What Biotech Teams Should Automate First with AI
A priority order for AI in biotech — highest-leverage, lowest-risk first.
Every biotech team I talk to wants to “use AI.” Few have a clear answer to where. The result is usually a flashy pilot that never touches the actual bottleneck. Here’s the order I’d automate in.
1. Data labeling and QA. If you train models on imaging, video, or behavioral data, labeling is almost certainly your bottleneck — slow, costly, inconsistent across annotators. This is where modern AI pays off first: LLM-assisted label auditing to catch disagreements and errors, plus specialized models to pre-label so experts review only the hard cases. It cuts manual burden and improves consistency at the same time.
2. The boring data plumbing. Most research orgs lose more time moving and reconciling data than to any analysis. Pipelines that ingest, clean, version, and standardize your data are unglamorous and enormously valuable — and a prerequisite for everything else. Do this before chasing fancier models.
3. Repetitive analysis and figures. If someone re-runs the same analysis and regenerates the same figures every week by hand, that’s a script — and increasingly, an agent. Reproducible, version-controlled analysis removes a whole class of human error.
4. Literature and document triage. Agentic workflows are genuinely good at first-pass triage: scanning new literature, summarizing with citations, flagging what a human should read. Keep the human in the loop for judgment; let the agent handle the volume.
Where to be careful. Don’t automate the decision — automate the toil that precedes it. Anything touching a regulated claim, a safety call, or a go/no-go needs a human accountable for it. And be honest about validation: an AI pipeline in a research-adjacent or regulated setting needs the same rigor you’d apply to any other method — documented, reproducible, tested.
The pattern across all four: AI’s highest-value role in biotech right now isn’t replacing scientists. It’s removing the manual burden between them and their decisions. Start where the toil is heaviest and the risk is lowest, prove it, then move up.
If you want a one-week read on where automation would actually move the needle for your team — and where it wouldn’t — that’s an AI opportunity assessment.