An AI proof of concept can look convincing while the production path remains unclear. Teams may still need to resolve data access, identity, inference capacity, system integration, security review, monitoring, and ownership after launch.
Begin with the workflow and decision the system is meant to support. Then trace the path from source data to model output and from output to user action. At each step, identify the system owner, access rule, quality check, failure response, and human review point.
Infrastructure planning should match expected demand and service needs. Teams need a view of compute options, data movement, latency, resilience, cost controls, and the operational team responsible for observing the service. These questions are related; resolving them in separate workstreams can hide important constraints.
Treat security, privacy, and governance as design inputs. Define what information may be used, who can access it, how changes are evaluated, and what happens when the system behaves unexpectedly. The right controls depend on the context and applicable requirements.
A readiness review is not a promise that a system is safe or production-ready. It is a structured way to expose unknowns, assign owners, and decide whether the next step should be a test, a redesign, or a wider deployment.
General educational content; not a client case study or professional advice. Confirm requirements with qualified specialists for your organization.