AI Adoption in Biopharma: Large Firms Lead the Way, Small Companies Follow

The growing promise of AI is gaining momentum in the biopharmaceutical sector, driven by the increasing adoption of AI/ML technologies, particularly among larger companies. According to Benchling’s 2023 State of Tech in Biopharma report, 67% of large biopharma companies (1,000+ employees) have already adopted AI/ML, compared to just 23% of small biopharma companies (<1,000 employees). This stark contrast highlights how large biopharma firms are leading the charge in AI adoption, making it a second-highest investment priority, just behind R&D data platforms. As a result, these companies are positioning AI as a critical tool to accelerate drug discovery and the broader R&D lifecycle. The majority of senior leaders at large biopharma companies are actively driving AI/ML initiatives, with 50% reporting top-down support to push these technologies forward, significantly outpacing the 28% of small companies with similar executive backing.

The impact of AI on biopharma is expected to accelerate in the coming years, with three-quarters of respondents at large companies anticipating AI/ML to significantly reduce timelines to key milestones within the next 12–24 months. As the AI-driven transformation in biopharma unfolds, the industry’s adoption of cloud-based scientific software and SaaS is also on the rise, with 23% of small biopharma and 17% of large companies already embracing these platforms. However, small companies are adopting a more cautious approach, primarily relying on pilots or proof-of-concept projects. As AI’s capabilities continue to be proven, the broader biopharmaceutical sector is set to benefit from faster drug discovery processes, enhanced decision-making, and optimized R&D operations, driven largely by the leadership and investment in AI by larger biopharma firms.

Artificial Intelligence (AI) in Healthcare Market: By Deployment: On Premise, On Cloud, Hybrid, By Components: Hardware, Software, Services. By Technology: Machine Learning, Deep Learning, Natural Language Processing, Others. By Application: Money transfers, Bill Payments, Airtime Transfer and Top-Ups, Travel and Ticketing, Merchandise and Coupons, By Operation Model: Unsupervised, Supervised, Reinforced, Semi supervised, Others. By Application: Disease identification and diagnosis, Personalized treatment and behavior modification, Drug discovery and manufacturing, Clinical trial research, Radiology and Radiotherapy, Smart electronic health record, Epidemic outbreak prediction, Virtual nurses, Repetitive jobs, Others. By Geography - North America (U.S, Canada and Mexico) Europe (Germany, France, UK, Netherlands, Denmark, Italy, Spain, and Rest of Europe) Asia-Pacific (China, Japan, South Korea, India, Australia, Singapore, and Rest of Asia-Pacific) South America (Brazil, Argentina and Rest of South America) Rest of the World (Middle East and Africa).

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