Category: AI SEO

Boost Your Google Ads Visibility in AI Overviews with These Strategies

I’ve discovered that AI Overviews are changing the way Google Search displays paid ads. Nowadays, it seems like there’s more pressure to get my ads to appear in AI-generated responses, as direct search results provide fewer opportunities for clicks.

Google suggests that Shopping, Performance Max, and AI Max for Search campaigns are best suited for this evolution. However, just choosing the right campaign isn’t enough. I need to ensure the quality of my feeds, optimize my landing pages, and use effective audience signals and creative content strategies to boost my ads’ chances.

Enable Google-Recommended Campaigns for AI Overviews

I’ve found that Google is quite clear about which campaign types are most likely to appear in AI Overviews. Interestingly, these opportunities are often overlooked by experienced marketers due lack of full control.

Despite this, I’ve come to understand that combining control with data and an understanding of search intent will benefit both me, as an advertiser, and the searcher. This involves strategizing beyond picking the right campaign types, focusing instead on fully optimized feed data and content alignment.

To boost my visibility in AI Overviews, I’ve enabled Google’s recommended campaigns to sync with the feature, particularly Shopping, Performance Max, and AI Max for Search, utilizing broad match keywords and smart bidding with final URL expansion.

Shopping Campaigns

Learning that the original keywordless campaign relies heavily on my data feed quality, I’ve focused on creating a well-built and optimized product data feed, using high-quality images, and ensuring my titles and descriptions are thorough.

I’ve realized how crucial the product data feed is in determining ad visibility for specific queries. When high-intent questions are asked, the AI Overview can feature a product carousel, enhancing the prominence of shopping results.

Performance Max Campaigns

In Performance Max, I’ve seen how keywordless campaigns utilize page content, data feeds, and audience insights to decide ad display. These inputs are key in determining ad visibility for queries.

Enabling Final URL expansion has allowed my ads to appear in more searches by leveraging page content for user query relevance.

AI Max for Search Campaigns

By using existing keywords as a starting point, AI Max for Search expands beyond to determine ad delivery strategies. This means keywords signal intent rather than dictate ad display.

I’ve noticed that AI Max uses search term matching and asset optimization to target queries unaddressed by traditional keyword targeting.

6 Best Practices for Ad Campaigns

To improve my chances of being featured in an AI Overview, I’ve optimized my campaigns by focusing on creative, copy, schema, and link-building techniques to reinforce brand authority.

1. Diversify Your Assets

With campaigns like AI Max and Performance Max, I’ve realized the importance of using varied creative assets. Incorporating informative headlines, descriptions, and visuals in multiple formats allows for diverse ad placements.

2. Use a Conversational Tone

Understanding Google’s approach, I’ve shifted from generic sales pitches to a conversational tone in my Responsive Search Ads, using language that assists the user rather than typical sales jargon.

3. Be Clear and Informative

By answering key questions succinctly, my ads now have a better chance of being highlighted in AI Overviews. A focus on information-rich landing pages has proven essential.

4. Check Schema Markup and Links

I ensure my schema markup is thorough and aligned with my content. Linking to reputable sources builds authority, and collaborating with my SEO team has enhanced these practices.

5. Guide Automation with Audience Signals

I recognize the lack of control in these campaigns, so I’ve guided automation using strong audience signals, exclusions, and negative keywords to refine my targeting strategies.

6. Regularly Monitor Campaigns

Regular monitoring is crucial for brand safety and profitability. Reviewing search terms, landing pages, and ad assets ensures my message remains consistent and aligned.

Adapt Your Approach for AI Overviews

Adapting to conversational AI Overviews requires me to focus on maximizing visibility on the SERP. Emphasizing data feed quality, content alignment, and creative diversity turns this shift into an opportunity for growth.

Inspired by this post on Search Engine Land.

May 27, 2026
Navigating AI Visibility: Macro Strategies for Success

AI visibility has transformed into a macro measurement challenge, and I’m here to guide you through building a foolproof framework to track recommendation trends effectively.

