Strategy decides who you serve, what you promise and how you will know it worked. You will turn a fuzzy goal into a one-page plan with a clear audience, position, channel mix and measures.

1. 1What marketing strategy is, and what it is not
2. 2From business goals to marketing goals
3. 3Reading a market without inventing numbers
4. 4Segmentation: grouping people by need
5. 5Choosing a target audience
6. Test 1 · 13 questions
7. 6Jobs to be done and the problems people pay to solve
8. 7Competitors and alternatives, including doing nothing
9. 8Positioning: the place you can own
10. Test 2 · 21 questions
11. 9The value proposition
12. 10Channels: going where your audience already is
13. 11Budgets and trade-offs
14. 12Objectives, key results and leading measures
15. 13The one-page marketing plan
16. Final Test · 34 questions

AI lab. Use AI to draft segment profiles and a competitor scan, then check each fact against a primary source and mark anything you could not confirm. The AI offers options; you choose the target and the position.

Good marketing starts by listening. You will plan interviews and surveys, read existing data and turn what people tell you into insight you can defend.

1. 1Questions worth answering
2. 2Secondary research and checking sources
3. 3Planning customer interviews
4. 4Running an interview that does not lead
5. 5Surveys: writing fair questions
6. Test 1 · 13 questions
7. 6Sampling and who is missing
8. 7Consent, privacy and research ethics
9. 8Coding interview notes into themes
10. Test 2 · 21 questions
11. 9Basic survey analysis
12. 10Personas built from evidence, not imagination
13. 11Customer journey maps
14. 12Synthetic respondents and AI summaries: uses and limits
15. 13Writing the insight brief
16. Final Test · 34 questions

AI lab. Have AI suggest themes from transcripts you have stripped of names, then trace every theme back to real quotes and drop any the transcripts do not support. Compare an AI 'synthetic' answer with your real interviews and note where they differ.

A brand is the promise people come to expect from you. You will build a brand platform, a voice and a messaging framework that a whole team can use without drifting.

1. 1What a brand is, plainly
2. 2Purpose, promise and proof
3. 3The brand audit: how you are seen today
4. 4Brand architecture: parent brands and sub-brands
5. 5Naming and trademark basics
6. Test 1 · 13 questions
7. 6Voice and tone
8. 7Visual identity: logo, color, type and images
9. 8Accessible and inclusive brand design
10. Test 2 · 21 questions
11. 9The messaging framework
12. 10Proof points and claims you can back
13. 11Brand guidelines people actually use
14. 12Keeping a brand consistent with AI tools
15. 13Measuring brand: awareness, consideration and trust
16. Final Test · 34 questions

AI lab. Write a voice guide, have AI draft five pieces in that voice, score each piece against the guide and fix the guide where the AI drifted. Run proposed names through the USPTO trademark search and note that only a lawyer can clear a name.

Marketing that misleads harms people and breaks the law, even when an AI wrote it. You will learn the main US rules in plain words and build habits that keep your work truthful, fair and private.

1. 1Why truth matters: ethics before law
2. 2The FTC Act: deceptive and unfair practices
3. 3Claims and substantiation: have the proof first
4. 4Comparative ads and the Lanham Act
5. 5Endorsements and influencers: the FTC Endorsement Guides
6. Test 1 · 13 questions
7. 6Reviews and testimonials: the FTC rule on fake reviews
8. 7Email and the CAN-SPAM Act
9. 8Calls and texts: TCPA consent basics
10. Test 2 · 21 questions
11. 9Privacy: state privacy laws, GDPR and opt-out signals
12. 10Children's data and COPPA
13. 11Special claims: health, green and "Made in USA"
14. 12AI-made content: disclosure, copyright and likeness
15. 13Building a compliance check into the workflow
16. Final Test · 34 questions

AI lab. Ask AI to flag risky claims and missing disclosures in sample ads, then check its flags against the primary sources (FTC guidance, the CAN-SPAM rules) and record what it missed or got wrong. AI is a second reader here, never the lawyer.

Content is how a brand shows up day to day. You will plan, draft, edit and publish content with AI, with a human editor's judgment and a fact-check on every claim.

1. 1Content strategy: purpose before volume
2. 2The editorial calendar
3. 3Briefs that get good work from people and AI
4. 4Writing for the web: clear, scannable, useful
5. 5Search basics and helpful content
6. Test 1 · 13 questions
7. 6Social platforms and formats
8. 7Short video and audio basics
9. 8Images and design with AI tools
10. Test 2 · 21 questions
11. 9Fact-checking and sourcing every claim
12. 10Rights: licences, music, likeness and AI images
13. 11Accessibility: alt text, captions and contrast
14. 12Editing and approval workflows
15. 13Measuring content: what helped and what did not
16. Final Test · 34 questions

AI lab. Draft with AI from your own briefs, then edit hard: verify every fact, remove invented quotes or numbers, and check images for rights and likeness problems. Keep before-and-after versions to show what you changed.

Performance marketing spends money to get measurable actions, so the numbers have to be honest. You will set up tracking, read results, run fair tests and report what worked without overstating it.

1. 1The funnel and the measures that matter
2. 2Tracking plans and clean data
3. 3Web analytics basics
4. 4Search advertising
5. 5Paid social advertising
6. Test 1 · 13 questions
7. 6Budgets, bids and pacing
8. 7Unit economics: acquisition cost, conversion and payback
9. 8A/B tests: design and sample size
10. Test 2 · 21 questions
11. 9Reading results: signal, noise and significance
12. 10Attribution and its limits
13. 11Incrementality and holdout tests
14. 12Privacy-safe measurement: consent and cookies
15. 13Dashboards and honest reporting
16. Final Test · 34 questions

AI lab. Have AI write the analysis code or spreadsheet formulas for a test, then recompute the key numbers yourself and confirm they match the raw data. Ask it to explain the result, and correct any claim it makes that the data cannot support.

Customers stay when the product and the follow-through keep their promise, and they pay what they believe is fair. You will design lifecycle programs on clean, consented CRM data and set prices that are clear and honest.

1. 1The customer lifecycle
2. 2CRM data: fields, hygiene and consent records
3. 3Segments and triggers
4. 4Onboarding journeys
5. 5Email programs that follow CAN-SPAM
6. Test 1 · 13 questions
7. 6Retention, churn and win-back
8. 7Loyalty and referral programs, honestly run
9. 8Customer lifetime value without wishful thinking
10. Test 2 · 21 questions
11. 9Pricing basics: value, cost and competition
12. 10Researching willingness to pay
13. 11Packages, tiers, discounts and promotions
14. 12Subscriptions and renewals: ROSCA and state auto-renewal laws
15. 13Honest prices: all-in totals and no fake scarcity
16. Final Test · 34 questions

AI lab. Use AI to draft journey emails and suggest segments from a sample CRM file, then check each email against CAN-SPAM basics and each segment for consent. Have AI model price scenarios, and check its math by hand.

The practicum puts the field courses to work on a real problem. You will scope, run and report a marketing project for a host organization, your own venture or a provided case, with written permission for any data you use.

1. 1Choosing a host or a case
2. 2Scoping the problem and the deliverable
3. 3The practicum agreement: goals, data and approvals
4. 4Getting to know the host's customers
5. 5Auditing the current marketing
6. Test 1 · 13 questions
7. 6Planning the work
8. 7Building with AI under the host's rules
9. 8Compliance and approval review
10. Test 2 · 21 questions
11. 9Launching or piloting
12. 10Measuring what happened
13. 11Handling setbacks and changes
14. 12Writing the practicum report
15. 13Hand-off and reflection
16. Final Test · 34 questions

AI lab. Work inside the host's AI policy: use only approved tools and never paste their private data into tools they have not approved. Log every AI-assisted step and how you or the host checked it.

Capstone Studio I sets up your capstone: a full go-to-market plan for a real or realistic product, cause or business. You will research, set the strategy and get the plan reviewed before you build anything.

1. 1The capstone brief and rubric
2. 2Choosing your product, cause or business
3. 3The research plan
4. 4Fieldwork: interviews and data
5. 5Synthesizing insight
6. Test 1 · 13 questions
7. 6Strategy and positioning
8. 7Brand and message choices
9. 8Channel and content plan
10. Test 2 · 21 questions
11. 9Pricing and the offer
12. 10The measurement plan
13. 11The compliance plan
14. 12Plan review and revision
15. 13Studio I checkpoint presentation
16. Final Test · 34 questions

AI lab. Use AI as a critic: ask it to argue against your strategy and list the holes in your evidence, then decide which critiques are fair and fix those. Record what you changed and why.

