Walk into SIP season with defensible AI fluency, not borrowed buzzwords
For first-year MBA students who want to answer the AI question confidently in any interview room, finish with a board-ready memo, and never get caught out on a second follow-up.
28 chapters, 91 lessons
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91
lessons, yours to run
Easy to pick up, built to get you the next job
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Every section below turns into something a hiring manager recognises, not just notes.
Understand how the machine actually works, in plain English
- You'll explain what a large language model is doing when it generates text, in language a sceptical interviewer or partner will respect, with zero maths or code
- You'll describe why models hallucinate and where that makes their output trustworthy or dangerous, so you can answer a second or third follow-up without flinching
- You'll build this from the first lesson, no technical background needed, just a browser and the willingness to think carefully
Read the business case for AI the way a hiring manager expects
- You'll assess where AI genuinely earns its place in a business and where the pitch is ahead of the evidence, using the Banyan Foods and Beverages case that runs through every week
- You'll name the specific layer of work AI changes in consulting, marketing, and finance roles, which is exactly what domain-specific interview questions are probing for
- You'll frame AI's business value in terms a hiring manager or client would recognise, not in terms borrowed from a tech blog
Argue both sides under pressure, including the confident no
- You'll identify the two interview failure modes, the buzzword answer and the engineer's answer, and practise the alternative that survives a second question
- You'll defend a limit on AI capability calmly and specifically, the rarest and most senior-sounding answer in any SIP room
- You'll stress-test your own answers using a live chat model as a sceptical interviewer before you walk into the real thing
Finish with a board memo you can show in an interview
- You'll produce a structured board memo on the Banyan Foods and Beverages case that ties together every concept from the four weeks into one polished, defensible artefact
- You'll leave with a real piece of work a recruiter can read, not a certificate of completion, proof that the capability is there and ready to deploy on day one of an internship
Finish this course and you can do all of this, no prior background required:
- You'll be able to explain how a large language model generates output and where it fails, at the level a strategy interviewer expects, with no technical background required to get there
- You'll be able to name the specific layer of work AI is already changing in your target domain, consulting, marketing, or finance, and cite a real example that survives a follow-up question
- You'll be able to argue both the case for and the case against an AI application in a structured business context, the exact skill a case interview panel is probing when it adds the AI twist
- You'll be able to state a specific limit on AI capability calmly and confidently, the confident no that reads as senior judgement rather than ignorance
- You'll be able to produce a board-standard memo that demonstrates AI fluency in writing, giving you a concrete portfolio piece when a recruiter asks what you've actually done with this knowledge
- You'll be able to assess an AI business case for where the evidence is solid and where the pitch is running ahead of it, a capability that matters from the first week of any strategy or finance intern
91 lessons, 28 chapters
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Most MBA programmes teach frameworks that are decades tested and genuinely useful, but none of them prepare you for the AI question that now appears in nearly every consulting case interview, marketing probe, and finance analyst round. You've used ChatGPT, but 'I've used ChatGPT' is not an answer that survives a second follow-up. The candidates who are losing offers aren't losing them because they said too little about AI, they're losing them because they said something enthusiastic they couldn't defend when the interviewer pushed back. And the core curriculum isn't going to fix this before SIP season arrives.
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
28 chapters · 91 lessons
Orientation (Pre-Week 1)
You'll run a live baseline interview simulation using a chat model, identify the specific sentence in your answer that goes vague under pressure, and map the exact AI question your target domain is already asking so you know precisely what you're preparing for.
