From MBA generalist to marketing AI specialist: the depth a brand interviewer actually tests for
For MBA students leaning into marketing who want to walk out of a finals round with a built project, a named deployment map, and the specialist-level answers a brand head or CMO cannot ignore.
29 chapters, 90 lessons
14-day refund on the yearly plan. Real pricing on the plans page, no surprises.
90
lessons, yours to run
Easy to pick up, built to get you the next job
No coding background needed, and you write code with Claude as you go.
Every section below turns into something a hiring manager recognises, not just notes.
Speak a brand head's language, not a textbook's
- You'll map what marketing functions at companies like Banyan Foods have actually deployed in production, not what vendors claim is possible, so you can name real levers in a real interview room.
- You'll learn to distinguish a genuine marketing AI deployment from a pilot that never scaled, the exact question a specialist interviewer asks and most candidates cannot answer.
- No coding background needed: every exercise runs in a frontier-model conversation or a no-code tool, with plain steps from lesson one.
Build something a hiring manager can actually see
- You'll construct a real marketing AI project on the Banyan Foods and Beverages case, the same brand your cohort has tracked since Course 1, so your work has context a recruiter can follow.
- You'll produce a finished artifact, not a slide deck of intentions, that you can open on a laptop in a finals room or attach to a job application as portfolio proof.
- Every build step is designed for someone who has never written a line of code and does not need to.
Interrogate vendor claims and AI cost economics the way a client would
- You'll learn to read a vendor's claimed accuracy number and ask the three follow-up questions that expose whether it was measured properly, skills a brand team or consulting engagement manager will pay for immediately.
- You'll apply honest evidence standards to marketing AI ROI claims, so you can advise a client or employer without being caught out by a number that does not survive one level of scrutiny.
- You'll practise the economics of deploying a frontier model in a marketing context, cost per output, scale thresholds, and where the business case actually holds.
Defend your work at finals-round standard
- You'll prepare and deliver a specialist defence of your Banyan marketing project, structured for the exact follow-up sequence a consulting engagement manager or brand head uses to separate generalists from specialists.
- You'll package your project, your deployment map, and your evidence standards into a CV-ready narrative that names specific capabilities the way a hiring manager recognises them.
- You'll practise before-and-after answer pairs so you can point, under pressure, to exactly what changed between your Course 1 generalist answer and your Course 2 specialist one.
Finish this course and you can do all of this, no prior background required:
- You'll be able to map named marketing AI deployments in production and explain what each one replaced, the answer a brand-side or consulting interviewer reaches for in a finals round.
- You'll be able to interrogate a vendor's accuracy or ROI claim with specific follow-up questions about measurement granularity, test periods, and failure cases, a skill a marketing analytics or consul
- You'll be able to build and present a no-code marketing AI project on a real case, giving you a portfolio artifact that answers 'show me something you've built' without requiring any prior coding expe
- You'll be able to run the cost economics of a frontier-model deployment in a marketing context and state clearly where the business case holds and where it does not, the kind of honest framing a clien
- You'll be able to defend your project under specialist follow-up, fielding questions about evidence standards, scale assumptions, and named levers without retreating to generalist filler.
- You'll be able to frame your AI capabilities in CV and interview language a hiring manager in marketing, brand strategy, or consulting recognises and values.
90 lessons, 29 chapters
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You finished Course 1 with a solid grasp of what AI can and cannot do for a business, and it got you through the shortlist round. But the finals room is different: the interviewer is a brand head or a consulting engagement manager who has actually run marketing campaigns, briefed creative agencies, and sat through vendor pitches claiming AI will double your conversion. They are not asking whether you know AI exists. They are asking which named lever, in which named marketing function, you would pull first at Banyan Foods, and why the vendor's evidence supports it. A generalist answer that worked at the SIP stage runs out of road in about four seconds. Meanwhile, your classmates who interned in marketing can already name real deployments. You need the same depth, built from a real project,
What you'll be able to do, module by module
Every module leaves you able to build and run something real. Here is the syllabus, in the order you work through it.
Curriculum
29 chapters · 90 lessons
Orientation (Pre-Week 1)
You can articulate, on the record, the exact gap between a generalist answer and a specialist one, and you can point to a specific missing fact in your own answer before Week 1 begins.
