What participants take away from the course

• A well-founded understanding of why AI transformation is sociotechnical and why traditional change models alone are no longer sufficient.
• Confident handling of Lewin, Kotter, ADKAR, Bridges, McKinsey 7-S, and Burke-Litwin and their targeted combination in the AI context.
• A repeatable, artifact-based approach ranging from mandate and diagnosis to target vision and architecture, all the way to institutionalization: Align, Structure, Enable, Anchor.
• Operational confidence in dealing with anxiety, resistance, and identity issues, as well as a clear understanding of psychological safety as a prerequisite for learning processes.
• Structural anchoring of governance, human-in-the-loop, and ethics, including the legal guardrails of the EU AI Act and GDPR.
• Practical knowledge on AI agents: architecture, application areas, and governance requirements with growing autonomy.
• Develop, present, and defend a reasoned transformation approach of your own for your professional context during the expert discussion.

These points are deliberately not an additive toolkit, but rather a cohesive development of competencies: whoever understands the socio-technical logic of AI transformation can deploy models more purposefully; whoever masters models can set up a change process more cleanly; whoever sets up a process cleanly can lead people more credibly. At the end of this chain lies the ability to anchor responsibility—technically, humanly, and legally—in such a way that AI transformation is not merely introduced, but becomes permanently viable.

What it is about

AI is not only changing individual tasks, but the architecture of companies. Knowledge work is being redistributed, decisions are being prepared increasingly with data and system support, routines are shifting into automation, and AI agents are increasingly taking over subtasks that humans previously coordinated.

This creates a new management task for managing directors: they must decide which work in the company will be performed in the future by humans, AI systems, agents, external specialists, or automated processes, and how this hybrid organization can function in a controlled, economically viable, and legally compliant manner.

The training course closes precisely this gap. It combines strategic AI transformation, organizational design, use case portfolio management, AI literacy, governance, and concrete implementation planning for small and medium-sized enterprises.

Who this course is made for

The program is aimed at managing directors in medium-sized businesses who want to not just try out AI in their own companies, but introduce, prioritize, and scale it in a structured way.

No prior technical AI knowledge is required. Participants are expected to be willing to bring a real corporate case study into their term paper and examine their own organization in terms of strategy, processes, roles, competencies, and risks.

Managing directors who have already launched initial AI initiatives and are now realizing that productivity, adoption, governance, data quality, and responsibilities do not happen by themselves will particularly benefit. The program is equally suited for companies that are facing their first scaling decision and want to avoid falling into tool-driven actionism, unclear mandates, or uncontrolled shadow usage right from the start.

• Target group: Managing directors of medium-sized companies
• Instructors: Norman Müller
• Duration: 46 teaching units
• Live classes: 16 teaching units in eight evening blocks
• Self-study: 20 teaching units of supervised self-study
• Term paper and exam: 10 EU
• Previous knowledge: No prior technical AI knowledge required
• Format Online evenings

Winter semester 2026

• Oct 8 to Nov 26, 2026, every Thursday from 5:00 PM – 6:30 PM

Spring semester 2027

• March 4 to April 22, 2027 every Thursday 5:00 PM – 6:30 PM

Teaching methods

The course combines scientific foundation with lived practice: keynote speeches and instructional discussions convey theoretical depth, case analyses and role plays make psychological dynamics tangible, and workshop sessions on mandate and diagnostic documents create direct practical transfer.
Group work, reflection exercises, and peer case consultations encourage interaction among participants, while transfer assignments after each evening apply what has been learned to their own transformation projects.

Completion and examination

The course concludes with a written term paper of 3,000 to 4,500 words. Anyone who passes the exam and has attended at least 80 percent of the live sessions will receive the certificate „AI Transformation Architect for Hybrid Organizations“.

The program of the certificate course