Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Siva Samy, CEO, ValGenesis
AI is no longer operating on the margins of life sciences. Instead, it is rapidly becoming embedded in discovery, development, validation, manufacturing, and quality operations. As AI systems begin influencing regulated decisions, global agencies are preparing for a future where oversight must evolve just as quickly as the technologies they evaluate.
Based on new emerging guidance and the accelerating pace of change, 2026 will be a defining year for how regulators view and govern AI. Below are five predictions for what life sciences organizations could expect and why preparation should begin now.
1: Explainability Becomes a Formal Regulatory Expectation
Regulators are moving away from tolerance for “black-box” models. By 2026, companies will be expected to clearly explain how their models work, how they were trained, and how they behave under different scenarios. The FDA’s Digital Health Framework already points strongly in this direction, emphasizing transparency, accountability, and lifecycle management.
AI that influences manufacturing, quality control, deviation decisions, or clinical support will be treated as part of the control strategy. This means every model must be able to demonstrate how it works, not just what output it produces.
2: AI Validation Becomes a Continuous Discipline
Unlike traditional static software, AI systems shift over time. They learn, drift, and change with new data. In 2026, the industry will begin recognizing that AI requires continuous validation not a single validation exercise at go-live.
This shift will also give rise to new internal roles — from professionals responsible for AI oversight across its lifecycle, to individuals who can read and interpret models, or specialists who sit at the intersection of quality, data science, and regulatory affairs. Organizations will need defined governance, monitoring, intervention mechanisms, and documented human authority over automated decisions.
3: Documentation Will Evolve from Static Requirements
Traditional documentation will no longer satisfy regulators evaluating AI. Agencies will expect narrative, evidence-based explanations that connect the behavior of a model to product risk and patient safety.
Companies will need to show how training data was selected, how bias was assessed, how performance drift is monitored, how exceptions are handled, and how human judgment is retained throughout the workflow. Regulators will move toward consistency and predictability in how these elements must be presented, which will raise the expectations for internal clarity and discipline.
4: AI Tools Begin Assisting Submissions and Regulatory Review
By 2026, AI will not only support internal operations, but it will begin shaping regulatory submissions themselves. Natural-language models will help teams structure documents, identify inconsistencies, gaps, anomalies and align content with regulatory expectations.
Agencies will adopt similar tools to review submissions more efficiently. This creates a two-sided AI ecosystem, where both industry and regulators are using machine intelligence to process complex data packages. With that shift will come pressure for shared definitions, clearer expectations, and more uniform documentation standards across regions.
5: Human Judgment Remains the Anchor
The most important shift in 2026 may be philosophical. As AI becomes more capable, regulators will emphasize that machines can assist, but humans remain accountable. Every model will need documented human authority, auditability, and override mechanisms.
Regulatory bodies will want reassurance that companies understand not only what a model does, but also when it should not be used. This balance of pairing automation with human-centered governance will become the defining principle of AI oversight in life sciences.
Looking Ahead
The year ahead will be shaped but by the interplay between technology, governance, and regulatory expectations. Transparency, continuous validation, narrative documentation, and preserved human oversight will become the pillars of regulatory readiness.
AI can accelerate the future of life sciences but only if organizations can explain it, control it, and prove its trustworthiness.
Companies that prepare early are sure to lead confidently in this this next phase.
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ABOUT THE AUTHOR
Before founding ValGenesis, Dr. Siva Samy held several technical and management positions at various biotech, pharmaceutical, and medical device companies in the fields of validation and Information technology. He has been credited with over fifteen research articles and holds a patent on �Validating and Maintaining Respective Validation Status of Software Applications, Manufacturing Systems and Business Processes’ by USPTO, and received a master’s degree in Analytical Chemistry, a Ph.D. in Medical Devices, and a post-doctoral from the University of Madras and University of Toronto.
