must have for going to production, especially when those agents
act autonomously. So this is why. Also Gartner predicts that
some agentic AI projects will fail early due to these hurdles.
How do you explain the difference between classical
SaaS automation and agentic
workflows to founders who may not have a technical
background? Yeah, I like really much the
analogy of a nice recipe that you're cooking. So
thinking Perhaps of traditional SaaS automation like a very
smart but very strict recipe. So it operates on A
simple if then logic. So if you use this ingredient, this will happen.
So for instance, if a new customer signs up, then you send a
welcome email. It's really fantastic for automating predictable
repetitive tasks. So you set it up and it runs reliably in the
background, but it can only do what exactly it was
programmed to do. So whenever something unexpected happens,
so for instance a new field pops up in the data or a step in
the process is changing, the automation is breaking.
The AgentIQ workflow hover is completely different.
So instead of this rigid recipe that we just discussed,
you can think of it as hiring a proactive goal oriented
chef or employee. So you don't give this employee a step by
step list, you give them a goal and then they figure out how
to achieve it. So for instance, instead of a workflow
that just says if a customer churns, send a survey, you might
give the agent the goal. Retain this customer, and the
agent can then autonomously reason, plan and take multiple
actions to achieve this goal. So it can analyze for instance
the customer's usage history to find a pattern. Check also
for any open support tickets or recent interactions.
Or also it could search the knowledge base for common issues that are related
to their usage. It can also send and draft the
personalized message that is offering a solution or a discount.
And then it can send the message and monitor the response.
And it's a huge really going away from executing predefined
tasks to really proactively making decisions and taking
actions. And here's another way to look at it.
So especially for founders and business leaders. So
traditional SaaS has also been a lot around great user
interface and a suite of features that a human uses to get a job done.
And the value is really in the tool itself, the dashboard, the button, the
reporting chart. So we've really measured success
by engagement metrics like daily active users or time spent in the
app. Whereas Agentix SaaS flips this a lot over
because the value is no longer about the ui, because the agent
doesn't need a beautiful dashboard to work, it works behind the scenes.
So talking to other services via APIs to get the job
done. So the new metric for success is the outcome.
So how well did the agent achieve its objective? Did it improve
the sales conversion rate or did it reduce customer support
ticket resolution time? So think about it like
this. Your customers aren't really buying software to use a dashboard,
they're buying it to solve a business problem. And that's where agents
get us closer to that outcome based business model. So it's really a
fundamental change from selling a tool to selling a solution.
Selling a tool and selling a solution. I think that's
a very important distinction. I may
add to our audience that you shared a lot of content with
us, including your AWS talks, where you contrast
operational improvement versus product innovations.
Where do agents fit best here? I would say
that agents really fit into both the operation improvement and the product
innovation. So they're really a unique type of AI that
doesn't just create content but also takes action. So it
makes them valuable both for the internal efficiency and
also for creating new customer facing products. So on the
operational side, agents act as proactive, goal driven
virtual collaborators. So this is where they drive significant
internal efficiency gains by automating complex multi step
tasks that traditional automation couldn't handle. So
let's take one example that is very popular also in the SaaS context. So it
could be onboarding where an agent would autonomously create a
series of action, drafting a welcome email, setting up access,
scheduling introductory meetings, sending follow
up reminders. So this really improves operational efficiency
and reduces manual processes within the company.
And on the product innovation side, agents are being used to
create entirely new customer facing features that were
previously impossible. They go really beyond being a simple
chatbot that we've accustomed to see and it becomes a core part
of the product. So for instance, think about a product pricing
agent here. So instead of just providing a dashboard with data,
this is an agentic feature that acts on behalf of the users
to find and implement an optimal price. So it takes a complex
goal here and coordinates with other agents for for instance a
demand analysis, web scraping, margin calculation
to make a decision and take an action such as updating the price
in a database. And really agents enable a new level
of personalized user experience and improved automation
that can be sold for instance for customers as a core product feature.
