A strong AI-focused MBA should not replace traditional management education with technical AI training. It should combine four pillars:

1. MBA fundamentals — economics, finance, accounting, strategy, marketing and operations.
2. Leadership and organisational capability — communication, negotiation, change, culture and mindful management.
3. AI and data literacy — machine learning, generative AI, analytics, experimentation and AI product management.
4. Responsible execution — governance, risk, regulation, cybersecurity, operating models and financial value.

This resembles the direction taken by programmes such as Wharton’s AI for Business major, Kellogg’s MBAi and NYU Stern’s Tech MBA. Wharton separates AI education into technical foundations and societal/ethical impact; Kellogg combines MBA, technical and integrated AI cores; NYU combines a business core, technology core and experiential projects. (Wharton OID)

How to use this curriculum​

Layer	What it is	When to use it
Programme benchmarks	Wharton, Kellogg, NYU, Oxford, Imperial, INSEAD	Design scope and depth before picking courses
Free MBA core	MIT OCW + OpenStax	Months 1–6 foundations
AI literacy track	Elements of AI, Google MLCC, Deeplearning.ai	Months 7–8
Governance track	NIST, EU AI Act, OECD, ISO 42001	Month 11
Playbook deep-dives	Consulting, product, FinOps, RAI articles on this site	Convert study into deliverables
Capstone	End-to-end AI transformation project	Month 12

Resources below are primarily (Free). Paid executive programmes are optional accelerators after you can already produce strategy memos, business cases and impact assessments.

Design principle: every module should end with an artefact you would show a client or board—not only notes.

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1. Curriculum benchmarks​

These pages show what leading schools currently treat as essential for an AI-capable business leader.

Free MBA curriculum benchmark​

Resource	Access	Why it matters
MIT Sloan MBA First-Semester Core	Free	Best free scaffold for a self-directed MBA
MIT OpenCourseWare	Free	Full Sloan catalogue: finance, strategy, ops, marketing, leadership
Harvard Business School Online catalogue	Paid / Free intros	Executive depth once foundations are solid

MIT’s collection covers economic analysis, data-driven decisions, managerial communication, organisational processes, financial accounting and electives such as finance, operations and marketing. It is the best free foundation for building this curriculum. (MIT OCW)

AI-focused programme benchmarks​

Programme	Focus	Link
Wharton AI for Business MBA major	Technical + societal/ethical AI	OID page
Kellogg & McCormick MBAi	MBA + technical + integrated AI cores	MBAi
Kellogg MBAi academic experience	Curriculum structure	Academic experience
Kellogg Analytics & AI pathway	Specialisation inside MBA	Pathway
NYU Stern Andre Koo Tech MBA	Business + technology + projects	Programme
NYU Stern Tech MBA coursework	Course map	Coursework
Oxford Artificial Intelligence Programme	Executive AI for leaders	Programme
Oxford MSc AI for Business	Technology + management degree	Announcement
Imperial MSc AI, Economics and Policy	AI + economics + policy	Programme
INSEAD AI for Business	Strategy, adoption, organisation	Overview · Curriculum

These are not all conventional MBAs, but together they benchmark AI strategy, economics, management, governance and implementation. (Kellogg MBAi)

Playbook upgrade: translate school curricula into engagement skills via AI consulting at MBB & Big Four and AI consulting strategy frameworks.

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Part I — Personal leadership and management

2. Mindful management​

Treat mindful management as a practical leadership discipline, not only meditation. It should develop:

• Attention and concentration
• Emotional self-regulation
• Self-awareness
• Active listening
• Empathy
• Resilience under pressure
• Awareness of cognitive bias
• Ethical judgement
• Reflective decision-making
• Ability to pause before escalating or automating decisions

Recommended resources​

Resource	Access	Link
Search Inside Yourself Leadership Institute	Paid / Free intros	siyli.org
MIT Search Inside Yourself	Internal / programme	MIT HR
Oxford Mindfulness — Workplace	Mixed	oxfordmindfulness.org
MIT Practical Leadership	Free	OCW 15.974 · Readings
MIT Inquiry-Driven Leadership	Paid exec	MIT Sloan Exec

Search Inside Yourself was developed at Google and combines mindfulness, emotional intelligence and leadership. MIT’s version emphasises conscious leadership, resilience, belonging and teamwork. (MIT HR)

AI-management application​

Use mindful management when:

• Reviewing an AI recommendation that appears overly confident
• Deciding whether to automate a sensitive process
• Handling disagreement between technical, risk and commercial teams
• Responding to an AI incident
• Avoiding automation bias
• Evaluating whether a decision is technically possible but organisationally irresponsible

Playbook upgrade: pair with Leadership: risk, governance and assurance and Responsible AI governance.

