# Implementation of Generative AI in Banking Customer Service and Knowledge Management ## Overview This case study examines the implementation of generative AI solutions across multiple major banks, with a particular focus on Discover Financial Services' production deployment of gen AI in customer service operations. The implementations demonstrate careful consideration of regulatory requirements, risk management, and practical operational constraints in the highly regulated banking sector. ## Technical Infrastructure and Foundation ### Cloud Platform Selection - Banks established strong cloud foundations before implementing AI - Multiple institutions chose Google Cloud as their primary AI platform - Vertex AI integration with Gemini was utilized for model deployment - Technical implementation of base infrastructure was relatively quick (weeks) - Focus on security and compliance requirements in cloud architecture ### Data Management - Emphasis on proper data governance and quality - Implementation of customer intelligence engines - Integration of PII data with appropriate security controls - Focus on making data both secure and usable ## Implementation Approach ### Risk Management Framework - Creation of dedicated "Generative AI CLO" organizational framework - Cross-functional team involvement including: - Development of "no regret rules" for AI usage - Structured intake process for ideas and experimentation - Risk-based prioritization of use cases ### Model Development and Deployment - Focus on RAG (Retrieval Augmented Generation) implementation - Integration with existing knowledge bases and documentation - Careful tuning of models for specific use cases - Implementation of domain-specific golden answers - Development of automated evaluation tools ### Human-in-the-Loop Components - Technical writers involved in content creation and validation - Expert agents providing feedback on model outputs - Implementation of feedback mechanisms: - Legal and compliance review of AI-generated content ## Production Use Cases ### Customer Service Enhancement - Problem: Complex customer queries requiring extensive procedure lookup - Solution: AI-powered document summarization and retrieval - Results: 70% reduction in agent search time - Search time reduced from minutes to seconds - Direct linking to source documents for verification ### Knowledge Base Management - Implementation of gen AI for technical writing assistance - Maintenance of human review processes: - Focus on procedure documentation improvement ## Challenges and Solutions ### Data Quality - Recognition of "garbage in, garbage out" principle - Implementation of data quality improvement processes - Business ownership of data quality - Automated quality checks and validations ### Regulatory Compliance - Development of explainability frameworks - Implementation of fairness checks - Maintenance of human oversight - Documentation of model decisions and processes ### Scale and Performance - Skill-by-skill rollout approach - Focus on measurable customer outcomes - Continuous monitoring of performance - Regular evaluation of risk boundaries ## Lessons Learned ### Organizational Readiness - Importance of cross-functional collaboration - Need for clear governance structures - Value of incremental implementation - Significance of business unit buy-in ### Technical Considerations - Platform selection importance - Integration with existing systems - Security and compliance requirements - Performance monitoring needs ### Future Directions - Expansion to additional use cases - Continuous improvement of models - Enhanced automation possibilities - Scaling successful implementations ## Impact Assessment ### Measurable Outcomes - Significant reduction in search times - Improved customer experience - Better agent satisfaction - Maintained regulatory compliance ### Business Benefits - Enhanced operational efficiency - Improved customer service quality - Better resource utilization - Reduced training requirements ## Best Practices Identified ### Implementation Strategy - Start with lower-risk use cases - Build strong governance frameworks - Maintain human oversight - Implement feedback mechanisms ### Technical Architecture - Use of enterprise-grade AI platforms - Integration with existing systems - Strong security controls - Scalable infrastructure ### Change Management - Focus on user adoption