A Cenevo survey finds that while over 60% of life sciences labs are exploring or piloting AI, only 5% have deployed it in production, with integration and data quality cited as key barriers.
Artificial intelligence (AI) adoption is widespread across life sciences laboratories, but the technology remains primarily in experimental stages, according to a survey from Cenevo.
The second annual survey of more than 110 life sciences professionals found that only 5% are currently using AI agents in production. The survey included respondents from clinical, manufacturing, chemistry, biology, and research and development environments.
While more than 60% of labs are exploring or piloting AI, 58% of researchers reported privacy or security concerns. Currently, organizations are prioritizing the technology for data analysis and interpretation, workflow automation and orchestration, experiment design and planning, and sample and inventory management.
Connectivity remains a significant barrier to implementation, according to the survey. More than half of respondents reported a lack of integration among systems, and one-third still rely on manual operations. Laboratory leaders identified the lack of integration between systems as the primary challenge, followed by managing unstructured or inconsistent data across instruments and teams.
Data quality and management issues were cited as a bottleneck by 42% of respondents, a decrease from 54% in the previous year. Investment priorities are shifting toward automation, AI-enabled software, and data infrastructure rather than standalone tools. Connecting laboratory information management systems and electronic lab notebooks is a priority for 50% of all organizations and 62% of small and medium-sized organizations.
“Exploring AI is very much now high on the agenda of labs; however, the actual production usage of agentic workflows is still limited at this stage,” says Keith Hale, CEO of Cenevo, in a release. “Concerns over fragmented data, as well as security and regulatory compliance, are hindering adoption, so labs are prioritizing connectivity, automation, orchestration, and data management to ensure they can fully benefit from what AI can deliver.”
