This is especially important as AI tools also provide brand insights vital for cross-organizational teams like customer experience and product marketing. By introducing AI strategically, you can extend its efficiencies to these multi-functional teams safely while addressing roadblocks more effectively.
Clear use cases
Your internal AI use policy should list all the licensed AI tools approved for use. Clearly define the purpose and scope of using them, citing specific use cases. For example, documenting examples of what tasks are self employed data low risk or high and which should be completely avoided.
Low-risk tasks that are not likely to harm your brand may look like the social media team using generative AI to draft more engaging posts or captions. Or, customer service teams using AI-assisted copy for more personalized responses.
In a similar vein, the AI use policy should specify high-risk examples where the use of generative AI should be restricted, such as giving legal or marketing advice, client communications, product presentations or the production of marketing assets containing confidential information.
“You want to think twice about rolling it out to people whose job is to deal with information that you could never share externally, like your client team or engineering team. But you shouldn’t just do all or nothing. That’s a waste because marketing teams, even legal teams and success teams, a lot of back office functions basically—their productivity can be accelerated by using AI tools like ChatGPT,” Rispin explains.
Considering the growing capacity of generative AI and the need to produce complex content quickly, your company’s AI use policy should clearly address the threat to intellectual property rights. This is critical because the use of generative AI to develop external-facing material, such as reports and inventions, may mean the assets cannot be copyrighted or patented.