Sellers on a team stopped trusting the AI-generated opportunity scores because the scores changed week to week without any indication of why. A consultant wants sellers to see which specific factors, such as days since last activity or contact engagement, drove a given score up or down. Which configuration addresses this?
Select an answer to reveal the explanation.
Short Explanation
When people stop trusting a number, the fix is almost never to change the number faster or replace it with a different number, the fix is to show your work. That is the whole idea here: sellers do not need the score to hold still, they need to see why it moved. Retraining the model on a tighter schedule does not add any of that visibility, the score could still swing around just as mysteriously, maybe more so. Swapping in a completely different forecasting feature answers a leadership-level revenue question, not an individual seller's question about their own deal. And handing sellers the keys to edit the scoring configuration is overkill and genuinely risky, since fiddling with model settings without training could throw off scoring for everyone, and it still would not explain anything on its own. What actually solves the trust problem is turning on the explanation view that breaks a score down into the factors that produced it.
Full Explanation
The correct answer is D. The score explanation or insight card is the feature designed to show sellers the specific contributing factors behind a given opportunity score, which directly restores trust by making the score's movement explainable rather than opaque. Option A is incorrect because retraining the model more often does not add any transparency; the scores could still change without sellers understanding why, and more frequent retraining could even increase volatility. Option B is incorrect because premium forecasting is a different capability focused on aggregate revenue and close-date projections, not on explaining an individual opportunity's score, so it does not address the sellers' concern at all. Option C is incorrect because giving sellers edit access to the scoring model's configuration is both unnecessary for explainability and risky, since untrained changes to model configuration could degrade scoring accuracy for the whole team rather than simply showing why a score changed. The scenario calls for visibility into scoring factors, which is exactly what the explanation card provides.