Capstone Studio II builds and tests the plan from Studio I. You will make the campaign, run a small real test or a clearly labelled simulation, and present the results with your AI-use log.

1. 1From plan to production schedule
2. 2Creative production with AI
3. 3Landing pages and offers
4. 4Email and lifecycle pieces
5. 5Pre-launch compliance check
6. Test 1 · 13 questions
7. 6Setting up tracking
8. 7Running the test
9. 8Watching early results without overreacting
10. Test 2 · 21 questions
11. 9Analysis
12. 10What to keep, change or stop
13. 11Writing the results report
14. 12Presenting to decision-makers
15. 13Final presentation and portfolio piece
16. Final Test · 34 questions

AI lab. Produce assets with AI, then check every claim, disclosure and consent step before anything goes live. Verify the reported numbers against the raw data, and label any simulated data as simulated.

Financial statements are the shared language of business. You will read, build and connect the income statement, balance sheet and cash flow statement, and learn the GAAP and IFRS basics behind them.

1. 1Why financial statements exist
2. 2Double entry and the accounting equation
3. 3Accrual accounting versus cash accounting
4. 4The income statement
5. 5The balance sheet
6. Test 1 · 13 questions
7. 6The cash flow statement
8. 7How the three statements connect
9. 8GAAP and IFRS: who sets the rules and where they differ
10. Test 2 · 21 questions
11. 9Revenue recognition: the five steps of ASC 606 and IFRS 15
12. 10Leases, inventory and other judgment areas
13. 11Notes, the audit report and the Form 10-K
14. 12Ratios: liquidity, profitability, leverage and efficiency
15. 13Quality of earnings and red flags
16. Final Test · 34 questions

AI lab. Have AI pull figures from a 10-K and compute ratios, then tie every number back to the page it came from and fix any it misread or invented. Note where it confused similar line items.

Financial planning and analysis turns plans into numbers and numbers into decisions. You will build driver-based budgets and rolling forecasts, explain variances and present scenarios leaders can act on.

1. 1What FP&A does
2. 2The planning calendar
3. 3Drivers: what really moves revenue and cost
4. 4Building a revenue forecast
5. 5Forecasting costs and headcount
6. Test 1 · 13 questions
7. 6Spreadsheet modeling standards
8. 7The operating budget
9. 8Variance analysis: budget versus actual
10. Test 2 · 21 questions
11. 9Rolling forecasts
12. 10Scenarios and sensitivity analysis
13. 11Unit economics and KPIs
14. 12Statistical and AI forecasts: uses and limits
15. 13Telling the story: commentary for leaders
16. Final Test · 34 questions

AI lab. Have AI suggest drivers and draft variance commentary, then check each sentence against the model and the actuals. Compare an AI or statistical forecast with your driver model and explain the gap instead of picking the number you like.

Corporate finance asks which projects are worth funding and how to pay for them. You will use the time value of money, the cost of capital and simple valuation methods, with every assumption written down.

1. 1The goal of the firm and who it serves
2. 2The time value of money
3. 3Interest rates and discount rates
4. 4Net present value
5. 5Internal rate of return and payback
6. Test 1 · 13 questions
7. 6Capital budgeting decisions
8. 7Risk and return, plainly
9. 8Cost of capital and WACC
10. Test 2 · 21 questions
11. 9Debt, equity and capital structure
12. 10Discounted cash flow valuation
13. 11Valuation by comparables
14. 12Sensitivity: how assumptions drive value
15. 13Writing the case for a business decision
16. Final Test · 34 questions

AI lab. Have AI build a first discounted cash flow model, then audit it: check the formulas, the timing of each cash flow and the discount rate, and list every error you found. Replace the assumptions the AI chose with your own sourced ones.

Everyone handles money, and people often ask whoever works in finance for help. You will learn the core ideas of personal finance well enough to run your own and explain them clearly, without giving anyone individual advice.

1. 1Education, not advice: where the line is
2. 2Income, paychecks and withholding
3. 3Budgets and spending plans
4. 4Emergency savings
5. 5Credit reports and credit scores
6. Test 1 · 13 questions
7. 6Borrowing: interest, APR and the true cost of a loan
8. 7Student loans, car loans and mortgages
9. 8Compound growth and inflation
10. Test 2 · 21 questions
11. 9Retirement accounts and workplace plans: how they work
12. 10Diversification, risk and fees as ideas
13. 11Insurance basics: what it is for
14. 12Scams, fraud and identity theft
15. 13Teaching money basics clearly
16. Final Test · 34 questions

AI lab. Use AI to draft explanations and examples, then check every rate, limit and tax figure against an official source for the current year. Remove anything that reads as advice to a particular person.

A profitable business can still fail if it runs out of cash. You will forecast cash week by week, manage working capital and bank relationships, and protect payments from fraud.

1. 1What treasury does
2. 2Profit versus cash
3. 3The cash conversion cycle
4. 4Working capital: receivables, payables and inventory
5. 5The 13-week cash flow forecast
6. Test 1 · 13 questions
7. 6Liquidity buffers and credit lines
8. 7Banking relationships and account structure
9. 8Payments: ACH, wires, cards and checks
10. Test 2 · 21 questions
11. 9Payment fraud and business email compromise
12. 10Short-term cash: safety and access first
13. 11Foreign exchange and interest-rate exposure basics
14. 12Debt covenants and lender reporting
15. 13The treasury dashboard
16. Final Test · 34 questions

AI lab. Have AI build the cash forecast from bank and ledger exports, then reconcile its opening balance and weekly totals to the bank statements. Test an AI-drafted payment request for fraud signs and write the checks a person must make before money moves.

Controls are how an organization makes sure the numbers are right and the money is safe. You will map financial risks, design internal controls on the COSO framework and learn, in plain words, what Sarbanes-Oxley asks of public companies.

1. 1Kinds of financial risk
2. 2Enterprise risk management basics
3. 3The fraud triangle
4. 4COSO internal control: the five components
5. 5Designing controls: preventive and detective
6. Test 1 · 13 questions
7. 6Segregation of duties
8. 7Reconciliations and the month-end close
9. 8Sarbanes-Oxley in plain words: Sections 302 and 404
10. Test 2 · 21 questions
11. 9IT general controls and system access
12. 10Auditors, the PCAOB and the audit cycle
13. 11Honest reporting: non-GAAP measures and disclosure
14. 12Controls for AI-assisted work
15. 13Testing controls and keeping evidence
16. Final Test · 34 questions

AI lab. Have AI draft a risk and control matrix for a process, then walk the process yourself and correct any control that does not match how the work is really done. Check that no one person can create, approve and pay the same item.

AI can speed up finance work, and it can also be confidently wrong with real money at stake. You will use AI for analysis, reconciliation and reporting with model-risk discipline, and protect customer data under the Gramm-Leach-Bliley Act (GLBA).

1. 1Where AI helps in finance, and where it fails
2. 2Invented numbers and how to catch them
3. 3Tie-outs: reconciling AI output to the source
4. 4What counts as a model
5. 5Model risk guidance: from SR 11-7 to SR 26-2
6. Test 1 · 13 questions
7. 6Validation and effective challenge
8. 7Monitoring models after launch
9. 8GLBA: privacy notices and the Safeguards Rule
10. Test 2 · 21 questions
11. 9Data handling: what never goes into an unapproved tool
12. 10Fair credit decisions: ECOA and adverse action notices
13. 11Explainability and documentation
14. 12AI risk frameworks: the NIST AI RMF and its generative AI profile
15. 13Writing an AI use policy for a finance team
16. Final Test · 34 questions

AI lab. Build the workflow and test it on cases where you already know the right answer; record every error and the control that would catch it. Use only synthetic or properly de-identified data.

The practicum puts your finance skills to work on a real problem. You will scope, deliver and report a finance project for a host organization, your own business or a provided case, with written permission for any data you use.

1. 1Choosing a host or a case
2. 2Scoping the question and the deliverable
3. 3The practicum agreement: data, confidentiality and approvals
4. 4Learning the host's finance processes
5. 5Getting and cleaning the data
6. Test 1 · 13 questions
7. 6Planning the analysis
8. 7Building with AI under the host's rules
9. 8Tie-outs and review
10. Test 2 · 21 questions
11. 9Drafting the findings
12. 10Testing recommendations with the host
13. 11Handling setbacks and changes
14. 12Writing the practicum report
15. 13Hand-off and reflection
16. Final Test · 34 questions

AI lab. Use only the AI tools the host approves, and keep their non-public data out of everything else. Log every AI-assisted step and how you tied its output back to the source.

