About this programme.
Artificial intelligence is reshaping every industry — but most business professionals lack the frameworks to evaluate AI opportunities, manage AI projects, and lead AI-driven transformation responsibly. This 7-week course bridges the gap between technical AI concepts and business strategy.
You will learn how machine learning, natural language processing, and generative AI actually work (without needing to code), how leading companies are deploying AI to create competitive advantage, how to build a business case for AI initiatives, and how to navigate the ethical, regulatory, and workforce implications. Each week pairs conceptual foundations with real-world case studies, curated readings from top journals, and hands-on assignments that build your AI business acumen.
By the end of this course, you will be able to confidently evaluate AI vendors, lead cross-functional AI projects, and articulate an AI strategy for your organisation.
Your instructor
Prof. James Okafor
MIT Sloan faculty and former Google AI lead. Specialises in AI strategy, digital transformation, and technology leadership.
Curriculum — 7 weeks.
1
The AI Landscape: What Business Leaders Need to Know
Build a clear mental model of what AI, machine learning, and deep learning actually are. Understand the current state of AI capabilities, limitations, and where the technology is heading.
Tutorial
Demystifying AI for Business
This tutorial cuts through the hype to explain AI fundamentals in business terms. We cover supervised vs. unsupervised learning, neural networks, large language models, and computer vision — all without requiring any programming knowledge. You will learn to distinguish between genuine AI capabilities and marketing buzzwords.
Reading list
? **Required Reading:**
1. Agrawal, A., Gans, J. & Goldfarb, A. (2018). *Prediction Machines: The Simple Economics of Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1633695733
2. Davenport, T.H. & Ronanki, R. (2018). Artificial Intelligence for the Real World. *Harvard Business Review*, 96(1), 108–116.
3. Lee, K.-F. (2018). *AI Superpowers: China, Silicon Valley, and the New World Order* (Chapters 1–3). Houghton Mifflin Harcourt. ISBN: 978-1328546395
? **Supplementary:**
4. Mitchell, M. (2019). *Artificial Intelligence: A Guide for Thinking Humans*. Penguin. ISBN: 978-0374257835
5. Brynjolfsson, E. & McAfee, A. (2017). The Business of Artificial Intelligence. *Harvard Business Review*, July 2017. DOI: 10.1225/H03QGN
1. Agrawal, A., Gans, J. & Goldfarb, A. (2018). *Prediction Machines: The Simple Economics of Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1633695733
2. Davenport, T.H. & Ronanki, R. (2018). Artificial Intelligence for the Real World. *Harvard Business Review*, 96(1), 108–116.
3. Lee, K.-F. (2018). *AI Superpowers: China, Silicon Valley, and the New World Order* (Chapters 1–3). Houghton Mifflin Harcourt. ISBN: 978-1328546395
? **Supplementary:**
4. Mitchell, M. (2019). *Artificial Intelligence: A Guide for Thinking Humans*. Penguin. ISBN: 978-0374257835
5. Brynjolfsson, E. & McAfee, A. (2017). The Business of Artificial Intelligence. *Harvard Business Review*, July 2017. DOI: 10.1225/H03QGN
Assignment · 100.00 pts
AI Landscape Briefing
Write a 1,200-word executive briefing for a non-technical C-suite audience that explains: (a) what AI/ML is and is not, using clear analogies, (b) three current AI capabilities most relevant to your industry or a chosen industry, (c) two common misconceptions about AI that could lead to poor investment decisions. Use at least three references from the assigned readings. The brief should be formatted as a professional memo.
2
AI Use Cases Across Industries
Survey how AI is being deployed across finance, healthcare, retail, manufacturing, and professional services. Identify patterns in successful AI adoption and common failure modes.
Tutorial
AI Applications: From Theory to Revenue
This tutorial maps the AI application landscape across major industries. We examine recommendation engines, predictive maintenance, fraud detection, medical imaging, supply chain optimisation, and conversational AI — analysing what makes each deployment succeed or fail.
