From MBA generalist to consulting-track AI specialist: the depth that wins finals rounds
For MBA students on the consulting track who need to move beyond Course 1 fluency and build a named, evidence-backed AI capability they can defend in a finals round at a top-tier firm.
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.
Stop sounding like every other candidate in the room
- You'll identify the exact moment a generalist answer collapses under a specialist follow-up and learn to replace it with a named lever, a named function, and a number.
- You'll practise the four killer questions that land in consulting, marketing, finance, and operations finals rounds this placement season, so you walk in knowing exactly what the engagement manager is listening for.
- No coding background needed: every exercise runs in a frontier-model conversation you already know how to open from Course 1.
Map what consulting firms have actually deployed, not what vendors claim
- You'll build a named deployment inventory for the consulting function, the real tools, the real use cases, the real limitations, not a capability map copied from a slide deck.
- You'll learn to read a vendor claim the way a hiring manager does: asking for the baseline, the granularity, and the window where the model failed, not only where it succeeded.
- This is the kind of functional literacy a hiring manager pays for and most candidates never demonstrate.
Build a real project on a real case, no code required
- You'll continue the Banyan Foods and Beverages case from Course 1 and build one genuine consulting-track project using no-code tools and frontier-model projects.
- You'll produce a working artifact you can open on a laptop in any interview room, not a slide about AI in theory.
- Every build step is hands-on from the first session: plain instructions, no prior technical background assumed.
Defend your work like a specialist, not a student
- You'll apply honest cost and evidence standards to your own project, so when an engagement manager asks how you evaluated it, you have a real answer about what the model got wrong, not only where it looked good.
- You'll package your project and your before-and-after answer pairs into a finals-ready specialist defence that demonstrates you have actually lived inside the consulting function's AI problems.
- Hiring managers recognise this immediately: it is the difference between a candidate who read about AI and one who built something with it.
Finish this course and you can do all of this, no prior background required:
- You'll be able to answer a consulting engagement manager's specialist follow-up with a named lever, a named function, and a number rather than a capability definition.
- You'll be able to map the AI deployments that are actually live in consulting-track work, with the specificity a functional interviewer recognises as earned rather than recited.
- You'll be able to build and demonstrate a real no-code AI project on a named case, the kind of working artifact a hiring manager can see on your laptop rather than read about in a bullet point.
- You'll be able to interrogate a vendor's AI performance claim by requesting the baseline, the granularity, and the failure window, which is the evidence standard any credible consulting team applies.
- You'll be able to apply honest cost and evidence standards to your own project, including an account of where the model was wrong, which is what distinguishes a specialist defence from a student prese
- You'll be able to package your project, your revised answer pairs, and your specialist defence into a finals-ready portfolio that makes your consulting-track AI capability visible and verifiable to an
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.
Kept current with the stack
Claude Code, skills, and MCP move fast. Your licence includes every system update for as long as you're subscribed. Your system gets better, not obsolete.
You finished Course 1 with a solid generalist foundation: you can explain what an LLM does, name the fit test, and hold your own in a group discussion. Then you hit a finals round and the engagement manager asks which manual process in their client's due diligence workflow a model would actually replace tomorrow, and your answer has nothing to grab onto. The silence lasts two seconds and the interviewer has already decided. Most MBA candidates at this stage are SIP-safe but finals-vulnerable: they can describe AI, they cannot demonstrate that they have stood inside a function long enough to know where the real money and the real risk sit. That gap is not filled by reading another framework or watching another panel. It is filled by doing one real piece of consulting-track work on a real ca
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 replaying your own recorded answer, exactly which sentences a consulting engagement manager's specialist follow-up would expose as generic, and name the one specific fact you are missing that would have saved them.
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 Specialist Bar and the Consulting 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: From the Org-Chart Trap to the Re-Scoped RoadmapQuiz
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 Pitch, 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 Consulting 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 can open a working no-code project on a named case, name the specific deployment landscape of the consulting function, and explain honestly where their model failed is not one of many AI-aware MBAs: they are the one in the room who has actually done the work. That combination of functional depth and honest evidence standards is exactly what a consulting hiring manager is listening
From subscribing to running your first artifact
Subscribe and pick up where Course 1 left you
Log in, open the Orientation module, and revisit your Banyan Foods and Beverages case file from Course 1. The pre-week material stress-tests your existing 60-second explainer against a simulated engagement manager probe so you know exactly which sentences are still generalist filler before Week 1 begins.
