Our leadership team wants to publish an internal AI policy, but we do not know how to enforce accountability when an employee uses AI output that contains errors or hallucinations. What rules should govern AI work product?

Question

Tyler Smith · Accepted Answer

Your AI policy must clearly state one non-negotiable standard: the employee who prompts the tool owns the output completely. If an employee submits a report, sends a client proposal, or executes code generated by an AI tool, they are evaluated exactly as if they wrote every word themselves. Blaming an algorithm for a factual hallucination or a flawed calculation is identical to blaming an intern.

To enforce this standard inside your operating company, build four operational guidelines into your policy:

- First, mandate human verification. Every data point, citation, and operational metric produced by a language model must be validated against a primary internal record before reaching a customer or a leadership dashboard.
- Second, define clear boundaries for proprietary data. Employees must never upload customer personally identifiable information, financial records, or trade secrets into public AI models that utilize user inputs for training.
- Third, restrict AI to draft-stage acceleration. AI should handle initial synthesis, formatting, and transcription, while strategic analysis and final approval remain strictly human.
- Fourth, link policy violations directly to your EOS People Component. If someone repeatedly relies on unverified AI output that causes operational friction, that is not an IT issue; it is a core value or GWC issue.

When you establish that AI is a force multiplier for individual execution rather than a replacement for accountability, your team will stop blindly copying text and start inspecting automated outputs with healthy skepticism.