We want our team to leverage AI for drafting client deliverables, but we are terrified of quality degradation. How do we write a simple, operational AI policy that establishes a strict verification workflow for all AI-generated outputs before they reach our clients?

Question

Tyler Smith · Accepted Answer

To protect your company standards without slowing down your team, you need a strict validation policy. Do not ban AI. Instead, establish a policy focused on human accountability.

The core of this policy is simple: every piece of AI-generated output must have a designated human owner on your Accountability Chart who is entirely responsible for its accuracy. We call this the human in the loop model.

In your policy, define three distinct levels of content: standard operational data, client facing communications, and strategic advice. Standard operational data might require a quick spot check, while client facing deliverables must go through a formal verification checklist before release.

For example, your team can use AI to draft initial client reports, but the account manager must physically verify the key data points against your source systems. If a hallucination reaches a client, the accountability falls entirely on the human manager, not the tool.

Frame this policy around GWC. If a team member uses AI, they must demonstrate they get it, want it, and have the capacity to validate the work. Write down these verification steps directly in your documented processes under the EOS Process Component. This keeps your quality high while letting your team eliminate low value drafting tasks.