Clinical Development & CROs

The next competitive advantage in clinical development will be patient access, site enablement and evidence quality — not the number of hours billed.

If artificial intelligence turns pharmaceutical R&D into an evidence industry, every company supporting drug development will need to reconsider what it actually sells.

For contract research organizations, the question is frequently framed in dramatic terms:

Will AI replace CROs?

That is probably the wrong question.

A more useful one is:

Which CRO services will continue to command premium prices when much of the administrative, analytical and documentary work can be automated?

AI is unlikely to remove the need for clinical development partners.

It may remove the willingness to pay premium fees for work that no longer requires premium expertise.

Where AI Is Already Automating Clinical Development

The most visible gains from AI in clinical development are not yet coming from fully autonomous trial design or synthetic replacements for human evidence.

They are coming from activities that are repetitive, fragmented and information-intensive:

protocol digitization;
feasibility analysis;
site identification;
document drafting;
clinical data review;
coding and programming support;
monitoring prioritization;
regulatory-document preparation;
safety-case processing;
study-status consolidation.

Executives interviewed by Reuters in early 2026 described measurable operational applications.

Novartis said AI reduced a site-selection exercise for a 14,000-patient cardiovascular study from a process that normally took four to six weeks to a two-hour meeting. The company subsequently completed enrollment only 13 patients above its target.

GSK reported approximately £8 million in savings from digital and AI-supported enrollment and data processes in late-stage asthma trials and established a goal of accelerating clinical trials by approximately 15%.

Genmab and ITM described applications in clinical analysis, report generation and regulatory formatting. (Reuters)

Teva CEO Richard Francis summarized the near-term opportunity as improving “all the unsexy stuff” surrounding the scientific objective of bringing a medicine to market.

The comment is revealing.

AI is first attacking the coordination costs of clinical development.

The earliest disruption is not replacing clinical science. It is reducing the labor required to organize, document and transmit it.

The Economic Threat to the Billable-Hour CRO Model

Many traditional CRO contracts are built around:

full-time equivalents;
hours worked;
monitoring visits;
documents produced;
units processed;
duration of support;
project-management effort.

AI reduces precisely those inputs.

A regulatory document that previously required several writers, reviewers and formatters may be generated and checked with fewer hours.

A central-monitoring team may review a smaller number of sites because algorithms prioritize the highest-risk signals.

Clinical data management teams may process fewer manual reconciliations.

Feasibility assessments may require less time collecting and consolidating historical information.

This creates an economic contradiction:

Under a traditional commercial model, a CRO can reduce its own revenue by becoming more productive.

CROs will therefore need to choose among several responses:

retain productivity benefits while selectively reducing prices;
move from hourly to unit-based pricing;
license technology separately;
offer subscription-based intelligence;
negotiate milestone or performance incentives;
share part of the value created through faster execution;
redesign services around higher-value decisions.

The threat is not that every outsourced activity will return to pharmaceutical companies.

The threat is that clients will no longer accept large teams and long timelines for activities that technology can substantially compress.

Financial markets are already debating this issue.

Shares of IQVIA, Medpace and Charles River came under pressure in early 2026 as investors considered whether advanced AI agents could enable sponsors to internalize more work.

Industry specialists interviewed by Reuters argued that this interpretation underestimated the physical, regulatory and operational capabilities required to conduct clinical research. (Reuters)

Both positions contain part of the truth.

AI will commoditize some services.

It will also increase the value of capabilities that cannot be digitally replicated.

Research Sites Are Becoming Scarce Clinical Trial Infrastructure

AI can generate a protocol.

It cannot automatically create:

an experienced investigator;
a trained coordinator;
an infusion facility;
validated laboratory capacity;
access to a rare-disease population;
trust between a physician and a patient;
local regulatory expertise;
reliable adverse-event management;
long-term participant retention.

If AI increases the number of viable development programs, demand for high-performing sites may grow faster than site capacity.

That turns the research center into one of the most strategically constrained resources in clinical development.

The consolidated ICH E6(R3) Good Clinical Practice guideline reflects this broader view of clinical trial quality.

It emphasizes that trial design should be operationally feasible, avoid unnecessary complexity, adapt technology to participant characteristics and incorporate perspectives from patients, communities and healthcare professionals.

