A second annual survey from Cenevo reveals that while AI adoption is now widespread across life science laboratories, only 5 percent of labs have AI agents in full production. The survey, conducted in January 2026 with 113 professionals across R&D, discovery, chemistry, biology, clinical, and manufacturing, shows that most deployments remain experimental even as investment shifts toward connectivity and data infrastructure.
More than 60 percent of labs are exploring or piloting AI, but researchers are prioritizing data analysis and interpretation, workflow automation, experiment design, and sample management over agentic AI for scientific discovery. Fifty-eight percent of respondents report privacy or security concerns, though the survey indicates AI is expected to become a critical, long-term component of lab operations.
Generative AI and agents move toward production slowly
Fifty-seven percent of labs already use AI for data analysis, and a quarter report using generative AI in full production environments. Agentic AI-where agents perform discrete tasks previously handled by humans or manage multi-agent workflows-is still emerging. Twenty-seven percent of labs are exploring or piloting agentic approaches, but only 5 percent use AI agents in production today.
Keith Hale, CEO of Cenevo, said, "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. 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."
Connectivity and integration top investment priorities
Lab budgets are shifting away from standalone tools. Leaders are directing spending toward automation, AI-enabled software, systems integration, and data infrastructure. Connecting laboratory information management systems, electronic lab notebooks, and instruments is a key priority for 62 percent of small and medium-sized organizations and 50 percent of all organizations surveyed.
More than half of respondents say they lack integration among systems, and one-third still rely on manual operations. Progress is accelerating, however-last year's survey found that more than half of labs relied on manual operations. For scientists looking to build practical skills in this area, an AI Learning Path for Research Scientists covers lab automation and experimental design workflows that directly address these integration challenges.
Data remains a core bottleneck
Data quality, overload, and management issues still block AI adoption for 42 percent of respondents, an improvement from 54 percent in the previous year. The biggest problem, cited by 55 percent of respondents, is a lack of integration between systems. Difficulties managing unstructured or inconsistent data and data spread across instruments and teams follow closely behind.
These findings align with broader trends in AI for Science & Research, where data readiness and system interoperability consistently determine whether AI projects move beyond pilot stages.
Why this matters for science and research professionals
The survey confirms that AI is no longer a future consideration for life science labs-it is an active investment area where the bottleneck has shifted from interest to infrastructure. For researchers and lab managers, the immediate priority is not chasing agentic AI but ensuring systems are connected, data is structured, and workflows are automated. Organizations that solve integration and data quality problems now will be positioned to adopt production-grade AI agents when the technology matures. The gap between the 60 percent exploring AI and the 5 percent running agents in production represents both a warning and a roadmap.
Your membership also unlocks:
