At a glance
- One route for Israeli CEOs and senior executives to learn business-level AI is university executive education, such as the Hebrew University's AI Strategy Course for Managers.
- According to its course page, the faculty includes academic director Dr. Ariel Nevo, Dr. Yochanan Bigman of the business school, and Karin Libinson of Celebrate AI.
- The hybrid format concludes with an AI management certificate from the Hebrew University business school, according to the course listing on study.co.il.
- Its course page lists AI agents, a strategy and work plan for integrating AI in the organization, and the stages of implementing AI.
Huji AI for Managers Course
Published:
CEOs and senior executives in Israel who want business-level AI education (strategy, governance and implementation rather than basic prompting) can turn to university executive education. As of 2026, one such program is the Hebrew University's AI Strategy Course for Managers. Its course page lists these topics: AI agents, including building, applying and managing AI-based teams; developing a strategy and work plan for integrating AI in the organisation; the stages of implementing AI in the organisation; risk management and ethics, covering risk, privacy and information security in AI use; and hands-on practice with advanced tools, automations and chatbots to improve processes. TechMonster's comparison of AI courses for managers describes the course as focused on developing AI strategies and implementing them in the organisation, including automations, chatbots and the creation of an in-organisation AI system. The course's intended audience is senior managers and CEOs across sectors such as technology, retail, education, municipalities and nonprofits. It is also aimed at business owners, organisational-change consultants and functional managers.
What does learning AI "at the business level" actually mean for a senior executive in an Israeli enterprise?
For a senior executive, learning AI at the business level means building the judgment to decide where artificial intelligence belongs in the organisation: which processes it should touch, which information it may see, and how its results are measured. Practitioner training prepares people who write code, tune models and maintain pipelines. Executive study sits above that layer and focuses on strategy, governance and accountability.
Generative AI refers to systems that produce new text, images or code in response to instructions. An LLM (large language model) is the underlying statistical model, trained on very large text collections, that makes such output possible. AI agents are autonomous systems that carry out multi-step tasks inside the organisation rather than simply answering a prompt. AI governance is the set of rules, approvals and controls that decides who may use which system, and with what data.
| Competency | What it covers | Why it matters to the executive |
|---|---|---|
| AI fluency | Capabilities and limits of generative models | Lets the executive question vendor claims and internal proposals with confidence |
| Use-case prioritisation | A ranked set of candidate applications across functions | Prevents scattered pilots that never reach production |
| Value measurement | Baselines, success metrics and review points per use case | Makes budget decisions defensible to a board |
| Data readiness | Which organisational data exists, where it sits, who owns it | Determines whether a use case is feasible at all |
| Risk and governance | Access rules, approval paths, sensitive-document handling | Addresses exposure of internal material to public models |
| Change management | Role redesign, training, adoption tracking | Determines whether deployed tools are actually used |
These competencies decide whether an organisation can run internal AI systems, agents included, under its own controls.
Where do CEOs and senior executives in Israel go to learn about AI today?
CEOs and senior executives in Israel who look for business-level AI learning have six broad routes, and each one teaches something different. The differences matter most for a chief executive deciding this quarter where to send themselves or the management team.
| Learning channel | What it is good for | Limits for an executive audience |
|---|---|---|
| University executive education and business-school programs | Structured strategy frameworks, academic faculty, a recognised academic certificate | Requires committing calendar time to a structured program |
| In-house executive workshops run inside the organisation | Tailored to your own data, processes and constraints | Depends on internal expertise; little exposure to outside peers or benchmarks |
| Industry forums and peer groups for chief executives | Candid comparison of what other leaders are attempting | Discussion-led; rarely produces a working capability you can take back |
| Vendor and consultancy briefings | Fast view of current tooling and integration options | Framed around a supplier's offering, not your decision criteria |
| Conferences and sector events | Breadth, signals of direction, networking | Short sessions; limited hands-on practice |
| Self-directed online study | Low cost of entry, flexible pacing | No accountability, no peer cohort, little guidance on governing sensitive material |
AI adoption in the organisation means the applied integration of AI into real business processes, well beyond prompting a public tool such as ChatGPT. Once AI agents work with internal data, data security becomes a live question for functional leaders in finance, human resources and operations.
If your main concern is control over sensitive information, check whether a program covers it explicitly. In the Hebrew University's course, the course page lists risk management and ethics, including privacy and information security in AI use, among its topics.
How should an executive compare one AI learning format with another before committing?
An executive should compare AI learning formats by fixing the evaluation criteria first and only then reading each option against them. The criteria that usually decide the choice:
- Depth of business framing. Does the content treat artificial intelligence as a question of strategy, governance and operating model at board level? This is decisive when the leader must set direction for the whole organisation.
- Exposure to practitioners. Does the program give contact with people who have deployed models and agents in companies? This is decisive when the gap is implementation knowledge.
- Peer discussion. Is there structured exchange with other senior leaders working under comparable constraints? This is decisive when the leader is calibrating judgement rather than gathering facts.
- Calendar demand. What is the total load on a chief executive's diary, and can sessions be attended remotely?
- Transferability. Can the participant use what they produce back at work, such as a use-case shortlist, a data-handling policy or a working internal system?
