AI is rapidly entering life sciences. Research, medical writing, regulatory affairs, market access — few functions are untouched by the conversation. The technology is advancing fast, investment is accelerating, and the pressure to act is real.
But speed of adoption and quality of results are not the same thing. And in a highly regulated industry where scientific accuracy, traceability, and compliance are non-negotiable, how AI is implemented matters as much as whether it is.
The organisations pulling ahead are not those deploying AI fastest. They are those deploying it correctly.
The Problem Isn’t the AI. It’s Where You Put the Human.
The industry default is what’s called “Human in the Loop”. AI generates an output, a human reviews it at the end, expert judgment added as a final check. It sounds responsible. In practice, it produces generic, unreferenced content that no regulator, payer, or medical expert will trust. Prompt engineering gets left to individuals, quality varies wildly, and adoption stalls. One launch event, then silence.
The shift that changes everything is not a technology upgrade. It’s a design principle: Human in the Lead.
The difference is where expert knowledge enters the process. In a Human-in-the-Lead model, domain expertise is embedded before AI runs, not after. Experts define the logic, the nuance, and the regulatory strategy upfront. AI then executes systematically, at scale, with traceable outputs. Human review doesn’t disappear, it becomes more focused, more valuable, and over time, dramatically faster.
This was the central argument at the KP-Morgan New Dawn Summit on March 11th, 2026 in Berlin, not whether AI belongs in life sciences, but how to design it so it actually works.
The Numbers Behind the Principle
The results from real implementations are clear. For Systematic Literature Reviews, a traditional baseline of 1,179 hours drops to 668 (−43%) in Phase 1 and 357 (−70%) in Phase 2. Title/abstract screening alone falls from 83 hours to 6. For regulatory dossier generation, 1,602 baseline hours become 1,003 in Phase 1 (−37%) and 542 in Phase 2 (−66%), shortening the entire process by more than 70%.
Quality Doesn’t Drop. It Rises.
The instinct when seeing 60–70% effort reductions is to assume quality suffers. The data says the opposite. In an independent benchmark by a global pharma organisation, OneRay.ai outperformed dedicated private instances of Claude and Gemini on seven of eight criteria including medical accuracy, clinical depth, referencing, and strategic value. On first iteration, AI accuracy scores closely matched human expert benchmarks across all measured dimensions, with remaining gaps driven by data structure and file format handling, not content quality. Overall Cover-In Score: approximately 91%.
When expert knowledge is embedded upfront, AI isn’t guessing. It’s executing against a calibrated set of instructions built from the best thinking in your organisation.
One Pilot. One Baseline. One Proof Point.
Six to eight weeks, one use case, a real baseline. That’s the fastest path from “we’re exploring AI” to “here are the numbers.” The question is not whether AI will change your function. It will. The question is whether your organisation will shape how.
The AI advantage belongs to those who lead it.
FFI Ventures with it’s precision AI solution OneRay.ai works with pharma and biotech organisations to implement Human-in-the-Lead AI across medical, regulatory, market access, and commercial functions. Ready to move from exploration to evidence?