0.1 Why Every MBA Interview Is Now an AI Interview4 items
- 0.1.1 The curriculum gap: what recruiters ask vs. what the program teaches🔒
- 0.1.2 How AI questions actually show up by domain (the consulting case twist, the marketing GenAI probe, the finance 'what changes for analysts' question)🔒
- 0.1.3 The two failure modes: the engineer's answer and the buzzword answer, both dissected from real interview transcripts🔒
- 0.1.4 What 'defensible fluency' means, and the two-follow-up test you'll use all course🔒
0.2 Meet the Case Study: Banyan Foods & Beverages Wants an AI Strategy4 items
- 0.2.1 The board's mandate and the 9-item AI wishlist (what the company thinks it needs)🔒
- 0.2.2 Company facts you'll carry all trilogy: revenue, SKUs, warehouses, distributors, the young D2C channel🔒
- 0.2.3 The stakeholders (CEO under analyst pressure, skeptical CFO, an eager CMO, the strategy head, the one IT manager who runs everything)🔒
- 0.2.4 Your role as the incoming trainee, and why the board memo in Week 4 is your deliverable🔒
0.3 The 4-Week Roadmap: SIP-Safe by Week 43 items
- 0.3.1 Skill map: understand the machine, judge the business fit, argue both sides, deliver it, and why in that order🔒
- 0.3.2 The 4 portfolio artifacts and the CV line you'll walk away with🔒
- 0.3.3 The honest time budget: 4 to 5 hours a week alongside first-semester academics🔒
0.4 Setting Up Your Toolkit5 items
- 0.4.1 Access you'll need: a frontier model account, a spreadsheet tool, your phone's voice recorder, and Banyan's provided document pack🔒
- 0.4.2 Your first structured model conversation: asking the model to explain itself, then probing it twice🔒
- 0.4.3 The recorder habit: your first 60-second timed answer (it will be bad, that's the baseline)🔒
- 0.4.4 Troubleshooting: free-tier limits, and what to do when the model refuses or rambles🔒
- Orientation checkpoint: are you actually set up?Quiz
Week 1: How the Machine Actually Works (No Math, No Hand-Waving)
You'll explain in plain, defensible language what a large language model is actually doing, why it sometimes produces confident nonsense, and where that makes its output useful or risky, without writing a line of code or touching a single formula.
1.1 What an LLM Actually Does3 items
- 1.1.1 The next-word machine: what 'trained on text' really means, taught through what the model gets eerily right and confidently wrong🔒
- 1.1.2 Why it can draft your marketing plan but can't count the r's in strawberry: pattern completion vs. computation🔒
- 1.1.3 The 60-second explainer, version 1: your own words, recorded, timed🔒
1.2 Why It Makes Things Up (and Why That's Not a Bug That's Getting 'Fixed Soon')4 items
- 1.2.1 Hallucination through the Banyan incident: the confident market-share figure with the nonexistent citation🔒
- 1.2.2 The mechanism in plain language: plausibility is the objective, truth is a frequent side effect🔒
- 1.2.3 What reduces it (grounding in documents, retrieval) and what doesn't (asking it to be honest)🔒
- 1.2.4 The two-follow-up drill: defending your hallucination explanation against a skeptical model🔒
1.3 The Capability Map: What AI Can, Can't, and Can-With-Caveats Do5 items
- 1.3.1 The three buckets taught through ten real business tasks sorted live🔒
- 1.3.2 Classical ML vs. GenAI: forecasting and fraud scoring existed before ChatGPT, and interviewers notice who knows that🔒
- 1.3.3 The vocabulary that must be exact: training, fine-tuning, RAG, agents, one sentence each, no more🔒
- 1.3.4 The stale-example trap: why 'Netflix recommendations' now signals weak prep, and building your fresh example bank🔒
- Week 1 checkpoint: the machine and its limitsQuiz
1.4 Talking to the Machine Like a Professional3 items
- 1.4.1 Prompting as delegation: context, task, format, constraints, the same skill as briefing a junior🔒
- 1.4.2 Using the model as a sparring partner: the skeptical-interviewer pattern you'll use all trilogy🔒
- 1.4.3 Where the model helps your MBA workload honestly (and the academic-integrity line)🔒
1.5 Case Study Checkpoint: Briefing Banyan's Strategy Head3 items
- 1.5.1 The strategy head asks you, pre-meeting: 'In plain words, what can this technology actually do for us?'🔒
- 1.5.2 Building your answer from the capability map, with the hallucination warning included🔒
- 1.5.3 The recorded 90-second briefing, survived against two follow-ups🔒
1.6 Week 1 Portfolio Artifact: Your 60-Second Explainer Bank3 items
- 1.6.1 Assembling the recorded explainers: what an LLM is, why it hallucinates, ML vs. GenAI, what RAG fixes🔒
- 1.6.2 The written capability map as the companion one-pager🔒
- 1.6.3 Checklist: every explainer survives the two-follow-up test, contains zero false claims, and lands under 75 seconds🔒
Week 2: The Business Lens, Where AI Earns Its Place
You'll assess a real business situation, anchored in the Banyan Foods and Beverages case, and argue specifically where AI creates genuine value and where the business case is thinner than the pitch, in the language a consulting or finance interviewer is actually listening for.