Through my experiences, I’ve learned that the funnel query pathway (FQP) is the ideal framework for measuring AI visibility. By assessing the FQP quarterly, I can derive actionable strategic insights.

I’ve coined this transformation the micro-macro shift. Traditional micro (ranking) metrics from search engines are no longer sufficient to measure AI visibility due to the opaque nature of AI engines.

In the AI-driven world, we must embrace a macro measurement approach, akin to economics evolving new measurement disciplines for broader economic systems.

The AI landscape operates under a brand-user-algorithm (BUA) opacity, where four layers veil every AI-era brand recommendation process.

The multi-layered opacity impacts everything from brand perception to conversion rates, and understanding this opacity is crucial.

Utilizing micro-strategies in an AI environment is futile. Instead, my focus shifts to macro-level insights, acknowledging that consistency over time is key, not momentary precision.

In 2026, search remains micro, while assistive and agent modes adopt macro approaches. The right measurement strategy for your business hinges on understanding each mode’s environment and data.

Search enables user control with clear metrics. Having been trained in this mode, I recommend maintaining micro strategies for search-based operations, supplemented by macro methodologies.

Assistive recommendations come from engines like ChatGPT. Unfortunately, I can’t see the decision data, making micro assessments impossible and macro the only viable option.

Agents autonomously make purchases, providing a clear but limited view of their decision-making. The conversion insight remains macro, even if initiation is observable.

Given buyers’ ever-changing reliance on different surfaces, adopting a macro approach remains inevitable, ensuring I stay adaptable to any environment they opt into.

As I shift forward with macro metrics, measuring becomes more about trends. Tracking consistent methodologies over eight quarters offers reliable strategic clarity.

In the busy world of AI decision-making, patience and consistency are key to staying ahead. I prioritize stable methodologies to gain competitive insights over time.

Inspired by this post on Search Engine Land.

May 27, 2026
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
Harnessing SEO: Focus on High-Intent Traffic for Greater Impact

I’ve noticed that not every organic visit deserves the same consideration these days. It’s become evident that I need to hone in on high-intent pages to truly measure SEO success and its impact on my business.

Recently, HubSpot rebranded its flagship conference from INBOUND to UNBOUND. This change wasn’t merely cosmetic; it represented a strategic pivot away from old-school SEO strategies that emphasized top-of-funnel traffic.

Modern search dynamics are nudging us closer to a zero-click environment. Trust me, the click-through rate curve is rapidly evolving. Studies show that around 60% of searches now conclude without a single click leading to the open web.

I’ve also observed that the discovery layer of search has shifted significantly. Nowadays, buyers are researching vendors within platforms like ChatGPT and Perplexity before they even consider clicking a traditional blue link.

Attribution has become increasingly complex. The modern buyer journey is fragmented, often starting with AI-assisted search and only finalizing on my website when the prospect is ready to make a decision.

This shifting landscape distorts my SEO reports if I focus solely on traffic as a success indicator. I’ve decided it’s time to pivot and redefine how I present traffic data to marketing leadership, ensuring that my reports align more closely with business impact.

A lively discussion on LinkedIn, led by Peter Rota, debated whether to completely retire organic traffic as an SEO metric. The consensus, I’ve found, is to use traffic with caution, always considering intent and the actual revenue it drives.

While organic traffic isn’t inherently bad, relying on it solely as a KPI lacks context and could be misleading. Adam Heitzman pointed out that it’s essential for traffic metrics to come with intent-based context for more accurate reflections of performance.

In a situation where low-intent traffic is reduced and focus is shifted towards high-intent pages, I’ve noticed that although overall visits might drop, conversions and revenue can actually increase due to better-quality traffic.

This understanding has led me to differentiate between top-of-funnel visits and more meaningful page interactions, thereby filtering out the data noise and focusing on what really matters in my dashboards.

Rand Fishkin beautifully summarized that top-of-funnel marketing feels like ‘rented land’—and he’s right. Buyers are now finding most basic information elsewhere, opting for instant answers on platforms like Reddit, TikTok, and within LLMs.

As of now, generic informational traffic is dwindling. Ironically, many SEO efforts are still devoted to content types most vulnerable to AI-driven change, such as FAQs and long-form articles.

