Based on insights from over 100 global R&D organizations, this benchmark report reveals a critical gap between digital ambition and operational reality. While AI/ML remains a strategic priority, 62% of organizations are still in the early stages of digitalization constrained by siloed data, legacy systems, and limited systems integration. Scientists continue to spend time preparing and searching for data, limiting productivity and delaying innovation. The findings highlight that effective AI adoption depends on foundational data readiness—centralization, accessibility, and workflow alignment. The message is clear: without addressing data infrastructure, the promise of AI-driven R&D will remain out of reach.
This benchmark report exposes the gap between AI ambition and the digital reality in R&D. With 62% of organizations still early in their digitalization journey, siloed data and legacy systems remain major barriers. The path to AI-enablement starts with data centralization, integration, and alignment across scientific workflows.
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