AI Will Not Close the Readiness Gap in Clinical Trials. It Will Expose It.

Author(s): Joel Selzer

The risk in an AI-first trial is not that teams use the tools. It is that they defer to them without the comprehension to know when the tool is wrong.

In the span of a few weeks this spring, the FDA made its direction unmistakable. The agency launched two proof-of-concept real-time clinical trials that stream data to regulators as it is generated, expanded its internal AI platform so that AI now sits on top of its review infrastructure, and published an expanding set of channels for sponsors to engage on the use of AI across trial design and conduct. The regulator is industrializing AI across the lifecycle, and faster than most sponsors expected.

The industry response has been loud and largely uniform. Every conference agenda and vendor booth now carries a version of the same promise: adopt the right AI, and the hard parts of running a trial get easier for everyone. In an environment where sponsors traditionally expect different levels of site performance, the implicit assumption is that AI is a leveler, that better tooling will narrow the distance between a site that executes well and one that struggles.

I want to challenge that assumption directly. AI is not a magic wand that can be waved around and instantly improve site performance. Applied to the work of running a trial, it more likely widens the distance between teams that are ready to execute a protocol and teams that are not.