Feature|Articles|September 14, 2026

The Execution Gap: Why Clinical Trial Infrastructure Has to Be Built Before AI Can Deliver

Clinical trials have spent decades perfecting data capture, but the execution layer underneath it, including decision workflows, data collection design, and lab connectivity, remains fragmented in ways that limit what AI can realistically deliver and that quietly compromise the scientific validity of the data itself.

Clinical research has built sophisticated infrastructure for capturing and storing trial data, but the workflows, design decisions, and connective architecture that determine whether that data is trustworthy and actionable have lagged behind. Artificial intelligence (AI) is arriving on top of a foundation that, in many places, has not yet been completed.

Related to this article

© putilov_denis - © putilov_denis - stock.adobe.com

October 14th, 2026

Real-Time Clinical Trials Depend on a Scalable EHR-to-EDC Connection

Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.

© photon_photo - © photon_photo - stock.adobe.com

October 6th, 2026

Beyond the Molecule: Why Radiopharmaceutical Trials Require a New Operating Model

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

October 8th, 2026

What It Takes to Harmonize AI Across Fragmented Oncology Health Systems

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.

October 2nd, 2026

What ClinOps Professionals Just Starting With AI Should Keep Top of Mind

In this video interview, Claire Riches, VP of clinical solutions at Citeline, offers practical guidance for clinical operations teams beginning their AI journey—making the case that the tools are more accessible than many assume and that waiting to start is a competitive risk in itself.

September 30th, 2026

The Control Plane for Agentic Clinical Operations: Governing Autonomous Digital Actors in Regulated Trials

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

© FlowStudio - © FlowStudio - stock.adobe.com

September 29th, 2026

Real-World Evidence Is Moving Upstream: Is the Data Infrastructure Ready?

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

October 9th, 2026

AI and Patient Recruitment: Can Predictive Promise Drive True Transformation?

Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.

October 7th, 2026

Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.