Long-form guides, explainers, and operating playbooks on how AI is being applied across drug discovery, development, manufacturing, and the clinic — from the Allbio.AI team and the life sciences community.
Articles here are analysis, guides, and opinion written by community practitioners and the Allbio.AI team — how to think, decide, staff, buy, govern, or measure across workflows. A single workflow described end to end belongs in Use Cases. Vendor/service provider-authored articles and case studies about their own product or service are commercial content and live with that listing in Products & Services.
Measuring real return on AI initiatives in biopharma requires tracking true operational outcomes rather than model throughput. To avoid self-deception, teams must establish honest baselines, account for human review time, and set firm stop conditions.
What to hire, in what order, and what to stop pretending you can do internally — a pragmatic staffing plan for mid-size biotechs.
Generic AI training fails in the lab because it ignores biological context and bench realities. A practical, staged internal curriculum builds real capability by teaching verification, data safety, and workflows without disrupting lab work.
Validating AI in regulated life science settings requires moving beyond traditional software testing toward risk-based assurance. By defining clear intended use boundaries and managing model drift, operating teams can deploy machine learning while satisfying quality expectations.
What would you like a deeper guide on? We commission and co-author the most requested pieces.
