AI, digital twins and connected insights: The future of clinical trials is here

That clinical development isn’t built for speed, agility or efficiency may be the worst-kept secret in pharma. For more than a decade, leaders have envisioned a digital transformation—but that vision has always remained just out of reach. At last, the prerequisites are in place to reinvent clinical development for the digital age.

The convergence of three forces has brought us to the tipping point:

• Data foundation. The volume and variety of available data has surged—but what’s different now is access. Years of investment in data infrastructure mean that information once trapped in siloed systems is now increasingly findable, accessible, interoperable and reusable (FAIR). Clinical development teams can collect, analyze and share insights across platforms in near real time.
• AI’s emergence. Breakthroughs in AI—especially agentic AI and large language models (LLMs)—are unlocking new frontiers in drug development. Our ability to rapidly train and fine-tune models for domain-specific tasks makes it possible not only to automate labor-intensive processes but to solve problems once thought intractable.
• A readiness to disrupt. As pharma’s current business model reaches its limits, innovative leaders are poised to disrupt the status quo. Whether it’s a new generation of digital-native leaders or a wave of C-suite executives in legacy-building mode, today’s decision-makers have what they need to create the future of clinical development.

Acting now matters. 

Clinical development has long followed a linear process—design in the present, wait years for results and then belatedly adjust course, often after incurring months or years of wasted effort and missed opportunity. Instead, what if AI agents continuously captured and analyzed every data point at the source, simulated the full spectrum of in-flight and future trials and delivered real-time insights to help leaders course-correct in the moment? And what if a universal CRM, powered by data and AI, could suddenly dissolve the arbitrary barriers between patients, sponsors and sites—transforming clinical trials from episodic events to connected and continuous journeys?

The result would be the most significant shift in how clinical trials are designed, run and experienced in decades. Companies that act quickly to upgrade their development operating model will experience an array of benefits, including higher asset success rates, faster study startups, fewer patient dropouts, shorter trial durations and significantly lower cost per trial.

So, what will it take for companies to start measuring their clinical pipelines in years, rather than decades? 

Visualize the future of clinical trials

Click on each icon to see what digital transformation of clinical trials could look like. 

Clinical development is fraught with complexity, uncertainty and high stakes. Development leaders often make pipeline decisions that cost millions—if not billions—of dollars based on incomplete data scattered across siloed teams. The result?

• Fragmented strategies that lack long-term cohesion
• Inefficient resource allocation that inflates costs and delays progress
• Reactive firefighting that wastes time and increases risk

Houston, we have a solution. 

In a digital, AI-powered clinical development model, R&D Mission Control will transform decision-making by integrating specialized AI agents that optimize pipeline strategy, resource allocation and risk management—eliminating fragmented processes and reactive firefighting. This real-time command center will offer development leaders an unprecedented level of visibility, foresight and control over clinical programs.

It’s based on three pillars:

• Future casting pipelines. Specialized agents simulate multiple development scenarios, forecasting market demands, changes to the regulatory environment and pipeline interdependencies. They recommend which assets to advance or pause—maximizing ROI across the portfolio by eliminating guesswork, reducing bias and ensuring that portfolio vision, not personal stake or internal politics, drives decisions. 
• Resource optimization. Allocation agents continuously monitor the deployment of people, budgets and infrastructure to pinpoint operational bottlenecks before they become roadblocks. They recommend next best actions that help leaders maximize resources, accelerating timelines by dynamically reallocating staff to critical milestones and uncovering hidden efficiencies.
• Predictive risk management. Risk-focused AI monitors external signals and internal metrics to detect threats sooner, flag potential crises and suggest proactive interventions. 

But data and simulations alone don’t alter outcomes. Acting on them does. Tomorrow’s portfolio leaders won’t just monitor data streams; they’ll leverage AI-driven forecasts to make strategic decisions, in real time and at the push of a button, reallocating budgets, adjusting personnel and pivoting or pushing forward with confidence. 

The ideal clinical trial strikes the perfect balance between scientific rigor and operational reality. It needs to be accessible, minimally burdensome and built around the patient experience. It must be designed to generate high-quality evidence through robust study design and clinical relevance. And it also must be optimized to maximize recruitment, minimize site burden and be compliant.

Unfortunately, data-collection requirements, recruitment considerations and patient centricity often find themselves in conflict. Optimizing for one dimension may compromise another. Even worse: As clinical trial complexity grows, causing data requirements to surge, trial designers still default to gut feel and past experience. The result?

• Inefficient protocols that slow recruitment, overburden sites and increase amendments
• Conflicting priorities between patient centricity, scientific rigor and operational feasibility
• Suboptimal trial designs that lead to costly delays, unnecessary risk and higher failure rates

Instead of human intuition, imagine a team of specialized AI agents running an infinite number of trial simulations to uncover the perfect trial design—every single time. This is the In Silico Slingshot.