Let's go a little bit into what you've learned. Business
models and use cases. You scaled a startup
yourself as country manager from that lens, where
do you see the biggest opportunities for
agents in sas? So if I think about
the time when I was in that startup as a country
manager, so I can tell you that the
opportunities for AI agents aren't really just about efficiency,
they're about strategic survival and growth. So I was
working here with Back Market, that is a French refurbished
marketplace. And in here I was one of the first
employees where I was focused on scaling the business in Germany
and ensuring that profitability
leading really to triple digit growth in just six months. And it
was of Course as you can imagine a lot of work and I
saw really firsthand how critical it is to get ahead of the curve.
And if I had agents at my side I would have been a lot
quicker because I was just on my own at the time to scale the market.
So agents really represent a profound shift from traditional
software and give really companies a huge competitive advantage.
So perhaps going back on where I would have seen
this accompany my time in the startup. So
as you know you're always at the beginning, especially you are
fighting for every new customer. So agents can really be a
game changer. So instead of a one size fits all approach,
agents can really hyper personalize the entire customer acquisition
funnel. So they can monitor market data, analyze
competitor moves and then draft tailored outreach emails
for different customer segments. So if you think about a predictive analytics
agents, it could analyze a user behavior on your website and
score their likelihood of converting, which then allows the sales
team to focus on the highest potential leads first. So really
about turning data into actionable intelligence at scale,
which is of course very essential for a growing business.
And if I think about my role at Backmarket here
I was working with also the building a
supply network and identify new partners. Because we
were operating from France to the new market, I had to also work
with existing relationships from there and provide them also
with data driven feedback, improve also
their sales and also further scale the market. So
I could imagine myself automating a significant portion of that work
now. So, so the agents could monitor for instance the key
account health, they could analyze also user generated
feedback from the support tickets and social media and even
also predict potential churn of a customer before it happens. So
this frees up a lot of our time to focus on high touch
strategic relationships rather than routine check ins. And
it's not really about replacing that human element, but also
making us then more productive and impactful so that really
contributing also to revenue growth and customer retention.
And lastly I think also the future as mentioned
in SaaS is not just about kind of beautiful interfaces as
well. AI agent don't need that. They interact directly with
functions and data. So this means that the product itself can become
agent first and even no UI in some cases
with the agent serving as the primary interface for the user.
So for instance a user could simply ask an agent to create a new campaign
targeting our top 10 customers in Germany who haven't made a
purchase in 90 days. And the agent could then execute the multi
step workflow across various systems from the CRM to the
marketing automation tool. And that doesn't only
simplify the user experience, but also allows SaaS
companies to focus on building a powerful interconnected back end rather
than just a front front end UI.
We were talking, I was wondering, should SaaS founders
treat agents as a feature, as an
enabler or completely new product category?
The short answer here is it's not really. As always, a one size
fits all solution. So let's start really with the most
common and lowest risk approach. So integrating a
Genai agent as a new product feature. So this is
ideal for improving existing workflows or adding a clear
value add without really completely
overwhelming your business model. For example, if you are a
CRM company, because I also work with software vendors, you could
introduce an agent that automatically drafts follow up emails for your
sales teams. This really provides direct,
measurable improvement in productivity and can be a strong selling point.
So when I was in a. Com manager I would have really loved that as
well. So the business impact can be also
very clearly stated because you have improved automation and
also personalized user experience. But also you
could always become more ambitious here. So for instance, if we think about an
enabler agent that works to streamline entire
processes, freeing up human workers for more strategic
tasks. So for instance a data analyst agent
that handles data cleanup and organization, or an
agent that autonomously triages customer
support tickets. So this really fundamentally changes how a
customer team works with your software, diving
also into a deeper level of operational
improvement. And I would have also loved that my time in back market, for instance.
Lastly, instead of building an agent that is living
inside an existing product, the agent, and that's
really the disruptive part that I see some customers of
mine working on, is that the agent is the product.
So this means really building a new standalone offering that
is centered alongside the agent's capabilities. So here
the agent isn't just an assistant, but it's really an
autonomous system that takes a goal, for instance optimizing
a product's price, and then reasons, plans and executes
multiple actions to achieve it. So it might use other agents to
analyze demand, scrape competitor websites and calculate
profit margins before finally updating the price in the
database. So as with all the topics around
AI for getting started, focus on
the business impact and feasibility of your use case. So if
you're just starting, consider launching an agent as a feature to test the
market and gather user feedback. But regardless of the
path that you choose, remember that the agents work best when they can interact with
the outside world. So really investing in a strong
infrastructure foundation as well. And we have all the
tools available for all different sites, kinds of builders to make
this happen. You've mentioned already
a few use cases, automatic follow up emails for
sales, triage of support.
What do you think are the most promising SaaS use cases for an
agentic workflow? From what I observe right
now, the most powerful agentic use cases
in SAS today is in BI and data analysis. So
instead of really just displaying the data, agents would actively
transform it into actionable insights. So imagine for
instance a multi agent system for product pricing optimization
that we mentioned, so finding the best price. They can also monitor
KPIs and detect anomalies in real time, so alerting you
really to problems before they become a real issue.
And also it is possible to generate reports in plain language
which also makes complex data accessible to non technical
teams. And agents can also go a step further. So
automating data driven workflows. Think about inventory
optimization or churn prevention by proactively
identifying at risk accounts and recommending retention
strategies. So for instance I'm working with a fintech startup that is focused
on building AI to generate dashboards in natural language from customer
transaction data. So their solution identifies anomalies
and financial data and recently received also
further approval for going beyond that for shaping the product
roadmap. And if we think about marketing and
sales, these are also really promising use cases as we've seen also previously
with generative AI. So agents are moving beyond the
simple content generation to orchestrating entire
campaigns. So you can call those the digital strategist of a
marketing team which handles a lot of the multi step processes
with autonomy. So for instance think about a system that can create
and send newsletters based on user behavior. This can mean
really hyper personalized ways of creating content
at scale, unique messages, even creating
personalized videos for individual users based on their behavior.
And this is really interesting for media and entertainment companies out there
and also optimizing campaigns dynamically. So while
really monitoring the performance and reallocating ad spend and
real time for better roi, that's something that I used also at
Amazon Advertising previously. That's also a great use
case. So really automating that entire lead
lifecycle from intent based qualification to
automated sales handoff also saves your team actually
countless hours on manual tasks for instance in the sales space.
And yeah I'm also working on with an E commerce customer
that is even using these ideas to dynamically
generate content really disrupt the way that we are currently
thinking about E commerce. And another great
example that I really enjoy is also that we talked a little bit
about the triage topic. But for instance imagine an
agent with memory that can remember the past interaction
preferences and issues across multiple sessions.
So there's a couple of companies I work with that
are also using this for a great
impact. For instance auto resolving 8,000 out of 16,000
customer tickets which then includes a 4% increase
in net promoter score. And that means also that the
AI solution processed five times more tickets than human agents in
its first week and this success transformed their customer
service so allowing the human agents to focus on high value
interactions and the same ICOs with each R software
that is looking into in product chatbots to
also help their customers with you know,
agentic capabilities thinking about tasks like
summarizing CVs and analyzing employee feedback
conversations.
Guys, we will be back after short ad break
talking about culture change management and an
outlook here.
Guys welcome back from our ad break
being here for the Last quarter of
2 very long and extensive interviews that Jennifer prepared
very well. Thank you very much and let's dive straight
in in your few what role do
ecosystems and marketplaces play in
scaling agent based SaaS Solutions?
If I think about my experience with software companies for a long time, I've
seen that they try to be a one stop shop building
pretty much every single feature that customers could ever need.
But as also agentic based solutions evolve,
we are seeing also even for agents, that one small agent
can't do it all. So manually managing these
different agents from different vendors can lead to how I call it
like agent coordination chaos where there's a
conflicting logic and inconsistent customer experience.
And the solution is to move towards a multi agent system
which is kind of the technical heart of a marketplace. So
imagine in that scenario that a supervisor agent
acts like like a project manager, so he takes the complex
problem and delegates subtasks to specialized agents.
