The AI Hype is Over. Now Comes the Hard Part: Implementation in Clinical Trials

By Jenna Levenson, PhD, RN, MS

Artificial Intelligence has dominated biotech headlines for years, promising faster drug development, smarter trials, and better patient outcomes. But now that the initial excitement is settling, the industry is facing a more difficult question: who is actually making it work?

The truth is, most biotechnology companies are still in the early stages of AI adoption. Real-world implementation—especially in clinical trials—requires more than just algorithms and polished dashboards. It demands clean, structured data; cross-functional alignment; and a regulatory framework that can handle the complexity of adaptive, machine-driven insights. Without these elements in place, even the most advanced AI tools will fall short.

Take site selection, for example. AI platforms can model site performance based on historical enrollment, patient diversity, and operational metrics. But without standardized data inputs and centralized oversight, the predictions become unreliable. Similarly, protocol optimization sounds promising until companies face outdated IRB processes or unclear regulatory expectations that stall progress.

We are entering a new phase—one where the winners will not be the loudest AI adopters, but those who build sustainable, compliant systems around the technology. The U.S. Food and Drug Administration’s evolving AI/ML guidance makes it clear: oversight and explainability are non-negotiable.

If your team is evaluating AI tools or vendors, it is time to ask the hard questions:

·      Are we operationally and ethically ready for AI?

·      Are our trial teams trained to interpret and act on AI outputs responsibly?

·      Are we documenting our decision-making processes to meet regulatory scrutiny?

The AI buzz may be fading, but the demand for execution excellence is only beginning.

Let’s Talk: How is your organization preparing for AI implementation in clinical trials?