Harvard Business School — Executive AI Programs

Profile Updated: 2026-06-24 | Programs Covered: 4 | Data Sources: Official HBS pages + deep web research

Table of Contents

1. Institution Overview
2. Program 1: Generative AI Strategy and Execution (On-Campus Executive)
3. Program 2: AI Essentials for Business (HBS Online)
4. Program 3: Competing in the Age of AI (In-Person + Virtual)
5. Program 4: AI for Leaders (HBS Online)
6. Full AI Program Matrix
7. Faculty Deep Dives
8. The HBS Case Method — Explained
9. Limitations and Critical Assessment
10. Sources

Institution Overview

Harvard Business School (HBS) is arguably the world's most brand-premium business school. Its Executive Education division serves thousands of senior leaders from 100+ countries annually. HBS's core competitive advantages are: the Case Method (discussion-driven, no-lecture learning), world-class research faculty, and a global alumni network of 90,000+. In AI, HBS has built a full spectrum from 4-day on-campus immersion to 4-week asynchronous online, priced $1,850–$15,500.

HBS AI Program Ecosystem (as of 2026):

Program 1: Generative AI Strategy and Execution (On-Campus Executive)

Key Facts

Target Audience

Designed exclusively for senior-level leaders: CEOs, C-suite executives, board members, and decision-makers leading organizational transformation across financial services, healthcare, manufacturing, consumer goods, media, and technology. Explicitly states "no technical background required"—designed for strategic governance, not engineering.

Faculty

• Rajiv Lal (Co-Chair) — Stanley Roth, Sr. Professor of Retailing; Head, Marketing Unit. Undergraduate engineering at IIT Kanpur; PhD Industrial Administration from Carnegie Mellon. Former Stanford GSB professor. Research: retail innovation, IoT commercialization, data monetization. Author of Retail Revolution; 80+ teaching cases.
• Suraj Srinivasan (Co-Chair) — Philip J. Stomberg Professor of Business Administration; Head of MBA Elective Curriculum. BITS (electrical engineering + physics) → IIM Calcutta MBA → HBS PhD (2004, George S. Dively Award). Creator of this program and the HBS MBA elective Generative AI for Business Leaders. Research spans three domains: AI strategy & governance, corporate governance & boards, financial reporting & risk. Former entrepreneur (supermarket and pharmacy chain in India); former University of Chicago accounting professor (2004–2008).

Curriculum Modules

(Case names not publicly disclosed; materials sent ~2 weeks before program start. Topics drawn from official curriculum description.)

1. Business Model Disruption: How generative and agentic AI is reshaping competitive dynamics
2. High-Impact Opportunity Identification: Scanning frameworks and prioritization for AI investments
3. Responsible AI Governance: Enterprise AI governance frameworks, compliance, and risk management
4. Strategy-to-Execution Translation: Roadmapping from AI ambition to scalable deployment
5. Workforce Transformation: Human augmentation, reskilling, organizational culture change
6. Technical Decision-Making: Non-engineering technical fluency required for executive oversight
7. Hands-on AI Tool Experience: Direct experimentation with leading AI tools

Teaching Methodology

• Harvard Case Method: Each session centers on a real company case; participants lead discussion, faculty guide rather than lecture
• Peer learning: Cross-industry, globally diverse cohort of C-suite peers
• Distinguished guest speakers: Industry leaders sharing frontline AI implementation experience
• Immersive campus residency: 4 days on HBS campus with structured networking dinners and informal sessions

Certificate

HBS Executive Education Certificate of Completion (Harvard University seal).

Participant Feedback

Formal public reviews are limited. Suraj Srinivasan's LinkedIn describes the program as helping senior leaders build "a practical pathway from AI ambition to measurable results." HBS Executive Education's official social media post emphasizes "achieving real enterprise AI impact." The fact that the July 2026 session reached waitlist status quickly suggests strong demand.

Program 2: AI Essentials for Business (HBS Online)

Key Facts

Lead Faculty

Karim R. Lakhani (Co-Instructor) - Title: Dorothy & Michael Hintze Professor of Business Administration; Co-Chair & Co-Founder, Harvard D³ (now HBS AI Institute) - Education: McMaster University (electrical engineering + management) → MIT SM (Technology & Policy) → MIT PhD (Management) - Core research: Technology management, AI and digital innovation, platform economics, open-source communities, large-scale innovation contests - Key partnerships: NASA (Tournament Laboratory), Harvard Medical School, Broad Institute, TopCoder, Linux Foundation - Signature work: Competing in the Age of AI (2020); pioneer of field experiments in innovation research

Marco Iansiti (Co-Instructor) - Title: David Sarnoff Professor of Business Administration - Education: Harvard University AB Physics (summa cum laude, 1983) → Harvard PhD Physics (1988, Robbins Physics Prize) - Core research: Digital transformation, AI impact on strategy and business models, business ecosystems, platform competition - Advisory work: Meta, Amazon, Microsoft, Intel, and many Global 1000 traditional companies - Entrepreneurship: Co-founded Model N (NASDAQ: MODN); Co-founder and Co-Chairman of Keystone.ai (AI transformation consulting) - Signature work: Competing in the Age of AI (2020); The Keystone Advantage (2004); One Strategy (2009) - Impact: 4 of last 6 years on HBR's list of top 10 articles of the year

Guest Instructors (from official materials): Dr. Alan Qi (VP and Chief AI Scientist, Ant Group); leaders from Moderna, Mozilla, Amazon.

