Data Strategy for Enterprise AI

• Learn from faculty at the frontier of enterprise AI. Developed by ESMT Berlin faculty and grounded in cutting-edge research at the intersection of AI, data, and enterprise transformation.
• Work through the real decisions, not the theory. Executive frameworks, diagnostics, and simulations built around the exact tradeoffs, from architecture, ownership, and vendor dependency, that your organization is facing right now.
• Build with peers who carry the same weight. Learn alongside senior leaders from diverse industries, all responsible for making their organizations' data layers AI-ready.
• Leave with tools, not slides. Frameworks and decision models built to be applied inside your organization from day one.
• See past this cycle. Explore the next wave of questions forming around sovereignty, resilience, and next-generation AI systems.

• You are a mid- to senior-level leader seeking to create lasting business impact through AI-enabled capabilities grounded in strong enterprise data foundations.
• You are responsible for AI, data, digital, technology, analytics, innovation, architecture, or enterprise strategy decisions.
• You are ready to move your organization beyond fragmented data environments toward a coherent enterprise data strategy that supports scalable, resilient AI.
• You seek frameworks and practical guidance to navigate the tradeoffs around data assets, architecture, economics, operating models, trust, and long-term competitiveness.

What you'll work through

1. Enterprise data readiness for AI and autonomous systems
Data pedigree, provenance, lineage, traceability, trustworthiness, and AI readiness.

2. Strategic data advantage and competitive differentiation
Proprietary, public, and synthetic data strategies, data asymmetry, feedback loops, and network effects.

3. Enterprise intelligence architecture
Centralized versus federated models, lakehouse approaches, cloud versus sovereign infrastructure, interoperability, and resilience.

4. Investment, ownership, and dependency at the data layer
Compute and storage economics, CapEx versus OpEx, RAG versus fine-tuning tradeoffs, build versus buy, vendor and SaaS dependency, and the operating model choices that follow from them.

5. Trust, resilience, and regulatory evidence
Privacy, cybersecurity, GDPR and EU AI Act considerations where relevant, auditability, and operational resilience.