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Category: Opinion

TurboQuant: Revolutionizing AI with Entity-Driven SEO

I believe the launch of TurboQuant will revolutionize AI and SEO as we know it. This cutting-edge algorithm from Google drastically reduces the computing power and energy needs by allowing the massive compression of LLMs and vector search engines.

Imagine using six times less memory and achieving eight times the speed without compromising accuracy. That’s how TurboQuant dramatically lowers the cost of running AI tasks.

As search engines evolve from simply listing links on a SERP to providing immediate AI-generated overviews, it’s crucial for us in the SEO industry to adapt. We need to focus on creating meaningful, trustworthy content and understand its impact on searches.

Before AI became prevalent, SEO was grounded in basic keywords and topics, which inefficiently represented user intent. High costs and energy consumption hindered mapping true meaning across the web, but now TurboQuant uses an advanced compression method, PolarQuant, to transform data into manageable coordinates. This breakthrough allows Google to process complex ideas far more efficiently.

TurboQuant can match exact search meanings in real time, thanks to its ability to understand user intent using past searches and real-world contexts.

The near-zero indexing lead time of TurboQuant eradicates delays between publication and ranking. Trusted publishers will gain instant recognition for their expertise, while the system also blocks manipulation and spam from appearing.

We must prepare for the fast-approaching era where AI summaries become the norm in responding to most queries. Thin content, which adds no original value, will vanish because AI can now summarize the web almost instantly, making unique viewpoints and genuine data irreplaceable.

Developing trust and authority with original thoughts, data, and experiences will prove essential, as AI-generated summaries merely consolidate existing information.

The focus of our SEO strategies should be to become a source AI recommends reliably, not just rankings based on keywords. TurboQuant maintains a more reliable index of facts by validating them against its real-time knowledge base.

This new system tracks a brand’s strength across various platforms, reinforcing the necessity of improving our knowledge graph as a trusted source.

With TurboQuant handling vast information without delays, hyper-personalization is set to explode in ways we’ve previously not imagined. AI agents could remember extensive user interactions to provide extensive personalization.

TurboQuant’s capability to integrate various signals into a cohesive perception of a brand’s value demands a strategic shift toward consistent, omnichannel representation.

We’ve prioritized quantity over quality for far too long in this industry. TurboQuant signals the end of this era, as it necessitates creating high-quality, meaningful content that establishes us as trusted entities.

Delivering a reliable message with a clear voice will guide how our messages are distributed and our brand credibility.

Inspired by this post on Search Engine Land.

May 13, 2026
Boost Your Brand’s AI Recommendations with Clarity and Relevance

Over the past few years, I’ve been inundated with advice on generative engine optimization (GEO) – everything from AI citation checklists to technical guides for structuring content for large language models.

Most GEO guidance revolves around a key premise: To be visible in AI-generated answers, your content must be structured, authoritative, and easy to extract.

In my view, this advice, while valuable, falls short if your brand isn’t yet eligible for consideration in AI-generated results.

The underlying assumption is that ticking those boxes makes your brand eligible for AI-generated answers. However, many brands overlook the fact that they aren’t even being considered.

To get past this hurdle, we need to address an underappreciated factor that many GEO enthusiasts miss.

Traditional SEO has taught us to seek visibility through rankings, believing that higher rankings translate into more clicks and better outcomes. Many have now adapted this mindset to AI, aiming for citations or inclusions in AI-generated answers.

However, AI systems don’t just rank; they filter and select entities based on signals, determining eligibility before weighing options.

Without eligibility, many brands risk being excluded from the AI recommendation set right from the start.

Brands often misprioritize, focusing on extractability before establishing clarity, which results in missed opportunities.

It’s critical to understand the difference between qualification (being eligible to join the candidate set) and selection (being chosen from that set).

AI-driven search changes the game. While traditional SEO ranks pages, AI selects entities, such as branded products and concepts, interconnected in a web of knowledge.

This shift means we must prioritize entities over pages. An entity might excel in traditional search yet remain ambiguous in AI-generated answers.

Common issues lie in clarity and relevance. AI systems ask: Can I identify and associate this entity accurately?

If definitions are inconsistent across platforms or names vary, brands struggle to pass this threshold.

Clarity is the cornerstone. When AI or search engines see your brand, clarity allows them to understand exactly who you are.

For example, when I noticed my common name, Mariana Franco, was causing confusion, I changed it to “Maryanna.” This helped ensure that my identity was distinct and recognizable to AI systems.

