What challenges did Principal Financial Group face before AI implementation?

Principal Financial Group needed to analyze massive volumes of customer interactions securely and in compliance with regulatory standards. They aimed to understand call drivers, topics, sentiment, and improve the overall omnichannel customer experience.

What was the impact of the AI solution?

Processed over 1 million calls, producing 63 million speech segments for analytics. Enabled deep customer interaction insights for improved call routing, sentiment analysis, and upsell opportunities. Expanded analytics across email and other digital channels for a unified view of customer interactions. Improved employee productivity and customer experience using generative AI enhancements.

Source

ExpandedEvidence: Low25/100

Principal Financial Group Enhances Customer Insights with AWS Post Call Analytics Solution and Amazon Bedrock

Use case typeCustomer experience analyticsUpdated Jun 13, 2026

Principal Financial Group improved omnichannel customer experience by analyzing millions of contact center interactions across voice, email, and chat using AWS Contact Center Intelligence Post Call Analytics solution with Amazon Bedrock. They implemented an automated workflow with Amazon Transcribe Call Analytics, AWS Step Functions, Amazon S3, and Lambda to transcribe and analyze calls integrated with Genesys Cloud CX. Enhanced transcripts were enriched with contact trace record metadata, enabling advanced analytics and generative AI insights to improve call routing, upsell identification, and customer self-service improvements.

Organization: Principal Financial Group
Industry: Finance
Location: United States
Published: November 2023

Reported outcomes

Strategic outcomes

Customer experience & trustImproved omnichannel customer experienceNew product / capabilityEnabled advanced interaction analyticsBetter decisions & insightGenerated deeper customer interaction insightsMarket & geographic expansionExpanded analytics across digital channels

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Principal Financial Group
Provider: AWS
Maturity: Unknown
Linked source: AWS Machine Learning Blog

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Customer Interaction Analytics
2Omnichannel Analytics
3Generative AI Insights

Principal Financial Group needed to analyze massive volumes of customer interactions securely and in compliance with regulatory standards.
They aimed to understand call drivers, topics, sentiment, and improve the overall omnichannel customer experience.

Implemented AWS CCI Post Call Analytics (PCA) with call transcription, metadata enrichment, and integrated contact center data.
Utilized foundational generative AI models on Amazon Bedrock for advanced insights and decision support.
Collaborated with AWS teams to extend open-source PCA capabilities, integrating Genesys CTR metadata and building custom ML models for topic and intent identification.

Technologies

AWS Contact Center Intelligence Amazon Transcribe Call Analytics AWS Step Functions Amazon S3 AWS Lambda Amazon QuickSight Amazon QuickSight Q Amazon Bedrock

Processed over 1 million calls, producing 63 million speech segments for analytics.
Enabled deep customer interaction insights for improved call routing, sentiment analysis, and upsell opportunities.
Expanded analytics across email and other digital channels for a unified view of customer interactions.
Improved employee productivity and customer experience using generative AI enhancements.

Architecture

Architecture includes AWS Step Functions for workflow orchestration, Amazon S3 for storage, Amazon Transcribe Call Analytics for transcription, AWS Lambda for custom processing, Amazon QuickSight for visualization, and generative AI models on Amazon Bedrock for advanced analytics.

Sources & evidence1

Evidence: Low25/100Evidence strength