Picture this: Content visibility is up, but traffic to your website is way down. More than half of Google searches today end in no clicks, according to Search Engine Land. And consumers are looking everywhere — from Google’s AI Overviews to Reddit — for instant solutions to fit their needs.

Is this your reality? Welcome to the rebirth of how people find information.

Payoffs from traditional SEO tactics used to be huge. Now, AI has effectively given everyone access to unlimited, personalized knowledge on a diverse set of channels, and Google Search is losing users to AI search engines like ChatGPT.

The once reliable marketing playbook has officially been disrupted. You can no longer count on one distribution channel, like search, to do all of the work for you. As a brand, you need to diversify your content across channels to meet buyers where they are.

With the rise in AI adoption, one of those channels is AI search. When your audience is finding information in large language models (LLMs), it’s time to optimize your content strategy for both humans and machines.

The Scoop on AI Engine Optimization (AEO)

AI usage has been increasing since 2023. A recent McKinsey survey found that 78% of organizations used AI in at least one business function in 2024, compared to 55% the year prior. This widespread adoption is fundamentally changing how people consume information.

As Google and other search engines roll out more AI features, businesses are facing a unique paradox: they’re seeing fewer clicks even if their rankings and impressions improve. That’s because AI engines are increasingly becoming the first stop for product discovery.

It is worth noting, however, that the buyer’s journey hasn’t changed. Users still identify a pain point, determine a solution, find the right product for that solution, and ultimately make a purchase. But the channels guiding those steps have, and AI search is shaping the first three phases more and more.

Traditional SEO focused on surfacing the best resources through search engine results pages (SERPs). Content was designed to address simplified search queries, where users make multiple search attempts and perform manual research to compare results.

But AEO prioritizes surfacing the best answers directly through LLMs. This means developing content that satisfies specific, natural language queries, where users learn from the AI engine and ask conversational follow-up questions.

Succeeding in the AEO environment depends on two things: choosing the right topics and designing content with intent.

Choosing the Right Topics

AI engines rely on vector embeddings to understand the relationships between words, concepts, and entities. This means that brands need to build strong semantic associations between their content and the product categories they want to own.

For example, a project management software company should target keywords beyond “project management tools” and create depth across related topics such as “resource allocation,” “workflow automation,” and “team collaboration best practices.” That way, AI engines can begin to associate the brand with the entire product category.

Topic selection is about claiming a semantic territory and fully owning it, rather than chasing down individual keywords. You can do this in three ways:

• Category saturation: Developing clusters of content that fully explore a topic category, from definitions to advanced use cases.
• Context-rich answers: Addressing nuanced, conversational queries like “How do small businesses manage projects with limited resources?” rather than only short, keyword-driven questions.
• Personalization at scale: Creating variations of content tailored for different industries, business sizes, or roles. This allows AI engines to pull the most relevant response for each user context.

AEO rewards breadth and depth of context. The more complete and interconnected the content is, the better the AI can understand it and recognize it as authoritative.

Designing Content with Intent

AI engines prioritize content that is both accurate and structured for machine readability and retrieval. It’s a strategic balance between factual authority, semantic completeness, and structured storytelling.

There’s value in consensus-driven, widely corroborated information. Citing credible sources, linking to structured data, and presenting verified facts increases the likelihood of being cited. But to stand out, content should also include information gain — insights or data that can’t be found elsewhere.

For example, a marketing firm that publishes a “Top Emerging Marketing Trends” article could cite widely available data but also include proprietary findings from its own research team to increase its chances of showing up in AI search results.

LLMs also index and retrieve content in “chunks.” This means each paragraph or section in your piece of content should stand alone as a complete thought.

A paragraph that explains how workflow automation tools support tasks like audience segmentation and lead scoring is far more valuable than one that simply references an earlier point. This completeness ensures the content can be understood and retrieved without relying on surrounding context.

Another important factor here is entity association. Content that clearly identifies and connects entities (like companies, tools, or processes) helps AI engines understand information in context. Writing techniques like using semantic triples make this easier.

Here’s what that looks like in practice:

Semantic triple: “HubSpot’s CRM helps sales teams track leads.”

• Subject: The entity being described (HubSpot’s CRM)
• Predicate: The relationship or property (helps)
• Object: The value or related entity (track leads)

Great content alone no longer guarantees visibility. Breaking through today requires meeting prospects where they are with content that is accurate, comprehensive, and easy for both humans and AI to understand.

