Tag: AI Search

Mastering AI: New Strategies for Local Search Success
AI has infiltrated nearly every industry, becoming an integral part of apps, company processes, and even daily life. As someone who’s been navigating the local SEO landscape since its inception, I’m witnessing a significant change in user search behavior and the types of responses they receive.

Back in the day, a local business could achieve high rankings simply by optimizing its website, polishing up the Google Business Profile, securing around 50 citations, and soliciting customer reviews. However, in today’s AI-driven search world, these efforts are just foundational.

To succeed in AI-driven local searches, it’s crucial to influence what the wider web communicates about your business, or in simpler terms, build brand awareness.

Consider local search as a form of digital word-of-mouth.

These questions are at the core of what AI systems evaluate when users request local business recommendations. Here’s how I work on shaping the reputation signals these advanced search engines rely on.

How to Conduct Competitor Research for AI Visibility

One initial step in developing an AI search strategy is figuring out which brands large language models (LLMs) recommend most frequently and understanding their strategies.

Identify Businesses Frequently Mentioned in AI Responses

Since AI responses change frequently, I found it essential to run the same query multiple times to discern patterns.

I run the most common brand-related searches at least 20 times in my chosen LLM. Whether you do this manually or employ software like Gumshoe or Waikay, these tools can help synthesize prompts based on your business details and indicate how often your brand appears.

Pinpoint the Sites AI Cites Most Often

After identifying competitors, I turn my attention to the sources LLMs tap into. Analyzing results can be done manually or with the aforementioned tools.

Getting Your Brand Mentioned on Key Sites

Armed with a list of essential sites, I strive to have my brand featured there.

If blogs are primary AI sources, I offer to contribute expert content. For mentions in podcasts or on YouTube, I seek opportunities to guest feature. The ultimate aim is brand amplification.

Building Reviews for AI Consideration

For years, Google has dominated as the primary channel for discovery, leading businesses, like mine, to focus primarily on garnering Google reviews. However, to excel in AI outcomes, reviews across multiple platforms are vital.

Diversify Your Review Collection Strategy

I recommend seeking reviews on various platforms such as Yelp, BBB, Facebook, and others pertinent to your industry. Regular reviews on multiple sites can bolster your brand’s visibility and enhance rankings in traditional search results.

Refine Your Approach to Requesting Reviews

Generic review requests are ineffective. Providing clear direction enhances the quality of feedback, steering customers toward experiences or product aspects AI models might query.

For instance, if you run a plumbing service, a polished review request could resemble this:
```
Hi [Name],

Thank you for choosing us for your hot water tank repair. If you could take a moment, please leave a review on [Link to Platform] and share how we met your needs:

— What plumbing issue did we resolve?
— Was our service up to your expectations?
— Did our plumber arrive punctually and display professionalism?
— Was the cost justifiable for the service quality?

Your review is invaluable to us and beneficial for others seeking quality plumbing services.

Thank you!
[Your Name]
```
AI systems directly reference review content, so securing detailed feedback is crucial.

Always Respond to Reviews

If you haven’t started responding to reviews, now’s the time. AI systems evaluate the content in review responses.

Establish an Everywhere Presence

AI systems scour the web for even rare mentions of your business. Thus, maintaining a presence across multiple platforms is essential, including:
- YouTube.
- Reddit.
- Industry forums.
- Social media, especially LinkedIn.
- Industry publications.
- Local and hyperlocal blogs.
- Local news sites.
- Local and industry podcasts and video channels.
- Best-of lists in your city or industry.
- Press releases.
Engage actively on platforms that resonate with your audience. Tools like Sparktoro can help identify where your audience is most active, enabling focused efforts.

Creating AI-Optimized Content That Stands Out

Today’s content strategies must cater to both humans and machines, demanding alterations in content structuring.

Research by Dan Petrovic into Google’s “grounding snippets” reveals that Google prioritizes sentences closely aligned semantically with the query and those positioned early in the text.

Deliver Key Information Promptly

While humans might savor a thoughtfully crafted introduction, LLMs laser focus on specific answers.

