Explain what drives a model's predictions to non-technical stakeholders

Help me explain what drives my model's predictions to a non-technical audience. What the model does and how it is used: [e.g. "scores leads weekly; sales calls the top 20%"] Model type: [e.g. gradient boosted trees, logistic regression] Performance in plain terms: [e.g. "top 20% of scores contain 55% of conversions"] Interpretability outputs: [PASTE feature importance, SHAP summary, coefficients, partial dependence descriptions, or example explanations for individual predictions] Audience and their likely concerns: [e.g. "sales leaders who think the model ignores relationships"] Produce: 1. What the model does in 2-3 sentences, with no jargon. 2. The top drivers in plain English, with direction ("customers who logged in fewer than twice in the last month are much more likely to cancel"). Group technical features into business concepts. 3. Two or three worked examples of individual predictions explained simply. 4. How good it is, framed against the current approach or a baseline, and how often it will be wrong. 5. What it does not mean: clarify that drivers are associations, not proven causes, unless causal work was done. 6. Limits and safeguards: where the model is weaker (segments, new customers), and when a human should override it. 7. Likely questions from this audience with short answers. Rules: do not overstate accuracy or claim causation. Avoid terms like SHAP, log-odds, or AUC in the main text; put any needed technical notes in a short appendix.