Agentic AI for SaaS – Workflows, Use Cases & EU AI Act
- Jörn Menninger
- Sep 18
- 20 min read
Updated: Sep 19
🚀 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
Agents vs Automation: Why Dashboards Are Dying
From Feature to Product: Three Paths for Founders
High-ROI Use Cases: Pricing, BI & Support
Multi-Agent Marketplaces & Governance
Culture Change: AI Literacy, Whisperers & Playgrounds
EU AI Act: Risk, Logs & Trust as Differentiator
SaaS 2028 Outlook: 15% of Work on Autopilot
Key Takeaways
FAQ
Closing & Resources
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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:
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.
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.
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:
Map risk before launch. Know which category your agent falls into.
Build in logs and explainability. High-risk agents require human-in-the-loop oversight and auditable workflows.
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.”
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📄 Startuprad.io – Interview with Jennifer Grün (AWS)
Topic: Agentic AI and the Future of SaaS
Part 1 — Opening & Early Context
[00:00:00]
Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Think of agents as an extension of your team who can own a workflow end-to-end instead of just helping out. Founders aren’t just using agents for conversation or Q&A. They’re running financial validation, multi-agent support workflows, supply-chain orchestration, and content management — all without needing humans for every trigger point.
For example, startups like Qonto use multi-agent systems to process, validate, and reconcile split payments in minutes — work that used to take hours or even days. With agentic AI, when a startup lands a big customer or suddenly grows its user base, the agents can scale instantly to match demand without doubling headcount or losing efficiency. That’s a fundamentally new way to accelerate growth.
Agents are equipped with feedback loops that improve themselves in production — learning from data, success, and failure. Companies no longer need to manually update processes all the time.
[00:01:10]
Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Welcome to Startuprad.io, your podcast and YouTube blog covering the German startup scene with news, interviews, and live events.
This is our second episode in cooperation with AWS. Together with Jennifer Grün, we’re exploring how SaaS is being rewritten by agents. If you’re building software today, your business model might not survive the next wave.
Jennifer is a Senior Specialist for Generative AI and Machine Learning at AWS, and formerly worked as a Country Manager and Account Manager in the startup segment. She now helps founders 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.
[00:02:42]
Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
At AWS, Jennifer helps startups and enterprises 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.
[00:03:07] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Thank you, Joe. Really enjoy being back on the show.
[00:03:10] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
It’s my pleasure. Let’s dive into context and trends. Gartner lists agentic AI as a top strategic trend for 2025 and beyond. From your perspective, why are agents more than just hype?
[00:03:30] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
First, it’s important to recognize that agentic AI is not just another round of automation. Traditional automation follows scripts and rules. Things get done, but only within tightly defined boundaries.
Agents, on the other hand, set goals, make independent decisions, adapt strategies, and take action across complex multi-step processes with minimal human input. Think of them as proactive team members who own workflows end-to-end.
Founders are already deploying agents far beyond chat or Q&A. They’re using them for financial validation, multi-agent support, supply-chain orchestration, and content management. Qonto, for example, processes split payments in minutes.
With agentic AI, when growth surges, agents scale to match demand — without doubling headcount. They learn from feedback loops in production, spotting issues, escalating, adapting campaigns, reordering inventory, or shifting customer interactions before problems escalate.
Research shows organizations adopting agentic AI are already seeing measurable business outcomes: higher customer satisfaction, productivity boosts, and ROI achieved quickly. That’s why investors are drawn to agentic startups — not just for the technology, but because autonomous systems deliver compounding value as the business scales.
Of course, there are downsides: security, governance, and robust guardrails are essential. Gartner predicts some agentic AI projects will fail early due to these hurdles.
[00:06:15] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
How do you explain the difference between classical SaaS automation and agentic workflows to founders who may not have a technical background?
[00:06:27] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
I like the analogy of cooking. Traditional SaaS automation is like following a strict recipe: if a new customer signs up, send a welcome email. It’s predictable and reliable, but rigid. If something unexpected happens — a new data field or a change in the process — the automation breaks.
Agentic workflows are different. Imagine hiring a proactive, goal-oriented chef. You don’t give them a recipe; you give them a goal. For example: retain this customer. The agent can analyze usage data, check support tickets, query the knowledge base, then draft and send a personalized retention message. It monitors the response and adapts.
This is a leap from executing predefined tasks to autonomously making decisions and taking action.
