Operationalizing AI in Life Sciences: 2025 Benchmark Report
Operationalizing AI in Life Sciences: 2025 Benchmark Report
Life sciences organizations lead in AI maturity but face compute constraints that limit scaling from pilot to production.
Life sciences organizations lead in AI maturity but face compute constraints that limit scaling from pilot to production.
Life sciences organizations face a high-stakes paradox: Demand for faster innovation is rising while funding and scaling capabilities remain under pressure.
While 82% of life sciences companies fall into the top two AI maturity tiers, 58% cite inadequate compute resources as a challenge. Organizations are diversifying beyond hyperscalers, with 33% of workloads running on alternative providers to address storage connectivity and data locality constraints.
Infrastructure strategies evolve as organizations move from hyperscaler dependence to diversified approaches
Infrastructure strategies evolve as organizations move from hyperscaler dependence to diversified approaches
Life sciences organizations are shifting infrastructure strategies. With 33% of workloads now on alternative providers versus 28% on hyperscaler clouds, successful scaling requires providers capable of meeting the speed, sensitivity, and discipline needed to move AI from pilot to enterprise foundation.
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