CDAO West Coast schedule

• How to align AI strategy with business & product roadmaps?  

• Talent & organizational structure: What AI teams look like at different stages of AI maturity? 

• Overcoming internal resistance to AI adoption.  

• Fine-tuning LLMs for industry-specific applications: Overcoming hallucinations and improving contextual accuracy. 

• Adopting vector databases & retrieval-augmented generation (RAG) to optimize knowledge-based AI applications. 

• Building enterprise-scale AI copilots: Automating workflows while ensuring human oversight and trust. 

• Data consolidation vs. sprawl – how to centralize AI-ready data without duplication. 

• Predicting & managing AI analytics costs – best practices for CFOs and CDOs. 

• Cloud vs. hybrid vs. on-prem strategies – balancing speed, security, and sustainability.  

20 min Breakout Session + 20 min Discussion Groups

• Revolutionizing Customer Insights: Learn how CoastHills Credit Union leveraged SurveyMonkey and Power BI to deliver next-day analytics, transforming raw customer feedback into actionable strategies that drive organizational success. 

• Accelerating Decision-Making: Discover how dynamic Power BI dashboards empowered leadership teams with real-time visibility into key metrics, enabling faster, data-driven decisions. 

• Streamlining Operations: Discover how CoastHills optimized customer feedback workflows, enhanced efficiency, and aligned those workflows with strategic business goals. 

• Bridging the Business-Analytics Gap: Learn how to foster collaboration between business leaders and front-line teams to deliver world-class customer service by combining innovative solutions with practical, measurable results. 

• How ML models identify anomalies and detect fraud in real time. 

• Why predictive AI improves fraud detection accuracy and reduces false positives. 

• Best practices for analyzing high-volume transactions instantly. 

• Ensuring transparency, explainability, and bias mitigation in fraud prevention. 

• How machine learning models identify cyber threats, fraud, and anomalies in real time. 

• Leveraging AI for predictive security, insider threat detection, and fraud prevention. 

• Ensuring explainability, governance, and compliance in AI-driven security frameworks. 

• Real world examamples on securing transactions, preventing breaches, and strengthening cyber resilience. 

• How machine learning enhances trading, credit approvals, and fraud prevention in real-time. 

• The role of cloud-based AI architectures in financial forecasting, risk modeling, and customer insights. 

• Practical applications of AI in high-frequency trading, customer engagement, and automated financial advisory services. 

• How cloud-based AI architectures are revolutionizing real-time analytics in finance. 

• Leveraging AI for fraud prevention in decentralized finance (DeFi) and digital banking. 

• Navigating AI regulations in real-time financial decisioning. 

Moderator: Ashlyn Lackey, Director, Innovation Strategy, Prudential Financial  

1 - Annie Ross, Vice President, Analytics Solutions, Global Banking, J.P. Morgan Chase 

• How AI enhances financial data sharing & aggregation through open banking APIs. 

• Privacy & security risks – Balancing data access with regulatory compliance (GDPR, CCPA, PSD2). 

• How banks can develop new revenue models based on AI-driven financial recommendations. 

• AI-driven credit scoring for financial inclusion – Leveraging alternative data from open banking APIs & digital footprints. 

• How AI models adjust dynamically based on spending patterns & financial behavior. 

• How AI is customizing financial products based on customer history & predictive analytics. 

• Building AI Governance Frameworks for Enterprise Adoption – Defining structured governance models to ensure compliance, security, and scalability. 

• How to align AI initiatives across multiple business units for enterprise-wide impact. 

• Regulatory Impact on AI Governance: Adapting to emerging regulations (GDPR, AI Act, SEC, and industry-specific mandates). 

• MLOps & Cost-Effective AI Scaling: Ensuring AI models remain reproducible, observable, and continuously optimized across the enterprise. 

Moderator: Jeremy Welland, Chief Data Officer, Gusto 

• What investments should AI & data leaders prioritize in 2025? 

• What organizational shifts will be needed for AI-driven companies? 

• Actionable steps attendees can take within 6-12 months. 

• How to align AI strategy with business & product roadmaps?  

• Talent & organizational structure: What AI teams look like at different stages of AI maturity? 

• Overcoming internal resistance to AI adoption.  

Jeremy Welland, Chief Data Officer, Gusto (Moderator) 

• Fine-tuning LLMs for industry-specific applications: Overcoming hallucinations and improving contextual accuracy. 

• Adopting vector databases & retrieval-augmented generation (RAG) to optimize knowledge-based AI applications. 

• Building enterprise-scale AI copilots: Automating workflows while ensuring human oversight and trust. 

• Data consolidation vs. sprawl – how to centralize AI-ready data without duplication. 

• Predicting & managing AI analytics costs – best practices for CFOs and CDOs. 

• Cloud vs. hybrid vs. on-prem strategies – balancing speed, security, and sustainability.  

• Leveraging AI-driven biomarker discovery to accelerate target identification & drug repurposing. 

• Using digital twins & synthetic data to simulate clinical trials and optimize patient recruitment. 

• Integrating multi-omics data for precision medicine & personalized treatment pathways. 

• How real-time predictive analytics enhances ICU decision-making and patient outcomes. 

• Leveraging AI to detect deterioration, reduce readmissions, and improve patient monitoring. 

• Best practices for ensuring seamless adoption and clinician trust in AI-driven decision support. 

