How AI Fabricated My Entire Supplier Analysis

The context poisoning problem supply chain leaders must understand

AI Supplier Analysis

I recently tested an AI tool to speed up supplier performance analysis. I uploaded a CSV of on-time delivery data and asked it to summarize the trends. In seconds, the model gave me a polished table: average lead times, defect rates, supplier scores, and even commentary that looked like it came straight from a consulting deck.

It seemed perfect - until I cross-checked one “supplier quote” in the CSV. No match. I tried another. Again, no match.

The model had fabricated entire supplier comments and even produced inaccurate summary stats. The analysis looked professional but was built on hallucinations.

This is the new risk supply chain executives face when using AI for supplier analysis: context poisoning.

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What is context poisoning?

When you upload supplier data into an AI tool and ask for insights, you’re doing a form of Retrieval-Augmented Generation (RAG). The AI pulls from your dataset, but also from its own prior outputs and memory. Without proper safeguards, it can’t always distinguish between real supplier data and its own generated content.

Here’s how it spirals in supply chain analysis:

1. AI fabricates a supplier delay reason (“Supplier X faced strikes in June”).

2. It then references that fabricated reason as “evidence” in its trend summary.

3. It finally recommends shifting spend away from Supplier X based on the false trend.

That’s context poisoning: fake AI-generated content becomes treated as true input for decision-making. In supplier management, this can quickly lead to flawed sourcing decisions, contract missteps, and damaged relationships.

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How to prevent AI hallucinations in supplier analysis

AI doesn’t have to be unreliable. The key is structuring your workflow to force the system to acknowledge what it knows versus what it’s guessing.