Preparing for End-to-End AI-Enabled Clinical Trials

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: McKinsey envisions end-to-end AI-enabled trials. What steps should clinical operations leaders take now to prepare for this future?

Agrawal: Yeah, look, as I was mentioning, there are different areas where AI could actually impact everything from improved clinical trial designs that get you better, faster trials, but also hopefully trials that may have a better chance of success by choosing the right trial design parameters, removing a lot of the white space through decision making with greater conviction on some of the more debatable areas, reducing some of the operational time, both from startup and during the conduct and the closeout, through using AI, agentic AI, and agents to do some of the manual tasks faster. I think that an AI-enabled clinical trial, end to end, truly incorporates all of this across the value chain.

I think if I look at where we are today, I don't think the technology is 100% there, but I think the technology is way more mature than what has actually been incorporated into the clinical trial setting, in the clinical trial space. And so when I think about your question of, what do teams need to do, I think there are a few elements here.

Like, number one, getting clarity around, like we were talking about, where are the places in the clinical trial ecosystem that AI and these technologies can be incorporated. Second is going about it very systematically in terms of building the solutions and most importantly, shifting the mindset and culture where people are actually embracing technology and using it for day-to-day decision making.

I think when we look across the board, 80% of the technology fails, not technology transformations in this space of clinical development failed, not because the technology is not ready, but because we either didn't have the right people with the right mindset to actually adopt it, or we didn't change our, what I call, workflows enough to incorporate how this technology fits into our decision-making process, right? And I think that transformation is actually quite important.

The last thing I would touch upon is talent. I think that we are upon a unique moment where technology is moving quite fast. I think that a lot of people in clinical operations, when we talk to leaders in clinical operations, a lot of the people grew up in the quote, unquote, conventional way of doing clinical trials, right? And truly, the biggest technology change that many people have seen over their careers is back 30 years ago, when you switched from paper to EDC and sort of, you know, digital-based, sort of data recording, and sort of got into that.

But since then, this is probably now the biggest change that is happening in terms of digitization of clinical trials. And I think the question is, what does the talent of the future look like that is able to do clinical trials, but in a much more digital and AI-assisted way. In fact, some of the companies that we talked to have started to think about, no one actually learns clinical trials in their college, right? You learn it on the job once you join an institution. And so do we actually bring people in who are more digitally native and digitally savvy and teach them clinical trials versus folks who would sort of be more on the other side of the spectrum, which is, you know, I know something about clinical trials, and then try to teach them digital. So the question is, how do we groom the sort of talent and workforce of the future that is much more savvy with AI and tools so that they're better able to incorporate and embed this in the clinical trial setting and embrace it.