Highlighting trust, AI-human interaction, and ethical considerations. 

• How evolving FDA, HIPAA, and global regulatory frameworks impact AI adoption in clinical decision-making and patient data management. 

• Best practices for explainability, bias mitigation, and risk assessment in AI-powered healthcare applications. 

• How policymakers and industry leaders are shaping guidelines for responsible AI deployment in medical research and patient care. 

• How federated learning aligns with HIPAA, GDPR, and evolving global AI regulations while enabling AI-driven breakthroughs in healthcare. 

• Discussion on how hospitals, pharmaceutical companies, and research institutions are leveraging federated learning for drug discovery, clinical trials, and diagnostics while ensuring data security. 

• Strategies for harmonizing distributed healthcare data across multiple organizations, EHR systems, and geographies. 

• Cross-industry lessons and future outlook. 

• Identifying optimal clinical trial sites using machine learning and real-world data. 

• Enhancing recruitment with AI-driven stratification models for better trial outcomes. 

• Reducing enrollment time, dropouts, and operational inefficiencies with automation. 

• Aligning AI-driven patient selection with FDA, HIPAA, and fairness standards. 

(15 min Breakout Session + 15 min Discussion Groups) 

• The Role of Trust in AI Adoption – How patient confidence and physician buy-in impact AI deployment in healthcare? 

• Explainable AI (XAI) - Best practices for ensuring transparency, fairness, and interpretability in clinical decision support systems. 

• Navigating compliance and ethical AI risks to meet HIPAA, FDA, and global healthcare AI regulatory standards while prioritizing patient safety. 

• Ensuring data interoperability, privacy, and governance for AI-driven healthcare advancements. 

• Building AI Governance Frameworks for Enterprise Adoption – Defining structured governance models to ensure compliance, security, and scalability. 

• How to align AI initiatives across multiple business units for enterprise-wide impact. 

• Regulatory Impact on AI Governance: Adapting to emerging regulations (GDPR, AI Act, SEC, and industry-specific mandates). 

• MLOps & Cost-Effective AI Scaling: Ensuring AI models remain reproducible, observable, and continuously optimized across the enterprise. 

• What investments should AI & data leaders prioritize in 2025? 

• What organizational shifts will be needed for AI-driven companies? 

• Actionable steps attendees can take within 6-12 months. 

• How to align AI strategy with business & product roadmaps?  

• Talent & organizational structure: What AI teams look like at different stages of AI maturity? 

• Overcoming internal resistance to AI adoption.  

Jeremy Welland, Chief Data Officer, Gusto (Moderator) 

• Fine-tuning LLMs for industry-specific applications: Overcoming hallucinations and improving contextual accuracy. 

• Adopting vector databases & retrieval-augmented generation (RAG) to optimize knowledge-based AI applications. 

• Building enterprise-scale AI copilots: Automating workflows while ensuring human oversight and trust. 

• Data consolidation vs. sprawl – how to centralize AI-ready data without duplication. 

• Predicting & managing AI analytics costs – best practices for CFOs and CDOs. 

• Cloud vs. hybrid vs. on-prem strategies – balancing speed, security, and sustainability.  

• Unifying in-store and online customer data for a seamless shopping experience. 

• Using AI to track consumer behavior in real time across multiple channels (e-commerce, mobile, physical stores). 

• Optimizing product recommendations and promotions based on historical and real-time data. 

• How AI anticipates supply chain disruptions and optimizes real-time decision-making. 

• Using AI-driven simulations to model demand shifts, supplier risks, and inventory fluctuations. 

• Enhancing agility, cost efficiency, and fulfillment accuracy with intelligent automation. 

• Discover how AI reshapes marketing, promotions, and real-time customer interactions. 

• Explore how generative AI creates product descriptions, ad creatives, and personalized recommendations. 

• See AI-driven chatbots, virtual stylists, and assistants transform customer engagement. 

• Learn best practices for aligning AI-generated content with brand voice, ethics, and regulations. 

• How generative AI, voice commerce, and computer vision enhance personalization and shopping experiences. 

• Enabling seamless, intuitive product discovery through speech recognition and image-based search. 

• Leveraging AI to generate personalized recommendations, virtual assistants, and interactive shopping experiences. 

• How to align data architecture, governance, and business objectives to support enterprise-wide analytics and AI. 

• Best practices for modernizing data ecosystems with data mesh, lakehouse, and multi-cloud strategies. 

• Ensuring data integrity, security, and regulatory alignment while enabling seamless access across business units. 

• Breaking down silos and building a data-driven culture that empowers decision-making and innovation. 

Mike Doll, VP of Data, The Guitar Center Company 

• Building AI Governance Frameworks for Enterprise Adoption – Defining structured governance models to ensure compliance, security, and scalability. 

• How to align AI initiatives across multiple business units for enterprise-wide impact. 

• Regulatory Impact on AI Governance: Adapting to emerging regulations (GDPR, AI Act, SEC, and industry-specific mandates). 

• MLOps & Cost-Effective AI Scaling: Ensuring AI models remain reproducible, observable, and continuously optimized across the enterprise. 

Moderator: Jeremy Welland, Chief Data Officer, Gusto 

• What investments should AI & data leaders prioritize in 2025? 

• What organizational shifts will be needed for AI-driven companies? 

• Actionable steps attendees can take within 6-12 months.