Go from MBA generalist to finance-track AI specialist a hiring manager will actually fight over
For MBA students leaning into finance who want to walk out of finals rounds with a named deployment, a built project, and a specialist-level defence no generalist candidate can match.
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 the specialist's language, not the generalist's
- You'll identify exactly which AI deployments are live inside finance functions today, named tools, named use cases, not capability maps
- You'll answer a VP-Finance's killer question with a specific lever, a specific number, and a specific evidence standard, not a definition
- No coding background required: you start with frontier-model conversations and no-code tools from lesson one, the same ones a finance team would actually use
- You'll build the kind of answer that slows down under pressure because it's drawn from real deployment knowledge, the tell every interviewer notices
Build a real finance-track project on a real case
- You'll continue the Banyan Foods and Beverages case from Course 1 and fork it into a finance-specific build using no-code tools and frontier-model projects
- You'll produce a working project artifact, not a slide deck of intentions, that demonstrates what you actually built and why it holds up
- Every build step is hands-on from the first session: no prior technical experience is assumed or needed
- Hiring managers in finance and consulting recognise a built artifact instantly as proof of capability, not just awareness
Run the economics and interrogate the evidence
- You'll stress-test a vendor AI claim the way a finance professional actually should: baseline comparison, granularity check, held-out test period
- You'll apply honest cost and evidence standards to a proposed deployment so you can tell a CFO or engagement manager what the numbers actually mean
- You'll learn to ask the one clarifying question that separates a specialist from a reciter: which metric, over what period, against what baseline
Defend your work at finals-round standard
- You'll package your Banyan finance project into a finals-ready specialist defence with before-and-after evidence a CV interviewer can probe
- You'll practise the exact follow-up sequence used in real consulting finals rounds, so silence is never the answer again
- Any MBA student can do this: the course is structured so you build confidence alongside capability, step by step, with mentor review throughout
Finish this course and you can do all of this, no prior background required:
- You'll be able to name specific AI deployments active in finance functions and explain exactly what they do and don't replace, the answer a consulting engagement manager or VP-Finance is actually list
- You'll be able to deliver a specialist answer to a regulated-decision or evidence-standard question that names a lever, a baseline, and a granularity rather than offering a definition.
- You'll be able to interrogate a vendor AI claim on behalf of a client or employer, identifying whether the headline metric is measured at the right level and over a meaningful test period.
- You'll be able to present a built project artifact from the Banyan case as portfolio proof that you've done the work, not just read about it, which is the distinction a hiring manager in finance or co
- You'll be able to defend your project under specialist follow-up using a before-and-after evidence structure that translates directly onto your CV and into an interview room.
- You'll be able to apply honest cost and evidence standards to an AI deployment proposal, giving a CFO or engagement manager a credible basis for a go or no-go recommendation.
90 lessons, 29 chapters
Built by practitioners, not influencers
Every agent, skill file, and dataset is drawn from 25+ years and 50+ Oracle ERP implementations across pharma, manufacturing, semiconductor, and distribution.
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You finished Course 1, you can explain what an LLM is, you passed the generalist screen, and now you're in a finals room with someone who actually runs a finance function. They don't want your capability map. They want to know which specific lever you'd pull, in which specific process, with which specific evidence standard, and they can tell within four seconds whether you've ever actually stood inside that function or whether you're reciting clean notes from someone who has. Most MBA candidates hit that wall and go quiet. The ones who don't are the ones who spent time building something real, not just reading about what's possible.
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 the exact difference between what a generalist interviewer accepts and what a finance-track specialist probes for, and you can record a 60-second answer that names at least one specific lever, function, or number 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 Seat, the Mandate, the ArcQuiz
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 Vendor GauntletQuiz
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 Finance 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🔒
A candidate who walks in with a named finance-track deployment, a built artifact from a real case, and a vendor-gauntlet evidence standard they can articulate under follow-up is not one of many MBAs who did an AI module. They are the one who actually did the work, and that is the distinction finance and consulting hiring managers say they are looking for and rarely find.
From subscribing to running your first artifact
Subscribe and open the pre-week orientation
Log in, access the Banyan Foods and Beverages case materials, and complete the specialist-bar orientation before Week 1 begins. No setup, no installation, no coding background needed.