Reading list
? **Required Reading:**
1. Iansiti, M. & Lakhani, K.R. (2020). *Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World*. Harvard Business Review Press. ISBN: 978-1633697621
2. Ransbotham, S. et al. (2020). Expanding AI's Impact with Organizational Learning. *MIT Sloan Management Review*, 62(1). DOI: 10.7551/mitpress/12588.003.0003
3. Fountaine, T., McCarthy, B. & Saleh, T. (2019). Building the AI-Powered Organization. *Harvard Business Review*, 97(4), 62–73.
? **Supplementary:**
4. Ng, A. (2017). *AI Transformation Playbook*. Landing AI (whitepaper, available at landing.ai)
5. McAfee, A. & Brynjolfsson, E. (2012). Big Data: The Management Revolution. *Harvard Business Review*, 90(10), 60–68.
1. Iansiti, M. & Lakhani, K.R. (2020). *Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World*. Harvard Business Review Press. ISBN: 978-1633697621
2. Ransbotham, S. et al. (2020). Expanding AI's Impact with Organizational Learning. *MIT Sloan Management Review*, 62(1). DOI: 10.7551/mitpress/12588.003.0003
3. Fountaine, T., McCarthy, B. & Saleh, T. (2019). Building the AI-Powered Organization. *Harvard Business Review*, 97(4), 62–73.
? **Supplementary:**
4. Ng, A. (2017). *AI Transformation Playbook*. Landing AI (whitepaper, available at landing.ai)
5. McAfee, A. & Brynjolfsson, E. (2012). Big Data: The Management Revolution. *Harvard Business Review*, 90(10), 60–68.
Assignment · 100.00 pts
Industry AI Opportunity Map
Choose an industry you know well (or aspire to work in). Research and document at least five AI use cases currently deployed in that industry, plus three potential use cases that have not yet been widely adopted. For each, specify: the business problem addressed, the type of AI/ML involved, estimated business impact, and implementation complexity. Present your findings as a structured opportunity matrix (table format) accompanied by a 1,500-word narrative that identifies the single highest-priority AI opportunity and justifies your recommendation.
3
Building the Business Case for AI
Learn to evaluate AI projects using business frameworks. Understand total cost of ownership, ROI estimation for AI, build vs. buy decisions, and how to structure AI pilot programmes.
Tutorial
AI Economics and Project Evaluation
This tutorial provides the financial and strategic frameworks for evaluating AI investments. We cover how to estimate ROI when outcomes are uncertain, structure proof-of-concept pilots, make build-vs-buy-vs-partner decisions, and present AI business cases to sceptical stakeholders.
Reading list
? **Required Reading:**
1. Agrawal, A., Gans, J. & Goldfarb, A. (2022). *Power and Prediction: The Disruptive Economics of Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1647824198
2. Brock, J.K.-U. & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. *California Management Review*, 61(4), 110–134. DOI: 10.1177/0008125619874615
3. Tarafdar, M., Beath, C.M. & Ross, J.W. (2019). Using AI to Enhance Business Operations. *MIT Sloan Management Review*, 60(4), 37–44.
? **Supplementary:**
4. Ross, J.W., Beath, C.M. & Mocker, M. (2019). *Designed for Digital*. MIT Press. ISBN: 978-0262042888
5. Bughin, J. et al. (2018). Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. *McKinsey Global Institute Discussion Paper*.
1. Agrawal, A., Gans, J. & Goldfarb, A. (2022). *Power and Prediction: The Disruptive Economics of Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1647824198
2. Brock, J.K.-U. & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. *California Management Review*, 61(4), 110–134. DOI: 10.1177/0008125619874615
3. Tarafdar, M., Beath, C.M. & Ross, J.W. (2019). Using AI to Enhance Business Operations. *MIT Sloan Management Review*, 60(4), 37–44.
