1、AI in Biotech:A Guide to Data InfrastructureReadinessBy Adrien Laurent,CEO at IntuitionLabs 12/8/2025 30 min readai in biotechdata infrastructuremachine learningfair datadata governancedrug discoverydata qualitydata integrationIntuitionLabs-AI Software for Pharma&BiotechAI in Biotech:A Guide to Data

2、 Infrastructure Readiness 2026 IntuitionLabs.ai-North Americas Leading AI Software Development Firm for Pharmaceutical&Biotech.All rights reserved.Page 1 of 15Executive SummaryThe convergence of advanced AI techniques and biotechnology promises transformative breakthroughs indrug discovery,precision

3、 medicine,and synthetic biology.However,realizing these benefits requires a solidfoundation of data infrastructure.Biotech organizations must prepare their data systems and processesbefore deploying AI fixing silos,ensuring high-quality and interoperable data,and building scalable computingplatforms

4、.This report identifies the key prerequisites and fixes needed to make biotech AI-ready,synthesizinginsights from industry studies,expert reports,and case examples.We find that:Data Consolidation&Integration:Biotech data is often fragmented across lab instruments,databases,and external sources.Pre-A

5、I fixes include building unified data repositories(lakes or warehouses),applyingcommon schemas and ontologies,and breaking down silos(scientist and department silos)so data can bequeried and merged()().Data Quality&Curation:Poor or inconsistent data can derail AI.Over 7080%of data scientists time is

6、spent on cleansing and wrangling data(www.aiuniverse.xyz)().Before AI is applied,organizations must implement rigorous ETL pipelines,data validation,and annotation processes.Thisincludes handling missing values,correcting errors,de-duplicating records,and standardizing formats(e.g.gene nomenclature,