Free Resource
AI Readiness Checklist
Score your organization across 6 dimensions. Find the gaps before you spend a dollar on models or infrastructure.
30 items. Takes 10 minutes. Used by engineering leaders at companies deploying AI on IBM Cloud and hybrid infrastructure.
1. Data Quality & Access
- Centralized data catalog or lake with documented schemas
- Automated data quality checks running in CI/CD
- PII classification and masking policies enforced
- Data lineage tracked from source to model input
- Cross-department data sharing agreements in place
2. Infrastructure & Compute
- GPU or TPU capacity provisioned (cloud or on-prem)
- Container orchestration (Kubernetes/OpenShift) for model serving
- Autoscaling configured for inference endpoints
- Model registry with versioning and rollback
- Monitoring stack covering latency, throughput, and error rates
3. Governance & Compliance
- AI ethics review board or approval workflow
- Model bias testing automated in the pipeline
- Explainability layer (SHAP, LIME, or equivalent) integrated
- Audit trail for every model decision in regulated workflows
- Quantum-safe encryption roadmap for sensitive payloads
4. Talent & Organization
- Dedicated ML engineering or MLOps headcount
- Cross-functional AI steering committee with exec sponsorship
- Prompt engineering guidelines documented and trained
- AI literacy program for non-technical stakeholders
- Vendor evaluation framework for build vs. buy decisions
5. Strategy & Roadmap
- AI use cases prioritized by business impact and feasibility
- Proof-of-concept to production timeline under 90 days
- Integration plan mapping AI outputs to existing workflows
- Competitive intelligence on what peers are deploying
- Board-level AI investment thesis documented
6. ROI Measurement
- Baseline metrics captured before AI intervention
- A/B testing framework for AI-augmented vs. manual workflows
- Cost-per-inference tracked against business value generated
- Quarterly AI ROI review with kill criteria for underperformers
- Total cost of ownership model including compute, talent, and ops
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