The premium drinks industry has long understood the value of a good story. The vineyard has legendary soil. Winemakers have origin stories. Canon events will be held at the distillery. There is a secret ritual in the basement that rivals that of the Knights Templar. Some sommeliers have divine revelation (John 2:1-11, in case you don’t believe me).
Each story has evolved to be tied to the brand, and it’s difficult to separate the two.
The unpleasant reality of 2026 is that good stories don’t matter for LLMs (Large-Scale Language Models).
The way we discover brands has changed quite a bit. Search has leapfrogged from lists of links to AIs that attempt to tell their own stories, based on opaque curation, summarization, and recommendation systems. This move poses a challenge to the beverage industry and the brand discovery strategies they have relied on for the past few decades.
Because being a good brand to an AI bot means something entirely different than what it means to a temporary human overlord.
prompt insight
Jordan Brannon from Coalition Technologies explains how to connect your business and the brands and services you sell to an AI search platform.
We humans still start everything with a prompt.
But prompts are trickier than keywords. They will be longer (20+ words on average), more personal, and more situational. Given the nature of common AI usage behaviors, AI also tends to seek more creative input. Your old Google search might have been ‘premium mezcal suppliers UK’.
Google Gemini’s prompt might ask LLM to “create a trusted premium mezcal product with a meaningful sustainability story that works in London’s upscale bars.”
This single prompt can also incorporate some additional hidden searches through a technique called query fan out (QFO). (Not to be confused with the more colorful GTFO).
The AI may search for premium mezcal brands, sustainability claims, suitability for hotel bars, UK distribution, trade reliability, reviews, press coverage and competitor comparisons before providing a single answer.
Traditional keyword research is built on measuring demand. We were able to see terms, search volumes, related queries, and measures of competition.
But today’s AI doesn’t have or share that data. All ChatGPT reports are internal and only shared with selected publishing partners. Gemini visibility is similarly opaque, and even experiences on Google.com like AI Overview tend to be less transparent in Google Search Console and Analytics.
Next, step one is to restructure your keyword research efforts for the prompt and initiate possible tracking mechanisms.
prompt library
Build an instant library based on real-life purchasing situations faced by your customers. Aim for 25-50 prompts that reflect how different audiences (B2B or D2C) seek recommendations. Don’t forget the trend towards “create your own” type prompts.
Group these by category, geography, use case, social proof, comparison, and audience.
It’s impossible to keep track of every possible phrase, so consider the phrases that are most economically important to your brand. Think about how existing keywords (for which there is sufficient data) can evolve into new LLM prompts.
From there, assess whether the prompt is branded, category-driven, or a decision prompt. Many premium brands are like Narcissus, placing too much emphasis on brand awareness. All this will tell you is whether people who already know you can find you. Usually that’s not a problem.
Brand Prompts We recommend tracking the three buckets of brand prompts separately. Category prompts; and prompts for decisions.
Growth happens with category and decision visibility.
You also need to shift your focus from ranking your own URLs to understanding what citations appear in the AI’s answers. Coalition Tech has done a lot of testing with AI rank tracking tools, most of which allow you to understand mention rate, citation rate, top recommendation rate, and sentiment.
multi-platform
Brands and businesses should monitor numerous AI search engines to see how relevant and recognizable they are to their company. Picture istock
Finally, remember to cast a wide net when it comes to fast tracking.
ChatGPT has the market share and recognition, but Gemini is steadily growing. Claude has skyrocketed in recent months. Google’s Unified AI Experience, AI Overview, and AI Mode are also worth tracking.
Using (or working with an agency that uses) multiple rank tracking tools may help here. Many people tend to prioritize one or two major LLMs over reliable measurement across the market.
When evaluating the “ranking”, do not forget to monitor the sentiment.
LLM companies are responding to previous criticisms of being too optimistic by training and enhancing their models to be more critically slanted.
A recent Coalition study published in Forbes showed that Grok was the most negative of the major LLMs, followed by ChatGPT. ChatGPT tended to include some negative brand or product content in three out of four conversations.
Additionally, as interest in e-commerce through LLMs grows, so too does skepticism about chatbot marketing claims. In a recent test we conducted on a brand, ChatGPT described the product’s claims as “gimmicky.” This is largely based on a five-year-old criticism found in an archived Reddit thread.
Track your prompts to find out where you rank. But tracking sentiment can help you understand where your brand story isn’t compelling enough for an AI or AI agent.
Think beyond the website
This is an important tactical step.
