Data Strategy for Enterprise AI - Executive Education
Fact sheet
Graduation
Executive Education
Taught language
Englisch
Specialization
Digital Transformation, Innovation
Program Emphasis
Data Analytics
Course options
Certificate program
Locations
Berlin
AI is only as powerful as the enterprise data strategy behind it.
Organizations are rapidly adopting AI, but many still face a deeper challenge: how do you build the enterprise data foundations required for scalable, trusted, and sustainable AI?
While access to AI models is becoming increasingly widespread, competitive advantage depends on strategic choices around data assets, architecture, infrastructure, economics, trust, resilience, and operating models.
The Data Strategy for Enterprise AI program equips leaders with the frameworks, strategic lenses, and practical tools needed to architect enterprise data as a defensible engine for AI.
Organizations are rapidly adopting AI, but many still face a deeper challenge: how do you build the enterprise data foundations required for scalable, trusted, and sustainable AI?
While access to AI models is becoming increasingly widespread, competitive advantage depends on strategic choices around data assets, architecture, infrastructure, economics, trust, resilience, and operating models.
The Data Strategy for Enterprise AI program equips leaders with the frameworks, strategic lenses, and practical tools needed to architect enterprise data as a defensible engine for AI.
Program Emphasis
- Enterprise data readiness for AI and autonomous systems
Data pedigree, provenance, lineage, traceability, trustworthiness, and AI readiness - Strategic data advantage and competitive differentiation
Proprietary, public, and synthetic data strategies, data asymmetry, feedback loops, and network effects - Enterprise intelligence architecture and strategic infrastructure choices
Centralized and federated models, lakehouse approaches, cloud and sovereign infrastructure, interoperability, and resilience - The economics of enterprise data and AI infrastructure
Compute, storage, and lifecycle economics, CapEx and OpEx, and the business tradeoffs between RAG and fine-tuning - Build vs. buy decisions at the enterprise data layer
Vendor strategy, proprietary middle tiers, SaaS dependency, ecosystem choices, and sovereignty implications - Trust, resilience, and compliance in enterprise data strategy
Privacy, cybersecurity, regulatory constraints, auditability, and operational resilience - Enterprise data operating models and strategic ownership choices
Data ownership, dependency models, interoperability priorities, and operating choices - Executive challenge and board-level decision making
Strategic pressure testing of enterprise data choices under regulatory, economic, technical, and competitive constraints
Important Dates
Dec 10-11, 2026
Tuition / Fees
Duration of study
Participants
- You are a mid- to senior-level leader seeking to create lasting business impact through AI-enabled capabilities grounded in enterprise data foundations.
- You are responsible for AI, data, digital, technology, analytics, innovation, architecture, or enterprise strategy decisions.
- You want to move beyond fragmented data environments toward a coherent enterprise data strategy that supports resilient AI.
- You seek frameworks and practical guidance to navigate strategic choices around data assets, architecture, economics, operating models, trust, and long-term competitiveness.
