Opening Remarks

From Strategy to Impact: Building an AI-Enabled Clinical Quality Organization

As artificial intelligence becomes increasingly embedded across clinical development, quality leaders must establish the governance, processes, and cross-functional collaboration needed to ensure AI delivers measurable value while maintaining GCP compliance and inspection readiness.

• Defining a strategic framework for evaluating and implementing AI solutions across Clinical Operations and Clinical Quality
• Establishing governance models that align Quality, Clinical Operations, Data Management, and Technology teams around responsible AI adoption
• Balancing innovation with regulatory expectations to maintain data integrity, patient safety, and GCP compliance
• Identifying opportunities to improve efficiency, risk management, and quality oversight through practical AI applications
• Driving organizational change and stakeholder engagement to ensure AI initiatives deliver sustainable business and quality impact

Rebuilding the Clinical Trial Operating Model: How AI is Transforming Protocol Design, Quality, and Execution

AI is reshaping clinical development beyond automation — transforming how organizations design protocols, manage complexity, govern decisions, engage cross-functional teams, and execute trials with greater speed, quality, and intelligence.

• From Static Protocols to Intelligent Clinical Trial Frameworks — Explore how AI is changing protocol development by identifying complexity, improving consistency, supporting risk-based decisions, and enabling teams to move from standardized templates toward dynamic, data-driven protocol strategies
• Redefining Clinical Operations Through AI-Enabled Decision Making — Examine how AI is impacting the clinical trial operating model by connecting clinical operations, quality, safety, and study teams with the right insights and the right stakeholders at the right time
• Creating Governance for AI-Driven Clinical Development — Discuss how organizations can build practical AI governance strategies that define oversight, validation, accountability, and the balance between human expertise and AI-enabled recommendations
• Scaling AI Across the Clinical Trial Lifecycle — Explore how digital protocols, structured data, and AI-powered workflows can reduce bottlenecks, improve timelines, enhance quality, and create a more connected clinical development ecosystem

Validation in Clinical Research: Building Trust in the Next Generation of AI-Enabled Clinical Systems

As AI becomes increasingly integrated into clinical technologies and vendor platforms, organizations must evolve validation, governance, and oversight practices to ensure AI-enabled systems improve study quality while maintaining regulatory compliance, data integrity, transparency, and patient safety.

• Explore where AI delivers the greatest value across clinical development while establishing appropriate human oversight and accountability for AI-assisted decision-making in regulated environments
• Examine how sponsors can validate AI capabilities embedded within clinical platforms, manage evolving AI models, monitor vendor updates, and establish continuous validation throughout the system lifecycle
• Discuss practical approaches for evaluating AI-generated outputs, ensuring traceability and explainability, maintaining data integrity, and determining when AI-generated recommendations should be accepted or challenged
• Learn how Clinical Operations, Clinical Quality, IT, Data Management, and Compliance teams can collaborate to develop scalable AI governance, validation, risk management, and oversight processes that enable innovation while maintaining inspection readiness

Navigating FDA Expectations for AI in Clinical Quality and GxP Environments

As organizations accelerate the adoption of AI across clinical development and quality functions, understanding evolving FDA expectations and implementing appropriate governance frameworks have become essential. This panel will explore how industry leaders are balancing innovation with risk management, validation, and regulatory compliance while enabling the responsible use of AI across GxP processes.

• Understand the latest FDA perspectives and emerging expectations surrounding AI and machine learning technologies in regulated environments
• Explore approaches to AI governance, validation, risk assessment, and human oversight to ensure data integrity and quality
• Discuss practical applications of AI across clinical quality, documentation, audits, investigations, and CAPA management
• Examine the challenges associated with vendor oversight, model updates, and maintaining compliance throughout the AI lifecycle
• Share lessons learned and strategies for preparing organizations for future regulatory expectations and evolving industry standards

Beyond Compliance: Building Quality Frameworks for AI Adoption Across Clinical Trials

As AI becomes increasingly embedded into clinical research, sponsors and sites must determine how to balance innovation with quality, compliance, and inspection readiness. This session explores practical approaches for evaluating AI-enabled technologies, managing risk, and creating effective sponsor-site partnerships.

• How sponsors can evaluate AI tools being used by clinical sites and ensure appropriate oversight
• Managing data integrity, security, validation, and compliance risks associated with AI adoption
• How AI can support patient recruitment, clinical operations, and trial efficiency while maintaining quality standards
• The evolving role of quality teams in establishing governance frameworks for AI-enabled clinical systems
• Determining the right balance between AI automation and human oversight, especially as AI increasingly influences clinical decision-making

AI-Enabled Clinical Quality: From Document Review to Quality Intelligence

As AI and advanced analytics continue to evolve, clinical quality organizations have new opportunities to improve document review, internal audits, quality investigations, and risk identification while transforming quality data into actionable insights that enable more proactive and informed decision-making.

• Apply AI and analytics to support quality processes including issue classification, CAPA development, root cause analysis, investigation summaries, and conclusion generation
• Enhance internal audits by reviewing trial data, site performance, protocols, and documentation to identify trends, gaps, and emerging risks earlier
• Utilize QMS and Veeva systems to create quality briefs and comprehensive data stories that provide greater visibility across studies and quality events
• Improve the identification and management of unresolved CAPAs, recurring issues, and quality signals through more intelligent analysis and prioritization
• Explore practical applications of AI to increase consistency, efficiency, and oversight while maintaining human review and regulatory compliance

Leading the AI Transformation: Change Management Strategies for Clinical Quality and Operational Excellence

Successfully implementing AI across clinical quality requires far more than deploying new technology — it demands strong quality leadership, cross-functional collaboration, and organizational change management to transform processes, empower teams, and deliver measurable business value while maintaining compliance, inspection readiness, and trust.

• Explore how Quality Assurance is evolving from compliance oversight to a strategic leader in AI governance, organizational transformation, and continuous quality improvement
• Discuss practical strategies for overcoming resistance to AI adoption, building organizational confidence, and preparing teams through effective change management, communication, and training
• Learn how Clinical Operations, Clinical Quality, IT, Digital, Regulatory, Data Science, and business leaders can collaborate to establish governance, define ownership, and successfully implement AI across the organization
• Examine how AI is transforming clinical quality processes — including risk management, deviation management, oversight, monitoring, and operational decision-making — while maintaining GCP compliance, data integrity, and regulatory expectations
• Explore how organizations can balance AI-driven insights with human expertise to ensure transparency, accountability, and sustainable adoption as AI capabilities continue to evolve
• Hear real-world lessons on measuring AI adoption, operational performance, organizational readiness, and business impact to ensure AI becomes a long-term business transformation initiative rather than simply another technology deployment

Day One Concludes