Many of these quick responses come from websites other than branded websites. A recent Search Engine Land publication found that Reddit, YouTube, LinkedIn, Wikipedia, Forbes, Yelp, and G2 contribute significantly to AI recommendations based on Peec AI analysis.
Beverage marketers now need to think more like distributors. It’s not enough just to have your story published. It must now be propagated by a machine’s authoritative source.
Premium drinks brands should look to trade publications, distributor pages, venue partners, buyer interviews, commentary, educational content, YouTube tastings, LinkedIn founder articles, trusted reviews, retailer listings, restaurant menus, and even community discussions as part of their marketing methods.
This is not link building in the old sense. It’s citation construction.
Regular links provide a path to your site and “link juice.” Citations are reused by LLM to provide evidence that your brand is included in the answer.
The nature of today’s AI response engines is not to drive traffic to your site in the first place. It’s about occupying and interacting with the consumer, with an off-page click almost the final step.
Given that their websites are only relevant near the bottom, brands should consider how they can leverage relationships and marketing connections to tell the entire story from top to bottom of the funnel.
Will sameness prevail?
Many marketers love the opportunity to tell different stories across different channels and highlight different aspects of the brands they represent.
However, AI values sameness highly. It’s right at their foundation.
For AI systems to understand entities, they need consistency and clear, repeatable connections that help make brand messages the “next right word.”
An entity is a concept such as a brand, person, or product. The human mind works magic, making connections that help complete our understanding of a single entity. Computer systems have been trying to recreate it for a long time.
Ensuring that the attributes of a given entity are consistently represented on the web can help the machine navigate some challenges. Make sure that specific manufacturing methods are named the same across retailers.
Don’t get too creative with adjectives that might be more important in the prompt results you’re looking for. For example, switching a product’s description from luxury to craft or sustainable to disruptive can backfire, and the LLM may see the product as none of those things.
But so is matter.
Remember that while you’re looking for a consistent way to represent entities, the AI wants your descriptions to have a little more substance.
Describing something as premium or sustainable is not appropriate if the LLM wants to reinterpret the content on its own (especially if the LLM wants to question your marketing talk). Define and reinforce what is premium or sustainable in a material way, rather than just claiming it. Speak exclusivity in measurable terms. Document your brand’s sustainability efforts and practices.
In doing so, you’ll find that ChatGPT and other tools begin to creatively describe different entities, relying heavily on the details you provide.
The aforementioned brand demonstration, which highlighted a dated reddit post, showed that by providing detailed descriptions of specific processes in a company’s production process (including some independently verified test results), ChatGPT could easily replace the “gimmick” language it relied on to question its product promotions.
What would a robot drink if it dreamed of sheep?
AI agents are currently shopping online to determine which sites perform best for AI searches.
For now, most drink discoveries involve people. Even with AI, someone is still doing the shopping. But agents have already moved from research experience to comparison and trading.
Harvard Business Review highlighted this shift, noting that an increasing proportion of shoppers are no longer humans, but are increasingly being researched, compared, and even purchased by AI on their behalf. The article specifically points out that ChatGPT is moving deeper into product discovery and seller transactions, Google is launching its Universal Commerce Protocol, and Amazon is releasing tools to let agents shop on other retailers’ sites.
Early research on agent shopping should give marketers pause. A secondary summary of HBR articles reported eight common promotion mechanisms and found that only one consistently behaved in the way marketers expect from human buyers. Tactics such as scarcity cues, countdown timers, strike-through pricing, and coupons failed to interest agents and, in some cases, backfired.
Of course, robot shoppers won’t stop marketing. This means we need to ensure our brand and marketing is both easy to read for agents and desirable for people.
AI agents won’t be seduced by pictures of bottles or vague words of scarcity. They’re more likely to respond to verified attributes, trusted sources, clear pricing, and (possibly) human reviews.
Beverage brands are not in a position to succeed here, but they need to take it seriously. Years of product testing and distribution suddenly become data available for AI agents to consume and consider as part of their designated activities.
One last thing to note here. Agents don’t browse the web by looking at UX like humans do. They are looking at the code and considering possible presentations. You also need to make sure you understand what technical obstacles are being introduced to your users’ navigation experience.
end?
Premium drinks brands that address these aspects of marketing in the age of AI will fight another day and tell a different story. We don’t fully understand how this story will end, but we do know that AI will play an increasingly important role.
* Jordan Brannon is president and COO of Coalition Technologies. He is a search marketing leader who has pioneered AI search strategies, with a focus on helping brands transition from traditional SEO to ensure strong visibility and positive sentiment within AI-driven discovery environments.