To cater to this, I ensure that my crucial points shine in the opening paragraphs, with the rest of the content bolstering these points.

Addressing the Right Questions

This revolves around keyword research and understanding query fan-out. It’s about pinpointing what queries bring visitors to my business and ensuring my site acts as an answer hub for these inquiries.

For local outfits, essential questions might include:
- What do you do?
  - What services or products are available?
  - Who is your target audience?
  - What problems do you address?
- Where are you located?
  - Which neighborhoods or cities do you serve?
  - Is service delivery on-site, or do clients visit your premises?
- What are your business hours?
  - Do you provide emergency or immediate services?
  - Do you operate during weekends and holidays?
- How can clients contact you?
  - What’s the booking procedure?
  - Do you provide quotes or consultations?
  - Is it appointment-only, or do you allow walk-ins?
- Why should someone opt for your services?
  - What differentiates you from the competition?
  - Do you hold any awards or certifications?
  - Are you renowned for a specific product or service?
- What are the costs involved?
  - Are there discounts or packages available?
- What do other clients say about you?
  - Can you share reviews and testimonials?
  - Do you provide case studies or before-and-after visuals?
- Answers to common queries.
- Demonstrating authority and expertise:
  - What’s your process like?
  - Do you impart knowledge through tips, guides, or blog posts?
Incorporating tools like AlsoAsked can enhance this question discovery process.

Once addressed on your site, ensure consistency of answers across the web, including citations, guest posts, and press releases.

Craft Machine-Friendly Content Structures

Local businesses often list their services as follows: “Services include: plumbing, drain cleaning, pipe repair, etc.”

To improve, I utilize semantic triples for better machine comprehension.

A semantic triple comprises:
- [Subject] + [predicate] + [object]
The subject pertains to what’s being defined, the predicate explains its relation to the object, and the object elaborates on the subject.

For instance:
- [Rescue Plumbing] [is] [a plumbing company in Denver].
- [Rescue Plumbing] [offers] [drain cleaning services].
Swapping out “we” with the brand name provides machines the unambiguous signals they need, improving clarity about your services.

Introduce Fresh Perspectives

AI searches rely heavily on information gain. Thus, I ensure my content contributes new insights rather than restating existing details.

LLMs are drawn to articles that expand their understanding of your brand, industry, and locality.

I leverage personal and vocational expertise to answer niche questions and share unique job experiences, ensuring I rank for AI searches where my competitors don’t feature.

AI Visibility Checklist

Enhancing AI visibility requires more than focusing on your website and Google Business Profile. This checklist covers reviews, citations, content, and brand signals crucial for AI evaluation.
- Revamp your local SEO strategy. Continue refining your website and Google Business Profile while enhancing brand visibility online.
- Identify and analyze your competitors’ content and citation methodologies.
- Find sources LLMs cite within your niche and location; ensure your brand features on these platforms.
- Seek reviews across varied platforms, optimize your review requests, and respond to all feedback.
- Boost your presence on blogs, social media, forums, YouTube channels, podcasts, and in the press.
- Offer unique, informative, and comprehensive content on your website and across web platforms. Use semantic triples to deliver essential information concisely.
This exploration of localized AI search can be far more expansive, but I hope I’ve held your interest. Ensure you check back for upcoming discussions!

