How to Treat AI as Your Customer, Not Just a Channel

Soon after AI search became reality, so did the terms for optimizing it: “GEO,” “AIO,” “AEO, “AI SEO,” “LLM optimization,” and more. But there's risk in equating AI optimization with the tactics, shortcuts, and quick wins that rule other marketing channels. Despite the similarities, trying to fit AI search into traditional SEO parameters is a tricky game. We recently covered why ranking isn’t the goal in AI search — it’s a new search landscape where consumers can discover, research, and buy content all within a single AI platform, sometimes without any need to visit an external website at all.

In fact, as Google and OpenAI embrace and promote agents shopping on behalf of customers with new protocols for agentic commerce, AI is more and more like a customer you need to serve — not a channel you must optimize. More and more, we’re seeing agents as the bridge between your customers and your products.

To gain visibility in AI search, you need to go beyond surface tactics and make a change at the deep strategy level. Shift your mindset to treat AI bots and agents as a new type of customer demographic — an always-on intermediary that interprets your brand for human buyers and requires its own targeted marketing strategy. The goal is now to reliably inform and influence the agents working on behalf of your human consumers.

In this post, we’ll walk through how reframing AI systems as your newest customer demographic can help shift priorities inside your marketing org, and what questions to ask before you add more optimizations to the backlog.

New audience, new perspective

You wouldn’t market your products for adult men as you would for adult women, or either as you would for children. Imagine it: if a new customer segment appeared, with different preferences for how information is found and evaluated, you’d revisit your marketing approach. Consider how you would treat AI systems the same way. Instead of asking how to “rank” for them, start with what this audience needs to truly know your brand, understand your products, and represent you accurately when they cite you in AI platforms.

Serving this new audience also means understanding its role in the new buyer journey. Agents are becoming the personal shoppers, the customer service line, the trusted recommendation from a friend. Everything a customer needs to know about your brand and products, from discovery to purchase and beyond, must first be known by AI systems.

This means mapping where your information lives online, ensuring it’s current, consistent, and agent-ready, and understanding where you’re absent or misrepresented. It also means aligning product, content, and data decisions so that the basics hold up across platforms.

This is company work, not just channel work

In agentic commerce, AI agents are tasked with finding the best and most satisfying answer to a consumer’s query. That means they need to understand which brands are trustworthy, aligned with that consumer’s personalized circumstances (such as their values, habits, and even budget), whether they have a product that answers the need, if that product is in stock, and so on. In essence, they need to understand everything a human customer might, but the way they access and digest that information is different. 

The sources that shape how AI systems present your brand are usually core inputs, ones that train AI models about your brand:

• Accuracy & consistency: Your policies and specs should tell the right story, consistently, across all your brand’s pages, feeds, and public-facing documents.
• Freshness: Know how quickly details like price, hours of operation, or inventory changes go live, and how quickly these attributes are found by search and AI systems.
• Authority: Your content (and its authorship) should display expertise and authoritativeness around your industry. Your reviews should reflect a brand and products trusted by consumers, and your third-party sources, like PR, should reinforce all of this.
• Accessibility: Essential info should be machine-readable, coded in a language that bots and agents can understand (for example, HTML and markdown are simple for AI agents to read, but content in JavaScript is invisible.)

If these inputs are wrong or messy, no optimization tactic can fix the downstream output. If they’re solid, placement and visibility in AI conversations follows more naturally. You can already see that this is foundational work spanning more than just digital marketing — ensuring AI systems know your brand well and comprehensively means assessing and optimizing every place online they might find you.

To learn more about how AI systems find and use content, read our article How AI Platforms Source Content (& How to Influence Them).

Optimize for AI by asking these foundational questions

You wouldn’t begin marketing to a new demographic without fully understanding their goals, needs, objections, and habits. Get curious about how AI agents will approach the customer journey, doing similar market research as you might for any new segment.

Reframe how you think about AI agents as a customer by asking questions like:

1. Where do AI systems find information about us? Understand where AI gets its brand intel. How does it determine competitor comparisons? Does it find product details on Google? Does it find customer sentiment on Reddit, or reviews sites? Use reliable log data to know if AI bots are accessing your policy pages, About section, product pages, etc.
2. What types of information does AI trust? Think Wikipedia, government websites, content with expert authorship, consistent facts across different sources, and so on. Understand what’s being cited in conversations that align with your customer intent, note the sources, and think about how to appear there.
3. What influences its “opinions” and outputs? Does it offer positive or negative sentiments that align with what you see from reviews sites, Reddit, or forums? How does your brand show up in those spaces?
4. How does it like to receive information? Some humans prefer text to video or vice-versa. AI systems will have preferences too — for instance, content coded in JavaScript is obscured, but HTML and markdown are clear to them. Do they like structured data or natural language? 
5. Where does it find specific vertical data? Research where it sources travel data, like live information and ticket prices. Does that come from Google, partnerships between the AI platform and travel brands, somewhere else? What product feeds does it ingest (and not ingest)? Where does it find shipping and inventory information?

Finding the answers requires research, study, and testing. Afterward, you’ll need to ask questions about your brand to define next steps:

• Where does information about our brand live today? List the public sources you own, such as site sections, documentation, policy pages, FAQs, feeds, and any marketplace or partner listings.

