A new report released by Lab Innovations explores how artificial intelligence (AI) is being applied across UK laboratories—and what managers must consider as digital tools reshape scientific work. Produced in collaboration with experts from leading science and technology institutions, the publication examines both the promise of AI-driven efficiency and the governance frameworks needed to maintain scientific integrity.
The contributors include Marie Oldfield, PhD, AI lead at the Institute of Science and Technology (IST); Joan Ward, deputy chair of the IST; Maya Carlyle, principal AI engineer at the National Physical Laboratory; and Duncan Lugton, head of policy and impact at the Institution of Chemical Engineers. Their collective insights highlight the need for careful planning as laboratories move from experimentation to full-scale integration of AI.
Skills and governance emerge as defining factors
“Laboratories require clear guidance, skills development, and strong governance to ensure the technology adds value rather than undermining scientific standards,” said Carlyle. The report warns that without adequate training and oversight, hasty implementation could introduce new forms of error or bias.
While many institutions are optimistic about AI’s potential to accelerate discovery and streamline workflows, the report notes that adoption remains uneven. Some facilities have advanced data infrastructure and technical expertise, while others are still evaluating how to align digital tools with existing quality systems and regulatory frameworks.
According to Nicole Hawkins, marketing manager at Easyfairs, which organizes Lab Innovations, the findings reflect a pivotal moment for the sector. “Implementing AI effectively and ensuring scientists maximize these tools will be a significant challenge,” she said. “Lab Innovations provides a forum where leading experts can come together to examine these questions, share experiences, and shape how AI will be applied in practice.”
Building readiness for responsible AI use
The publication emphasizes that organizations investing early in governance and workforce capabilities are better positioned to secure long-term benefits. Training programs, clear accountability structures, and transparent data practices are among the factors linked to successful adoption.
The report will be distributed to policymakers, funders, and laboratory leaders to inform future decisions about digital transformation in science. It is also available to delegates attending the Lab Innovations conference, where sessions on AI readiness and laboratory modernization will continue the discussion.
Preparing your lab for responsible AI use
For laboratory managers, the findings underscore the importance of building digital literacy and establishing oversight frameworks before integrating AI tools into core operations. Assessing staff readiness, setting clear data standards, and prioritizing explainable algorithms can help labs harness AI responsibly while maintaining scientific rigor.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
