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