What does it mean to make meaningful change?
More than a decade ago, Medable was founded on a simple premise: bringing treatments to patients faster. What we encountered when we first started was an industry relatively moving at a fixed pace, approving only about 50 new treatments a year on average. This average remained constant since the year 2000, no matter how much R&D investment and new technology got poured in.
As the decade progressed and we developed our data platform, eCOA, eConsent, and Televisit tools, a vision began to take shape. This vision was a series of ambitious goals that we believe we can achieve within our lifetimes: our 1:1:1 vision.
More importantly, we’ve made meaningful, measurable, tested progress towards radically redefining how fast clinical trials can move, and how they can be made easier for every stakeholder involved in them. This rapid evolution of Medable and the industry has been meaningful, with clients in top-10 pharma deploying our latest technologies, helping us take major steps towards accomplishing our 1:1:1 vision.
The vision of 1:1:1
Our vision for the future of clinical trials is simple: instead of the 10-12 years that it takes to bring treatments to market, we envision a future of:
- 1-day study startup
- 1-day patient enrollment
- 1-year study conduct
As you might guess, getting to 1:1:1 takes far more than incremental improvement. It requires a fundamental shift in the tools and systems that run clinical trials from fragmented silos to intelligent, integrated, autonomous systems. Simply put, this means moving from systems of record to systems of outcome.
Today, highly trained clinical experts spend too much of their time navigating systems, assembling information, and reconciling signals rather than applying their expertise to what actually matters: patient safety, therapy effectiveness, and scientific progress. This is due to the friction and whitespace that is inherent within clinical trials.
Removing friction to make progress
Medable's progress toward 1:1:1 rests on two distinct but connected capabilities.
Generative AI in Study Studio removes friction at the front end. It takes on the design and build bottlenecks that traditionally sat with humans, compressing study builds from an industry-standard 16-20 weeks down to as little as 4-6 weeks, with some studies going from protocol to build in a single day.
Agentic AI in the Agent Platform removes friction across the rest of the lifecycle: study conduct, monitoring, documentation, and decision-making. Rather than requiring more human oversight, these agents act as collaborators that take on defined pieces of the work with human-in-the-loop review at every step.
Together, these two capabilities are how a company that started with digitizing outcomes science through eCOA and decentralized trial tools, then aligned clinical operations at the program level through Medable Studio, has arrived at agentic collaboration as the current stage of its evolution. Each stage compounded on the one before it.
Closing the operational whitespace
In 2017, Tufts CSDD found it took an average of 8.1 days just to enter patient visit data into site EDCs. That gap is a good example of the "operational whitespace" that has quietly slowed trials since technology first entered them: not a single point of failure, but dead time accumulating between systems and decisions.
Generative AI in Study Studio compresses study startup and improves data capture, but much of the remaining delay lives in cross-system workflows and inter-phase decisions during conduct. That's exactly where agentic AI is built to work: aggregating information across systems, surfacing trends and risks, and recommending next-best actions, all while preserving governance and credentialed control.
The data: Tufts CSDD models the impact
Claims about removing friction are only useful if they hold up to independent scrutiny. In August 2026, Tufts Center for the Study of Drug Development published an analysis assessing Medable's Clinical Monitoring Agent specifically, using benchmarked oncology program and trial data from Tufts alongside contract value and experience data from Medable. The findings quantify, for the first time, what agentic AI is worth in a real drug development program:
- Expected net present value (eNPV) gains of approximately $7.5 million for a phase 2 trial, $11.3 million combined across phase 2 and phase 3 development, and $21 million for a phase 3 trial
- Return on investment of an estimated 64x for phase 2 and 82x for phase 3 clinical trials
- Direct operating cost savings in on-site monitoring of approximately $4.4 million per phase 2 study and $5.6 million per phase 3 study
- Development timeline acceleration of approximately 18 weeks, driven by faster patient enrollment (109-119 fewer days), earlier database lock (about two weeks shorter closeout), and earlier realization of future revenue
- Administrative task efficiencies valued at roughly $600,000 (phase 2) and $1.7 million (phase 3) in CRA time that can be reallocated to other studies
"To our knowledge, this is the first time that eNPV modeling based on actual use and benchmark data has been applied to quantify the net financial impact of an agentic AI solution deployed to support a drug development program," said Ken Getz, Tufts CSDD Executive Director. He noted the value was driven by operational efficiencies such as fewer on-site visits, reduced travel costs, accelerated enrollment, and faster database lock.