0.1 The Specialist Bar4 items
- 0.1.1: Why Course 1 fluency survives the generalist and dies against the specialist🔒
- 0.1.2: The four killer questions (one per track) and what answering from experience sounds like🔒
- 0.1.3: Choosing your track honestly: the placement-market map by function🔒
- 0.1.4: What carries forward from Course 1: retrieving your own Banyan assessment and board memo🔒
0.2 Re-Entering Banyan — In Role4 items
- 0.2.1: Your seat, your boss, your mandate: the track's engagement brief🔒
- 0.2.2: The track's stated ask, and the Course 1 workbook entries that already hint it's not the whole story🔒
- 0.2.3: The track's stakeholders and the one relationship that will decide your success🔒
- 0.2.4: The Banyan data pack for your track: what you've been given, first inspection🔒
0.3 The 4-Week Roadmap3 items
- 0.3.1: The shape of every track: landscape, build, deepen, defend, and why that order🔒
- 0.3.2: The 4 artifacts, and why the built project with its failure section is the CV centerpiece🔒
- 0.3.3: The honest time budget: 6 to 8 hours a week and where they go🔒
0.4 Toolkit Setup5 items
- 0.4.1: The track's tool stack at the no-code ceiling: what to sign up for and what each tool is for🔒
- 0.4.2: Loading the Banyan pack into a frontier-model project and verifying it answers from the documents🔒
- 0.4.3: First smoke test: one small track-specific task run end to end🔒
- 0.4.4: Troubleshooting: access, upload limits, and the free-tier reality🔒
- Orientation Quiz: The Role, the Client, the Marketing MandateQuiz
Week 1: The Landscape — What Your Function Has Actually Deployed
1.1 The State of AI in Your Function3 items
- 1.1.1: The deployment map: what's live, at which named companies, with what published results🔒
- 1.1.2: The graveyard: named or well-documented failures in this function, and the pattern behind them🔒
- 1.1.3: Classical ML vs. GenAI in this function: which problems belong to which🔒
1.2 The Function's Real Levers3 items
- 1.2.1: Where money is actually made or lost in this function, mapped for a company Banyan's size🔒
- 1.2.2: Which levers AI can touch, which it can't, and the fit test re-run at function depth🔒
- 1.2.3: Practice: three real company moves in this function, audited as substance or theater🔒
1.3 Reading the Stated Ask3 items
- 1.3.1: Your track's brief re-read against the levers: what's missing, what's assumed🔒
- 1.3.2: The discovery pass: interrogating the Banyan pack for the real problem🔒
- 1.3.3: The reveal lands: the track-specific truth under the stated ask, found by your own inspection🔒
1.4 The Vendor and Startup Map3 items
- 1.4.1: Who sells into this function: categories, named players, and what each actually delivers🔒
- 1.4.2: Build-buy-wrap re-run at function depth for the track's opportunity🔒
- 1.4.3: The questions that expose a weak vendor, drafted for later use🔒
1.5 Case Study Checkpoint: The Corrected Brief3 items
- 1.5.1: Writing the revised problem statement for your track, the Week 1 reveal made formal🔒
- 1.5.2: Pressure-test: the model as your track's boss, pushing back on the correction🔒
- 1.5.3: Cross-track sidebar: what the other three functions found in the same company🔒
1.6 Week 1 Portfolio Artifact: The Function AI Landscape Brief3 items
- 1.6.1: The two-pager: deployment map, graveyard patterns, vendor map, and your corrected Banyan brief🔒
- 1.6.2: The 12-deployment example bank, started🔒
- 1.6.3: Checklist: current examples only, every claim sourced, survives the specialist's two follow-ups🔒
Week 2: The Build — Your Project Starts
2.1 The Core Tool Skill3 items
- 2.1.1: The track's core workspace: documents/data in, structured output out, with the model as analyst🔒
- 2.1.2: The quality gate: how this function judges output🔒
- 2.1.3: First real run on the Banyan pack, and the planted data trap that must be caught here🔒
2.2 Building v13 items
- 2.2.1: Scoping the smallest version that proves the concept for your track's corrected brief🔒
- 2.2.2: The build, step by step, at the no-code ceiling🔒
- 2.2.3: Testing it honestly: designing your own checks before admiring the output🔒
2.3 What It Got Wrong — The First Failure Log3 items
- 2.3.1: Running the v1 against realistic cases until it breaks, and cataloguing how🔒
- 2.3.2: Classifying the failures: data problems vs. model limits vs. your setup🔒
- 2.3.3: The fix pass: what improved, what's inherent, what needs a human in the loop🔒
2.4 The Week 2 Twist3 items
- 2.4.1: The track's twist lands (see CANON.md Section 5 for the exact locked twist)🔒
- 2.4.2: Handling it with the Week 1-2 machinery: re-scope, add a gate, flag the bias, fix the measurement🔒
- 2.4.3: The incident note: documenting the twist and response in the professional format the function uses🔒
2.5 Case Study Checkpoint: v1 Demo to Your Boss3 items
- 2.5.1: The demo structure: what works, what it got wrong, what's next, failures shown on purpose🔒
- 2.5.2: Recorded 3-minute demo, then the model as boss asking the uncomfortable questions🔒
