How to design a brand for machine-mediated discovery

For most of marketing history, brands have been designed for one audience.

The visual identity, the messaging architecture, the tone of voice, all of it built around a human being. Someone with a problem. Someone scrolling, searching, skimming. Someone whose attention you needed to earn in three seconds or less.

But now there’s a new audience in the room.

Large language models (LLMs) are now active participants in the discovery journey. When a prospective buyer asks AI to recommend a supplier in your category, or prompts AI to compare the leading options, or reads a Google AI Overview that summarises the competitive landscape – a machine is making decisions about your brand. Deciding how to describe it or whether it’s worth recommending at all.

So, does the machine understand your brand as well as it should? And what can you do to influence it?

Key takeaways:

• LLMs are now active participants in buyer discovery. You need to design your brand for both human audiences and machines. 
• Finding your AI perception score – how LLMs describe your brand today – is a good starting point. 
• The gap between how AI describes your brand and how you’d describe it is a growth opportunity.
• You can gain a better understanding of how visible your brand really is in AI-driven search by answering 10 quick questions. 

Two audiences. One brand.

You can’t write one version of your brand for humans and a different one for machines. 

The signals LLMs use to understand your brand are the same signals humans encounter – your website, content, press coverage, reviews, social presence and thought leadership. There’s no separate ‘machine-facing’ layer you can engineer in the background.

What you can do is design your brand with both audiences in mind from the start. 

• Make your positioning clear enough that a human can grasp it immediately and specific enough that a machine can accurately represent it. 
• Structure your content so buyers can skim it to find what they need and so that an AI system can extract your core proposition and repeat it faithfully.

It’s all about building a brand coherent enough, consistent enough and well-corroborated enough that the machine doesn’t have to guess.

Understand how the machine sees you

Before you can design for machine-mediated discovery, you need to understand how AI systems currently describe your brand. 

• Check your AI perception score. There’s a few ways you can do this. One is to ask all the popular AI tools to describe your brand. Ask them individually. Look at how they characterise you – what words they use, what they omit, where they’re vague. This gives you an idea of how your brand is currently understood, synthesised from everything the machine has been trained on.
• Aggregate across platforms. Don’t rely on a single LLM. Different systems weight different sources and their characterisations of your brand will vary. Mapping those variations gives you a composite picture. If every model describes you differently, that’s a consistency problem. If every model describes you the same way (but not the way you’d choose) that’s a positioning problem.
• Quantify share of voice, favourability and sentiment. Ask AI systems to compare you to your competitors. Who are the leading players in your category? What are the strengths and weaknesses of each? Which brands would they recommend to a specific buyer? Pay attention to whether your brand is named, how prominently and in what context. 

What is an AI perception score and how do I measure it?  An AI perception score is a structured assessment of how Large Language Models currently describe and characterise your brand. You can conduct a basic version yourself by asking ChatGPT, Gemini and Perplexity to describe your organisation, compare you to competitors and answer the questions your buyers are likely asking. Aggregating those responses gives you a composite read of how your brand is understood across AI platforms. StrategiQ goes further. We use tools that analyse and aggregate every instance of your brand appearing in AI search – across ChatGPT, Perplexity, Google AI Overviews and beyond – to build a comprehensive picture of your AI perception. Where you’re showing up, for which searches, with what sentiment, and where the gaps are relative to your competitors. Share of voice, favourability and competitive positioning, all mapped in one place.

Map your brand alongside your competitors

The next step is to understand how LLMs position you in relation to everyone else.

• Know your competitive set. LLMs don’t just describe individual brands, they construct categories. They decide who the relevant players are and how different brands map against the attributes buyers care about. Who does the machine think your competitors are? The answer may surprise you. Category leaders are sometimes absent from the conversation entirely. Understanding how the machine constructs your competitive landscape is the starting point for influencing it.
• Understand relative performance on key attributes. Ask AI how you compare on the dimensions that matter to your buyers – trust, expertise, innovation, delivery, outcomes. The pattern that emerges tells you where your brand has a clear, well-evidenced position and where it doesn’t. The gaps in AI’s understanding of your brand are, almost always, the gaps in your content, your coverage and your authority signals.
• Identify which searches you appear in – and which you don’t. Ask AI to answer the questions your buyers are actually asking. Monitor whether your brand is surfaced. Every time your brand doesn’t appear in an AI response a buyer is relying on, you have already lost a consideration.
• Learn from the brands the machine respects. Ask AI to describe your most respected competitors in detail – the language it uses, the attributes it leads with, what makes them credible in its assessment. What are they doing that you’re not? 

Build the signals LLMs trust

The brands AI describes most confidently are the ones with a clear, well-documented point of view, visible not just on their own platforms but reflected back through industry coverage, community discussion and independent sources the machine already trusts.

• Reddit and community presence. LLMs weight community platforms heavily as they represent unfiltered third-party opinion. Brands with a genuine, useful presence in the communities where their buyers think out loud accumulate authority that translates directly into how AI systems characterise them. It’s about showing up consistently in the places that matter. If your category has an active Reddit community, it belongs in your brand strategy.
• Structured content that answers real questions. AI systems parse content for direct, specific answers. If your website and thought leadership is written to impress a general reader rather than address the precise questions your buyers are actually asking, the machine will struggle to extract and repeat your positioning. Answer the questions your buyers are asking. That content is significantly more likely to be surfaced than content that is well written but doesn’t directly address anything in particular.
• Third-party validation. Your website is one data point. LLMs need to see your brand validated in multiple independent sources before they’ll characterise it with confidence. Earned media, analyst mentions, award recognition, industry publication contributions – these are the authority signals that corroborate what you claim on your own platforms.
• Individual authority. LLMs weight individual voices as well as organisational ones. Thought leadership under your senior leaders’ names – in industry publications, on LinkedIn, in panels that generate written coverage – builds a layer of authority that brand content alone cannot replicate. The machine learns that your organisation contains experts. That distinction matters when it’s deciding who to recommend.

The audit

If you’re a senior leader wanting to move from diagnosis to action, the starting point is a systematic audit of how AI currently understands your brand.

StrategiQ is offering 10 complimentary AI Visibility Reviews for senior leadership teams navigating this shift.

Do you have something to confess?

Answer 10 quick questions to understand how visible your brand really is in AI-driven Search.

Reveal your confession score.

Sources and references

1. The Great Visibility Shift, Panel – StrategiQ and GTA – Future of Work
2. Tracking AI driven journeys
3. Our human-centric AI strategy
4. Search Engine Land (2025) — State of Search Q1 2025: zero-click searches in the US rose from 24.4% (March 2024) to 27.2% (March 2025); organic clicks fell from 44.2% to 40.3%. searchengineland.com