We run procurement for a large beverage distributor with operations across five ERP instances, and until recently our supplier data was a mess. Duplicate records, inconsistent naming conventions, missing contact info—you name it. We knew AI-powered risk modeling and automated sourcing could help us, but every pilot failed because the models couldn’t make sense of our supplier master.
We invested in a centralized supplier MDM hub with API-first integration and change data capture. Machine learning now detects duplicates and standardizes records automatically, cutting manual review by about 70%. We also built out cross-domain links so supplier data connects cleanly to product catalogs and customer records, which turned out to be critical for traceability and recall scenarios.
The results have been solid: onboarding cycles are faster, compliance validation is automated, and our procurement team finally trusts the data enough to rely on AI recommendations for sourcing decisions. The biggest lesson? You can’t bolt AI onto dirty data. Data quality and governance have to come first, or you’re just amplifying the chaos.