Overview

By 2026, most enterprises are no longer struggling to start with Generative AI, they are struggling to scale it meaningfully. What began as a wave of pilots and proofs of concept is now exposing a deeper structural reality: enterprise AI does not fail at the model level, it fails at the point of integration, governance, and operational adoption.

The core challenge is no longer experimentation, but execution under real enterprise constraints — fragmented data landscapes, legacy architecture, unclear ownership of AI capabilities, and governance models that were not designed for autonomous or semi-autonomous systems.

In this session, Dr. Sebastian Kaiser, Head of AI Implementation at Munich Re, shares what changes when AI moves from innovation labs into regulated, global-scale enterprise environments. Drawing on hands-on implementation experience, he explores the practical realities of turning GenAI from isolated use cases into a governed, scalable business capability.

Discussion points:

• Why do most GenAI initiatives still fail to move beyond pilots — even in digitally mature organizations?
• Where does AI scaling actually break down: data, architecture, governance, or decision ownership?
• What trade-offs emerge when AI shifts from experimentation to embedded enterprise decision-making?
• How should ownership of data, platforms, and AI capabilities evolve in complex global organizations?
• What does “production-grade AI” really mean under real regulatory, security, and operational constraints?
• Where are enterprises unintentionally creating new risk exposure as AI becomes more pervasive?
• What structurally separates organizations that are scaling AI successfully from those stuck in pilot cycles?
• As AI becomes business-critical infrastructure, how is accountability shifting across the C-suite?