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AI MBA (Non-STEM) — Zurich Track

Beyond AI literacy. Toward institutional control

An AI MBA for decision-makers, not system builders 

The Non-STEM AI MBA is designed for professionals who must make decisions about AI, rather than build AI systems themselves. It addresses AI and data science at the level of judgment, capital, and organizational control—where analytical outputs intersect with strategy, governance, and execution. Instead of emphasizing mathematical formalism or model construction, the program focuses on how AI actually functions in real business environments: where it adds value, where it fails, and how its limitations should shape executive and institutional decisions. Participants develop the ability to evaluate AI claims critically, communicate effectively with technical teams, and translate analytical insights into responsible operational and strategic judgment.

The program combines online coursework with a mandatory in-person residency, reflecting the view that institutional judgment cannot be formed through content consumption alone. The online phase establishes a shared analytical foundation, while the residency phase requires participants to work under sustained time pressure, collective scrutiny, and direct supervision. Completion of the Non-STEM AI MBA certifies not technical proficiency, but demonstrated capacity for disciplined reasoning, decision-making under constraint, and institutional responsibility in AI-driven contexts.

Zurich single track

The Zurich Track is the canonical completion path for the Non-STEM AI MBA. Participants complete a four-week, full-time residency in Zurich focused on analytical judgment, institutional reasoning, and decision-making under constraint.

The residency emphasizes rigorous case analysis, collective problem framing, and disciplined evaluation of AI claims in real organizational settings. Rather than technical model construction, the focus is on understanding where AI succeeds, where it fails, and how analytical outputs should—and should not—inform executive decisions.

Completion of the Zurich Track certifies that the participant has demonstrated sustained analytical judgment and institutional discipline under direct supervision

Benefits of single track

Zurich-only participants stay in a single analytical environment for the full residency. There is no context switching, no re-framing for a second institutional culture, and no disruption of cognitive momentum.

A single-city residency reduces presentation inflation, rhetorical over-optimization, and “executive polish” effects. Besides, Zurich disproportionately benefits participants who are ocused on reasoning quality over positional and seeking intellectual closure rather than global signaling.

Because Zurich mirrors the tone of the coursework more closely, the transition from online to in-person feels smoother, less theatrical, and more academically continuous

Lecture Note

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SIAI MBA Note
Math & Stat for MBA I
SIAI MBA Note
Math & Stat for MBA II
SIAI MBA Note
Scientific Programm
SIAI MBA Note
Machine Learning
SIAI MBA Note
AI in Digital Marketing
SIAI MBA Note
Deep Learning
SIAI MBA Note
AI in Business Cases
SIAI MBA Note
Reinforcement Learning
SIAI MBA Note
Investment Case
SIAI MBA Note
Data Visualization
SIAI MBA Note
Data Management
SIAI MBA Note
Data-based Decision Making
AI MBA (non-STEM)

at a glance

HIGH QUALITY ONLINE CLASSES

ONLINE TA SESSIONS

12 COURSES & 1 DISSERTATION

8 WEEKS FOR TWO COURSES

2 COURSES PER WEEK

1 YEAR PROGRAM

Learning Outcomes

DATA ANALYTIC TRAINING FOR ABSTRACT THEORY TO REAL APPLICATION

CASE STUDIES FOR APPLYING COMPUTATIONAL SCIENCE TO BUSINESS

AI MBA (Zurich)

Admission Examination
Admission Examination
  • No official admission examination
  • Following documents will be thoroughly reviewed
    • Statement of Purpose
    • Undergraduate transcript

Please note that if you have not done any STEM education during your undergraduate or even in graduate studies, we recommend you to try MBA AI programs' business track or non-STEM MBA tracks. Even if you have applied for tech track, if no record of mathematical training is found, the offer letter will be given to biz track.

Program Structure
How classes work
  • 3-hour Video-recorded classes per week (Required)
  • 1-hour support sessions per week  (Selective)
  • Final exam / term paper 1-week after the end of the course
  • Total 12 courses, 2 courses for 1 term
  • 1 term for 8 weeks
Tuition Fees
Tuition Fees
  • Application fee : CHF 200.- (Non-refundable)
  • Administration fee : CHF 2,000.- (Non-refundable)
  • Courses : CHF 3,000 per course
    – 2 courses per term (Bi-monthly payment)
    – 1 course for 5 ECTS*

Graduation requirements

  • Coursework – 60 ECTS* wth average 60% or above
  • Dissertation – 30 ECTS*
    • Tech track: 20,000 words academic essay or equivalent mathematical/programing application
    • Biz track: Case study equivalent to tech track's dissertation
    • 6 months support course (CHF 6,000)

*ECTS – European Credit Transfer and Accumulation System

Admission
Admission

Class module : Online only

Credit : 90 ECTS / (Level / EQF 7)

