Electronic lab notebooks (ELNs) were introduced to modernize laboratory work. But new research from Sapio Sciences suggests many scientists now see them as obstacles rather than enablers of discovery.
In a survey of 150 scientists across biopharma R&D, contract research organizations (CROs), clinical diagnostics, and pharmaceutical manufacturing in the US and Europe, dissatisfaction with ELNs was widespread. Despite years of investment in digital lab infrastructure, only 62 percent of respondents said their ELN helps them work efficiently. Just 5 percent reported being able to analyze experimental results independently using their ELN.
The inefficiencies are translating directly into higher costs. Nearly two-thirds of scientists (65 percent) said they have repeated experiments because prior results were difficult to find or reuse. Such duplication consumes reagents, instrument time, and specialist labor, while slowing decision-making across R&D teams.
“Most ELNs were designed to document experiments, not to support scientific reasoning,” says Mike Hampton, Chief Commercial Officer at Sapio Sciences. “But modern research demands faster movement from data to decisions.”
The survey highlights persistent usability and configuration issues. More than half of respondents (56 percent) said their ELN is overly complex and slows them down. Seventy-one percent said ELNs are difficult to configure or adapt to new workflows, rising to 84 percent among pharmaceutical manufacturing scientists.
Workflow rigidity remains a key limitation. Only 7 percent of scientists said their ELN can be adapted to new assays or experimental approaches without specialist support. Manual data handling is also common: 51 percent reported spending excessive time importing and exporting data, with even higher rates in US-based labs.
As ELNs struggle to support analysis, scientists are increasingly turning to public generative AI tools. Forty-five percent of respondents said they use public AI models through personal accounts to assist their work, despite the associated risks to data security, intellectual property, and compliance.
“Scientists aren’t trying to bypass governance,” says Sean Blake, Chief Information Officer at Sapio Sciences. “They’re compensating for tools that can’t help them analyze results or determine next steps.”
When asked about the future of ELNs, expectations were clear. Ninety-six percent of scientists said next-generation systems must help interpret data, not simply record it. Ninety-five percent want conversational, text-based interfaces, and 78 percent are interested in voice interaction to support hands-free lab work.
Demand for AI capabilities varies by discipline, from molecular binding simulations in biopharma to genetic sequence optimization in diagnostics and CROs.
The findings suggest that second-generation ELNs may have reached their limits — and that laboratory software must evolve from passive record-keeping tools into active partners in scientific analysis if they are to keep pace with modern research.
Newsletters
Receive the latest pharmaceutical news, personalities, education, and career development – weekly to your inbox.