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3. Leadership and change management​

An AI leader must lead people through uncertainty—not merely select models. Study:

• Leadership styles; situational and adaptive leadership
• Power, influence and organisational politics
• Psychological safety and team design
• Motivation and stakeholder management
• Conflict resolution
• Transformation leadership and change resistance
• Capability building and human–AI collaboration

Recommended resources​

Resource	Access	Link
MIT People and Organizations	Free	15.668
MIT Organizational Leadership and Change	Free	15.317
MIT Leadership Lab	Free	15.974
MIT Leadership in an Exponentially Changing World	Paid exec	Executive
HBS Online leadership courses	Paid	Catalogue
OpenStax Organizational Behavior	Free	OpenStax

MIT’s leadership material emphasises feedback, reflection, practice and organisational experience—not leadership as pure theory. (MIT OCW)

AI-management questions you must answer​

• Which jobs will be augmented, redesigned or removed?
• What capabilities must employees develop?
• Who owns AI decisions?
• How do we prevent “shadow AI” use?
• How do we communicate limitations without creating fear?
• How do we redesign performance measures after automation?
• How do we create trust without exaggerating AI capability?

Change plan minimum contents​

Workstream	Include
Stakeholder impact	Who gains / loses influence, status or tasks
Role changes	Augment / redesign / retire job families
Training	Role-based curricula and practice environments
Communication	Narrative, cadence, channels, rumour control
Adoption metrics	Usage quality, not vanity logins
Resistance management	Legitimate concerns vs blocking behaviours
Support	Super-users, floorwalkers, escalation paths
Feedback loops	Incident → product → policy learning

Playbook upgrade: Organisation-wide transformation, Develop people & capability, Change and adoption frameworks, banking delivery & change.

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4. Organisational behaviour and organisation design​

Study individual and group behaviour, culture, motivation, incentives, informal networks, decision rights, centralised versus federated structures, communities of practice, knowledge management, organisational learning, diversity and inclusion, and performance management.

Core links​

• OpenStax Organizational Behavior (Free)
• MIT People and Organizations (Free)
• MIT Organizational Processes (Free)
• MIT Organizational Leadership and Change (Free)

AI organisation-design patterns​

An AI-focused MBA should teach you to design:

Pattern	When it fits
Central AI Centre of Excellence	Standards, platforms, scarce expertise
Federated AI teams in business units	Domain depth and local adoption
Hub-and-spoke operating model	Shared platform + local delivery
AI product teams	Persistent products with roadmap ownership
Model-risk committees	Regulated or high-stakes decisions
Responsible AI boards	Cross-functional policy and escalation
AI platform teams	Shared tooling, evaluation, observability
Data-product teams	Reusable data assets as products
AI champion networks	Grassroots adoption and shadow-AI reduction
Human-oversight functions	High-risk review and accountability

Playbook upgrade: Regional Data & AI CoE, Data & AI Centre of Excellence roadmap, Create an exceptional culture.

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Part II — Strategy and business fundamentals

5. Business and corporate strategy​

Study industry analysis, competitive advantage, resource-based view, capabilities, value chains, corporate and portfolio strategy, business models, positioning, differentiation, cost leadership, market entry, ecosystems, platforms, network effects, scenario planning and strategy execution.

Best resources​

Resource	Access	Link
MIT Strategic Management I	Free	15.902 · Lecture notes
MIT Strategic Management II	Free	15.904
MIT Technology Strategy	Free	15.912 · Notes
HBS Online Business Strategy	Paid	Courses
INSEAD AI for Business	Paid exec	Curriculum

MIT’s strategy material covers business and corporate strategy, technology transformation and frameworks such as industry analysis and the resource-based view. Technology Strategy connects technology choices with value creation, value capture and organisational innovation. (MIT OCW)

AI strategy analysis grid​

For every AI opportunity, analyse:

Where to play

• Which industry, segment, workflow and geography?
• Internal productivity or customer-facing product?

