# Panel Discussion on Enterprise Generative AI Implementation ## Overview This panel discussion brings together AI leaders from major technology companies and consultancies to discuss the practical challenges and solutions for implementing generative AI in enterprise settings. The panelists include: - Camilia Aryafar - Senior Engineering Director, Google Cloud AI - Shri Ror - CEO/Co-founder, Symbol AI - Shingai Manga - Head of AI Education, Chain ML - Manas Buya - AI/Data Practice Lead, Deloitte ## Key LLMOps Challenges and Considerations ### Model Selection and Evaluation - Organizations need frameworks to evaluate different models for specific use cases - Domain-specific models are becoming increasingly important versus general-purpose models - Companies should develop internal evaluation paradigms based on their data and customer needs - Need to establish clear metrics and KPIs to measure success ### Infrastructure and Scaling Considerations - Building redundancy into model deployments - Ensuring reliable and responsive systems at scale - Meeting SLAs when scaling to billions of users - Managing document processing at enterprise scale - Developing surrounding infrastructure beyond just the LLMs ### Implementation Strategy - Start with value-driven approach rather than technology-first - Focus on existing applications that can be enhanced with GenAI - Consider using service providers rather than building from scratch - Implement human-in-the-loop processes where appropriate - Set up guardrails and safety measures ### Production Readiness Assessment - Data and platform maturity varies significantly by industry - Need proper data infrastructure before GenAI implementation - Regulated industries require additional compliance considerations - Important to have organizational AI literacy and skill readiness ## Best Practices for Production Deployment ### Development Approach - Adopt experiment-driven development culture - Start with POCs to validate use cases - Implement iterative improvement processes - Focus on measurable business outcomes ### Use Case Selection - Identify repetitive tasks suitable for automation - Look for opportunities in: ### Technical Implementation - Consider using specialized models for different tasks - Build evaluation frameworks for model selection - Implement proper monitoring and observability - Ensure scalable infrastructure ### Risk Management - Extra considerations for regulated industries - Implementation of proper security measures - Compliance with industry regulations - Data privacy protections ## Emerging Trends and Future Directions ### Agent-based Systems - Growing interest in autonomous agents - Need for better control flow in prompt engineering - Development of specialized agents for specific tasks - Integration of factchecking and redaction capabilities ### Domain Specialization - Movement toward industry-specific models - Development of internal model-to-data frameworks - Real-time model selection based on task requirements - Focus on domain expertise in model development ### Productivity Enhancement - Emphasis on developer productivity tools - Improved search and summarization capabilities - Integration with existing workflows - Focus on augmentation rather than replacement ## Implementation Recommendations ### Team Structure - Mix of general software engineers and AI specialists - Strong emphasis on infrastructure and operations - Solution engineering roles for bridging technical and business needs - Domain experts for regulated industries ### Getting Started - Begin with existing applications and workflows - Partner with service providers for initial implementations - Focus on measurable business objectives - Start with lower-risk use cases ### Common Pitfalls to Avoid - Don't implement technology without clear business cases - Avoid rushing to production without proper evaluation - Don't ignore regulatory requirements - Consider organizational readiness ## Industry-Specific Considerations ### Healthcare - Special attention to regulatory compliance - Focus on patient data privacy - Opportunities in clinical notes processing - Need for high reliability and accuracy ### Financial Services - Strict regulatory requirements - Need for robust security measures - Focus on risk management - Importance of audit trails ### Technology Sector - Generally more advanced in adoption - Focus on scaling and performance - Emphasis on developer tools - Greater tolerance for experimentation