Survey highlights growing role of AI in modern life science laboratories

Cenevo has released new survey findings revealing that while artificial intelligence (AI) adoption is now widespread across life sciences laboratories worldwide, only 5 percent of labs are using AI agents in full production.

The research, conducted in January 2026 with 113 life sciences professionals across R&D, discovery, chemistry, biology, clinical, and manufacturing environments, shows how organizations are approaching AI, where they are investing, and why connectivity and data remain critical barriers to scaling AI in the lab.

AI in life sciences labs remains largely experimental

Cenevo conducted its second annual survey of more than 110 life sciences professionals to understand the current state of AI adoption in digital lab operations. The findings show that more than 60 percent of labs are exploring or piloting AI, but most deployments remain at an experimental stage.

Researchers are prioritizing AI for data analysis and interpretation, workflow automation and orchestration, experiment design and planning, and sample and inventory management. At this stage, fewer organizations are using agentic AI for scientific discovery and decision making.

While 58 percent of respondents report privacy or security concerns around AI, the survey indicates that AI is expected to be a long-term, critical component of lab operations.

Generative AI and agents begin to move into production

The survey shows that 57 percent of labs are already using AI for data analysis. A quarter (25 percent) report using generative AI in full production environments.

Usage of AI agents to perform discrete, previously human tasks or more complex multi-agent workflows is emerging, with 27 percent of labs exploring or piloting agentic approaches. However, only 5 percent report using AI agents in production today.

Connectivity and integration top investment priorities

Lab budgets are shifting to address connectivity, integration, and data challenges rather than standalone tools. Lab leaders say their investment priorities are focused on automation, AI-enabled software, systems integration, and data infrastructure and analytics.

Connecting laboratory information management systems (LIMS), electronic lab notebooks (ELNs), and instruments is a key priority. This is reported by 62 percent of small and medium-sized organizations and 50 percent of all organizations surveyed.

For the second consecutive year, scientists indicated that connectivity is essential to maximizing the benefits of AI. More than half of respondents say they lack integration among systems, and one-third still rely on manual operations. However, progress in automation is accelerating, as last year’s survey found that more than half of labs relied on manual operations.

Data remains a core bottleneck for AI adoption

Data continues to be a major barrier to AI in life sciences labs. While 42 percent of respondents report that data quality, overload, and management issues are blocking AI adoption, this is an improvement from 54 percent in the previous year’s survey.

Lab leaders still face significant challenges in making effective use of their data. A lack of integration between systems is cited as the biggest problem by 55 percent of respondents, closely followed by difficulties managing unstructured or inconsistent data and data spread across instruments and teams.

“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,” said Cenevo CEO Keith Hale. “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.”

About the survey

In January 2026, Cenevo surveyed the life sciences industry to understand the trends and innovations that matter most in digital lab operations. A total of 113 people completed the survey. The findings represent the current state of automation and data management, as well as the evolution of AI in life sciences and its potential impact on lab operations.

Survey respondent breakdown by organization type:

• 37% from large pharma/biotech (>5,000 employees)
• 18% from mid-size pharma/biotech (500–5,000 employees)
• 18% from small (start-up) pharma/biotech (<500 employees)
• 14% from academic institutions
• 13% from CROs and other organizations

Other organization types specified included industrial R&D, healthcare, education, and materials sectors.

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