Course Content Framework

(Reconstructed from official syllabus PDF, blog posts, and course description)

Week 1: AI Foundations and the Business Landscape - Machine learning and deep learning (non-technical, strategic lens) - How AI is disrupting industry competition — introduction to the AI Factory concept - AI economics: scale effects, network effects, data flywheels

Week 2: Predictive Analytics and AI Applications - Predictive modeling in business decision-making - AI-driven personalization and recommendation systems - Case studies: Amazon, Ant Financial, Alibaba

Week 3: The Business Logic of Generative AI - Generative AI capabilities, limits, and commercial opportunities - Frameworks for building AI-first strategies - Cross-industry generative AI application cases

Week 4: Responsible AI and Strategic Execution - AI ethics, bias, and privacy risk management - Enterprise AI governance frameworks - Building an organizational AI transformation roadmap

Pedagogical Format: Interactive touchpoint every 3–5 minutes (polls, quizzes, problem-solving exercises); real company case discussions; peer discussion groups; course reading: Competing in the Age of AI.

The AI Factory Concept (Theoretical Core)

The AI Factory framework by Lakhani & Iansiti is the conceptual spine of this course: - Definition: A self-reinforcing system that continuously collects data → processes through algorithms → deploys software at scale - Essence: Replaces "judgment-intensive, human-bottlenecked" traditional decision trees with algorithmic decision-making - Competitive implication: AI Factories enable scale without mass, hyper-personalization, and dynamic market responsiveness - Canonical examples: Amazon product recommendations, Ant Financial credit scoring, Netflix content distribution

Participant Feedback

Asma AlNabouda (government/public sector): Praised Lakhani and Iansiti's depth; highlighted peer interaction and diverse perspectives. Plans to apply AI-first strategy frameworks to government public services. Review is entirely positive — no criticisms recorded.

Post-Completion Benefits

• Membership in HBS Online Official Networking Group (alumni community)
• Access to exclusive HBS Online events and networking opportunities

Program 3: Competing in the Age of AI (In-Person + Virtual)

Key Facts

Core Faculty

• Marco Iansiti (Program Chair) — same as above; co-author of the book this program is built on
• Iavor I. Bojinov — Richard Hodgson Fellow, Assistant Professor of Business Administration, HBS; Co-PI of the AI and Data Science Operations Lab; Harvard Statistics Department affiliate; Harvard Data Science Initiative faculty affiliate. Research: statistical methods for AI operationalization at scale, rigorous enterprise experimentation design

Program Positioning

Targets strategy leaders, product managers, digital transformation officers, technology operations leaders, and government AI policymakers. Goes broader than "Generative AI Strategy and Execution"—covers the full AI-era competitive strategy (algorithmic decision-making, infrastructure governance, ethics, and network effects).

Program 4: AI for Leaders (HBS Online)

Key Facts

Core Faculty

• Karim R. Lakhani (Co-Instructor)
• Iavor I. Bojinov (Co-Instructor)

Positioning vs AI Essentials

Full AI Program Matrix

Faculty Deep Dives

Karim R. Lakhani

• Title: Dorothy & Michael Hintze Professor of Business Administration, HBS
• Institutional Roles: Co-Chair & Co-Founder, Harvard D³ / HBS AI Institute; Founder & Co-Director, Laboratory for Innovation Science at Harvard (LISH); Principal Investigator, NASA Tournament Laboratory
• Research Agenda: AI and digital innovation; open-source community dynamics; platform economics; blockchain (cautious stance — "foundational, not disruptive"); field experiments for innovation at scale
• Signature Works: Competing in the Age of AI (HBR Press, 2020); numerous high-cited HBR articles
• Core Thesis: AI will become the operating system of all industries; competitive advantage derives from the systemic integration of data flywheels, algorithmic decision-making, and AI Factory architecture

Marco Iansiti

• Title: David Sarnoff Professor of Business Administration, HBS; Co-Chairman, Keystone.ai
• Entrepreneurial Track Record: Co-founded Model N (enterprise revenue management SaaS, publicly traded NASDAQ: MODN); Co-founded Keystone.ai (AI transformation advisory)
• Advisory Roster: Meta, Amazon, Microsoft, Intel, and large Global 1000 traditional enterprises
• Research Agenda: Pathways to digital transformation; AI's structural impact on business models; business ecosystems and platform competition; R&D-to-operations technology integration
• Signature Works: Competing in the Age of AI (2020); The Keystone Advantage (2004); One Strategy (2009); Technology Integration
• Core Thesis: The "AI Factory" liberates companies from human-capital scaling constraints, enabling "scale without mass"; the biggest AI transformation barrier for traditional companies is operating model inertia, not technology

