AI Digital Twins: Fixing Pharma’s Billion-Dollar Bottleneck

LAGUNA BEACH, CA – December 02, 2025 – The pharmaceutical industry is grappling with a well-known, multi-billion-dollar problem: an 80 to 85 percent failure rate for clinical trials to meet their patient enrollment timelines. This chronic inefficiency, often rooted in an archaic site selection process reliant on outdated spreadsheets and slow manual queries, creates a significant bottleneck that delays patient access to potentially life-saving therapies and inflates R&D costs. Now, a new wave of technology aims to dismantle this bottleneck, and companies are racing to prove their mettle.

Laguna Beach-based startup Ryght AI is stepping into the spotlight with a bold claim: its platform can shrink the months-long process of finding and vetting clinical trial sites into a matter of minutes. The company is set to demonstrate its technology in a live webinar on December 17, showcasing a solution built on "AI Site Twins"—dynamic digital replicas of every clinical research site in the world. This move signals a significant shift from reactive problem-solving to proactive, data-driven trial design, a change that could have profound implications for sponsors, research sites, and patients alike.

Deconstructing the Digital Twin

The term "digital twin" has moved from manufacturing and aerospace into the life sciences, representing a virtual model of a physical object or system. While some companies focus on creating digital twins of patients, Ryght AI has taken a different approach by modeling the entire research site ecosystem. These aren't static snapshots; the company’s "AI Site Twin Network" comprises dynamic profiles of what it claims are all 100,000 clinical trial sites globally.

This is achieved through a sophisticated blend of generative and agentic AI. Autonomous software agents continuously scour public and proprietary data sources—from trial registries like ClinicalTrials.gov to academic publications and operational histories—to build and update each twin. The platform synthesizes vast amounts of structured and unstructured data to reflect a site's real-time operational capacity, historical performance on similar trials, primary investigator expertise, and even its regulatory track record.

During its upcoming demonstration, Ryght AI plans to showcase its "Network Navigator," which analyzes a trial protocol and, within moments, identifies a list of best-fit sites. This stands in stark contrast to the traditional method, where a Contract Research Organization (CRO) might spend weeks or months sending out mass feasibility questionnaires, waiting for responses, and manually collating data that may already be out of date. By simulating trial enrollment against these dynamic site profiles, sponsors can not only find the right sites but also optimize their protocols before the study even begins.

The Business Case for AI-Driven Feasibility

For pharmaceutical executives and investors, the value proposition extends far beyond technological novelty. The financial stakes are immense. With the cost of bringing a single drug to market exceeding $2.6 billion and timelines stretching over a decade, every delay is costly. The failure to enroll patients on time is a primary driver of these delays. Industry data suggests that 11% of selected sites fail to enroll a single patient, while 37% under-enroll, forcing costly trial extensions or expansions.

This is where the business case for platforms like Ryght AI becomes compelling. By automating and accelerating site identification and feasibility, the technology directly targets the operational friction that erodes budgets and timelines. Ryght AI claims its "Feasibility Accelerator" can compress a process that traditionally takes several months into less than three weeks. It does this by automatically generating pre-populated feasibility questionnaires using data from the AI Site Twins, allowing site staff to simply verify information rather than starting from scratch.

The competitive landscape for clinical trial optimization is heating up, with major players like Veeva Systems offering unified cloud platforms and others like Unlearn.AI focusing on patient-level digital twins for synthetic control arms. However, Ryght AI is betting that its unique, site-centric approach, powered by a combination of generative and agentic AI, will provide a decisive edge. By creating an intelligent, self-updating global map of research capabilities, the platform aims to transform site selection from a speculative art into a data-driven science, improving the return on R&D investment and de-risking development pipelines.

Easing the Burden on the Front Lines

While the benefits for sponsors and CROs are clear, the innovation also promises a significant impact on the clinical research sites themselves—the front-line hubs where medical science meets patient care. These sites are often inundated with redundant requests and administrative tasks that divert resources from their core mission.

Ryght AI's platform is designed to alleviate this burden. The pre-population of feasibility forms is a key feature, as site coordinators often spend countless hours filling out similar information for different sponsors. By having an AI agent handle the initial data entry for their review, site personnel can reclaim valuable time. This efficiency gain allows them to focus on higher-value activities, such as patient engagement and protocol execution.

Furthermore, the system’s real-time communication and dashboard features foster greater transparency between sponsors, CROs, and sites. This addresses another common pain point: the communication gaps and outdated information that can lead to mismatched expectations and wasted effort. Strategic partnerships with academic medical centers like Emory University and Keck Medicine of USC suggest that research institutions are actively seeking such solutions to streamline their operations and enhance their capacity to participate in cutting-edge research.

The Ultimate Payoff: Accelerating Hope for Patients

Ultimately, the convergence of AI and clinical research is about more than just efficiency and cost savings. The true measure of its success will be its impact on human health. By accelerating the entire clinical trial process, these technologies hold the promise of bringing new and potentially life-saving treatments to patients faster.

For every month shaved off a development timeline, patients with debilitating or terminal illnesses gain precious time. This is particularly critical in areas like oncology and rare diseases, where treatment options are limited and the patient populations for trials are small. AI-driven platforms can more accurately identify pockets of eligible patients through optimal site selection, making it feasible to conduct trials that were previously challenging to staff.

The broader trend is unmistakable: the pharmaceutical industry is at an inflection point. The adoption of AI is no longer a peripheral experiment but a central pillar of strategy for remaining competitive and fulfilling the mission of improving global health. As technologies like AI Site Twins mature, they will not only reshape trial logistics but will also enable more sophisticated, adaptive trial designs, pushing the industry closer to the long-sought goal of personalized medicine. The journey from protocol to patient is being redrawn by data, and the entire healthcare ecosystem stands to benefit.