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World Congress 2026 Europe • Jul 10, 2026 • Session details

The Missing Layer Between Enterprise Data and AI Agents

Jannis Eickenroth , Sebastian Klenke

Operational failure scales infinitely when agentic AI runs on fragmented enterprise data. Discover how a Trusted Intelligence Layer bridges this critical gap to safely scale autonomous agents.

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#1 about 4 min

Digitalizing Germany's savings banks network at massive scale

Providing personalized digital banking to 50 million customers across 350 entities requires robust infrastructure.

#2 about 3 min

Why agentic AI forces companies to fix data debt

Automating business actions uncovers the hidden operational risks of relying on inconsistent enterprise data.

#3 about 3 min

Automating banking workflows with the SAI Pilot assistant

Utilizing an on-premise AI assistant prepares meeting summaries to save thousands of manual hours.

#4 about 4 min

Prototyping autonomous AI agents for management reporting tasks

Future AI systems will actively analyze KPIs and delegate operational tasks rather than passively presenting dashboards.

#5 about 3 min

Implementing the trusted intelligence layer for AI semantics

Constructing specialized domain models and knowledge graphs bridges the gap between raw data tables and AI understanding.

#6 about 2 min

Four core principles for building trustworthy enterprise intelligence

Translating accurate semantics into reliable operational intelligence relies on clear data meaning and automated reasoning.

#7 about 3 min

Maintaining trust and digital sovereignty via on-premise AI

Operating a secure AI platform entirely on-premise enables rapid innovation while safeguarding consumer finance data.

#8 about 2 min

Establishing architectural boundaries and guardrails for agentic execution

Hardcoding behavior controls inside the core banking system guarantees every AI decision remains strictly explainable.

#9 about 2 min

Routing inference across specialized open-source foundation models

Directing specific tasks to optimized models improves enterprise reliability better than adopting massive general-purpose models.

#10 about 4 min

Embedding continuous AI governance within standard deployment pipelines

Integrating model testing and logging natively into deployment pipelines tracks performance without waiting for retrospective legal documents.

#11 about 3 min

Centralizing access policies with standardized LLM gateways

Managing AI protocols via centralized model context interfaces avoids fragmentation and ensures tools act reliably together.

#12 about 2 min

Prioritizing AI architecture and robust operations over model selection

Focusing engineering resources on overarching platform operations secures trust and compliance more sustainably than chasing benchmark models.

Matching moments

2:52 min

Designing agentic AI solutions for the enterprise

Damir Dobric · Coffee With Developers

2:52 min

Scaling generative AI use cases across large enterprises

Mike Butcher Mike Butcher +3 · WWC 2024

2:32 min

Securing enterprise artificial intelligence through foundational model mandates

Frederik Gregaard Frederik Gregaard +1 · WWC Europe 2026

1:40 min

Addressing data sovereignty and compliance blind spots within AI

Sebastian Kister Sebastian Kister · WWC Europe 2026

3:52 min

Establishing a structured framework for enterprise AI

Julian Joseph · LIVE

40 sec

Introduction to building real-world AI agent solutions

Dennis Zielke Dennis Zielke +1 · WWC 2025

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