AI is proliferating in every major marketing cloud in nearly every marketing point solution, but most organizations are still experimenting with it. There are many use cases for AI that span every role in marketing from advertising to social, web, content, analytics and more. How you apply AI to marketing should reflect how you do your marketing. If you do a lot of outbound email, there are AI engines to enhance list selection and segmentation, subject lines and dynamic content. If you do a lot of social, there are AI engines to analyze sentiment, identify influencers, and post content. If you do a lot of web marketing, there are chatbots and recommendation engines to assist with site navigation. And many more.
The difficulty is most marketers do all of those things at scale and each requires a different kind of AI engine. This creates the potential for technology proliferation, so marketers should be careful to avoid silos in the AI layer of their infrastructure. The goal should be for every interaction to enhance every other interaction regardless of whether a bot does it. So the AI engines need to be connected.
It's important to start down the learning curve with AI as there are new technologies, practices, governance and skills needed. The primary way most marketers will interact with AI is by feeding data or content into the engines. Marketers should think of AI as a new way to cook, and their expanding datasets as ingredients they can use to create exciting new experiences for themselves and their audiences. As with everything in the kitchen it can be a little daunting or even dangerous if you don't know what you're doing. You don't need to know exactly how the microwave works, but you do need to know that you shouldn't put metal in it and that some things like eggs tend to explode. Once you get the basics down its about how creatively you can use your data to not only do the same old marketing better, faster, cheaper, but also to create new value and experiences for your customers.