The effect compounds at the portfolio level. "For a sponsor with 20 active indications, deploying a clinical monitoring agent across phase 2 and 3 studies could generate as much as $226 million in incremental portfolio eNPV," said Dr. Pamela Tenaerts, Chief Medical Officer at Medable. "For a sponsor with 50 active indications, that figure could jump to as much as $565 million. Bottom line? We now have evidence demonstrating sizable value creation of agents in clinical research, helping break longstanding barriers."
Inside Medable’s Agent Platform: Agent use cases
None of this works without a foundation built for regulated environments. Every agent on the platform operates with GxP and regulatory context built in, configurable logic through prompts, skills, and triggers, secure connectors into clinical and enterprise systems, credential isolation and role management, AES-256 encrypted and ALCOA+ compliant audit trails, and a validation framework aligned with 21 CFR Part 11, Annex 11, ICH GCP, and CSA. Human-in-the-loop oversight is configurable throughout, with escalation pathways and flags for anything that needs a person's judgment.
Within that foundation, key agents are doing the bulk of the work today.
Clinical Monitoring Agent
Built to continuously execute monitoring and augment the CRA with consistent, protocol-driven actions across sites. It brings all site data into a single real-time view, standardizing ingestion across CTMS, EDC, and RBQM systems, detects risk using predefined logic, recommends actions for human approval, and writes back to systems of record with full traceability. At a top-5 pharma company, it reduced CRA task time by 62%, earned 100% positive feedback on satisfaction and ease of use, and saw 93% of users say they'd use it weekly, freeing up to 8 hours a week per CRA.
Study Oversight Agent
Where the Clinical Monitoring Agent works at the site level, this agent works one level up, giving teams real-time visibility into study risk, vendor performance, and CRA performance across both individual studies and the broader portfolio. It's the layer that turns site-by-site monitoring into program-wide oversight, so leaders can see where a portfolio needs attention without waiting for a status roll-up.
TMF Agent
Built to keep trial master files audit-ready continuously rather than in periodic scrambles. It automates document tagging, classification, and filing, standardizes intake from inboxes, folders, and portals, auto-tags across languages, and produces ready-to-file outputs for Veeva and other eTMF systems, with built-in QC, reconciliation, and continuous missing-document detection. At a top-5 pharma company, it correctly classified 99% of documents with 90% first-match accuracy and cut quality findings by more than 60% compared with manual processing.
DDF Agent
Built to lay the foundation for end-to-end agentic clinical development. It transforms clinical trial protocols into machine-readable data, supporting adaptive, real-time decision-making across the rest of the platform rather than leaving protocol intent locked in static documents.
PI Summary and Review Agent
Embedded directly in the eCOA workflow rather than sitting alongside it. It continuously synthesizes captured data, surfaces what's changed since a principal investigator's last review, flags material updates, and supports configurable 21 CFR-compliant e-signatures, all in support of, not replacement for, the judgment only a PI can exercise.
Intelligent Consent
AI-powered eConsent and TeleVisit that streamline consent creation, review, and deployment in one participant app, so the enrollment moment itself becomes faster and less friction-prone rather than a separate workflow bolted onto the rest of the trial.
Together, these agents are how the operational whitespace actually closes, with consistent, auditable action happening continuously in the background of a trial.
The path forward towards achieving 1:1:1
The path to 1:1:1 won't be defined by a single breakthrough, but by the steady removal of friction across every stage of clinical development, generative AI compressing the front end in Study Studio, agentic AI closing the whitespace everywhere else. What look like isolated inefficiencies are really systemic gaps between people, data, and decisions, and the Tufts CSDD findings are the first independent evidence of just how much value closing those gaps creates. 1:1:1 is not just an aspiration. It is a direction of travel, and the work to realize it has already begun.