By consistently using this unique name variant across all my online assets, I reduced ambiguity within a week, making it easier for systems to recognize me as an entity.

Relevance is another crucial factor. Does the web associate your brand with relevant topics consistently and strongly?

This involves appearing alongside related entities, demonstrating expertise through in-depth content, and being referenced by well-known entities in your field.

Once qualified, a brand becomes part of the candidate pool, applying GEO strategies to increase the chance of selection.

Credibility becomes vital at this stage. You need corroboration from reputable sources to enhance your credibility.

Multiple credible mentions and appearances in media, reports, and podcasts bolster your visibility and reliability.

Extractability, or how easily an AI can generate answers from your content, is crucial once in the candidate set.

To ensure extractability, organize your content clearly, prioritizing concise, context-independent answers.

Testing your brand’s appearance in AI tools can reveal whether you’re recognized or recommended. A search using ‘best [your category]’ illuminates inclusion gaps.

If AI recognizes your brand but doesn’t recommend it, focus on building selection signals — credibility and extractability.

For comprehensive visibility, prioritize clarity and relevance to ensure eligibility, then focus on credibility and extractability to strengthen your standing.

Start by ensuring name consistency and clarity — the foundation of being recognized as a distinct entity.

Your About page should explicitly define your brand, utilizing schema to integrate into AI systems.

In AI’s expanding landscape, qualified entities will thrive, making consistent clarity and corroboration more critical than ever.

Inspired by this post on Search Engine Land.

May 13, 2026
Unlock Local Visibility: Harness AI in Local Search Now
I recently discovered how AI is revolutionizing the way customers find local businesses. Tools like Google AI Overviews, Gemini, and Ask Maps are paving the way for more detailed, conversational searches.

It’s clear to me that traditional search rankings are no longer the sole factor in gaining visibility. Ensuring your business details are complete and accurate—like your Google Business Profile, reviews, and local content—can make a big difference.

I’m excited to join SOCi and Google for an exclusive webinar, Winning the Next Era of Local Visibility, on June 3. It’s a golden opportunity for anyone looking to stay ahead of the curve.

During this webinar, I look forward to learning:
- How AI is transforming local search dynamics.
- The types of signals that AI considers for recommendations.
- Strategies to boost visibility on Search, Maps, and Gemini.
- The implications of Ask Maps for your brand.
I’m convinced that AI is already shaping customer discovery, so it’s crucial to ensure your business isn’t left behind.

Register now to secure your spot.

Inspired by this post on Search Engine Land.
May 11, 2026
Boost Your Site’s Relevance: Aligning Intent Over Technical SEO
These days, simply fixing technical SEO issues on my site isn’t enough to make a significant impact.

When my site achieves technical parity with competitors, the ranking focus shifts from infrastructure to relevance. Google evaluates relevance based on how well my content aligns with search intent.

Let’s explore how I can make my site more relevant.

Why an intent mismatch may be suppressing my site’s performance

An intent mismatch happens when the content on my page doesn’t meet user expectations. If the page isn’t relevant or the signals sent are mixed, it results in poor behavior signals, like users bouncing off the page without finding answers.

These signals suggest to Google that my page doesn’t satisfy the query, causing ranking drops, fewer users viewing the page, and worsening behavior signals. It’s a situation that technical SEO alone won’t solve.

Technical SEO improvements may no longer make a difference

Initially, when I start an SEO strategy, improvements come quickly. If my website lags in technical standards, resolving crawl errors, addressing duplicate content, boosting page speed, and adding schema can result in significant gains.

However, once these changes place my site on par with competitors, Google evaluates sites based on user query satisfaction. Now, my technical foundation is solid, but the rules have changed.

Intent alignment becomes the primary improvement focus here.

Signals that reinforce search intent

Various elements affect a page’s intent and Google’s decision on whether it matches. These include:
- Click-through rate.
- Engagement signals.
- Core Web Vitals.
- Schema type.
- Internal linking anchor texts.
- URL structure.
Click-through rate (CTR)

My CTR can be influenced by factors like my title tag, meta description, URL structure, and schema, all measured against intent.

If my title tag is well-optimized yet mismatched with user queries, CTR will drop. Google sees low CTR as a relevance signal and adjusts rankings.

Engagement rate

Intent misalignment can harm time-on-page, scroll depth, and interaction rates. A user searching to purchase something might exit immediately if they land on a how-to guide. Similarly, a user seeking an emergency plumber might bounce from a page lacking contact details.

