While AI is a hot topic as of late, it’s been around for a long time.
At our agency, Mekanism, we are seeing beyond the hype and exploring how AI will impact all aspects of marketing, including research, strategy, creativity, and performance.
And one area poised for disruption? Advertising.
Here, let’s explore how private AI tools can transform your advertising strategy.
An AI Tool is Only as Powerful As the Data You Provide
Through our adoption of AI tools there’s one common trait we’ve noticed in our evaluations: There’s nothing inherently special about many of the AI tools out there. What’s special is the inputs we put into them, and the data we provide each tool.
Like many people will tell you with AI: It’s garbage in, garbage out. You want to make sure that the dataset you are using with any AI tool is the best you can provide.
This means collecting whatever first-party data you can to make your outputs from AI as productive and personalized as possible.
For instance, to receive a powerful AI-generated marketing plan, you’ll want to incorporate client expectations, first-party data around the consumers you’re trying to reach, past examples of campaigns and their performance, etc.
Our thesis for the solution to this problem: The future of AI in advertising will be setting up bespoke internal AI tools that safeguard clients’ data, and providing personalized marketing.
The Five Elements You’ll Need to Incorporate Into Your AI Tools for Stronger Advertising
There are a few key elements that a corporate AI tool will need to be successful when it comes to advertising. Let’s jump into those, now.
1. A shared prompt library.
A shared prompt library is a collective resource across your organization that allows for anyone to share their best prompts for completing work. By sharing this information, you help onboard your team members to better leverage AI.
Consolidating and protecting your prompt libraries also address privacy concerns. Prompt libraries help centralize the knowledge around AI, and reduce any loss of productivity when people leave the organization.
2. A document library.
A document library inside of an internal AI tool is the personalized training that you bring to any LLM (large language models). This library is the “brain” of your organization’s AI and should include any relevant documentation that can train the AI to provide more personalized results.
The library can include a brand’s past campaigns, competitors’ campaigns, results of campaigns, data around your consumer, and results from past brainstorms.
3. Brand tone and voice guidelines.
As part of that library, there should be a Brand Tone and Voice Guideline document that clearly states what would and wouldn’t be in any communications from your brand. This document should be weighted heavier than others in training to help maintain your brand in any generated content.
4. Approval flows.
An internal AI tool should also include an approval flow that allows any generated content from the AI to be audited and checked for things like hallucinations and biases before being used outside of the tool.
As part of this approval process, other things can be checked by AI, such as any claims being made with citation or any regulatory issues that certain brands may encounter within the used language. This approval flow is key to keeping the work human. By applying the good taste that only a human can harness, we can avoid work that feels robotic.
5. Security.
Lastly, and most importantly, these tools should include a robust suite of security measures to make sure that all generations remain private before they receive approval to become public. This security should also keep the document library secure, and perhaps offline, to better protect any first-party data provided to the LLM.
What Personalized Results Look Like with Private AI Tools
With significant first-party data added into a private AI tool, a company could expect results that are both personalized as well as potentially predictive in performance. It’s a tall order to place on generative AI, but with enough past performance information, AI could produce responses that mimic the best practices of top performers from the past.
Asking a simple question like, “create 10 ads about going back to school” would yield results not only with more brand-appropriate responses using private AI, but also with predicted results alongside each response.
These tools could also plug directly into the API’s of e-commerce platforms, as well as social platforms, to track organic and paid content performance to optimize its generations in real time.
Private AI Tools That Continue To Learn
If our private AI tools are learning from qualitative data points such as click-throughs, likes and shares, why not qualitative, as well? That’s truly the power of LLM tools, the ability to manipulate and compute the written word just like numbers. These tools will be able to also take into account consumer sentiment via comments and reviews to create better generative outputs for brands.
One area Mekanism is currently exploring is collecting and measuring the rich conversation with TikTok comments to better understand what consumers are thinking. With the waning usefulness of social listening from platforms like Twitter, comments in videos are becoming increasingly more important.
A common workflow for our social strategy team whenever they are researching a brand or topic is to pull the comments of the top videos in that space and then run those conversations through an LLM like ChatGPT’s Code Interrupter to better understand topics of conversation. After this data is entered into ChatGPT, our strategies can then have a “conversation” with these consumers and ask them more questions based on this data to better craft their understanding of the brand or topic.
What’s Next
So many organizations are currently looking around and asking how they will use AI, and many are running into the same issues around copyright and security. Our hope is that we’ve provided a framework on how the advertising and marketing industry can move forward with adoption of these tools by investing in private AI.
If we want AI tools to meet our expectations of the future, we’ll need to provide more useful data. And, in order for everyone to feel safe doing so, developers of these tools will need to provide options for organizations to run these tools on-site, off the cloud, or with strict security options.
It’s a pretty exciting time out there for humans and AI.
