More than 400 clinical research professionals across sponsors, CROs, and sites contributed to the WCG CenterWatch 2025 AI Benchmarking Report, released this month. The survey gives trial operations teams a missing piece: a shared, role-specific baseline to measure their own AI adoption against industry-wide data rather than anecdote.
A segmented view of adoption
AI enthusiasm is widespread, but the report reveals a gap between appetite and operational discipline. It segments answers by stakeholder type because adoption at a large sponsor looks nothing like adoption at a community site with three staff members. Collapsing those realities into one number would mislead. Instead, the report lets organizations see how they compare with peers doing similar work.
Operations executives can benchmark their organization's AI maturity against peers using the segmented data-a practice covered in resources on AI for Operations. The benchmark framing is deliberate: it shows not just where the industry is trending, but where a specific team sits relative to the field.
Regulatory pressure and the cost of commitment
The FDA issued draft guidance in January 2025 establishing a risk-based credibility framework for AI and machine learning models used in regulatory decision-making. The agency expects sponsors to demonstrate, not merely assert, that their AI tools are fit for purpose. That shift puts a premium on documented adoption evidence of the kind this report captures.
Separate published data on AI-enabled clinical activity shows average implementation costs near $1.1 million per activity, climbing to $3.2 million for data quality and cleaning solutions. Those numbers reframe AI adoption as a capital allocation decision, not a technology enthusiasm exercise. For many operations leaders, the dollars require the same rigor as any major equipment or site investment.
The site perspective
Smaller and mid-tier research sites consistently reported lower AI readiness. The report suggests a future where these sites use the benchmarks in contract and partnership conversations with sponsors. If site qualification processes start incorporating AI capability assessments tied to this kind of industry-wide data, the report's influence becomes measurable and durable.
Why this matters for operations professionals
The benchmark data gives operations teams direct input for budgeting, vendor evaluation, and internal capability planning. You can identify exactly where your organization's AI adoption sits-and where the gaps are most expensive to ignore. As regulatory expectations rise and per-activity costs average seven figures, operating without a peer-calibrated baseline becomes a risk in itself. The report provides that baseline, not as a static figure, but as a mirror split by role and organization size.
Your membership also unlocks:
