Our employees are eager to use AI tools, but we need a practical, operational AI policy that focuses on maximizing productivity without overwhelming them with rules. How do we design this policy?

Question

Tyler Smith · Accepted Answer

To build a practical, productivity-focused operational AI policy for your team, you must avoid long, complex legal documents that nobody reads. Instead, focus on a few simple guidelines that encourage safe automation of cumbersome tasks while keeping your data secure.

Your policy should establish that while employees are encouraged to use these tools to eliminate low-value tasks, they maintain complete accountability for the final output. If an AI agent drafts an email with an error, the employee who sent it is fully responsible. This maintains the core EOS® principle of accountability across every seat on your Accountability Chart.

Keep the policy simple by focusing on three main rules:
- Never upload proprietary customer data or sensitive financial records into public models.
- Always review and edit any output before sending it to a client or using it in operations.
- Document any automated workflow as a system-dependent SOP so the process can be repeated.

By framing the policy around productivity and personal accountability, you empower your team to innovate without creating unnecessary administrative bottlenecks or exposing your business to security risks.