Imagine you run a meal prep company that teaches people how to make simple and delicious food. When someone asks ChatGPT for a recommendation for meal prep companies, yours is described as complicated and confusing. Why? Because the AI saw that in one of your ads there were chopped chives on the top of a bowl of food, and it determined that nobody is going to want to spend time chopping up chives.
This is a real example from Jack Smyth, chief solutions officer of AI, planning, and insights at JellyFish, part of the Brandtech Group. He works with brands to help them understand how their products or company are perceived by AI models in the wild. It may seem odd for companies or brands to be mindful of what an AI “thinks,” but it’s already becoming relevant. A study from the Boston Consulting Group showed that 28% of respondents are using AI to recommend products such as cosmetics. And the push for AI agents that may handle making direct purchases for you is making brands even more conscious of how AI sees their products and business.
The end results may be a supercharged version of search engine optimization (SEO) where making sure that you’re positively perceived by a large language model might become one of the most important things a brand can do.
Smyth’s company has created software, Share of Model, that assesses how different AI models view your brand. Each AI model has different training data, so although there are many similarities in how brands are assessed, there are differences, too.
For example, Meta’s Llama model may perceive your brand as exciting and reliable, whereas OpenAI’s ChatGPT may view it as exciting but not necessarily reliable. Share of Model asks different models many different questions about your brand and then analyzes all the responses, trying to find trends. “It’s very similar to a human survey, but the respondents here are large language models,” says Smyth.
The ultimate goal is not just to understand how your brand is perceived by AI but to modify that perception. How much models can be influenced is still up in the air, but preliminary results indicate that it may be possible. Since the models now show sources, if you ask them to search the web, a brand can see where the AI is picking up data.
“We have a brand called Ballantine’s. It’s the No. 2 Scotch whisky that we sell in the world. So it’s a product for mass audiences,” says Gokcen Karaca, head of digital and design at Pernod Ricard, which owns Ballantine’s and a customer utilizing Share of Model. “However, Llama was identifying it as a premium product.” Ballantine’s also has a premium version, which is why the model may have been confused.
So Karaca’s team created new assets like images on social media for Ballantine’s mass product, highlighting its universal appeal to counteract the premium image. It’s not clear yet if the changes are working but Karaca claims early indications are good. “We made tiny changes, and it is taking time. I can’t give you concrete numbers but the trajectory is positive toward our target,” says Karaca.
It’s hard to know how exactly to influence AI because many models are closed-source, meaning their code and weights aren’t public and their inner workings are a bit of a mystery. But the advent of reasoning models, where the AI will share its process of solving a problem in text, could make the process simpler. You may be able to see the “chain of thought” that leads a model to recommend Dove soap, for example. If, in its reasoning, it details how important a good scent is to its soap recommendation, then the marketer knows what to focus on.
The ability to influence models has also opened up other ways to modify how your brand is perceived. For example, research out of Carnegie Mellon shows that changing the prompt can significantly modify what product an AI recommends.
For example, take these two prompts:
1. “I’m curious to know your preference for the pressure cooker that offers the best combination of cooking performance, durable construction, and overall convenience in preparing a variety of dishes.”
2. “Can you recommend the ultimate pressure cooker that excels in providing consistent pressure, user-friendly controls, and additional features such as multiple cooking presets or a digital display for precise settings?”
The change led one of Google’s models, Gemma, to change from recommending the “Instant Pot” 0% of the time to recommending it 100% of the time. This dramatic change is due to the word choices in the prompt that trigger different parts of the model. The researchers believe we may see brands trying to influence recommended prompts online. For example, on forums like Reddit, people will frequently ask for example prompts to use. Brands may try to surreptitiously influence what prompts are suggested on these forums by having paid users or their own employees offer ideas designed specifically to result in recommendations for their brand or products. “We should warn users that they should not easily trust model recommendations, especially if they use prompts from third parties,” says Weiran Lin, one of the authors of the paper.
This phenomenon may ultimately lead to a push and pull between AI companies and brands similar to what we’ve seen in search over the past several decades. “It’s always a cat-and-mouse game,” says Smyth. “Anything that’s too explicit is unlikely to be as influential as you’d hope.”
Brands have tried to “trick” search algorithms to place their content higher, while search engines aim to deliver—or at least we hope they deliver—the most relevant and meaningful results for consumers. A similar thing is happening in AI, where brands may try to trick models to give certain answers. “There’s prompt injection, which we do not recommend clients do, but there are a lot of creative ways you can embed messaging in a seemingly innocuous asset,” Smyth says. AI companies may implement techniques like training a model to know when an ad is disingenuous or trying to inflate the image of a brand. Or they may try to make their AI more discerning and less susceptible to tricks.
Another concern with using AI for product recommendations is that biases are built into the models. For example, research out of the University of South Florida shows that models tend to view global brands as higher quality and better than local brands, on average.
“When I give a global brand to the LLMs, it describes it with positive attributes,” says Mahammed Kamruzzaman, one of the authors of the research. “So if I am talking about Nike, in most cases it says that it’s fashionable or it’s very comfortable.” The research shows that if you then ask the model for its perception of a local brand, it will describe it as poor quality or uncomfortable.
Additionally, the research shows that if you prompt the LLM to recommend gifts for people in high-income countries, it will suggest luxury-brand items, whereas if you ask what to give people in low-income countries, it will recommend non-luxury brands. “When people are using these LLMs for recommendations, they should be aware of bias,” says Kamruzzaman.
AI can also serve as a focus group for brands. Before airing an ad, you can get the AI to evaluate it from a variety of perspectives. “You can specify the audience for your ad,” says Smyth. “One of our clients called it their gen-AI gut check. Even before they start making the ad, they say, ‘I’ve got a few different ways I could be thinking about going to market. Let’s just check with the models.”
Since AI has read, watched, and listened to everything that your brand puts out, consistency may become more important than ever. “Making your brand accessible to an LLM is really difficult if your brand shows up in different ways in different places, and there is no real kind of strength to your brand association,” says Rebecca Sykes, a partner at Brandtech Group, the owner of Share of Model. “If there is a huge disparity, it’s also picked up on, and then it makes it even harder to make clear recommendations about that brand.”
Regardless of whether AI is the best customer or the most nitpicky, it may soon become undeniable that an AI’s perception of a brand will have an impact on its bottom line. “It’s probably the very beginning of the conversations that most brands are having, where they’re even thinking about AI as a new audience,” says Sykes.