Required documents 

  • Bachelor diploma and transcript (mandatory)
  • Graduate school diploma and transcript (if applicable)
  • Statement of Purpose
Course Curriculum
Course Curriculum

1 Year for 12 courses

1 Term for 8 weeks with 2 courses

Requirement for graduation

  • Coursework: 60 ECTS (12 courses)
  • Dissertation: 30 ECTS
    • Technical track: 20,000 words or above and technical interpretation of the topic
    • Business track: Case study equivalent to tech track's dissertation

Core Course Description

Q&A

The technical track shares most of undergraduate STEM education required for AI/Data Science, which is also taught at PreMSc AI/Data Science. This is the most chosen program by students who look for a quick study for AI/DS without grad school level Math & Stat. Course materials are mostly at Junior to Senior years of undergraduate programs in the US top research schools. Many parts of our lecture notes are publicly shared (check above 'Lecture Note' link) so as to help students to understand what we teach and what are the examination questions. For business track, since we do not assume students to be well-equipped to STEM's math, we replace examinations with essays. The biz track is for students to look through what real AI/Data Science field is running, and be conversant to data scientists.
Students must earn 50% or above on average in coursework and should pass dissertation. Grading scale of the school is available from school's regulation. Students must finish the coursework within two years and should pass dissertation in the following year
Students can choose to write dissertation during the 2nd semester or after the coursework. If to finish the dissertation by the end of 2nd semester, the graduation will be September of the year, otherwise September of the following year. The dissertation support course offers total 6 meetings with students. Students are to meet instructors via online meeting and all conversations will be recorded for personal and peer review purposes.
We do not expect students to be software engineers. We want students to be a data/research scientist with scientific programming skills. How fast you type codelines or how long code have you debugged, for example, are irrelevant experience for us. We want students to convert mathematical reasoning to program code to materialize thought experiments. Most courses provide sample codes for assignments and examinations.
There only are two course differences between two programs. BUS502 and BUS503 are focused on BigData-based model optimization, which are typical in IT sectors. In-depth discussions of recommendation engine, multi-touch attribution models are well-known examples that are shared between AI Marketing and Data Science, for example. BUS504 and BUS505 are for Finance track courses that covers recently adopted financial models in Corporate Financd and Financial Investments. It is not like we help you to create an AI model that beats fund managers. We focus more on comparing traditional financial theories with new approaches that are, in fact, only an extension of old models. In the end, AI/Data Science is just computationally heavy statistics. We help students to understand that.
AI Engineers from technologically 2nd-tier institutions think running AI code libraries is AI/Data Science. We want our students to pre-check the data to identify potential correlations that may affect denseNet's performance, thus propose a revised model to either re-design the NeuralNet or change the data structure accordingly. At the end of the day, all good models are DGP (Data-Generating-Process) optimised variations of existing models, not a copy of other company's working code. This is our version of 'Critical Thinking'
Computer programming indeed is essential in every sub-discipline at SIAI, but students are given ample amount of study materials. For starters, Issues in Computer Programming (OPT101) , a short prep course given to all incoming students, covers a number of programming issues that are basics for later courses. In addition to that, for every problem set and term paper, a set of guideline code lines are given so as to provide right direction.
Unfortunately, as an online business school, we do not provide any offline support for learning. However, students are free to ask to the course forum. Professors and TAs are available within a short reach of forum posting.

MBA, but not the same MBA

Most MBAs are designed around management routines, soft skills, and generalized leadership frameworks. This program is not. The Non-STEM AI MBA is built for professionals who must evaluate, govern, and decide on AI-driven initiatives without becoming engineers themselves. It focuses on how analytical systems actually function inside organizations—where data, incentives, institutional constraints, and uncertainty collide—rather than on idealized models or abstract strategy.

Unlike mass-market MBAs that treat AI as a toolset or trend, this program treats AI as an object of judgment. Participants are trained to interrogate AI claims, understand the limits of data-driven reasoning, and translate analytical outputs into accountable business and institutional decisions. The emphasis is not on coding or theory for its own sake, but on disciplined reasoning: knowing when AI should inform a decision, when it should be constrained, and when it should be rejected.

The program combines online analytical preparation with a mandatory, full-time residency, reflecting the view that institutional judgment cannot be developed through content consumption alone. Completion certifies not familiarity with AI concepts, but demonstrated capacity to reason, decide, and take responsibility in AI-influenced business environments—something mass-market MBAs are structurally unable to provide.

AI MBA balances theory and practice to foster top-tier business man in AI businesses

Be one of them!
Course Curriculum
Department Contact Info

Master of Business Program

[email protected]

Mon – Fri 9:00 A.M. – 6:00 P.M.

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