Agentic AI for SaaS – Workflows, Use Cases & EU AI Act

🚀 Management Summary

How do you scale SaaS in Germany or Europe without doubling headcount? That’s the core question behind the rise of agentic AI.

Unlike traditional SaaS automation, which runs predefined workflows, agentic AI equips software with goal-driven agents. These agents don’t just execute instructions; they reason, plan, adapt, and act across multiple systems with minimal human intervention. The result is a shift from dashboards and manual inputs to outcome ownership—measured by retention, pricing accuracy, campaign performance, and ticket resolution, not by time spent in-app.

In this long-form pillar article, based on insights from Jennifer Grün, Senior Specialist for Generative AI & ML at AWS, we’ll explore how agentic AI is rewriting SaaS business models and what founders need to know about compliance, marketplaces, and culture change.

📚 Table of Contents

1. Agents vs Automation: Why Dashboards Are Dying

2. From Feature to Product: Three Paths for Founders

3. High-ROI Use Cases: Pricing, BI & Support

4. Multi-Agent Marketplaces & Governance

5. Culture Change: AI Literacy, Whisperers & Playgrounds

6. EU AI Act: Risk, Logs & Trust as Differentiator

7. SaaS 2028 Outlook: 15% of Work on Autopilot

8. Key Takeaways

9. FAQ

10. Closing & Resources

    
    

🚀 Meet Our Sponsor

AWS is proud to sponsor this week’s episode of Startuprad.io.

The AWS Startups team comprises former founders and CTOs, venture capitalists, angel investors, and mentors ready to help you prove what’s possible.

Since 2013, AWS has supported over 280,000 startups across the globe and provided $7Billion in credits through the AWS Activate program.

Big ideas feel at home on AWS, and with access to cutting-edge technologies like generative AI, you can quickly turn those ideas into marketable products.

Want your own AI-powered assistant? Try Amazon Q.

Want to build your own AI products? Privately customize leading foundation models on Amazon Bedrock. 

Want to reduce the cost of AI workloads? AWS Trainium is the silicon you’re looking for.

Whatever your ambitions, you’ve already had the idea, now prove it’s possible on AWS.

Visit aws.amazon.com/startups to get started.

Agents vs Automation: Why Dashboards Are Dying

Agentic AI replaces scripted workflows with goal-seeking agents that deliver business outcomes—not dashboards.

The distinction between automation and agentic AI is fundamental. Automation is like a recipe: “if a new lead signs up, send a welcome email.” It works for predictable, repetitive tasks but breaks when the environment shifts. If the data format changes or a new field appears, automation stalls.

Agents, by contrast, behave more like employees with initiative. They’re given a goal—such as “retain this customer”—and then independently analyze context, plan a series of actions, execute them, and adjust based on results. This might involve scanning CRM usage logs, reviewing recent support tickets, pulling from a knowledge base, drafting a personalized outreach, and monitoring response.

For SaaS founders, this means value shifts away from beautiful interfaces and dashboards. Customers aren’t buying software to admire graphs—they’re buying solutions to business problems. In an agentic SaaS world, metrics like customer retention, conversion uplift, and time-to-resolution become the north star. Dashboards still exist, but they’re supporting artifacts; the outcome delivered by the agent is the real product..

From Feature to Product: Three Paths for Founders

SaaS founders can ship agents as features, enablers, or standalone products—a strategic spectrum from low risk to disruptive.

Jennifer Grün highlights a spectrum that helps founders decide how ambitious to be with agents:

1. Agent as a Feature

   The lowest-risk entry point. Here, the agent is embedded into an existing product, augmenting a workflow. A CRM that drafts follow-up emails for sales reps is a prime example. It boosts productivity, provides measurable ROI, and strengthens product stickiness without forcing a complete pivot.

2. Agent as an Enabler

   This step goes deeper by reshaping how the product operates internally. An enabler agent might autonomously triage customer tickets or clean up large data sets for analysts. These agents transform internal efficiency and become integral to how teams work with the product.

3. Agent as the Product

   The boldest path. The agent itself is the offering. For instance, a dynamic pricing optimization agent that scrapes competitors, runs demand analyses, calculates margins, and directly updates product pricing in the database. Here, the agent owns the outcome end-to-end and becomes the product’s identity.

   
   

This tiered model lets startups test adoption with smaller, safer features before moving into enabler or product territory. Founders should evaluate business impact, user feedback, and compliance feasibility before deciding which level to pursue.

High-ROI Use Cases: Pricing, BI & Support

Today’s most impactful SaaS agentic workflows: pricing optimization, BI anomaly detection, and support ticket triage.

• Pricing Optimization

  A classic multi-agent workflow. One agent scrapes competitor sites, another forecasts demand elasticity, and a third ensures margin viability. Together, they recommend (or directly implement) optimal pricing in real time. This replaces manual dashboards with direct revenue-impacting action.

