Value Creation with AI at Scale: Strategy, Tech, People and Governance

1) AI Strategy & Value Potential Analysis of business models, value narratives, and strategic target visions for AI; evaluation of value creation logic and success metrics. 2) AI Platform & Ecosystem Strategy Technological architecture decisions (e.g., cloud, MLOps) and design of partner ecosystems to enable scalable AI solutions. 3) People Strategy & AI Leadership Success factors for talent development, change management, organizational structure, and leadership behavior in AI adoption. 4) Responsible AI & Execution Strategy Integration of regulatory requirements (incl. EU AI Act), ethical principles, and governance structures into AI execution plans. 5) Generative AI in Practice (BCG Excursion) Live demonstrations of current GenAI applications, discussion of impact measurement, scaling strategies, and lessons learned from real-world transformation projects.

After completing the course, students can 1) analyse real-world AI adoption cases to distil recurring success and failure patterns in value creation 2) evaluate strategic, technological, organisational and ethical drivers (incl. EU AI Act) that determine whether AI initiatives deliver business impact 3) design a coherent AI value-creation playbook for a chosen company, covering strategy, tech stack, people and governance 4) justify and communicate the expected value and risk-mitigation logic clearly in a professional report and executive-level presentation

The assessment is conducted as group work with around six students per team and consists of: a) a written project report (in form of a slide deck of about 60 content slides) - 70% b) a presentation to a decision-making audience followed by a Q&A session - 30% Each group presents its concept in a 10 minute presentation, followed by a Q&A session of up to 5 minutes. The presentation is addressed to a fictional senior management audience and follows the format of a management pitch. Optionally, guest speakers from industry or consulting may be invited to join the jury. The objective is to transfer the insights from scientific studies short and precise to senior management and analyze real AI case studies and develop a viable AI value creation concept. The presentation aims to train target group-oriented communication and the confident defense of results in front of a decision-making audience. Evaluation criteria: Written report – analytical depth, consistency of value logic, consideration of responsible AI aspects, clarity of argumentation, clarity and effectiveness of slides, and professionalism. Presentation – persuasiveness of the pitch, quality of answers during the Q&A, and professional delivery.

For successful participation in the module, students are expected to have a basic understanding of business strategy or digital transformation in order to contextualize AI use cases within value creation frameworks. In addition, very good English skills (at least B2 level) are required. Beneficial – but not mandatory – are basic knowledge of artificial intelligence and machine learning, as well as fundamentals of statistics (e.g. descriptive statistics, regression analysis).

Lectures: provide a compact theoretical foundation for AI value creation. Case studies: apply concepts to real company examples, fostering analytical thinking. Guest lectures: offer current industry insights and highlight success and failure patterns. Group work: enables students to develop their own value creation playbook, strengthening teamwork, conceptual, and presentation skills.