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.

Key facts

Key benefits

Learn from academic excellence: Developed by ESMT Berlin faculty and grounded in cutting-edge research and industry practice at the intersection of AI, data, and enterprise transformation.
Combine strategy and practice: Work with executive frameworks, diagnostics, simulations, and hands-on sessions built around real organizational challenges.
Join a carefully selected peer cohort: Learn with leaders from diverse industries and organizational contexts, all focused on designing the enterprise data layer behind successful AI.
Apply immediately: Use frameworks, decision models, and tools directly within your organization.
Stay ahead: Explore emerging questions around enterprise architecture, sovereignty, resilience, trust, compliance, and next-generation AI systems.

Your profile

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.

Topics

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

2 days

€3,000

Apply now

♡

Next start date: December 10, 2026

Data Strategy for Enterprise AI

Format

Face-to-face

Price

€3,000

Location

Berlin

Duration

2 days

Language

English

Apply now See all dates

Next dates

Data Strategy (AID.1)

Location

Berlin

Next Intake

Dec 10-11, 2026

Duration

2 days

Format

Face-to-face

Tuition

€3,000

Apply now

Day 1

Architecting the enterprise data engine

Explore the strategic data choices that must be made before enterprise AI can scale.

Day 2

Operating and stress testing the data engine

Translate strategic choices into an integrated enterprise data approach, then test decisions under executive scrutiny.

Tangible outcomes

By the end of the program, participants will have built a board-ready data asset and architecture blueprint.

This blueprint covers:

enterprise data readiness assessment
strategic data advantage logic
architecture and infrastructure decisions
economics and investment priorities
operating model and ecosystem choices
trust, resilience, and compliance implications
enterprise data action plan

Executive Education participants in the classroom

Why ESMT?

Ranked #1 business school in Germany and among the best worldwide
Based in Berlin, a hub for entrepreneurship, technology, and innovation
Executive education combining academic rigor, strategic relevance, and practical application

Apply this program toward your AI Executive Certificate

This program counts toward ESMT Berlin’s AI Executive Certificate. Complete any mix of AI programs totaling 6 learning days and earn a credential aligned with the EU AI Act.

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Complete 6 learning days in total

Earn your AI Executive Certificate

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