• Business Intelligence (BI) Agents

  Traditional BI produces charts. Agentic BI highlights anomalies, surfaces insights in plain language, and recommends actions. Instead of waiting for analysts to interpret a dashboard, a BI agent might alert a sales lead: “Your churn risk spiked 12% in mid-market accounts this week. Suggested next step: targeted retention campaign.”

• Customer Support Agents

  Support is an early proving ground. Memory-enabled agents can resolve repetitive tickets automatically while escalating complex cases. In one AWS case study, agents resolved 8,000 of 16,000 tickets in their first week, improving NPS by 4%. That freed human agents to focus on strategic, relationship-building interactions.

• Marketing & RevOps Agents

  The next wave is orchestration. Imagine an “autonomous campaign strategist” that creates, launches, and optimizes multi-channel outreach, adjusting spend in real time based on results. These workflows shift SaaS from efficiency tools to growth engines.

Multi-Agent Marketplaces & Governance

The future of SaaS is multi-agent ecosystems, orchestrated by supervisors with strict governance and security guardrails.

One agent alone rarely solves complex workflows. That’s why multi-agent systems—structured like teams—are gaining traction. A supervisor agent acts like a project manager, delegating subtasks to specialist agents.

In a pricing use case:

• The scraper agent gathers competitor data.

• The demand agent forecasts customer behavior.

• The margin agent calculates profitability.

• The supervisor integrates these insights and decides.

Marketplaces emerge when these agents can be plugged in from multiple vendors, creating best-of-breed ecosystems. Instead of one SaaS vendor building everything, startups focus on a single “killer agent” and integrate with partners.

However, governance is critical. Without oversight, “agent coordination chaos” can lead to contradictory outputs, inconsistent customer experiences, or even vulnerabilities. AWS and startups like Lakera emphasize that trust requires robust guardrails, auditing frameworks, and explainable security layers.

Culture Change: AI Literacy, Whisperers & Playgrounds

Agent adoption depends on culture change—AI literacy, “AI whisperers,” and low-risk experimentation environments.

Technology adoption often fails not because the tech isn’t ready, but because people aren’t. Jennifer Grün outlines three culture levers:

• AI Literacy

  Training must be role-aligned. Beginners need simple, low-risk workflows to test. Advanced employees benefit from tactical modules and integration exercises. A one-size-fits-all training program breeds frustration and resistance.

• AI WhisperersThese are bilingual leaders—fluent in both technical AI and day-to-day business. They explain AI’s value to non-technical staff, act as translators, and reduce fear. Pairing whisperers with senior leaders accelerates adoption by combining authority with practical knowledge.

• AI Playgrounds

  Sandboxes where employees can test agents without risk of breaking production. AWS PartyRock is one example, enabling employees to remix apps or run experiments in a safe environment. These playgrounds encourage hands-on experimentation, create excitement, and generate proof points that build momentum.

  
  

The lesson: adoption is less about software deployment and more about human adoption curves. Culture-first companies accelerate agent value capture.

EU AI Act: Risk, Logs & Trust as Differentiator

The EU AI Act enforces risk-tiered compliance. Startups that bake in transparency, logs, and oversight can turn regulation into competitive advantage.

• The EU AI Act is the first comprehensive AI regulation. It classifies systems into four categories:

  • Unacceptable Risk: banned (e.g., social scoring).

  • High Risk: strict documentation, transparency, oversight (e.g., hiring, healthcare).

  • Limited Risk: transparency obligations (e.g., bots must disclose themselves).

  • Minimal Risk: low/no regulation (e.g., spam filters).

  For SaaS founders, the implications are clear:

  1. Map risk before launch. Know which category your agent falls into.

  2. Build in logs and explainability. High-risk agents require human-in-the-loop oversight and auditable workflows.

  3. Shared responsibility. Both SaaS vendors and customers (deployers) are liable.

  
  

  While this increases complexity, it can also differentiate. Enterprise buyers trust compliant vendors. Startups that deliver compliance “out of the box” win deals over those who treat it as an afterthought.

  Case in point: Swiss startup Lakera built an entire business on compliance-first agent testing, providing guardrails and adversarial stress tests as a service..

Agentic AI for SaaS 2028 Outlook: 15% of Work on Autopilot

By 2028, one-third of enterprise apps will embed agentic AI; 15% of work decisions will run fully on autopilot.

The SaaS landscape is changing rapidly. Gartner forecasts that by 2028, AI agents will be deeply embedded in everyday productivity apps, with oversight becoming increasingly rare.

For SaaS founders, this means the core design question shifts:

• Am I building a workflow or an agent?

• Is the task complex enough to justify reasoning?

• Do I need one agent or a multi-agent ecosystem?

The winners will not treat agents as add-ons but as the product’s heartbeat. SaaS will evolve from productivity helpers to outcome owners.

Key Takeaways

• Agents ≠ automation. They reason, plan, and act autonomously.

• Founders can ship agents as features, enablers, or products.

