AI Readiness Assessment: A Practical Framework

AI readiness measures whether an organization has the data, tooling, people, and governance to build, deploy, and operate AI applications reliably and ethically. Before embarking on an ambitious AI roadmap, a thorough assessment is crucial to identify strengths and weaknesses.

Assessment Domains

Data & Infrastructure

This is the technical foundation. Key dimensions include data availability and quality, the maturity of your data labeling processes, data lineage tracking, and the robustness of your infrastructure (compute, storage, and MLOps).

People & Skills

Successful AI requires a blend of cross-functional capabilities. This includes data engineers, ML engineers, AI-focused product managers, and crucially, domain-specific subject matter experts (SMEs) who understand the business context.

Governance & Compliance

This domain covers the rules of the road. It involves establishing clear policies for data privacy, rigorous model testing protocols, and procedures for model rollback and change control to ensure AI is used responsibly.

Process & Maturity

This assesses your operational readiness. It looks at the maturity of your processes, such as CI/CD for models, the ability to reproduce experiments, real-time monitoring of models in production, and a clear incident response plan for when things go wrong.

Scoring & Thresholds

A simple way to score your readiness is to rate each domain on a scale of 1–5. A score of 4–5 suggests you are production-ready. A score of 2–3 indicates you are pilot-ready but will need investment to scale. A score of 1 means foundational work is required before any significant AI project begins.

Creating a Gap Remediation Plan

Based on your scores, draft a prioritized list of remediation items. These could include data cleanup initiatives, building out an MLOps pipeline, or hiring for key skills. Assign owners and budgets to each item and focus on quick wins to build momentum and demonstrate progress.