Inspired by this post on Search Engine Land.
May 26, 2026
Dive Deeper: How AI Search Rewards In-Depth Content
When I think about the future of AI in search engines, I’m reminded of a statement by Nick Fox, Google’s senior vice president of Knowledge & Information. He believes that as AI begins handling simpler search queries, we need to focus on crafting content that’s richer with human perspectives—something AI summaries simply cannot replicate.
As I ponder how our content can remain relevant in the age of AI, I remember Fox’s advice shared during the Google Marketing Live 2026 interview with Ben Smith of Semafor. Here, he emphasized that quality content must transcend surface-level answers to truly shine.
Consistency is key. Fox noted that our approach to ranking in AI search remains similar to traditional methods. It’s all about crafting exceptional content.
- “The way to optimize for AI search is the same way to optimize for search. Create great content.”
He advised, though, that moving beyond basic summaries is crucial.
- “The additional piece of advice we give is go beyond the surface level.”
According to Fox, while AI summaries might address initial queries, the content that truly excels goes further, answering deeper layers of questions.
- “If you assume that the AI will provide sort of a first-level response, high-level framing, the best content that will do the best within AI is one that goes one level deeper, two levels deeper, and is really helpful there.”
It got me thinking—how does Google distinguish “deeper” content from just longer pages?
The human touch AI can’t duplicate. I find it intriguing that Google’s new AI search guidelines emphasize the value of content AI can’t easily reproduce. These guidelines caution against creating “commodity” content that merely echoes others or is readily generated by AI models.
Producing content that offers little in unique insight is discouraged, whereas content rich with expert or personal experience goes far beyond the ordinary, and that stays with me during content creation.
During the interview, Fox highlighted the web’s future role, emphasizing the need for human perspective in AI-driven search results.
- “If you’re looking to buy something, you don’t just want to hear what the AI says. You want to hear from someone who’s used it. What did they think? What did they experience? What was amazing about it? That kind of rich human content is invaluable.”
- “As humans, we want to hear from other humans. We crave human perspectives and experiences.”
Addressing traffic concerns. I’m aware that Google’s focus on human experience underscores the web’s value, even as AI summaries cut down on organic search traffic clicks that traditionally supported such enriching content.
- Unfortunately, the interview didn’t touch upon how AI summaries might shrink organic search traffic or counteract these drops.
Changing search habits. Observing people has shown me that search behavior is evolving, influenced by conversational AI tools. As Fox pointed out, queries are becoming more intricate and detailed.
- “The questions that people are asking now are these two-, three-, four-sentence queries.”
He highlighted how natural-language searches now include more context, offering intricate prompts rather than short keyword phrases. Google didn’t accompany this with specific data, but I’ve noticed the change in my own search habits.
Why this matters to us. In our pursuit of creating content that stands out, AI-generated responses with basic summaries mean we must offer original reporting, share firsthand experiences, or deliver valuable analyses not available in generic AI answers.
The interview. For those interested, you can watch the complete interview with Nick Fox on the future of AI and search.
Watch this video on YouTube
Digging deeper. If you’re curious about the nuances of Google’s AI search guidance, you might find this article worth exploring.

Inspired by this post on Search Engine Land.
May 26, 2026

Unlocking AI Search: Making Your Brand Truly Machine-Readable

As I delved into audits across Prince Edward Island, one issue stood out: businesses with significant expertise weren’t visible to AI systems because their knowledge wasn’t rendered into a machine-readable format.

Despite their leadership in biotech, manufacturing, and other sectors, critical business information was often trapped in PDFs, behind forms, or muddled in vague marketing copy. It was also disconnected from structured data systems that AI engines need for verification.

We’re living in a world where 88% of companies are integrating AI. Yet, McKinsey notes that 86% of leaders admit to being unprepared for its daily integration.

Many brands mistakenly equate AI visibility with being featured in a Gemini summary or a ChatGPT result, without solidifying the structured digital groundwork needed for ongoing visibility.

AI Visibility: The Basics Before the Buzz

If you’re only focusing on large language model (LLM) responses, you’re lagging. LLM visibility reflects authority—it doesn’t build it.

According to a study by Responsive, 22% of B2B buyers now use generative AI for vendor research. Traditional search use is expected to drop by 50% by 2028 as AI solutions become the go-to answer engines, as Gartner predicts.

Now, discovery happens through synthesizing answers rather than listing URLs. Until you’re part of the Knowledge Graph as a verified entity, your brand’s visibility will be inconsistent.

The Insights from 19 Case Studies: Expertise Powers AI Search

AI systems value concrete, structured data over descriptive text. Brands chasing fleeting AI mentions without anchoring their data won’t achieve lasting visibility, but those establishing structured data relationships will always be recognized.