? **Supplementary:**
4. Ross, J.W., Beath, C.M. & Mocker, M. (2019). *Designed for Digital*. MIT Press. ISBN: 978-0262042888
5. Bughin, J. et al. (2018). Notes from the AI Frontier: Modeling the Impact of AI on the World Economy. *McKinsey Global Institute Discussion Paper*.
Assignment · 100.00 pts
AI Business Case
Develop a comprehensive business case for an AI initiative at a real or hypothetical company. Your 2,000-word proposal should include: (a) problem definition and current-state analysis, (b) proposed AI solution with technical approach described in business terms, (c) build vs. buy analysis, (d) financial projections (costs, expected benefits, timeline to ROI) using realistic assumptions, (e) risk assessment and mitigation strategy, (f) proposed pilot plan with success metrics. Format as a professional business case document.
4
Data Strategy & AI Infrastructure
Understand the data foundations required for successful AI. Cover data governance, data quality, feature engineering concepts, and the modern data stack — all from a business perspective.
Tutorial
Data as a Strategic Asset
This tutorial explains why data strategy is the prerequisite for AI success. We cover data governance frameworks, data quality dimensions, the concept of data products, cloud vs. on-premise decisions, and how to assess your organisation's data readiness without needing to be a data engineer.
Reading list
? **Required Reading:**
1. Redman, T.C. (2008). *Data Driven: Profiting from Your Most Important Business Asset*. Harvard Business Review Press. ISBN: 978-1422119853
2. Davenport, T.H. & Bean, R. (2022). Is Your Company Actually Using the Data It Collects? *Harvard Business Review*, April 2022.
3. Janssen, M., van der Voort, H. & Wahyudi, A. (2017). Factors Influencing Big Data Decision-Making Quality. *Journal of Business Research*, 70, 338–345. DOI: 10.1016/j.jbusres.2016.08.007
? **Supplementary:**
4. Provost, F. & Fawcett, T. (2013). *Data Science for Business*. O'Reilly. ISBN: 978-1449361327
5. Stobierski, T. (2021). Data Governance: What It Is and Why It Matters. *Harvard Business School Online* (article).
1. Redman, T.C. (2008). *Data Driven: Profiting from Your Most Important Business Asset*. Harvard Business Review Press. ISBN: 978-1422119853
2. Davenport, T.H. & Bean, R. (2022). Is Your Company Actually Using the Data It Collects? *Harvard Business Review*, April 2022.
3. Janssen, M., van der Voort, H. & Wahyudi, A. (2017). Factors Influencing Big Data Decision-Making Quality. *Journal of Business Research*, 70, 338–345. DOI: 10.1016/j.jbusres.2016.08.007
? **Supplementary:**
4. Provost, F. & Fawcett, T. (2013). *Data Science for Business*. O'Reilly. ISBN: 978-1449361327
5. Stobierski, T. (2021). Data Governance: What It Is and Why It Matters. *Harvard Business School Online* (article).
Assignment · 100.00 pts
Data Readiness Assessment
Conduct a data readiness assessment for an organisation (your employer, a case study company, or a hypothetical business). Your 1,500-word report should cover: (a) an inventory of key data assets, (b) an assessment of data quality across the dimensions of accuracy, completeness, timeliness, and consistency, (c) an evaluation of current data governance practices, (d) identification of the top three data gaps that would prevent successful AI deployment, and (e) a prioritised roadmap of recommendations. Use at least two frameworks from the readings.
5
AI Ethics, Bias & Responsible AI
Navigate the ethical challenges of AI deployment. Cover algorithmic bias, fairness metrics, explainability, privacy implications, and emerging AI regulation worldwide.
Tutorial
Building AI You Can Trust
This tutorial confronts the hardest questions in AI deployment: How do you detect and mitigate bias in models? What does explainability mean in practice? How do you comply with evolving regulations like the EU AI Act? We examine real cases where AI went wrong and what governance structures prevent these failures.
