Winning hearts, minds and models: Brand building in an AI age
AI hasn't just given marketers new tools. It's created a new audience: large language models (LLMs) and AI agents that influence what people see, consider and buy.
Right now, many brands show up in AI answers by luck, not design. That's risky. If LLMs and agents are going to steer choices, you need a plan to be present, consistent and recommended.
Search isn't dying, it's having babies
Overall search is up, but zero-click behavior is eating into site visits. People get what they need from AI summaries and never click through.
Ranking still matters because many AI answers start with a web crawl. But you also need citations inside the AI-generated summaries. That's where GEO (generative engine optimization) comes in.
What GEO actually does
GEO shapes how generative systems perceive, interpret and present your brand. It's about structuring content so LLMs can digest it and choose you as a trusted answer.
Success isn't just traffic. Track visibility, perception and citation rates across models. As models become multimodal, text, images, audio and video all feed the machine's view of your brand.
New search behaviors to plan for
- Generative search: AI summaries inside results pages that cannibalize clicks. Goal: be cited as a credible source in those summaries.
- Conversational research: Longer sessions in tools like ChatGPT and Gemini (8-14 minutes vs. 1-3 for traditional search, per Similarweb). Goal: get your expertise into the content these systems learn from.
- Agentic buying: AI agents will research and purchase on a user's behalf. Goal: ensure content is visible, structured and your site has the hooks agents need to execute (feeds, APIs, schema).
SEO isn't dying. It's splitting into new surfaces and behaviors. Treat GEO and SEO as connected, not competing.
How this connects to brand building
Most GEO talk lives in the lower to mid funnel. But AI answers can now reflect your brand's message at the top of the funnel too. Your brand story should inform both human ads and machine outputs.
Start thinking about brand through an LLM lens. How should models describe you? Where are you cited? Do AI summaries mirror your positioning or muddle it?
Humans vs. LLMs: design for both
Humans have short attention spans and limited memory. We respond to emotion, story and clear distinctiveness.
LLMs thrive on structure, depth and consistency. They value well-formed, information-dense content. Many brands now have higher AI awareness than human awareness. Closing that gap is a smart first goal.
Your brand story is now data
A strong narrative still builds emotional connection. It also acts as a signal models can learn from. If your footprint consistently communicates "helpfulness" or "Vorsprung durch Technik," models will use it to inform recommendations.
Consistency is non-negotiable. If your ads say one thing and your structured content says another, both people and machines will discount you.
Make your guidelines machine-readable
LLMs are literal. "Warm" tone is vague; "friendly, accessible, colloquial" is actionable. Test prompts, reverse-prompt your outputs, and translate brand voice into clear, checkable attributes.
- Define tone with specific descriptors and banned phrases.
- Provide do/don't examples the model can mirror.
- Encode rules into prompts, templates and system messages used across tools.
Category entry points (CEPs) in a conversational era
Queries are longer and more personal. A user won't ask "best noise-cancelling headphones" - they'll add flight length, comfort needs and battery anxiety.
Build associations across the web of CEPs your category triggers. That's product, content and comms working from the same brand strategy.
Campaigns engineered for LLM readability
Earned media is indirect training data. Reddit threads, Meta posts and YouTube videos often end up in model inputs. Campaigns that trigger credible coverage can lift your "share of model."
Make work that people talk about and that AI can summarize cleanly: clear themes, distinctive assets, quotable lines and obvious product tie-ins. Emotional first, but AI-friendly by design.
Get agent-ready
- Use clean product data: titles, specs, FAQs, pricing, availability, returns, warranties.
- Implement schema.org, product feeds and APIs so agents can evaluate and transact.
- Ensure your chatbot/agent mirrors your brand voice and values in real interactions.
- Watch retail ecosystems adopting agents, like Amazon's Rufus.
A practical checklist
- Audit your Share of Model: Measure brand mentions, citation sources and message accuracy across top LLMs.
- Map your CEPs: Prioritize the situations and needs that drive queries. Build content and proof around each.
- Structure your content: Deep, skimmable pages with headings, comparisons, FAQs and references. Add schema.
- Build authority signals: Credible third-party coverage, expert quotes, reviews and data the models can cite.
- Align creative with machines: Distinctive brand assets and story for humans; precise, consistent language for models.
- Define machine-ready TOV: Convert tone to specific traits, examples and constraints for prompts and templates.
- Design AI-friendly campaigns: Clear narrative, memorable line, strong product linkage and earned-media potential.
- Prepare for agents: Feeds, APIs, clean PDPs, policy clarity and frictionless checkout flows.
- Instrument new metrics: AI citation rate, model SOV, message alignment, zero-click impact and agent-driven revenue.
- Run experiments: Test content formats, prompt strategies and campaign angles. Iterate based on model outputs and sales.
Metrics that matter
- Share of Model by task (research, compare, recommend, buy)
- Accuracy and consistency of brand descriptors in model outputs
- Citation sources and quality (are the right pages getting referenced?)
- Zero-click effects on organic traffic and conversions
- Agent-attributed revenue and assisted conversions
Level up your team
If your marketing org needs a focused upskill on GEO, agentic AI and brand voice for machines, explore this certification built for marketers: AI Certification for Marketing Specialists.
The bottom line
Brand communication now builds two kinds of availability: mental for people and model for machines. Do the work to win both.
When your story is consistent, your content is structured, and your products are accessible, you set the conditions for choice - by humans and by agents.
Your membership also unlocks:
