Shobhit Shrotriya: Pharma is Moving from AI Pilots to Production

George Magrath: AI-native Biotechs Have Arrived

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.

Paulius Ojeras: AI is Changing the Clinical Trial Cost Equation

AI is about to force a much bigger conversation in clinical research than most people realize. In this episode, Ram Yalamanchili, CEO of Tilda Research, and Paulius Ojeras, VP of Clinical Operations at Perceive Biotherapeutics, dig into how AI could change the economics of running studies, not just by improving quality and accelerating timelines, but by putting real pressure on the traditional CRO pricing model. If the work takes fewer hours, gets done faster, and delivers better outputs, what exactly should sponsors still be paying for? That question leads to one of the most fascinating parts of the discussion: whether clinical development is headed toward rebates, new pricing structures, and a very different definition of value.

Shobhit Shrotriya: Pharma is Moving from AI Pilots to Production

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.

Ash Jayagopal: Closing the Clinical Research Innovation Gap

Clinical trials have become more scientifically sophisticated, yet many of the operational challenges behind them remain stubbornly unchanged. In this episode of Breaking Protocol, Ram Yalamanchili speaks with Ash Jayagopal, Chief Scientific and Development Officer at Opus Genetics, about the realities of running clinical trials in the era of gene therapy and ultra-rare diseases. Ash brings a rare perspective from the front lines of ophthalmology drug development, where some programs target patient populations measured in the hundreds rather than the thousands. In these environments, traditional clinical trial infrastructure begins to break down. Finding patients becomes a global search problem. Published prevalence numbers often prove unreliable. Registries require constant maintenance. And clinical trial planning still depends on fragmented datasets that were never designed for modern drug development. The conversation explores why patient identification remains one of the most persistent bottlenecks in clinical trials. Ash explains how inaccurate diagnostic coding, inconsistent genetic testing, and fragmented clinical data make it difficult to identify eligible patients even when they technically exist within healthcare systems. Registries and centers of excellence have helped improve visibility, but they still require significant manual effort to maintain and query. Ram and Ash also discuss how automation, data infrastructure, and emerging AI tools could fundamentally change this landscape. If patient registries, clinical data, and eligibility criteria could be integrated and continuously updated, trial sponsors could move from a “needle in a haystack” search to a far more targeted model of recruitment. The potential for AI-assisted patient identification, registry management, and trial planning represents a major opportunity for modernizing clinical operations. Beyond patient recruitment, the discussion turns to regulatory innovation. Ash outlines how agencies such as the FDA are beginning to adapt to the realities of rare disease drug development, including more flexible manufacturing requirements and adaptive trial designs such as Bayesian approaches. These changes acknowledge the practical reality that some gene therapies may require only a handful of manufacturing batches to treat an entire patient population. Finally, the conversation examines why certain regions outside the United States sometimes move faster in early clinical development. Special regulatory pathways, investigator-initiated trials, and rapid proof-of-concept mechanisms can accelerate early studies, though Ash emphasizes that the fundamentals remain unchanged: successful trials still depend on strong clinical networks and centers of excellence that know where the patients are. At its core, this episode explores a simple but important question: clinical science is advancing rapidly, so why does clinical trial execution still lag behind? The answer may lie in how the industry modernizes its operational infrastructure. For leaders in biotech, clinical development, and clinical operations, this discussion offers a candid look at where the system works today, where it breaks down, and how emerging technology could reshape the future of clinical trials.

Krishna Cheriath: AI is Disrupting Clinical Research Already

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 leadership For 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.