Thus, SEO has evolved from simply creating content to becoming an information architect. As the case studies reveal, expertise remains a key signal that AI systems can interpret.

Case No.	Entity	Industry	The discovery	The SME solution
1	BioVectra	Biotech	Technical authority trapped in PDFs	Encoded cGMP data into facts
2	Wyman’s	Food manufacturing	Sustainability was a narrative	Structured supply chain schema
3	Murphy Hospitality Group	Hospitality	Invisible venue specifications	Constructed event logic
4	Invesco	FinTech	Opaque compliance data	Built regulatory ground truth
5	Sekisui Diagnostics	MedTech	Innovation lacked readability	Engineered diagnostic logic triples

Why SEOs Must Focus on Education

The main obstacle to AI readiness is the gap in education. We must evolve into information architects, comprehending our clients’ business logic deeply.

SEOs as Subject Matter Experts

Understanding is foundational. For instance, auditing a biotech firm requires a grasp of compliance as keen as their lead scientist’s.

AI relies on structured context for accurate answers. Vague marketing language feeds insufficient responses.

Clients Must Prepare Their Data

Data quality and governance activation equate to maximizing AI-driven value. SEOs must educate clients on digital presence impacting AI brand perception.

Focus on True AI Authority

Appearing in a ChatGPT reply isn’t the goal; becoming an authoritative node in the Knowledge Graph is. It ensures visibility across AI platforms like Gemini and Claude.

AI advancements will persist rapidly. SEOs and clients not prioritizing structured data will be left behind in AI discovery systems.

Inspired by this post on Search Engine Land.

May 22, 2026

Unveiling Reddit’s Impact on AI Search Dynamics
I often find myself explaining Reddit’s role in AI search. It’s frequently underestimated, yet its influence extends well beyond training data.

Clients frequently ask how AI training, licensed access, and retrieval systems can affect SEOs and AI strategies, particularly concerning Reddit.

Here are the typical questions I receive:
- Should I engage with Reddit to boost my brand visibility?
- Is advertising on Reddit beneficial if AI uses Reddit for training?
- Our CEO suggests creating a subreddit for each product. Is that wise?
- Why does Google’s AI reference a Reddit thread criticizing my product?
These inquiries often conflate three separate but interrelated concepts:
- Training data.
- Licensed or real-time access.
- Citation and retrieval systems.
Although connected, they serve different purposes. Understanding these distinctions impacts how we approach SEO and AI citations, especially as Reddit increasingly appears in AI-driven results.

Let’s demystify AI training, access, and citation. You might think, “ChatGPT was trained on Reddit,” means every post is directly stored in its memory—an incorrect assumption.

Training AI is akin to education. Kids learn concepts like using the Pythagorean theorem without remembering specific textbook answers. Similarly, AI learns conversational patterns, not individual Reddit posts.

AI doesn’t remember specific threads but discerns key discussion points from Reddit, like consumer preferences on r/RockTumbling.

Reddit partnerships with Google and OpenAI in 2024 enabled a transition from static datasets to ongoing access, allowing AI to stay updated on Reddit dialogs.

If AI training is like schooling, licensed access is a continuous flow of information akin to subscribing to a newspaper.

AI can cite Reddit, not because it’s preferential part of the training, but finds it useful for real-time querying, just like humans might refer to yesterday’s conversation.

Reddit’s prominence in AI results impacts my SEO strategy, yet it’s not only due to formal partnerships. Reddit’s depth in human experiences enhances its informational value.

Reddit offers what many websites lack: practical user insights and diverse opinions. Where official sites provide features, Reddit adds authentic experiences and user narratives.

Rather than mimicking Reddit, I focus on fostering authentic discussion by leveraging user insights from reviews, interviews, or forums, enhancing the context around my content.

I’ve realized that prioritizing nuanced details and showing reasoning can increase credibility, making my content more relatable in subjective decision-making scenarios.

Ultimately, integrating firsthand experiences and transparency can elevate content strategy, aiding systems that synthesize human input into AI insights.

