Survey finds AI adoption growing in life sciences laboratories
More than 60% of life sciences laboratories are exploring or piloting artificial intelligence technologies, but only a small proportion have deployed AI agents into production environments, according to a new survey from laboratory technology company Cenevo.
The survey of 113 life sciences professionals found that AI adoption continues to accelerate across research, discovery, chemistry, biology, clinical and manufacturing laboratories, although concerns around data quality, security and system integration remain significant barriers.
According to the findings, 57% of respondents are already using AI for data analysis and interpretation, while 25% have deployed generative AI tools in production environments. However, only 5% reported using AI agents in production, despite growing interest in agentic workflows.
Researchers appear to be focusing AI investment on practical laboratory applications, including data analysis, workflow automation, experiment planning and inventory management, rather than AI-driven scientific decision-making.
The survey also highlights continuing challenges around laboratory connectivity and data management. More than half of respondents reported a lack of integration between systems, while around one-third still rely heavily on manual operations.
Connecting laboratory information management systems (LIMS), electronic laboratory notebooks (ELNs) and analytical instruments remains a key priority, particularly among small and medium-sized organisations.
Data quality and management continue to be major obstacles to AI adoption. While 42% of respondents cited data-related issues as a barrier, this represents an improvement from the 54% reported in Cenevo's previous survey.
Keith Hale, chief executive of Cenevo, said laboratories are increasingly interested in AI but are focusing first on establishing the infrastructure required to support it.
“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,” he said.
“Concerns over fragmented data, as well as security and regulatory compliance, are hindering adoption, so labs are prioritising connectivity, automation, orchestration and data management to ensure they can fully benefit from what AI can deliver.”
The findings suggest laboratory investment is increasingly being directed towards automation, AI-enabled software, systems integration and data infrastructure rather than standalone tools.
Privacy and security also remain concerns, with 58% of respondents reporting reservations about AI adoption.
The survey covered organisations ranging from large pharmaceutical and biotechnology companies to academic institutions, contract research organisations and industrial R&D operations.
While the findings suggest AI remains at an early stage of deployment in many laboratories, they also indicate a shift from experimentation towards building the data and connectivity foundations required for wider adoption in the future.
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