Applied AI Transformation Management

Programme structure

MAI is structured as a three-semester full-time master's programme with 90 ECTS. Six English-language core modules (CORE01 to CORE06) form the backbone of the curriculum and cover the subject-specific, methodological, governance and execution-related competences needed to take responsibility for AI initiatives in organisations. In the individual elective profile of 24 ECTS, students deepen their work in a specialisation of choice — in German or English. The master's thesis of 24 ECTS concludes the programme; it is written in English and may be carried out in cooperation with an industry partner.

The programme is aligned with level 7 of the German Qualifications Framework. Teaching, learning and assessment formats follow a constructive-alignment logic and combine classical formats (lectures, tutorials) with workshops, project-based learning, flipped-classroom elements and industry-coupled sprint formats.

Module catalogue (CORE01–CORE06)

CORE01 — AI Transformation & Change, Leadership, Org Design · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

Students learn to plan, manage and reflect on AI-driven transformation initiatives. They understand classical and agile change approaches, transformational leadership and the interplay between organisation, culture and technology.

CORE02 — Governance, AI Compliance, Ethics, Data Ownership · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

Students learn to apply regulatory frameworks (in particular the EU AI Act, data protection law and liability regimes), to design governance models for AI and data ecosystems and to clarify data ownership in practice. Ethical implications are reflected upon systematically.

CORE03 — AI Use-Case Discovery & Value Realization (Process, Data & Stakeholders) — Project · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

Students identify AI use cases along processes, data and stakeholders, evaluate them with value contribution logics (Output-Outcome-Impact, KPI/OKR) and prioritise them in portfolios. They make Go/No-Go recommendations on a sound scientific basis.

CORE04 — Technical AI Foundations: Applied ML, Data Pipelines & Decision Systems · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

Students know the relevant model classes of applied machine learning, master data preparation and feature engineering and understand the end-to-end life cycle of AI-based systems from data acquisition through to model operations.

CORE05 — AI Transformation Execution & Scaling Lab · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

Students learn to move AI solutions from pilot to scaled, reliable operations. They master methods from MLOps, roll-out management, monitoring as well as incident and change management and develop sustainable operating models.

CORE06 — AI Transformation Sprint Lab (Industry Project) · 6 ECTS · English · Sem. 1 (WS) and Sem. 2 (SS)

In an industry-coupled sprint format, students tackle a real AI transformation initiative under realistic organisational, technical, legal and time constraints. They take responsibility for analysis, design, delivery and impact reporting.

Elective profile (24 ECTS)

The elective profile of 24 ECTS is composed from the MAI elective catalogue, which is published at the beginning of each semester. It allows individual profile-building in a specialisation of choice. Modules can be taken in German or English, depending on availability. Decisions about adding further modules to the catalogue are made by the study commission.

The specific elective catalogue for summer semester 2027 will be published before the first cohort starts.

Master's thesis (24 ECTS)

The master's thesis (24 ECTS) is an independent scientific treatment of a complex question from the field of AI-driven transformation. It is written in English and may be carried out in cooperation with an industry partner. Decisions on exceptions to the language rule are made by the examination board. The master's thesis demonstrates that the graduate is able to address a real transformation question in a methodologically sound, evidence-based and audience-appropriate manner.

Teaching, learning and assessment

Teaching and learning

Modules combine lectures, tutorials, workshops and seminars with project-based learning, flipped-classroom elements, case-based learning and structured reflection and transfer assignments. CORE06 (Sprint Lab) is dominated by self-organised project work with an industry partner.

Assessment

The assessment formats are specified per module in the module catalogue. They include written exams, written assignments, project work and presentations, as well as combinations thereof. Module examinations are conducted in English; electives follow the assessment language of the host module.

Downloads

• Study and examination regulations (SPO MAI, special part) — link to follow once published
• Module catalogue (status: 27 March 2026, Rev0) — View in the HTWG Indigit app
• Admission regulations (§ 27 ZuSMa) — link to follow once published
• Programme flyer (4 pages) — View PDF