So in the pricing example we had earlier, supervisor
agent might send tasks to web scraping agent together competitor
data, another one to a demand analysis agent and the third
one to a margin calculation agent. And this
collaborative approach between the agent is really a natural way to build
complex workflows just like in our usual teams. And
marketplaces are really the logical extension of this.
So this allows companies to easily discover and plug in
these specialized agents. And this ecosystem
model I believe leads us to the biggest business benefit
which is specialization. So a marketplace
allows everyone to focus on building one or two
highly specialized, let's call them best in class agents
who rather than trying to create a monolithic product that does
everything. So this is really a
fundamental shift that enables a more like
how we Call it also best of breed approach. So take a retail
company for instance. So instead of having a single
company having to build agents for every possible e commerce task, from
product recommendation to payment processing, you can source them from
an ecosystem of specialized providers. So we're already
seeing this in some cases with companies co developing
repeatable AI solutions with partners that are addressing
specific use cases like customer support automation.
So this shows that partnerships and ecosystems are a really key
way to scale successful solutions into repeatable patterns.
And this shift also changes the business
model, with the new standard being a usage based or value
based model, which we discussed in an earlier episode, where
customers pay for the outcomes the agents deliver, not just for access
to the software, but as in all the time
you always with a lot of agents working together, you also have to take
into consideration governance and security challenges.
Thinking, for instance, if a malicious tool could try to trick an agent
into performing a harmful action. So this is a real
risk that you need to think about early. And this is where
the marketplace plays its most crucial role. So
for an ecosystem to succeed, it must build on a foundation of
trust. So this means having rigorous governance, security and
compliance standards for all agents in the ecosystem so
customers can confidently combine agents from different vendors without
fear of misuse or data breaches. According to
some discussions that I've been having is that securing
these systems requires a new security approach that
combines AI specific protection like guardrails with
traditional application security controls and robust
operational monitoring, which for instance is also a key part
of agent core in aws.
We've been talking about a lot about the agents, the
marketplaces, the areas where they will be
most able to help a startup. But
let's talk a little bit about the people aspect, the culture and change
management. A big theme in your AWS talks is
creating bought in teams. What practical step
can founders take to build an
internal AI literacy and
reduce the potential resistance? Yeah,
if I think about that one, leaders for me have to really champion
the change. So I see this a lot also in the enterprise customers
I work with. So according also to a recent article, and
also based on my experience, only a third of the companies actually have
an AI strategy. So many of the companies I work with
dive into AI out of a fear of missing out without
really clarity on what problem they're trying to solve. So it really
has to start with a clear purpose. So what will AI help us
improve? Is it customer experience? Efficiency, innovation?
And that clarity which is voiced from the top, for instance, gives the
team the reason to care. And also what I Find very important
is that you shouldn't treat everyone the same. So you should start
conducting an assessment in your team. So where are the people
currently? Are they a beginner, intermediate, advanced, and then
you can tailor the training accordingly. So I just came out of actually a workshop
with our internal team, so we tailor it to the different audiences over
here. And this really role aligned approach
ensures that you're hitting the people where they are and making
sure that you deliver what they need. So also letting people get their
hands dirty really with low stake experiments. So
drafting internal mock ups, basic summarization
and also letting people fail fast in a safe sandbox environment
is really important because while
also still going a step further experimentation is important.
It also works best when you pair it with structured training. So
making sure that you have short targeted modules that are tailored to
different job functions. And I work also
personally a lot with our AI champions and in the
different teams. So these are really early adopters who love the
tools and can show others how to apply them in context.
So they create a lot of opportunities for peer learning, team
sandbox projects. So we are doing that quite a lot here at
AWS as well. They are trying out tools and
they're also sharing back what they learned, but
also pairing this with clear AI policies that
also brings people clarity so they don't experiment in the dark
or worry about violating guidelines. Because as I've seen
it a lot of times is that people can resist
AI because they feel it might replace their autonomy or they just
don't trust it. So treating AI with room to adjust or
override, seeing it really as a sparing partner
and also sharing small but real wins in your teams, for
instance, saying okay, I used for instance Agentic
or another workflow to cut my reporting preparation
time in half or, or guess what happened when our
team tried this cool tool.