Core Web Vitals (CWV)

LCP, INP, and CLS measure page load speed. A slow transactional page frustrates users ready to buy, whereas informational article readers are more patient.

While CWV thresholds matter everywhere, they heavily impact conversion and behavior on high-intent pages.

Schema type

Schema markup explicitly tells Google the page content type. Contradictory content and schema signals send Google a wrong intent signal, affecting traffic.

Internal linking anchor texts

Internal link anchor text informs Google about the linked page’s intent. If a transactional page’s links use informational text like “learn more about X,” intent signals get diluted.

URL structure

Google uses URL patterns to infer page type. For instance, URLs in /blog/ are seen as informational. A product page in a blog path may struggle with ranking expectations.

Cannibalization and canonicalization

Multiple pages targeting the same keyword with different intents dilute Google’s signal, hindering ranking. Using canonical tags can emphasize the preferred page for a keyword, consolidating or redirecting when necessary.

How to fix intent misalignment

Let’s consider a common intent mismatch and steps I can take to audit and fix it.

What an intent mismatch looks like

If someone searches for “financial analysis software,” they intend to purchase software, a highly transactional query. Targeting this keyword with an informational blog post explaining DIY analysis creates a mismatch.

These users want to compare features and pricing or book a demo. Therefore, targeting the keyword with a dedicated page outlining features and pricing is optimal, aligning with user needs and boosting conversions.

Identify the intent of my pages

To remedy intent mismatches, I start by compiling top-performing keywords and manually checking their Google rankings. This research shows what type of page and content best suits these keywords.

See what my competitors are doing

By researching competitors’ pages targeting my keywords, I note elements they include, such as tables, comparisons, or videos, which can inform improvements on my pages.

Measure my page’s performance based on intent metrics

After making page improvements, I track performance indicators like clicks, rankings, and time on page to evaluate the effectiveness of changes.

Technical SEO and intent need to work together

Technical SEO is vital; it lays the groundwork. Pages that aren’t properly crawled won’t rank to their full potential, regardless of intent alignment.

Intent alignment, however, dictates how high a technically sound page can rank and its conversion rate. Every page should have clearly defined intent supported by technical signals for reinforcement.

Inspired by this post on Search Engine Land.
May 8, 2026
Maximize AI Visibility: Influence, Signals, and Citations

I’ve seen how crucial it is to understand that AI visibility starts long before users hit that search bar and ends with citations.

These insights are vital in shaping what gets seen, summarized, and cited by AI systems.

Currently, the focus has shifted towards improving the AI ROI story, and I’m right in the thick of it, learning what strategies truly work.

This year, attending SMX Advanced will be more enlightening than ever, bringing unique perspectives and strategies.

Let’s dive into why influence matters everywhere, and how it impacts AI citations.

Rand Fishkin’s study, ‘Influence Happens Everywhere,’ reveals that, although Google commands the majority of search traffic, it’s the influence happening outside of search that truly dictates what people look for online.

For many, wandering through social media or news sites builds their understanding and interest long before the actual search occurs.

Despite the exciting growth of AI tools, achieving a stable presence online requires understanding how fragmented channels contribute to this influence.

When crafting content, it’s essential to dominate the influence phase so thoroughly that an AI assistant doesn’t just suggest your brand—it demands it.

That’s the strategic thrust behind the discussions at SMX Advanced in Boston and why I align my content calendar accordingly.

My colleagues at Search Engine Land are among those shaping these discussions. Insights from thought leaders like Dave Davies and Carolyn Shelby are invaluable.

They emphasize the importance of structured visibility signals and entity recognition, helping AI systems select the right brands to highlight.

In my own analysis, the various AI models like ChatGPT, Perplexity, and others have unique methodologies for selecting sources, reinforcing the idea that an engaged, multi-platform strategy is critical.

So, what does full-stack content truly mean today? It’s more than crafting blog posts; it’s about commanding entire topics with authority and depth, enhanced by AI tools like Jasper’s Enterprise Suite.

The ability to integrate real-time data, identify competitive content gaps, and create diverse multimedia content packages mean we’re shifting from simply generating content to dominating entire narratives.

But AI tools can only serve the overarching strategy if our content offers the original insights that help us stand out in AI retrieval systems.

This year, Purna Virji’s insights at SMX Advanced will challenge us to think critically about the real ROI in AI investment.

