Clinical Researcher—August 2026 (Volume 40, Issue 4)
TRIALS & TECHNOLOGIES
Alison Holland
The clinical research industry is approaching an inflection point.
For years, the conversation about accelerating drug development has focused on scientific discovery: better target identification, faster molecule design, stronger predictive models, and, more recently, the growing role of artificial intelligence (AI). AI investment is now beginning to change the front end of the pipeline. More candidates can now be identified, prioritized, and advanced with unprecedented speed in the preclinical space.
But that progress has exposed a different constraint: how to evaluate these promising therapies as they transition into humans at a pace the current clinical development system can sustain.
The tension is increasingly visible in the regulatory environment. In April 2026, the U.S. Food and Drug Administration (FDA) signaled its intent to reduce “dead time” or white space in drug development—saying it accounts for about 45% of the traditional 10-to-12-year drug development timeline. In May, the agency followed with a request for feedback on approaches that would allow sponsors and contract research organizations (CROs) to report endpoint data as they are generated, rather than waiting for traditional, phase-based submission cycles and cited pilot studies by AstraZeneca and Amgen as examples.
The agency is signaling more than process refinement. Rather, it is a call to upend how trials are run, how evidence is reviewed, and how operational readiness may be judged in the years ahead.
The Emerging Logic of Real-Time Clinical Trials
The FDA’s Real-Time Clinical Trials (RTCTs) initiative reflects a simple but consequential idea: if scientific evidence can be generated continuously, regulatory review should not remain bound to long periods of delay, reconciliation, and administrative handoff.
The conventional development model is still largely episodic. Data are collected over time, then cleaned, aggregated, reviewed, and submitted at predetermined milestones. Each stage introduces lags and white spaces or downtime. Safety signals may be technically available well before they are operationally visible. Endpoint trends may emerge long before they are discussed in a form that can support decision-making. Regulatory engagement, in turn, often follows the cadence of the package rather than the cadence of the evidence.
A real-time model challenges that logic. Instead of treating review as something that happens after the fact, it imagines a development process in which regulators, sponsors, and study teams can engage more continuously with emerging data. That does not eliminate the need for rigor. If anything, it raises the bar. Data must be cleaner, more structured, and more interpretable earlier in the trial lifecycle, not just at the point of submission.
For clinical research, that is a profound operational change.
Discovery Has Accelerated, But Trial Execution Has Not
The deeper issue is easy to see. AI is compressing timelines in early research faster than clinical operations can absorb the output. Sponsors may soon face a pipeline shaped not by scarcity of candidates, but by scarcity of human trial capacity.
Essentially, the bottleneck in drug development is moving downstream to clinical trials. The limiting factor is increasingly not whether a plausible molecule can be found, but whether in-human studies can be activated, monitored, adapted, and kept inspection-ready at pace.
That is especially relevant in therapeutic areas where complexity is already high. Adaptive oncology programs, biomarker-driven studies, rare disease development, and multi-cohort protocols all demand a level of operational responsiveness that legacy trial models struggle to support. In those environments, delays are rarely caused by a single catastrophic failure. More often, they are the accumulated result of fragmented systems, repetitive manual tasks, deferred issue resolution, and limited real-time visibility.
Clinical development is not simply facing a technology gap. It is facing an operating model gap.
Why RTCTs Change the Infrastructure Question
In RTCTs, continuous review requires continuous readiness, which means the clinical technology stack can no longer function primarily as a collection of disconnected systems of record. Capturing information is no longer enough. Trial infrastructure must also help interpret, route, prioritize, and maintain the quality of that information while the study is in motion.
Two capabilities become especially important.
The first is continuous evidence generation. Patient and site data must be captured in structured forms that are reviewable as they are created, not merely stored for later reconciliation. This includes familiar tools such as those designed for electronic informed consent (eConsent), electronic clinical outcomes assessment (eCOA), and electronic patient-reported outcomes (ePROs), but the real question is whether those inputs remain traceable, usable, and decision-ready in near real time.