Inspired by this post on Search Engine Land.
May 21, 2026
AI Search in Multilingual Regions: Lessons from Catalonia

When I think about AI search, I realize it’s more than just translating or localizing results. It’s about deciding which sources, narratives, and realities emerge on top. This complex system is incredibly fascinating to me, especially when I consider how multilingual regions like Catalonia challenge these AI search systems.

The unique geography of Catalonia, where Catalan and Spanish languages coexist, serves as an excellent stress test for AI technology. It’s intriguing to see the underlying patterns unfold when the same queries are entered in both languages across platforms like Google AI Overviews and ChatGPT.

In Catalonia, a query like Tradicions de Sant Jordi shows how AI systems can sometimes misidentify the language, often tagging Catalan as Occitan. This discovery was both surprising and revealing, shedding light on broader problems that transcend multilingual spaces.

Consider this: an AI system operating out of Barcelona with a local IP may choose the less prevalent language of Occitan over Catalan, a decision that feels bizarre given Catalonia’s linguistic and geographical context.

This issue isn’t isolated. In January 2023, Google acknowledged downgrading Catalan results in favor of Spanish, which sparked dissatisfaction among users. The subsequent updates improved things somewhat, but the root language-identification errors persist, affecting how AI synthesizes information today.

My journey into this topic has involved documenting AI search variations across Hispanic markets, observing how it often treats diverse Spanish-speaking regions as uniform, ignoring their unique contexts. However, in Catalonia, where geography remains constant, the retrieval patterns unfold in more distinct and educational ways.

For me, multilingual regions expose the foundational defaults in retrieval systems. Here, users can switch languages and observe firsthand how the system reallocates meaning, authority, and even the language of an answer.

The reality is, the same issues will likely emerge in seemingly monolingual markets, manifesting in different ways as AI technology advances.

Inspired by this post on Search Engine Land.

May 21, 2026
2026 AI Traffic Insights: ChatGPT Fades as Claude & Gemini Rise

I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.

You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.

The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.

Inspired by this post on HiGoodie Blog.

May 21, 2026
Transform Your Search with Google’s Innovative AI Agents

I’m excited to share that Google has announced some transformative updates to its search capabilities. These updates include the introduction of information agents and enhanced agentic experiences that will elevate how we interact with search. Google’s AI will continuously scan the web, ensuring we receive the most current information, much like a personal assistant would.

In a recent announcement, Google revealed new search agents, focusing on information agents and additional agentic functionalities within Google Search. These information agents are designed to monitor the web for changes to our tasks, seamlessly supporting us on our journey through various challenges and questions.

Liz Reid, the head of Google Search, stated, “We’re entering the era of Search agents, where you can easily create, customize, and manage multiple AI agents for your many tasks, right in Search.” This new era provides a unique opportunity to tailor search experiences to our specific needs.

Information Agents. These agents are designed to keep us informed about our questions and tasks. Google’s agents will intelligently sift through the internet—exploring blogs, news sites, social posts, and accessing the freshest real-time data on finance, shopping, and sports, to ensure we receive the most relevant updates on our inquiries.

The information agents will then compile an “intelligent, synthesized update” that not only provides the necessary information but also enables us to take action.

The Example. Envision yourself apartment hunting. You can simply input all your specific requirements, and your agent will continuously scan listings, alerting you whenever a match surfaces. Similarly, if you’re keen on not missing any sneaker collaborations from your favorite athletes, your agent will notify you about new releases.

Availability. These exciting capabilities are set to roll out this summer, initially available to Google AI Pro & Ultra subscribers.

Agentic Experiences. Google is also extending its agentic booking capabilities within Google Search to encompass new tasks like finding local experiences and services. Imagine effortlessly booking a private karaoke room for an exact time and with specific food options, all handled by Google Search.

Google will provide the most current pricing and availability information, along with direct links for purchase, streamlining experiences across various services, including home, repair, beauty, and pet care. These features are expected in the U.S. this summer.

Personal Intelligence Expanding. In addition, Google has revealed plans to broaden its Personal Intelligence feature within AI Mode, now reaching around 200 countries and territories, supporting 98 languages.