What I see a lot of successful companies do is to celebrate
these internal successes and showcase those broadly.
So not really just as metrics, but really as proof points
so you can get the flywheel spinning and get more people excited,
making also these AI tools really accessible. So ensuring
that employees know what's available and how to access it, that's
also really key. So we have also an AWS Party Rock
which has an easy way to create apps from
scratch. You can also have any other tools. Making
just people try this out for themselves is so much more
worthwhile than just talking about it. And that really
brings a lot of excitement.
You often mentioned in like all the documentation you
provided to us, you often mention AI
Whisperers, what exactly do you mean
and how can they accelerate the adoption
inside, for example, SaaS companies.
I would like to think about AI whispers really as a kind of
translator or a bridge builder. So they are
the person who is equally comfortable speaking the language of a data
scientist, but also the language for instance of a customer service
representative. So their primary skill is not just the
technical expertise, but the ability to really understand how
AI can solve real world business problems and then clearly
communicate that to non technical teams. For instance,
this is really important because about 88% of
employees according to study of Gartner are non technical.
So the Iwhisperers take the complex technical
concepts and make them accessible. So it helps really to
demystify the technology and shift the conversation
from fear to opportunity. So they're really the human element that
ensures that AI isn't just a project for the tech department, but
a tool for everyone. And where I see this coming
to life even more is that if you pair an AI
whisperer with a senior leader, that is really strategic
because it can really accelerate AI adoption across the entire
organization. So imagine the leader that provides a
top down vision, authority and resources that
are needed for a large scale change and they can set the
strategic direction and signal to the entire company that AI
is a priority. However, the leaders might not have really the
ground level insight into how AI can practically solve day to
day problems. And that's where the AI whisperer comes in.
So it provides the bottom up practical knowledge so they can
work with different teams, identify specific pain points
and also suggest high impact, low risk use cases that
are perfect for a pilot. For instance. So think about reducing
manual processes or, or like a lot of people or
almost everyone is doing right now, speeding up software development.
So when you combine this leader's strategic
authority with the AI whisperer's practical underground
knowledge, you really create a great momentum for change.
So you have these great example proof points and
the leader can then champion these successes from the top. And that really
creates a powerful feedback loop that builds momentum and trust
and also builds and bridges the gap between an
AI vision and a real world implementation.
I actually had to smile when I went through your material because you
also talk about AI playgrounds. As a father of two,
I do have a total different association with playgrounds. But
those AI playgrounds, how do they
encourage a safe experimentation and build
culture of innovation? Actually I like a
lot the analogy of the playground as you just said, from the
children's perspective, because if we take ourselves
back to that time, we really enjoyed to learn a lot of different
things. So we didn't always think about a goal in mind. And
that's also a mindset that we like to take also to
AI because you are then experimenting
in a sandbox. So really a risk free environment where you
can get hands on, you're not afraid of breaking
anything or really having any massive cost
attached. It's really a low stakes space for iterative
experimentation and thinking outside the box. Instead
of just reading lots of things out there about what AI
can do, you, you actually get to build and test things.
So this is especially important for startups because
AI of course isn't a magical solution for every program. So
playgrounds really allow you to quickly test a new idea
and see if it really has a business impact before you actually
commit significant resources. So they're all about moving
from a theoretical idea to a practical prototype
quickly. And how do these playgrounds
specifically help if you think about the startup context,
for startups that are often short on time and money,
AI playgrounds really provide a way to build quick
prototypes, do a test drive of use cases
without the need for full scale development team or complex
infrastructure. As I mentioned, we have for instance a free
platform that you can also try out just on your phone called plus Party Rock,
which allows you to start with a prompt, remix an app or build
from scratch so you can create simpler applications.
