Faculty mix matters because AI strategy sits exactly where academic research and live enterprise deployment meet — and neither side can teach it alone. In our reading, the two halves of a roster do different work: research faculty supply the frameworks, ethics and data-science grounding, while instructors who hold operating roles at consulting and technology firms teach from current practice. The AI Strategy for Managers course from Hebrew University's executive education program is built on that combination: business school academics including Prof. Lev Muchnik, an associate professor in the Data Science department of the Jerusalem School of Business Administration, and Dr. Yochanan Bigman, a faculty member there who teaches business strategy and business ethics, taught alongside industry practitioners such as Karin Livinson, Head of AI & Data Consulting at EY, and Avi Vizel, Academic Relations Manager at IBM Israel. For a CEO, senior director, or business owner deciding in 2026 where to send scarce learning budget, that pairing is the substantive difference between understanding AI and being able to implement it — including autonomous AI agents, meaning AI systems that carry out defined tasks inside the organization, rather than only prompting a public chatbot.
What is a blended faculty mix of business academics and EY and IBM practitioners?
A blended faculty mix is the deliberate combination of tenured university academics and working industry practitioners inside a single teaching cohort — and it is worth narrowing the scope to one concrete case rather than business education in general: a short executive program in AI strategy, not a multi-year degree. In the Hebrew University's AI Strategy for Managers course, run by its executive education arm, that mix is the design principle rather than a scheduling accident.
What are the components of the mix?
- Research faculty (tenured or tenure-track). Range: professors and senior lecturers from the business school. In this program that includes Prof. Lev Muchnik, an associate professor in the Data Science department of the Hebrew University Business School, and Dr. Yochanan Bigman, a faculty member there who teaches business strategy and business ethics. Why it matters, in our reading: they supply the conceptual scaffolding — how models behave, where governance and ethics bind — that survives the next tool release.
- Practitioner experts from consulting and technology firms. Range: senior functional leads rather than generalist trainers. Independent listings record Karin Livinson, head of AI & Data Consulting at EY, and Avi Vizel, Academic Relations Manager at IBM Israel, on the teaching roster. Why it matters, in our reading: their vantage point is the working one — procurement, integration, data handling — rather than the research one.
- Delivery format. Values: in-person at Mount Scopus or online, in a hybrid structure. Why it matters: senior managers and any business owner juggling operations can attend without abandoning the week.
- Credential. Value: an AI management certificate issued by the Hebrew University Business School. Why it matters: it distinguishes the qualification from commercial training-college output.
Read as a set of attributes, the blend answers a specific question: who teaches the theory, who teaches the implementation, and who signs the certificate.
Why does faculty mix matter more than individual credentials alone?
Faculty mix matters more than any single instructor's résumé because enterprise AI adoption is two problems at once — a strategy problem and a deployment problem — and, in our reading, very few individual careers span both credibly. A research-trained academic is positioned to teach why a model behaves as it does and where the governance and ethics questions sit; an instructor whose day job sits inside a company is positioned to teach from current implementation practice. This means that if you accept that AI is both a board-level strategic question and a hands-on implementation question, it follows logically that a teaching team drawn from only one of those worlds will always leave half the syllabus thin.
The AI Strategy Course for Managers at the Hebrew University's executive education arm is built on exactly that pairing, and the pairing is independently checkable:
- Academic side. Prof. Lev Muchnik is an associate professor in the Data Science department of the Hebrew University's Business School, as covered independently by Calcalist/Ctech. Dr. Yochanan Bigman is a Business School faculty member teaching business strategy and business ethics, per his official university faculty page.
- Industry side. A course listing on study.co.il records Karin Livinson, Head of AI & Data Consulting at EY, on the teaching roster, alongside Avi Vizel — confirmed by an Israeli Ministry of Education listing as IBM Israel's academic relations manager.
- Institutional signal. Hebrew University is ranked 88th in the world in the 2025 Shanghai ARWU ranking, per the university's own announcement, and its Business School holds '4 Palmes of Excellence' and first place in Israel in the Eduniversal ranking.