I’m particularly interested in seeing how Google Vids is democratizing video content by eliminating the high entry barriers of previous video production methods.

Now, video content can be produced and localized for a multitude of markets rapidly, a paradigm shift in how we engage audiences across the globe.

The standards AI is setting for content — whether text, video, or multimedia — require a strategic framework that aligns with evolving platforms like GEO and AEO.

For those in the trenches like me, adjusting focus towards an integration of structured data and earned media becomes imperative.

The real challenge isn’t in the buzzwords but effectively navigating the volatile landscape of AI-driven citations.

I recognize the adjustments needed in approach, especially when considering the stark differences in referral and conversion rates from traditional search versus AI platforms.

So, practical actions for the rest of 2026? Audit your AI presence thoroughly, stop gating original research, secure your place in vibrant communities, and refine your focus towards citatability rather than simple visibility.

Ultimately, the brands ready to adapt will continue to thrive in this AI-enhanced environment.

Indeed, the bots are crawling, and it’s time I ensured my brand is worth citing.

Inspired by this post on Search Engine Land.

May 4, 2026
Discover Why ‘Ugly’ Ads Could Boost Your Marketing Success
For years, I’ve been told to stick to a set of guidelines: always use top-notch creatives, maintain a polished brand, follow scripts, and adhere to platform-recommended formats.

Lately, while navigating ad accounts or simply scrolling through feeds, I’ve noticed something intriguing. The ads that grab my attention often defy these rules. They’re less polished, scrappier, and sometimes referred to as ‘ugly ads.’ What’s fascinating is that they’re outperforming the traditional, polished ones.

More brands are deliberately breaking so-called best practices to stand out. It’s important to remember that these practices represent an average of what worked for others in the past. By the time a strategy becomes a platform-recommended rule, it might have already lost its edge.

This is why defying best practices can lead to success — but only if you understand the reasons behind them.

Why Breaking Best Practices Enhances Ad Performance

Before diving into what to change, it’s crucial to understand the rationale behind existing rules. Platforms like Meta and TikTok have dual objectives:
- They aim for you to spend money on ads.
- They want to keep users engaged on their platforms.
The best practices they promote are designed to ensure a seamless experience, encouraging ads to resemble others. The issue is that familiarity eventually breeds invisibility. When I adhere too closely to the rules, my ads risk blending into the background noise, overlooked by users.

Highly-produced ads often scream ‘this is an ad,’ prompting users to skip them before my message hits home. In contrast, when my ad resembles something a friend might share, users’ defenses remain down longer, potentially transforming a scroll into a conversion.

This is why many top-performing ads today don’t appear traditionally polished or on-brand. They break patterns instead. Consider:
- Grainy phone footage.
- Notes app screenshots.
- Green-screened reactions or commentary videos.
- Other lo-fi formats that outperform studio-quality creatives.
Source: TikTok Ads Manager

To implement this, I started intentionally reducing my production value and experimented with formats like point-of-view (POV) shots tailored to various personas.

Dig deeper: TikTok ad creative has a shorter shelf life. Here’s how to keep up

Founder-Led Ads: Reviving the Human Touch

Many brands have adopted guidelines that make them seem faceless and untouchable. They refrain from showing a messy office, an unpolished founder, or anything that challenges their corporate script. However, others are discarding that playbook, embracing founder-led ads that deviate from the polished executive version.

There’s a catch.

Breaking the rules works only when it’s genuine. I’ve learned that faking authenticity is easy to spot and can backfire. This was evident in a viral series of videos where McDonald’s CEO appeared to present a new burger, but his execution was criticized for being stiff and unconvincing.

As shown in a Dineline video, his performance appeared staged. Contrarily, Burger King’s president presented their burger with no hesitation, offering a genuine and relatable moment.

The distinction was evident: One was a product pitch, and the other felt authentic.

If my leadership doesn’t genuinely believe in the product, neither will my customers. Rule-breaking should allow us to be real, rather than simply appear unpolished.

Source: Dineline on YouTube

The Comment Hook Hijack

You’ve probably encountered video hook best practices like ‘show the product in the first two seconds and state the value prop clearly.’ Sound familiar?

Imagine my ad starting with a screenshot of a negative comment, like one for a skincare product stating, ‘This probably smells like old socks, and does it even work?’ My ad would then show the founder confidently disproving this in an unscripted manner, applying the product.

Though this breaks the positive-association rule, it leverages viewers’ curiosity about digital conflicts. By the time they realize it’s an ad, they might already be engaged.

