🚀Interactive Checklist
AI Project Readiness Checklist
Validate readiness before launching AI and ML initiatives.
20 items0%
Critical items (marked ★) carry 4–5× weight. Weighted score reflects project readiness, not just preparatory task completion.
Problem & Use Case Definition
Validate that the problem is well-defined and AI is the right approach.
0/5
Define a clear, measurable business problem that AI/ML will solve — not a solution in search of a problem.★ Critical
Quantify the expected business value (revenue, cost savings, risk reduction) with realistic assumptions.★ Critical
Validate that AI/ML is the appropriate approach — not a simpler rules-based or statistical method.
Identify success criteria and KPIs that will be used to evaluate model performance in production.
Secure executive sponsorship with committed budget and decision-making authority.
Data Readiness
Confirm that the data needed to train and operate the model is available and fit for purpose.
0/5
Verify that sufficient labelled training data exists or can be acquired within budget and timeline.★ Critical
Assess data quality: completeness, accuracy, consistency, and representativeness for the target population.
Confirm data access permissions, privacy compliance, and consent requirements are met.
Establish data pipelines for ongoing model training, validation, and inference.
Identify and mitigate potential data biases that could affect model fairness.
Team & Skills
Ensure the team has the expertise to deliver and maintain the AI solution.
0/5
Confirm availability of core roles: data scientist, ML engineer, data engineer, and domain expert.★ Critical
Assess the team's experience with the specific ML techniques required (NLP, computer vision, forecasting, etc.).
Plan for ongoing model maintenance, monitoring, and retraining — not just initial development.
Establish clear RACI for model development, validation, deployment, and production support.
Identify training needs and allocate budget for upskilling if capability gaps exist.
Infrastructure & Tooling
Validate that the technical environment supports the full ML lifecycle.
0/5
Confirm compute infrastructure (GPU/TPU, cloud ML services) is provisioned and budget-approved.★ Critical
Select and standardise MLOps tooling: experiment tracking, model registry, CI/CD for ML, and monitoring.
Define the deployment strategy: batch vs. real-time inference, API design, and scaling requirements.
Implement model monitoring for data drift, performance degradation, and concept drift in production.
Establish rollback procedures and canary deployment processes for model updates.