Inspired by this post on Search Engine Land.

May 19, 2026
Discover Google’s Revolutionary Intelligent Search Box

Today, I want to share some exciting news. Google has unveiled its most significant change to the search box in 25 years. This new feature, known as the “Intelligent Search Box,” is designed to provide us with an easier way to access AI search capabilities.

This innovation is powered by the latest technology, the Gemini 3.5 Flash.

Here’s How It Looks. Google completely redesigned the search box to give us more space for longer and deeper queries. As I type my search, the box will expand, supported by an AI-powered suggestion tool that goes beyond simple autocomplete, according to Google’s Head of Search, Liz Reid.

What’s even more impressive is the ability to search with text, images, files, videos, and even my Chrome tabs. It’s truly versatile!

Let me show you what this looks like:

This innovation puts Google’s most powerful AI tools right at our fingertips, enabling us to ask questions more easily, as explained by Liz Reid from Google.

Seamless Transition to AI Mode. Google also made it easier to switch to AI Mode with their new AI Overviews feature, which is now available globally on both desktop and mobile. Initially launched to many in January, it’s now fully operational.

Here’s how it works:

Why It Matters to Us. The transformation of the Google Search Box impacts how we search and potentially changes the type of traffic Google sends our way. It may encourage more users like me to switch to AI Mode for deeper answers, possibly leading to fewer direct clicks on our websites.

While change can be challenging, it’s also inevitable. Google’s CEO Sundar Pichai emphasized how our expectations from Google Search evolve—from individual queries to ongoing conversations and now to agentic workflows. As the world’s most-used product, Google is determined to stay ahead of our needs.

Inspired by this post on Search Engine Land.

May 19, 2026
Discover How AI is Transforming Google Search Queries

I’ve noticed that Google Search Query Reports are moving towards AI-driven interpretations, reflecting inferred intent rather than exact user searches.

What’s happening. Google has clarified that the search terms in Search Query Reports might not precisely match what users typed. Instead, the system displays the “closest approximation” due to the complexity of modern search behaviors.

What’s behind it. It’s fascinating how heavily AI now influences Google Ads’ matching systems. Rather than depending solely on specific keywords, Google increasingly interprets user intent, context, and behavioral signals to decide which ads to display.

Why we care. For those of us in advertising, Search Query Reports might become less of a mirror reflecting user language and more of a summarized representation of intent. This shift might complicate query analysis, decisions on negative keywords, and strategy around match types.

Discovered by. This update was brought to my attention by Adsquire founder, Anthony Higman, on an official Google help page discussing ad group and asset group prioritization in Google Ads.

The bottom line. Google Ads continues its evolution from keyword matching to AI-driven intent modeling, meaning we might have less insight into the exact searches that activate our ads.

Inspired by this post on Search Engine Land.

May 13, 2026
Transform Your Link Building with Citation Optimization
AI search is reshaping how SEO visibility is understood. It can often overlook high-ranking brands in buyer answers, urging us to refocus our strategies. Our mission as link builders is to optimize the sources AI systems use to retrieve and cite information.

Link building has evolved significantly over the years. Traditionally, visibility was measured by keywords, rankings, links, and click-through traffic. Although these metrics are still crucial, their influence, especially at the top of the funnel, has diminished.

There’s a seismic shift in how prospective customers resolve their issues. Today, buyers no longer compress their queries into keywords. Instead, they interact with AI systems using natural language, providing context to make informed decisions tailored to their needs.

If we ignore this change, we’re in for visibility nightmares that outdated metrics can’t explain. As link builders, our role has always been about more than just accumulating links. We must earn visibility on pages that convert.

Modern link building requires us to focus more closely on decision-making, understanding what buyers need, ensuring the information’s existence, and discerning which sources AI can trust and utilize.

That’s why our focus should shift towards citation optimization.

AI search changes the landscape of SEO visibility. Top-of-the-funnel strategies are still relevant, but they don’t yield the same impact as before. Ranking for key topics remains beneficial, as does maintaining visibility in searches and sources AI systems refer to for decision-stage prompts.

