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Executive AI Forum in Swiss

Where AI, capital, and institutional judgment converge

A closed forum on AI, capital, and institutional power 

Across industries, AI is increasingly presented as a solved problem—widely adopted, operationally mature, and commercially decisive. In practice, however, most deployments remain fragmented, over-promised, or institutionally misaligned. The Executive AI Forum in Zurich is convened in response to this gap between narrative and reality, bringing together senior leaders who must assess AI not as a technology trend, but as a system embedded in capital allocation, governance structures, and long-term decision-making.

This is neither a training program nor a product showcase, and it differs fundamentally from conventional academic conferences. The forum is designed as a closed, analytical setting where participants examine AI from first principles—economic constraints, statistical limits, institutional incentives, and failure modes that rarely surface in public discourse. Discussions are structured to prioritize judgment over tools, and governance over implementation, allowing participants to recalibrate strategy with a clearer understanding of what AI can—and cannot—deliver at scale.

The Zurich forum is organized around two complementary tracks: the Council Track and the Executive Track. While distinct in format and depth, both tracks engage with the same core questions at the intersection of AI, capital, and institutional judgment. Typical event schedule is the 1st and 2nd week of June, respectively.

Council track

The Council Track is designed for participants engaged in AI at the level of institutional design, regulatory coordination, and long-term economic governance.

Discussions operate at a depth comparable to senior forums within research universities, central banking institutions, and multilateral organizations, where AI is evaluated not as a tool, but as a structural force shaping capital flows, policy boundaries, and institutional credibility. Sessions emphasize rigorous debate, cross-institutional perspectives, and first-principles reasoning, and are conducted under conditions of discretion appropriate to their scope.

Selected Council Track participants are invited to SIAI’s annual Gulf retreat in Saadiyat, UAE in December

Executive track

The Executive Track is structured as a guided exposure to real-world AI deployment across institutional and industrial contexts.

Similar in spirit to executive inspection programs used in advanced manufacturing and infrastructure policy, the track emphasizes observation, contextual explanation, and strategic interpretation rather than technical instruction. Participants engage with AI systems as they exist in practice—through briefings, site-anchored discussions, and applied case perspectives—developing the ability to assess claims, risks, and operational reality at the level of executive decision-making.

The two tracks differ in depth and format, but share a common focus on AI as a system of governance, capital allocation, and institutional responsibility.


Executive AI Track

at a glance

COUNCIL TRACK

3-5 DAY EVENT

EXECUTIVE TRACK

AI AT CONTROL LEVEL

CONFERENCE TRACK

BUSINESS ORIENTED

Zurich event

COUNCIL TRACK FOR INTELLIGENT MEETINGS AND CONTINUED FRIENDSHIP

EXECUTIVE TRACK FOR CASE STUDIES, BUSINESS APPLICATIONS, AND INSIGHTS

MBA in AI & Big Data

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
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

Prep classes are available

  • LaTeX for assignments and paper writing
  • Programming prep for Python

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

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

Scholarship

  • If 70% or above in admission examination
  • RA/TA opportunities
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. 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.

English requirement
For non-native English speakers, should meet one of the following criteria

For non-native English speakers, should meet one of the following criteria by graduation

  • High school or University level diploma from an all-English program
  • TOEFL iBT 100/120 or above (with each section at least 21/30)
  • IELTS 7 or above (with each section at least 6.5/9)
  • Pass grade from SIAI’s internal English course

 

Internal English course

  • Course fee: CHF800
  • Course schedule
    • July~Aug (8 weeks)
    • Live session
    • 3 hours per week (usually weekend)

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.

Executive AI Forum (Private)


Clarity on AI, before strategy becomes irreversible.

AI is now discussed as if its strategic implications were already understood: budgets allocated, vendors selected, and institutional commitments quietly made. Yet for many organizations, the most consequential decisions around AI are being taken under conditions of incomplete information, misaligned incentives, and public narratives that discourage honest reassessment. Once capital, regulation, or reputation is committed, reversing course becomes costly—sometimes impossible

The Executive AI Forum exists to address this moment. It provides a closed setting in which senior participants can examine AI before decisions harden into doctrine. Discussions are grounded in economic constraints, statistical limits, and institutional realities that rarely appear in public forums. Rather than promoting adoption or resisting it, the forum focuses on understanding where AI genuinely changes outcomes—and where it does not.

For participants responsible for long-term strategy, capital allocation, or policy design, the value of the forum lies in recalibration. It offers a rare opportunity to stress-test assumptions, compare institutional perspectives, and regain strategic clarity before commitments become path-dependent. The result is not consensus, but sharper judgment—aligned with the responsibilities that senior roles actually carry.

Participation in the Executive AI Forum is by invitation only, or through nomination by partner institutions.

Access to institutional-grade judgment about AI — before capital, policy, or reputation is committed

Be AI leader not follower!
Department Contact Info

Executive AI MBA Program

[email protected]

Mon – Fri 9:00 A.M. – 6:00 P.M.