Capstone Studio I sets up your capstone: a financial plan and decision package for a real or realistic organization. You will analyze its statements, build the forecast and frame the decision before you recommend anything.

1. 1The capstone brief and rubric
2. 2Choosing your organization and its decision
3. 3Gathering statements and data
4. 4Statement analysis
5. 5Business drivers and assumptions
6. Test 1 · 13 questions
7. 6Building the three-statement model
8. 7Model review and error checks
9. 8The cash forecast
10. Test 2 · 21 questions
11. 9Framing the decision
12. 10Valuation or capital budgeting analysis
13. 11Scenario planning
14. 12Peer and AI critique
15. 13Studio I checkpoint presentation
16. Final Test · 34 questions

AI lab. Ask AI to stress-test your model for formula errors, circular references and unrealistic assumptions. Confirm each finding yourself before you change the model, and log it.

Capstone Studio II turns the model into a recommendation an owner or a board could act on. You will add the risk and controls review, finish the decision case and present it with your model-risk and AI-use files.

1. 1From model to recommendation
2. 2Risk review of the decision
3. 3Controls and approvals for the plan
4. 4Funding options
5. 5Downside plans and trigger points
6. Test 1 · 13 questions
7. 6Model documentation
8. 7The model-risk file and validation
9. 8Privacy and data review
10. Test 2 · 21 questions
11. 9Writing the decision memo
12. 10Board-style slides
13. 11Preparing for hard questions
14. 12Final revisions
15. 13Final presentation and portfolio piece
16. Final Test · 34 questions

AI lab. Have AI play skeptical board members and answer their questions with evidence from your model. Verify any figure the AI quotes back to you before you use it.

A tour of the main lines of insurance (personal property and casualty, life, health and commercial) and how to read the policy behind each one. You learn what each line covers, what it leaves out, and why the policy wording, not the brochure, decides a claim.

1. 1Insurance as a Promise: Pooling, Premiums and Trust
2. 2Anatomy of a Policy: Declarations, Insuring Agreement, Exclusions and Conditions
3. 3Personal Auto: Liability, Collision, Comprehensive and Uninsured Motorists
4. 4Homeowners and Renters: Dwelling, Belongings, Liability and Loss of Use
5. 5Umbrella, Flood and Earthquake: The Gaps Standard Policies Leave
6. Test 1 · 13 questions
7. 6Life Insurance: Term, Whole and Universal
8. 7Annuities: Turning Savings Into Income
9. 8Health Insurance: Marketplace, Employer Plans, Medicare and Medicaid
10. Test 2 · 21 questions
11. 9Disability Income and Long-Term Care
12. 10Commercial Property and General Liability
13. 11Workers' Compensation and Commercial Auto
14. 12Professional, Cyber, and Directors and Officers Liability
15. 13Reading Policies Side by Side: A Coverage Comparison
16. Final Test · 34 questions

AI lab. Give an AI assistant a public specimen policy (never a real customer's file) and ask it to summarize coverage and exclusions. Check every statement it makes against the exact wording and mark each one correct, overstated or missing.

How insurers decide whom to cover, on what terms and at roughly what price: perils, hazards, classification and the underwriting file. You practise reading an application as an underwriter does and learn where fair risk classification ends and unfair discrimination begins.

1. 1What Makes a Risk Insurable
2. 2Perils, Hazards and Exposures
3. 3Adverse Selection and Moral Hazard
4. 4The Underwriting Process: From Application to Decision
5. 5Classification and Rating Variables
6. Test 1 · 13 questions
7. 6Personal Lines Underwriting: Auto and Home
8. 7Commercial Underwriting: Reading a Business
9. 8Life and Disability Underwriting: Medical Information and Its Limits
10. Test 2 · 21 questions
11. 9Data Sources: Reports, Inspections, Telematics and Outside Data
12. 10Credit-Based Insurance Scores and the Rules Around Them
13. 11Fair Classification Versus Unfair Discrimination
14. 12Guidelines, Underwriting Authority and Referrals
15. 13Writing an Underwriting Rationale Someone Else Can Check
16. Final Test · 34 questions

AI lab. Ask an AI to pre-screen the three applications against the guideline and flag missing information. Compare its flags with yours, find where it guessed instead of reading, and record any variable it leaned on that you would not be allowed to use.

The ideas behind insurance prices and reserves, explained without heavy math: frequency, severity, expected loss, credibility and loss development. Then the risks that break averages, catastrophes and a changing climate, and the tools used to carry them: catastrophe models, reinsurance, public programs and mitigation.

1. 1What Actuaries Do and Why It Matters to Everyone
2. 2Frequency and Severity
3. 3Expected Loss and the Pure Premium
4. 4From Pure Premium to Price: Expenses, Profit and the Combined Ratio
5. 5Credibility: How Much to Trust Your Own Data
6. Test 1 · 13 questions
7. 6Reserves and IBNR: Paying for Claims Not Yet Reported
8. 7Trends, Inflation and Why Last Year's Losses Are Not Next Year's
9. 8When Averages Break: Correlated Losses and Catastrophes
10. Test 2 · 21 questions
11. 9Catastrophe Models in Plain Terms
12. 10Reinsurance: Spreading the Biggest Risks
13. 11Climate Trends and the Limits of the Past
14. 12Availability and Affordability: FAIR Plans, the NFIP and Stressed Markets
15. 13Mitigation, Resilience and Parametric Cover
16. Final Test · 34 questions

AI lab. Have an AI write the calculation steps in a spreadsheet or Python and explain them. Recompute a sample of the figures yourself, then change one assumption and check whether the AI's explanation still holds.

What happens after a loss: first notice, coverage review, investigation, valuation, settlement and sometimes dispute, all under the good-faith duties of state fair claims practices rules. You also learn how insurers detect fraud without treating honest customers as suspects, and where AI belongs in a claim and where it does not.

1. 1The Claim as the Moment of Truth
2. 2First Notice of Loss and Early Contact
3. 3Coverage Analysis: Reading the Policy Against the Facts
4. 4Investigation: Statements, Photos, Records and Experts
5. 5Valuation: Actual Cash Value, Replacement Cost and Depreciation
6. Test 1 · 13 questions
7. 6Liability Claims: Negligence, Reserves and Negotiation
8. 7Settlement, Payment and Closing the File
9. 8Fair Claims Practices: Timeliness, Communication and Good Faith
10. Test 2 · 21 questions
11. 9Disputes: Appraisal, Complaints and Bad-Faith Exposure
12. 10Subrogation and Recovery
13. 11Fraud in Plain Terms: Hard Fraud, Soft Fraud and Padded Claims
14. 12Red Flags, Special Investigation Units and Reporting Duties
15. 13AI in Claims: Triage, Photo Estimates, Fraud Scores and the Human Decision
16. Final Test · 34 questions

AI lab. Use an AI to draft the claimant letters and a damage estimate from sample photos. Check every coverage statement against the policy and every estimate line against the photos and price list, and rewrite anything that reads like a denial before a person has decided.

How insurance is regulated in the United States: mainly by the states, coordinated through the NAIC and its model laws, with federal laws such as GLBA, HIPAA and the Fair Credit Reporting Act covering privacy, health data and consumer reports. You study rates and forms, market conduct, solvency and fair claims rules, and current work on unfair discrimination in algorithms, including the NAIC's model bulletin on insurers' use of AI and Colorado's rules on insurers' use of external consumer data.

1. 1Why the States Regulate Insurance: McCarran-Ferguson and Its Limits
2. 2The NAIC, Model Laws and How States Adopt Them
3. 3Licensing, Market Conduct and Consumer Complaints
4. 4Rates and Forms: Not Excessive, Not Inadequate, Not Unfairly Discriminatory
5. 5Solvency: Capital, Reserves, Examinations and Guaranty Associations
6. Test 1 · 13 questions
7. 6Unfair Trade Practices and Fair Claims Settlement Rules
8. 7Insurance Privacy: GLBA and State Privacy Rules
9. 8Health Insurance Rules: HIPAA Privacy, the ACA and Where Each Applies
10. Test 2 · 21 questions
11. 9Data Security and Breach Notice: The NAIC Insurance Data Security Model Law
12. 10Consumer Reports and Adverse Action: The Fair Credit Reporting Act in Insurance
13. 11Algorithms and Unfair Discrimination: Proxies, Testing and State Rules
14. 12The NAIC AI Model Bulletin and AI Governance Programs
15. 13Reading a Regulation and Writing a Compliance Checklist
16. Final Test · 34 questions

AI lab. Ask an AI to list the rules that apply and cite them. Open every citation at the official source (the state insurance department, the NAIC or the federal agency), strike anything invented or out of date, and keep a record of which citations held up.

