A new survey by Sapio Sciences has found widespread frustration among scientists using electronic lab notebooks (ELNs), with many reporting duplicated experiments, inefficient data use, and increased reliance on unregulated ‘shadow AI’ tools.

The study surveyed 150 scientists across biopharma R&D, contract research organizations, clinical diagnostics, and pharmaceutical manufacturing in the US and Europe. It found that only 62% of scientists believe their ELN allows them to work efficiently, and just 5% report being able to analyze experimental results independently without specialist support.

Duplication was highlighted as a major issue. Nearly two-thirds of respondents (65%) said they had repeated experiments because previous results were difficult to find or reuse, adding avoidable costs and delays.

Workflow and usability limitations were also noted: only 7% of scientists said their ELN could be adapted to new assays or workflows without specialist assistance, while 56% found the systems overly complex. Manual data movement was cited as a time-consuming task by 51% of respondents, rising to 81% in the U.S. and 72% in pharmaceutical manufacturing. Additionally, 71% described configuration difficulties, with pharmaceutical manufacturing reporting 84% frustration.

Mike Hampton, chief commercial officer at Sapio Sciences, said: “The survey clearly shows a growing mismatch between modern scientific practice and the capabilities of traditional ELNs. Most ELNs were designed as tools that focused on documenting experiments, not actively supporting scientists or guiding next steps. 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.”

The research also highlighted a rise in unregulated AI use. Nearly half of scientists surveyed (45%) reported using public generative AI tools through personal accounts to assist their work, despite potential security, IP, and compliance risks.

Sean Blake, chief information officer at Sapio Sciences, said: “Scientists aren’t turning to public AI because they want to bypass governance. They’re doing it because existing lab tools can’t help them analyze results or determine next steps efficiently. When AI capability isn’t available in governed environments, people will find it elsewhere, even when they do understand the risks.”

Survey participants expressed strong demand for next-generation ELNs with AI capabilities. Ninety-five percent wanted conversational, text-based interfaces, 78% requested voice interaction, and 96% said ELNs should help interpret data rather than simply capture it.

Scientists also cited discipline-specific AI needs, including:

• Retrosynthesis, toxicity, and solubility prediction (83% of diagnostics labs, 74% of biopharma R&D)

• Molecular binding simulations (71% of biopharma R&D)

• Genetic sequence optimization (65% of CROs, 63% of diagnostics labs)

Rob Brown, head of the scientific office at Sapio Sciences, said: “Our research clearly shows that second-generation ELNs have reached the limits of what scientists expect from them. As we shape the next generation of lab software at Sapio, the focus is on AI-enabled scientific analysis and design methods that keep scientists in control while actively supporting workflows, analysis and next-step decisions.”

The survey underscores the growing gap between ELN capabilities and modern laboratory needs, and the potential for AI-integrated tools to improve efficiency, reduce duplication, and provide actionable insights within regulated environments.