The Supplier Intelligence Copilot uses AI to interpret structured supplier information and help users understand potential risk drivers, supporting evidence, and possible next actions. Because these outputs can influence supplier investigations, a response should not be considered good simply because it sounds convincing.
The evaluation framework focuses on four questions:
The goal is to evaluate whether the Copilot is reliable enough to support human decision-making, not whether it can replace human judgment.
I would evaluate every response across five dimensions.
| Dimension | What we are checking |
|---|---|
| Evidence Grounding | Are claims supported by the supplier context provided to the model? |
| Risk Interpretation | Does the response correctly identify and explain the most relevant risk signals? |
| Recommendation Quality | Are suggested actions reasonable and connected to the evidence? |
| Uncertainty Handling | Does the model acknowledge missing or insufficient information instead of guessing? |
| Output Compliance | Does the response follow the required structure and include all expected fields? |
For example, if the supplier context shows deteriorating delivery performance but contains no information about financial health, the Copilot can discuss the delivery issue. It should not claim that the supplier is experiencing financial difficulties. That would fail the Evidence Grounding evaluation.
Before production use, I would create a small evaluation set containing representative supplier scenarios. The initial evaluation set could include cases such as:
Clear high-risk signals: Multiple supplier metrics indicate deterioration.
Mixed signals: Quality is improving while delivery performance is declining.
Healthy supplier: No meaningful deterioration is present.
Missing data: One or more important supplier signals are unavailable.
Conflicting signals: Different metrics suggest different conclusions.
Insufficient evidence: The available data does not support a strong recommendation.