More than 60% of life science laboratories are exploring or piloting AI, but data integration challenges continue to slow adoption, according to a new survey from Cenevo.

Artificial intelligence is becoming increasingly embedded within life science laboratories, although many organisations continue to face significant challenges around data management, connectivity and system integration.

That is according to Cenevo’s second annual survey of life science professionals, which examined trends in laboratory automation, digitalisation and AI adoption across the sector.

The survey gathered responses from 113 professionals working across R&D, discovery, chemistry, biology, clinical and manufacturing environments. Respondents represented a mix of large and small biopharma companies, academic institutions, contract research organisations and other research-focused organisations.

According to the findings, more than 60% of laboratories are currently exploring or piloting AI technologies, highlighting growing interest in the technology as organisations seek to improve efficiency and gain greater value from laboratory data.

Data analysis and interpretation emerged as the most common use case, with 57% of respondents reporting that they already use AI for these activities. Researchers also identified workflow automation and orchestration, experiment design and planning, and sample and inventory management as key areas where AI could support laboratory operations.

While generative AI is gaining traction, with 25% of respondents reporting production use, adoption of agentic AI remains at an earlier stage. Although 27% of laboratories are exploring or piloting AI agents and multi-agent workflows, only 5% have implemented the technology in production environments.

The survey also highlighted ongoing concerns about security and governance. More than half of respondents said privacy or security concerns were affecting AI adoption decisions.

At the same time, laboratories are increasingly prioritising investments in infrastructure that can support future AI initiatives. Respondents identified automation, AI-enabled software, systems integration and data infrastructure as key spending priorities.

Connecting laboratory information management systems, electronic lab notebooks and scientific instruments was cited as a priority by 62% of small and medium-sized organisations and 50% of respondents overall.

The findings suggest that many laboratories recognise the potential benefits of AI but continue to face practical challenges around data accessibility and interoperability.

Data-related obstacles remain one of the most significant barriers to wider adoption. While 42% of respondents reported that data quality, overload and management issues are limiting AI initiatives, this represented an improvement from last year’s survey, when 54% cited the same concerns.

More than half of respondents said a lack of integration between systems remains their biggest challenge, while others pointed to difficulties managing unstructured data and information spread across multiple instruments and teams.

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.”

The results suggest that while interest in AI continues to grow across life sciences, organisations are increasingly recognising that effective data management and connected laboratory systems will be essential to achieving long-term value from the technology.