The AI Hangover in Biotech: What We Need Now Is Operational Clarity, Not More Hype
By Jenna Levenson, PhD, RN, MS
AI in biotech promised moonshots—and we believed it.
But after billions spent on “transformative” tools and underbaked pilots, many biotech teams are waking up to an expensive reality: the return on investment is lagging, leadership is confused, and regulatory pathways are still catching up.
In my last newsletter, I made the case that AI in biotech is overhyped—and it is costing us millions.
Today, I want to go one step further and say this:
The biggest threat AI poses to biotech is not failure—it is distraction.
Distraction from the basics, from execution, from knowing your protocol inside and out, from collecting quality data, from listening to the patients you serve.
AI Will Not Fix Poor Trial Design - Or Bad Data - Or Unrealistic Timelines.
I have worked on AI-driven protocol builders that spit out 80-page “optimized” documents with no regard for feasibility. I have seen site-selection tools that ignore site capacity and rely on outdated claims data. I have consulted with biotech startups who bet their seed funding on chatbots for trial recruitment—before even validating their inclusion/exclusion criteria.
This is not innovation.
This is misallocation.
What We Need Instead: A Return to Operational Rigor
Here’s what biotech companies need to prioritize before layering on another AI tool:
Tight Protocols: If your endpoints are not crisp and your eligibility criteria are vague, AI will only scale your confusion.
Site Partnerships: No AI will replace meaningful relationships with sites that actually enroll patients.
Data Strategy: Garbage in, garbage out. If your data is siloed, inconsistent, or missing context, predictive models will mislead you.
Regulatory Navigation: A flashy algorithm will not get you to an IND if your submission package lacks clarity or consistency.
Let’s not outsource our thinking. Let’s empower it.
Smart AI Starts with Smart People
There is a place for AI in biotech. But it must follow clear scientific and operational logic. Not replace it.
Want to integrate AI into your development pipeline responsibly? Start with a risk-benefit framework. Know what you’re solving for. Measure what matters. And involve experts who know how trials actually run—not just how models are trained.
Let’s Talk Strategy, Not Just Software
If you are rethinking how your team is using AI—or wondering whether that next AI vendor pitch is really worth it—Let’s talk. I help biotech leaders cut through the noise and refocus on what moves trials forward: clarity, feasibility, and smart execution.
Download my free biotech workbooks (1–8).