AI & DATA

Production AI readiness: data, evaluation, governance and operating controls

Move an AI use case from prototype enthusiasm into a measurable production system with explicit data, evaluation and human-control boundaries.

11 min guide

Define the decision the model influences

Start with the business action, risk and acceptable failure modes rather than selecting a model first.

Identify source ownership, freshness, quality checks, protected fields and how training or retrieval data changes over time.

Use representative cases, edge conditions and human review criteria so model quality is tied to operating acceptance.

Log model versions, inputs, outputs, latency, exceptions and handoffs with enough context for investigation and rollback.

Document approval, access, monitoring, incident response and change responsibilities before scale increases.

Data platforms, AI products, model operations, analytics and governed automation.