- 2.5.3: Cross-track sidebar: the other functions' twists🔒
2.6 Side Quest: Your Build in a Different Industry3 items
- 2.6.1: Re-pointing the v1 pattern at a second industry🔒
- 2.6.2: What transferred, what didn't, and why🔒
- 2.6.3: The one-page transfer note, a second interview story earned🔒
2.7 Week 2 Portfolio Artifact: The Working v1 + Failure Log4 items
- 2.7.1: Packaging the build with its documented failure section🔒
- 2.7.2: The honest README: what it does, what it doesn't, what a real deployment would need🔒
- 2.7.3: Checklist: reproducible by a stranger, failures documented, no overclaims🔒
- Week 2 Quiz: The Landscape and the BuildQuiz
Week 3: Depth — Economics, Evidence, and the Vendor Gauntlet
3.1 Costing It Properly3 items
- 3.1.1: The full cost model for your track's project at Banyan scale🔒
- 3.1.2: The benefits case built honestly: what's measurable, what's assumed, labeled🔒
- 3.1.3: The spreadsheet: a defensible one-tab model the CFO could audit🔒
3.2 Evidence Standards3 items
- 3.2.1: What counts as proof in this function: pilots vs. backtests vs. A/B results vs. case studies🔒
- 3.2.2: Reading a vendor's evidence like a specialist🔒
- 3.2.3: Designing the pilot that would actually prove your track's case🔒
3.3 The Vendor Gauntlet3 items
- 3.3.1: The Week 3 twist: a real-looking vendor pitch for your track's problem, deliberately 80% credible🔒
- 3.3.2: The evaluation: your Week 1 questions deployed, the pitch's three weaknesses found🔒
- 3.3.3: The recommendation: buy, pilot, or pass, with reasoning a specialist would sign🔒
3.4 The Regulation and Risk Layer at Depth3 items
- 3.4.1: The track's specific exposure🔒
- 3.4.2: The controls that let the project proceed anyway🔒
- 3.4.3: Updating the project's risk section, retrieving Course 1's risk framework🔒
3.5 Case Study Checkpoint: The Stress Test3 items
- 3.5.1: The confrontation, per track🔒
- 3.5.2: Defending with the Week 3 machinery🔒
- 3.5.3: What you conceded and why, the maturity move, documented🔒
3.6 Week 3 Portfolio Artifact: The Business Case Pack4 items
- 3.6.1: Cost model plus benefits case plus pilot design plus vendor evaluation, assembled🔒
- 3.6.2: Every assumption labeled, every number sourced or flagged as estimate🔒
- 3.6.3: Checklist: auditable by a hostile CFO, no unlabeled assumptions🔒
- Week 3 Quiz: Depth, Evidence, and the Agency FightQuiz
Week 4: Defend and Package — The Finals-Ready Finish
4.1 The Final Build Pass3 items
- 4.1.1: v2: incorporating the Week 3 stress-test outcomes🔒
- 4.1.2: The final failure log: everything it still gets wrong, honestly ranked🔒
- 4.1.3: Freezing the artifact: version, date, and the if-I-had-another-month section🔒
4.2 Case Study Checkpoint: The Final Readout3 items
- 4.2.1: The Week 4 twist: your slot is halved and a senior skeptic joins🔒
- 4.2.2: The recorded final readout, then the model as the senior skeptic🔒
- 4.2.3: The engagement retro: original ask vs. corrected brief vs. what got built🔒
4.3 CV Language and the Verification Gauntlet3 items
- 4.3.1: The exact CV lines this project supports, written to survive campus verification🔒
- 4.3.2: The verbal defense of each line: 30-second and 2-minute versions, recorded🔒
- 4.3.3: The overclaim audit: what you must NOT say🔒
4.4 The Specialist Question Bank3 items
- 4.4.1: The track's real finals-round question pool, mapped to which unit answers each🔒
- 4.4.2: The follow-up trees: where each question goes at depth🔒
- 4.4.3: Practice round: five specialist questions cold, recorded, self-scored🔒
4.5 Staying Current in Your Function4 items
- 4.5.1: The track's source list: what to read weekly for 15 minutes🔒
- 4.5.2: Refreshing the example bank: the maintenance habit through placement season🔒
- 4.5.3: The lateral-role map: post-MBA AI-adjacent titles emerging in this function🔒
- Course Final: The Marketing Track End to EndQuiz
4.6 Week 4 Portfolio Artifact: The Packaged Project3 items
- 4.6.1: The full package: built project, failure log, business case, readout recording, CV lines🔒
- 4.6.2: The one-page project summary for the placement file🔒
- 4.6.3: Checklist: a stranger can understand it in 5 minutes; a specialist can probe it for 20🔒
Employers hiring into marketing, brand strategy, and consulting are increasingly shortlisting candidates who can name a real deployment, interrogate the evidence, and show a built artifact, not just candidates who can explain what an LLM is. Stacking this course's specialist depth on top of Course 1's foundation puts you in the small group who can do both, which is the group a hiring manager calls
From subscribing to running your first artifact
Subscribe and pick up exactly where Course 1 left off
Log in and open the Orientation module. Your Banyan Foods and Beverages case file carries over from Course 1, so you are not starting from a blank slate. The first exercise runs in the same frontier-model tool you already know, no new software to install.
