Breaking Protocol

George Magrath, MD, CEO of Opus Genetics, joins Ram Yalamanchili to discuss how Opus is approaching portfolio-scale biotech development with multiple active clinical programs, something unimaginable without the use of AI. George believes the traditional single-asset biotech models may no longer be sustainable and that AI-native operations allow smaller teams to execute far more efficiently than ever before.With the rapid advancement of AI in discovery, clinical development has now become the biggest bottleneck in biotech. George also shares his perspective on rare disease development, operational scalability, and why the future of biotech is driven by the companies that have figured out AI-native clinical trial execution.

In this episode, Ram Yalamanchili sits down with Shobhit Shrotriya, Managing Director of Global Life Sciences R&D Operations at Accenture, to unpack what it will actually take for AI to move beyond mere pilots to full production in clinical research. Drawing on deep AI expertise, as well as decades of experience in clinical operations, Shobhit explains why most organizations are still thinking too narrowly about AI, why pilot fatigue is real, and why point solutions often fail to solve the underlying workflow problem. The conversation explores the full evolution of clinical data operations, from paper-based studies and early EDC adoption to today’s push toward AI-led transformation. Along the way, Ram and Shobhit dig into the harder questions most vendors and sponsors still avoid: fragmented data ecosystems, weak governance, poor process redesign, limited interoperability, and the importance of building systems that can actually scale in regulated environments. They also tackle one of the most important issues in enterprise AI adoption: trust. Shobhit makes the case for responsible AI frameworks, human-in-the-loop decision making, and a more realistic approach to evaluating what “failure” actually means in AI pilots. The result is a practical, executive-level discussion for leaders in pharma, biotech, CROs, and clinical data science who want to understand where AI can create real value and where the industry still has work to do.

Enterprise software has dominated how companies operate for decades. Krishna Cheriath, Head of Clinical Research Data and AI at Thermo Fischer Scientific, believes that model is being broken by AI teammates. In this episode of Breaking Protocol, Krishna joins Ram Yalamanchili to discuss how AI teammates are fundamentally disrupting the enterprise technology stack itself. Instead of navigating layers of applications and workflows, future knowledge workers will interact directly with data through AI agents that reason, plan, and act.Krishna brings a rare perspective at the intersection of technology, AI, and pharmaceutical R&D. As Head of Digital and AI for Clinical Research at Thermo Fisher Scientific and former Chief Data & AI leader at Zoetis and Bristol Myers Squibb, he has spent decades deploying enterprise technology inside some of the world’s largest life sciences organizations.The conversation explores why clinical trials still struggle with timelines and operational complexity, why automation alone has not delivered the expected breakthroughs, and why AI may represent a fundamentally different paradigm.They also discuss the growing importance of AI fluency across the life sciences industry and why both individuals and organizations must rethink how they learn and adapt in an era of AI-augmented work.Topics covered include:• Why clinical trial operations have not improved as much as expected• The limits of automation in drug development workflows• Why AI could disrupt the entire enterprise software model• The concept of a human-AI workspace replacing traditional applications• How AI fluency will shape the future of clinical research leadershipFor leaders in pharma, biotech, and clinical research, this conversation offers a clear look at how AI may reshape both the technology stack and the way scientific organizations operate.

In this episode of Breaking Protocol, Ram Yalamanchili sits down with Paula Brown-Stafford, CEO of Allucent and one of the early architects of the CRO industry.From joining Quintiles as its 23rd employee to leading a purpose-built CRO focused on biotech innovation, Paula shares how the industry evolved from pure operational execution to intelligence-driven partnership. She explains why the real differentiator today is not scale, but regulatory intelligence, therapeutic depth, and the ability to navigate complex rare disease and cell and gene therapy trials.The conversation also examines the economics of drug development, shifting regulatory pathways, and how AI teammates can reduce time and cost without compromising quality. For sponsors facing capital constraints and increasing pressure to deliver results, this episode offers a grounded perspective on how the CRO model must evolve to create measurable strategic advantage.If you are leading clinical development, R&D, or portfolio strategy, this discussion will challenge how you think about partnership, value, and execution.

What happens when a retina specialist with a background in computer science takes on the inefficiencies of clinical research?In this insightful conversation, Dr. Mark Barakat of Retina Macula Institute joins Tilda CEO Ram Yalamanchili to explore how AI is transforming the day-to-day reality of clinical trial execution.They discuss the growing operational burdens on site staff, the silent cost of turnover, and the bottlenecks that limit research capacity. Dr. Barakat shares his firsthand experience adopting AI in a high-volume ophthalmology research site—including what’s working, what’s not, and why he believes AI will become a core collaborator, not a threat.From automating data entry and managing re-consent workflows to long-term visions of AI-assisted imaging and protocol compliance, this episode offers a grounded, site-level perspective on AI’s real potential in trial operations.⸻What you’ll learn:- How AI is reducing operational burden for clinical research coordinators and site staff- Why research sites struggle with staff burnout, turnover, and training and how AI can help- The hidden inefficiencies in clinical trial workflows (EDC, source-to-CRF, informed consent)- Real-world examples of AI improving regulatory compliance and data quality at ophthalmology sites- How sites are using AI to manage high trial volume without increasing headcount- Why adoption of AI in clinical research is slow—and what’s changing now- The role of AI in imaging analysis for retina trials, including OCT segmentation and atrophy tracking- How AI can level the playing field across high- and low-performing clinical trial sites- Practical considerations for bringing AI into day-to-day research operations- What sponsors need to know about site enablement, trial scalability, and AI’s role in quality assurance

ICON Eyecare experiences 66% data quality improvement and 90% increase in efficiency. Dr. James Fox of ICON Eyecare (Grand Junction, CO) discusses what it really takes to scale clinical research today. He walks through the burnout his team faced, the turning point that led to adopting AI teammates, and the measurable improvements in data quality, team capacity, and growth. He unpacks the staffing paradox in research, the trust that's at stake, and what it means for the future when AI and humans work side-by-side. His site, running 30 trials with a lean team, is a case study in what happens when AI meets the real-world pressure of clinical operations.

A powerful conversation with Dr. George Magrath, CEO of Opus Genetics and seasoned ophthalmologist, as he shares how AI is reshaping clinical trials, and the incredible impact of AI teammates on his clinical research program. From cutting trial timelines to boosting enrollment, George reveals real-world wins in biotech and gene therapy for rare childhood blindness. Opus' near sci-fi levels of science is restoring vision for blind adults. Learn how AI is easing site coordinator burden, accelerating research, and making once-impossible treatments a reality.