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

David Cambra

Senior Director, Clinical Quality Assurance · Eisai

Panel Discussion: 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

Niloy Shah, M.S.

Vice President, Research & Development Quality · Replimune

Nick Hargaden

Senior Director, TMF · Genmab

Compliance in the Loop: Building Quality Frameworks for AI Adoption

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
• Finding ways to build compliance frameworks into the iterative process of model-based knowledge generation — when critical thought must take precedence
• Managing data integrity, security, validation, and compliance risks associated with AI adoption
• How AI can support sponsor-site partnerships that champion accelerated patient recruitment, agile 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

Abby Statler, PhD, MPH, MA

Senior Director, Clinical Quality Assurance · AVEO Pharmaceuticals, Inc.

Beyond the AI Use Case: Building the Business Case for Clinical Quality Transformation

AI delivers value only when organizations combine technology with the right business strategy, governance, processes, and workforce capabilities to drive sustainable improvements across Clinical Quality.

• Shift the conversation from AI tools to measurable business outcomes and enterprise value
• Build the organizational capabilities — including people, processes, governance, and AI skills — needed to support successful digital transformation
• Create executive-ready business cases that justify investment through quality improvements, efficiency gains, and risk reduction
• Apply AI across Clinical Quality functions, including Quality Management Systems, CAPA, inspection readiness, safety, pharmacovigilance, and process excellence
• Develop scalable AI programs that enable continuous innovation while supporting compliance, quality, and long-term operational success

AI Immersion Lab: See It. Experience It. Transform Clinical Quality.

Go beyond presentations and experience AI in action as leading technology providers and life sciences organizations deliver live demonstrations showcasing how innovation is transforming clinical quality, improving compliance, strengthening inspection readiness, and driving measurable operational impact.

In this interactive immersion lab, attendees will:

• See live demonstrations of AI-powered solutions supporting clinical quality, quality management, inspection readiness, and risk-based oversight
• Experience innovative technologies that streamline document review, automate quality workflows, strengthen CAPA management, and improve inspection readiness
• Explore how AI is enabling proactive risk identification, predictive quality analytics, and smarter decision-making across the clinical trial lifecycle
• Learn practical implementation strategies, governance considerations, and lessons learned from organizations deploying AI in regulated GCP environments
• Engage directly with technology innovators during live Q&A to better understand capabilities, implementation approaches, and real-world business impact
• Leave with actionable ideas and practical solutions that can be evaluated and applied within your own clinical quality organization

See It. Experience It. A dynamic series of rapid-fire demonstrations where attendees will experience cutting-edge technologies firsthand, interact with solution experts, ask questions in real time, and discover practical innovations they can bring back to their own organizations.

Day One Concludes