Given this shift, it’s crucial for me to track pages based on their transactional value—those that AI can’t easily replace. I’ve narrowed my focus to four main areas: homepage, pricing pages, products and solutions pages, and money content pages.

Focusing my reporting on these key pages allows me to cut through the noise and concentrate on the traffic truly affecting my business’s bottom line.

For example, when a prospective B2B buyer starts searching for a modern CX platform, they’ll go through AI search, Google verification, and eventually land in the dark funnel for conversion.

Understanding these layers helps me recognize which organic traffic is significant enough to report, enhancing my insights into customer journeys and how they interact with my website content.

I know I must move away from outdated traffic analysis techniques to embrace more effective, modern reporting standards that focus on directional trends and macro shifts indicative of real business impact.

By focusing on page health instead of unreliable keyword-level reporting and monitoring branded search volume as an AI visibility proxy, I can capture a more accurate view of my current impact.

Inspired by this post on Search Engine Land.

May 22, 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

Mastering Pre-Search Visibility: The SEO Pyramid Guide

I’ve come to realize that my buyers often have a shortlist in mind even before hitting Google. It’s fascinating how these pre-search decisions form. Here’s my take on how I influence those vital conversations that put my brand on that list.

The customer journey used to kick off with a simple search, but it’s evolved beyond that point. By the time potential buyers type a query into Google, they usually have some brands in mind. They’ve watched Instagram Reels featuring a product repeatedly, read threads on Reddit with unanimous recommendations, and seen similar endorsements in Facebook groups.

Google is now more of a confirmation tool than a starting point. When someone searches, they’re looking to confirm their assumptions, not to browse aimlessly.

The key question is, did my brand make it onto their mental shortlist before they began searching? In most cases, being visible on comparison platforms is crucial for this.

So, where is this shortlist actually built? Peer-driven decisions are made in various industry-specific environments

By the time these interactions prompt a Google search, choices are often boiled down to specific comparisons like “brand X review” or “brand X vs. brand Y.” Being mentioned in those off-SERP discussions is usually more influential than ranking for a head term.

It’s worth noting that platforms like Reddit won’t hold the spotlight forever as visibility there is inherently temporary. Yet the basic behavior remains constant: people ask their peers before consulting search engines. My strategy focuses more on participating in these conversations rather than just chasing trending platforms.

Dig deeper into strategies to ensure pre-search visibility and why your brand might not be included in AI recommendation sets.

The two objectives of search everywhere optimization, or SEvO, form the backbone of my campaigns:

Direct visibility ensures my brand appears where buyers are narrowing options, measurable by direct search traffic and specific branded queries. Engine comprehension, on the other hand, leverages each brand mention next to relevant problems or solutions to enhance AI system recommendations.

Steve Jobs famously said, “You can’t connect the dots looking forward; you can only connect them looking backward.” I can’t see how these efforts gel until they start appearing in AI responses and the buyer conversations.

To measure effectively, I keep tabs on things like brand mention volume and trends in branded searches. These indicators suggest that pre-click visibility is working.

When it comes to Search Everywhere Optimization, the strategy I use is all about getting discovered where my buyers spend time, even before they think to search for brands like mine.

The Search Everywhere Optimization Pyramid organizes my efforts:

The groundwork is Audience Platform Research, guiding me to where my customers are likely making their decisions.

Setting up effective alert systems is key to knowing when relevant topics surface, helping me know when my brand should join the conversation.

Next up comes credibility through industry publications, earning my brand recognition in places potential buyers trust.

Then I focus on distribution, ensuring my content reaches my audiences effectively and keeps them engaged.

Finally, I create and refine my own content to support everything from below, nudging my brand into view when buyers are in that crucial decision-making phase.

Understanding that conversation is ongoing helps me navigate future shifts, even as specific platforms rise and fall in popularity.

If my goal is making it to the buyer’s shortlist, I need to ensure visibility not just on SERPs but across all the web spaces they engage with. Through consistent and deliberate steps, the pyramid ensures that my brand is more than just a search result — it’s part of the discussion.