How insurance reaches people (captive and independent agents, brokers, direct and digital sales, managing general agents and embedded insurance) and how to serve customers honestly once they arrive. You practise needs conversations, plain-language explanations and complaint handling, and you design AI assistants that say what they are and hand off to a licensed person when they should.

1. 1How Insurance Is Sold: Agents, Brokers, Direct and Digital
2. 2Captive and Independent Agencies, MGAs and Wholesale Brokers
3. 3Embedded Insurance and Partner Channels
4. 4Producer Duties: Licensing, Appointments and Errors and Omissions
5. 5Needs Conversations: Asking Before Recommending
6. Test 1 · 13 questions
7. 6Suitability and Best Interest in Annuity Sales
8. 7Explaining Coverage Plainly, Including What Is Not Covered
9. 8Quotes, Binding and the First Policy Year
10. Test 2 · 21 questions
11. 9Service Work: Changes, Billing, Renewals and Cancellations
12. 10Complaints and Recovery: Making a Bad Moment Right
13. 11Access for Everyone: Language, Disability and Clear Writing
14. 12AI Assistants in Sales and Service: Disclosure, Limits and Handoffs
15. 13Measuring Service Without Gaming It
16. Final Test · 34 questions

AI lab. Build a simple AI assistant from a written knowledge sheet and test it with hard questions: coverage advice, a cancellation, a customer in distress. Log every answer that gave advice only a licensed person should give, and fix its instructions until it hands those off.

The technology changing insurance (telematics, parametric cover, digital claims, outside data vendors and machine-learning models) and the discipline of using it responsibly. You learn model governance, fairness testing and explainability well enough to write the documentation a regulator, a customer and your own team would ask for.

1. 1Insurtech in Plain Terms: What Changed and What Did Not
2. 2Usage-Based and Telematics Insurance
3. 3Parametric Insurance and Index Triggers
4. 4Machine Learning in Pricing, Underwriting and Claims
5. 5Generative AI in Insurance Work: Drafting, Summaries and Search
6. Test 1 · 13 questions
7. 6Outside Data and Model Vendors: Due Diligence
8. 7Model Governance: Inventory, Validation and Monitoring
9. 8Fairness Testing: Choosing Metrics and Reading Results
10. Test 2 · 21 questions
11. 9Explainability: Reason Codes, Feature Importance and Their Limits
12. 10Explaining a Decision to a Customer
13. 11Human Oversight: When a Person Must Decide
14. 12Security, Privacy and Data Minimization in AI Systems
15. 13Writing a Model Card for an Insurance Model
16. Final Test · 34 questions

AI lab. Use an AI coding assistant to build the model and a feature-importance chart. Check that the reasons it reports match the model's real behavior by changing inputs and watching the output, and note where its explanations were confident but wrong.

Applied work on a real or realistic insurance task, at your workplace, a nonprofit, your own agency or in a provided simulation; EducationAI does not arrange or guarantee placements. You scope the work, do it with AI tools, keep a verification log, protect any real customer information and present the results to someone able to judge them.

1. 1Choosing a Practicum Setting and Getting Permission
2. 2Scoping the Work: Problem, Users and Limits
3. 3Confidentiality and Data Handling in Insurance Work
4. 4Mapping the Current Process
5. 5Planning the AI Assistance and the Human Checks
6. Test 1 · 13 questions
7. 6First Build or First Draft
8. 7Testing With Real or Realistic Cases
9. 8Midpoint Review and Course Correction
10. Test 2 · 21 questions
11. 9Fairness, Privacy and Compliance Check
12. 10Second Build: Fixing What Testing Found
13. 11Writing the Practicum Report
14. 12Presenting to a Reviewer
15. 13Reflection: What Worked, What the AI Did, What You Would Change
16. Final Test · 34 questions

AI lab. Work with AI throughout the practicum and keep a dated verification log: each AI output, how you checked it, and whether you kept it, fixed it or threw it out.

The first half of the capstone: choose a real insurance problem, research it, and design an AI-assisted solution with its fairness, privacy and compliance plan settled before you build. The term ends with a reviewed design and a working first version.

1. 1Choosing a Capstone Problem That Matters
2. 2Stakeholders: Customers, Staff, Regulators and the Business
3. 3Researching the Problem and Earlier Solutions
4. 4Defining Success and How You Will Measure It
5. 5Data: What You May Use and What You Must Not
6. Test 1 · 13 questions
7. 6Regulatory Scan for Your Capstone
8. 7Designing the Human and AI Roles
9. 8Design Review
10. Test 2 · 21 questions
11. 9Building the First Version
12. 10Test Plan: Accuracy, Fairness and Edge Cases
13. 11Running the First Tests
14. 12Documenting Decisions as You Go
15. 13Studio Review: Design and First Version
16. Final Test · 34 questions

AI lab. Use AI to research earlier approaches and draft the design, verifying every source it cites. Then have the AI argue against your design and record which objections you accepted and why.

The second half: finish, test and document the solution, then present it as you would to an insurer's leadership team or a regulator. Everything the AI did is disclosed, and every claim about results rests on your own tests.

1. 1Second Build: Acting on Test Results
2. 2Fairness Testing in Depth
3. 3Explainability: Writing Reasons People Can Use
4. 4Privacy and Security Review
5. 5The Compliance File: Mapping Rules to Features
6. Test 1 · 13 questions
7. 6User Testing With Customer and Staff Roles
8. 7Measuring Results Honestly
9. 8Limits, Risks and What Could Go Wrong
10. Test 2 · 21 questions
11. 9The Governance File: Inventory, Monitoring and Ownership
12. 10Writing the Capstone Report
13. 11Preparing the Presentation
14. 12Presenting and Taking Hard Questions
15. 13Handing Off: Next Steps and Lessons Learned
16. Final Test · 34 questions

AI lab. Use AI as a red team: ask it to find unfair outcomes, privacy leaks and misleading explanations in your finished work. Reproduce each problem it claims before you fix it, and list the ones that were false alarms.

What managers actually do all week, and how to do it well: setting direction, building trust, delegating and helping people grow. Written for new managers and for experienced ones who want to name what they already do and do it on purpose.

1. 1From Doing the Work to Getting Work Done Through Others
2. 2What People Need From a Manager
3. 3Trust: How It Is Built and How It Breaks
4. 4Motivation Without Gimmicks
5. 5Setting Clear Expectations
6. Test 1 · 13 questions
7. 6Delegation: What to Hand Off, to Whom and How
8. 7One-on-Ones That Matter
9. 8Developing People: Stretch, Support and Growth Plans
10. Test 2 · 21 questions
11. 9Managing Former Peers, Older Colleagues and Remote Staff
12. 10Recognition and Fairness
13. 11Your Own Workload, Calendar and Energy
14. 12Managing Up and Across
15. 13Your Management Operating Rhythm
16. Final Test · 34 questions

AI lab. Role-play a first one-on-one and a delegation conversation with an AI playing a team member with a stated personality and situation. Review the transcript yourself, mark where you were vague or talked over them, and ask the AI only for evidence from the transcript, not praise.

How groups become teams that deliver, and the communication that keeps them there: meetings, writing, listening and hard conversations. You practise the everyday messages managers send and the conflicts they have to settle.

1. 1Groups Versus Teams: Purpose, Roles and Norms
2. 2Psychological Safety in Practice
3. 3Team Charters and Working Agreements
4. 4Hybrid and Remote Teams
5. 5Running Meetings People Do Not Dread
6. Test 1 · 13 questions
7. 6Writing for Managers: Updates, Decisions and Requests
8. 7Listening and Asking Good Questions
9. 8Disagreement and Conflict on a Team
10. Test 2 · 21 questions
11. 9Hard Conversations: Bad News, Missed Commitments and Behavior
12. 10Inclusive and Cross-Cultural Communication
13. 11Briefing Leaders Briefly
14. 12Communicating Through a Crisis
15. 13Team Health Checks and Retrospectives
16. Final Test · 34 questions

AI lab. Draft team messages with AI, then ask it to read each message as three different team members would. Check its readings against what you meant, rewrite wherever tone or facts slipped, and choose the final words yourself.

How to make good decisions with incomplete information, and how to make them ethically when interests collide. You learn simple decision tools, the common traps of judgment, and a way to test a choice against your duties to people inside and outside the organization.