• Where do AI agents find content on our site? Learn whether AI agents are visiting your site, finding content, and sharing it in AI search. An AI visibility solution like Botify can help here.
• Where do our facts conflict? Note regional or version differences and designate a canonical explanation. Also identify stale content to update or archive.
• How are we described in our category today? Within any owned or third-party content where you can see sources or summaries, check for brand inclusion, omissions, or mismatches with your current facts.
• Are there content gaps for our customers’ buyer journey? List what customers try to do at each funnel stage (e.g. learn, compare, buy, use) and make sure your site provides clear, up-to-date content aligned with those tasks. Also pay attention to low-frequency but high-importance edge cases where missing or vague information creates frustration, costs, or risk (e.g. policy exceptions, compatibility, accessibility.)
• What information changes most, and how fast do we update it? List facts that change often, like inventory, price, hours, and policies, and identify standard edit-to-live timelines.
• Which signals of credibility are visible? On priority pages, discern whether expertise and authorship (where appropriate) is clear, if timestamps and dates are visible, and if citations or references are present where relevant.
• Is my key content easy to read, both by people and AI agents? Note whether or not page and feed fields values match. Also document whether key facts are visible without issues like being hidden behind tabs or extra clicks.
• Are identifiers stable across systems? Are you using the same model numbers, store locations, and any other identifiers across all CMS, PIM, feeds, and pages?
• What technical barriers do we control? Have your technical team assess things like crawl blockers on your site, heavy client-side rendering, brittle pagination, thin sitemaps, slow endpoints, and so on.
• What is our canonical brand source? Maintain your source-of-truth pages for positioning, definitions, and policy summaries. Align other pages to this source and retire or label conflicting materials.
• How will we learn and adjust? You’ll want to run prompt and query checks, plus site and feed audits, on a set schedule. Use a simple error list to track fixes needed (e.g. omission, outdated, contradiction, formatting). Consider how you’ll test to confirm improvements stick.

You don’t need to answer all these questions to start. Choose the few with the highest risk or visibility in your category and begin there.

Practical next steps by vertical 

Every category will have nuance regarding where AI systems find and use content. What it always comes back to, however, is knowing your customers’ intent, buyer journey, and desired outcome, and making sure all the content and facts that support it are accessible to and optimized for AI. We’ve included common considerations below:

E-commerce

Customers need reliable product truth. Focus on complete attributes, standardized IDs, clear specification (spec) tables, shipping and returns policies, and timely pricing and stock updates if you offer them. Common fixes include normalizing variant data, avoiding specs locked inside images, and making sure product detail pages and feeds say the same thing.

Travel

‍Decisions hinge on availability and rates shown on your owned platforms, plus clear rebooking and cancellation rules, amenity definitions, and local restrictions where relevant. Typical fixes include consolidating scattered policy content and keeping hours or cancellation notifications current. 

Healthcare‍

Readers look for author credentials where appropriate, references to recognized guidance, update stamps, and sections that explain side effects or contraindications when relevant. Common fixes are updating or labeling legacy articles and making sure references are visible. 

Financial services‍

Customers look for clarity around APRs or other fees, eligibility, risk language, and date-stamped rate updates. Move critical disclosures out of PDFs when possible and align product pages across owned sources. 

For your vertical, start with identifying broad, strategic realities and work inward from there.

Track metrics that matter

If AI agents are your new audience, you also need to start tracking KPIs that map to their processes: 

1. Visibility: The presence rate in percentages for how often your brand or URLs appear in AI-generated responses. 
2. Share of voice: How visible your brand is versus your competitors.
3. Competitor trends: Benchmarking your visibility progress over time against your competitors.
4. Citations share: The percentage of AI-generated answers that reference and link to your brand.
5. Average best position: How highly you’re cited, on average, when your domain is linked to in AI responses.
6. Sentiment analysis: The proportion of answers that mention you positively, neutrally, or negatively.
7. Platform visibility: Which models are surfacing your brand and products (ChatGPT, Perplexity, Gemini, etc.)
8. Intent visibility: How visible your brand is across different customer journey intents: awareness, research, and decision.
9. Accuracy against your brand truth: When public answers surface facts about your brand, check them against your current, published data. Use your canonical sources (policy pages, specs, pricing tables) as the yardstick. 
10. Business outcomes: Watch assisted traffic, conversion, retention, and support deflection on your properties. When you run changes like clarifying a policy hub or standardizing product specs, annotate those releases and look for correlated movement. 

AI Visibility in Botify Analytics can get you tracking these KPIs now and actioning insights sooner. AI Visibility gives you an observable view of how you show up across AI search by pulling your own first-party data to track inclusion and share of voice, prompt-level insights, and competitor comparisons across platforms like ChatGPT, Google AI Mode, and Perplexity, with regular refreshes.

Strategy matters more than tactics 

If you approach AI search with the mindset of optimizing a channel, you’ll end up missing the forest for the trees. To strategize successfully, think of AI as a new customer that meets your brand through what you publish and how clear, current, and accessible it is to AI agents.

Focus on what you control: keep facts consistent, remove barriers on your properties, understand where and how AI is gathering information about your brand and products, and foster a culture of testing, studying, and iteration to know what works. That steady discipline is enough to build a useful AI customer persona and improve how your brand shows up in AI-mediated discovery.