Survey reveals frustrations and future needs for electronic lab notebooks
Sapio Sciences announced the results of new research examining scientists’ sentiment around electronic lab notebooks (ELNs) and AI tools in modern laboratory environments.
The study reveals widespread frustration with existing lab software, leading to repeated experiments, inefficient data use, and a growing reliance on unauthorized shadow AI. 150 scientists were surveyed across U.S. and European labs in biopharma R&D, contract research organizations, clinical diagnostics, and pharmaceutical manufacturing.
Despite the significant investments made in digital lab technology, ELNs often fail to support effective scientific work. Only 62% of scientists say their ELN allows them to work efficiently, and just 5% report being able to analyze experimental results without specialist support.
Additionally, duplication is a persistent issue. Nearly two-thirds of scientists, 65%, say they have had to repeat experiments because prior results were difficult to find or reuse, driving avoidable costs and delays across lab teams.
The survey highlights several ways today’s ELNs are falling short:
- Workflow rigidity: Only 7% of scientists say their ELN can be adapted to new assays or experimental workflows without specialist support, limiting scientists’ ability to respond quickly as research evolves. Separately, just 5% of scientists say they can analyze experimental data without additional support.
- Usability issues: 56% of scientists say their ELN is too complex and slows them down.
- Manual data movement: 51% spend too much time importing and exporting data, rising to 81% among U.S.-based scientists and 72% in pharmaceutical manufacturing.
- Configuration difficulties: 71% of scientists say ELNs are hard to configure or adapt, with above-average frustration in pharmaceutical manufacturing at 84%.
The research also shows how these frustrations are reshaping behavior in the lab. Almost half of scientists surveyed, 45%, say they use public generative AI tools through personal accounts to support their work, despite the security, IP, and compliance risks associated with shadow AI.
When asked what they want from the next generation of ELNs, scientists consistently emphasized interaction, guidance, and interpretation rather than documenting experiments alone. Ninety-five percent want conversational, text-based interfaces, while 78% want voice interaction. Almost all respondents, 96%, say future ELNs must help interpret data, not simply capture it.
Scientists also want built-in, field-specific AI capabilities, with demand varying by discipline:
- Retrosynthesis, toxicity, and solubility prediction: 83% of diagnostics labs and 74% of biopharma R&D
- Molecular binding simulations: 71% of biopharma R&D
- Genetic sequence optimization: 65% of CROs and 63% of diagnostics labs
The findings suggest scientists are not looking to relinquish control but to work with AI tools that actively support reasoning and interpretation within governed lab environments.
