Normalized claim
Time: 50% decrease
Reduced time spent responding to customer inquiries and RFPs by approximately 50%.
Principal Financial Group enhances workforce productivity with generative AI using QnABot and Amazon Q Business
Use case typeFraud detectionUpdated Jun 13, 2026
Principal Financial Group, a global financial company serving about 64 million customers, faced challenges managing vast unstructured internal data for quick responses to inquiries. They needed a compliant, secure AI solution to improve internal information access and workforce productivity. Principal deployed an intelligent self-service role-based chatbot using QnABot on AWS integrated with Microsoft Azure Entra ID, leveraging Amazon Q Business and Amazon Bedrock foundation models for advanced generative AI capabilities including query processing and summarization. The solution provides secure, compliant AI-powered answers from indexed internal documents like SharePoint data. It achieved about a 50% reduction in time responding to client inquiries and RFPs, with over 95% of queries receiving accepted or improved answers, boosting productivity across work roles. The platform is scalable with real-time monitoring dashboards, user feedback loops, and safeguards for responsible AI deployment, maintaining high accuracy and relevancy. Future expansion includes adding many new use cases building on this successful generative AI assistant foundation.
- Organization
- Principal Financial Group
- Industry
- Finance
- Location
- United States
- Published
- November 2024
Reported outcomes
+99%
quantified impactAdoption & scale
−50%time+95%quantified impact
Strategic outcomes
Customer experience & trustReduced response time for inquiries and RFPsBetter decisions & insightImproved answer relevance and accuracySpeed & agilityBoosted workforce productivityRisk & complianceEnabled secure compliant AI platform
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Quantified impact: 95% increase
Maintained over 95% accepted or improved answers by users, with 99% of documents deemed relevant and up-to-date.
Normalized claim
Quantified impact: 99% increase
Maintained over 95% accepted or improved answers by users, with 99% of documents deemed relevant and up-to-date.
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- Principal Financial Group
- Provider
- AWS
- Maturity
- Production
- Linked source
- AWS Machine Learning Blog
Principal deployed an intelligent self-service role-based chatbot using QnABot on AWS integrated with Microsoft Azure Entra ID, leveraging Amazon Q Business and Amazon Bedrock foundation models for advanced generative AI capabilities including query processing and summarization
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1Conversational AI
- 2Knowledge Management
- 3Productivity Enhancement
- Handling large volumes of heterogeneous unstructured data across multiple formats and sources, making quick and accurate information retrieval difficult for employees.
- Need for secure, compliant, and responsible AI capable of understanding natural language queries and providing up-to-date, role-specific answers.
- Limitations of manual search processes leading to inefficiencies and high labor time requirements in responding to customer inquiries, RFPs, and internal support requests.
- Built an intelligent AI chatbot named Principal AI Generative Experience using QnABot on AWS integrated with Microsoft Azure Active Directory for role-based access control.
- Leveraged Amazon Q Business for advanced generative AI responses and Amazon Bedrock foundation models for semantic matching, query disambiguation, answer lookup, and summarization.
- Developed custom frontend chat interface using AWS Lex Web UI, and continuous improvement via user feedback, monitoring dashboards, and adherence to responsible AI governance and security.
- Implemented indexing improvements for handling SharePoint datasource securely and allowed users to attach files dynamically for real-time querying.
- Reduced time spent responding to customer inquiries and RFPs by approximately 50%.
- Maintained over 95% accepted or improved answers by users, with 99% of documents deemed relevant and up-to-date.
- Significantly boosted productivity allowing users to focus on strategic decision-making rather than manual search tasks.
- Provided a secure, scalable generative AI platform with strong governance ensuring compliance, ethical AI use, and data privacy.
Architecture
The architecture includes QnABot integrated with AWS services such as Amazon Bedrock, Amazon Q Business, Amazon OpenSearch Service, AWS IAM Identity Center, and data connectors for SharePoint and S3, supporting a secure, compliant conversational AI platform with real-time monitoring and user feedback integration.
Sources & evidence1
Evidence: Medium50/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Quantified outcome available
- Technical implementation details available
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: Blog PostPublished: Nov 15, 2024Publisher: AWS Machine Learning BlogEvidence: VendorConfidence: Medium
AI-generated summary. Verify important details with the linked sources before relying on this case.
Explore related AI use cases
Customer evidence
Provider evidence
Browse the catalog
Was this useful?
Community
Comments
No published comments yet.