--- schema_version: "1" id: 76670af0301584fc6ecb03a03f3685dde6929f5c607ed7c44fdf33f6d6fa4c37 title: Implementation Tasks for Predictive AI Solutions collection: admin version: current locale: en-us portal_url: https://docs.salesforce.com/admin/current/en-us/data/264-0-0/c360_a_ai_predictive_implement.md canonical_url: https://help.salesforce.com/s/articleView?id=data.c360_a_ai_predictive_implement.htm&release=264.0.0&type=5 source_site: help.salesforce.com content_type: documentation content_hash: ac07b54ed525d20b5e4e51785d2dac6711baa89face8b0f4ecec66def283c8de description: The process of implementing predictive AI solutions typically involves a series of tasks. products: - Data 360 release: "264" --- # Implementation Tasks for Predictive AI Solutions The process of implementing predictive AI solutions typically involves a series of tasks. ### Required Editions

Available in: All Editions supported by Data 360. See Data 360 edition availability.

## Common Tasks

#	Task	Description	More Information
1	Define the Outcome to Predict	Define an outcome that you want to improve. It’s most likely a key performance indicator (KPI) for your business. Most organizations have many outcomes that are candidates for improvement. Start with one outcome that’s significant in your business. Over time, you can develop and apply predictive AI models to many different outcomes concurrently.	AI Strategy AI + Data: Project Planning
2	Assemble the Training Data	Design and populate the training data (in a Data Model Object, or DMO) to contain the historical data you want the model to learn from. The DMO contains tabular data that you normalize for model training. You can use Salesforce data as well as data that’s external to Salesforce.	Prepare Your Training Data
3	Create the Model	Based on the training data, create and configure a predictive model that tells Einstein how to analyze your data. Models provide the settings and preferences that Model Builder uses to generate insights.	Create Predictive AI Models From Scratch
4	Evaluate Model Quality	Assess performance and refine the predictive model.	Evaluate Model Quality Address Data Issues
5	Predict and Improve Outcomes	Predict outcomes and get prescriptions using the model you deployed.	Get Predictions, Prescriptions, and Top Predictors

## Iterative Refinement and Continuous Improvement Implementation is an iterative process rather than a linear one. Model Builder is designed for rapid exploration, experimentation, and implementation. You learn as you go. Every step of the way, you use built-in feedback to check your results, review your assumptions, ask new questions, make adjustments, and try again. Add a column to your training data. Change model settings. Resolve alerts, refine thresholds, and filter data. As you fine-tune your approach, each improvement can help lead you to better operational outcomes.