2.1 The Subtraction Skill: Real AI Opportunities vs. AI Theater3 items
- 2.1.1 The five-question fit test (real pain, why AI over rules, error tolerance, data availability, cost-to-value) taught through two real corporate AI announcements, one substantive, one theater🔒
- 2.1.2 The tell-tale signs of theater: no error tolerance stated, no data mentioned, timed to earnings calls🔒
- 2.1.3 Practice pass: running three real company AI announcements through the fit test🔒
2.2 Case Study Checkpoint (Early): Auditing Banyan's 9-Item Wishlist5 items
- 2.2.1 The wishlist, item by item, through the fit test, the course's central reveal🔒
- 2.2.2 The six that aren't AI: analytics, process, and data problems wearing an AI costume, and why calling this out is a career-maker not a career-risk🔒
- 2.2.3 The two or three that are real: demand forecasting, trade-promotion optimization, and the distributor-assistant question mark🔒
- 2.2.4 Writing the audit memo: respectful, specific, and impossible to dismiss🔒
- Week 2 checkpoint: the wishlist auditQuiz
2.3 The Money: AI Unit Economics an Interviewer Will Respect3 items
- 2.3.1 What AI actually costs: per-use pricing, the pilot-vs-scale cost cliff, and the hidden data-cleanup line item🔒
- 2.3.2 Back-of-envelope: costing Banyan's distributor assistant at 900 distributors times realistic usage🔒
- 2.3.3 Why most enterprise AI pilots die: the demo-to-production gap, taught through well-documented failure patterns🔒
2.4 Build, Buy, or Wrap: The Decision Every Company Faces4 items
- 2.4.1 The ladder (use a tool, buy a product, wrap a model, build custom) and who should be where🔒
- 2.4.2 Data as the actual moat: why Banyan's distributor data matters more than any model choice🔒
- 2.4.3 The vendor landscape in one map: model makers, application companies, and the consulting and SI layer, who sells what🔒
- 2.4.4 Applying the ladder to Banyan's two real opportunities🔒
2.5 Side Quest: The Same Audit in a Different Industry3 items
- 2.5.1 Pick one: a bank's or a hospital chain's public AI announcements, audited with the same fit test🔒
- 2.5.2 What changed across industries, what didn't🔒
- 2.5.3 Reflection: the one-page 'fit test travels everywhere' note, your first cross-industry interview story🔒
2.6 Week 2 Portfolio Artifact: The Banyan AI Opportunity Assessment3 items
- 2.6.1 The one-page assessment: wishlist audit, the real opportunities, the economics, the build-buy-wrap call🔒
- 2.6.2 Pressure-testing it against the model playing a skeptical CFO🔒
- 2.6.3 Checklist: what makes this artifact quotable in an SIP interview🔒
Week 3: Judgment, Risk, Both Sides, and the Questions With No Clean Answer
You'll construct and defend both sides of an AI argument under mock interview pressure, practise stating a specific limit calmly rather than hedging, and work through the ambiguous scenarios where a confident no is the most credible answer in the room.
3.1 The Risk Sweep: What Goes Wrong When AI Ships3 items
- 3.1.1 The failure taxonomy through real public incidents: hallucination in customer-facing contexts, bias in decisions about people, privacy leaks, over-reliance🔒
- 3.1.2 Which risks are managed vs. accepted vs. walked away from, the framework a board actually uses🔒
- 3.1.3 The twist lands: a competitor's customer chatbot makes news for a costly wrong promise, and Banyan's board wants to know 'can that happen to us?'🔒
3.2 The Regulation Picture in 20 Minutes3 items
- 3.2.1 The EU AI Act's risk-tier logic (the only structure worth memorizing), India's current approach, and the direction of travel🔒
- 3.2.2 What regulation means for Banyan practically: the distributor assistant vs. anything touching credit or hiring🔒
- 3.2.3 The interview version: answering 'should AI be regulated?' without sounding like a headline🔒
3.3 Arguing Both Sides: The GD Engine3 items
- 3.3.1 The both-sides method taught on 'AI will destroy more jobs than it creates,' steel-manning each position before choosing🔒
- 3.3.2 Drilled on the live GD pool: AI in hiring, deepfakes and elections, AI in education, regulation vs. innovation🔒
- 3.3.3 The GD entry itself: claiming a position in the first 20 seconds without hogging the floor (full delivery training is Course 3, the content weapon is built here)🔒
3.4 The Questions With No Clean Answer3 items
- 3.4.1 'Should banks use AI for loan approvals?' The answer that shows maturity vs. the cheerleader answer, both written out🔒
- 3.4.2 The honest-uncertainty move: how to say 'the evidence is mixed' and sound senior, not evasive🔒
- 3.4.3 Practice: three no-clean-answer questions, both sides argued, positions taken, recorded🔒
3.5 Case Study Checkpoint: The Board Gets Nervous4 items
- 3.5.1 Post-incident, the CFO challenges your Week 2 economics AND the board wants risk answers, the same meeting🔒
- 3.5.2 Revising the assessment: risk columns added, the distributor assistant re-scoped to human-checked drafts🔒
- 3.5.3 The recorded 2-minute board answer: 'can that happen to us?' Honest, specific, calm🔒
- Week 3 checkpoint: the board gets nervousQuiz
3.6 Week 3 Portfolio Artifact: The Both-Sides Brief3 items
- 3.6.1 Three GD-ready topics, each with both positions steel-manned and your stance stated🔒
- 3.6.2 The risk-sweep one-pager for Banyan as the applied companion🔒
- 3.6.3 Checklist: no strawmen, every claim sourced or hedged honestly, each position speakable in 60 seconds🔒
Week 4: The Board Memo and the SIP-Safe Finish
You'll produce a structured, mentor-reviewed board memo on Banyan Foods and Beverages that integrates every concept from the previous three weeks into a single polished artefact you can reference or share in any interview conversation.