Suraj Srinivasan

• Title: Philip J. Stomberg Professor of Business Administration; Head, MBA Elective Curriculum, HBS
• Distinctive Background: STEM + finance + entrepreneurship; former founder/operator of a supermarket and pharmacy chain in India
• Research Triangle: ① AI strategy and governance ② Corporate governance and boards ③ Financial reporting and risk management
• Program Authorship: Creator of the executive program Generative AI Strategy and Execution and the MBA elective Generative AI for Business Leaders
• Unique Lens: Bridges AI governance with board-level accountability, equipping non-technical executives to make sound technology decisions

Rajiv Lal

• Title: Stanley Roth, Sr. Professor of Retailing; Head, Marketing Unit, HBS
• Academic Background: IIT Kanpur mechanical engineering → Carnegie Mellon PhD Industrial Administration; Former Stanford GSB professor (from 1982)
• Research Specialty: Retail innovation, IoT commercialization barriers, data monetization models
• Industry Practice: Extensive work with retail and consumer goods companies; author of 80+ teaching cases

The HBS Case Method — Explained

All programs in this profile use the HBS Case Method as their primary pedagogical vehicle. Understanding it is essential to evaluating program value:

Mechanics: 1. Participants pre-read a real business case (with data, decision context, time pressure) 2. Class sessions are participant-driven discussions—professors guide, not lecture 3. A typical 80-minute session features 20–40 participant contributions 4. Sessions close with a faculty "wrap"—synthesizing transferable frameworks from the messiness of debate

What distinguishes it from lecture-based executive education: - Knowledge internalized through "thinking while debating" rather than passive listening - Cases are designed as genuine dilemmas with no obvious right answer—training decision quality, not knowledge recall - Cohort diversity is a feature, not incidental: your banking-sector peer may illuminate your manufacturing challenge

AI program adaptation: HBS AI programs combine case discussions with hands-on AI tool sessions, specifically designed to break down the "AI belongs in the tech department" mental model.

Limitations and Critical Assessment

Price vs. Replaceability

Depth Constraints

• No technical implementation: All programs explicitly target non-technical leaders — valuable for governance decisions, but insufficient for those who need to build AI systems
• Case time lag: HBS cases take 12–18 months from research to publication; in fast-moving GenAI this creates an inherent staleness risk
• Execution gap: Programs deliver frameworks and strategic vocabulary, but actual technical stack choices, engineering team building, and vendor evaluation are out of scope
• Competitive program count: Per Poets & Quants (April 2026), Stanford GSB leads business schools in AI course offerings (~30 identifiable AI courses); HBS counts only ~5. HBS wins on brand density, not volume

Who Should Attend vs. Not

Strong fit: - Large-company C-suite executives who need to frame AI strategy for boards and shareholders - Executives who need "Harvard endorsement" to unlock internal AI budgets - Senior managers who need structured AI strategic vocabulary but lack time for deep technical study

Weak fit: - Practitioners seeking technical depth (prompt engineering, RAG, fine-tuning, MLOps) - Independent founders or early-stage startups (high time cost; lower ROI vs. self-directed learning) - People already deeply familiar with the AI Factory / Competing in the Age of AI framework (limited incremental content)

Sources

• Generative AI Strategy and Execution — HBS ExEd Official Page
• HBS ExEd AI Programs Overview
• AI Essentials for Business — HBS Online Official Page
• AI Essentials for Business — Harvard University Course Catalog
• AI for Leaders — HBS Online Official Page
• Competing in the Age of AI — HBS ExEd In-Person
• Competing in the Age of AI—Virtual — HBS ExEd
• Driving Digital and AI Strategy — HBS ExEd
• Karim R. Lakhani — HBS Faculty Profile
• Karim R. Lakhani — HBS AI Institute
• Marco Iansiti — HBS Faculty Profile
• Marco Iansiti — Wikipedia
• Suraj Srinivasan — HBS Faculty Profile
• Rajiv Lal — HBS Faculty Profile
• Iavor I. Bojinov — HBS Faculty Profile
• HBS ExEd Full 2026 Program Schedule (PDF)
• AI Essentials vs AI for Leaders — HBS Online Blog
• What to Expect from AI Essentials — HBS Online Blog
• Competing in the Age of AI — Original HBR Article (2020)
• AI Factory Concept — TechTalks Deep Dive
• The AI Boom in Executive Education — Poets & Quants
• AI for Business Courses at Top B-Schools — GMAC
• Student Review: AI Essentials for Business — LinkedIn
• HBS ExEd Facebook Post: GenAI Program Faculty Announcement