LONDON, UK. June 22, 2026 – New findings from a Sapio Sciences commissioned survey suggest that unauthorised AI adoption is widespread among scientists. Seventy-seven percent of respondents said they use public AI tools during laboratory work, and almost 45 percent access those services through personal accounts. The results point to significant concerns regarding data protection, compliance, and confidence in research outcomes.
The survey also revealed that only 5 percent of scientists can conduct experimental analysis independently within their organisation’s approved systems.
Shadow AI refers to AI technologies used without formal authorisation from corporate IT and security teams. While often adopted to improve efficiency, such tools can introduce risks involving data governance, intellectual property, and regulatory compliance.
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 experts say the trend reflects the realities of modern biopharmaceutical research. Scientists frequently use public AI applications to evaluate data, improve experimental protocols, and assist with scientific reasoning. Although ELNs and laboratory management platforms are common, they do not always provide the analytical support researchers require.
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.”
Survey participants also highlighted operational challenges. Fifty-six percent said their ELN reduces efficiency, while 65 percent reported repeating experiments because historical results were difficult to retrieve, understand, or reuse.
Generative AI tools have gained traction because they offer immediate support, helping users summarise information, organise thoughts, and simplify analytical tasks.
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.”
Industry specialists emphasise that AI is not inherently the issue. The greater concern is when AI usage takes place outside approved systems that govern scientific processes.
Sean Blake believes the answer lies in embedding controlled AI capabilities into existing workflows. AI Lab Notebook platforms are emerging to support this shift by enabling scientific reasoning within trusted environments.
Scientists are not seeking to replace expertise with automation. Instead, they want tools that support faster and more effective decision-making.
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.”
