AI Implementation Checklist for San Diego Biotech Companies
A practical checklist for biotech and life science companies in San Diego preparing to implement AI systems. Covers data, compliance, infrastructure, and vendor selection.
San Diego’s biotech cluster — from Torrey Pines to Sorrento Valley — is one of the most AI-ready in the country. The data exists. The use cases are clear. The regulatory context is well-understood.
What’s missing, for most companies, is a clear starting framework for implementation.
This checklist is what we run through with every biotech client before starting any AI project.
Phase 1: Data readiness
Document inventory
- Identify the primary document types (clinical trial data, lab reports, regulatory filings, literature)
- Assess document format distribution (PDFs, structured databases, EHR exports, Excel)
- Estimate volume (number of documents, update frequency)
- Identify data owners and access controls
Data quality
- Sample 100 documents and manually review for quality
- Identify OCR quality issues in scanned documents
- Assess consistency of field names and formats across document types
- Identify any PII or PHI that requires special handling
Data infrastructure
- Confirm cloud environment (AWS, Azure, GCP) and available services
- Identify current data storage locations
- Confirm backup and retention policies
- Assess current ETL or data pipeline infrastructure
Phase 2: Compliance review
HIPAA considerations
- Identify if any data contains PHI (Protected Health Information)
- Confirm Business Associate Agreements with any AI vendors
- Review data handling requirements for your specific data types
- Confirm audit logging requirements
21 CFR Part 11 (if applicable)
- Assess whether AI system outputs will be used in regulated activities
- Identify electronic signature requirements
- Plan for system validation documentation
- Identify audit trail requirements
ITAR (if applicable)
- Identify any defense-related research data
- Confirm export control classification of your data
- Assess cloud provider compliance (GovCloud requirements)
Internal compliance
- Loop in legal/compliance team before any vendor contracts
- Review IP ownership provisions in AI vendor agreements
- Confirm data residency requirements
Phase 3: Use case prioritization
For each potential AI use case, score it on:
Impact (1–5): How much time or money does this save? Feasibility (1–5): How structured is the data? How clear is the task? Risk (1–5, inverted): What’s the consequence of an error?
Start with high impact + high feasibility + low risk use cases.
Common high-value biotech AI use cases:
- Clinical trial document extraction (high feasibility — structured templates)
- Literature review and evidence synthesis (high impact — expensive currently)
- Regulatory document automation (high impact — time-consuming currently)
- Lab report analysis and anomaly detection (medium — depends on data quality)
- Drug interaction screening (medium — high accuracy requirements)
Phase 4: Infrastructure requirements
Compute
- Assess whether you need on-premise GPU infrastructure
- Identify cloud GPU availability in your region
- Estimate inference cost per query at expected volume
- Plan for scaling requirements
Security
- Review network architecture for AI services
- Confirm VPC/private endpoint requirements
- Identify monitoring and alerting requirements
- Plan for model access controls
Integration
- Map existing systems that AI will integrate with
- Identify API availability for each system
- Confirm authentication and authorization patterns
- Assess data synchronization requirements
Phase 5: Vendor selection
Questions to ask every AI vendor:
- Where does our data go during inference?
- Is our data used to train your models?
- Do you have a BAA (Business Associate Agreement)?
- What’s your SOC 2 Type II status?
- Can you deploy in our cloud environment?
- What does ongoing support look like post-deployment?
- Who owns the IP in outputs generated by your system?
Red flags:
- Vague answers about data handling
- No BAA available
- “We’ll figure out compliance later”
- Demo-only experience, no production deployments
Phase 6: Pilot design
A well-designed pilot:
- Has a specific success metric defined before it starts
- Runs for a defined time period (4–8 weeks typical)
- Has a clear decision criterion for “go to production” vs “stop”
- Includes IT and security from day one
- Has a named system owner post-deployment
- Includes budget for ongoing maintenance
If you’re working through this checklist and want expert guidance, that’s exactly what our AI Readiness Audit covers — in 2 weeks, with a written deliverable you keep.
Solvren AI is San Diego’s AI implementation agency for biotech, healthcare, and defense companies.