How Agentic AI Accelerates Healthcare Research and Innovation

Preparing Data Foundations and Security Guardrails

To achieve these gains, life sciences organizations must first address data readiness.

“When you want to do a deep dive into your own portfolio, you have to ensure you have organized data,” Ries stresses.

Often, poorly designed schemas or unstructured archives must be reindexed and enriched with metadata before they’re useful.

“You want meta tags that have a deeper description,” Ries says. “That way, when you do a search, the agent has an easier time finding information.”

This step is critical to ensure accuracy, reduce noise and avoid wasting time on irrelevant or low-quality information.

Healthcare data also demands rigorous privacy and compliance controls. From Ries’s perspective, the principle is straightforward: Treat AI agents like human employees with role-based permissions.

“When you’re building agentic AI, you’re only going to give that agent permission to the right level of database,” Ries says.

Using established access controls ensures that agents can’t overreach into sensitive patient information.

“You want to maintain your security posture for an agent, just as if it were a person.”

RELATED: Why does agentic AI make zero trust more important than ever?

Managing Expectations and Measuring Impact

For all its promise, agentic AI isn’t a silver bullet: Researchers still need to validate findings and refine outputs. Instead, the value lies in surfacing connections humans might overlook.

Ries suggests healthcare organizations should evaluate success by focusing on everyday research tasks; if agentic AI can automate those, it frees researchers to focus on higher-value work. Ultimately, he says, agentic AI represents a new interface to data and systems across healthcare.

“People see agentic AI as being an easier way to interface with all of their systems, no matter what they are,” he says.

That simplification, however, is exactly what makes it so powerful: giving every researcher a tireless assistant that can accelerate discovery, reduce administrative burden and support innovation at scale.

“We’re not at the point of Star Trek, where there’s a robot that magically solves everything,” Ries says. “But it’s definitely going to give everyone in research an assistant that can help them speed things along.”

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