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Building a corporate AI policy that actually works

Published

Building a corporate AI policy that actually works

Published

Corporate AI policies can be challenging to develop. While a normal policy sets rules from the ground up, AI use is now so widespread that a policy has to adapt and build on the current situation. Still, while the train has already left the station, leaders can at least decide the route and the destination. 

If AI has been governed by nothing more reliable than individual judgment in your business so far, it’s time to set out a policy. The policies that actually work pair clear rules with a sanctioned tool that makes the approved path easier than the unapproved one. 

This guide and accompanying policy template are designed to be used together: read the guide to understand the framework, then adapt the template for your organisation.

What’s in the guide?

A practical governance framework covering the five things any enforceable AI policy needs to get right:

  • Approved tools: why vague guidance leaves the product decision to the individual.
  • Prohibited inputs: what needs to be named explicitly (and why a general warning isn’t enough).
  • Data classification: a three-tier framework for the grey area between obvious and restricted.
  • Output ownership: where the gap between consumer tool terms and enterprise agreements creates real exposure.
  • Incident response: what needs to be documented before something goes wrong.

The guide also covers how to get the policy adopted across three different internal audiences, as well as what to measure at 90 days to know whether it’s actually working.

A document titled "What a Functional AI Policy Needs to Cover" with a purple header. It lists five key areas: approved tools, prohibited inputs, data classification, output ownership, and incident response. The background features a photo of three business people in a meeting. The tone is professional and informative.

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