Oracle has launched its Life Sciences AI Data Platform, a unified solution that merges the company’s enterprise-scale data capabilities with agentic AI to help life sciences organizations turn fragmented real-world data into actionable, evidence-based intelligence. The platform, announced on January 23, 2026, addresses a longstanding challenge in the industry: the sheer volume and complexity of real-world data from EHRs, insurance claims, patient registries, wearables, genomic repositories, and clinical trial systems often makes it difficult to extract timely, reliable insights for drug discovery, development, and post-approval activities.

The platform operates as a governed data lakehouse on Oracle Cloud Infrastructure, ingesting and harmonising structured and unstructured data while preserving full lineage and auditability. At its core are specialised AI agents that can autonomously interpret natural language queries, decompose them into logical steps, retrieve relevant data, apply statistical and machine learning techniques, validate results, and deliver fully referenced answers with confidence scores. For instance, a researcher could ask: “What is the real-world overall survival in elderly patients with metastatic NSCLC treated with osimertinib after progression on first-line therapy?” The agent would locate appropriate RWE cohorts, adjust for confounders, perform survival analysis, and return a detailed, cited response in minutes.

Oracle has emphasised compliance and trust from the ground up. The platform supports de-identification, pseudonymisation, and federated querying to meet stringent global privacy standards (HIPAA, GDPR, PDMA, etc.). Every agent action is logged, data transformations are tracked end-to-end, and outputs include transparent reasoning traces and source citations critical requirements for regulatory-grade real-world evidence generation.

The launch includes pre-built connectors to major EHR vendors, claims aggregators, genomic databases, and clinical trial repositories, reducing integration time from months to days. Early adopter feedback from several top-20 pharmaceutical companies highlights significant reductions in time spent on evidence synthesis, faster cohort identification for trials, and improved confidence in regulatory submissions.

Doug Kehring, Executive Vice President at Oracle Health & Life Sciences, said: “Life sciences organisations have more data than ever, but turning it into insight remains slow and error-prone. Our AI Data Platform changes that equation by combining massive scale with agentic intelligence that reasons, plans, and acts delivering fast, trustworthy answers ready for regulatory scrutiny.”

The platform supports a wide range of use cases across the R&D continuum: target identification using real-world genetic and phenotypic patterns, trial feasibility and site selection, comparative effectiveness and safety studies, label expansion strategies, and personalised treatment simulation. It is also designed to support federated learning and collaborative research across sponsor organisations, CROs, and academic partners without compromising data sovereignty.

Oracle’s move reflects its strategic push to become the preferred data and AI foundation for global life sciences, building on its existing Cloud for Life Sciences offerings and recent investments in clinical trial and real-world data capabilities. As regulatory bodies increasingly accept real-world evidence and AI-driven insights, platforms like this are expected to play a pivotal role in shortening development timelines and improving the probability of success for new therapies.