Inspired by this post on Search Engine Land.

May 21, 2026
Is Zero Click Marketing Evolving with New AI Branded Links?

On May 7, 2026, something remarkable happened that completely shifted the landscape of AI-driven brand traffic. As I watched, ChatGPT quietly launched the most significant single-day transformation I’ve seen all year.
Overnight, the referrals from OpenAI to various brand sites practically doubled. It felt like each mention of a brand by ChatGPT was suddenly more valuable—because they turned into clickable referrals directly to the brands’ homepages.

Inspired by this post on Try Profound Blog.

May 19, 2026
Unlocking SEO’s Future with AI: Why Expertise Still Matters

I’ve often pondered the impact of AI on our work as SEO professionals. As AI takes over repetitive tasks, those of us who can strategically guide its use will find our skills even more valuable.

By now, you’ve likely heard the dire predictions:

Verizon’s CEO, Dan Schulman, cautioned that AI might push U.S. unemployment rates to 20%-30% in the next few years.

Anthropic’s CEO, Dario Amodei, warned of AI wiping out a significant portion of entry-level white-collar jobs.

According to Ford’s CEO, Jim Farley, AI could replace half of white-collar workers in the U.S.

SEO, a field I’ve been passionate about for years, is certainly in the crosshairs. But does this mean our careers are at risk? Not necessarily.

The landscape is evolving, yes. But if you’ve been in SEO as long as I have, you’re no stranger to adaptation.

Our roles have always demanded that we wear many hats, from being technical analysts to creative strategists. AI won’t replace this expertise—it’ll replace superficial approaches to SEO.

Success will belong to those who understand search behavior deeply, link it to business outcomes, and make insightful decisions.

The version of SEO many remember is already outdated. I’ve practiced SEO since before it even had a name, and every so often, someone claims that “SEO is dead.” While the field has changed drastically, it’s far from deceased.

SEO, as interpreted today, requires understanding how people search for your offerings and knowing how to meet their needs across various platforms. This journey is only just beginning for those of us in the know.

In a time where everyone can leverage AI tools, the real differentiator is how adeptly we employ these tools to achieve our visions.

Even now, some people believe that writing SEO prompts in AI means they can call themselves experts. But SEO isn’t just about title tags or decoding search engines; it’s about understanding user psychology and combining technical systems with strategic execution.

With AI, we’re entering a new phase requiring new skills. We’ll work more efficiently by incorporating AI into essential SEO tasks. The depth of our conversations with AI will be key to our differentiation.

Here’s a look at how I’ve begun integrating AI into my workflow for greater productivity and insight:

AI can help with the basics—like generating metadata—where precision takes precedence over creativity. We can use AI for better recommendations and design, allowing developers to work with better-prepared resources.

AI is also instrumental in drawing insights from GSC, GA4, and tools like Semrush to gather actionable user data and preferences.

Another frontier is using AI to prototype and improve upon web design layouts, thereby streamlining collaboration with designers and developers.

AI’s presence in analytics is similarly transformative. Though GA4 initially posed a setback for established workflows, AI allows us to develop new, more insightful reports.

Ultimately, my career’s foundation isn’t just in managing tasks that AI could handle. It’s in understanding customers, reading data for insights, and connecting these insights back to real-world results.

Like many others in our field, I’ve witnessed great companies start with grassroots efforts, which have only grown with time. As AI continues to evolve, its role isn’t one of replacement—but of empowerment.

SEO isn’t fading—it’s transforming, waiting for us to lead it into a new era.

Inspired by this post on Search Engine Land.

May 19, 2026
Mastering AI Visibility: A New Framework for Success

I often get asked in 2026, “How do we measure this?” when it comes to AI visibility.

People want to know if their brand is appearing in ChatGPT or if Perplexity is recommending them. They also wonder if their work on AI grounding last quarter made any impact.

The truth is, the solution doesn’t exist yet. Anyone offering a straightforward dashboard for tracking your brand’s presence in AI spaces across search, assistive, and agent modes is just making an educated guess.