No single model can capture the full complexity of a clinical trial. Much like a spacecraft using planetary gravity to finetune its trajectory, the In Silico Slingshot uses an “agent-of-agent” model to pull the trial protocol into perfect alignment across scientific, operational and regulatory priorities.

With the In Silico Slingshot, model agents—each one advocating for a core priority of trial design—assess and optimize the protocol using virtual patients to simulate everything from cellular-level interactions with investigational drugs to recruitment dynamics, population variability and protocol performance across diverse trial scenarios.

Individual agents are responsible for:

• Synthetic protocol management that authors multiple synthetic protocol designs, which can then be evaluated via in silico trials
• Virtual patient cohort creation that generates synthetic patient cohorts using real-world data (RWD) and historical trial data leveraging AI foundation models
• Treatment simulation that models the administration and treatment effects on synthetic patients, simulating drug behavior, pharmacological effect and system-level interactions
• Outcomes prediction that forecasts clinical outcomes by applying statistical or machine-learning techniques to map treatment-simulation outcomes to clinically relevant endpoints
• Analysis and decision-making that analyzes trial results to adapt and optimize trials based on probability of success and the related commercial opportunity
• Operational simulation that simulates operational elements, such as site, enrollment, cost, quality and timeline, based on analysis of protocol, cohorts and predicted outcomes

Overseeing them all is a central orchestrator—the agent of agents—that weighs each input and proposes the optimal design compromise, achieving a balance between scientific rigor, operational feasibility, patient centricity and financial viability. It’s like running a multidisciplinary design subcommittee in real time, with none of the delays.

Freed from manual and repetitive trial-design tasks, trial teams can shift to more strategic ones such as strengthening alignment, ethical considerations and trial execution. In this future, human expertise remains essential for safeguarding patient interests, guiding AI outputs and validating assumptions.

From simulated design to fully in silico trials

With In Silico Slingshot, data is no longer just a byproduct of clinical trials used for regulatory submissions. It’s the fuel that refines our models, which in turn shape all future trial designs. By generating and validating synthetic data, in silico trials will not only accelerate regulatory submissions and enable sponsors to run fewer trials with smaller patient cohorts—they’ll also unlock the next evolution in clinical research.

A potential first step? Replacing traditional placebo arms with virtual controls drawn from robust historical and synthetic datasets. Instead of assigning half of participants to a taxing trial experience without receiving the investigational therapy, sponsors can use well-established placebo data to simulate outcomes and spare patients unnecessary burden.

Over time, this capability will pave the way for fully in silico trials—rapid, low-burden studies conducted using virtual cohorts to assess therapeutic viability. Promising therapies could reach the market faster, with safety and efficacy continuously monitored in the real world. 

Healthcare today is distressingly fragmented, making clinical research feel disconnected from standard healthcare. The result?

• Patients must actively seek out and enroll in trials.
• Sites struggle to manage multiple, disconnected technologies.
• Siloed organizations lack seamless data-sharing, causing delays, inefficiencies and missed insights.

Enter the “Biosphere,” a seamless clinical trial ecosystem.

The Clinical Trial Biosphere creates a unified environment where AI-powered insights seamlessly connect sites, patients and operations. No longer an isolated step in the healthcare journey, trials become an embedded, intuitive and frictionless part of the overall healthcare landscape.

The biosphere comprises four foundational elements:

• A universal CRM. A single platform integrates commercial, medical and clinical data, synchronizing site performance, patient demographics and engagement preferences. More than a data repository, this CRM delivers seamless experiences for both sites and patients, making trials feel like a natural extension of routine healthcare.
• AI-driven site intelligence. Every site’s performance history, patient demographics and engagement patterns are centrally visible. AI-driven insights match sites to the most relevant trials and dynamically adjust engagement strategies—accelerating activation and optimizing site selection.
• Proactive patient engagement. AI agents build patient relationships long before recruitment begins, providing continuous personalized support through automated chat, appointment scheduling and engagement tracking. By the time a trial launches, a motivated and well-informed patient pool is ready to enroll.
• An expanded, high-performing site network. The biosphere extends beyond major academic centers to include nontraditional sites like primary care clinics, outpatient facilities and telehealth hubs. AI-driven workflows guide site staff step by step, ensuring consistency, reducing site burden and expanding patient access to trial opportunities.

A new era of ‘invisible’ clinical trials

People don’t magically transform from patients into trial participants and then later into customers. Trial sites don’t shift overnight from research hubs into care providers. They’re the same people and places across an asset’s life cycle. The problem is that clinical research today often treats them as separate entities.

A unified trial biosphere, powered by a centralized CRM, eliminates this disconnect. By embedding trials into routine care, it streamlines enrollment, reduces site burden and enhances patient experience—all without adding complexity.

When trials run alongside everyday healthcare, patients and providers get the support they need, exactly when they need it, without the friction of trial logistics. The result? An invisible clinical trial experience that simplifies participation, strengthens engagement and results in fewer enrollment delays that derail trials.

