Salesforce AI-powered predictive analytics play a crucial role in customer relationship management (CRM), sales forecasting, and lead scoring. However, ensuring the reliability of predictions remains a challenge. Traditional conformal prediction methods provide uncertainty quantification but often lack adaptability to business-specific data distributions. To address this, we introduce SalesConformal, a novel adaptive conformal prediction framework tailored for Salesforce applications. Our approach learns dynamic partitions of the input space, ensuring homogeneous non-conformity scores across interpretable customer segments. This method integrates seamlessly with Salesforce AI models, producing more reliable prediction intervals and group-conditioned guarantees for lead conversions, sales forecasting, and customer retention. Additionally, the learned partitions aid in data collection and model selection, enhancing overall CRM decision-making. Experimental results on Salesforce sales and marketing datasets demonstrate that SalesConformal improves worst-case prediction reliability, reducing uncertainty in critical business forecasts.