AI in Operations (AIO)

Report - Identifies ways to improve manufacturing operations using AI through data from real production environments in top 25 global pharmaceutical companies. Access Here —>

Poster - Assess your AI readiness across Culture, Governance & Organization, Data Management, and Tools & Techniques. Access Here —>

Report - Conduct a Self-Assessment for your AI readiness related to Culture, Governance & Organization, Data Management, and Tools & Techniques. Access Here —>

Presentation - This presentation from the AI Summit 2024 outlines a comprehensive data management framework integrated throughout the AI/ML lifecycle—from initiation through deployment and retirement—emphasizing that data management (80% of AI systems) is the critical foundation for regulatory compliance, model validation, and lifecycle governance in pharmaceutical and medical device applications. Access Here —>

Report - This paper proposes a Pragmatic AI Validation (PAIV) framework that extends traditional computer system validation to address unique AI challenges such as data quality, model drift, and adaptive algorithms through additional governance processes and continuous monitoring. Access Here —>

Report - This guideline establishes a comprehensive framework for managing AI datasets throughout their entire lifecycle—from collection and preparation through training, testing, production monitoring, and secure disposal—with particular emphasis on pharmaceutical and medical device applications. It addresses critical requirements including algorithm intent understanding, data quality and governance, bias mitigation, privacy protection, and regulatory compliance to ensure AI systems are both high-performing and trustworthy. Access Here —>