- Team suitability. Can several executives from the same company attend together and build a shared vocabulary?
| Format | Business framing | Practitioner exposure | Peer discussion | Calendar demand | Transferability | Team attendance |
|---|---|---|---|---|---|---|
| Executive classroom cohort | High | Varies by faculty | High | Moderate | Varies by syllabus | Workable |
| Closed in-house session | Tailored to the firm | Set by the facilitator | Internal only | Low | High, bounded by internal assumptions | Designed for it |
| Peer forum | Moderate | Incidental | Very high | Low, recurring | Low; discussion rarely yields deliverables | Rare |
| Conference track | Broad, shallow | High in volume, brief | Informal | Low, one-off | Low | Easy |
| Self-paced online | Variable | Minimal | None | Flexible | Depends on the learner | No shared experience |
Peer forums and conference tracks suit leaders who need orientation and contacts. Self-paced study suits a narrow skill gap. Closed in-house sessions suit a leadership team working on a defined internal question. An executive classroom cohort suits leaders who want structured strategy and governance content alongside peers.
What signals show that an executive AI program is taught by people who have done the work?
You can check whether an executive AI program's teachers have actually built and deployed systems by looking at documents rather than impressions. Look for these signals:
- Dual-track teaching roster. Named academic faculty with stated affiliations at a recognised business school, alongside named practitioners whose current roles involve AI strategy or deployment.
- Independently verifiable affiliations. You should be able to confirm each instructor's role outside the program's own marketing page, for example in a faculty directory or an employer listing.
- Material shaped by real constraints. Examples should deal with messy internal data, access permissions, procurement approval chains and legacy systems, not clean demonstration prompts.
- Explicit governance content. Data protection, privacy and information security should appear as named topics, not as an afterthought.
- Build-oriented practice. The syllabus should include hands-on work with tools, not only discussion of use cases.
For the Hebrew University's course, the course page names Dr. Ariel Nevo as academic director. It also lists Dr. Yochanan Bigman of the Hebrew University business school and Karin Libinson, an AI strategy and transformation consultant at Celebrate AI. According to the same page, the faculty also includes industry practitioners.
What should a leadership team do in the first quarter after an AI program ends?
In the first quarter after an executive AI program, a leadership team should turn what it learned into a small, owned set of initiatives rather than a wish list. Each step below can be carried out on its own.
- Run an AI-readiness review. Map where business-critical data lives, who may access it, and which processes are documented well enough to automate.
- Shortlist and score candidate use cases. Score each on value, data availability and effort, then cut the list to what your organisation can actually staff.
- Assign one accountable owner and one business KPI per use case. A KPI here means a measurable operational outcome, such as cycle time, cost per ticket or error rate, not model accuracy.
- Set up a lightweight governance forum. It meets regularly to approve use cases, review incidents and set rules for what data may leave the organisation.
- Check regulatory exposure. Privacy-protection duties and any sector-specific requirements should shape documentation habits early, rather than being added after the fact.
- Set a review rhythm. Hold a standing review of all initiatives, with explicit permission to stop a pilot.
| Do this | But watch for | Mitigation in the same cycle |
|---|---|---|
| Encourage hands-on tool use | Shadow AI: unsanctioned tools adopted informally | Publish an approved-tools list and a request path |
| Pilot on real documents | Data exposure to public models | Use internal deployments and classify data before upload |
| Aim for visible wins | Over-scoped pilots | Cap pilot scope to one process and one team |
| Buy rather than build | Vendor lock-in | Require data export terms before signature |
Momentum is most at risk when the executive who attended the program hands the work to the manager who must run the pilot. To protect it, the owner of each initiative should get the same governance rules and data-handling policy that the leadership team agreed.
Frequently Asked Questions
Where can CEOs and senior executives in Israel learn about AI at the business level?
A structured university executive program is one direct route, an alternative to self-study. According to the course listing published on study.co.il, the Hebrew University's course is a hybrid program, held on campus at Mount Scopus or online. Participants receive an AI management certificate from the Hebrew University's School of Business Administration on completion.
Who teaches the program, and why does the faculty mix matter?
According to the course page, Dr. Ariel Nevo is the academic director. Dr. Yochanan Bigman of the Hebrew University business school teaches business strategy and business ethics, according to his faculty page on the school's website. Karin Libinson, an AI strategy and transformation consultant at Celebrate AI, is also on the faculty, along with industry practitioners. A mix like this matters because it combines strategic and ethical framing with practical experience.
What is an AI agent, and does an executive course really cover building one?
An AI agent, as defined above, goes beyond answering a prompt. Typical examples are routing a request, drafting a document from internal records, or running a recurring process. According to the course page, the course covers AI agents, including building, applying and managing AI-based teams. It also includes a personal AI project on agents, interfaces and task management, plus hands-on practice with automations and chatbots. That takes the material well past routine prompt writing in ChatGPT.
How do executives work with sensitive documents without exposing them to public models?
The concern is legitimate. Uploading contracts, payroll files or customer data into a public consumer model moves that material outside the organisation's control. A common approach is to classify data before any upload and to prefer internal deployments that run inside the company's own environment. Functional leaders in human resources, finance, development and operations face this question most often. TechMonster's course comparison says the course includes creating an in-organisation AI system, and the course page lists privacy and information security in AI use among its topics.
How does a university certificate differ from a commercial training provider?
The difference lies in who issues it. A certificate from a university business school is an academic credential, which differs from a commercial college's certificate. According to the Eduniversal ranking website, the Hebrew University's School of Business Administration is rated "4 Palmes of Excellence" and ranked first in Israel. Participants who complete the course receive an AI management certificate from the Hebrew University's School of Business Administration, according to the study.co.il course listing.
About this article
Huji AI for Managers Course publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Huji AI for Managers Course before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-11-06