So I've seen some colleagues using this for finding the perfect
recipe for cooking or for more kind
of use cases, planning a trip, finding
apartments so there's a lot of room for
imagination. And when the team then knows that
they can't break the system, they're more likely to be creative and try
new things. So you're not focused on getting the technology
perfectly right from the start, but you are focused on what business
value you can create and exploring different use cases.
We had also here in our teams, reducing manual
processes in territory, planning faster software development,
or even creating personalized user experience for customers
in the sales team for instance. That really allows a
startup as well to quickly evaluate a use case based on
what is the business impact and feasibility. And you can really see
and test the idea to see how it could help the business and whether
it's even possible to build. So I've been also working on the
commercial side with kind of the, the simulating different
shoe types and seeing how, what which ones make
it more towards the like what what meet the,
the needs of the the consumers. So likewise you
can use these playgrounds as well to prioritize use
cases and avoid really investing in projects that don't have a clear
path to success.
We've been talking about change management that is often underestimated
in startups. What lessons from your own leadership experience
would you pass on here? So I think
the common understanding quite often that I hear from people is that
when we think of change, we often think of a new software tool or a
new process. But when we think about today,
the changes are quite profound. So if we also pull it
back to research from Gartner, by the end of even
this year and beyond, there will be some of our virtual and hybrid
meetings will be attended by agents, not just humans.
And if we go a step further, we also expect that by
2028 generative AI is really expected to
be woven into our day to day work and some of it might
not require that much of an oversight as it does now.
And what is exciting also that it's not just
for the tech teams as mentioned also it's also we have
a large market to address for people that are non technical.
And this means that there's already a lot of culture shift
that is happening right now from your marketing, your
sales team, human resources, all of these different
departments. And if we ask ourselves then
so if the change is so constant, why do
startups so often underestimate the challenge?
For me that brings me a little bit back to my time when I was
a country manager for Germany in back market
where I was managing change in a fast paced environment. Because
we were really growing very fast as a company, we
doubled ourselves within a few months and I was
also responsible for developing, growing the business in the German
market. So this means a lot of focus on customer acquisition,
profitability, providing data driven feedback,
developing action plans for our partners for the founders
to also help us pinpoint how to improve the sales. So
this really required me to have many different heads
having the information, managing change,
advocating for new ways of working, while also
making sure that the human element is at the forefront of it
all. So in the early days we were very focused
on building a product and finding the market fit, especially as it was a
new market in Germany. So the entire team quite
often we met in a single room, we have very informal
chats around pizza and change really happens
organically. But as you know and as when you scale,
that informal approach is just not happening
as much anymore and can become also actually a major liability.
So there's a bigger barrier then
for effective communication, for instance from upper management
and sometimes also an inability
to address resistance in the company. So
this means that also when we put this into a larger
perspective is that a significant number of change
initiatives fail. Because even when we look at the
research, sometimes even as high as 60 or 70%, it
doesn't really mean that the change itself was bad, but it's because
the people side of the equation was in some ways also
mishandled. And what I learned also from my time
in startups is that people won't embrace change just
because you tell them to. So they really need to understand the vision behind
it and how it benefits them personally.
So you have to shift the narrative from changes happening to
us to actually we are shaping this change together.
And this is especially true in my experience in startups
that are hyper growing, like you know, from a few people to then over
100 employees, there will be some people that you
know are making part of that change, but some that are feeling left out as
well because so many things are changing constantly
as your team is scaling up. And this doesn't just
mean new tech or processes. So you really need to focus on the people
have to live with it every day, making, you know, sure
that you have a two way conversation, not really a top down mandate
involving also employees. Then early in the process identify
change champions, which we just also shared earlier together for instance
with the I whisperers. Those are
really impactful people that can advocate for the new way of working
and for a major change. One tip is also
consider running a small pilot program with a subset of your team.
And this allows you to test a change, gather feedback
and make adjustments before rolling it out to the entire company and
making sure that you have an agile approach here that mirrors
actually the iterative mindset of startup.