Traditional SaaS success has been measured by engagement with the tool itself: dashboards, buttons, time spent. Agentic SaaS flips the script: the value is no longer the UI but the outcome achieved. Did the agent increase conversion rates? Did it reduce ticket resolution times?
Customers aren’t buying software to use dashboards — they’re buying outcomes. And agents finally let SaaS companies sell outcomes directly.
[00:09:31] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
That’s a very important distinction. In your AWS talks you contrast operational improvement versus product innovation. Where do agents fit best?
[00:09:53] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Agents fit into both.
On the operational side, they act as proactive, goal-driven collaborators. For example, in onboarding: an agent can draft a welcome email, set up access, schedule meetings, send reminders — reducing manual work and improving efficiency.
On the product innovation side, agents become new customer-facing features. Take a pricing agent: instead of showing a dashboard with data, the agent autonomously analyzes demand, scrapes competitor sites, calculates margins, and updates the database price.
Agents enable a new level of personalization and automation that moves from “feature add-ons” to core product capabilities.
Part 2 — Use Cases, Back Market, and SaaS Product Strategy
[00:11:46] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Let’s go into what you’ve learned from your own leadership. You scaled a startup yourself as a Country Manager. From that lens, where do you see the biggest opportunities for agents in SaaS?
[00:12:04] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
When I was Country Manager at Back Market, a French refurbished marketplace, I saw firsthand how hard it is to scale quickly. In Germany we achieved triple-digit growth in six months — but it was a huge manual effort. If I’d had agents then, scaling would have been faster and less overwhelming.
For early-stage startups, agents are about more than efficiency; they’re about survival and growth. Instead of a one-size-fits-all approach, agents can hyper-personalize the entire acquisition funnel: monitoring market data, analyzing competitor moves, drafting outreach emails tailored to segments.
Predictive analytics agents can analyze user behavior, score conversion likelihood, and help sales prioritize the most promising leads. This turns data into actionable intelligence at scale, critical in a fast-growing startup.
Agents can also monitor account health, analyze feedback from support tickets and social media, and even predict churn before it happens. This frees teams to focus on strategic relationships instead of routine check-ins.
It’s not about replacing human work — it’s about making humans more impactful.
And beyond that: SaaS products themselves may no longer need a beautiful UI. Agents don’t require dashboards. They work behind the scenes via APIs, making no-UI experiences possible. For example, a user could simply ask an agent: “Create a new campaign targeting our top 10 German customers who haven’t purchased in 90 days.” The agent then executes across CRM, marketing automation, and analytics tools.
That’s a fundamental shift in SaaS design.
[00:15:51] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Should SaaS founders treat agents as a feature, an enabler, or a completely new product category?
[00:16:01] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
There’s no one-size-fits-all.
1. Agents as a FeatureThe lowest-risk approach: integrate a GenAI agent as a feature. Example: a CRM vendor adding an agent that drafts follow-up emails for sales teams. It’s measurable, improves productivity, and enhances the core product.
2. Agents as an EnablerMore ambitious: an agent that streamlines end-to-end processes. For example, an agent that triages support tickets or a data-cleaning agent for analysts. This changes how teams interact with your software, driving operational transformation.
3. Agents as the ProductMost disruptive: build the agent as a standalone product. Here the agent isn’t just a tool — it’s the product itself. For instance, a pricing optimization agent that autonomously scrapes competitor sites, analyzes demand, calculates margins, and updates prices.
Regardless of the model, success depends on business impact and feasibility. Start with agents as a feature, validate, then expand. But remember: agents are most valuable when they can interact with the outside world. Infrastructure is key.
[00:18:51] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
You’ve mentioned use cases like automated follow-up emails and support triage. What do you think are the most promising SaaS use cases for agentic workflows?
[00:19:07] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Right now, the strongest SaaS use cases for agents are in business intelligence and data analysis.
Instead of just displaying dashboards, agents transform data into actionable insights. Multi-agent pricing systems can find optimal prices, monitor KPIs, and detect anomalies in real time. They can generate plain-language reports that make complex data accessible to non-technical teams.
Agents also automate data-driven workflows: inventory optimization, churn prevention by identifying at-risk accounts, or recommending retention strategies.
I’m working with a fintech startup that uses AI to generate dashboards in natural language from transaction data. Their agents detect anomalies and are now shaping product roadmaps.
In marketing and sales, agents are moving beyond simple content generation. They orchestrate campaigns: drafting emails, segmenting audiences, reallocating ad spend in real time. Think of them as digital strategists that personalize at scale.
