The State of Generative AI
Martech’s buzz around Generative AI shows no signs of stopping any time soon. With the rise of tools like ChatGPT, Generative AI’s ability to create text, images, or other data from simple prompts has unsurprisingly excited customers and marketers alike. But while Generative AI is a powerful asset in any marketer’s toolkit, it’s also not a one-size-fits-all solution for every workflow or content creation problem.
This blog will provide three practical tips to help you understand the real impact of Generative AI on your business and how to leverage its benefits effectively, moving beyond the hype and misconceptions.
Tip #1: Choose Use Cases to Test Generative AI
To understand the strengths and limits of Generative AI, you need to test the tool for yourself. To do so, consider specific use cases:
Short form text is the best place to start. Try out quick copy examples that would make a big impact due to their frequent use, such as CTAs or email subject lines. Da Vinci’s Subject Line Generator, as depicted below, is the perfect example of a quick AI win.
Long form text can be the next step once you get a sense of how best to prompt the AI. While Generative AI is not yet developed enough to create a long, publishable piece of content, the solution is a great option for getting past writer’s block—the AI can generate a rough draft, leaving you to simply adjust and edit as needed.
Image generation from GPT models still has a long way to go. Beyond the ethical implications that marketers need to look out for, such as a lack of diversity or copyright infringement, open source options cannot create marketing collateral. Whether it’s extra fingers and toes appearing or off-brand fonts and colors, marketers are still quite limited when it comes to open source image generation.
While text generation appears to be more straightforward, marketers have to think outside the box to make the most of Generative AI for images. For example, instead of using Generative AI for an entirely new image, use it to enhance existing images. This could look like filling in missing backgrounds or cutting items you don’t want in the final product. In the case of images that need a little more workshopping, consider automated tools that can use your existing branded content and layer it with real-time data personalization for the utmost relevance.
Tip #2: Review How Generative AI Fits Within Brand Guidelines
Whether it’s tone of voice or stylistic rules, every organization needs guidelines to ensure a cohesive brand presence. It’s for this reason that marketers can’t rely on open source AI alone; with data inputs from several sources, the output will simply never be on-brand enough.
To make the most of Generative AI, marketers need their GPT models to be solely trained on their brand’s guidelines and fed their own proprietary data, creatives, and assets. By avoiding the commingling of data with other vendors and sources, you can ensure that the output is branded correctly and relevant to your audience.
As the Large Language Models (LLMs) used in these processes often require massive data sets for training, investigate if you have the volume of data required for your AI to generate on-brand content. Alternatively, see if the Generative AI solution can ingest your already written brand guidelines and be trained through that method.
Tip #3: Create a Guide to Use Generative AI Responsibly
Much concern exists around how marketers are using AI, especially as a growing number of AI mishaps appear in mainstream media. Marketers should get ahead of any potential problems by creating documentation that outlines how they plan to use Generative AI ethically and responsibly. This source of truth should cover these key topics:
Data Decisions
Feeding customer and personal data into Generative AI is not a task to treat lightly. Decide under which circumstances you can input this data and when it must be avoided—a key factor to keep in mind when making this decision is knowing when data is commingled and when it is not.
Legal Ramifications
Legal guidelines are still evolving, especially when it comes to copyright. As many AI models are trained on open source data, some copyrighted materials trickle in and blur the lines when it comes to the output’s ownership. To get ahead of any future AI laws and policies, be sure to tag and separate your AI-generated materials from the rest of your content.
AI’s Trustworthiness
Generative AI models are trained to recognize patterns, meaning that if the dataset is not wholly accurate or based on facts, the output will be similarly skewed. This can make the output of Generative AI difficult to control, especially when it’s open source. That means defined rules around human oversight are critical to the Generative AI process, otherwise you risk spreading misinformation.
AI Code of Ethics
To override biased or inaccurate outputs, lay out the rules and regulations for human oversight—and not just for your team. Be sure to define how you will keep outside vendors accountable in the goal of avoiding any potential biases or stereotypes.
Use Generative AI to its Full Potential
With this understanding of Generative AI in place, you’ll be able to leverage the tool’s strengths ethically all while avoiding its weakness. Check out the related resources below to understand all the other ways you can use AI effectively in your marketing messaging.