1. 1The Decisions Managers Make, and Which Matter Most
2. 2Framing the Decision Before Choosing
3. 3Options, Criteria and Simple Decision Matrices
4. 4Reading Data Without Being Fooled
5. 5Biases in Judgment and How to Check Them
6. Test 1 · 13 questions
7. 6Deciding With Others: Consult, Consent or Decide
8. 7Reversible and Irreversible Decisions
9. 8Risk, Uncertainty and Pre-Mortems
10. Test 2 · 21 questions
11. 9Ethics at Work: Duties, Consequences and Character
12. 10Conflicts of Interest, Honesty and Speaking Up
13. 11Fairness When Resources Are Scarce
14. 12Using AI as a Second Opinion, Not the Decider
15. 13The Decision Record: Writing Down Why
16. Final Test · 34 questions

AI lab. Have an AI generate options and argue the strongest case against your preferred choice. Verify any fact or number it uses, label which arguments changed your mind, and write the final decision and its reasons in your own words.

Getting defined work done on time and within budget: scope, schedule, risk, people and communication. You learn plan-driven and agile approaches, run a small project from charter to close, and use AI to draft plans that you then check against reality.

1. 1What Makes Something a Project
2. 2Project Charters and Scope
3. 3Work Breakdown and Estimating
4. 4Schedules, Dependencies and the Critical Path
5. 5Budgets and Resources
6. Test 1 · 13 questions
7. 6Risk Registers and Issue Logs
8. 7Stakeholders and Status Reporting
9. 8Agile Basics: Backlogs, Sprints and Retrospectives
10. Test 2 · 21 questions
11. 9Choosing an Approach: Plan-Driven, Agile or Hybrid
12. 10Change Control and Scope Creep
13. 11Vendors and Contracts in Plain Terms
14. 12Recovering a Project in Trouble
15. 13Closing a Project and Capturing Lessons
16. Final Test · 34 questions

AI lab. Ask an AI to draft the work breakdown, estimates and schedule. Check every dependency and estimate with the people who would do the work, or against the simulation's data, and record which AI estimates were off and by how much.

Turning a strategy into work people actually do, and leading the change that requires. You learn to translate goals into priorities and measures, to spot why plans stall, and to lead people through change with honesty about what it will cost them.

1. 1Strategy in Plain Words: Where to Compete and How to Win
2. 2From Strategy to Priorities
3. 3Goals and Measures: Objectives, Key Results and Scorecards
4. 4Resource Allocation and Saying No
5. 5Operating Cadence: Reviews That Drive Action
6. Test 1 · 13 questions
7. 6Why Execution Stalls
8. 7Aligning Teams Across the Organization
9. 8Why Change Is Hard for People
10. Test 2 · 21 questions
11. 9Planning a Change: The Case, the Picture and the Path
12. 10Communicating Change and Listening to Resistance
13. 11Training, Support and Making the New Way Easy
14. 12Making Change Stick and Measuring Adoption
15. 13Leading a Change You Did Not Choose
16. Final Test · 34 questions

AI lab. Use AI to play the people your change affects (a skeptical veteran, a stretched supervisor, an eager new hire) and stress-test your plan. Check whether its objections are realistic by comparing them with real interviews or published case material, then revise.

The people practices with the most legal and human weight: feedback, performance reviews, hiring, leave, complaints and ending employment, with the plain basics of US federal employment law (equal employment opportunity, the ADA, the FLSA and more). It teaches you when to act and when to call HR or a lawyer; it is educational, not legal advice.

1. 1Feedback That Helps: Specific, Timely and Fair
2. 2Performance Conversations and Reviews
3. 3Performance Problems: Documentation and Improvement Plans
4. 4Writing a Job Description That Matches the Real Job
5. 5Structured Interviews and Fair Selection
6. Test 1 · 13 questions
7. 6Equal Employment Opportunity: Title VII, Age Discrimination and Equal Pay
8. 7The ADA at Work: Disability, Accommodation and the Interactive Process
9. 8Wage and Hour Basics: The FLSA, Overtime and Exempt Status
10. Test 2 · 21 questions
11. 9Leave and Pregnancy: The FMLA and the Pregnant Workers Fairness Act
12. 10Harassment, Retaliation and Protected Activity
13. 11Onboarding That Works
14. 12Ending Employment Fairly and Lawfully
15. 13AI in Hiring and Performance: Screening, Monitoring and Bias
16. Final Test · 34 questions

AI lab. Have an AI draft the job description and interview questions, then audit them: flag any question that touches a protected characteristic, any requirement not tied to the real job, and any wording that could put off qualified applicants. Check each legal point against the official agency source, such as the EEOC or the Department of Labor.

How to run a team where AI does part of the work: what to delegate to it, how to oversee it, how to keep quality and fairness, and how to bring people along. Includes the rules forming around automated decisions in hiring and employment, such as New York City's bias-audit law and Illinois's amendments to its Human Rights Act, and how to write a team AI policy people will follow.

1. 1What Changes When AI Joins the Team
2. 2Mapping Work: Tasks for People, Tasks for AI, Tasks for Both
3. 3Delegating to AI: Clear Instructions and Checkable Outputs
4. 4Oversight: Review Levels Matched to Risk
5. 5Quality Control and Error Logs
6. Test 1 · 13 questions
7. 6Data, Privacy and Confidentiality in AI Use
8. 7Fairness in AI-Assisted Hiring and Promotion
9. 8The Rules on Automated Employment Decisions
10. Test 2 · 21 questions
11. 9Monitoring, Trust and Employee Privacy
12. 10Skills, Training and Changing Roles
13. 11Measuring Whether AI Is Actually Helping
14. 12Writing a Team AI Use Policy
15. 13Leading People Who Are Worried About AI
16. Final Test · 34 questions

AI lab. Delegate three real or provided team tasks to an AI with written instructions, review the outputs at the level your plan sets, and keep an error log. Compare the time saved with the time spent checking, and change the review level where the evidence says to.

Applied management work on a real or realistic team, at your workplace, a nonprofit, a volunteer group or in a provided simulation; EducationAI does not arrange or guarantee placements. You scope a problem, act on it with AI tools, keep a verification log, protect people's privacy and present the results to someone able to judge them.

1. 1Choosing a Practicum Setting and Getting Permission
2. 2Scoping a Management Problem Worth Solving
3. 3Confidentiality and Working With People's Information
4. 4Listening First: Interviews and Observation
5. 5Planning the AI Assistance and the Human Checks
6. Test 1 · 13 questions
7. 6First Intervention or First Draft
8. 7Trying It With the Team
9. 8Midpoint Review and Course Correction
10. Test 2 · 21 questions
11. 9Fairness, Legal and Ethics Check
12. 10Second Round: Fixing What You Learned
13. 11Writing the Practicum Report
14. 12Presenting to a Reviewer
15. 13Reflection: What Worked, What the AI Did, What You Would Change
16. Final Test · 34 questions

AI lab. Work with AI throughout the practicum and keep a dated verification log: each AI output, how you checked it, and whether you kept it, fixed it or threw it out.

The first half of the capstone: choose a real management challenge, diagnose it with evidence, and design an intervention with its human and AI roles, fairness and privacy plan settled before you start. The term ends with a reviewed design and first results.

1. 1Choosing a Capstone Challenge That Matters
2. 2Stakeholders and Who Has a Say
3. 3Diagnosing the Problem: Evidence Before Solutions
4. 4Defining Success and How You Will Measure It
5. 5People, Privacy and Permission
6. Test 1 · 13 questions
7. 6Legal and Ethics Scan for Your Capstone
8. 7Designing the Human and AI Roles
9. 8Design Review
10. Test 2 · 21 questions
11. 9Launching the First Version
12. 10Gathering Honest Feedback
13. 11Reading the First Results
14. 12Documenting Decisions as You Go
15. 13Studio Review: Design and First Results
16. Final Test · 34 questions

AI lab. Use AI to research how others have handled similar challenges and to draft the design, verifying every source it cites. Then have the AI argue against your diagnosis and record which objections you accepted and why.

The second half: run the intervention, measure what happened, and write the playbook another manager could follow, then present it as you would to senior leaders. Everything the AI did is disclosed, and every claim about results rests on evidence you gathered.

1. 1Second Round: Acting on What You Learned
2. 2Measuring Results Honestly
3. 3Fairness and Unintended Effects
4. 4Making It Stick: Handover, Training and Ownership
5. 5Writing the Management Playbook
6. Test 1 · 13 questions
7. 6Costs, Time and What It Took
8. 7Limits, Risks and What Could Go Wrong
9. 8Peer and Stakeholder Review
10. Test 2 · 21 questions
11. 9Writing the Capstone Report
12. 10Preparing the Presentation
13. 11Presenting to Leaders
14. 12Taking Hard Questions
15. 13Next Steps and Your Management Philosophy
16. Final Test · 34 questions

AI lab. Use AI as a critical reviewer: ask it to find unfair effects, weak evidence and gaps in your playbook. Check each criticism against your own records before acting on it, and list the ones that did not hold up.