Work through each week's live sessions and hands-on build
Attend daily live sessions, complete the Do This exercises using frontier-model conversations and no-code tools, and log your MISTAKE_LOG and INSIGHT_LOG entries as you go. Mentor review is built into the rhythm.
Submit your finished finance-track project and deliver your specialist defence
Package your Banyan finance-track artifact and deliver a finals-ready defence with before-and-after evidence you can use directly in interviews and on your CV.
How a finance-track student handles the regulated-decision killer question in a finals round
The question lands
The engagement manager says: 'Our client's CFO wants to use an AI model to flag payment-term exceptions before approval. The vendor says it's 90% accurate. Should the client proceed?' The generalist candidate says AI can improve decision-making and asks whether the vendor is reputable. The finance-track specialist pauses, then asks one clarifying question before answering.
The clarifying question
You ask: 'Is that 90% measured on the client's own transaction distribution, or on the vendor's benchmark dataset? And which exception type, value, frequency, or counterparty risk, because blending those into one accuracy number hides where the model actually fails.' The engagement manager leans forward. That question only gets asked by someone who has actually sat with a model output that looked right on average and broke on the case that mattered.
The evidence standard
You walk through the three checks you applied during Week 3's vendor-gauntlet exercise: baseline comparability (is the vendor's 90% measured the same way the client measures today's manual exception rate), granularity (does accuracy hold on high-value or cross-border transactions, not just the easy volume), and held-out test period (was the model evaluated on a period that includes a reporting quarter-end, since that is exactly where exception volumes spike and naive models break).
The recommendation
You say: 'I'd recommend a structured pilot on one transaction category with a held-out quarter-end window before any board sign-off. The regulated-decision standard here isn't average accuracy, it's worst-case accuracy on the transactions that carry the most liability.' You reference the Banyan case: you built exactly this framing in Week 2 and defended it in Week 4. The engagement manager stops taking notes and starts asking follow-up questions instead. That is the tell.
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 completed Banyan Foods and Beverages finance-track project artifact built with no-code tools and frontier-model projects
A finals-ready specialist defence deck with before-and-after evidence structured for CV use and interview rooms
A logged MISTAKE_LOG and INSIGHT_LOG workbook covering every major exercise in the course, usable as interview preparation material
A recorded 60-second specialist answer, revised against mentor and model feedback, demonstrating the delta between your generalist starting point and your finance-track finish
A vendor-gauntlet evidence checklist you can apply to any AI claim in a finance or consulting context
You'll get the most from this if
MBA students in their finals season who are leaning into finance, consulting with a finance client, or any role where AI in a regulated decision context will come up in the room
Course 1 graduates who passed the generalist screen and now need a finance-specific build and a specialist defence to survive the next round
MBA students with no coding or technical background who want to demonstrate real AI capability through a built project, not just familiarity with the concept
Anyone who has sat in a finals room and felt a generalist answer run out of road under a specialist follow-up
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. Every build in this course uses no-code tools and frontier-model conversations, the same ones introduced in Course 1. The course is designed so that hands-on work starts from lesson one without any prior technical experience assumed. If you can open a browser and type a prompt, you can complete every exercise.
Does this course work if I haven't completed Course 1 of the MBA AI Trilogy?
This is Course 2 of the trilogy and it continues the Banyan Foods and Beverages case from Course 1, so you'll get the most out of it if you've completed Course 1 first. The orientation module is specifically designed to help you gauge where you're starting from and fill any gaps before Week 1 begins.
What is the refund policy?
There is a 14-day refund policy. If you complete the orientation and the first week's exercises and decide the course isn't right for you, you can request a full refund within 14 days of purchase. We'd rather you make the right call early than feel stuck in a course that isn't serving you.
How does the course stay current if AI tools and deployments are changing so fast?
The course content is updated to reflect real deployment evidence as it becomes available. Enrolment includes access to updated materials, and the live daily sessions mean you're working with current examples rather than a frozen snapshot from a recording made months ago.
Can I use the project artifact and defence materials commercially, for example in client work or as part of a job application?
The artifact you build is your own work product. You can use it in job applications, interviews, and as portfolio evidence. The course materials themselves, the frameworks, exercises, and case structure, are licensed for personal educational use. If you have a specific commercial use case in mind, reach out to the academy directly before proceeding.