It also reinforces that poorly designed or poorly conducted trials waste the efforts of participants and investigators and may generate unreliable results. (ICH E6(R3))

This has an important consequence for CROs.

Selecting a research site will no longer be enough.

The next competitive frontier will be enabling the site to perform.

Site selection will become site enablement.

From Clinical Trial Site Selection to Site Enablement

Traditional feasibility frequently asks whether a site has access to the required patient population, infrastructure and staff.

The answers are often based on questionnaires, investigator estimates and historical performance that may not accurately reflect the current environment.

AI can improve feasibility by combining:

electronic health-record signals;
epidemiological data;
historical enrollment;
current competing trials;
staff capacity;
startup timelines;
screen-failure patterns;
protocol complexity;
geographic and socioeconomic factors.

But predictive site selection solves only part of the problem.

Even a correctly selected center may fail if it receives an impractical protocol, too many disconnected systems, inadequate training or insufficient operational support.

Research Site Capability Assessment

Before activation, the CRO could evaluate:

actual patient availability;
investigator and coordinator capacity;
therapeutic-area expertise;
equipment and laboratory readiness;
pharmacy and investigational-product capabilities;
technology infrastructure;
data-security maturity;
competing-study burden;
previous quality findings;
likely barriers to recruitment and retention.

The objective should be to replace optimistic feasibility estimates with verifiable operational evidence.

Clinical Trial Workflow Redesign

The protocol must be translated into the reality of the research center.

Who identifies potential participants?

Who reviews eligibility?

How are laboratory, imaging and pathology results transferred?

Which activities can occur remotely?

How are adverse events escalated?

Which data remain in the local clinical record, and which are transmitted to the sponsor?

Where are the points most likely to generate deviation, delay or participant burden?

AI can help map these workflows, identify bottlenecks and compare alternative operational designs.

But the redesign must involve the professionals who will actually execute the study.

Research Site Technology Implementation

Research centers are increasingly expected to operate multiple technologies:

electronic data capture;
clinical trial management systems;
electronic trial master file interfaces;
electronic informed consent;
ePRO and eCOA;
electronic source systems;
digital health devices;
remote-monitoring tools;
patient-matching applications;
safety-reporting systems;
sponsor-specific portals.

The CRO that merely delivers these systems transfers complexity to the center.

The CRO that integrates them, trains users and simplifies workflows creates value.

Continuous Site Training and Operational Support

Training can move beyond a protocol presentation at study initiation.

AI-supported systems can provide:

role-specific guidance;
just-in-time reminders;
simulations;
protocol-deviation prevention alerts;
answers linked to approved study documents;
retraining triggered by emerging risks;
multilingual and accessible materials.

Human oversight remains essential.

The objective is not to allow an unvalidated chatbot to interpret the protocol independently. It is to provide controlled access to consistent and traceable guidance.

CROs and site-management organizations may also provide:

remote study coordinators;
centralized prescreening;
regulatory-document support;
contract and payment administration;
participant-navigation services;
technology support;
multilingual engagement;
specialist resources shared across centers.

This model could be particularly valuable for community hospitals and smaller research centers that have access to relevant patients but lack the infrastructure of major academic institutions.

AI-Assisted Patient Identification — Without Algorithmic Exclusion

The phrase patient profiling should be used carefully.

It may describe legitimate analysis of clinical characteristics to identify potential trial participants.

But it may also imply opaque classification, behavioral prediction or the exclusion of people considered operationally inconvenient.

A more appropriate concept is:

AI-assisted patient identification, matching and engagement.

Large language models and other AI systems can analyze:

diagnoses;
laboratory results;
medications;
genomic findings;
imaging and pathology reports;
clinical notes;
disease progression;
previous treatments.

These tools can convert complex eligibility criteria into searchable logic and identify records that merit review by a qualified professional.

The evidence is promising, but it must be interpreted carefully.

TrialGPT, a large-language-model framework evaluated in Nature Communications, retrieved more than 90% of relevant trials while examining less than 6% of the initial trial collection.

Its criterion-level matching achieved 87.3% accuracy in manual evaluation and reduced screening time by 42.6% in a user study.

