Real-time Analytics · Risk Management
Sanofi needed a faster, more objective way to decide which R&D programs to advance, accelerate, or stop while weighing costs, clinical trial enrollment timelines, unmet need, competition, and each program's probability of success across a very large body of internal data.
Reported impact
Sanofi reports faster, smarter, and more objective portfolio decisions, earlier warnings on slowing clinical trial recruitment, sharper study design discussions, and a broader cultural shift toward higher-quality strategic conversations in R&D governance.
Sanofi built and deploys Plai, an agentic AI application developed with Aily Labs, to support portfolio governance across its R&D value chain. The system aggregates more than one billion data points from across Sanofi and combines them with feasibility insights and real-time learnings from global trial sites. On top of that data foundation, Plai runs AI and machine-learning analyses to predict key value drivers such as R&D costs, clinical trial enrollment timelines, and a program's probability of success. It then serves those outputs conversationally through real-time, personalized what-if scenarios that teams can debate during governance reviews. Plai is embedded directly into molecule progression meetings and is also accessible more broadly to project teams and decision makers, extending AI-assisted portfolio analysis across the company while keeping humans in the loop under Sanofi's responsible AI policy.
R&D data from across Sanofi, plus feasibility insights and real-time learnings from global trial sites, are aggregated into Plai. The application analyzes those inputs to predict costs, enrollment timelines, and probability of success, then delivers conversational what-if scenarios and recommendations to governance meetings, project teams, and decision makers.
"AI makes recommendations to optimize our portfolio, such as accelerating the strongest opportunities... while halting those with a lower probability of success."
"It even talks, providing real-time and personalized 'what if' scenarios that guide governance discussions."
"Our industry-leading agentic AI app, ‘Plai’—developed with AI platform company Aily Labs—aggregates more than one billion data points across Sanofi."
"Plai is also helping with clinical trial enrolment, providing early warnings when recruitment slows and recommending adjustments."
"Plai participates in all governance meetings, where molecule progression decisions are made."
"The tool predicts value drivers including R&D costs, clinical trial enrolment timelines, and a given program’s probability of success."
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Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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