AI Readiness Checklist
A structured framework to assess, prepare, and execute your organization's AI transformation. Track progress locally and export your results.
Data & Infrastructure
0/5Centralized & clean data pipeline
Unified data warehouse/lake with automated cleaning, deduplication, and quality checks.
Data labeling & annotation processes
Standardized workflows for human-in-the-loop tagging, validation, and version control.
Scalable compute access
On-demand cloud/GPU provisioning with cost monitoring and auto-scaling policies.
Data versioning & lineage tracking
Tools like DVC, MLflow, or Delta Lake to track datasets, transforms, and model inputs.
Real-time data ingestion capabilities
Streaming pipelines (Kafka, Kinesis) for live model scoring and feedback loops.
Strategy & Governance
0/5Clear AI use cases aligned to business goals
Prioritized initiatives with explicit ROI targets, not just tech experiments.
Success metrics & KPI framework
Defined benchmarks for accuracy, latency, cost, and business impact pre-deployment.
AI governance & ethics guidelines
Documented policies on fairness, transparency, accountability, and human oversight.
Vendor & partner evaluation criteria
Scoring matrix for APIs, LLMs, and third-party tools covering cost, reliability, and data rights.
Phased rollout & pilot program plan
Controlled sandbox testing before production deployment with clear rollback procedures.
Talent & Culture
0/5Cross-functional AI task force formed
Dedicated squad spanning data, engineering, product, legal, and domain experts.
Upskilling & training program
Internal workshops, certifications, and sandbox environments for non-technical staff.
Clear roles for ML engineers & data scientists
Defined career paths, ownership boundaries, and collaboration handoffs.
Change management & internal communication
Transparent roadmap sharing, FAQ docs, and feedback channels to reduce AI anxiety.
Incentive structure for AI innovation
Recognition programs, hackathons, and OKR alignment to drive adoption.
Security & Compliance
0/5Data privacy & regulatory compliance audit
GDPR, CCPA, HIPAA, or industry-specific checks with consent management workflows.
Model security & vulnerability assessment
Protection against prompt injection, data poisoning, model extraction, and adversarial attacks.
Bias detection & fairness testing protocols
Regular auditing of training data and outputs across demographic and operational segments.
IP & licensing review for AI tools/models
Verification of training data rights, model commercialization terms, and output ownership.
Incident response plan for AI failures
Automated kill switches, human override protocols, and post-mortem documentation.
Implementation & Scaling
0/5MVP scope & validation criteria defined
Minimum viable product with clear success thresholds before full investment.
CI/CD pipeline for model deployment
Automated testing, staging, blue-green deployments, and feature flagging.
Monitoring & drift detection setup
Real-time tracking of performance degradation, data drift, and latency spikes.
Feedback loop for continuous improvement
User ratings, error reporting, and automated retraining triggers integrated into workflow.
Scalability & cost optimization strategy
Right-sizing models, caching layers, quantization, and multi-region deployment plans.