How to win

• Proprietary data; workflow integration; lower operating cost
• Faster decisions; trust and compliance; domain evaluation
• Distribution; UX; human expertise combined with AI

Build, buy or partner

• Build internally; managed cloud AI; SaaS; open-weight models
• Fine-tune / customise; specialist partnership

Strategic risks

• Commoditisation; vendor dependency; model obsolescence
• Data-access limits; regulation; weak adoption
• Competitor response; differentiation failure

Playbook upgrade: Strategy frameworks, From experimentation to enterprise advantage, EMEA AI go-to-market, Set direction and priorities.

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6. Digital innovation and technology strategy​

Study digital transformation, adoption and diffusion, disruptive innovation, technology life cycles, platform strategy, product ecosystems, architectural innovation, data strategy, digital operating models, legacy modernisation, API economies, cloud economics and technology portfolio management.

Recommended links​

• MIT Technology Strategy · Readings (Free)
• MIT Managing Innovation and Entrepreneurship · Syllabus (Free)
• HBS Online (Paid)
• Oxford AI Programme (Paid)

MIT’s innovation course connects processes, incentives, portfolio management and commercialisation—strategy and implementation together. (MIT OCW)

Digital and AI strategy pack (produce this)​

1. Current business capabilities
2. Data maturity
3. Technology architecture
4. AI use-case portfolio
5. Target operating model
6. Build-versus-buy decisions
7. Governance model
8. Workforce transformation
9. Investment roadmap
10. Value-measurement framework

Playbook upgrade: Enterprise AI solution engineering, What is AI solution engineering, Vendor technology evaluation.

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7. Microeconomics for managers​

Study supply and demand, elasticity, consumer choice, production and costs, marginal analysis, market structures, monopoly and oligopoly, game theory, information asymmetry, externalities, pricing, competition policy and regulation.

Recommended links​

• MIT Economic Analysis for Business Decisions (Free)
• OpenStax Principles of Economics 3e (Free)
• OpenStax Economics PDF (Free)

MIT focuses on business demand, costs, pricing, market power and regulation. (MIT OCW)

AI-focused economics applications​

Apply microeconomics to AI subscription, usage-based and token pricing; freemium; price discrimination; switching costs; data network effects; marginal inference cost; model commoditisation; marketplace design; platform competition; and the economic value of automation.

Playbook upgrade: Commercial value frameworks, AI FinOps / commercial design, Model FinOps roadmap.

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8. Macroeconomics​

Study GDP and growth, inflation, interest rates, employment, productivity, fiscal and monetary policy, business cycles, exchange rates, trade, public debt, financial stability, technological unemployment and industrial policy.

Recommended resources​

• OpenStax Principles of Economics 3e (Free)
• IMF Macroeconomic Diagnostics (Free / registration)
• IMF Financial Programming and Policies (Free / registration)
• IMF Macroeconometric Forecasting (Free / registration)
• World Bank Open Learning Campus (Free)

IMF courses cover fiscal, monetary, external and financial-sector analysis, diagnosis and forecasting. (IMF)

AI-focused macro questions​

• How could AI affect labour productivity and which occupations are augmented?
• How might AI investment affect interest-rate-sensitive sectors?
• Could compute infrastructure become strategically important?
• How will governments regulate cross-border AI services?
• How could AI alter wage inequality and skill premiums?
• What happens when AI supply chains depend on a few countries or firms?

Follow annual trends via the Stanford AI Index 2026 (investment, adoption, performance, policy, economics). (Stanford HAI)

Playbook upgrade: Industry / domain knowledge for translating macro forces into client propositions.

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9. Financial accounting and management accounting​

Study income statement, balance sheet, cash-flow statement, accrual accounting, revenue recognition, capitalisation versus expense, financial ratios, cost accounting, activity-based costing, budgeting, variance analysis, management control, working capital and internal controls.

