Artificial Intelligence/Machine Learning

As sponsors push for faster, more representative studies, AI-enabled matching and EHR integration are emerging as operational levers, but bias, explainability, and governance will determine whether the technology scales.

Agentic AI is arriving in clinical operations with genuine capability to absorb administrative work, but the organizations that will realize lasting value are those willing to redesign accountability structures, financial workflows, and operating models before deploying the tools on top of them.

AI platforms that absorb trial data reconciliation could compress operations toward a small, accountable team.

Biomarker-informed trial enrollment appears straightforward for identifying responders and optimizing efficiency, but failures to account for demographic variation in cut-off selection, causality versus correlation, and composite biomarker complexity risk excluding viable patients and exacerbating disparities.

AI is reshaping clinical research faster than any technology before it; but for sponsors and CROs, adoption without GxP-grade governance, validation, and human oversight isn't a strategy. It's a risk.

Agentic AI will put the CRO operating model to the test—and force the efficiency and accountability questions the industry can no longer avoid.

New collaborations with Causaly, Databricks, and Microsoft anchor a broader push to embed artificial intelligence directly into clinical trial decision-making and operational workflows.

In this Q&A, Raviv Pryluk, PhD, CEO and co-founder of PhaseV, discusses the data standardization bottleneck that sits between raw trial data and real-time analysis, what the FDA's continuous monitoring pilot will require to scale beyond large pharma, and why an ecosystem approach is the only path to making real-time trial oversight a practical reality.

Clinical research associates are evolving from compliance-focused manual monitors to data-enabled strategic site partners, but organizations need analytics training, clear operating models, and active change management to make the shift stick.

New eNPV modeling applied to operational data from an AI clinical monitoring agent finds ROI multiples as high as 82x, with time savings and monitoring cost reductions identified as the primary value drivers.

In this Q&A, Richard Graham, PhD, co-founder and chairman of the board at TruTechnologies, discusses why six decades of manual site-level processes continue to undermine clinical trial execution, what national initiatives like Operation TrialBlazer leave unaddressed, and why meaningful modernization has to start with data collected at the point of care.

Real-time AI-enabled decision-making only accelerates development when trials have coordinated operational infrastructure for randomization, supply management, and workflows capable of responding to new insights without disrupting execution or compliance.

Under-enrollment isn't news to teams running oncology trials, and the instinct is to spend more on reach. The evidence, however, says the biggest leak is something else entirely and that most recruitment budgets focus on the least valuable part of the problem.

Real-world oncology data requires hybrid curation combining clinical expertise with technology to address variability in documentation, missing data, and complex concepts like lines of therapy that are often implicit rather than explicitly recorded in EHRs.

As agentic AI, automated data harmonization, and real-time monitoring reshape clinical development, the organizations seeing meaningful results are those that have invested in unified data infrastructure and disciplined governance rather than cycling through pilots without the foundation to scale them.

AI agents operate only within workflows, but most organizations lack unified workflow management systems, making it difficult to identify where agents should be deployed or ensure they integrate effectively across connected business processes in clinical research.

AI churn—repeatedly restarting initiatives before scaling them—stems from organizational execution gaps rather than technology limitations, but agentic AI amplifies these gaps by requiring connected systems, trustworthy data, and disciplined governance from the start.

Clinical R&D modernization stalls through incremental optimization of individual workflows, but meaningful systemic change requires leaders to visualize structural relationships, understand hidden incentives, and identify leverage points that benefit the whole system rather than parts.

The traditional global-to-local communication model creates fragmented content through sequential adaptation, but a local-first approach using structured data and AI-orchestrated generation can embed stakeholder terminology and preferences from the start, reducing rework while improving clarity and relevance.

Eligibility criteria alone rarely predict enrollment, which depends more on trust, hope, and fear, requiring relationship-based community engagement paired with behavioral readiness assessments rather than transactional outreach.

AI-enabled systems medicine could influence which populations are selected for trials, how biomarkers are validated, how products are positioned, how evidence is generated after launch, and how value is demonstrated to payers.

In this Q&A, Liz Beatty, co-founder and chief strategy officer at Inato, discusses how real-time patient data is reducing non-enrolling sites, why sponsor-specific technology remains the biggest barrier to adoption, and what a shift toward cross-asset site partnerships could mean for enrollment efficiency.

The FDA's launch of real-time clinical trial proof-of-concept studies signals a fundamental shift in regulatory oversight, one that most sponsors are not yet equipped to meet and that demands urgent investment in unified data infrastructure, quality-by-design practices, and protocol digitization.