4.1 Writing the Board Memo3 items
- 4.1.1 The memo structure boards actually read: one page, recommendation first, economics and risks visible, theater explicitly declined🔒
- 4.1.2 Drafting it from your Week 2 assessment and Week 3 revisions, the workbook chain pays off🔒
- 4.1.3 The language pass: cutting every buzzword, keeping every number🔒
4.2 Case Study Checkpoint: Presenting to the Banyan Board4 items
- 4.2.1 The final twist: you get 5 minutes on the agenda, not 15, compressing without losing the subtraction🔒
- 4.2.2 The recorded 5-minute presentation, then the model as three different board members asking hostile follow-ups🔒
- 4.2.3 The retro: comparing your Week 0 cold recording to this, the visible delta🔒
- Course final: presenting to the Banyan boardQuiz
4.3 Making It Placement-Ready3 items
- 4.3.1 The CV line that survives verification: how the Banyan work becomes 'Conducted AI opportunity assessment framework applied to FMCG operations' and what you say when probed🔒
- 4.3.2 The 60-second SIP answers, final versions: 'do you think our clients should use AI?' and 'what do you know about AI?' per target domain🔒
- 4.3.3 What NOT to claim: the overclaim audit of your own materials🔒
4.4 The AI Question Bank: What They Actually Ask3 items
- 4.4.1 The real SIP question pool by domain, mapped to which course unit answers each🔒
- 4.4.2 The follow-up tree: where each question goes when the interviewer probes, and where your knowledge ends (saying so gracefully)🔒
- 4.4.3 Practice round: five questions cold, recorded, self-scored against the two-follow-up test🔒
4.5 Where You Go Next3 items
- 4.5.1 The trilogy path: Course 2's domain fork after SIP results, Course 3 before finals, and what carries forward automatically🔒
- 4.5.2 Staying current for 10 minutes a week: the source list and the weekly-brief habit (stale answers are visible answers)🔒
- 4.5.3 The 30-day pre-SIP checklist🔒
4.6 Week 4 Portfolio Artifact: The Banyan Board Memo + Recorded Presentation3 items
- 4.6.1 The final memo, the 5-minute recording, and the follow-up survival log🔒
- 4.6.2 Packaging all 4 artifacts into one SIP-prep folder🔒
- 4.6.3 Checklist: what makes the full set defensible under a partner's probing🔒
Employers at consulting firms, banks, and brand companies are not looking for candidates who have memorised AI headlines; they are looking for the rare candidate who can assess a specific claim, state a specific limit, and put both in writing under pressure. Finishing this course with a reviewed board memo and a tested two-sides framework puts you in a different conversation from classmates who ar
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One subscription unlocks all four weeks of lessons, the Banyan Foods and Beverages case study, workbook templates, and the preview lesson. No waiting for cohort start dates. Open it the afternoon you sign up.
Work through four to five hours a week, starting with Orientation
Begin with the baseline exercise in Orientation: paste a single prompt into any frontier chat model, answer the AI interview question it fires back, and log exactly which sentence went vague. That is your starting point. Every subsequent lesson builds from there, no code, no maths, just structured thinking and hands-on practice with the Banyan case.
