1、2026BiotechAIReportBreakthroughs,bottlenecks,andthepowershiftshapingbiotechsAIfutureA handful of tools have broken out of pilot mode and become everyday parts of R&D for biotech AI leaders.These are the use cases scientists now trust and rely on:literature review and knowledge extraction(76%adoption

2、),protein structure prediction(71%),scientific reporting(66%),and target identification(58%).These early killer apps succeed because they operate where data is clean and local,results are easily verifiable,and they fit naturally into a scientists workflow.AI is transforming R&D long before the first

3、 AI-designed drug.Biotech is hitting a ceiling with AI in complex,regulated science.AI in biotech has found its first killer apps.The effects of AI are showing up earlier in the pipeline,where decisions about targets,constructs,and experiments set the trajectory for everything that follows.Half of t

4、hose adopting AI in biotech already report faster time-to-target,56%expect meaningful cost reductions within two years,and 42%see an uplift in accuracy and hit rates with scientific models.In a field where development takes 10-12 years,upstream improvements compound.Faster cycles,smarter decisions,a

5、nd fewer dead ends matter enormously.AI adoption drops sharply in areas like generative design(42%adoption),biomarker analysis(40%),and ADME prediction(29%)and IND submissions(24%).The limitation is more often the data environment than the models themselves.In these domains,data lives across a dozen

6、 systems,key metadata is often missing,and verifying outputs can take longer than the experiments themselves.Yet these are stages where decisions are complex and consequential,and where teams say they want AI to help next.Over the next two years,organizations expect to move from task-level copilots