Business Problem
Procurement had data that contained large volumes of unstructured textthat were difficult to interpret consistently, limiting automation andincreasing dependence on manual expertise.
Actions Taken
· Utilized semantic matching capabilitiesto interpret and structure unformatted procurement text.
· Applied document- and text-levelunderstanding to support classification workflows.
· Enabled algorithmic matching betweenfree-text entries and standardized classification structures, with humanvalidation where required.
Outcomes Achieved
· Improved interpretability of unstructureddata.
· Supported AI-driven spend classificationaccuracy and consistency.
· Reduced dependency on manual interpretationfor recurring activities.