Reading list
? **Required Reading:**
1. O'Neil, C. (2016). *Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy*. Crown. ISBN: 978-0553418811
2. Jobin, A., Ienca, M. & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. *Nature Machine Intelligence*, 1, 389–399. DOI: 10.1038/s42256-019-0088-2
3. Floridi, L. et al. (2018). AI4People — An Ethical Framework for a Good AI Society. *Minds and Machines*, 28, 689–707. DOI: 10.1007/s11023-018-9482-5
? **Supplementary:**
4. Buolamwini, J. & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. *Proceedings of Machine Learning Research*, 81, 1–15.
5. European Commission (2021). *Proposal for a Regulation Laying Down Harmonised Rules on AI (EU AI Act)*.
1. O'Neil, C. (2016). *Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy*. Crown. ISBN: 978-0553418811
2. Jobin, A., Ienca, M. & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. *Nature Machine Intelligence*, 1, 389–399. DOI: 10.1038/s42256-019-0088-2
3. Floridi, L. et al. (2018). AI4People — An Ethical Framework for a Good AI Society. *Minds and Machines*, 28, 689–707. DOI: 10.1007/s11023-018-9482-5
? **Supplementary:**
4. Buolamwini, J. & Gebru, T. (2018). Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. *Proceedings of Machine Learning Research*, 81, 1–15.
5. European Commission (2021). *Proposal for a Regulation Laying Down Harmonised Rules on AI (EU AI Act)*.
Assignment · 100.00 pts
Responsible AI Framework
Develop a Responsible AI Framework for an organisation that is deploying (or planning to deploy) AI. Your 2,000-word deliverable should include: (a) a set of AI ethics principles tailored to the organisation, (b) a bias detection and mitigation protocol for one specific AI use case, (c) an explainability strategy appropriate to the stakeholders involved, (d) a governance structure (roles, review processes, escalation procedures), and (e) a compliance checklist mapped to either the EU AI Act or your local regulatory framework. Cite at least four readings.
6
Generative AI & the Future of Work
Understand large language models, image generation, and multimodal AI. Learn how generative AI is transforming knowledge work and how to develop an organisational strategy for its adoption.
Tutorial
Generative AI: Beyond the Hype
This tutorial provides a business leader's guide to generative AI: how large language models work, what they can and cannot do, enterprise deployment patterns, prompt engineering fundamentals, fine-tuning vs. RAG, and workforce implications. We examine early adopters and their results.
Reading list
? **Required Reading:**
1. Mollick, E. (2024). *Co-Intelligence: Living and Working with AI*. Portfolio/Penguin. ISBN: 978-0593716717
2. Eloundou, T. et al. (2023). GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. *arXiv preprint*. DOI: 10.48550/arXiv.2303.10130
3. Dell'Acqua, F. et al. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. *Harvard Business School Working Paper* 24-013.
? **Supplementary:**
4. Brynjolfsson, E., Li, D. & Raymond, L. (2023). Generative AI at Work. *NBER Working Paper* No. 31161. DOI: 10.3386/w31161
5. McKinsey Global Institute (2023). *The Economic Potential of Generative AI: The Next Productivity Frontier*.
1. Mollick, E. (2024). *Co-Intelligence: Living and Working with AI*. Portfolio/Penguin. ISBN: 978-0593716717
2. Eloundou, T. et al. (2023). GPTs Are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. *arXiv preprint*. DOI: 10.48550/arXiv.2303.10130
3. Dell'Acqua, F. et al. (2023). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality. *Harvard Business School Working Paper* 24-013.
? **Supplementary:**
4. Brynjolfsson, E., Li, D. & Raymond, L. (2023). Generative AI at Work. *NBER Working Paper* No. 31161. DOI: 10.3386/w31161
5. McKinsey Global Institute (2023). *The Economic Potential of Generative AI: The Next Productivity Frontier*.
Assignment · 100.00 pts
Generative AI Strategy Proposal
Your CEO has asked you to develop a generative AI strategy for the organisation. Write a 2,500-word strategic proposal that: (a) identifies the five highest-value use cases for generative AI in your organisation or chosen industry, ranked by impact and feasibility, (b) details an implementation plan for the top use case including technology selection, data requirements, and change management, (c) analyses workforce implications with a reskilling/upskilling plan, (d) addresses risk mitigation (hallucinations, IP concerns, data leakage), and (e) proposes metrics for measuring success at 90, 180, and 365 days.
