I pay attention to what people read. It’s a good tell for where the industry is going.
At the halfway point, I went back through the numbers. Most of what we publish is about AI, and I believe in it as much as anyone: what it can do now, what’s still hype and where it’s taking us. AI will change how trials run more than anything since the industry moved off paper.
But AI was only half of what you read.
AI Needs a Home: Inside the Workflows That Drive Trials
The most-read post of the year. It’s the thing I keep saying. The teams that get real value from AI won’t be the ones running the most tools. They’ll be the ones who decide how those tools plug into real trial decisions: who reviews what, in what order, with what context. The advantage is in the connections, not the tools.
The advantage is in the connections, not the tools.
How to Reduce Eligibility Risk and Prevent Protocol Deviations
This one is Cathy Tyner’s, our head of clinical strategy, and it earned its spot. Enrolling a patient who doesn’t qualify is a protocol deviation that puts data, timelines and patient safety at risk. She shows how centralized eligibility review keeps those calls consistent, including a sickle cell trial where manual eligibility decisions were taking three times longer than the protocol allowed. A standardized process brought every one back inside the window. That’s the operational specificity people came for.
The Next Competitive Advantage in Clinical Trials Is Execution Intelligence
The post you stayed with longest, by a wide margin. Most teams can’t easily answer who decided what, when, or why one study ran smoothly while another stalled. AI doesn’t fix that on its own. It needs structure to work against. Execution is something you design deliberately, not something you reconstruct after the fact.
They aren’t two conversations. They’re one.
How Imaging Workflows Get Derailed: Four Fixes Teams Can Use Now
Imaging anchors a lot of oncology and cardiology trials. The coordination behind it still runs on fragmented systems and manual trackers, which is how you end up with missing data, delays and avoidable PHI exposure. Concrete failure points, concrete fixes. Another operational post that outdrew most of our thought leadership.
The pattern in the numbers is this: the AI stories and the hands-on operational posts drew almost exactly the same readership. They aren’t two conversations. They’re one. AI earns its place where the day-to-day work is hardest: the eligibility calls, the imaging handoffs, the decisions that stall a trial when they go wrong. That’s the work worth building for.
If any of this maps to what you’re working through, let’s talk.
Abraham Gutman, Founder & CEO, founded AG Mednet with a focus on the operational challenges that slow clinical trials down after data is captured. He has over 20 years of experience at the intersection of clinical research and technology.