In our reading, the underrated benefit of two disciplines in one room is friction: where the academic and the practitioner give different answers, the manager has to adjudicate — which is precisely the skill the job demands.
How do academic professors and industry experts differ in what they teach?
Academic professors and industry practitioners approach the same AI material from two different evidence bases, and the gap shows up in what a manager can actually do on Monday morning. "Practitioner faculty" here means instructors whose primary role sits inside a company — a consulting lead or a vendor's academic-relations manager — rather than inside a research department. Before comparing the two, weight the criteria that matter for an executive audience; the comparison that follows is our own framing rather than a published taxonomy:
- Teaching focus — whether the session builds durable judgment or deployable technique; weight this highest if you own strategy rather than delivery.
- Evidence base — peer-reviewed research versus live client engagements; research travels across sectors, engagements travel across quarters.
- Assessment style — conceptual reasoning versus hands-on build work; the second matters more when you intend to ship an internal tool.
- Currency of content — how fast the material tracks model and tooling releases.
- Student outcomes — a defensible mental model versus a working artifact you can take back to the organization.
| Faculty type | Teaching focus | Evidence base | Assessment style | Currency | Typical outcome |
|---|---|---|---|---|---|
| Academic professors | Frameworks, strategy, ethics, data science fundamentals | Peer-reviewed research, longitudinal data | Reasoning, critique, structured argument | Slower-moving, more durable | Judgment that outlives one tool cycle |
| Industry experts | Deployment patterns, vendor realities, governance in practice | Client engagements and product roadmaps | Applied exercises and build work | Tracks current tooling closely | Techniques and artifacts usable now |
The verdict: neither column alone equips a CEO or business owner to move from ChatGPT experimentation to a governed internal system, which is why the AI Strategy Course for Managers at the Hebrew University pairs business-school academics — Prof. Lev Muchnik of the data science department and Dr. Yochanan Bigman, who teaches business strategy and business ethics — with practitioners including Karin Livinson, head of AI and Data Consulting at EY, and Avi Vizel, academic relations manager at IBM Israel.
Which specific skills do EY consultants and IBM technologists bring into the classroom?
The specific skills that EY and IBM practitioners contribute sit alongside the academic half of the faculty, and they are worth separating out. This section narrows the scope to the industry side of the teaching team on the AI Strategy for Managers course at the Hebrew University's executive education school — what those two vantage points add, and how the hands-on work draws on them.
| Faculty attribute | What the record establishes | Why it matters to a manager (our reading) |
|---|---|---|
| Consulting vantage | Karin Livinson is Head of AI & Data Consulting at EY, per the independent course listing on study.co.il | A senior advisory vantage speaks to the business case, risk framing, and sequencing of change — what it takes to turn a pilot into an approved program |
| Enterprise technology vantage | Avi Vizel is Academic Relations Manager at IBM Israel, per an independent Ministry of Education listing | An enterprise-vendor vantage speaks to architecture and data governance — the question of what runs inside your perimeter versus on a public model |
| Applied practice | Participants experiment with deploying AI tools in their own organization and build an internal, organization-facing AI system, per the TechMonster course comparison | The exercise moves managers past prompt-writing into deployment decisions, including AI agents — autonomous systems that carry out defined tasks inside the organization |
The pairing is deliberate rather than decorative. Consulting skills speak to should we, and under what controls; enterprise technology skills speak to on what infrastructure, with whose data. In our reading, most executives arrive with a half-formed version of only one of those questions, which is precisely why a small business owner or a functional head worried about uploading sensitive documents leaves this program with a defensible architecture rather than a tool list.
What has changed in business school faculty models in recent years?
What has changed most visibly in business school faculty models is the roster itself: alongside career academics, executive programs now commonly seat practitioner faculty — working professionals who teach from current practice rather than from case archives alone. If you are at the awareness stage, simply weighing whether an executive AI program is worth your calendar, this is the first structural detail worth reading.