Let's go a little bit into the risk and the
outlook Uber is currently
leading with AI regulations. We had a
lot of AI act content already,
but I'm asking you, how will this shape the SAS
models built on agents? So
firstly, I believe that, or also what I've been seeing
so far is that the EU AI act doesn't really treat all the
AI equally. So as you have seen,
there's a classification of systems into four risk
categories. So the unacceptable, high, limited and
minimal risks. So this means that the flexible
structure around it means that you have to determine where the product
lands before shipping a single feature. So if you think
about the agentic, this means
that you're thinking about agentic SARS for instance, used
in consequential domains like hiring, lending or
healthcare. So requirements here of course quite high. So you have
to offer a total transparency, maintain up to date technical
documentation, keep logs, ensure really explainability
and also install mechanisms for human oversight.
And SAS teams here will need to be able to explain how
and why an AI or agent made the decisions and really
be able to show this part of the system life cycle for
audit purposes. If we go a step lower than for
the limited risk systems, that means for instance customer support,
chatbots or recommendation engines. So for those
rules mostly mandate transparency. So for instance you're talking to a
bot here, but it doesn't go as deep on documentation or
oversight as in the previous bucket where we
have also minimal risk AI that would be your spam filter
which have almost no new requirements and then
unacceptable risk. The these would be outrightly banned ways,
for instance social scoring for the public sector. And
if we explore this further. So for any kind
of SAS company or not SaaS company, the first major
challenge is mapping the agentic functionality
into these buckets. So building an AI that touches
hiring or financial processes. And the act also
changes the game by making both the SaaS vendor and the customer
so the deployer responsible for compliance. So
you cannot just hand off the models or agents and walk away.
So you really need to think about compliance support into the products,
think audit logs, risk monitoring dashboards and user
controls for then being able to do model
oversight. And human oversight is then also a legal
requirement. So even the most autonomous agents need a human
in the loop architecture when dealing with high risk.
So it also for me also is an interesting one here because
compliance quite often is not just a legal burden, but can also
be a market advantage. So as you have seen, surely, or
also in one of the other episodes, there are high fines, for instance
35 million or 7% of global turnover. So the cost
of getting this wrong is really high. But I see also a lot
of potential for startups who embrace compliance by offering
security by design, robust documentation and
explainable AI that helps them win, trust and
unlock larger enterprise deals. So giving you an example,
for instance startups like the Swiss startup Lakera, they
saw the gap and build a business on it. So they create stress
test suites that simulate real world attacks and scenarios, think
about prompt exploits, adversarial contexts,
and they help developers discover reliability gaps before
attackers do. And also their platform embeds advanced
defenses, so toxic content filters, guardrails for
context windows or input sanitization directly
into the agentic orchestration pipelines. So
they pair also that automated evaluation framework with model
alignment tools. So that agentic SaaS isn't just
robust, but also explainable and safe by Design.
So to sum this up is for a SaaS company, really
building with agents is really to determine the risk classification.
So you need to establish clear boundaries and permissions for your agents.
So if you think about an agent that helps customers find furniture
online, that would likely be a limited risk, but an
agent that automates a hiring process would be high risk.
So the AI act here places the burden on both the
provider and of the AI system and the deployer, so the
customer. So it's a shared journey of responsibility.
And we also happy to help you over there. We have actually an interesting article
where we describe also how we as AWS help on the EU AI
Act. And yeah, you'll need to basically maintain
detailed records of your AI system's life cycle from
data governance to performance logs. You have to
explain this really clearly. And we help for instance with
AI Service card and the safety framework that gives
customers also information on a model's intended use
limitation and responsibilities design choices because it's super important for
us. And yeah, as, as we've seen with this
example, there's a lot of potential here
to also transform compliance into real competitive
advantage because security is a timeless principle.
And when your customers then know your product is trustworthy
and compliant, it really builds a foundation of trust.
So it's not really about just avoiding defiance, but really about
gaining market trust and also
additional market share. So I find this a really interesting space.
Coming to our last question here. Sitting here
together for almost three hours. Let's give it your
best, let's give it your last. Gartner predicts by
2028 AI will be so embedded in
productivity apps that oversight will be rare.
What does this mean for SAS founders today?
