A bold bet to speed up clinical trials

The story

A top-10 biopharma struggled with slow, uneven enrollment across late-stage studies. Historical planning relied on averages and static curves. Twenty three percent of trials had missed timelines in the prior three years, consistent with sector data. The company carried too many underperforming sites and paid for avoidable delays. Finance estimated 15 to 25 million euros per year in incremental cost tied to site selection and recruitment shortfalls.

We built an AI operating model around three capabilities and a clear governance spine. First, an ensemble enrollment forecaster used structured feasibility inputs, prior site performance, and real world data to predict monthly patients by site, country, and indication. The model surfaced variance drivers and flagged probable nonperformers before activation. Second, a real-time telemetry layer ingested EDC, IRT, and feasibility refreshes to track recruitment, screen failures, dropout sources, and diversity mix by site. It detected anomalies and pushed alerts when curves deviated from plan. Third, a Next Best Action engine converted insights into recommended actions. It proposed site triage, micro-budgets for outreach, pre-screen protocol clarifications, and investigator coaching. A command PMO owned decisions with country teams. Data science sat with operations, not in a silo. We validated model lift on five retrospective studies, then piloted in two live programs with pre agreed decision rules.

We protected execution. The team wrote inspection ready documentation for model logic, data lineage, and decision thresholds. We defined equity guardrails, tracked participant mix against target populations, and added targeted community outreach where underrepresentation appeared. The platform landed as part of the sponsor’s standard study start up and governance, not as a side project. We also ran a proof of concept with quantum inspired feature selection for site risk signals. Classical models remained the workhorse. The exploratory method slightly improved early risk ranking in sparse geographies and now sits on the roadmap.

Results were concrete within two quarters. Forecast error for month-by-month enrollment dropped by 35 to 45 percent versus the baseline approach. The sponsor activated 18 to 28 percent fewer sites for the same target, with no loss in data quality. Median time to first 100 randomized patients improved by 6 to 9 weeks. Studies saw a 25 to 40 percent reduction in persistent underperforming sites. Budget variance on recruitment tightened meaningfully. Finance attributed 4 to 7 million euros in avoided cost per large global study. Participant diversity moved closer to epidemiology, with 6 to 10 percentage point improvements in two pilots. The organization now plans enrollment as an active, model informed process with clear decision rights and transparent evidence.