FDA AI Inspections Prompts Industry Readiness Strategy Rethink
FDA's expanding use of artificial intelligence tools in regulatory operations is poised to transform how pharmaceutical, biotechnology, and medical device companies prepare for and manage inspections, according to a new analysis by former FDA Office of Enforcement director David Elder.
In a guest column published on pharmaceuticalonline.com, Elder, now a principal at Eliquent Life Sciences, said FDA has already begun incorporating AI into a range of agency functions, including clinical data review, marketing application assessments, adverse event signal detection, import screening, and inspection activities.
According to Elder, FDA’s AI tool,ELSA, is increasingly being used to support inspections and remote regulatory assessments by helping investigators analyze large volumes of information and identify potential compliance signals more rapidly than traditional review methods. "FDA's inspection tactics continue to evolve and the agency's adoption of ELSA as its AI tool is just the next stage of evolution," Elder wrote.
Unlike conventional inspection techniques that rely heavily on manual record review, keyword searches, and spreadsheet analysis, AI can evaluate information across multiple datasets simultaneously, potentially revealing patterns and relationships that might otherwise go unnoticed.
Elder emphasized that AI-generated findings would not automatically constitute regulatory violations. Instead, the technology is expected to serve as a signal-detection tool, highlighting issues that warrant further human review and investigation. FDA investigators could use AI to evaluate individual records, such as determining whether the root-cause analysis in an out-of-specification investigation appears adequately supported. However, Elder said the technology's greatest value emerges when it is supplied with large datasets from multiple sources and repeatedly queried to uncover trends.
Among the records FDA may request and analyze are complaint databases, deviation and investigation logs, corrective and preventive action (CAPA) records, change-control documentation, risk management files, and standard operating procedures. The agency can also supplement company data with information from adverse event reporting systems, recall databases, field alert reports, marketing applications, and records from prior inspections.
Rather than resisting the agency's growing use of AI, companies should implement their own qualified AI systems to improve inspection readiness, Elder recommended. He argued that organizations should integrate AI-assisted trend analysis into existing quality systems and governance processes, including management reviews, complaint review boards, and CAPA committees.
"A long-standing rule of thumb is that an inspection should never find anything in a company's data that the company didn't already know," Elder wrote. He also recommended that firms incorporate AI tools into internal audits and mock inspections to more closely mirror FDA's evolving inspection practices.
Beyond preparation, Elder said companies can use AI during active inspections to evaluate the same datasets requested by FDA investigators and anticipate potential concerns. By identifying possible signals in real time, manufacturers may be able to gather supporting documentation, assess risks more quickly, and prepare more comprehensive responses to inspector questions.
Even when inspection observations are issued, AI could help companies rapidly assess the scope and impact of findings and develop corrective action strategies before the inspection concludes, he said.
Despite AI's growing role, Elder stressed that the technology's effectiveness depends on the quality of the data it analyzes and the expertise of the people interpreting the results. The outputs are only as reliable as the data that inform it and the individuals who apply it," he wrote, adding that AI must remain subject to rigorous human oversight and critical thinking.
Elder noted that FDA has already begun signaling increased scrutiny of industry AI applications. One example cited was an April 2026 Warning Letter involving what the agency described as inappropriate use of artificial intelligence in pharmaceutical manufacturing (see earlier story).
Law firm Morgan Lewis recently put out a client alert (see story) noting that the Warning Letter drew a clear line from the agency: companies remain fully accountable for compliance, regardless of whether AI tools are involved. “This Warning Letter sends an unambiguous message that reliance on AI is not a defense against regulatory violations,” the Morgan Lewis analysis noted, adding that FDA expects human oversight to remain central to all regulated activities.
The warning also highlights emerging risks as pharmaceutical and biotechnology companies accelerate adoption of AI across their operations, according to the law firm. While AI can streamline documentation and support compliance workflows, FDA’s action indicates that insufficient validation or overreliance on automated outputs could trigger enforcement.
As FDA continues integrating AI into its regulatory toolkit, Elder concluded that companies that proactively adopt similar technologies while maintaining strong quality systems and human oversight will be best positioned to navigate future inspections successfully.