The transition toward automated labs remains in a transitional phase. Cenevo’s research, which gathered insights from 113 professionals across R&D, clinical, and manufacturing sectors, highlights a shift in budgetary priorities. Rather than chasing standalone AI tools, lab leaders are funneling capital into systems integration, data infrastructure, and automation. This strategic pivot is driven by the fact that 55 percent of organizations still struggle with fragmented data and a lack of interoperability between electronic lab notebooks and laboratory information management systems.

Security concerns continue to act as a significant barrier to entry, with 58 percent of surveyed professionals citing privacy and security risks as primary deterrents. Despite these hurdles, progress is evident compared to previous years. The number of labs relying entirely on manual operations has decreased, and concerns regarding data overload have dropped from 54 percent last year to 42 percent today. Cenevo CEO Keith Hale noted that while AI remains high on the industry agenda, the path to widespread adoption requires solving core issues related to regulatory compliance and inconsistent data structures before agentic workflows can become the standard.