The Enterprise AI Readiness Checklist: 12 Questions Before You Deploy

Deploying AI agents in production is an infrastructure decision, not just a product decision. Before any agent touches a production environment, your team should be able to answer twelve questions across four domains: data governance, security, reliability, and organizational readiness. Most enterprises arrive at this checklist after their first incident. We built it so you can arrive at it before.

On data governance: Where does training data come from, and has it been reviewed for bias and representativeness? What data does the agent access at runtime, and does it have the minimum necessary permissions? How long are conversation logs retained, and who can access them? Is PII handled in compliance with applicable regulations, and is that handling documented? On security: Is the deployment VPC-native or does data traverse a third-party network? What are the authentication and authorization controls on the agent API? How is prompt injection mitigated, and has it been tested adversarially?

On reliability: What is the failover behavior when the underlying model is unavailable? Are SLOs defined and monitored? Is there a kill switch that can disable the agent within minutes if unexpected behavior is observed? On organizational readiness: Does your support team know how to handle escalations from AI-assisted workflows? Is there a documented process for users to flag incorrect agent outputs? Has legal reviewed the use case and any contractual implications of AI-generated outputs?

The teams that navigate AI deployments without incident are not the ones with the most sophisticated models. They are the ones who treated deployment readiness with the same rigor they apply to any other production infrastructure change. This checklist is a starting point. The real work is the institutional knowledge you build as you answer each question honestly.

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