Merchandising & Buying
Supplier spec sheet extraction
Extract structured product data from unstructured supplier spec sheets, PDFs, and email attachments. Outputs clean records ready for system upload.
70–85%
reduction in manual data entry time
Use case assessment
Negligible commercial impact. Saves time at the margins but won't move the needle on revenue or profit.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Supplier documents vary significantly in format — the extraction model needs exposure to the range of formats you receive before you can rely on it.
Build a validation step for anomalous outputs (blanks, implausible values) before records reach the system.
Map the target data schema first: knowing exactly what fields you need makes prompt design and validation much more precise.
Consider whether the root problem is the input format — a structured supplier portal or standardised template may solve the problem more cleanly than processing unstructured documents downstream.
Experiment starter: Collect 50 spec sheets from your top five suppliers. Define the target extraction schema. Run a prompted extraction and measure field-level accuracy. Aim for over 90% accuracy on mandatory fields before proceeding to automation.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in Merchandising & Buying
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