Recommended links​

• MIT Financial Accounting · Syllabus (Free)
• MIT Introduction to Financial and Managerial Accounting (Free)
• MIT Management Accounting and Control (Free)

MIT’s financial accounting course takes a decision-maker perspective—connecting numbers to economic events. (MIT OCW)

AI-focused accounting topics​

Learn to account for AI software development, cloud and model-consumption costs, data acquisition, consulting, implementation, capitalised versus expensed development, vendor commitments, research and experimentation, and AI-related provisions.

Management accounting should also measure:

Metric	Why
Cost per successful task	Links spend to outcomes
Cost per conversation / resolved case	Contact-centre and support AI
Human-review cost	HITL is often the hidden OPEX
Rework and failed-response cost	Quality and eval debt
AI infrastructure cost	Platform and GPU / cloud share
Cost avoided through automation	Benefits realisation

Playbook upgrade: banking commercial case, Performance Engineering and AI FinOps.

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10. Corporate finance and valuation​

Study time value of money, DCF, NPV, IRR, cost of capital, capital budgeting, risk and return, CAPM, capital structure, working capital, company valuation, real options, scenario and sensitivity analysis, and M&A.

Recommended resources​

Resource	Access	Link
MIT Finance Theory I	Free	15.401 · Downloads
MIT Finance Theory II	Free	15.402
OpenStax Principles of Finance 2e	Free	OpenStax
Damodaran free class collection	Free	Class list
Damodaran Corporate Finance online	Free	Webcast · Home

MIT Finance Theory I covers valuation, risk, capital budgeting and portfolio theory. Damodaran provides a complete free MBA-level corporate-finance and valuation collection. (MIT OCW)

AI investment business case​

For an AI project:

$$\text{NPV}=\sum_{t=1}^{n}\frac{CF_t}{(1+r)^t}-I_0$$

Where CF_t = incremental project cash flow, r = risk-adjusted discount rate, I_0 = initial investment, n = evaluation period.

Cost / benefit bucket	Examples
Initial costs	Discovery, data prep, integration, security, model evaluation, legal, training, change
Recurring costs	Inference, cloud, licences, monitoring, human review, support, evaluation, incidents
Benefits	Revenue, handling time, error rate, conversion, retention, compliance losses avoided, faster delivery

Always model base, upside and downside scenarios. Do not present a single-point NPV as certainty.

Playbook upgrade: AI solution engineering framework — business case, Commercial realism, Practical core frameworks.

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11. Operations and supply-chain management​

Study process analysis, capacity, bottlenecks, queueing, inventory, forecasting, quality, lean, Six Sigma, revenue management, supply-chain coordination, risk pooling, outsourcing, service operations and business continuity.

Recommended links​

• MIT Introduction to Operations Management (Free)
• MIT Operations Strategy · Lecture notes (Free)
• MIT Supply Chain Planning (Free)
• MIT D-Lab Supply Chains (Free)

MIT covers process design, capacity, inventory, production control, supply-chain design, quality, revenue management and operational risk. (MIT OCW)

AI for operations — and AI operations itself​

Use AI to improve operations: demand forecasting, predictive maintenance, intelligent routing, dynamic scheduling, inventory optimisation, quality inspection, contact-centre automation, fraud detection, document processing, supply-risk intelligence.

Also study AI as an operating system: request queues, throughput, latency, model capacity, rate limits, human-review queues, failure recovery, vendor outages, cost controls, model fallback, service-level objectives.

Playbook upgrade: Delivery and programme management, MLOps / LLMOps, banking architecture & operating model.

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12. Marketing and customer strategy​

Study market research, segmentation, targeting, positioning, customer value, branding, pricing, acquisition, retention, CLV, channels, product-market fit, marketing analytics, experimentation and customer journeys.

Recommended resources​

• MIT Marketing Management · Lecture notes (Free)
• MIT Marketing Management: Analytics, Frameworks and Applications (Free)
• HBS Online marketing courses (Paid)

MIT’s MBA marketing material combines opportunity assessment, strategy, customer value and analytics. (MIT OCW)

AI-marketing topics and metrics​

Topics: personalisation, recommendations, propensity and churn models, dynamic pricing, generative content, search and discovery, conversational commerce, AI sales assistants, synthetic research, attribution, brand and reputational risk.