Open the course and start building your consulting-track project
Week 1 maps what the consulting function has actually deployed. Week 2 is the build: you start your real Banyan project using no-code tools and a frontier-model project, following plain step-by-step instructions with no coding background required. Mentor review and daily live sessions keep you on track.
Defend your artifact and leave with a finals-ready package
By Week 4 you have a working project, an honest evidence record showing where your model was wrong as well as where it was right, and a practised specialist defence you can deliver under pressure in a consulting finals round. You walk out with something real in your portfolio, not a reflection on AI in theory.
How a Banyan Foods board deadline becomes a specialist answer
The board deadline lands on your desk
Banyan's CEO wants to present an AI roadmap to the board in three weeks. You open your Course 1 board memo and immediately see the problem: it names capabilities, not levers. A consulting engagement manager asking 'what do you actually do first' needs a specific process, a specific function, and a specific number in your first sentence.
You map what is actually deployed, not what is possible
Using the Week 1 deployment landscape for the consulting function, you identify the named tools already running in comparable client engagements and the specific manual processes they touch. You note the baseline each one was measured against, because without a baseline the board presentation is a vendor pitch, not an evidence-backed recommendation.
You build the Banyan project artifact
In Week 2 you set up your no-code project and build the first working version on the Banyan case. No coding required: plain step-by-step instructions get you to a running artifact inside the first session. Mentor review keeps the build grounded in what a real consulting team would actually deliver.
You apply the vendor gauntlet to your own work
Week 3's economics and evidence module forces you to ask the same questions of your own project that you would ask a vendor: what is the baseline, what is the granularity, and where in the Banyan data did the model get it wrong. This is the step that turns a student project into a defensible piece of consulting work.
You deliver the specialist defence
In Week 4 you package the Banyan artifact, the evidence record, and your before-and-after answer pairs into a finals-ready defence. When the engagement manager asks 'where did your model fail,' you have a real answer. That answer, not the project itself, is what puts you on the shortlist.
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 AI project built on the Banyan Foods and Beverages consulting case, openable on a laptop in any interview room
A named deployment inventory for the consulting function, recording real tools and real failure modes rather than vendor claims
A before-and-after answer file: your original generalist responses to the consulting killer question alongside your revised specialist answers, timestamped and ready for CV defence
An evidence record for your Banyan project showing where the model performed and where it failed, built to honest cost and evidence standards
A practised finals-ready specialist defence, tested under simulated engagement-manager follow-up questioning
You'll get the most from this if
MBA students who completed Course 1 and are targeting consulting-track roles, internships, or final placements at firms where the finals round includes a functional specialist
Second-year students preparing for finals rounds who need to move from SIP-safe generalist fluency to the named, evidence-backed depth an engagement manager probes for
MBA candidates who want a real working artifact on the Banyan case in their portfolio rather than a reflection on AI in theory
Students with no coding or technical background who need to build and demonstrate a genuine AI project using no-code tools
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
I have no coding background at all. Will I be able to build the Banyan project?
Yes, and this is built into the course design from the start. Every build step in Week 2 uses no-code tools and frontier-model projects with plain step-by-step instructions. You do not write a single line of code. If you completed Course 1, you already have everything you need to begin. Hands-on mentor review during daily live sessions means you are never stuck without support.
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 and builds directly on the fluency and workbook entries from Course 1. The Orientation module is designed to surface exactly what you are carrying in from Course 1 before Week 1 begins. Students who have not completed Course 1 will find the specialist bar in Week 1 significantly harder to meet because the baseline skills and the case context are both assumed.
What is the refund policy?
There is a 14-day refund window from the date of purchase. If you complete the Orientation module and start Week 1 and decide the course is not right for you within that period, you can request a full refund. The free preview lesson lets you check the pace and format before you commit.
How does the course stay current if AI tools change after I enrol?
The deployment landscape and tool examples are updated as the course runs. Because the core skill being built is how to evaluate and interrogate AI deployments rather than how to operate any one specific tool, the fundamentals you practise on the Banyan case remain valid even as individual products change. Update notes are pushed to enrolled students whenever the Week 1 or Week 3 material is revised.
Can I use the Banyan project artifact and the work I build here in job applications and client work?
Yes. The artifact you build is yours. You can include it in your portfolio, reference it in interviews, and use the skills you develop in professional work. The licence covering course materials applies to the instructional content, not to the project you personally produce during the build weeks.