A 90% reduction in time spent aggregating data across systems sounds like the kind of number that ends debates about AI adoption in clinical trials. It hasn’t. Venu Mallarapu, Chief Transformation and AI Officer at eClinical Solutions, points to that figure alongside a 25% reduction in cycle times as evidence of what the technology already delivers, yet the gap between proven performance and widespread deployment remains wide.
The friction is partly regulatory. The FDA’s draft guidance on AI in drug development is non-binding, which leaves sponsors designing validation strategies without a fixed target to meet. The EMA and FDA did jointly release guiding principles for good AI practice in drug development, but principles are not requirements, and sites and sponsors building compliance frameworks around guidance that may still shift have real incentive to wait. Meanwhile, a separate joint study by eClinical Solutions and Hobson & Company found that AI-enabled platforms also cut report and data quality check time by 75% per new study, meaning the operational case is already quantified even as the regulatory case is still being written.
Organizational bottlenecks compound the regulatory uncertainty. Clinical data teams are often structured around the manual workflows that AI is meant to replace, and retraining staff or reorganizing data governance takes time that trial sponsors running against enrollment deadlines rarely feel they have. eClinical Solutions counts 18 of the top 50 pharmaceutical companies among its clients, and added more than 20 net new customers in 2025, which suggests growing appetite at the top of the market. But appetite at large sponsors does not automatically move mid-size biotechs or CROs, where the cost of implementation and the risk of a failed validation carry more weight relative to available resources.
The honest read is that adoption is not slow because the technology is unproven. It is slow because the surrounding infrastructure, regulatory frameworks, workforce readiness, and data governance models, has not caught up to what the tools can already do. The clearest near-term signal to track is whether the FDA finalizes its AI guidance with binding validation requirements, because that single event will either accelerate adoption by removing ambiguity or slow it further by raising the compliance floor.
Source link: https://www.eclinicalsol.com/news/what-are-the-key-bottlenecks-preventing-rapid-adoption-of-novel-technologies/
Moe Alsumidaie, MBA, MSF, is founder and Chief Editor of Vanguard Publications, which publishes Clinical Trial Vanguard, Pharma Vanguard and BullScope, and Head of Research at CliniBiz. He has two decades in clinical trial operations and data science, with earlier roles at Genentech, Abbott Vascular and Stanford University Medical Center, and is a guest lecturer in clinical trial sciences at Rutgers University.