The second is continuous operational oversight. Study teams need live visibility into enrollment, protocol adherence, data quality, site performance, and emerging safety risks. More importantly, they need a practical way to act on those signals without creating yet another layer of manual work.
Agentic technology brings both capabilities.
The Case for an Agentic Operational Layer
Clinical research has spent the last decade digitizing workflows. The next phase is about coordinating them automatically, continuously, autonomously.
Agentic technology goes beyond static automation. Agents can continually monitor large and varied source systems to detect patterns across workflows, surface issues that require action, and support operational decision-making at a scale human teams alone often struggle to manage. Agents don’t replace humans but reduce the amount of human effort spent chasing administrative tasks, reviewing low-yield queues, or stitching together fragmented operational signals.
That distinction matters. In clinical trials, the highest-value human work is not document routing or routine triage. It is clinical judgment: evaluating risk, interpreting context, deciding when a signal matters, and determining what action is appropriate. If agentic tools are useful, it is because they help preserve human attention for exactly those decisions.
For example, a clinical monitoring agent might identify data trends, site anomalies, or potential protocol deviations before they become embedded operational problems. A document-focused agent might support trial master file quality by classifying content, checking completeness, and flagging issues at ingestion rather than weeks before inspection.
In both cases, the goal is not autonomy for its own sake. It is sustained operational control in a trial environment that is becoming more continuous, more data-intensive, and less tolerant of lag.
Continuous Trials Will Reward Different Strengths
If the industry is moving toward a model in which evidence generation and regulatory engagement become more continuous, then success will depend less on whether organizations have digitized isolated functions and more on whether they can keep an entire study continuously reviewable.
Sponsors and CROs must be able to run multiple programs without losing oversight, adapt execution without creating disorder, and maintain submission-readiness throughout study conduct rather than at the end of a study. It also favors infrastructure designed for signal flow, workflow coordination, and governed intervention—not simply data collection.
If the boundaries between Phase I, II, and III become progressively less operationally rigid, then administrative handoffs become a more visible source of inefficiency. The organizations best prepared for that future will not necessarily be those with the most sophisticated drug discovery engines. They will be the ones that can translate continuous evidence into continuous action.
A More Useful Industry Question
The industry has often framed innovation in clinical development as a question of tools: centralized or decentralized, risk-based or traditional, digital or manual, AI-enabled or not. Those distinctions still matter, but they may be too narrow for what is coming next.
A more useful question is whether trial operations are being designed for a world in which evidence moves faster than legacy processes can handle.
If the answer is no, then the consequences will show up quickly: too many candidates entering too few efficient development pathways, too much manual review for too little insight, and too much operational friction between emerging data and meaningful decisions.
If the answer is yes, then RTCTs may become more than a regulatory initiative. They may become the mechanism that finally aligns the speed of discovery with the speed of development.
The Real Opportunity
The FDA’s program rewards sponsors who can generate clean, structured, continuously flowing evidence. Modern agentic platforms were built for this model, not adapted to it. Continuous data collection and real-time signal reporting enable the critical decisions (when to stop, pivot, or accelerate) to happen alongside evidence generation, rather than being deferred to an end-of-study review. Failing faster is the most efficient path to protecting patients and concentrating resources on programs with the strongest evidence base.
The promise of RTCTs, therefore, is not just shorter timelines. It is also the possibility of building a clinical development system that learns and responds while a study is underway, rather than after momentum has already been lost.
RTCTs represent a transformative, and necessary, change for sponsors, CROs, sites, and regulators alike. The volume of potential therapies is rapidly rising. The pressure to make better decisions earlier is increasing. And the cost of waiting for legacy operational models to catch up is likely to be measured not only in months and dollars, but in missed opportunities for patients.
Medable Chief Customer Officer Alison Holland (alison.holland@medable.com) brings more than 30 years of experience conducting clinical trials to guide customers as they evolve into adopting modern, digital, and decentralized approaches. Previously she was Global Vice President and General Manager for general medicine at Covance. She has managed more than 350 clinical trials, working successfully with biotech organizations and global pharma on some of their most critical initiatives.