However, the principal evaluation used synthetic patient cohorts, meaning the results demonstrate technical potential rather than universal readiness for unsupervised clinical deployment. (Nature Communications)

A 2026 randomized evaluation using retrospective oncology records found that human–AI collaboration could improve prescreening accuracy and potentially include a broader patient population while reducing false-positive eligibility assessments. (Nature Communications)

The appropriate role is therefore prescreening and decision support, not autonomous eligibility determination.

AI should identify patients who may benefit from access to a trial — not the patients who are most convenient for the trial.

When Clinical Trial Patient Matching Becomes Unethical Profiling

A defensible patient-matching system should:

use variables relevant to clinical eligibility;
provide reasons for each recommendation;
preserve professional review;
record model versions and data sources;
measure false-negative as well as false-positive rates;
evaluate performance across demographic and clinical groups;
minimize access to identifiable data;
comply with consent, privacy and institutional requirements;
allow errors to be corrected.

A problematic system would:

automatically exclude patients predicted to be difficult to retain;
use income, address or digital behavior as proxies for compliance;
prioritize participants by estimated operational cost;
infer sensitive conditions without an appropriate legal and ethical basis;
conceal the reason for exclusion;
contact patients without appropriate authorization;
reproduce historical underrepresentation because previous trial populations were not diverse.

This distinction matters because a patient-matching model can create two different types of error.

A false positive increases review workload and may lead to an unsuccessful screening visit.

A false negative may silently deny a patient access to a potentially relevant clinical study.

Organizations must therefore resist evaluating these systems only by how much labor they save.

They must also measure who the algorithm fails to identify.

AI-Based Patient Retention Should Trigger Support — Not Exclusion

AI can also identify participants at increased risk of missed visits or withdrawal.

This capability can improve clinical trials, but only when the prediction is used to remove barriers.

Relevant barriers may include:

transportation;
travel distance;
inflexible scheduling;
language;
caregiver responsibilities;
difficulty using digital devices;
adverse events;
visit complexity;
financial burden;
lack of understanding of trial requirements.

A responsible retention system would use these signals to offer:

transportation assistance;
flexible scheduling;
remote visits where appropriate;
home nursing;
technical support;
translated materials;
patient navigators;
caregiver support;
reimbursement assistance;
simplified reminders.

It should not be used to avoid enrolling participants who may require greater support.

Retention prediction should change the support offered to a patient — not the patient’s right to be considered.

FDA guidance on clinical trials with decentralized elements reinforces this principle.

Sponsors should ensure that digital technologies and telecommunications are available to participants who might otherwise be excluded for socioeconomic reasons.

The guidance also emphasizes training, qualified personnel, clear management of remotely identified adverse events and coordination among local healthcare providers and contracted services. (FDA)

New Technology and Compliance Requirements for Research Sites

Research sites will face two different categories of expectations.

Regulatory and Good Clinical Practice Responsibilities

The fundamentals remain:

participant rights, safety and well-being;
investigator oversight;
qualified staff;
informed consent;
reliable source records;
data integrity;
privacy;
appropriate delegation;
safety reporting;
control of computerized systems;
documentation of roles and responsibilities.

Technology does not transfer accountability away from the investigator or sponsor.

ICH E6(R3) states that roles should be clear and documented and that sponsors and investigators retain responsibility and oversight for delegated activities. (ICH E6(R3))

New Commercial and Operational Expectations

Sponsors and CROs are also likely to expect research sites to demonstrate:

verifiable recruitment data;
faster startup;
structured access to electronic records;
prescreening capabilities;
cybersecurity readiness;
support for eConsent and digital technologies;
remote-monitoring readiness;
rapid response to risk signals;
reliable retention metrics;
interoperability;
staff availability;
technology-support capacity.

Not all these expectations are formal regulatory requirements.

Some will become commercial conditions for being selected.

This creates a risk.

Large academic institutions and consolidated site networks may be able to invest in data warehouses, integration teams, cybersecurity and AI-enabled prescreening.

Smaller centers may have excellent investigators and access to underrepresented patient populations but lack the necessary infrastructure.

Technology could therefore concentrate trials in organizations that are already powerful.

Or it could be used to make a broader network of centers research-ready.

CROs will help determine which outcome occurs.

