A 15-minute Self-Assessment for AI Readiness Diagnostic of Genomics Platforms
Built after watching three clients discover mid-implementation that their platforms couldn't support the AI capabilities they'd already committed to building. Use this before you sign a vendor contract or allocate engineering resources.
- 12 diagnostic questions across 4 critical categories for AI readiness
- Every "no" comes with a real fix timeline and cost estimate
- Built for VP Bioinformatics, CTO, Head of Data Science
12 Questions Across 4 Sections
Data Infrastructure
Reference database versioning, computational lineage from FASTQ to variant calls, structured phenotype data access
Model Operations / MLOps
Shadow deployment for safe model comparison, GPU compute access, explainability infrastructure your genetic counselors can use
Validation & Compliance
Documented validation SOPs, comprehensive audit logging, AI-specific incident response
Operational Readiness
Expert override workflows, per-sample cost tracking, safe model retraining without breaking production
Why This Matters, and What You Walk Away With
Scoring Reveals Real Readiness Gaps
Score 0-3 "Yes" answers, and you're 12-18 months and $600K-$1M from an AI-ready platform. Score 10-12 and you're ready to select a use case today. Most organizations land somewhere in between, and the gap matters.
The Same Three Gaps, Every Time
Based on 20+ genomics platform assessments, the same three gaps show up again and again: no explainability infrastructure, incomplete data lineage, and no model versioning for safe deployment.
Real Costs, Real Timelines
Every "no" comes with a real fix timeline and a real cost estimate, so you can budget accurately instead of guessing.
- A concrete Yes/Partial/No score across all 12 dimensions of AI readiness, not a vague maturity label
- Fix timelines and cost ranges for every gap, so you can build a real budget before your next planning cycle
- A prioritized punch list: which gaps block AI deployment entirely versus which ones just slow it down
- A defensible answer when leadership asks "why isn't AI in production yet"