Every operation is a set of processes, and many of them grew up without ever being designed on purpose. This course teaches you to watch work where it happens, map it honestly, measure how it flows and redesign it with the people who do it.

1. 1What operations is: turning inputs into something people value
2. 2Processes, systems and the work nobody wrote down
3. 3Walking the process: watching work where it happens
4. 4SIPOC: suppliers, inputs, process, outputs and customers on one page
5. 5Flowcharts and swimlane maps: who does what, and where work waits
6. Test 1 · 13 questions
7. 6Value stream mapping: following one order from request to delivery
8. 7Measuring a process: cycle time, lead time, throughput and work in progress
9. 8Little's Law: why more work in progress means longer waits
10. Test 2 · 21 questions
11. 9Bottlenecks and the theory of constraints in plain terms
12. 10Value-adding, necessary and wasted steps: sorting the map
13. 11Designing the future state: remove, combine, reorder, simplify
14. 12Standard work and written procedures people actually use
15. 13Rolling out a process change: pilots, training and checking that it stuck
16. Final Test · 34 questions

AI lab. You give AI your interview notes and have it draft the swimlane map, step list and timing table. You then walk the real process, mark every place the AI's map differs from what actually happens, and recompute the timing figures yourself before anything goes in the report.

Quality means the work meets the need, every time, without heroics. You learn the useful ideas from lean and Six Sigma in plain terms (no belts, no jargon contests) and run a real improvement cycle backed by data.

1. 1What quality means: meeting the need, every time
2. 2The cost of poor quality: scrap, rework, returns and lost trust
3. 3Lean ideas in plain terms: value, flow, pull and the eight wastes
4. 45S and visual management: a workplace that shows its own problems
5. 5Mistake-proofing: designing steps so errors can't happen or get caught early
6. Test 1 · 13 questions
7. 6Variation: why the same process gives different results
8. 7Six Sigma ideas in plain terms: define, measure, analyze, improve, control
9. 8Collecting quality data: check sheets, sampling and measurements you can trust
10. Test 2 · 21 questions
11. 9Pareto charts: finding the few causes behind most of the trouble
12. 10Root cause analysis: five whys, fishbone diagrams and testing the cause
13. 11Control charts: telling ordinary variation from a real change
14. 12Plan-Do-Check-Act and kaizen: small improvements run as experiments
15. 13Quality management systems: ISO 9001 and its 2026 revision in plain words
16. Final Test · 34 questions

AI lab. AI suggests likely root causes and builds your Pareto and control charts from your data. You recompute the centre line and limits in a spreadsheet, and you test at least one AI-suggested cause against real evidence before accepting or rejecting it.

Goods and services reach customers through a chain of suppliers, carriers and warehouses, and every link has a cost and a risk. This course covers how demand moves through that chain, how to buy well, and how goods are stored, shipped and moved across borders.

1. 1The supply chain from raw material to customer, and back again
2. 2Demand: forecasts, orders and why they never match exactly
3. 3The bullwhip effect: how small changes in demand grow upstream
4. 4Sourcing strategy: make or buy, one supplier or several
5. 5Finding and vetting suppliers: capability, quality, references and risk
6. Test 1 · 13 questions
7. 6Requests for quotes and proposals: writing a spec suppliers can bid on
8. 7Total cost of ownership: why price is only part of the cost
9. 8Purchase orders, contracts and the three-way match
10. Test 2 · 21 questions
11. 9Managing suppliers: scorecards, reviews and working through problems
12. 10Transportation basics: parcel, less-than-truckload, full truckload, rail, ocean and air
13. 11Warehousing and fulfillment: receiving, putaway, picking, packing and shipping
14. 12Moving goods across borders: Incoterms, customs and tariff rules that change
15. 13Responsible sourcing: labor, environmental and ethical checks on suppliers
16. Final Test · 34 questions

AI lab. AI drafts the request for quote and a first comparison of the quotes. You check every number against the actual quotes, verify supplier claims against independent sources such as business registrations and references, and list every assumption the AI made that you had to correct.

Too much inventory ties up cash and too little lets customers down; too little capacity makes lines and overtime. You learn the working tools for deciding what to stock, when to order, what a process can really make and how to schedule work and people fairly.

1. 1Why inventory exists: buffers, batches and bets on demand
2. 2What inventory costs: holding, ordering, stockouts and obsolescence
3. 3ABC analysis: spending your attention where the value is
4. 4Reorder points and lead time: knowing when to order
5. 5Safety stock: buffering against uncertain demand and supply
6. Test 1 · 13 questions
7. 6Economic order quantity: the classic trade-off and its limits
8. 7Inventory accuracy: cycle counting and fixing record errors
9. 8Capacity: what a process can make, and what it actually makes
10. Test 2 · 21 questions
11. 9Utilization and why running at 100% makes lines grow
12. 10Planning capacity: matching people and equipment to demand
13. 11Scheduling work: priority rules, due dates and sequencing jobs
14. 12Scheduling people: shifts, coverage, fairness and rest
15. 13Sales and operations planning: one plan the whole business agrees on
16. Final Test · 34 questions

AI lab. AI writes the spreadsheet formulas or a short Python script for reorder points, safety stock and the schedule. You hand-calculate two items to confirm the results, test edge cases such as zero demand and a doubled lead time, and check the schedule against the constraints the people on it raise.

In a service the customer is part of the process, so waiting, mistakes and kindness all happen in front of them. This course covers designing services, managing queues and peaks, staffing to demand, recovering from failures and deciding what to hand to self-service and AI.

1. 1What makes services different: the customer is in the process
2. 2Service blueprints: front stage, back stage and the line of visibility
3. 3Customer journeys: the moments that matter and where things break
4. 4Demand in services: peaks, no-shows and reservations
5. 5Queues: why lines form even when capacity looks fine
6. Test 1 · 13 questions
7. 6Managing waits: appointments, arrival rules and how waiting feels
8. 7Staffing to demand: forecasting volume and building rosters
9. 8Service standards and service-level agreements people can keep
10. Test 2 · 21 questions
11. 9Service recovery: fixing a failure and keeping the customer
12. 10Measuring service: response time, resolution, satisfaction and effort
13. 11Self-service and AI assistants: what to hand off and what to keep human
14. 12Field service: visit windows, routes and having the right parts
15. 13Designing a service people trust: accessibility, clarity and consistency
16. Final Test · 34 questions

AI lab. AI drafts the blueprint from your notes and a staffing estimate from volume data. You check the blueprint against an observed visit, recalculate the staffing numbers, and review every AI-drafted customer message for accuracy, tone and accessibility before it is used.

An operation that hurts people or falls over at the first disruption is not well run. You learn OSHA basics in plain terms, how to find and control hazards, and how to plan for the things that go wrong, from a broken machine to a lost supplier; it is educational and never replaces official OSHA text or a qualified safety professional.

1. 1Why safety comes first: people, the law and the cost of getting it wrong
2. 2OSHA basics: the OSH Act, the General Duty Clause, standards and state plans
3. 3Workers' rights and employer duties, including protection from retaliation
4. 4Finding hazards: walkthroughs, job hazard analysis and asking the people doing the work
5. 5The hierarchy of controls: eliminate, substitute, engineer, administer, protect
6. Test 1 · 13 questions
7. 6Common hazards in plain terms: chemicals and hazard communication, stored energy and lockout, falls, heat and PPE
8. 7Recordkeeping and reporting: injury logs and what must be reported within hours
9. 8Incident investigation: near misses, root causes and fixing the system, not blaming the person
10. Test 2 · 21 questions
11. 9Operational risk: what can go wrong, how likely, how bad
12. 10Failure mode and effects analysis in plain terms
13. 11Single points of failure: suppliers, systems, equipment and people
14. 12Business continuity: keeping going through outages, storms and shortages
15. 13Exercises and after-action reviews: practising before it's real
16. Final Test · 34 questions

AI lab. AI drafts the job hazard analysis and risk register. You check every regulatory reference against OSHA's official text on osha.gov or the eCFR, walk each task with the person who does it, and mark every hazard the AI missed or got wrong.

You can't improve what you can't see, and you shouldn't automate what you can't watch. This course covers choosing the few measures that matter, building dashboards people read, and putting automation and AI agents to work with tests, approvals, logs and a person in charge.