Submit your board memo and walk into SIP season with proof of fluency
By the end of Week 4 you'll have produced a finished board memo on Banyan Foods and Beverages that a mentor reviews. That memo is your portfolio artefact: a real piece of written work showing an interviewer or recruiter exactly what defensible AI fluency looks like in practice.
The consulting case twist: Banyan Foods and Beverages mid-case
The interviewer adds the AI line
You are twenty minutes into a market-entry case for Banyan Foods. The interviewer pauses and says: 'Assume Banyan can now use an AI model to draft the initial demand analysis. Does that change your recommendation on whether to enter?' You recognise this as the case-twist pattern from Week 1 of the course.
Identify which layer of work has actually shifted
Using the business lens from Week 2, you separate the first-draft work, compiling category data, running demand-sizing calculations, and structuring the memo from the judgment call: whether the assumptions underlying that draft are reliable for Banyan's specific route-to-market in a new geography. You note that the first layer has shifted; the second has not.
Name the specific limit calmly
You tell the interviewer that the AI draft accelerates the analysis but does not change the entry recommendation, because the recommendation hinges on distribution assumptions and local regulatory risk that the model cannot verify from public data. You state the limit plainly rather than hedging. Week 3 practice with the confident no is what makes this feel natural rather than defensive.
Handle the follow-up
The interviewer asks: 'How would you know whether to trust the model's demand estimate?' You draw on Week 1 to explain that you would check whether the model's sources are current and specific to the category, and flag one scenario where the estimate would be unreliable: a market where distribution data is thin and the model is likely interpolating from adjacent categories.
Close with the board memo logic
The interviewer asks you to summarise the AI dimension for Banyan's board. You apply the Week 4 memo structure directly: one sentence on what the model can do, one sentence on where human judgement remains essential, and one sentence on the governance question the board actually needs to answer. Two minutes. No buzzwords. Every claim defensible.
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, mentor-reviewed board memo on the Banyan Foods and Beverages case, ready to reference or share in any SIP interview conversation
A personal INSIGHT_LOG workbook tracking your baseline answer, every specific gap you identified, and how your thinking developed across four weeks
A domain-specific AI question map covering consulting, marketing, and finance, with a specific claim and a specific limit written out for each, grounded in named examples
A reusable interview simulation prompt that turns any frontier chat model into a sceptical interview partner you can run before every recruiting conversation
A two-sides framework for structuring AI arguments under pressure, tested against the Banyan case and ready to deploy on any case twist or GenAI probe
You'll get the most from this if
First-year MBA students heading into SIP recruiting who have used AI tools but cannot yet explain or defend them under interview pressure
MBA students targeting consulting, marketing, or finance roles where AI questions now appear routinely in case interviews and technical rounds
Students who want a concrete portfolio artefact, not just conceptual knowledge, to show during the recruiting season
Anyone who has prepared a generic 'what I think about AI' answer and knows it will not survive a domain-specific second question
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 any coding or technical background to do this course?
None at all. The course is built from the ground up for MBA students with no technical background, and the description is explicit: no code, ever. Every concept is explained in plain business language, and the only tools you need are a browser and a frontier chat model like Claude.ai, both of which you almost certainly already have. The hands-on exercises start in Orientation and stay practical throughout, no maths, no engineering.
Will this work for my specific interview domain, consulting, marketing, or finance?
Yes. The Orientation module maps the AI question as it actually appears across all three domains, the consulting case twist, the marketing GenAI probe, and the finance analyst question, and the course builds domain-specific frameworks for each. The Banyan Foods and Beverages case is used throughout, giving you a single worked example you can adapt to your target domain rather than learning three separate things.
What is the refund policy if it is not right for me?
There is a 14-day refund period. If you work through the course and it does not deliver what is described on this page, you can claim a full refund within 14 days of purchase, no complicated process.
How does the course stay current given how fast AI is moving?
The course is designed around durable reasoning skills, how to assess a claim, how to identify a limit, how to argue both sides, rather than specific tool versions that date quickly. When the curriculum is updated to reflect material changes in the recruiting environment, enrolled students receive access to the updated content.
Can I use the board memo and workbook outputs for job applications or as portfolio pieces?
Yes. The work you produce, the board memo, the INSIGHT_LOG, the domain frameworks, is yours to use. You built it on the Banyan Foods and Beverages case, which is a fictional teaching case, so there are no commercial confidentiality issues. You can reference it in interviews, include it in a portfolio, or share it with a recruiter as evidence of the capability. That is the entire point of building it.