1) The document discusses challenges in predictive analytics projects including finding ideas, assessing impact, prioritizing projects, developing and deploying models, and post-implementation monitoring. 2) It describes moving from manual processes to automating workflows to using decision-making and machine learning to drive innovation. New data sources like prescriptions and driving records can help leverage new predictors for better customer experiences. 3) A framework is presented for evaluating analytics projects based on their value, data and process integration requirements, urgency, and ease of implementation to identify high priority projects that maximize return. The top priority projects identified were predictive underwriting, pricing models, and customer journey analytics.