This paper builds upon Stassen et al. (2025), “Recommendations for Artificial Intelligence Application in Continued Process Verification,” to provide a detailed interpretation of the European Medicines Agency (EMA) Draft Annex 22 – Artificial Intelligence (July 2025 draft) – within the context of GMP-regulated pharmaceutical manufacturing. The draft annex introduces a structured, risk-based framework for AI adoption, defining expectations for intended use, validation, lifecycle management, explainability, and human-in-the-loop (HITL) oversight. This manuscript examines the alignment between Annex 22 provisions and CPV recommendations, identifies convergence, operational challenges, and areas for future development, and proposes a roadmap for compliant AI implementation. Conceptual frameworks and workflow illustrations depict the harmonization of CPV principles, lifecycle management, and emerging regulatory expectations. This review supports harmonized AI governance and readiness for Annex 22 compliance across the pharmaceutical sector.

The European Qualified Person (QP) holds unique legal responsibility for certifying batch release, ensuring patient safety, and maintaining regulatory compliance. Modern pharmaceutical manufacturing—especially in biologics, ATMPs, and personalized medicines—generates complex, multi-site, data-rich environments that challenge traditional oversight. Artificial Intelligence (AI) offers predictive analytics, anomaly detection, and trend recognition to support decision-making but cannot currently replace the QP’s scientific judgment or legal accountability. The forthcoming Annex 22 introduces Human-in-the-Loop (HITL) frameworks that embed human oversight within AI-supported processes, aligning technological advancement with regulatory control. This review explores evolving QP responsibilities, core and emerging competencies, AI integration within Quality Risk Management (QRM) and GMP frameworks, and ethical considerations, illustrated with practical case studies. By developing AI literacy and applying HITL oversight, QPs can more effectively translate technological potential into transparent, science-based, and patient-centered batch-release decisions.

This review paper explores the transformative impact of Artificial Intelligence (AI) on Continued Process Verification (CPV) in the biopharmaceutical industry. Originating from the CPV of the Future project, the study investigates the challenges and opportunities associated with integrating AI into CPV, focusing on real-time data analysis and proactive process adjustments. The paper highlights the importance of aligning AI solutions with regulatory standards and offers a set of comprehensive recommendations to bridge the gap between AI9s potential and its practical, compliant, and safe application in pharmaceutical manufacturing. Emphasizing transparency, interpretability, and risk management, the research contributes to establishing best practices for AI implementation, ensuring the highest quality pharmaceutical products while meeting regulatory expectations. The conclusions drawn provide valuable insights for navigating the evolving landscape of AI in pharmaceutical manufacturing.

Responsible decision-making in pharmaceutical manufacturing increasingly occurs within complex, distributed, and rapidly evolving environments. While the European Qualified Person (QP) holds defined regulatory accountability, modern decision contexts extend beyond compliance verification and require integration of scientific understanding, lifecycle knowledge, and organisational governance. Building on recent discussions surrounding technological evolution and regulatory expectations, this review explores how professional judgement operates in environments characterised by uncertainty, accelerating timelines, and expanding organisational interfaces.Specification compliance alone often provides insufficient confidence for decision-making. Confidence increasingly emerges through process understanding, integration of multidisciplinary expertise, and development of coherent scientific narratives that connect data, process behaviour, and patient expectations. Accelerated environments also reveal how governance structures and organisational systems influence decision quality, highlighting the importance of clear accountability, trusted expertise, and independent judgement.This paper introduces the concept of patient-relevant decision quality and discusses how experienced professionals contribute to continuity of understanding across lifecycle stages. Seen through this lens, the QP represents one perspective within a broader system of responsible decision-making, where organisational maturity, principled leadership, and stewardship of judgement support scientifically grounded and ethically sound outcomes.