3 Tips To Curate A Clear Internal AI Policy

AI is good for many commercial purposes. But just like anything else you do in the corporate world, it’s all about how you do it, and not necessarily if you do it alone. Most companies are trying to yield the benefits of AI to some degree, but as every commercial entity is in that boat, a lack of clear thinking and correct implementation can denigrate your service.

We’re all aware of companies stuffing their products with AI features we could have done without thus far, or perhaps promising us conveniences that have yet to materialize. Look at Samsung’s “generative background” option in their smartphones; it’s been referred to as a gimmick thanks to most of the delivered images looking vague and clearly generated.

But how you use AI within your team matters just as much as how you structure it externally. Staff want to save time in their jobs, but it’s easy for this to harm the quality or safety of internal workflows if this isn’t carefully handled.

In this post, we hope to help you with three tips to curate a clear internal AI policy:

Be Very Direct About Privacy

Sometimes OpenAI doesn’t need to know your most intimate financial information because your in-house accountant doesn’t want to calculate it all themselves. You have to spell out exactly what counts as sensitive information, because people interpret that differently and what seems obvious to you might not be obvious to someone rushing through a task on a Tuesday afternoon.

If someone pastes something sensitive into an AI tool because they didn't realize it was off limits, that's on the policy being too loose or not communicated well enough. For that reason, direct language about what stays private can save you from potentially having your information leaked. We’re not suggesting the AI companies are spying, but you don’t know what that information yielding could mean. Best not to risk it.

Set Up Your Own User Agents

Running your own AI agents internally gives you control over how the technology gets used and what data it touches. With the agent services, you’re not relying on third party tools that might store information somewhere you can't see or change their terms overnight without warning you first.

If you spend a little time building agents that fit your workflows means you can set the rules, monitor how they're being used, and adjust things as you go, you can better build in safeguards that make sense for your team, like checking for inter rater reliability to make sure outputs are consistent and trustworthy across different uses. It takes more effort upfront than just signing up for a subscription service, but you end up with something tailored to what your business needs and not a convenient but poorer option.

Be Clear About Technical Jargon

Your team can't follow a policy if they don't understand what half the words mean. AI comes with a lot of terminology that sounds technical for the sake of sounding technical, and if you're writing internal guidelines full of phrases nobody outside of a machine learning course would recognize, people will just ignore them.

Break things down, then, and explain what you're asking people to do in terms that make sense for their day-to-day work. If you need to use a specific term, define it right there so nobody has to go hunting for what it means or guess based on context. 

With this advice, you’ll be certain to curate a clear and helpful internal AI policy.