Tracking queries we assume users might ask, or adapting search keywords as a best guess, won’t cut it. These prebuilt lists often miss the mark as they choose easily mapped or ideal scenarios that don’t reflect reality.

The visibility question itself is valid, but the precise answer everyone seeks simply isn’t feasible.

Brands looking for perfect AI-era visibility KPIs are chasing a mirage. Instead, we need a methodology inspired by economic measurement of complex systems—this is where my Funnel Query Pathway comes in.

This unique approach serves as strategy, measurement, and analysis, unlike traditional metrics that were reliable when search rankings were predictable and measurable.

Now, we must rethink our approach in a complex AI landscape, asking new questions and measuring different signals.

Your customers search everywhere. Make sure your brand shows up.

The SEO toolkit you know, plus the AI visibility data you need.

Start Free Trial

Get started with

I studied economics at Liverpool John Moores University, which gives me a unique perspective on measurement challenges where traditional tools fail at larger scales.

As with macroeconomics dealing with vast, unobservable systems, AI visibility is too opaque and personalized for old tools. We need macro principles to guide AI-era brand measurement.

AI systems have similar structural complexities as macroeconomics:

Opacity hinders visibility into the system’s state, with AI algorithms operating like a black box. Personalization means users receive unique outputs from the same inputs, influencing the visibility paths.

With expanding possibilities across apps, systems, and devices, AI environments now introduce variables that weren’t present in traditional search models.

The Funnel Query Pathway methodology focuses on these macro aspects, shifting away from keyword mapping to a broader approach focused on cohorts and intent at the node level.

AI-era acquisition begins at the conversion moment projected upward, contrary to traditional funnel methods.

Inspired by this post on Search Engine Land.

May 19, 2026
3 Key Elements Your SEO Audits Can’t Succeed Without

AI can elevate SEO and GEO audits dramatically, but only if you equip it with the right data, methodology, and human oversight.

As someone deeply involved in the world of B2B tech SEO, I find it fascinating how AI is reshaping our strategies. However, I’ve noticed a trend among clients who provide AI-generated audits—what I term ‘naive audits.’ While these reports often appear detailed, they miss crucial components. When I inquire about their basis, data sources, or methodology, they frequently crumble under scrutiny.

This gap between expectation and delivery inspired me to propose a simple framework focusing on three critical elements—context, methodology, and human oversight—to ensure AI-driven audits provide genuine value.

Imagine asking an advanced language model, like Claude or ChatGPT, to perform a simple SEO task, such as optimizing a blog post. The result? A 1,600-word detailed analysis filled with assumptions and errors, due to lack of access to the full content or appropriate keywords. Sounds familiar, right?

Despite the capabilities of models like Claude, I discovered severe limitations. For instance, it couldn’t read the original article, basing its recommendations on search snippets instead. Not only was the suggested keyword, ‘intelligent data tiering,’ void of search volume, but the analysis itself was flawed as well.

Ensuring an audit is grounded in reality requires agents that are self-sufficient and well-informed. They must include an understanding of content, an appropriate methodology, and concise, actionable recommendations. I believe in empowering busy writers by offering bite-sized guidance rather than overwhelming them with lengthy reports.

When building a page audit agent, I follow these essential steps: pre-scraping webpage content, leveraging keyword tools, accessing top URLs for key queries, and aligning recommendations with structured content outlines—all while maintaining a human in the loop to ensure accuracy and practicality.

So, when asking AI to execute GEO/AEO audits, one must be cautious of potential pitfalls. The knowledge base for AI in these emerging fields is riddled with speculative insights and inconsistent data. That’s why partnering with experts actively engaged in experimentation remains invaluable.

Ultimately, my CaML framework—short for Context, Methodology, and Human in the Loop—ensures that AI audits are comprehensive and substantial. Just as a camel is equipped to withstand the harsh desert environment, a well-prepared AI agent should be resilient to the challenges of digital landscapes.

Envision a future where SEO roles are redefined, focusing on strategic guidance and unique insights rather than laborious manual tasks. Our agency’s transition to an agent-first model embodies this shift, and I’m excited to be on this transformative journey.

Inspired by this post on Search Engine Land.

May 18, 2026