Customer service is another major area. I’ve seen companies resolve 8,000 out of 16,000 tickets automatically, with a 4% NPS boost. Agents with memory can recall customer history across sessions, creating continuity that traditional bots can’t.
Even HR software is adopting agentic features — for example, summarizing CVs or analyzing employee feedback.
The impact is clear: in one e-commerce client, agents processed 5x more tickets than human agents in the first week, freeing humans for high-value interactions.
Part 3 — Ecosystems, Marketplaces & Change Management
[00:22:58] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Welcome back from our short ad break. Jennifer, let’s dive into the role of ecosystems and marketplaces. How do they matter for scaling agent-based SaaS solutions?
[00:23:40] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
For a long time, software companies tried to be “one-stop shops,” building every feature customers might ever need. But as agentic solutions evolve, it’s clear: one agent can’t do it all.
If companies try to manually manage agents from multiple vendors, they risk agent coordination chaos — conflicting logic, inconsistent user experiences.
The solution is multi-agent systems and marketplaces. Think of a supervisor agent acting like a project manager: breaking down a complex problem, delegating subtasks to specialized agents. For pricing, that might mean:
one agent scrapes competitor data,
another analyzes demand,
a third calculates margins.
Together, they collaborate like a human team.
Marketplaces make it easy to discover and integrate these specialized agents. The benefit is specialization: instead of one monolithic product, we get best-of-breed agents that can be combined into powerful workflows.
Take retail: instead of one vendor building agents for recommendations, payments, and logistics, each function can come from a specialized provider. I already see co-development of repeatable AI solutions with partners in customer support automation.
But ecosystems also require trust. Governance, security, and compliance are critical. Without guardrails, malicious tools could manipulate agents. Successful marketplaces will require shared security standards so customers can safely combine agents from different vendors.
[00:27:33] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
We’ve talked about agents and marketplaces. But let’s pivot to the people side: culture and change management. You’ve said it’s often underestimated. What practical steps can founders take to build AI literacy and reduce resistance?
[00:28:06] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Leaders must champion change. Too often, companies dive into AI out of FOMO without clarity on the problem they’re solving. Start with a clear purpose: customer experience, efficiency, innovation? That clarity gives teams a reason to care.
Next: don’t treat everyone the same. Assess whether employees are beginners, intermediate, or advanced, then tailor training. At AWS, we run workshops aligned to roles. Pair training with low-stakes experiments — summarization, mockups, sandbox projects — so teams can fail fast in a safe space.
Structured training matters too: short, targeted modules per function. Pair that with AI champions inside teams — early adopters who show others practical use cases. This peer-to-peer model accelerates adoption.
Crucially, provide clear AI policies. Ambiguity breeds fear. When employees know the rules, they experiment confidently.
And celebrate wins. Share stories like: “We cut reporting prep time in half with an agentic workflow.” These proof points spin the flywheel.
Finally: make tools accessible. Platforms like AWS PartyRock let anyone prototype agents from scratch. Nothing beats hands-on experimentation to build confidence.
[00:31:48] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
You often mention AI Whisperers. What exactly do you mean, and how do they accelerate adoption?
[00:32:07] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
I think of AI Whisperers as translators and bridge-builders. They speak both the language of data scientists and of business teams. Their role isn’t just technical expertise — it’s making AI accessible, shifting conversations from fear to opportunity.
Around 88% of employees are non-technical (per McKinsey research). Whisperers explain AI in plain language, demystifying concepts.
Paired with senior leaders, they’re powerful. Leaders bring vision, authority, and resources. Whisperers bring practical insights, identify pain points, and suggest low-risk, high-impact pilots.
Together they create a feedback loop: strategy from the top, grounded experiments from the bottom. That combination accelerates AI adoption across the organization.
[00:34:38] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
You also talk about AI playgrounds. As a father of two, playground means something different to me. What do AI playgrounds mean in this context?
[00:34:58] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
I actually like the children’s analogy. Playgrounds are low-stakes spaces to explore, experiment, and learn by doing. That’s what AI playgrounds are: safe sandboxes for hands-on experimentation.
For startups, time and money are scarce. Playgrounds let teams prototype ideas quickly without committing big resources. On PartyRock, for example, I’ve seen teams build apps to plan trips, generate recipes, or simulate customer campaigns — in minutes.
The point isn’t getting it perfect. It’s discovering business impact before investing heavily. Startups can test: does this use case deliver impact and is it feasible? If yes, scale it. If not, pivot early.