Site Enablement as a Service

A significant new opportunity is the development of site enablement as a service.

A CRO, site network or specialized vendor could offer:

readiness assessments;
standardized technology;
electronic-record integration;
cybersecurity support;
remote coordination;
centralized regulatory services;
AI-assisted prescreening;
participant navigation;
quality coaching;
performance dashboards;
shared specialist staff.

Instead of repeatedly selecting the same small group of high-performing centers, sponsors could invest in expanding global research capacity.

The potential benefits include:

access to new patient populations;
reduced competition for the same investigators;
greater geographic representation;
improved diversity;
stronger community-site participation;
more predictable startup;
sustainable development of local research expertise.

The future relationship between CROs and research centers should therefore be less transactional.

A center should not be treated as the final operational vendor in a long chain.

It is the infrastructure on which the entire evidence system depends.

The future of site management is not tighter control. It is better enablement.

The CRO as a Clinical Intelligence and Site-Enablement Platform

The most defensible CROs will combine four elements:

IQVIA’s position illustrates this model.

During concerns that AI could displace parts of its business, CEO Ari Bousbib argued that the company’s proprietary healthcare-information assets were becoming more valuable, not less. (Reuters)

Its financial results also show that clinical-development outsourcing remains substantial.

At the end of the second quarter of 2026, IQVIA reported an R&D Solutions backlog of $34.2 billion and record quarterly net new bookings above $3.1 billion. (IQVIA Investor Relations)

ICON is similarly building an AI and data layer across the clinical trial lifecycle.

Its Orbis platform, developed with Microsoft infrastructure, is intended to support protocol optimization, feasibility, site identification, startup, monitoring, data review, regulatory documentation and patient and site engagement.

ICON CEO Barry Balfe described AI as becoming “core infrastructure” for the industry. (ICON Investor Relations)

Parexel launched ParexelAI in 2026 and highlighted partnerships covering more than 800 research sites and 2,100 provider locations for patient identification and clinical trial access.

It also reported reducing IND and NDA authoring timelines through regulatory automation.

These are company-reported results and require independent validation, but they demonstrate where CRO investment is moving. (Parexel)

The competitive advantage will not come from adding a generic chatbot to existing services.

It will come from converting operational history into better decisions.

Clinical Trial Data Is a Moat Only When It Can Be Used

CROs possess decades of information about:

site performance;
recruitment;
screen failure;
retention;
protocol deviations;
data queries;
startup timelines;
regulatory delays;
monitoring findings;
country-level execution;
costs.

But historical data do not automatically create a competitive advantage.

They must be:

structured;
current;
comparable;
legally reusable;
representative;
linked to outcomes;
accessible within validated systems.

A site that recruited successfully five years ago may have a different investigator, staff, competing studies and patient population today.

A model trained on historical high-performing sites may repeatedly recommend those same institutions, excluding emerging centers and reinforcing market concentration.

The data moat is therefore not simply accumulated volume.

It is the ability to maintain reliable, contextual and continuously updated evidence about execution.

How AI Will Affect Other Pharmaceutical R&D Vendors

eClinical and Clinical Data Management Providers

Routine reconciliation, query generation and data review will become increasingly automated.

Value will move toward:

interoperability;
continuous data quality;
provenance;
real-time risk detection;
integration across clinical, laboratory and digital-device data.

Biostatistics and Statistical Programming

Basic code and standardized outputs will become faster to produce.

The premium will move toward:

study design;
simulation;
causal inference;
adaptive methods;
independent validation;
regulatory interpretation.

Medical Writing and Regulatory Services

First drafts, consistency checks and document formatting will become more automated.

Human value will concentrate in:

scientific argument;
benefit–risk interpretation;
regulatory strategy;
source verification;
accountability.

Central Laboratories, Imaging and Digital Pathology

AI can improve measurement, classification and signal detection.

The strategic moat remains:

biological samples;
validated assays;
standardized procedures;
image archives linked to outcomes;
biomarker expertise;
regulatory-grade quality.

Patient Recruitment Companies and Site Networks

Simple advertising and database searches may become commodities.

Value will depend on:

trusted clinical relationships;
access to patient populations;
prescreening integration;
participant navigation;
retention support;
local execution.

