AI Readiness Checklist for Pharmaceutical & Life Sciences

EDC and CTMS data is structured and accessible for trial optimization and analysis.

Compound libraries and genomic datasets are stored in AI-ready formats for drug discovery.

Electronic records and signatures comply with FDA 21 CFR Part 11 requirements.

Adverse event reports from multiple sources flow into a structured signal detection pipeline.

Claims, EHR, and patient registry data is curated for real-world evidence analytics.

Laboratory data from LIMS is integrated with research databases for experiment tracking.

Historical submission documents and regulatory correspondence are indexed and searchable.

Batch records and quality control data are digitized for process optimization models.

Cloud infrastructure meeting pharmaceutical & life sciences security and compliance requirements is operational.

A secure API gateway manages access to AI services with authentication and rate limiting.

Infrastructure for deploying and serving ML models is available with appropriate latency.

Continuous integration and deployment pipelines support AI model versioning and rollback.

Sufficient compute resources (GPU/TPU) are available for model training and inference.

A centralized model registry tracks model versions, metadata, and deployment history.

Model performance monitoring tracks accuracy, drift, latency, and resource usage.

Data encryption in transit and at rest meets FDA 21 CFR Part 11 (electronic records) requirements.

Domain experts who understand AI capabilities are identified to champion adoption and validate outputs.

Data scientists with pharmaceutical & life sciences domain expertise are on staff or available.

Staff across the organization receive training on AI tools, limitations, and responsible use.

AI projects include domain experts, data scientists, engineers, and compliance.

MLOps engineers manage model deployment, monitoring, and lifecycle in production.

Product managers translate business needs into AI requirements and manage AI roadmaps.

Teams can evaluate and manage AI vendors for pharmaceutical & life sciences applications.

The organization invests in AI education through conferences, certifications, and knowledge sharing.

A formal AI policy defines acceptable use, risk levels, and approval workflows aligned with FDA 21 CFR Part 11 (electronic records).

An ethics framework addresses fairness, transparency, and accountability for pharmaceutical & life sciences AI.

AI models undergo structured validation testing before deployment to production.

Regular bias audits evaluate AI models across relevant demographic and operational dimensions.

An incident response plan addresses AI model failures, biased outputs, and data issues.

Structured change management guides organizational transitions from AI implementation.

Vendor AI solutions undergo proper evaluation for quality, compliance, and data handling.

Post-deployment monitoring tracks model performance, drift, and business impact continuously.

High-impact AI use cases for pharmaceutical & life sciences are documented with expected outcomes.

An ROI model quantifies AI impact including Drug candidate identification time reduction and Clinical trial recruitment rate and screen failure rate.

C-suite executives actively champion AI initiatives with dedicated budget and resources.

A 2-3 year AI roadmap aligns with the organization's pharmaceutical & life sciences strategic plan.

AI maturity is benchmarked against pharmaceutical & life sciences industry peers and leaders.

AI initiatives directly map to improving key stakeholder metrics and satisfaction.

Organization embraces AI experimentation with acceptance of measured failures.

Strategic partnerships with AI vendors, cloud providers, and domain experts are established.