Human-AI Collaboration in Clinical Operations

In a recent video interview with Applied Clinical Trials, Gaurav Agrawal, Senior Partner at McKinsey & Company, highlighted insights from the company’s new report on clinical operations. Agrawal outlined how AI can accelerate development timelines by optimizing trial design, reducing operational “white space” between phases, and streamlining study startup, monitoring, and closeout activities. He emphasized the value of human-AI collaboration, noting that AI provides insights for protocol design, site selection, and monitoring while humans contribute strategic oversight and relationship management. Agrawal also highlighted the growing need to integrate trials into routine care and expand access through trial-naive sites and simplified protocols, as well as the role of digital tools in decentralizing assessments and enhancing patient-centricity. Looking ahead, he stressed that preparing for end-to-end AI-enabled trials will require systematic adoption of technology, cultural and workflow transformation, and cultivating digitally savvy clinical operations talent.

The below interview transcript was lightly edited for clarity.

ACT: The report highlights human-AI collaboration as a new standard. What does that look like for clinical operations teams?

Agrawal: Yeah, I think the look, I think, you know, different people call it different things, human-AI collaboration, AI-assisted copilot, etc., etc. And I think there are a lot of areas where we realize that AI can add a lot of power in terms of insights and getting work done, but in the near future is not autonomous enough to sort of just run the whole thing. And so the question is, where does that collaboration most make sense?

In terms of human and AI, I think there's three or four areas similar to the ones that I had described upfront, in terms of clinical protocols, both in terms of design of the protocols, but also writing and authoring of the protocols, is an area where I think it could be very helpful.

Second is sort of this whole decision-making, which is a complex web of considerations around which clinical sites do we go to, which countries do we go to? There are strategic considerations that companies have, but there are also just fundamental facts around which clinical sites do we think will perform better in terms of patients and enroll better for our study. And I think there are areas where humans can bring a little bit more of that overarching strategic perspective, and AI can do a lot of the rest of the stuff to essentially provide insights and that collaboration on how they work together to make the right decisions. Better decisions than an AI-only or human-only model is actually where we see things going.

I think the third thing I would say is, I think this is something that is still evolving. But if you look at the clinical monitors, which is a pretty large field force, if you will, in every clinical operations organization, whether it's internal or based in a CRO, I think the role on how they interact with sites is actually changing from a purely sort of checklist compliance sort of measure to being more targeted around AI helping with sites to actually go to, and why, where they see compliance risks. I think that's sort of going to become the norm over a period of time. And so AI-assisted monitoring, and then monitors, of course, elevating their role to be more site relationship-based. So I think those are sort of some of the interconnectivity that we see between AI and humans.