Real-World Evidence Providers

Demand will increase for:

external control arms;
safety evidence;
comparative effectiveness;
post-market learning;
value and reimbursement evidence.

Competitive advantage will depend on data quality, longitudinal depth, population representativeness and credible causal methods.

Preclinical CROs and Research Laboratories

AI may increase the number of compounds and hypotheses requiring experimental validation.

These vendors can benefit if they combine physical capabilities with:

automated laboratories;
digital pathology;
predictive toxicology;
advanced translational models;
validated alternative methods.

From Billable Hours to Outcome-Based CRO Contracts

The commercial model must eventually reflect the new value proposition.

Possible structures include:

bonuses for recruitment performance;
payments linked to startup acceleration;
gain sharing based on verified time or cost reduction;
incentives tied to data quality;
platform subscriptions;
analytics licensing;
hybrid service-and-software contracts;
risk-sharing arrangements;
payments for creating new site capacity.

The relevant outcomes must be defined carefully.

Rewarding enrollment alone could encourage inappropriate pressure or inadequate screening.

Rewarding speed alone could compromise quality.

A balanced model should consider:

participant safety;
population diversity;
screen-failure rates;
retention;
protocol deviations;
data quality;
startup time;
milestone achievement;
investigator and participant experience.

The CRO of the future will not be paid only for performing activities. It will be paid for reducing uncertainty without compromising trust.

Which CROs and R&D Vendors Will Win?

Potential winners include:

CROs with proprietary and usable operational data;
organizations with strong site and patient networks;
companies that improve center capability rather than merely monitor it;
vendors that integrate technology with therapeutic and operational expertise;
organizations able to demonstrate measurable improvements in time, quality and access;
regional CROs with deep knowledge of local sites and patient populations;
platforms that reduce complexity for investigators and participants.

Potential losers include:

providers dependent primarily on labor arbitrage;
organizations selling isolated systems;
vendors whose technology adds work for research sites;
companies unable to demonstrate data provenance or model performance;
CROs that use AI only to reduce internal headcount;
providers whose pricing remains disconnected from delivered value.

The Future CRO Will Sell Confidence

Clinical trials are not simply information-processing exercises.

They are distributed systems involving patients, investigators, healthcare institutions, regulators, laboratories, technologies and sponsors.

AI can make those systems faster and more intelligent.

It cannot remove the need for trust, accountability, clinical judgment and physical execution.

The CRO of the future will not merely sell labor.

It will sell:

access;
intelligence;
site capability;
patient support;
execution;
evidence quality;
confidence.

The most valuable CROs will not be those that use AI to distance themselves from research centers and participants.

They will be those that use AI to build stronger relationships with them.

AI will not eliminate clinical development partners. It will redefine which partners deserve to be paid a premium.

Sources and Further Reading

Reuters, Drugmakers Turn to AI to Speed Trials and Regulatory Submissions. Read the report
Reuters, AI-Led Selloff in Contract Research Firms May Be Misjudging Disruption Risk. Read the report
ICH, E6(R3) Guideline for Good Clinical Practice. Read the guideline
FDA, Conducting Clinical Trials with Decentralized Elements. Read the guidance
Jin et al., Matching Patients to Clinical Trials with Large Language Models, Nature Communications, 2024. Read the study
Parikh et al., Human–AI Teaming for Oncology Trial Prescreening, Nature Communications, 2026. Read the study
Teodoro et al., A Scoping Review of Artificial Intelligence Applications in Clinical Trial Risk Assessment, npj Digital Medicine, 2025. Read the review
Lu et al., Artificial Intelligence Tools for Optimising Recruitment and Retention in Clinical Trials, BMJ Open, 2024. Read the review
ICON, Microsoft Partnership for AI-Enabled Clinical Development. Read the announcement
Parexel, Launch of ParexelAI. Read the announcement
IQVIA, Second-Quarter 2026 Results. Read the results
Reuters, IQVIA Backs Its AI Strategy as Analysts Question the Impact on Its Business. Read the report
IQVIA Institute, Global R&D Trends 2026. Read the report

Originally published on LinkedIn on August 9, 2026.

Category: Clinical Development & CROs

AI Will Not Eliminate CROs. It Will Redefine What Clients Pay Them For