To really make it count, brands need a smarter approach to distribution that amplifies content across the channels where buyers are already paying attention.

From Distribution to Amplification

This tactical AI-driven shift in search and discovery is outlined in HubSpot’s Loop Marketing playbook, which helps businesses evolve as customer habits change.

There are four stages in the Loop:

1. Express who you are: Define your taste, tone, and point of view.
2. Tailor your approach: Use AI to make your interactions personal.
3. Amplify your reach: Diversify your content across channels for humans and bots.
4. Evolve in real-time: Iterate quickly and effectively.

AEO fits right into this playbook at the Amplify stage, where the focus is on diversifying your channel mix to engage customers where they are.

The components of the Amplify stage were historically seen as one simple play: distribution. But these tactics now influence LLM citation volume in the AI search era.

Here’s a quick breakdown.

Diversify your channel mix.

This has been discussed in detail as AEO takes center stage as a new channel for information and product discovery. The key to diversification is embracing channels with more upside. This includes AEO, but also channels like community forums and video that are showing big returns.

According to Statista, Reddit is seeing significant increases in daily active users across regions with approximately 50 million users in the U.S. Statista also reports that YouTube had over 2.5 billion global viewers as of February 2025.

Your channel strategy needs to reflect changing industry trends and follow your audience’s behaviors. The goal isn’t to be everywhere — you want to be on the platforms where your message makes the most impact.

Engage buyers in real time where intent is highest.

When someone reaches your website, they’ve already signaled high intent. They’re no longer casually browsing. They’re actively evaluating whether your product or service can solve their problem.

That makes the on-site experience just as important as the channels they came in on.

Delivering value in these moments requires immediacy. Buyers expect instant answers, personalized recommendations, and smooth pathways to action.

A software company might integrate an AI assistant that surfaces relevant tutorials or comparison pages the moment a visitor begins researching features. The goal isn’t to overwhelm with information but to anticipate the next question and serve it up before the buyer bounces.

Real-time engagement also means removing friction. Fast load times and intuitive navigation help to create an experience that feels effortless. After all, buyers are more likely to convert when they don’t have to work too hard to find information.

Activate trusted creators.

While the power of influence is shifting from traditional search to LLMs, it’s also moved from polished brand channels to trusted individuals.

Audiences today are more likely to believe a product review from a respected YouTuber or an honest LinkedIn post from an industry expert than from a business press release.

Partnering with creators — like YouTubers or industry experts — builds credibility by transferring trust. These voices already have established relationships with the communities your brand wants to reach, which makes them invaluable for amplification.

Scale content production with AI.

If it isn’t clear by now, the demand for fresh, relevant content across multiple platforms is sky high. AI can give you the leverage to meet that demand without breaking the bank on headcounts or budgets.

Use AI to help you increase production, but use it wisely and don’t forego human involvement. You can ask AI to:

• Transform long-form content (blog posts, whitepapers) into bite-sized assets (social media posts/graphics, short-form video).
• Personalize copy for different audience segments to ensure consistent messaging at scale.
• Handle busy work and time-consuming tasks like research and copyediting.

The result is a content engine that moves faster, adapts more easily, and frees teams to focus on creativity over production.

Experiment with next-gen advertising.

Advertising is entering a phase where personalization and interactivity are no longer nice-to-haves. Static banners and generic pre-rolls are giving way to AI-generated campaigns that adapt in real time.

For example, a SaaS company might run LinkedIn video ads that automatically highlight different product features depending on the viewer’s job title. A CFO sees the ROI dashboard while the sales manager sees the pipeline tracking tools.

The common thread is relevance. By experimenting with new ad formats and technologies, brands can meet audiences with timely messages that feel personal and position themselves ahead of competitors who are still relying on old methods.

Riding the Seismic Shift in Discoverability

AI is reshaping how buyers make decisions. No surprise there.

Like a game of telephone, your business website now becomes essential in influencing the AI engines that influence humans to take action and buy from you. The journey to product discovery is spread across LLMs, communities, creators, and dynamic brand experiences.

Winning in this new era means creating content that both humans and machines can trust, and showing up in the spaces where buyers are already engaged.

The companies that adapt won’t just be found — they’ll be recommended, cited, and surfaced at the exact moments when intent is highest.