Artificial intelligence in Pega Sales Automation for Insurance™ helps you to proactively assess risks on deals in the pipeline, coach newly recruited sales representatives, and identify leads that have a high probability of being convertible to opportunities.

Before using artificial intelligence insights with Pega Sales Automation for Insurance, you must activate the feature for your implementation and then train Pega’s adaptive models for artificial intelligence.

To configure your application for artificial intelligence, log in to Pega Sales Automation for Insurance and complete the following steps:

1. Activating artificial intelligence
2. Verifying Decision Strategy Manager (DSM) nodes
3. Importing historical data
4. Truncating data sets
5. Deleting existing models
6. Running the data flows for opportunity insights
7. Running the data flows for sales coach
8. Runing the data flows for lead ranking (B2B selling mode)
9. Runing the data flows for lead ranking (B2C selling mode)
10. Enabling preloaded NBAs

If you installed the Pega Sales Automation for Insurance sample application, reset the artificial intelligence sample data to its original form by using the Tools menu in the Sales Ops portal.

Activating artificial intelligence

1. In the navigation pane of App Studio, click Settings > Application Settings.
2. To see all of the AI capabilities, on the Features tab, select the Artificial intelligence insights - opportunity insights, lead ranking, and sales coach check box.
3. Enable any of the listed AI capabilities by selecting their individual check boxes.
4. Click Save.
5. In the header of App Studio, click Switch Studio, and then click Dev Studio.
6. In the navigation pane of Dev Studio, click Records > SysAdmin > Job Scheduler, override the job schedulers listed above, and then set the Enable job scheduler toggle to true for each of the following rules:
   • CRMIOpportunityInsights
   • DailySnapshotsForAE
   • DailySnapshotsForLead
   • RecommendationsForRep

Verifying Decision Strategy Manager (DSM) nodes

1. In the navigation pane of Dev Studio, click Configure > Decisioning > Infrastructure > Services.
2. Verify that each of the following services contains a node with a status of Normal:
   • Decision Data Store
   • Adaptive Decision Manager
   • Data Flow
   • Real-time Data Grid

Importing historical data

1. In the navigation pane of Dev Studio, click Configure > Application > Distribution > Import.
2. Click Choose File, browse for and select the HistoricalData file from your distribution media, and then follow the instructions in the import wizard.
3. Repeat these steps for the SAIOpportunityInsightTrainingData and SAISalesRepEffectivenessData files.

Pega-provided historical data consists of a snapshot of data from a production environment for various models.

Truncating data sets

1. In the header of Dev Studio, in the search field, search for and select the pxDecisionResults data set of the Data-Decision-Results class.
2. ClickActions > Run.
3. In the Operations field, select Truncate.
4. Click Execute.
5. Repeat steps 1 to 4 for the PreviousStages data set of the SA-SR class.

Deleting existing models

1. In the header of Dev Studio, click Configure > Decisioning > Models Management.
2. Select all of the existing models:
   • PredictWin
   • PredictMoveNextStage
   • PredictCloseDate
   • BaseWinModel
   • LeadRanking
   • PredictEffectiveness
3. Click Delete Models.

Running the data flows for opportunity insights

Run the data flows for opportunity insights in order to pass the incoming data to the adaptive models, so that the system calculates opportunity insights.

1. In the header of Dev Studio, in the search field, search for and select the StoreBusinessOpportunitySnapshots data flow.
2. Click Actions > Run.
3. On the Data flow test run form, click Start.
4. Repeat steps 1 to 3 for each of the following data flows:
   • TrainBusinessOppFromHistory
   • StoreIndividualOpportunitySnapshots
   • TrainIndvOppFromHistory

Running the data flows for sales coach

Run the data flows for the sales coach in order to pass the incoming data to the adaptive models, so that the system calculates sales coach suggestions.

1. In the header of Dev Studio, in the search field, search for and select the StoreSalesRepSnapshots data flow of the PegaInsCRM-Data-SFA-SalesRepPredictors class.
2. Click Actions > Run.
3. On the Data flow test run form, click Start.
4. After receiving a confirmation message with a successful result, repeat steps 1 to 3 for the CaptureEffectivenessOutcomes and StorePropensitySnapshots data flows of the PegaInsCRM-Data-SFA-SalesRepPredictors class.

Running the data flows for lead ranking (B2B selling mode)

Run the data flows for lead ranking in order to pass the incoming data to the adaptive models, so that the system calculates the B2B lead scores.

1. In the header of Dev Studio, in the search field, search for and select the StoreLeadSnapshots data flow.
2. Click Actions > Run.
3. On the Data flow test run form, click Start.
4. Repeat steps 1 to 3 for the CaptureLeadOutcomes data flow.
5. To set up predictors and calculate lead score for existing leads and store them in the lead predictor table, run the InitialiseLeadPredictorTable activity.

Running the data flows for lead ranking (B2C selling mode)

Run the data flows for lead ranking in order to pass the incoming data to the adaptive models, so that the system calculates the B2C lead scores.

1. In the the header of Dev Studio, in the search field, search for and select the StoreIndividualLeadSnapshots data flow.
2. Click Actions > Run.
3. On the Data flow test run form, click Start.
4. Repeat steps 1 to 3 for the CaptureInvidualLeadOutcomes data flow.
5. To set up predictors and calculate lead score for existing leads and store them in the lead predictor table, run the InitialiseLeadPredictorTable activity.

Enabling preloaded NBAs

1. In the navigation pane of Dev Studio, click Records > SysAdmin > Dynamic System Setting, and then search for the UsePreloadedNBA dynamic system setting.
2. Set the UsePreloadedNBA dynamic system setting to true.