Public-Sector AI Readiness: What to Assess Before Selecting a Model

Model selection is often treated as the beginning of an AI program. For mission-driven organizations, it should come after the organization understands the use case, data, risk, operating environment, and people who will own the outcome. It's a natural instinct: a new capability appears, and the first question is which vendor to buy from—when the more useful first question is whether the organization is actually ready to operate it responsibly.

Start with mission value

Define the decision, workflow, or service the AI capability is expected to improve. Establish what success looks like and what human judgment must remain in the process. A technically impressive model cannot rescue an unclear use case. If no one can articulate what would count as failure, the pilot will be judged on enthusiasm rather than outcomes—which rarely survives budget scrutiny the following year.

Assess data readiness

Identify the information the use case requires, where it lives, who owns it, its sensitivity, quality, retention constraints, and whether users are authorized to expose it to the proposed workflow.

Assess governance and accountability

Clarify ownership, acceptable use, review requirements, procurement considerations, incident handling, documentation, and who can stop or modify the system when risk changes.

Assess security and integration risk

Review identity, access, data protection, model/tool boundaries, logging, integrations, agent permissions, third-party dependencies, and likely abuse paths.

Assess workforce readiness

Users need to understand what the system can and cannot do, how to validate outputs, how to protect data, and how to report problems. Adoption without workforce preparation can create shadow processes and inconsistent controls.

Readiness turns AI selection from a technology purchase into an accountable operating decision. Readiness work takes days to weeks, not months—but skipping it tends to cost months later, in the form of a stalled or reversed rollout.