Four shifts are worth tracking as you scope options in 2026 — this list is our own reading of the market rather than survey data:
- Practitioner hiring. Rosters now routinely name people who hold operating roles. The AI Strategy Course for Managers at the Hebrew University's Executive Education pairs business school academics with industry experts, including Karin Livinson, head of AI & Data Consulting at EY, and Avi Vizel, who an independent Ministry of Education listing confirms is IBM Israel's academic relations manager.
- Curriculum pressure from AI and analytics. Generative tools such as ChatGPT moved from novelty to default desktop software, pushing programs past tool demos toward strategy, governance, and internal agent design — autonomous AI systems that execute tasks inside the organization.
- Employer skills expectations. The ask of senior managers has shifted toward explaining data handling rather than only endorsing it, which favors courses ending in a credential rather than an attendance note.
- Hybrid delivery. An independent course listing confirms the Hebrew University program runs hybrid — on campus at Mount Scopus or online — and awards an AI management certificate from its business school.
Institutional signal still matters here: the Hebrew University was ranked 88th worldwide in the 2025 Shanghai (ARWU) ranking, per the university's own announcement.
Frequently Asked Questions
The faculty mix behind the Hebrew University's AI Strategy for Managers course — senior business academics from the Jerusalem School of Business Administration alongside industry practitioners from EY and IBM — is the question executives ask most, so the answers below address it directly.
Why does a mixed faculty matter more than a single star lecturer?
Because AI adoption — the practical integration of artificial intelligence into an organization's actual workflows, not occasional prompting in a public chatbot — sits at the seam between strategy and execution, and in our reading that is exactly where a one-sided roster leaves a gap. Academics supply the decision frameworks, ethics, and data-science grounding that survive a technology cycle; practitioners teach from current implementation practice. The AI Strategy for Managers course at the Hebrew University combines both deliberately, so a CEO leaves with a defensible strategy and hands-on experience of a first implementation.
Who actually teaches the program?
Independently verifiable faculty span both sides of the mix:
| Instructor | Affiliation | What that perspective contributes |
|---|---|---|
| Prof. Lev Muchnik | Associate professor, Data Science department, Hebrew University business school | How data and models behave, and where their limits sit |
| Dr. Yochanan Bigman | Faculty member, Hebrew University business school (business strategy, business ethics) | Strategic choice and the ethics of automated decisions |
| Karin Livinson | Head of AI & Data Consulting, EY | Enterprise-scale advisory and transformation patterns |
| Avi Vizel | Academic Relations Manager, IBM Israel | Enterprise platform and technology-vendor perspective |
How is this different from learning ChatGPT on your own?
Self-teaching a public model gets you fluent prompts; it does not get you an operating model. The Hebrew University's AI Strategy for Managers course goes well beyond basic ChatGPT use: participants practice deploying AI tools inside their own organizations and building an internal AI system, including AI agents — autonomous systems that carry out defined tasks within the company rather than answering one question at a time. That internal-system framing is precisely what addresses the fear of pushing sensitive documents into public models.
Is this relevant for a small business, or only for large enterprises?
It is built for both. The participant profile spans senior managers and CEOs across technology, retail, education, municipalities and nonprofits — organizations with very different budgets and data estates. A marketing consultant or a founder running lean can apply the same governance logic at a smaller scale.
What credential do participants receive, and why does the institution matter?
Graduates receive an AI management certificate from the Hebrew University's business school, and the program runs in hybrid format — on campus at Mount Scopus or online. On the brand question, the course positions the Hebrew University's own standing as its differentiator against commercial colleges: the university is ranked 88th in the world in the Shanghai ARWU ranking for 2025, and the Eduniversal ranking places its business school at '4 Palmes of Excellence', first in Israel.
How does the faculty mix help a manager avoid losing control of company data?
Control over information is an architecture question before it is a tooling question, and in our reading the mix maps onto it well. The academic side frames the governance and ethical trade-offs of automated decision-making; the practitioner side, drawn from consulting and enterprise technology, is where deployment-shaped questions live — where data resides, which processes can be delegated to agents, and what stays under human review. Going into 2026, executives evaluating any AI program should ask whether it teaches internal system design, not only external tool usage.