CRO Timeline 1981-2012

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is shifting trial risk management from reactive to proactive—enabling sponsors to pressure-test protocols and anticipate pivots before a single patient is enrolled.

In this Q&A, Sylviane de Viron of CluePoints and Abigail Dirks, MS, of Tufts CSDD discuss the findings of a collaborative study quantifying the financial value of RBQM, why time savings emerged as the largest driver, and what sponsors struggling to justify adoption now have that they didn't before.

Sponsor oversight of outsourced CRO work is often robust in practice but fails inspection because oversight decisions are fragmented across systems and lack an audit trail, requiring sponsors to document oversight as a connected operating system with clear decision records, escalation pathways, and issue resolution from start to finish.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is expanding endpoint selection beyond the bounded experience of a single sponsor team—and surfacing options that traditional design thinking might never have considered.

Cell and gene therapy access expands into community care settings only when operational coordination, site readiness, and supply chain standardization become first-order priorities equivalent to manufacturing capacity, requiring standardized processes, digital integration, and distributed logistics networks.

Decentralized trial models have demonstrated real gains in enrollment performance and patient access, but operational switching costs, fragmented technology stacks, and unresolved gaps in the patient-site relationship raise a question the industry has been slow to confront directly.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

Why clinical development’s next AI bottleneck is not the model, but the system around it.