How to create content for both Google and AI search (not two different strategies)

Overview: Content now has to work for two audiences: human readers and AI systems. That doesn’t require two separate strategies, but it does require more deliberate structure. Human-facing content still needs to be engaging, useful, and on-brand, while AI-facing content should answer specific questions clearly in formats that are easy to extract. FAQs, comparison content, and strong third-party validation can all improve visibility in AI-generated answers. The fundamentals remain the same: know your audience, answer the real question, bring credible expertise, and use a consistent brand voice.

In a recent conversation with GeekWire, I talked about how content strategy is changing as buyers increasingly use both traditional search and AI assistants to research vendors. The challenge for marketers is to serve both without splitting the brand into two different content strategies.

Buyers don’t research the way they used to. According to G2’s 2026 AI Search Insight Report, 51% of B2B software buyers now start their research with an AI chatbot more often than Google, while 71% use AI chatbots somewhere in the software research process.

They still browse websites and scan search results, but they also go straight to AI assistants for very specific answers: Does this vendor have SOC 2 compliance? What are the top revenue orchestration products, compared side by side? 

That shift can make content leaders feel like they’re suddenly running two different programs. In reality, it’s one strategy with two audiences: humans and the machines (LLMs) that increasingly shape what humans see.

As I’ve said many times, “it might begin to feel like you’re doing two different activities… but really it’s about aligning the two.” The goal is to keep one brand voice and one coherent content strategy while formatting and structuring content so it performs in both environments.

Why content strategy now has two audiences

When someone comes to your website, they’re often open to exploring. You’re designing for engagement: attractive pages, brand-forward storytelling, and high-level value propositions.

AI assistants are different. People typically use them for utilitarian tasks; getting specific information fast, often in a preferred format. That means the underlying intent changes:

• Website behavior can look like browsing a magazine.
• LLM behavior looks like asking a direct question and expecting a direct answer.

So yes, the same brand must speak to two different consumption patterns. The solution isn’t duplicating your marketing into two separate worlds. It’s aligning one strategy to meet both sets of needs.

What is AEO (answer engine optimization)?

AEO is the practice of structuring content so AI systems can easily extract and reuse it as a direct answer to a specific question - often in a concise, clearly formatted way - rather than requiring a reader to browse and interpret a full page.

What’s actually different between SEO and AI search 

The fundamentals are unchanged. You still need to know your audience, answer the real question, show credible expertise, and keep a consistent brand voice and language.

What changes is the structure.

With traditional SEO, content teams have historically focused heavily on keyword placement and on-page behaviors; getting people to arrive and then browse through related pages.

With AI assistants, the “browse” step often disappears. The content that performs is content that’s easy to pick up and reuse: clear questions, clear answers, and information presented in a format that maps to how people prompt.

That’s why your content approach needs to hold two ideas at once:

• Keep the site engaging and brand-forward.
• Ensure key information is also available in direct-answer formats.

Tactical moves that work for both Google and AI search

There are a couple of practical formats I consistently see working because they serve humans and are easy for LLMs to extract.

FAQs: specific question, specific answer

As I explained to GeekWire, “A lot of websites have FAQs. That’s excellent because it’s a specific question and a specific answer.” That structure is naturally aligned with how people ask questions in AI assistants.

FAQs also benefit humans: they reduce friction, shorten evaluation cycles, and create clarity.

Comparison and listicle content: “top vendors, compared”

If you publish a blog post with a theme like “the top five solutions in revenue orchestration,” you’re creating content that closely matches the way people use AI assistants: they ask for specific options, comparisons, and recommendations in an easy-to-scan format. Structuring content around those questions can make it easier for AI systems to identify and use the relevant information in a response.

That doesn’t mean changing your voice to appeal to an algorithm. Focus on making the content clear, well structured, and genuinely useful to the person asking the question.

The credibility layer: why third-party validation matters more now

LLMs don’t just rely on what you say about yourself. Third-party validation becomes a major lever for credibility and visibility.

In practice, that means:

• PR coverage and earned media
• Analyst rankings and reports
• Customer stories
• Broader third-party discussion (including places like Reddit)

These sources can show up as citations or supporting evidence in AI responses. As I told GeekWire, “the more you can ensure that your brand and product… are represented through things like earned media… the higher likelihood that they’ll get picked up in an LLM as a credible source.”

This changes how content leaders need to think about visibility. What you publish on your own site still matters, but so does the broader body of third-party content, coverage, and commentary that shapes how your brand is represented.

What’s changing next for AI search and content 

AI-platform advertising is emerging as a new consideration. I’ve referenced examples like OpenAI having ads, and how this can be oriented around top topic areas rather than classic keyword mechanics. 

The important point for content strategy is simple: this is a separate channel with different intent. It shouldn’t distract from the core work of building content that answers real questions clearly and credibly.

One brand voice, two formats

It’s tempting to treat “AI search” as a new discipline that replaces everything that came before. I don’t see it that way.

The fundamentals are still the same:

• Know your audience.
• Answer the real question.
• Bring credible expertise.
• Maintain consistent brand and language.

What’s changed is how information gets discovered and reused. By adapting the structure with FAQs, comparison posts, and direct-answer sections, marketers can make one content strategy work across both traditional search and AI-assisted research.

For the broader context on how these changes fit into the revenue technology landscape, read Abhijit Mitra’s perspective on what revenue orchestration is and how AI agents are changing revenue work.

Frequently asked questions about creating content for Google and AI search 

What is AEO (answer engine optimization)?

AEO is structuring content so AI systems can extract and cite it as a direct answer to a question, rather than requiring a person to browse a page to find the information.

How is writing for AI search different from writing for SEO?

SEO content often emphasizes keyword placement and browsing behavior: getting someone to your site and guiding them through related information. AI search content is typically used to answer a specific question quickly, so it benefits from clear structure: direct questions, direct answers, and formats that are easy to extract.

Do FAQs actually help with AI search visibility?

Yes. FAQs are a strong format because they pair a specific question with a specific answer, which makes them more likely to be picked up by LLMs, while also improving clarity for human visitors.

Does third-party coverage affect whether AI assistants cite your brand?

Very important. Research cited in this article shows that a large share of brand mentions in AI search comes from third-party sources rather than brands’ own websites. That includes earned media, analyst coverage, reviews, comparison pages, customer stories, and community discussion. For marketers, that means AEO is not only an on-site content discipline; visibility also depends on how consistently and credibly your brand appears across the broader web.

Where should marketers focus offsite for AEO?

Focus on the third-party sources AI systems are most likely to use when answering your buyers’ questions, such as earned media, analyst reports, reviews, comparison pages, customer stories, and relevant community discussions. The goal is not to appear everywhere; it is to be represented accurately and credibly in the sources that shape AI-generated answers.

Do I need a separate website or content strategy for AI search?

Not necessarily. It can feel like two different activities, but one aligned strategy can serve both audiences. The key is to keep one brand voice while structuring content in formats that work for human readers and for AI systems.