Playgrounds encourage creativity, reduce fear, and help teams build a culture of innovation.
[00:38:17] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
We’ve been talking about culture and change. What lessons from your own leadership experience would you pass on?
[00:38:28] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Change isn’t just about tools — it’s about people. At Back Market, we doubled headcount in months while expanding to Germany. Change happened constantly, and informal communication broke down.
The reality: 60–70% of change initiatives fail, often because the people side is mishandled. Employees don’t embrace change just because leadership mandates it. They need to see the vision, understand personal benefits, and feel part of shaping it.
Involve employees early. Identify change champions and AI whisperers who can advocate. Run small pilot programs, gather feedback, adjust, then scale.
Treat change as a two-way conversation, not a top-down directive. That’s what builds buy-in and resilience.
Part 4 — Regulation, EU AI Act & The Outlook to 2028
[00:43:21] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Let’s move into regulation. The EU AI Act has been a big topic already on this show. Jennifer, how do you see it shaping SaaS models built on agents?
[00:43:42] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
The EU AI Act doesn’t treat all AI equally. It classifies systems into four risk categories:
Unacceptable risk (e.g. social scoring in the public sector) → banned.
High risk (e.g. hiring, lending, healthcare) → strict requirements: transparency, documentation, logging, human oversight.
Limited risk (e.g. customer support chatbots, recommendation engines) → lighter rules, mostly transparency.
Minimal risk (e.g. spam filters) → almost no new requirements.
For SaaS startups, the first challenge is mapping your agent’s functionality into these buckets. If your agent touches hiring or financial processes, you’ll fall into the high-risk category. That means building transparency, explainability, audit logs, and mechanisms for human oversight into the product.
Compliance isn’t just a legal requirement; it’s becoming a market differentiator. Startups that embrace compliance can win enterprise trust faster. The penalties for non-compliance are steep — up to €35m or 7% of global turnover. But the upside is credibility in enterprise sales.
We see startups like Lakera already building businesses around compliance: offering stress-test suites for adversarial attacks, input sanitization, and guardrails for orchestration pipelines. They turn compliance into a product and a competitive edge.
At AWS, we help customers navigate this with tools like AI Service Cards and our Responsible AI Framework, which document intended use, limitations, and safeguards.
The key message: compliance and governance aren’t just red tape. They’re an opportunity to win market share by building trustworthy SaaS agents.
[00:49:24] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
Let’s close with a future outlook. Gartner predicts that by 2028, AI will be so embedded in productivity apps that oversight will be rare. What does that mean for SaaS founders today?
[00:49:47] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Imagine routine tasks — invoice processing, contract generation, help desk support — handled almost entirely by agents. By 2028, Gartner expects one-third of enterprise apps to include agentic AI, with around 15% of work decisions fully on autopilot.
This shifts SaaS design from tools that help humans to platforms that own outcomes end-to-end. Founders need to ask: am I building a workflow, or am I building an agent? Does the task require reasoning or just automation?
Multi-agent systems will be central: specialized agents managed by a central coordinator. The good news: it’s never been easier to build. Frameworks like LangGraph and CrewAI, plus platforms like Amazon Bedrock Agents, give startups the building blocks. You can spin up agents that fetch data, execute API calls, self-improve through feedback, and orchestrate sub-agents.
We already see finance startups validating invoices in minutes rather than hours — with one agent orchestrating data extraction, rule checks, and BI analysis.
But not all projects will succeed. Complexity, evaluation, and security challenges mean some agentic ideas won’t make it to production. High-error-cost tasks will still need human-in-the-loop oversight, especially under regulation.
The winners will be SaaS founders who move beyond productivity enhancements to operational ownership. Customers don’t want dashboards — they want results. Agents that deliver faster outcomes, reduce manual work, and unlock new value will dominate.
And as Werner Vogels, Amazon’s CTO, often says: “Everything fails all the time.” That mindset is critical. Build guardrails, evaluation metrics, and resilience into your agentic SaaS products.
The next few years will show whether your app is just an assistant — or whether it becomes the owner of outcomes.
[00:55:25] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
That’s a powerful conclusion. Jennifer, thank you for spending almost three hours with us across two episodes. It was a pleasure having you as a guest.
[00:55:40] Jennifer Grün | Senior Specialist for Generative AI and Machine Learning | AWS
Likewise, Joe. I really enjoyed our conversation and look forward to the next one.
[00:55:55] Jörn “Joe” Menninger | Founder & Editor-in-Chief | Startuprad.io
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