LONDON, UK. June 22, 2026 – Scientists are increasingly turning to public AI tools outside approved workplace systems, according to newly released research from Sapio Sciences. The survey found that 77 percent of respondents use unauthorised AI as part of their research activities, while nearly 45 percent access those tools through personal accounts. The findings point to growing concerns around governance, security, and the handling of sensitive scientific information.
Only a small minority of respondents, just 5 percent, said approved systems allow them to analyse experimental data independently.
Shadow AI is the term used to describe AI technologies adopted without the knowledge or approval of an organisation’s IT and security teams. Such practices can increase exposure to compliance issues, data leaks, and intellectual property risks.
Sean Blake, Chief Information Officer at Sapio Sciences, said: “Shadow AI tends to emerge where official digital tools fail to support how modern science is practised.
“When platforms cannot support interpretation, comparison, or decision-making at the required pace, scientists work around them.”
Sapio Sciences reports that shadow AI has become a common feature of contemporary biopharma R&D. Researchers frequently rely on public AI systems to help evaluate results, optimise protocols, and structure scientific thinking. Existing ELNs and laboratory management systems often support record-keeping effectively but may not fully address analytical requirements.
Sean Blake added: “Many ELNs are optimised for documentation and retention rather than scientific reasoning. Interpretation and comparison frequently require informatics queues, manual exports, or external analysis.
“Scientific progress rarely stalls at data capture. It more often stalls during interpretation, when results must be translated into decisions. When official tools cannot support that transition efficiently, scientists adapt.”
The research found that 56 percent of scientists believe their ELN slows day-to-day work. Additionally, 65 percent said they have repeated experiments because earlier findings were difficult to access or interpret.
Public AI platforms have become attractive because they provide fast, conversational support that can summarise findings, organise information, and reduce workload.
Sean Blake noted: “This usage reflects rational tradeoffs rather than defiance. From an infrastructure perspective, shadow AI reflects unmet demand within official systems.
“Typically, companies tend to respond by restricting the use of shadow AI. Blanket policies reduce exposure, but they rarely change behaviour.”
Experts suggest the greatest risks arise when AI tools operate separately from approved scientific systems.
Sean Blake argues that organisations should embed governed AI capabilities directly into laboratory workflows. AI Lab Notebook technology is one example of this approach, helping researchers interpret data within established processes rather than relying on external applications.
Researchers are looking for tools that enhance efficiency while preserving scientific oversight and judgement.
Sean Blake concluded: “The challenge is designing infrastructure that supports both control and innovation. Focusing solely on restriction reduces confidence. Embedding intelligence within approved systems regains visibility.
“The choice is no longer whether AI belongs in the lab. It is whether intelligence remains outside official systems or is embedded where scientific decisions are actually made.”