1. 1Choosing measures that matter: outcomes, drivers and a few vital signs
2. 2Leading and lagging indicators
3. 3Good data in: definitions, sources and data quality checks
4. 4Designing a dashboard people use: one screen, clear charts, honest scales
5. 5Targets, thresholds and alerts without alarm fatigue
6. Test 1 · 13 questions
7. 6Where automation fits: rules, integrations and robotic process automation
8. 7AI in operations: forecasting, reading documents, triage and drafting
9. 8AI agents: tools, permissions and what an agent can actually do
10. Test 2 · 21 questions
11. 9Human in the loop: approvals, review queues and escalation
12. 10Testing an automation before it touches real work
13. 11Monitoring and logs: catching an agent when it goes wrong
14. 12Governing AI in operations: privacy, security, vendor questions and the NIST AI Risk Management Framework
15. 13Leading the change: roles, training and what people do with the time saved
16. Final Test · 34 questions

AI lab. You build the agent or automation with AI tools, for example one that sorts incoming requests or drafts purchase orders for approval. You write the test cases, review every output in the trial run, measure its error rate on your own test set, and decide which actions it may take alone and which need sign-off.

The practicum puts the whole toolkit to work on a real operation: your workplace, a volunteer organization or a small business that agrees to host you, or a detailed case provided in the course if you have none. You scope a problem, work with the people involved, test a change safely and report honestly on what happened.

1. 1Choosing a practicum setting: your workplace, a volunteer group or a provided case
2. 2Permission, confidentiality and handling real people's data
3. 3Scoping the problem: a charter with goal, boundaries and owner
4. 4Stakeholder interviews: what people need from a change, and what they fear
5. 5The baseline: measuring how the process performs today
6. Test 1 · 13 questions
7. 6Finding causes with the people who do the work
8. 7Choosing countermeasures: impact, effort and risk
9. 8Planning a small, safe test
10. Test 2 · 21 questions
11. 9Running the test and keeping a daily log
12. 10Reading the results honestly, including what didn't work
13. 11Standardizing what worked: procedures, training and visual controls
14. 12Handing off: owner, measures and a check-in schedule
15. 13The practicum report: telling the story with evidence
16. Final Test · 34 questions

AI lab. AI works as your analyst and scribe: it summarizes interview notes (with consent and names removed), drafts the charter and analyses the before-and-after data. You check every quote against your notes, recompute the key figures, and keep a log of what the AI did and what you changed.

The first capstone term takes one real operation from proposal to a pilot-ready design. You map and measure the whole system, find what limits it, design the future state with its people, and plan a safe pilot that includes a supervised AI or automation layer.

1. 1Choosing a capstone operation worth improving
2. 2The capstone proposal: problem, scope, measures and limits
3. 3Mapping the whole system: process, suppliers, people and data
4. 4Building the baseline dashboard
5. 5Diagnosing: bottlenecks, quality losses and waste
6. Test 1 · 13 questions
7. 6Safety and risk review before any change
8. 7Designing the future-state operation
9. 8Planning inventory, capacity or staffing for the new design
10. Test 2 · 21 questions
11. 9Designing the AI and automation layer with human checkpoints
12. 10Costs, benefits and what you will deliberately leave alone
13. 11Peer and mentor review of the design
14. 12The pilot plan: scope, success measures, stop rules and rollback
15. 13Design review: making the case for the pilot
16. Final Test · 34 questions

AI lab. AI drafts the maps, the baseline dashboard and the pilot plan from your notes and data, and the AI teacher plays a skeptical reviewer who challenges the design. You validate each draft against observation and source data, and record which challenges changed the design.

The second capstone term runs the pilot, measures it honestly and leaves the operation in the hands of an owner who can keep it going. You finish with a case study and a presentation that shows what changed, what didn't and why.

1. 1Pilot readiness: people, materials, data and safety
2. 2Launching the pilot and the daily stand-up
3. 3Tracking the pilot on the dashboard
4. 4Handling surprises: the issue log and quick fixes
5. 5Checking the AI layer: sampling outputs and logging overrides
6. Test 1 · 13 questions
7. 6Mid-pilot review: keep, change or stop
8. 7Measuring results against the baseline
9. 8Separating real improvement from noise
10. Test 2 · 21 questions
11. 9Standardizing: procedures, training and controls
12. 10The hand-off package: owner, measures, risks and review dates
13. 11Writing the case study: what changed, what didn't and why
14. 12Presenting to the people who decide
15. 13Retrospective: what you would do differently and what comes next
16. Final Test · 34 questions

AI lab. The AI layer you designed runs in the pilot under your supervision. You sample its outputs every day, log each time a person overrode it and why, and use that log to decide whether it stays, changes or comes out before hand-off.

A venture needs a problem people care enough about to pay to solve, and no amount of effort makes up for a weak one. This course teaches you to find problems worth solving, talk to the people who have them, and decide honestly whether to go on.

1. 1What a venture is: solving a problem people will pay to have solved
2. 2Where good problems come from: your work, your community and your own frustrations
3. 3Founder-problem fit: why you, and why this problem
4. 4Sizing a problem honestly: who has it, how often and how much it hurts
5. 5Writing a problem statement and the assumptions underneath it
6. Test 1 · 13 questions
7. 6Customer discovery: learning before building
8. 7Finding people to talk to without spamming anyone
9. 8Interviews that work: asking about what people did, not what they might do
10. Test 2 · 21 questions
11. 9Listening for pain, workarounds and what people already spend
12. 10Taking notes and sorting what you heard
13. 11Jobs to be done: what customers are really trying to get done
14. 12Competitors and alternatives, including doing nothing
15. 13Deciding: go on, change the problem or walk away
16. Final Test · 34 questions

AI lab. AI helps draft the interview guide and sorts your notes into themes. You check the guide for leading questions, confirm that every theme is backed by real quotes in your notes, and never let AI-invented customer personas stand in for real conversations.

An offer is what you sell, to whom, and what it promises; a business model is how money flows so the venture can last. You learn to shape an offer, set and test a price honestly, and choose a model that fits your customers and your life.

1. 1From problem to offer: what you sell, to whom and what it promises
2. 2The value proposition: outcomes, not features
3. 3Packaging: products, services, subscriptions and bundles
4. 4Guarantees, terms and promises you can keep
5. 5Pricing basics: cost-plus, competitor-based and value-based
6. Test 1 · 13 questions
7. 6Talking about price with customers without guessing
8. 7Price tiers and anchoring, without fake discounts or false urgency
9. 8Business models: how the money flows
10. Test 2 · 21 questions
11. 9One-time sales, subscriptions, services, marketplaces and licensing
12. 10The business model canvas: the whole idea on one page
13. 11Channels and partners: how customers find you and buy
14. 12Testing a price: pre-orders, deposits and small experiments
15. 13Choosing your first model and writing it down
16. Final Test · 34 questions

AI lab. AI generates offer variations and a pricing model. You check every cost and margin by hand, delete any claim the AI wrote that you can't back up, and test the offer with real prospects rather than AI-simulated ones.

A minimum viable product is the smallest real thing that tests your riskiest assumption with real people. You learn to choose what to test, build it quickly with AI and no-code tools, check what the AI built, and decide what to do with what you learn.

1. 1What a minimum viable product is, and what it is not
2. 2Picking the riskiest assumption to test first
3. 3Kinds of MVP: landing page, concierge, behind-the-curtain, pre-sale and prototype
4. 4Scoping hard: the one thing it must do
5. 5Building with AI tools: no-code, low-code and AI-written code
6. Test 1 · 13 questions
7. 6Checking AI-built work: testing, security basics and reading what it made
8. 7Design that's good enough: clear, accessible and trustworthy
9. 8Getting it in front of real people
10. Test 2 · 21 questions
11. 9Measuring what matters: sign-ups, first use and coming back
12. 10Watching people use it: feedback loops that tell the truth
13. 11Build, measure, learn: deciding what to change
14. 12When to stop, pivot or keep going
15. 13From MVP to version one: what to rebuild properly
16. Final Test · 34 questions

AI lab. You build the MVP with AI coding or no-code assistants. You read and test what they produced, run a checklist (forms work, payments in test mode, accessibility basics, a privacy notice that matches what you collect), and list every AI-made part you checked.

Early customers come from conversations, not from big budgets. This course covers honest selling, the first customers, positioning, content and a small set of channels done well, plus the consent and disclosure rules that apply when you market by email, text and reviews.