• Pricing, BI, and support triage are the highest-ROI use cases today.

• Multi-agent marketplaces demand governance and guardrails.

• The EU AI Act turns compliance into a sales differentiator.

• Culture-first adoption (literacy, whisperers, playgrounds) unlocks value.

• By 2028, SaaS will evolve into decision ownership platforms.

FAQs

Q1: What is agentic AI in SaaS?

Agentic AI equips software with agents that set goals, reason, plan, and act—delivering outcomes, not dashboards.

Q2: How is agentic AI different from automation?

Automation follows scripts. Agents adapt, self-improve, and coordinate across systems with minimal human input.

Q3: What are the top SaaS use cases?

Pricing optimization, BI anomaly detection, and customer support triage.

Q4: What is a multi-agent marketplace?

A system where supervisor agents orchestrate specialized agents—like project managers with expert teammates.

Q5: What does the EU AI Act mean for SaaS founders?

You must classify risk, log decisions, and provide oversight. Done right, compliance builds enterprise trust.

Q6. How do startups adopt agents faster? 

Invest in AI literacy, empower whisperers, and create playgrounds.

Q7/ Will agents replace humans? No—agents augment humans by removing repetitive tasks and freeing time for strategy.

Q8. How can compliance be a GTM wedge? Transparency and explainability reassure enterprise buyers.

🧵 Closing & Resources

Market Lens (Expert Commentary)

Agentic AI is arriving faster than most SaaS founders expect. Waiting risks irrelevance. Those who align technology, compliance, and culture will define the next SaaS era.

Pro Tip

Start with low-risk agent features to learn adoption patterns, then expand into enabler and product plays.

Stat Spotlight

By 2028, 15% of business decisions will run fully automated via agents.

Founder Quote (Jennifer Grün, AWS)

“Your customers aren’t buying dashboards—they’re buying outcomes. Agents get you there.”

🎥 The Video Podcast

🎧 The Audio Podcast

Subscribe here: https://linktr.ee/startupradio

• Spotify

• Apple Podcasts

• Castbox

• Good Pods

🚪 Connect with Us

• Partner with us: partnerships@startuprad.io

• Subscribe: https://linktr.ee/startupradio

• Feedback: https://forms.gle/SrcGUpycu26fvMFE9

• Follow Joe on LinkedIn: Jörn Menninger

📝 About the Author

Jörn “Joe” Menninger is the founder and host of Startuprad.io — one of Europe’s top startup podcasts. Joe's work is featured in Forbes, Tech.eu, and more. He brings 15+ years of expertise in consulting, strategy, and startup scouting.

Give us Feedback!

Let us know who you are and what you do. Give us feedback on what we do and what we could do better. Happy to hear from each and every one of you guys out there! 

https://forms.gle/Qp53eVuc9P1RMqWj8

The Host & Guest

The host in this interview is Jörn “Joe” Menninger, startup scout, founder, and host of Startuprad.io. And guest is Jennifer Grün, Senior Specialist for Generative AI and Machine Learning at AWS

Reach out to them:

Joe on LinkedIn

Jennifer on LinkedIn

📅 Automated Transcript

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:00:00]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:01:10]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:02:42]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:03:07]:

Thank you Joe. Really enjoy being back on the show.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:03:10]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:03:30]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:04:33]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:05:25]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:06:15]:

How do you explain the difference between classical SaaS automation and agentic workflows to founders who may not have a technical background?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:06:27]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:07:11]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:08:00]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:08:45]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:09:31]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:09:53]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:10:50]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:11:46]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:12:04]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:13:15]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:14:24]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:15:30]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:15:51]:

We were talking, I was wondering, should SaaS founders treat agents as a feature, as an enabler or completely new product category?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:16:01]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:16:42]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:17:48]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:18:51]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:19:07]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:20:00]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:21:05]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:22:20]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:22:58]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:23:40]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:24:58]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:26:10]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:27:33]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:28:06]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:28:54]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:29:54]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:31:20]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:31:48]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:32:07]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:33:18]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:34:23]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:34:38]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:34:58]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:36:04]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:37:41]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:38:17]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:38:28]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:40:06]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:41:44]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:42:59]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:43:21]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:43:42]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:44:51]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:45:53]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:46:56]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:48:05]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:49:03]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:49:24]:

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?

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:49:47]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:50:48]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:52:19]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:53:36]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:54:44]:

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.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:55:25]:

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.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:55:40]:

Likewise. Really enjoyed myself and and yeah looking forward perhaps to the next one in.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:55:45]:

A way that would be awesome. Have a good day. Bye bye.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:55:49]:

Thank you. Bye bye.

Jörn 'Joe' Menninger | Founder and Editor in Chief | Startuprad.io [00:55:55]:

That's all folks. Find more news streams, events and interviews@www.startuprad.IO. remember, sharing is caring.

Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS [00:56:08]:

Same.

📝 Copyright: All rights reserved — Startuprad.io™