How branding changes when AI is your customer

Today, many brand interactions occur through non-human interfaces such as search engines, voice assistants, and generative AI. These systems don’t “feel” your brand the way a person does. Instead, they process it, interpreting meaning from structured data, consistent tone, and machine-readable relevance. Machines can now translate your brand before it ever reaches your audience.

Large language models (LLMs) prioritise coherence and consistency, parsing sentiment and analysing metadata. While visual identity remains important, machines primarily consume the structure, message, and meaning inside your content. 

This is redefining discoverability, because as AI directly answers more queries, your brand’s visibility hinges on its interpretability. If your content lacks structure, consistency, and semantic richness, your brand won’t be referenced, regardless of how beautiful or polished it looks. This means you have to treat language as infrastructure, designed particularly for AI gatekeepers. 

Because user prompts often provide such rich context, AI can grasp specific situations and tailor answers individually. Meaning: LLMs and AI search platforms offer a massive opportunity for brands to connect with audiences at the precise right moment.

Building content for machines first and humans second requires an understanding of how AI processes information. This involves using Schema.org to structure web content and managing brand definitions in knowledge graphs. It also means moving beyond keywords to focus on “entities,” or consistent, clear references for your brand, products, and ideas. This prevents confusion and helps AI understand relationships. 

To associate your brand with a topic, your online content must frequently link them. Write directly and concisely, adding rich context. Strategic repetition of key terms aids AI comprehension. All content should be accurate, verifiable, and linked to trusted sources. 

Since LLM systems can store content in chunks, sections should be standalone, and semantic HTML is crucial for identifying purpose. FAQs and question-phrased headings also enhance LLM comprehension.

Your traditional tone-of-voice guide needs an upgrade for both human creators and AI models:

• Within your brand pillars, specify keywords, phrases, sentence structures, and acceptable sentiment ranges. 
• Create a brand terminology glossary, detailed grammar and punctuation rules, and preferred voice (like your English teacher, most LLMs prefer active versus passive). 
• Provide templates optimised for schema markup and entity extraction for various content types. 
• Establish guidelines for tagging existing content for AI training, defining metrics for evaluating AI-generated content against brand standards, and setting up feedback loops for continuous model refinement.

Your tone-of-voice guide no longer belongs solely to copywriters. Now, it’s also for LLMs. Generative AI will analyse your website’s language and synthesise your brand story for users who may never visit your site. So it’s up to you to engineer how your brand is perceived through every input. 

In short: branding is now about instruction, not just expression.

Semantic branding is essential, but it doesn’t need to (and shouldn’t) sacrifice human connection. 

Maintaining creativity and brand personality amidst strict semantic rules can be challenging. You risk content becoming overly formulaic, lacking emotional nuance, or failing to truly resonate. Getting it right requires finding a delicate balance that ensures “algorithm-aware” writing enhances, rather than constrains, brand expression.

This dual-channel approach of semantic clarity for machines and emotional resonance for humans is about integration, not sacrifice. The goal isn’t to write for machines, but to ensure machines understand what we write for humans. 

Branding ultimately builds relationships with people. While machines filter, brand content aims to engage, inform, and persuade human audiences. This means continuing to craft compelling narratives and evoke genuine emotion. Semantic optimisation helps AI find, understand, and expand content’s reach, but human validation remains the end goal.

Users who click through from chatbots to brand websites demonstrate high intent. This critical moment demands a seamless transition from machine-understood clarity to human-felt connection. 

The opportunity to convert is immense and relies on your ability to present precise, emotionally impactful, and consistent brand messaging. This calls for ongoing review by expert writers and designers, ensuring AI-optimised content is technically sound, deeply compelling, and aligned with human desires.

I’ve spent most of my career as a designer, where visual identity and human psychology were paramount. It feels slightly ironic how the very visuals I championed are becoming less central in an AI-first world. 

LLMs don’t “see” visual design. They extract rudimentary and often obvious sentiment from cues like typeface and color through associated metadata or descriptions. This presents an intriguing, though subtle, opportunity to strategically manipulate how LLMs portray your brand, subtly guiding their interpretation without direct visual understanding.

Jeff Bezos famously said, “branding is what people say about you when you’re not in the room.”

That’s still true, but now it’s about engineering that conversation with a non-human intelligence, ensuring it understands and “sees” your brand well enough to speak for it.

In an era where AI curates what we see and recommends what we buy, successful brands will optimise for both human psychology and machine learning. And that means designing for the models that decide what people see, while ensuring our human message cuts through the digital noise.