The AI Native Enterprise

Most organizations are experimenting with AI, but few are seeing meaningful impact. The limitation is not the models — it’s how companies adopt, structure, and govern their use.
To move beyond isolated use cases, we need to understand two things: the patterns that shape how new technologies succeed or fail, and the practical mechanisms required to operationalize them at scale.
This meetup brings both perspectives together — from lessons of past technology adoption to the concrete question of how to govern and integrate AI within the enterprise.

------------- Agenda ------------

1. Learning from the past: How tech adoption patterns shape the AI future
With Christian Scholz, Schwarz Digits, 15min + Q&A
What can we learn from previous waves like cloud, mobile, or SaaS?
Christian explores how recurring patterns in technology adoption help us understand today’s challenges with AI — and how they point toward a viable solution space with agents and new system architectures.

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Understanding these recurring patterns is only the first step. If the challenge is not the technology itself, but how organizations structure and control its use, the question becomes practical: how do we operationalize AI in a way that actually works?
One area where we already know things will change significantly is governance. How models are accessed, controlled, and embedded into enterprise systems will look fundamentally different. Moving from abstract patterns to real-world implementation requires new approaches to managing models, permissions, and integration at scale.

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2. Governing Models & MCP Access in Enterprises
With Kevin Rampelmann, Riverty/Bertelsmann, 15min + Q&A
As AI becomes embedded in enterprise workflows, governance becomes critical.
Kevin dives into how organizations can manage model usage, control access, and build scalable structures around MCP (Model Context Protocol) and beyond.
Expect practical insights on model governance, access control, and enterprise-ready AI architecture.

Who should join?
Enterprise architects and IT leaders, AI and data practitioners, Product and platform teams, anyone working on scaling AI beyond pilots

About the group
The AI Native Enterprise is a community for people who believe that AI is not just a feature, but a fundamental shift in how organizations are built and operated.

# Ontologies & Context Engineering: The Missing Layer in Enterprise AI

Most enterprise AI initiatives don’t fail because of models. They fail because of missing context.
As organizations move toward an AI-native operating model, it becomes clear that AI is not just another layer on top of existing systems—it needs a structured understanding of the enterprise itself. This is where ontologies come in.

## What this session is about

In this meetup, we’ll explore why ontologies are becoming a critical building block for context engineering in enterprise AI—and why they might be the difference between promising pilots and production-grade systems.
We’ll go beyond theory and focus on:

• How ontologies create a shared language across teams and systems
• Why LLM-based systems break without structured context
• How ontologies improve RAG, agent systems, and decision-making
• The role of ontologies in governance, explainability, and compliance
• How organizations are reintroducing semantic layers into modern AI architectures

## Why this matters now

Enterprise AI is entering a new phase:

• From prompts to systems
• From experiments to production
• From isolated use cases to AI embedded in workflows

But one question keeps coming up:
How do we make AI actually understand the business?
Ontologies are a big part of the answer.

## Who should attend

This session is designed for:

• Enterprise architects and platform engineers
• AI and ML engineers working on LLM systems
• Data and knowledge engineers
• CTOs, CDOs, and technical decision-makers
• Anyone moving from AI experiments to real systems

## What you’ll take away

After this session, you’ll have:

• A clear understanding of what ontologies are (and aren’t)
• A mental model for context engineering in enterprise AI
• Concrete ideas on how to integrate ontologies into your architecture
• A better sense of when ontologies are worth the effort—and when they’re not

## Format

• 20 min talk
• 20 min deep-dive discussion
• Open Q&A

------ From APIs to Agents: Why we need a new gateway layer ------

Many enterprise architectures today are still built around APIs: clearly defined interfaces, request-response patterns, and service-to-service communication.

But with the rise of AI agents, this model starts to break.
Agents don’t just call APIs. They plan, reason, use tools, interact with other agents, and continuously exchange context with LLMs. This creates entirely new challenges around security, governance, observability, and control.

This session dives deep into Agent Gateway, a new infrastructure layer designed specifically for agent-to-agent, agent-to-tool, and agent-to-LLM communication.

------ What we’ll explore ------

Why traditional API gateways fall short in agent-based systems
The shift from service-to-service to agent-to-everything communication
Core concepts behind Agent Gateway (MCP, A2A, tool integration)
Security, policy enforcement, and guardrails for autonomous agents
Observability: tracing prompts, decisions, and tool usage
Real-world architecture patterns for building agentic systems
How Agent Gateway fits into emerging “Agent Mesh” architectures

------ Speaker ------

Christian Posta (Solo.io)

Christian is a globally recognized expert in cloud-native architecture, service mesh, and distributed systems. As Global Field CTO at Solo.io, he works with enterprises to design and implement next-generation platforms—and is now at the forefront of AI-native infrastructure and agent systems.

------ Who should attend? ------

This meetup is for people who are building or thinking about the next generation of enterprise systems:
Platform engineers and architects, Cloud-native practitioners, AI engineers working with agents and LLMs, Anyone exploring how AI changes system design

------ How to join -------

This time, the meetup will be in the premises of Riverty in Kantstrasse 17, Berlin (4th Floor of Living Berlin mall). Riverty will provide pizza and drinks. Please bring your ID as you will need to sign an NDA (we are a regulated entity).

Most enterprises are experimenting with AI in software engineering — but few are seeing real, systemic impact. One of the reasons: Today, the conversation is still largely focused on individual productivity: How can we make a single engineer faster? How do we create “10x developers”?
But this perspective is too narrow. To unlock the full potential of AI, we need to move beyond isolated productivity gains and rethink the entire process from idea to production.

This session introduces a practical maturity model that captures how organizations actually evolve: From resistance and strict control to shadow usage and vibecoding to AI-augmented individual performance.
But there are further stages — where the real transformation begins: Not just optimizing developers, but fundamentally redesigning how software is built, integrated, and operated across the SDLC.

Georg Kreimer (ex-Celonis, ex-Hybris) developed a 5-level maturity model for AI in software engineering.
In this talk, he shares the model and discusses what it actually means in practice — across organization, processes, tools, training and governance.

-------- What to expect --------
- A realistic view on how enterprises adopt AI in engineering
- Why most companies are stuck at early maturity levels
- What changes when moving from individual productivity to system-level thinking
- Practical implications for scaling AI across the SDLC

-------- Who should join? --------
Engineering leaders and architects, Platform and DevOps teams, AI and data practitioners, anyone working on bringing AI into software development beyond experimentation

-------- About the group --------
The AI Native Enterprise is a community for people who believe that AI is not just a feature, but a fundamental shift in how organizations are built and operated.