1. 1Selling as helping: finding the fit, not forcing it
2. 2Your first ten customers: who they are and where they gather
3. 3Outreach that respects people: warm introductions, communities and honest cold email
4. 4The sales conversation: questions, listening and a clear next step
5. 5Handling objections and hearing a real no
6. Test 1 · 13 questions
7. 6Proposals, quotes and closing cleanly
8. 7Positioning: owning one clear idea in a customer's mind
9. 8Content that earns attention: writing, video and showing your work
10. Test 2 · 21 questions
11. 9Search, social and email: picking one or two channels and doing them well
12. 10Email and text rules in plain terms: consent, CAN-SPAM and the TCPA
13. 11Endorsements, reviews and testimonials: what the FTC's rules require
14. 12Tracking cost per customer and what is actually working
15. 13A 90-day marketing plan you can run alone
16. Final Test · 34 questions

AI lab. AI drafts outreach messages, content and a channel plan. You fact-check every claim, personalize each message before sending (no mass AI spam), check consent, unsubscribe and disclosure rules, and label AI-made images where it matters.

Founders need to know whether each sale makes money and whether the bank account will last. You learn the three financial statements in plain words, unit economics, break-even and a 13-week cash forecast, and the main funding options plainly; it is educational, not tax, accounting or investment advice.

1. 1The three statements in plain words: profit and loss, balance sheet and cash flow
2. 2Costs: fixed, variable and the ones founders forget
3. 3Gross margin and contribution margin
4. 4Unit economics: what one sale or one customer really earns
5. 5Acquisition cost, lifetime value and payback, used with care
6. Test 1 · 13 questions
7. 6Break-even: how many sales cover the costs
8. 7Cash flow: why profitable businesses still run out of money
9. 8Building a 13-week cash forecast
10. Test 2 · 21 questions
11. 9Bookkeeping habits, a separate business account and money set aside for taxes
12. 10Bootstrapping and putting revenue first
13. 11Loans, lines of credit and SBA-backed lending in plain terms
14. 12Investors in plain terms: friends and family, angels, venture capital and crowdfunding
15. 13Reading a term sheet at a plain level, and when to call a lawyer or accountant
16. Final Test · 34 questions

AI lab. AI builds the spreadsheet model and explains each formula. You audit every formula, reconcile the figures to actual bank and sales records (or the provided case), run the best and worst cases yourself, and never send AI-produced numbers to a lender or investor unchecked.

Legal basics protect you, your customers and the people you work with, and ethics is how trust gets built on purpose. This plain-level course covers entities, contracts, hiring rules, intellectual property, privacy and consumer protection, with current notes such as FinCEN's August 2026 rule ending beneficial ownership reports for companies formed in the US; it is educational, not legal advice, so see a lawyer licensed in your state.

1. 1Why legal basics matter, and when to see a lawyer
2. 2Entity types: sole proprietorship, partnership, LLC and corporations
3. 3Taxes and entity choice at a plain level, including the S corporation election
4. 4Forming the business: state registration, a free EIN from the IRS, licences and permits
5. 5Contracts: what makes one, and the clauses to read twice
6. Test 1 · 13 questions
7. 6Working with clients and vendors: scopes of work, payment terms and signatures
8. 7Contractors or employees: classification, hiring paperwork and why it matters
9. 8Intellectual property basics: trademarks, copyright, patents and trade secrets
10. Test 2 · 21 questions
11. 9AI and ownership: what you can protect, and reading AI tools' terms of service
12. 10Privacy and data: collect less, protect it and say what you do
13. 11Consumer protection: honest advertising, pricing and subscriptions
14. 12Ethics for founders: honesty with customers, investors, workers and yourself
15. 13Building trust on purpose: policies, promises and owning mistakes
16. Final Test · 34 questions

AI lab. AI summarizes contracts and drafts plain-language policies. You check every statement against the official source (your secretary of state, the IRS, the USPTO, the U.S. Copyright Office, the FTC), mark everything that needs a lawyer, and never treat the AI's answer as legal advice.

An online store is a shopfront, a checkout, a warehouse and a customer-service desk in one. You learn to build and run a store on a hosted platform such as Shopify (named as an example, not an endorsement): honest product pages, payments, shipping, returns, sales tax, sourcing, speed, accessibility and the weekly numbers.

1. 1Ways to sell online: your own store, marketplaces and social selling
2. 2Choosing a hosted store platform: themes, apps, fees and lock-in
3. 3Store structure: home page, collections, product pages, cart and checkout
4. 4Product pages that sell honestly: photos, descriptions, specs and sizes
5. 5Payments, fraud and card security (PCI DSS) in plain terms
6. Test 1 · 13 questions
7. 6Shipping, delivery promises and the FTC's mail and internet order rule
8. 7Returns, refunds and customer service policies
9. 8Sales tax online: nexus, collection and getting help
10. Test 2 · 21 questions
11. 9Sourcing: making, wholesale, print-on-demand and dropshipping
12. 10Store speed, accessibility and search basics
13. 11Email, abandoned carts and repeat customers
14. 12Reading store analytics: conversion rate, average order value and returning customers
15. 13Running the store with AI: catalog updates, support drafts and weekly reports, all checked
16. Final Test · 34 questions

AI lab. AI drafts product descriptions, image alt text, policies and theme changes. You check every product fact against the real item, make sure each policy matches what you will actually do, test checkout end to end in test mode, and run an accessibility check before launch.

The practicum takes the venture work into the world: your own small venture, or a small business or nonprofit that agrees to host you. You make and deliver a real offer, keep honest records, fix what breaks and report what happened.

1. 1Choosing a practicum: your own venture or a small business that agrees to host you
2. 2Ground rules: permission, confidentiality, honesty and keeping people safe
3. 3Setting practicum goals you can measure
4. 4A weekly rhythm: plan, do, review
5. 5Customer conversations in the field
6. Test 1 · 13 questions
7. 6Making and delivering a real offer
8. 7Taking payment and keeping records
9. 8Keeping the promise: delivery, support and follow-up
10. Test 2 · 21 questions
11. 9Gathering feedback and reviews honestly
12. 10Reviewing the numbers: revenue, costs and time spent
13. 11Fixing what broke
14. 12Presenting to your host or an advisor
15. 13The practicum report: evidence, lessons and next steps
16. Final Test · 34 questions

AI lab. AI drafts weekly summaries, follow-up messages and the numbers review from your logs. You check each summary against what actually happened, send nothing to a customer without reading it, and record in the AI-use note what you changed.

The first capstone term takes one venture to launch, run by you with AI helpers you supervise. You set up the team of one (what you do, what AI does, what you hire out), get the business ready and launch.

1. 1Choosing the capstone venture: continue, pivot or start fresh
2. 2The capstone plan: problem, customer, offer, model and measures
3. 3The team of one: what you do, what AI does and what you hire out
4. 4Setting up AI helpers: assistants, agents and skill cards for routine work
5. 5Writing procedures so AI helpers and contractors do it your way
6. Test 1 · 13 questions
7. 6Checking AI work every week: a review routine you will keep
8. 7Tools and costs: a lean stack you can afford
9. 8Getting the business ready: entity, accounts, records and policies
10. Test 2 · 21 questions
11. 9Building the launch version of the product, service or store
12. 10The launch plan: audience, message, channels and date
13. 11Pre-launch tests: payments, delivery and support
14. 12Launch week
15. 13First-week review: what the numbers and customers say
16. Final Test · 34 questions

AI lab. You set up AI helpers for routine work such as support drafts, catalog updates and bookkeeping entries, each with a written procedure. You review a sample of their work every week, log what you corrected, and keep anything customer-facing or money-related behind your own approval.

The second capstone term runs the venture, measures it honestly and ends with a decision about growth, hiring and funding that you can defend. You present the venture and what you learned.

1. 1The operating rhythm: weekly numbers, customer notes and decisions
2. 2Unit economics from real data
3. 3Improving the offer from what customers say
4. 4Growth levers: more customers, bigger orders, more often
5. 5Choosing growth that won't break the business or you
6. Test 1 · 13 questions
7. 6Systems before people: what to write down before you hire
8. 7Your first hire or contractor: role, pay, the law and onboarding
9. 8Leading a small team: expectations, feedback and fairness
10. Test 2 · 21 questions
11. 9The funding decision: stay bootstrapped, borrow or raise
12. 10Risk check: legal, money, operations and reputation
13. 11The pitch to a lender or investor, told honestly
14. 12The capstone presentation
15. 13Retrospective and the next twelve months
16. Final Test · 34 questions

AI lab. AI builds the weekly dashboard from your sales and cost records and drafts the decision memo. You reconcile the dashboard to the records, test the memo's assumptions against your own numbers, and write the final decision yourself.