Open the course and build your marketing deployment map in Week 1
Week 1 walks you through what marketing functions have actually deployed in production, with named examples and honest evidence standards. You use no-code tools to audit real claims and start building the specialist vocabulary that Week 2's project depends on.
Build your Banyan marketing project and defend it at finals-round standard
Weeks 2, 3, and 4 take you from project brief to finished artifact to a rehearsed specialist defence. You interrogate the economics, apply evidence standards to vendor claims, and package everything into a CV-ready portfolio piece you can open in any interview room.
Banyan Foods gets a vendor pitch: can you defend the recommendation?
The scenario lands
Banyan's CMO has received a pitch from a marketing AI vendor claiming their tool will improve campaign targeting accuracy. You are the analyst on the engagement. The engagement manager asks you to assess the claim before the next client call.
You apply the evidence standard
Using the framework you built in Week 3, you ask three specific questions: Is the accuracy figure measured at the audience-segment level or blended across the whole campaign? What does the model do around a demand event like a festival launch, the exact window where naive targeting models fail? Can you see the segments where the backtest was wrong, not only the average? You log the vendor's responses against each question.
You run the economics
You use your Week 3 cost-economics worksheet to calculate the per-output cost of running the vendor's tool at Banyan's campaign volume, compare it against the claimed uplift, and identify the scale threshold at which the business case holds. You note one scenario where it does not.
You brief the engagement manager
You produce a short written assessment naming the one claim that holds under scrutiny, the one that does not, and the single piece of missing evidence Banyan should request before signing. Your language is specific: a named metric, a named campaign type, a named test window.
You defend it under follow-up
The engagement manager fires the specialist follow-up from your Week 4 defence rehearsal. You answer with the named lever, the evidence standard you applied, and the honest gap you acknowledged. No generalist filler. The answer slows down under pressure because you are retrieving something you actually built.
The toolkit you'll build and run in this program
You leave with working assets, not notes.
Real things you can demo in an interview.
A finished no-code marketing AI project built on the Banyan Foods and Beverages case, ready to open in any interview room as a live portfolio artifact.
A named marketing AI deployment map covering what functions have actually shipped in production, with honest evidence notes you built yourself.
A vendor interrogation framework: a set of specific follow-up questions for marketing AI claims, tested against real scenarios in Week 3.
A cost-economics worksheet for a frontier-model marketing deployment, completed on the Banyan case with your own assumptions and conclusions.
A finals-ready specialist defence, rehearsed and logged with before-and-after answer pairs you can reference in a CV conversation.
You'll get the most from this if
MBA students in their second year who completed Course 1 and are targeting marketing, brand strategy, or consumer goods roles where AI fluency is now expected at the finals stage.
MBA students preparing for consulting interviews on marketing or commercial mandates who need specialist depth, not just a capability map.
Career switchers using an MBA to move into marketing or brand roles and who want a built project to answer 'show me something you've done with AI.'
Any MBA student who found Course 1 useful but recognised their answers still ran out of road when a specialist pushed one level deeper.
Yearly plan: 14-day keep-the-system guarantee
14-day refund on the yearly plan. Finish the first two modules, and if you haven't run your first live pipeline, take a full refund.
Straight answers before you subscribe
Do I need a coding or technical background to take this course?
No. Every build exercise uses no-code tools and frontier-model conversations with plain step-by-step instructions. The course is designed on the assumption that you have never written a line of code, and the hands-on work starts in lesson one, not after a theory module.
Does this work if I did not take Course 1 of the trilogy?
Course 2 continues the Banyan Foods and Beverages case and assumes you are familiar with the foundational fit-test framework and the 60-second explainer habit from Course 1. If you completed Course 1, you pick up exactly where you left off. If you have not, we recommend starting there so the Orientation exercises make full sense.
What is the refund policy?
You have 14 days from the date of purchase to request a full refund, no questions asked. If the course is not the right fit for your stage or track, reach out within that window and we will sort it.
How does the course stay current if the AI landscape moves fast?
The curriculum is reviewed and updated when named deployments, evidence standards, or tool availability change materially. As a subscriber you receive those updates automatically, so your deployment map and vendor interrogation framework reflect what is actually live, not what was live when the course first launched.
Can I use the project and artifacts I build here in job applications and client work?
Yes. The Banyan case project, the deployment map, and the vendor assessment you produce are yours to use as portfolio proof in interviews, on your CV, and in professional work. The course is designed specifically so that the artifacts you build are ready to show, not just exercises you complete and file away.