A growing divide between the rapid adoption of artificial intelligence (AI) tools and the ability of staff to use them responsibly is emerging as a critical management issue in modern laboratories. According to the AI Reality Check report from Lab Innovations, more than 75 percent of researchers now use some form of AI in their work, yet only about one in four say they fully understand how these tools function.
This imbalance is more than a training problem—it’s a quality risk. Laboratories depend on accurate, reproducible results, but that reliability can erode when staff cannot validate the algorithms that shape data analysis. The report’s authors, including Marie Oldfield, PhD, AI lead at the Institute of Science and Technology (IST), warn that without meaningful oversight and education, laboratories may be unknowingly introducing bias, compromising regulatory compliance, or drawing incorrect conclusions from flawed outputs.
“Without AI literacy, lab personnel and their work are vulnerable to mistakes, ultimately undermining trust in the technology they’ve been directed to use,” the report notes.
Training shortfalls undermine responsible AI adoption
Despite years of advocacy for technical upskilling, funding for AI-specific training remains limited. The IST has petitioned multiple UK governments to allocate protected time and budgets for AI education, but progress has been slow. Many staff are still expected to learn on the job, often using tools that were selected for them rather than with them.
This approach leaves laboratories exposed. When researchers and technicians do not understand how an algorithm was trained, what data sets it draws from, or how its performance was validated, even simple tasks—such as setting decision thresholds or interpreting outputs—can become points of error. These oversights can propagate quickly through automated systems, undermining both scientific integrity and public confidence in AI-enabled research.
Accreditation and continuous learning close the gap
Structured accreditation programs are emerging as one solution. The IST’s Registered Technician in AI (RTechAI) credential helps scientists and technical staff demonstrate competence in assessing, validating, and applying AI tools responsibly. Such certifications, combined with formal continuing professional development (CPD), can build trust among funders, regulators, and collaborators that a laboratory’s use of AI meets ethical and technical standards.
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Yet, as Oldfield and co-author Joan Ward emphasize, professional development must be viewed as essential, not optional. In other sectors—medicine, engineering, and aviation—ongoing training is a regulated requirement. In the lab environment, CPD is often voluntary and underfunded. Shifting this mindset is crucial if laboratories are to keep pace with technological change.
Bridging inclusivity gaps in the AI transition
The skills divide is also creating unintended inequities. Older employees and underrepresented groups are less likely to have access to AI training or mentorship, putting them at risk of exclusion as digital systems replace manual processes. The IST cautions that an unequal transition could widen existing representation gaps within technical and research roles.
For lab managers, this adds a human-resources dimension to digital transformation. Ensuring equitable access to training and creating cross-generational learning opportunities can prevent the emergence of a two-tier workforce—one fluent in AI, the other left behind by it.
Making AI literacy part of lab quality culture
Laboratories that treat AI proficiency as a core component of quality management—not just a technical specialization—will be best positioned to adopt automation safely and efficiently. That begins with assessing the current state of staff literacy, identifying gaps, and integrating AI awareness into onboarding, SOPs, and regular audits.
AI has the potential to free scientists from repetitive tasks and accelerate discovery, but only if those implementing it understand both its capabilities and its limits. For lab leaders, closing the skills gap is no longer a peripheral concern—it is central to maintaining the standards of rigor, safety, and inclusivity that define good science.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
