MENA Newswire, SAN FRANCISCO: Global drugmakers are increasingly using artificial intelligence to compress the most time-consuming parts of clinical development, applying the technology to tasks such as selecting trial sites, screening participants, monitoring data flows and assembling regulatory documents. Executives and investors described the shift in recent industry briefings, as companies look for practical ways to shorten timelines and reduce manual work in late-stage programs.
The tools being deployed range from machine-learning systems that analyze performance and patient data to generative AI software that drafts and checks technical text. Companies have focused first on the operational steps that routinely slow trials, including identifying sites likely to recruit on time, aligning protocols with local requirements, and preparing standardized documents that can run into thousands of pages across global filings.
Novartis has cited one of the clearest examples of time savings. In launching a 14,000-patient, late-stage cardiovascular outcomes study tied to its cholesterol-lowering therapy Leqvio, the company said AI helped narrow and rank potential trial sites, turning what is typically a four- to six-week selection process into a two-hour session. Novartis said the approach supported enrollment that finished close to target.
GSK has reported measurable cost reductions from using digital tools that include AI in late-stage asthma studies. The company said it saved nearly £8 million by cutting manual work linked to data handling and study operations, underscoring why large manufacturers are investing in automation even when the underlying science of discovering new medicines remains complex and slow.
Operational automation in clinical development
Beyond trial execution, companies are using AI to accelerate regulatory submissions, where repetitive drafting and cross-checking can consume large teams for months. Several drugmakers have said they are applying generative AI to create first drafts of sections of clinical study reports, convert trial outputs into standardized templates and run consistency checks across tables, narratives and appendices before packages are finalized for regulators.
Some companies are also testing “agentic” systems designed to complete multi-step workflows with limited human input, such as pulling information from multiple internal databases, generating structured summaries and packaging results into submission-ready formats. Consulting firm McKinsey has estimated that more autonomous AI could lift productivity in clinical development by 35% to 45% over five years, a figure companies have cited as they scale pilots beyond single studies.
Regulatory guardrails for AI-generated evidence
Regulators have begun to formalize expectations for how AI is used when outputs may influence decisions on safety, effectiveness or quality. The U.S. Food and Drug Administration issued draft guidance in January 2025 laying out a risk-based framework for assessing the credibility of AI models for a defined context of use, including documentation and testing proportional to the role the model plays in evidence generation.
In January 2026, the FDA and the European Medicines Agency published joint guiding principles for “good AI practice” in drug development, describing broad governance and lifecycle considerations for applying AI across stages from clinical trials to manufacturing and safety monitoring. Separately, the FDA has also said it is deploying AI tools internally to help staff handle repetitive tasks in the review process.
Across the sector, executives have emphasized that current, verifiable gains are concentrated in execution and documentation rather than in producing breakthrough medicines through AI alone. As adoption broadens, companies are tracking where automation improves speed and quality in trial operations and submission preparation, while maintaining human oversight for clinical judgments and final regulatory accountability.