At its core, a clinical trial is about generating data and using it to understand an asset’s safety, efficacy and likely position in the market relative to other therapies. For too long, pharma companies have treated data as a byproduct of clinical research—an output to be cleaned, stored and submitted—rather than the fuel that powers clinical innovation.

Instead of powering real-time insights, data today remains trapped in slow, fragmented workflows that delay decision-making and limit the value of the data trials generate. The result?

• Teams spend more time cleaning and validating data than analyzing it for insights.
• Critical signals are missed, or detected too late to be useful, because data isn’t structured for real-time use.
• Data is collected for submission but only sparingly used for broader portfolio planning, predictive modeling or commercial impact.

It’s time for clinical development teams to replace classic data management with continuous, real-time data science. 

R&D Mission Control, the In Silico Slingshot and Clinical Trial Biosphere all depend on a constant flow of real-time data connecting every node in the clinical trial ecosystem.

In the future, each infinitesimal data point—whether from electronic health records systems, wearables, imaging or genomic sequencing—will arrive at the sponsor structured, validated and ready for immediate use with minimal human intervention required. No matter its origin, data flows and transforms seamlessly for immediate use and near-instant analytics.

The three elements of Data at the Speed of Light are:

• Data standardization at the source. With metadata and formatting rules defined at the protocol-design stage, teams ensure seamless ingestion, interoperability and reuse of data from devices, RWD and other sources.
• AI-driven quality control. Data teams deploy AI agents directly in site workflows to flag issues at the point of capture, trigger real-time corrections and continuously refine data integrity.
• Instant data analysis and submission-ready outputs. Agents structure and validate data as it’s collected, making it immediately available for analysis and regulatory submission—without delays.

Push-button document generation

With generative AI tools trained on comprehensive and connected data, teams can automate how they create trial documents. Instead of manually drafting and revising documents, study teams will simply validate AI-generated outputs, reducing administrative burden and accelerating cycle times from months to days.

From data management to data mastery

With the onset of Data at the Speed of Light, traditional data management will recede as AI handles routine data validation and cleaning while data scientists focus on higher-value tasks such as:

• Designing and maintaining the end-to-end data flow for seamless integration, interoperability and minimal friction at every stage
• Establishing and enforcing standards for data integrity, security and regulatory compliance, making AI-driven processes safe and ethical
• Partnering with clinical operations, biostatistics and regulatory teams to refine pipelines, incorporate new data sources and advance real-time analytics

In a future defined by speed, collaboration and continuous, real-time information, data will serve as the gravitational force that pulls every facet of clinical development into alignment.

By embedding intelligence and integration across the trial ecosystem, this transformation of the clinical development operating model will deliver improvements for each stakeholder involved.

Portfolio leaders will gain real-time, portfolio-wide visibility supported by agents helping them efficiently surface the next best action, freeing them to focus on strategic innovation, scenario planning and agile response to market or pipeline shifts.

Program and study teams can move beyond repetitive data tasks to provide high-impact strategic guidance, collaborating more closely with sites and internal stakeholders.

Sites will benefit from reduced administrative burden and clearer operational workflows, freeing up time and resources to focus on delivering exceptional patient care.

Patients will encounter fewer administrative obstacles and receive more personalized support, making trial participation a seamless part of their healthcare journey.

Companies that fully integrate AI, automation and real-time data across clinical development can expect to double their pipeline output in about half the time. Transformations don’t have to happen all at once—but each step compounds, creating additional impact across the clinical development continuum.

Before embarking on the journey to reinvent clinical development, leaders must be prepared to commit to deep, structural changes to long-ingrained processes. Here are three things they should bear in mind:

1. Don’t just sponsor the future. Build it.

Transformation of this magnitude requires senior executives to engage directly with teams to codesign the future operating model to ensure strategy aligns with real-world execution. This means embedding AI, digital and data-driven workflows into daily operations and modeling a culture of innovation.

2. Changing the human is just as important as changing the technology.

Leaders must rethink their talent mix to ensure they have the right skills and mindsets to thrive in an AI-powered environment. Leaders should foster a culture of cross-functional collaboration, experimentation and psychological safety, where new ideas and unconventional voices are valued. Transforming technology isn’t enough—leaders also must help change how people work.

3. Take smart risks—and choose the right partners

Breakthroughs require calculated risk-taking that strike a balance between boldness and discipline. Organizations must prototype, iterate and scale new approaches while derisking transformation through partners that combine domain expertise, AI and data strategy acumen and a proven ability to execute, not just build a great strategy on paper.

Ready to go deeper? Join us May 20 for a live webinar on the future of clinical development: Clinical development is changing: Your next step matters

The authors would like to thank Mike Martin, Vickye Jain, Sam Dowd, Ansh Srivastava and Gaurav Singh for their invaluable contributions.