Source: TikTok Creative Center

The Rebel’s Safety Net

I learned not to abandon all polished assets just yet.

Rule-breaking is strategic, and often misunderstood when the ’80/20 rule’ is ignored.

Switching completely to shaky phone footage isn’t wise. Keeping 80% of the budget in traditional ads while using 20% for testing unconventional ones can be effective.

Next testing campaign, I plan to try:
- The silent test: Running a silent ad with bold captions to stand out in a noisy feed.
- The UI ghost: Using static images resembling platform notifications to pause scrolling.
- The algorithmic trust fall: Disabling auto-optimizations in a campaign to test creative performance without constraints.
Don’t Follow the Rules; Understand Them

Best practices are a guide, not a strategy. To move beyond them, I do it systematically.

I start by questioning the rule’s existence, evaluating its current relevance, and testing its opposite in a structured manner. Comparing traditional and lo-fi approaches helps me understand user engagement better.

In an environment where brands play it safe, those who understand and strategically break the rules will capture attention and conversions. My goal is to learn faster than the competition, skipping guesswork.

Inspired by this post on Search Engine Land.
April 22, 2026
AI Search: Navigating New Reputation Risks Effectively
I remember the days when a Google search was akin to embarking on a quest for information. It was an adventure of navigating various links and forming my own opinions.

Nowadays, tools like AI Overviews, ChatGPT, and Perplexity condense all that information into a single, simplified answer. This transformation often strips away the finer details while amplifying certain perspectives.

This shift has redefined online reputation management. Now, search engines not only present information but shape the underlying narratives. This raises the stakes for brands, as even a top-ranking status doesn’t guarantee influence if AI stories tell a different tale.

For brands, the game has changed. Being number one doesn’t ensure visibility and influence anymore. The underlying narrative holds far greater power.

AI Narrative Formation: Crafting User Answers

AI platforms now utilize what I like to call ‘AI narrative formation.’ This process crafts the responses we receive from various search engines. Let me walk you through how this system works.

Source Pooling

These systems pull content from numerous sources. Contrary to expected reliance on peer-reviewed articles, they gather data from Reddit, YouTube, and social platforms like Instagram and TikTok.

Signal Weighting

Not all sources are equal. Often, a popular yet low-quality source can outweigh a singular, credible entry. A bustling Reddit thread with negative feedback might overshadow a well-researched Wikipedia page.

Narrative Compression

The summarization process compresses diverse inputs, often losing nuance along the way. Complex reputations are simplified into general statements like, ‘Users find this company untrustworthy.’

Continued Reinforcement

These summaries transcend their original context, getting shared and re-shared across social media. As these echoes return as new data, they further entrench the narratives in AI responses.

Explore deeper: How AI is Redefining Authority in Search

Unraveling a Finance Company’s Reputation in AI Search

To illustrate AI narrative formation, consider a recent case I worked on involving a financial company, which we’ll call Company X.

Company X’s reputation remained strong on traditional SERPs. High Trustpilot ratings and reputable endorsements were the norm until Google AI Overview threads surfaced a forgotten Reddit forum rife with grievances against them.

The AI Overview skewed the narrative, suggesting Company X had unresolved customer service issues, even though these concerns had been addressed years prior. This created a skewed perception that was hard to counteract.

The Amplified Risk from AI Searches

AI dramatically increases reputational risk through several mechanisms:
- The Spread of Negative Narratives: Negative content surfaces faster and more prominently than before.
- AI Hallucinations: Despite growing awareness, AI inaccuracies continue to deceive.
- The Snowball Effect: Repeated narratives gain momentum, complicating reputation management efforts.
It has become evident that in ORM, repetition often overrides accuracy.

Explore deeper: Generative AI’s Defamation Challenges

Auditing AI-Generated Narratives: A Step-by-Step Approach

Let’s consider a situation involving an AI-generated narrative challenge faced by CEO X of a well-known SaaS company.

After an out-of-context quote from CEO X’s podcast appearance went viral, AI summarized him unfavorably. Quickly, his reputation transformed negatively across major platforms.

Step 1: Mapping Queries

I initiated a process to understand what queries AI outputs were generating about CEO X. This helped identify the underlying issues.

Step 2: Capturing Outputs

Identifying repeated claims revealed how CEO X was perceived. Narratives from Google AI and ChatGPT were consistently portraying him negatively.