Avoid measuring only engagement. Prefer:

• Incremental conversion and revenue uplift
• Retention and customer lifetime value
• Cost of acquisition
• Brand trust, complaint rate, opt-out rate
• Hallucination or misinformation rate

Playbook upgrade: AI sales funnel to customer value, Go-to-market strategies, Persuading through stories.

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13. International business​

Study globalisation, trade, FDI, cross-border expansion, country risk, cultural differences, exchange-rate exposure, international supply chains, localisation, global integration versus local responsiveness, institutional differences, alliances, and political/regulatory risk.

Recommended resources​

• MIT Global Strategy and Organization · Notes · Readings (Free)
• World Bank Open Learning Campus (Free)
• OECD (Free)
• IMF training catalogue (Free / registration)

MIT examines internationalisation, global business models, country differences, multinational organisation, local adaptation and global integration. (MIT OCW)

International AI questions​

• Where may customer data be stored, and can models/data cross borders?
• Which languages and cultural contexts are supported?
• Does the use case fall under local high-risk AI regulation?
• Export controls on chips or models?
• Can outputs create discrimination in a local market?
• Which vendors are permitted in each jurisdiction?
• How should systems be localised, and who owns the IP?

Playbook upgrade: Privacy, legal and compliance, EU AI Act, EMEA AI GTM.

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Part III — Analytical and AI capabilities

14. Business analytics and decision science​

Study probability, statistics, regression, forecasting, decision analysis, optimisation, simulation, causal inference, A/B testing, visualisation, sensitivity analysis and decision-making under uncertainty.

Recommended resources​

• MIT Data, Models and Decisions · Summaries · Assignments · Cases (Free)
• Google Machine Learning Crash Course (Free)

MIT’s MBA decision-science course covers probability, statistics, regression, simulation and optimisation with management cases. Google’s course adds ML models, data preparation, production systems, LLMs and fairness. (MIT OCW)

Critical MBA distinctions​

Learn to separate:

• Correlation from causation
• Prediction from intervention
• Statistical significance from business significance
• Model accuracy from business value
• Average performance from subgroup performance
• Offline evaluation from production performance

Playbook upgrade: AI evaluation and quality assurance, Machine learning foundations.

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15. AI and machine-learning foundations​

An MBA student need not become an ML engineer, but should understand supervised/unsupervised/reinforcement learning; regression and classification; neural networks; train/validation/test; overfitting; features and embeddings; foundation models and LLMs; tokens and context windows; fine-tuning; RAG; agents; evaluation; monitoring; hallucinations; and limitations.

Beginner resources (Free / freemium)​

Resource	Link
University of Helsinki — Elements of AI	Helsinki Studies
AI for Everyone	Coursera
Google AI Essentials	grow.google
Generative AI for Everyone	Deeplearning.ai
Google Machine Learning Crash Course	developers.google.com

Elements of AI is non-programming; Google MLCC goes further into models, neural nets, embeddings, LLMs, production and fairness. (Helsinki)

Executive and business-focused resources​

Resource	Access	Link
Wharton AI for Business	Paid	Executive Education
Oxford AI Programme	Paid	SBS
INSEAD AI for Business	Paid	Curriculum
AWS Generative AI for Executives	Paid classroom	AWS blog
Microsoft AI Business Professional	Credential	Microsoft Learn

Wharton combines big data, ML, generative AI, deployment, ethics and governance. INSEAD extends into agentic AI, RAG, system-level thinking, experimentation, adoption and organisation design. (Wharton)

Playbook upgrade: LLM technical reading map, Models landscape, Agentic AI, RAG.

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16. AI product management​

Study problem discovery, Jobs to Be Done, user research, product-market fit, product strategy and roadmaps, prioritisation, prototyping, MVPs, experimentation, product analytics, human-in-the-loop design, AI evaluation, model feedback loops and responsible product design.

