How AI reads your brand and why meaning matters most

Will your brand be visible in the age of AI? The short answer: you need a meaningful brand or you’ll be commoditized.

The longer answer starts with what a meaningful brand is outside of AI. A logo is not a brand — it’s a visual identifier that recalls promises kept, problems solved and values demonstrated. Brands exist in the minds of the public.

In my book “Appreciated Branding,” I explain why earned meaning is more powerful than attention bought through interruption. In the AI era, attention will be harder to buy than ever.

How AI interprets brand meaning

When a customer asks an AI assistant what to buy, the system instantly examines the category, weighs solution options and serves the best choice for that situation and the person making the query.

Like Tom Cruise in “Minority Report,” moving his hand to see different information floating in front of him, only at light speed. In that moment, the assistant isn’t ranking logos. It’s harvesting meaning:

The problems your product solves, both rational and emotional.
The promises you’ve kept.
Proof of performance.
What others with similar values think of your brand.

Together, these form a personalized recommendation. If that meaning is clear, consistent and machine-readable, AI surfaces you as a solution. If it’s fuzzy or hard to crystallize, you’re invisible. Tom Cruise won’t see you.

Dig deeper: Generative AI is rewriting your brand story — make sure it gets it right

Gartner projects a 25% drop in traditional search by 2026 as queries shift to chatbots and virtual agents. The question is no longer if AI will change discovery, but whether it will recognize your brand and its meaning.

As AI systems increasingly mediate how people discover products, services and brands, the traditional tools of branding — logos, slogans, visual identity, color palettes — are still necessary but no longer sufficient.

What your brand means matters more than what your product does

In the pre-AI era, brand meaning shaped discovery through search engine optimization (SEO). Today, AI engines act like bouncers: they won’t let you in unless they know who you are.

What matters most is not what your product does but what your brand means — the associations, values, stories and trust signals it carries. These are the cues AI looks for when responding to a query. Building that meaning isn’t optional. It’s a strategic imperative.

How does brand meaning get read by AI?

AI models learn from multiple signals:

Content: Clarity, consistency and relevance. AI models are trained on text, metadata, reviews, social media, About pages, usage examples, etc If your messaging is vague or contradictory, the AI won’t reliably understand what you stand for.
User-generated content (UGC): Virtual gold mines for the associations real humans carry for a brand — what we think, value and expect. This feeds into how trustworthiness and relevance appear in AI summaries, recommendations and searches.
Structured data: Schema markup can make your brand easier for AI to interpret, though its influence on visibility is still debated.
Consistency across platforms: If your brand’s message, tone, values and mission aren’t consistent across your owned content (including social channels), you’re undermining clarity because AI isn’t seeing those cues reinforced.
Ethical behavior: How you signal data practices and authentic transparency matters. Otherwise, you risk damaging both human trust and the algorithm’s trust. (You don’t want to get on the wrong side of the algorithm. It doesn’t send warnings — it just ignores you.)

Dig deeper: GenAI is telling your brand’s story — with or without you

What should brands do now?

AI is already mediating discovery. To ensure your brand is visible and accurately represented, take these steps.

Audit your brand meaning: Use frameworks like Appreciated Branding to understand what people believe, value and associate with you today.
Clarify positioning and values: Hold up 2-3 core values that accurately reflect your brand’s behavior and distinguish you. Make them consistently available in your content.
Enhance your content strategy: Tell stories that bring those values to life. Focus less on optimizing for keywords, but more on how people actually talk.
Use reviews, UGC and social proof: Don’t treat them as supporting links — make them central to your brand story.
Test your visibility: Simulate discovery. Run prompts in ChatGPT and other AI systems to see how you’re represented. Are the answers accurate? If not, iterate until they are.
Practice authenticity, transparency and ethics: Avoid greenwashing and empty claims. If you use AI to create content, ensure it reflects your data and values. Otherwise, you risk becoming generic.
Align internal teams: The brand is everyone’s job. Marketing, content, product, customer service, social and analytics should all reinforce the same brand narrative. Mixed messages weaken clarity.
Safeguard sensitive data: Larger companies already use walled AI systems, but smaller teams often don’t realize employees may be feeding private data into public models. Emerging AI privacy platforms can help keep a clear boundary between internal knowledge and external systems.