Survey suggests scientists view ELNs as ‘glorified filing cabinets’
1 Feb 2026
Electronic lab notebooks (ELNs) are regarded by many laboratory professionals as little more than “glorified filing cabinets”, claim the authors of a new survey.
AI lab informatics platform Sapio Sciences polled 150 scientists across US and European labs spanning biopharma R&D, CROs, clinical diagnostics and pharmaceutical manufacturing.
Responses revealed that many allege poor usability is unnecessarily driving up R&D costs, with more than 6 in 10 (65%) of those commenting saying they have had to repeat experiments due to hard-to-find results.
The survey also revealed a widespread potential security risk with nearly half of respondents (45%) admitting they had used public AI models via their personal accounts to compensate for information gaps.
Fewer than two thirds (62%) of scientists said their ELN allowed them to work efficiently. Only 5% said they could analyse experimental results without specialist support, while just 7% said their notebooks could be adapted for new assays or experimental workflows
Time consuming manual data movement and configuration difficulties were also a source of regular complaints, particularly in the pharmaceutical sector.
Mike Hampton, chief commercial officer at Sapio Sciences, claimed his company’s survey showed a growing mismatch between “modern scientific practice and the capabilities of traditional ELNs”. Most notebooks, he said, were focused on documenting experiments rather than actively supporting users.
“Today, scientists are working with increasingly complex data and are expected to move from results to decisions faster than ever, yet many ELNs still function like glorified filing cabinets,” stated Hampton.
“When scientists can’t analyse data or easily build on previous experiments without additional support, frustration turns into real cost. Unnecessary experiment duplication wastes reagents, instrument time, and specialist labour. At the same time, it limits curiosity and slows the pace of discovery across biopharma R&D.”
Remarking on the findings that almost half of respondents confessed to accessing public generative AI tools through personal accounts to support their work, Sapio Sciences chief information officer Sean Blake highlighted the security, IP, and compliance risks involved.
However, he acknowledged: “When AI capability isn’t available in governed environments, people will find it elsewhere, even when they do understand the risks.”
When asked what they wanted from next generation ELN developments, an overwhelming majority (95%) called for conversational, text-based support, including more than three quarters (78%) wanting voice interaction.
Most added that, in future, next generation ELNs needed to be able to assist in data interpretation rather than data capture alone
Across sectors there was also a demand for field-specific AI capabilities, with specific requirements varying between groups.
Diagnostics and biopharma R&D labs emphasised retrosynthesis, toxicity, and solubility prediction, with the latter also requiring molecular binding simulations. Diagnostics labs together with CROs also said they needed more genetic sequence optimisation facilities.
