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A survey finds only 5% of labs use AI in production, with most still piloting the tech and citing integration, data quality as barriers.
A recent second annual survey conducted by Cenevo involving over 110 life science professionals, including those from clinical, manufacturing, chemistry, biology, and research and development environments, indicates that Artificial Intelligence (AI) adoption is prevalent in these laboratories. However, the technology is predominantly in its nascent, experimental stages. A mere 5% of the surveyed labs have fully integrated AI agents into their production workflows, signifying that widespread operational deployment is still largely aspirational. This highlights a significant gap between exploration and practical, in-production application of AI within the life sciences sector.
The survey identified several critical barriers impeding the seamless integration of AI into life science laboratories. Over half of the respondents cited a significant lack of integration among various existing systems as a primary challenge, forcing a third of labs to continue relying on manual operations. Furthermore, data quality and the complexity of managing unstructured or inconsistent data across diverse instruments and teams emerged as a substantial bottleneck for 42% of participants, though this figure represents a slight improvement from the previous year's 54%. Beyond technical challenges, 58% of researchers expressed notable concerns regarding data privacy and security, which also contributes to the cautious approach to full-scale AI deployment.
Life science organizations are strategically prioritizing AI for specific high-impact applications. These include enhancing data analysis and interpretation capabilities, automating and orchestrating complex workflows, optimizing experiment design and planning, and improving sample and inventory management. Reflecting these priorities, investment trends are shifting away from isolated tools towards more integrated solutions such as comprehensive automation platforms, AI-enabled software solutions, and robust data infrastructure. A key area of focus for integration efforts is connecting laboratory information management systems (LIMS) and electronic lab notebooks (ELN), identified as a priority by 50% of all organizations and a higher 62% among small and medium-sized organizations.
Keith Hale, CEO of Cenevo, underscores that despite the high interest in AI among labs, its actual use in production environments remains limited. he attributes this restraint to prevalent concerns over fragmented data, security vulnerabilities, and the complexities of regulatory compliance. To effectively overcome these challenges and fully harness the transformative potential of AI, Hale emphasizes that laboratories must prioritize improving connectivity between systems, advancing automation, refining workflow orchestration, and implementing robust data management strategies. These foundational steps are crucial for creating an environment where AI can deliver its promised benefits safely and efficiently.