AI & DATA

Production AI needs an operating model

A model or agent becomes a business system when data, evaluation, monitoring and human review surround it.

9 min guide

Define the decision boundary

State what the system may recommend, decide, change or escalate.

Document sources, freshness, permissions, lineage and quality expectations.

Measure unsupported outputs, edge cases, latency, cost and handoff quality.

Models, prompts, tools, data schemas and policies should change under controlled versions.

Monitoring and review must connect technical quality to real business outcomes.

Connect models, agents, data, permissions, evaluation and observability across enterprise workflows.

Define versioning, auditability, permissions, review, documentation and policy controls for AI systems.

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

Product delivery, cloud platforms, managed IT, security and technical operations.

Product engineering, subscriptions, customer operations, cloud delivery and scale.

Patient or member service workflows, secure systems, operational support and data integration.

Secure digital products, customer operations, data workflows, automation and technology controls.