Core SEO principles such as creating useful content, fostering trusted references, establishing authority, maintaining source consistency, ensuring clarity, and building strong links still matter. However, the traditional process has weakened.

We’ve built an entire SEO model around keywords, but they were always simplified representations of real problems. People had to translate their questions, constraints, fears, or decisions into keywords to use search.

AI changes this behavior. People ask questions naturally, add context, and describe their problems, what they know, and their obstacles. Although simple, this represents a significant mental shift for SEO teams—from focusing on keyword rankings to assisting people in solving problems.

Citation optimization involves guiding AI systems to useful source material for decisions rather than simply adding another link.

AI makes visible the questions buyers once asked sales directly. We’ve observed enterprises with vast search visibility still missing in critical AI-driven buyer queries.

Massive keyword searches and site traffic don’t guarantee presence in these AI-centric answers, as more focused questions tie closely to buyer pain points and services. Competitors often appear instead.

Google’s AI Mode may not recognize some brands due to a lack of context necessary to confidently recommend them for specific buyer questions.

These aren’t traditional keyword questions. They’re deeper buyer-side queries typically surfacing during sales interactions, aiming for clarification on fit, use cases, proof points, and implementation, traditionally held in sales reps’ knowledge.

Nowadays, buyers conduct this research independently when narrowing down options, confirmed by our recent behavioral study.

As link builders, it’s our responsibility to extract this valuable information from within our organizations, posting it where AI tools are likely to source answers, not just focusing on backlinks.

This necessitates access to essential sales and implementation diagnostics insights.

When these questions arise, simply covering keywords isn’t enough. It showcases demand but doesn’t highlight necessary buyer trust elements nor uncover unasked questions (known as FLUQs) essential for decision-level information AI systems require.

AI systems need materials to answer buyer questions. Tracking BOFU prompts lets us examine these surfaces.

Direct prompt data remains inaccessible, but synthetic prompts can reflect real buyer intent, guiding insight without treating single rundowns as conclusive.

We must begin by considering what sources AI systems access when responding to buyer problems.

This changes link-building strategy. We assess cited pages in AI responses asking if they provide detailed, accurate answers:
- Do they explain the offer?
- Do they compare options?
- Do they outline use cases?
- Do they provide proof?
The source mix varies by prompt, industry, and intent. At the funnel’s bottom, AI tools often cite LinkedIn, YouTube, third-party comparison pages, microsites, and competitive or vendor content.

AI systems work with what they can swiftly access, requiring page content prepared for easy consumption, like tables or comparisons.

Our job is to earn not just links, but to enhance material AI systems reference, aiding their brand decisions.

Don’t over-analyze a single prompt. Track multiple prompts for recurring gaps. If a brand is visibly missing from valuable prompt categories, that gap signals an area to investigate.

Citation optimization involves identifying influential pages and websites and ensuring they properly mention your offering to boost brand visibility and accuracy within AI context.

Remember PARSE: Source-led research starting points for SEOs and link builders. Track relevant unbranded prompts, identify repeatedly cited pages and domains, and review them closely.

Questions to consider:
- What sources shape the answer?
- Which pages compare options?
- Which provide a table, list, or framework AI systems can utilize?
- Which omit your brand while mentioning competitors?
- Where are you mentioned without enough context?
This approach produces a richer target list beyond mere backlinks. It’s about refining material AI might use to identify brand presence in an answer.

Incorporate your brand into cited pages, enriching existing mentions, or improving thin comparisons with clearer ones, adding tables, graphics, or explanations to create more valuable content chunks.

Links remain important but aren’t standalone solutions. You need more than anchor text; contextual material surrounding it is critical for AI understanding, forming effective citations.

Whether you’re managing link-building internally or with partners, seek more than just a backlink. Ask for comprehensive anchor context, including insights into the offer, use cases, beneficiaries, and reasons for its place in the AI-driven answer.

This marks the first step from traditional link building to the realm of citation optimization, enhancing both search and AI visibility.

Inspired by this post on Search Engine Land.
May 13, 2026