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Course overview

The 20 Lessons

1 of 20 unlocked

01Normalize Messy SaaS Vendor Quotes — Unit Conversion, Hidden Fees, Cross-Platform Verification02Review a SaaS Master Service Agreement for Traps — Auto-Renewal, Price Escalation, Data Portability03Compress a Messy Savings Ledger Into the One Page Your CFO Will Sign04The Price-Increase Counter: Make AI Show You What It Can't Know05SaaS License Audit — Checking Contracts Before Cutting Seats06Build the AI-Adoption ROI Case That Survives the CFO07Compare Three Consulting Bids With AI — The $15K Spread That's Really $75008Commodity-Price Drift Monitor — Make the AI Design the Checklist, Then Bring the Index09Auditing a Services SOW Bundle: Feed, Chain, Report10PO Price-Variance Audit — From Raw Extract to Three Director Actions11Freight Landed Cost — Normalizing Quotes That Cover Different Spans of the Same Trip12Make AI Cross-Reference Three Documents Before You Renew a Supplier13SaaS Consolidation: From Three Overlapping Tools to One Committee-Ready Memo14Build a Supplier Risk Register When Three of Your Data Fields Are Lying15Compare Two Freight Contracts by Making AI Build the Crosswalk16Turn Three Fleet Bids Into One Recommendation Your VP Can Sign17Build a Weighted Vendor Ranking from Three Data Sources18From Three Incompatible Equipment Proposals to One Number Your Capital Committee Can Approve19Merge Three Catalogs Without Deleting the One Part Someone's Safety Depends On20Turn Three Maintenance Bids Into One Five-Year Cost of Ownership
All lessons
Course overview

The 20 Lessons

1 of 20 unlocked

01Normalize Messy SaaS Vendor Quotes — Unit Conversion, Hidden Fees, Cross-Platform Verification02Review a SaaS Master Service Agreement for Traps — Auto-Renewal, Price Escalation, Data Portability03Compress a Messy Savings Ledger Into the One Page Your CFO Will Sign04The Price-Increase Counter: Make AI Show You What It Can't Know05SaaS License Audit — Checking Contracts Before Cutting Seats06Build the AI-Adoption ROI Case That Survives the CFO07Compare Three Consulting Bids With AI — The $15K Spread That's Really $75008Commodity-Price Drift Monitor — Make the AI Design the Checklist, Then Bring the Index09Auditing a Services SOW Bundle: Feed, Chain, Report10PO Price-Variance Audit — From Raw Extract to Three Director Actions11Freight Landed Cost — Normalizing Quotes That Cover Different Spans of the Same Trip12Make AI Cross-Reference Three Documents Before You Renew a Supplier13SaaS Consolidation: From Three Overlapping Tools to One Committee-Ready Memo14Build a Supplier Risk Register When Three of Your Data Fields Are Lying15Compare Two Freight Contracts by Making AI Build the Crosswalk16Turn Three Fleet Bids Into One Recommendation Your VP Can Sign17Build a Weighted Vendor Ranking from Three Data Sources18From Three Incompatible Equipment Proposals to One Number Your Capital Committee Can Approve19Merge Three Catalogs Without Deleting the One Part Someone's Safety Depends On20Turn Three Maintenance Bids Into One Five-Year Cost of Ownership

Lesson 19 of 20

Merge Three Catalogs Without Deleting the One Part Someone's Safety Depends On

Supplier Screening & Evaluation · MRO / Industrial Supplies

Synthetic case data — evidence from real ChatGPT, Claude, Gemini and Copilot runs.


📥 Download the practice file: case-file.txt — three distributor catalogs, exactly as they'd land on your desk, ready to paste into any AI tool (Copilot, ChatGPT, or Claude). You need this file to run the exercises below.


Atlas calls it a "Hex Bolt." Ironclad calls it a "Hex Cap Screw." Summit doesn't list the grade. Three catalogs, three naming conventions for the same half-inch of zinc-plated steel — and your VP of Operations wants them merged into one comparison table so he can cut two of these three distributors and stop bleeding tail spend.

So you have three price files open side by side. Different column layouts. Different SKU systems. And — the part that will bite you — different units. One quotes per each. One quotes per box of a hundred. One quotes per box of fifty, with the "fifty" hiding in a footnote. Line them up by the number in the price column and you will rank them exactly backwards.

That's the arithmetic risk. Here's the one that keeps category managers up at night. Two catalog lines can read "nitrile gloves, blue, large" and mean two different products — one rated for a chemical-handling station, one not. Merge them as a "duplicate," phase out the distributor that carried the safety-rated one, and the consolidation quietly deletes a part that a solvent crew's hands depend on. Nobody notices until the wrong gloves show up in the supply closet and EHS starts asking questions.

The recommendation this case produces: consolidate to Summit (cheapest on 8 of 12 comparable lines after per-piece normalization), retain Ironclad for two safety-critical items only it carries, and phase out Atlas — the distributor whose printed prices look lowest but is cheapest on zero lines once the units are converted.

You've done this merge by hand before: four hours of INDEX/MATCH, calling a rep to ask what "BX" means in their catalog, re-reading footnotes for pack quantities. An AI can do it in minutes. The question this lesson answers is not "can AI merge three catalogs" — it can, faster than you'd expect. The question is: which parts of the merge do you still have to check yourself, and how do you brief the AI so those parts are checkable in ten seconds instead of re-derivable from scratch?

We'll do it in three moves: feed the catalogs so units come out comparable, cross-check the matches so a spec difference doesn't get merged away, and compress the result into the one page your VP will actually sign.


You’ve read the free three. The other seventeen are where the misses live.

The first lessons show what a general-purpose AI catches on a procurement task. The rest show what it misses — the unit conversion that survives an expert prompt, the two clauses it lists but never connects, the table that contradicts its own arithmetic. That gap is the job.

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