Recommended resources​

• MIT New Enterprises (Free)
• MIT Nuts and Bolts of New Ventures · Videos (Free)
• MIT Managing Innovation and Entrepreneurship (Free)
• MIT Scaling Entrepreneurial Ventures (Free)

MIT entrepreneurship material covers customer identification, business models, financial projections, legal issues, financing and scaling. (MIT OCW)

AI product questions before you build​

1. What business decision or workflow is being improved?
2. Why is AI needed?
3. What happens when the AI is wrong?
4. What is the acceptable error rate?
5. Who reviews high-risk outputs?
6. What data is available?
7. What is the baseline without AI?
8. How will value be measured?
9. What is the fallback process?
10. Can the solution scale economically?

Playbook upgrade: AI product management roadmap, Learning Map: Product Management, AI opportunity discovery.

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17. AI strategy and economics​

This subject combines strategy, economics, finance and AI: value creation, productivity, automation versus augmentation, data as a strategic asset, platforms, model commoditisation, compute economics, open versus closed models, network effects, switching costs, vendor lock-in, unit economics, portfolios and strategic option value.

Core reading sources​

• Stanford AI Index 2026 (Free)
• Wharton AI and Analytics Initiative (Free / programme mix)
• Wharton AI for Business major (Programme)
• Kellogg Analytics and AI specialisation (Programme)
• INSEAD AI programmes (Paid)

Wharton explicitly combines technical understanding with business, economic, ethical, legal and societal implications. Kellogg applies AI foundations across strategy, finance, marketing, accounting, operations and organisational management. (Wharton OID)

Playbook upgrade: AI consulting strategy frameworks, Portfolio performance and value, Shaping major data & AI opportunities.

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Part IV — Governance, risk and professional execution

18. Responsible AI, governance and regulation​

Study AI governance, model risk, accountability, fairness, transparency, explainability, privacy, human oversight, auditability, documentation, impact assessments, third-party risk, incident management, model inventories, acceptable-use policies and regulatory compliance.

Essential official links​

Framework	Link
NIST AI Risk Management Framework	nist.gov
NIST AI RMF Playbook / AIRC	airc.nist.gov
EU AI Act policy page	digital-strategy.ec.europa.eu
Official EU AI Act Explorer	ai-act-service-desk.ec.europa.eu
OECD AI Principles	oecd.org
ISO/IEC 42001	iso.org

NIST helps organisations incorporate trustworthiness into design, development, use and evaluation. ISO/IEC 42001 specifies requirements for an organisational AI management system. The EU AI Act follows a risk-based structure. (NIST)

Governance deliverables to practise​

Create: AI policy; system inventory; use-case intake; risk classification; impact assessment; model card; data sheet; human-oversight plan; evaluation plan; vendor assessment; incident-response process; monitoring dashboard; Responsible AI committee ToR; AI audit evidence pack.

Playbook upgrade: NIST / EU AI Act, ISO/IEC 42001, ISO/IEC 27001, EU AI Act deep dive, IAPP AIGP, Responsible AI learning map.

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19. Communication, negotiation and executive influence​

Study business writing, executive presentations, storytelling, persuasion, negotiation, stakeholder communication, conflict management, board communication, crisis communication and cross-cultural communication.

Recommended resources​

• MIT Communication for Managers · Readings (Free)
• MIT Management Communication (Free)
• MIT Power and Negotiation (Free)
• MIT Negotiation and Conflict Management (Free)
• HBS Online negotiation and leadership (Paid)

MIT management communication focuses on writing, speaking, persuasive presentation and communication strategy. Negotiation courses use simulations and reflection. (MIT OCW)

AI executive communication​

Practise explaining one AI project differently to: board, CFO, CRO, CIO, data scientists, employees, customers, regulators, procurement and investors.

A board presentation should answer:

1. What problem are we solving?
2. What measurable value could it create?
3. Why is AI appropriate?
4. What evidence supports the proposal?
5. What are the material risks?
6. How are those risks controlled?
7. What decision is required from the board?

Playbook upgrade: Communication and executive articulation, Proposal mastery, Influence: psychology of persuasion, How to speak with confidence, Represent the firm externally.