Yeah, that's a really great question here and I would really like to unpack this
further. So imagine the day to day decisions
in a business. So you have the invoice processing contract
generation to help desk automation so
handled largely looking into the future, perhaps by
agents that set their own goals, solve problems and communicate
with other tools or without requiring actually constant
human checkpoints. And that's as we've seen before,
that's one way where the industry is going. So we expect that
one third of enterprise apps will include agentic AI by
2028. So this means that around
15% of all work decisions is fully on autopilot by
then. And this oversight burn is shrinking
and that changes everything for SaaS product design.
And this means also that Agenti elevates the SaaS
products from tools that automate routine processes to
platforms that Reason, adapt and act. So
founders now have to ask themselves. And that's what I advise in a lot of
conversations that I go to with
customers, is am I building a workflow or am I building an
agent? Because you have to really decide based on, you know, different
factors around the complexity, the cost of error, so many
factors that you take into consideration. And also is the
task complex enough to need reasoning or not just
automation? And do I need also multi
agent systems with specialized sub agents
where central agents manage high level goals.
And the good news here is it has never been easier
to start building those because we have so many tools
at our disposition that many startups are using like Landgraph Crew.
Also we launched Amazon Bedrock Agent Core just a couple of weeks back.
So this lets developers spin up agents that fetch data,
execute API calls, evaluate outputs and even
self improve via continuous feedback. So instead of
customer support tickets bottlenecking, really a help desk,
here the agent reads complex situations,
it orchestrates specialized micro agents to resolve
tasks, it verifies accuracy and delivers a
consolidated solution with minimal human intervention.
So I'm working also in finance with startups whose
solution also validates invoices in minutes rather than hours.
A single agent coordinates the data extraction, checks the supply
status, crunches rules, using business intelligence.
So really for SaaS founders that are pivoting towards
agentic AI, that means products will stop
being just helping humans, but they begin owning
outcomes end to end. And another thing that I find
really important in this context is that founders
need to ask themselves should they build custom agents or do
they want to build those off the shelf or do they want to partner
for parties like thinking about Amazon Q
strands. So so many different tools for different builders needs.
And this doesn't get easier with all of these different
frameworks and commercial solutions. Recently when I looked
into it, so there are thousands of partners, even like over 100,000
partners worldwide that are contributing to the agenti
deployment, so making it really
a huge market at the moment. So this means, as we discussed earlier, you need
to upscale your teams, secure high quality data
and architecture systems for flexibility, speed and security.
And you know, as we said also earlier, the road is
also not frictionless. So it's very likely that not all of the
agentic projects that you are thinking about are not
making it into production. It could be because of
complexity, implementation, evaluation, maintaining security
and also importantly, not all
tasks make sense for agents. So there might be
higher error costs, complex human judgment and regulatory
requirements that we will see increase. Or so that may mean that human
in the loop is required. So for those that are listening in today,
I think that by 2028 apps will really
embed Genti so they will win by delivering real
autonomous impact. So faster outcomes, reduced manual work
and new business value. So really the magic I
see here is in transitioning from productivity enhancement
to operational ownership. By the eye,
we're seeing so many great developments with open source
models, low code platforms and partner networks that
make these agentic developments accessible for
an increasingly large market. But of course
make sure you stay with these main frameworks in
mind that always hold true for software development. So you need evaluation
metrics and responsible guardrails because as Werner Focus,
our CEO suppose calls it, everything
fails all the time. So you really need to have this into
consideration. So really thinking about
what is will my app with the overseer, will it be the owner?
So the next few years will show how this is evolving.
But I think that the winners will be those that make agentic
AI not just a feature, but really a heartbeat and essential part of their business.
I think that are really great. Final
words Jennifer, thank you for being here,
spending like three hours, two hours recording two
episodes. Thank you very much. It was a pleasure having you as a guest.
Likewise. Really enjoyed myself and and yeah looking
forward perhaps to the next one in. A way that would be awesome.
Have a good day. Bye bye. Thank you. Bye bye.
That's all folks. Find more news streams,
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interviews@www.startuprad.IO.
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Sam.