Sumesh Nair by becoming a significant new voice in the field of AI (artificial intelligence) governance. He’s taking powerful steps in the right direction with the biopharmaceutical industry. His pioneering research is changing how we think about AI. Beyond a regulatory hurdle, it is a strategic competitive differentiator that can dramatically improve the efficiency and safety of drug development. Nair stresses that existing conventional IT validation frameworks are inadequate to cover many of the specific complexities involved with AI technologies.
Nair is a strong proponent of interpretable, human-understandable outputs from AI systems. This method creates accountability, particularly in areas such as signal detection and patient classification—often the most critical components of AI technology. To create a trustworthy governance structure, he identifies three essential elements: transparency, auditability, and reproducibility. These principles form the foundation for a governance maturity model that Nair has developed to help organizations benchmark and optimize their AI practices.
Nair brings 25 years of industry experience from Eisai Inc. and Genmab. His background has given him a unique perspective on what’s possible and what’s needed for AI governance across the biopharma landscape. His goal is to create a new regulatory bridge between the breakneck pace of technological innovation and the public’s regulatory trust needed for those innovations to flourish.
The Vision Behind Robust AI Governance
Sumesh Nair imagines an alternative, more foundational approach toward AI governance. He has a profound understanding of the transformative potential of AI and the regulatory vulnerabilities it now poses in clinical research and drug safety landscapes. His motivation comes from over eight years of experience running highly complex IT programs. His experience encompasses knowledge of pharmacovigilance, clinical systems, and GxP-regulated areas.
“My motivation stemmed from a deep recognition of both the transformative potential of AI and the regulatory vulnerabilities it introduces in clinical research and drug safety environments.” – Sumesh Nair
Nair notes that the speed at which AI technologies are being adopted has outstripped old validation and compliance models. This mismatch introduces major dangers, including algorithmic bias and model drift. These challenges all conspire to undermine the ability to deploy AI in a reliable manner in high-stakes clinical contexts.
Nair is adamant that next-gen maturity models need to be more than single-validation exercises. In addition, they need to provide for continuous monitoring and auditing of the processes. This change will help organizations address rapidly changing risks and remain compliant and ethical as the world around them changes.
“As someone with years of experience managing complex IT programs across pharmacovigilance, clinical systems, and GxP-regulated domains, I saw firsthand how the rapid adoption of AI outpaced traditional validation and compliance models,” – Sumesh Nair
Establishing Trust Through Transparency and Auditability
A key pillar of Nair’s governance approach is transparency. The idea that all AI systems should be transparent. He argues that in order for these systems to work and have an impact, they need to output things stakeholders can intuitively read and understand. This accountability is especially critical in emerging contexts like signal detection, risk classification, or patient categorization.
“For AI systems to be transparent, they need to generate interpretable outputs, especially in scenarios such as signal detection, risk classification, or patient categorization,” – Sumesh Nair
Beyond transparency, we need auditability to build trustworthy AI applications. Nair stresses a top-down approach to logging everything possible during model training, versioning, validation, deployment, and performance monitoring. This detailed practice is necessary to comply with Federal Highway Administration requirements.
“Auditability necessitates comprehensive logging of activities related to model training, versioning, validation, deployment, and performance monitoring,” – Sumesh Nair
Each step involved with a generative AI model—from when an algorithm should be retrained to how parameters are changed—needs bulletproof documentation, a full audit trail and timestamping. This level of detail may be tedious, but it is necessary to achieve compliance with Good Machine Learning Practices (GMLP) and uphold regulatory accountability.
“Every decision—from model retraining to parameter adjustments—must be documented with timestamps and traceability, which is essential for meeting regulatory standards and adhering to Good Machine Learning Practice (GMLP).” – Sumesh Nair
Innovative Frameworks for AI Governance
At Eisai Inc., Nair developed a standardized, repeatable, AI validation framework. This framework is compatible with GAMP 5 guidelines and integrates Good Machine Learning Practices (GMLP). This effort laid down solid pipeline model versioning control frameworks. It recently established a new, cross-functional AI Governance Council to guide AI governance efforts across departments and agencies.
During his tenure at Eisai, Nair successfully implemented major innovations that reduced unnecessary manual review time by 30%. This technical advancement assured the safe use of AI technologies in high stakes clinical environments. His work played a significant role in facilitating the FDA’s traditional approval process for Leqembi, a drug for Alzheimer’s disease, by validating and integrating multiple GxP systems.
Nair envisions that as artificial intelligence becomes increasingly integrated into biopharmaceutical research and development (R&D), benchmarks for AI governance will evolve from simple compliance checklists to dynamic, risk-based frameworks. This change will allow organizations to be nimbler in adapting to new challenges that arise, all while meeting their regulatory requirements.
“As artificial intelligence becomes more integrated into biopharmaceutical R&D, AI governance benchmarks will shift from compliance checklists to dynamic, risk-based frameworks,” – Sumesh Nair
He champions frameworks designed to satisfy the regulatory requirements of the present day. These frameworks prepare for family future challenges in the quickly changing paint menace of AI technologies.
