Findings reveal widespread frustration with electronic lab notebooks, driving experiment duplication and unauthorized AI use among laboratory professionals.
A new survey of 150 scientists across US and European laboratories reveals significant dissatisfaction with electronic lab notebook (ELN) systems, with researchers describing current platforms as inadequate for modern scientific workflows.
The study, conducted by Sapio Sciences, found that 62% of scientists say their ELN allows them to work efficiently, while just 5% report being able to analyze experimental results without specialist support. The research surveyed professionals across biopharma R&D, contract research organizations, clinical diagnostics, and pharmaceutical manufacturing.
“Most ELNs were designed as tools that focused on documenting experiments, not actively supporting scientists or guiding next steps,” says Mike Hampton, chief commercial officer at Sapio Sciences, in a release. “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.”
Experiment Duplication Drives Up Costs
The survey identified experiment duplication as a persistent problem, with 65% of scientists reporting they have had to repeat experiments because prior results were difficult to find or reuse. This duplication creates avoidable costs and delays across laboratory teams.
Additional workflow challenges include rigid system configurations, with only 7% of scientists saying their ELN can be adapted to new assays or experimental workflows without specialist support. More than half (56%) describe their ELN as too complex and report it slows down their work.
Data management issues also plague current systems. Fifty-one percent of scientists spend excessive time importing and exporting data, with this figure rising to 81% among US-based scientists and 72% in pharmaceutical manufacturing environments.
Shadow AI Use Increases
The limitations of current ELN systems are driving scientists toward unauthorized workarounds. The survey found that 45% of scientists use public generative AI tools through personal accounts to support their work, despite security, intellectual property, and compliance risks.
“Scientists aren’t turning to public AI because they want to bypass governance,” says Sean Blake, chief information officer at Sapio Sciences, in a release. “They’re doing it because existing lab tools can’t help them analyze results or determine next steps efficiently.”
Scientists Seek Interactive AI Capabilities
When asked about desired features in next-generation ELNs, scientists emphasized interaction and interpretation capabilities. Ninety-five percent want conversational, text-based interfaces, while 78% want voice interaction functionality. Nearly all respondents (96%) say future ELNs must help interpret data rather than simply capture it.
Scientists also expressed demand for built-in, field-specific AI capabilities. These include retrosynthesis, toxicity, and solubility prediction (requested by 83% of diagnostics labs and 74% of biopharma R&D), molecular binding simulations (71% of biopharma R&D), and genetic sequence optimization (65% of contract research organizations and 63% of diagnostics labs).
“Our research clearly shows that second-generation ELNs have reached the limits of what scientists expect from them,” says Rob Brown, head of the scientific office at Sapio Sciences, in a release.
The survey was conducted in November 2025 across laboratory environments in the United States and Europe.
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