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Recommended 12-month study plan

Months 1–2: MBA foundations​

Complete: MIT MBA core overview · Microeconomics · Financial accounting · Business statistics

Produce: Industry economics analysis · Three-statement company analysis · Basic regression exercise

Months 3–4: Finance, operations and marketing​

Complete: Corporate finance · Operations management · Marketing management

Produce: DCF valuation · Process map and bottleneck analysis · Market segmentation and positioning document

Months 5–6: Strategy, leadership and organisation​

Complete: Strategic Management I · Technology Strategy · Organisational Behaviour · Practical Leadership · Communication for Managers

Produce: Five Forces analysis · Capability assessment · AI target operating model · Five-minute executive presentation

Months 7–8: AI and analytics​

Complete: Elements of AI or AI for Everyone · Google ML Crash Course · MIT Data, Models and Decisions · Generative AI for Everyone

Produce: AI terminology guide · Model evaluation exercise · AI opportunity portfolio · Business experimentation plan

Months 9–10: AI product and innovation​

Complete: Managing Innovation · New Enterprises · AI product-management study · AI strategy and unit economics

Produce: AI PRD · Build-versus-buy assessment · AI business case · MVP evaluation framework

Month 11: Governance and international business​

Complete: NIST AI RMF · EU AI Act overview · OECD AI Principles · ISO/IEC 42001 overview · Global Strategy and Organization

Produce: AI impact assessment · Governance operating model · International deployment risk assessment · Third-party AI vendor questionnaire

Month 12: Capstone​

Complete an end-to-end AI consulting project.

Suggested capstone — Enterprise AI customer-service transformation​

Deliver:

1. Market and industry analysis
2. Customer journey
3. Current process analysis
4. AI use-case prioritisation
5. Target solution architecture
6. Data assessment
7. Financial business case
8. Operating model
9. Responsible AI assessment
10. Security and compliance controls
11. Change-management plan
12. Implementation roadmap
13. KPI framework
14. Board presentation

Playbook upgrade for the capstone: run the Banking customer-service AI series as a worked industry template, or ConsultAI OS as an engagement operating system.

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The most important MBA frameworks to master

Strategy​

PESTLE · Porter’s Five Forces · Value Chain · Resource-Based View · VRIO · SWOT · Strategy Choice Cascade · Playing to Win · Three Horizons · Scenario Planning · Wardley Mapping · Business Model Canvas · Platform strategy

Finance​

DCF · NPV · IRR · WACC · CAPM · Sensitivity analysis · Scenario analysis · Real options · Break-even · Unit economics · Economic value added

Operations​

SIPOC · Value Stream Mapping · Theory of Constraints · Little’s Law · Queueing · Capacity analysis · Lean · Six Sigma · Service blueprint · Process mining

Organisation and leadership​

RACI · RAPID · Stakeholder mapping · Kotter · ADKAR · McKinsey 7S · Team Topologies · Psychological safety · Situational leadership · Influence–interest matrix

AI management​

AI use-case portfolio · Build–buy–partner · AI maturity assessment · NIST AI RMF · AI impact assessment · Model card · Human-in-the-loop design · AI system inventory · AI risk taxonomy · Evaluation scorecard · AI unit economics · Model and vendor selection matrix

Catalogue detail: Strategy · Commercial value · Change and adoption · Responsible AI · Practical core frameworks.

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Best free minimum curriculum

For a manageable starting point, complete these first:

1. MIT Sloan MBA Core
2. MIT Strategic Management I
3. MIT Finance Theory I
4. MIT Operations Management
5. MIT Marketing Management
6. MIT Data, Models and Decisions
7. MIT Technology Strategy
8. Elements of AI
9. Google Machine Learning Crash Course
10. NIST AI RMF
11. EU AI Act Explorer
12. Stanford AI Index 2026

Completing these with assignments, case analyses and one substantial capstone provides a stronger foundation than passively watching dozens of unrelated business and AI courses.

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Verdict​

An AI-focused MBA is a management curriculum with AI as the operating context—not an ML engineering degree with softer electives. School benchmarks (Wharton, Kellogg, NYU, Oxford, INSEAD) agree on the blend: business core, technology literacy, experiential judgement and societal/governance literacy.

Use MIT OCW and OpenStax for free depth; use NIST/EU/OECD for governance; use this playbook to turn study into client-ready artefacts. The 12-month plan and month-12 capstone are the difference between a reading list and a professional capability.