This course focuses on the strategic management and organizational transformation of artificial intelligence (AI) in business contexts. Rather than emphasizing technical development, it prepares students to act as future decision-makers and leaders responsible for designing, evaluating, and implementing AI-driven transformation initiatives.

Students learn how organizations can systematically create value from AI by developing AI strategies, assessing AI maturity, identifying and prioritizing use cases, and managing the implementation of AI initiatives across the organization. The course builds on state-of-the-art frameworks such as AI maturity models, AI strategy approaches, and structured implementation logics (e.g., governance-driven vs. momentum-driven transformation paths).

By the end of the course, students are able to:

• Design AI strategies aligned with business objectives
• Assess organizational AI readiness and maturity
• Identify, evaluate, and prioritize AI use cases based on strategic and economic criteria
• Develop business cases for AI initiatives
• Make strategic decisions on AI sourcing (e.g., make-or-buy) and ecosystem partnerships
• Manage organizational change and cultural transformation in AI adoption
• Critically reflect on ethical, societal, and regulatory implications of AI

The course explicitly targets the management, strategy, and decision-making level of AI, not the technical implementation of algorithms.

Students work in interdisciplinary teams on a real-world business challenge provided by a practice partner. Each team takes on distinct managerial roles (e.g., AI strategist, business analyst, project manager) and develops a comprehensive AI strategy and implementation plan.

The course culminates in:

• a written strategy report, and
• a competitive pitch presentation to stakeholders, simulating real-world executive decision-making contexts.

The course is delivered in English and conducted in collaboration with international university partners in a digital and interactive format.
 

Content:

Part 1: AI as a Strategic Management Challenge

• AI as a driver of organizational transformation and competitive advantage 
• Strategic implications of AI across industries 
• Fundamentals of AI (only as far as needed for managerial decision-making) 
• Data as a strategic resource (not technical processing) 
• AI risks, limitations, and governance challenges 

Part 2: AI Strategy and Value Creation

• AI strategy development and alignment with business strategy 
• AI maturity assessment and readiness frameworks 
• Governance-driven vs. momentum-driven AI transformation approaches 
• Identification, evaluation, and prioritization of AI use cases 
• Business case development (ROI, cost-benefit, scalability) 
• Make-or-buy decisions and AI ecosystem strategies 

Part 3: Implementation and Organizational Transformation

• AI implementation planning and roadmap design 
• Change management and digital mindset transformation 
• Organizational roles, capabilities, and structures for AI 
• Ethical, legal, and societal implications 
• Managing AI projects and stakeholder communication 

Part 4: Applied AI Strategy Project

• Real-world case analysis with a practice partner 
• Development of an AI strategy and implementation plan 
• Team-based role execution (e.g., strategist, analyst, PM) 
• Final report and competitive executive pitch 

Keypoints: