From MBA generalist to operations AI specialist: the depth that wins finals rounds and gets you hired
Built for MBA students and early-career operations professionals who want to move beyond surface-level AI fluency and defend a real built project in front of the specialists who actually run supply chains, procurement teams, and vendor negotiations.
29 chapters, 90 lessons
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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.
Answer the killer operations question without flinching
- You will practise the exact specialist follow-up sequence used in real finals rounds and learn why a named lever beats a capability map every time
- You will map the AI deployments that supply chain and operations functions have actually shipped, not the ones vendors claim, so your answers are grounded in what is real
- No coding background needed: every exercise runs in a frontier-model conversation you already have open, plain steps from lesson one
- You will walk away able to tell a hiring manager exactly which operational lever you would pull, at which granularity, and why the vendor's blended number does not prove it
Build a real operations AI project on the Banyan Foods case
- You will scope and begin an actual project using no-code tools and frontier-model features, continuing the Banyan Foods and Beverages case from Course 1
- You will produce a working artefact grounded in a real operations scenario, not a slide deck about what AI could theoretically do
- This is hands-on from the first session, and every step is written for someone with zero prior coding or technical background
- The project becomes a concrete portfolio piece a hiring manager can ask you to demo on the spot
Interrogate vendor economics and evidence like an insider
- You will learn to spot when a vendor's accuracy claim is a blended average disguising SKU-level failure, and ask for the evidence that exposes it
- You will apply an honest cost and evidence standard to operations AI proposals, the standard a supply chain head uses in a real vendor gauntlet
- You will be able to state, in one sentence, why a held-out test period beats a vendor's own backtest as proof of performance
- These are the questions that signal to any interview panel that you have actually sat with a forecast that broke
Defend your specialist position in a finals-ready package
- You will prepare and deliver a finals-ready defense of your built project, structured the way a consulting engagement manager or supply chain head would interrogate it
- You will package your before-and-after answers, your project artefact, and your vendor interrogation skills into a CV-ready portfolio
- The defense format matches what top-tier finals rounds actually test, so you are practising the real thing, not a simulation of it
- You will be able to name the specific operations deployment you built, the evidence standard it was held to, and the gap it was designed to close
Finish this course and you can do all of this, no prior background required:
- You will be able to walk into a supply chain or operations finals round and answer a vendor-claim question at the granularity a specialist actually expects, SKU level, held-out period, failure cases i
- You will be able to present a real no-code operations AI project built on a named company case, and demo it on the spot rather than describing what you would have built
- You will be able to interrogate an AI vendor proposal using an honest cost and evidence standard, the kind that protects a business from buying a backtest dressed up as a forecast
- You will be able to map the AI deployments that operations and supply chain functions have actually shipped, and use that map to make your interview answers specific to the function and company in fro
- You will be able to package your built project, your before-and-after answer evidence, and your vendor interrogation skills into a portfolio that a hiring manager can verify, not just read about
- You will be able to state, unprompted and in one sentence, the difference between a generalist AI answer and a specialist one, which is itself the signal most finals-round candidates never manage to s
90 lessons, 29 chapters
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You finished Course 1, you can explain what an LLM is, you passed the generalist screen, and now you are in the finals room in front of someone who actually runs a supply chain or a vendor negotiation every day. They are not asking what AI is. They ask which SKUs, over what period, against what baseline, and your polished generalist answer has nowhere to go. Procurement and operations roles are filling with people who can point to a real built thing and defend the evidence behind it, and the candidate who can only describe AI in the abstract is being passed over, even with a strong GPA and a good case record. The gap is not intelligence or effort. It is that no one has put you inside a real operations scenario, handed you a vendor claim to interrogate, and made you defend your answer to a
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 identify, by name, the exact specialist follow-up sequence that exposes a generalist AI answer in a finals round, record your own 60-second answer against it, and log the specific missing fact that a supply chain or operations specialist would have caught immediately.
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 Seat, the ToolkitQuiz
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 Reveal and the Banyan Forecasting WorkbenchQuiz
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: Cost, Evidence, the Vendor Gauntlet, and the Risk SweepQuiz
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 Operations 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🔒
The operations candidates employers cannot stop talking about are the ones who built something real, interrogated a vendor claim at SKU level, and defended both in a room full of specialists. Stacking the named deployments, the no-code project, and the evidence standard from this course puts you in that category, and most hiring managers across supply chain and procurement have never seen that com
From subscribing to running your first artifact
Subscribe and pick up where the Banyan case left off
As soon as you subscribe, you get access to the orientation module and all four weeks of the Operations Track. You pick up the Banyan Foods and Beverages case you started in Course 1 and immediately apply it to a real operations context: forecasting, vendor claims, supply chain deployments that have actually shipped.
Open the course and start building from lesson one
Every lesson is designed for someone with no coding or technical background. You open a frontier-model conversation, follow plain written steps, and complete a hands-on exercise in the same session. By the end of Week 1 you have a named deployment map for operations AI and a recorded answer that has already survived a specialist follow-up.
Finish with a working project and a finals-ready defense
By Week 4 you have a real no-code operations AI project built on the Banyan case, a portfolio package with before-and-after evidence, and a practiced defense you can deliver to a hiring manager or finals-round panel without flinching. Mentor review and daily live sessions are built into the schedule so you are never stuck.
Interrogating a vendor forecast claim for Banyan Foods and Beverages
The vendor walks in with a number
A demand-forecasting vendor tells Banyan's supply chain head their model achieves 85% accuracy versus Banyan's current 68%. You are the MBA associate in the room. Your job is not to accept that number or reject it on instinct. Your job is to know exactly which questions turn it into evidence.
Check whether the two numbers are actually measuring the same thing
You ask whether the vendor's 85% is measured at SKU level across all active items in Banyan's range, or whether it is a blended average across a smaller, easier-to-forecast subset. Banyan's 68% is a blended number across all 260 SKUs. A vendor number built on their best 50 SKUs is not a comparison. It is a sales figure.
Test the model on the weeks that actually matter
You ask the vendor to show performance specifically in the weeks around a demand spike, the kind Banyan sees around Diwali and other seasonal peaks. A naive model trained on flat-demand history will fail exactly there. Vendors rarely surface that window unprompted. Asking for it signals to the supply chain head that you know where forecasts break.
Ask for the failure cases, not only the average
You ask the vendor to share the SKUs where their own backtest was most wrong, not the headline accuracy figure. The average always looks good. The distribution of errors tells you whether the model fails catastrophically on a few high-value items or fails gently across many low-value ones. For Banyan, catastrophic failure on a core SKU is a different risk than diffuse error across tail items.
Package the findings for the board
Using the no-code project you built in Week 2 on the Banyan case, you structure a one-page evidence summary: what the vendor claimed, what the adjusted comparison shows, what the failure-case data revealed, and what a held-out test period on Banyan's own historical data would need to look like before a procurement decision is justified. That document is the artefact you defend in Week 4.
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 working no-code operations AI project built on the Banyan Foods and Beverages case, ready to demo in an interview or include in a portfolio
A named deployment map of AI applications that operations and supply chain functions have actually shipped, grounded in real evidence standards
A vendor interrogation framework with the specific questions that expose a blended accuracy claim and ask for the held-out, SKU-level evidence that actually proves performance
A before-and-after answer bank showing your recorded generalist answer and its specialist-level revision, with the exact delta named, usable as CV defense material
A finals-ready project defense package structured for the specialist panel a top-tier consulting or operations employer puts in front of shortlisted candidates
You'll get the most from this if
MBA students on an operations or supply chain track who passed Course 1 and need the specialist depth that wins finals rounds, not just shortlist screens
Early-career procurement and supply chain professionals who want to demonstrate AI capability grounded in real operational scenarios, vendor economics, and evidence standards
MBA candidates preparing for consulting roles with an operations or supply chain specialism, where the finals round includes someone who has actually run the function
Anyone who has answered an AI question in an interview, felt it was going well, and then watched it collapse the moment a specialist asked one follow-up about granularity or evidence
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 exercise runs in a frontier-model conversation with plain written steps. The project in Week 2 uses no-code tools, and the vendor interrogation framework in Week 3 is built around business and evidence reasoning, not engineering. The course is designed specifically for operations and supply chain professionals who work with numbers and decisions every day, not with code.
Does this course work if I have not done Course 1 of the MBA AI Trilogy?
Course 2 continues the Banyan Foods and Beverages case from Course 1 and builds directly on the generalist fluency, the 60-second explainer bank, and the fit-test framework developed there. If you have not done Course 1, the orientation module is designed to surface exactly where your baseline needs strengthening before Week 1 begins, and the daily live sessions give you a place to close that gap quickly.
What is the refund policy if the course is not right for me?
You have 14 days from the date of purchase to request a full refund, no questions asked. We also offer a free preview lesson before you buy so you can check the level, the format, and the Banyan case context before committing.
How does the course stay current as AI tools and deployments change?
The curriculum is updated to reflect new named deployments, vendor developments, and changes in the operations AI landscape. When a module is updated, enrolled students get access to the revised content automatically. The daily live sessions are also where current developments get discussed in real time, so you are not relying on static material alone.
Can I use the project and portfolio materials I build here commercially, for example in a real client engagement or a job application?
Yes. The artefacts you build, including your no-code project on the Banyan case, your vendor interrogation framework, and your before-and-after answer bank, are yours to use in job applications, interview portfolios, and professional work. The Banyan Foods and Beverages case is a teaching case created for this course, and your built work on top of it belongs to you.