7
Leading AI Transformation
Synthesise everything into an AI leadership framework. Learn to build AI teams, manage organisational change, communicate AI strategy to boards and investors, and sustain AI-driven competitive advantage.
Tutorial
Becoming an AI-Ready Leader
This capstone tutorial brings together strategy, ethics, data, and implementation into a unified leadership framework. We discuss how to build and lead cross-functional AI teams, overcome organisational resistance, communicate AI strategy to boards, and create a culture of continuous learning around AI.
Reading list
? **Required Reading:**
1. Iansiti, M. & Lakhani, K.R. (2020). *Competing in the Age of AI* (Chapters 8–10). Harvard Business Review Press. ISBN: 978-1633697621
2. Westerman, G., Bonnet, D. & McAfee, A. (2014). *Leading Digital: Turning Technology into Business Transformation*. Harvard Business Review Press. ISBN: 978-1625272478
3. Tambe, P. et al. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. *California Management Review*, 61(4), 15–42. DOI: 10.1177/0008125619867910
? **Supplementary:**
4. Davenport, T.H. & Mittal, N. (2023). *All-In on AI: How Smart Companies Win Big with Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1647824679
5. Kaplan, A. & Haenlein, M. (2019). Siri, Siri, in My Hand: Who's the Fairest in the Land? On the Interpretations, Illustrations, and Implications of AI. *Business Horizons*, 62(1), 15–25. DOI: 10.1016/j.bushor.2018.08.004
1. Iansiti, M. & Lakhani, K.R. (2020). *Competing in the Age of AI* (Chapters 8–10). Harvard Business Review Press. ISBN: 978-1633697621
2. Westerman, G., Bonnet, D. & McAfee, A. (2014). *Leading Digital: Turning Technology into Business Transformation*. Harvard Business Review Press. ISBN: 978-1625272478
3. Tambe, P. et al. (2019). Artificial Intelligence in Human Resources Management: Challenges and a Path Forward. *California Management Review*, 61(4), 15–42. DOI: 10.1177/0008125619867910
? **Supplementary:**
4. Davenport, T.H. & Mittal, N. (2023). *All-In on AI: How Smart Companies Win Big with Artificial Intelligence*. Harvard Business Review Press. ISBN: 978-1647824679
5. Kaplan, A. & Haenlein, M. (2019). Siri, Siri, in My Hand: Who's the Fairest in the Land? On the Interpretations, Illustrations, and Implications of AI. *Business Horizons*, 62(1), 15–25. DOI: 10.1016/j.bushor.2018.08.004
Assignment · 200.00 pts
Capstone: AI Transformation Roadmap
Develop a comprehensive 12-month AI transformation roadmap for a real or hypothetical organisation. This capstone has two deliverables:
**Part A — Strategic Roadmap Document (3,000 words):** Cover vision and objectives, current AI maturity assessment, prioritised initiative portfolio (at least 5 initiatives across quick wins and strategic bets), talent and capability plan, technology and data infrastructure requirements, governance and ethics framework, change management approach, budget estimate, and KPIs.
**Part B — Board Presentation (10–12 slides):** Create a professional slide deck presenting the roadmap as if to a board of directors. Focus on strategic rationale, expected business impact, investment required, and risk management.
$447
$59725% OFF
Create account to enrol
Already have an account? Log in
- 7 weeks of structured content
- 28 video lessons & tutorials
- Curated academic reading lists
- Weekly assignments with feedback
- Live Google Meet sessions
- Discussion forums & peer network
- Certificate of completion
- Lifetime access to materials