Procurement AI Has a Data Problem and It’s Bigger Than You Think
Article Date: 2026-02-04T08:04:00-05:00
Article URL: https://www.supplychain247.com/article/procurement-ai-hallucinations-supplier-data-problem
Article Image: https://www.supplychain247.com/images/2025_article/AI-procurement-GettyImages-2183482739.jpg
Summary
New research from apexanalytix (interview with Danny Thompson, Chief Product Officer) warns that procurement AI is producing plausible but incorrect outputs — so-called “hallucinations” — because supplier data is fragmented, duplicated or out of date. The article explains how disconnected systems, weak governance and data decay leave AI models guessing rather than deciding, which produces false confidence, missed risk signals, failed automation and operational disruption. Thompson argues that cleaning and governing supplier master data is the prerequisite for procurement AI to be effective and safe.
Key Points
- Procurement AI hallucinates when it fills gaps in poor supplier data with plausible but incorrect answers.
- Fragmentation arises from multiple systems (ERP, procurement tools, spreadsheets) and a lack of unified governance.
- AI magnifies data problems: automated workflows can “fail fast” at scale, turning small data issues into big operational risks.
- Many procurement solutions labelled “AI” lack the volume of accurate supplier data needed to produce reliable insights.
- Organisations often treat supplier data as administrative rather than strategic, entrenching duplication and inconsistency.
- Fixes include centralised master data, clear ownership, standardised attributes and continuous validation/automated updates.
Context and Relevance
As procurement teams rush to adopt AI for sourcing, risk screening and automation, the quality of supplier data has become the critical dependency. The piece places supplier-data hygiene at the heart of any successful AI transformation in procurement: without trustworthy master data, AI projects risk creating blind spots, false positives/negatives and costly automation errors. For anyone responsible for procurement, risk or digital transformation, this is directly relevant to project strategy, governance and ROI.
Author note
Punchy: This isn’t a tech fad problem — it’s a structural one. The interview makes a clear case that data governance must come first. If you care about AI actually delivering value (not drama), read the practical bits about master data and ownership.
Why should I read this?
Short and blunt: don’t be the team that slaps AI on top of rubbish supplier records and then wonders why it breaks things. Read this to understand the real blocker to procurement AI — messy supplier data — and what to do about it before your automation turns into a liability.
Source
Source: https://www.supplychain247.com/article/procurement-ai-hallucinations-supplier-data-problem