Regulators open the AI floodgates in life sciences
The FDA’s simultaneous deployment of ELSA for drug protocol reviews and CDRH-GPT for medical device evaluation marked a sharp departure from regulatory caution. The agency accelerated this trajectory in December 2025, announcing agentic AI capabilities for all FDA employees, including advanced tools that plan, reason, and execute multi-step actions with human oversight to ensure reliable outcomes.
ELSA streamlines the pharmaceutical submissions process while CDRH-GPT analyses clinical and non-clinical datasets from device studies. Together, they transform how sponsors interact with regulators. Within months, companies pivoted from explaining AI capabilities to racing to match the agency's technological standards.
By adopting AI internally while mandating provenance and traceability, the FDA redefined compliance across drug and device development. Auditability evolved from a quality metric to a fundamental requirement for every function handling regulatory data, making transparent data lineage and AI-ready documentation prerequisites for approval, rather than competitive advantages.
With regulators moving faster than anyone expected, the floodgates are open. Sponsors are scaling AI programs, redefining compliance frameworks, and preparing for a new standard of transparency. The stage is set for the “Age of Accountable AI.”
In 2026, biopharma will move from proving AI works to proving it can be trusted at scale. Sponsors will operationalise the pilots of the past year, build systems that learn, document, and defend every decision. Human–AI collaboration will become a core performance metric, and regulatory confidence will become the new competitive edge. The next era belongs to those who can scale speed without losing traceability.
The question shifts from “Why AI?” to “How Fast?”
AI in life sciences is already moving from theory to execution. Sponsors no longer ask if they should use AI, but how fast they can scale it. Last year, teams ran pilots and ROI tests. Next year, they will embed AI across regulatory, medical-writing, and data-management workflows. The urgency comes from both directions.
Regulators are proving they can use AI responsibly, and boards are demanding measurable efficiency gains. The focus will shift from persuasion to execution: setting governance, unifying workflows, and choosing partners that can move at enterprise speed.
Success will depend less on model novelty than on clarity, provenance, and disciplined human oversight. Across the industry, hesitation will emulate risk, and implementation speed will define operational maturity.
Human + AI becomes the operating standard
The defining model for 2026 isn’t full automation; it’s accountable collaboration. AI systems now handle the scale and repetition that once consumed weeks of manual effort, while humans stay responsible for validation, interpretation, and regulatory judgment.
Defined review points, traceable edits, and signed audit trails will turn human oversight into part of the system itself. Each output will eventually carry its own lineage – who reviewed it, what data informed it, and when it changed. The FDA’s own definition of agentic AI reinforces this operating standard: systems designed to achieve specific goals through autonomous reasoning while incorporating built-in guidelines and human oversight.
The result will be a new kind of partnership where automation delivers efficiency and throughput while human experts contribute scientific nuance and interpretive rigour, together creating an operational standard that regulators can recognise, and which auditors can verify.
Infrastructure gives way to intelligence
The systems established in 2026, including unified data fabrics, orchestration engines, and human-AI governance, will set the stage for reasoning platforms that transform how evidence is created, reviewed, and approved.
The next phase of automation will connect the data itself, linking clinical databases, electronic health records, and real-world evidence into submission-ready formats through continuous reconciliation. Workflow engines will evolve beyond document automation to anticipate what comes next, surfacing the right data at the right moment and enforcing compliance automatically.
The competitive differentiator won’t come from scale alone, it will come from discipline. Organisations that treat governance, validation, and user fluency as first-class engineering problems will turn scepticism into informed confidence.
By decade’s end, regulatory platforms will evolve from drafting systems into reasoning engines that predict reviewer friction points, recommend evidence adjustments, and simulate approval scenarios. Experts will approve logic, rather than language, authorising AI-generated insights with traceable justification.
The age of accountable AI arrives in 2026
The changes ahead next year represent more than incremental improvement. They mark a fundamental shift in how the life sciences industry operates. Sponsors will move decisively from pilot programs to enterprise-scale deployment, embedding AI across regulatory, medical-writing, and data-management functions.
Human-AI collaboration will become the operating standard, with defined review points, traceable edits, and signed audit trails built into every workflow. Medical writing expertise will prove essential to this transition, showing what good looks like and helping sustain the behaviour change needed for long-term adoption. Governance and literacy will outperform scale as the true differentiators, rewarding organisations that invest in training their people as deliberately as they build their models.
The winners will measure maturity by accountability, not output. For sponsors ready to act, the opportunity is clear: build the infrastructure now, establish the governance frameworks, and prepare teams to work alongside AI systems that learn, document, and defend every decision.
The AI floodgates are open. The race belongs to those who can scale speed without losing traceability, and next year the AI race begins in earnest.
About the author
Anita Modi is the CEO and co-founder of Peer AI, a leading agentic AI platform for life sciences regulatory documentation. A strategic and operational leader at the intersection of healthcare and technology, she has led large-scale transformation, quality, cybersecurity, and strategy efforts at Science 37, and helped build and scale Genos. Known for driving innovation and high-performing teams in complex, regulated environments, Modi holds degrees in Quantitative Computational Biology and Finance from Princeton University and an MBA from Harvard Business School.