Step 3: Delving Through Sources

The next step involved examining the quality of sources contributing to these narratives, often outdated or lacking accuracy.

Step 4: Analyzing the Narrative Gap

This involved assessing discrepancies between AI narratives and his actual reputation, contextualizing the initial quote, and examining the long-standing perception of CEO X.

Step 5: Correcting and Replacing Sources

Finally, I focused on directly addressing, correcting, and replacing those negative narratives. This involved engaging directly with platforms that contributed to the misinformation and reinforcing positive content elsewhere.

Explore deeper: Responding to Negative AI Reviews

A New Perspective: From SEO to Narrative Management

The focus has shifted from merely achieving top SEO rankings to understanding and adapting to narrative shifts. We must rethink our strategy from content engagement to managing the narratives AI disseminates.

To succeed, it’s important to reinforce AI systems with quality inputs, including crafting high-quality content, pursuing credible mentions, disseminating structured data, and managing misinformation directly.

Inspired by this post on Search Engine Land.
April 3, 2026
Avoid These Costly Google Ads Mistakes for Ecommerce Success
Expanding beyond paid social? Discover how I learned to structure campaigns, control spend, and unlock demand without depending solely on the Meta playbook.

My paid social campaigns were thriving. I understood my audience intimately, had a tight creative process, and watched results improve each year. Naturally, when leadership proposed expanding into Google Ads, I was thrilled—envisioning it as a new revenue channel.

But sticking to our existing strategy only led to difficult conversations. Google demands different tactics—intent signals and campaign structures vary, and common budget-draining mistakes aren’t always obvious. Many brands mirroring their Meta strategy end up with flashy dashboards but disappointing balance sheets.

From my experiences, six frequent mistakes can cause substantial damage before they’re even noticed. They’re what I’ve seen most often with ecommerce brands transitioning to Google Ads—and each error is reversible.

Mistake 1: Treating Google like a retention channel

Utilizing Google Ads for retention and brand defense is possible, but relying solely on it as a strategy is problematic. I often notice brands new to the platform diving straight into Performance Max. Initially, the ROAS shines bright, making everyone happy. However, when the right question surfaces—”Are we truly growing or just capturing purchases?”—issues arise.

For example, a client approached me with branded search and retargeting doing most of the work in PMax—a mere tax on demand already created elsewhere, leading to stagnant revenue. Although ad spend was soaring, growth wasn’t.

Acquiring new customers requires a different setup, like:
- Shopping campaigns to highlight products to new audiences.
- Search campaigns centered on non-branded, high-intent keywords.
- Layered PMax configurations to bypass defaulting to easy conversions.
When Google grants vast access to new audiences, focusing solely on closing disregards most of this opportunity.

Dig deeper: Ecommerce PPC: 4 takeaways that shape how campaigns perform

Mistake 2: Not knowing how to leverage Google’s core levers

Although paid social expertise is somewhat transferable to Google, I’ve observed four major gaps. Let me share them with you in more detail.

Search intent: Social media ads interrupt, but search ads meet users actively seeking your offerings, transforming campaign structure, ad copy, and keyword targeting entirely.

Data feed optimization: An optimized product feed enhances visibility and targeting in Shopping or Performance Max campaigns.

Keyword research: Understanding match types and search intent is critical for reach and cost efficiency.

Landing pages: Engaging landing pages outperform product pages for high-intent but unfamiliar visitors.

Dig deeper: 7 Google Ads search term filters to cut wasted spend

Mistake 3: Allowing operational issues to interrupt campaign momentum

Consistent data is key for Google’s algorithms. Every unintended campaign pause can reset learning, causing weeks of degraded performance and wasted spend.

Common disruptions include:
- Payments: Bill lapses, leading to campaign pauses, overshadow the actual cost when factoring in downtime recovery.
- Tracking and feed integrity: Broken pixels and feed errors silently degrade performance.
Setting up automated alerts and regular audits can prevent these costly errors.

Mistake 4: Overly granular campaign structures

Detail-oriented advertisers may over-segment campaigns, believing it provides control. However, widespread budget allocation hinders Google’s automation from optimizing effectively.

Instead, tight, well-funded campaigns optimize better and are more manageable.

Dig deeper: How to find and fix the root cause of low conversions

Mistake 5: Leaving campaigns on Max Conversion Value without ROAS targets

Max Conversion Value aims for conversion volume, neglecting cost efficiency. A realistic ROAS goal encourages the algorithm to maximize efficiency. Setting this correctly is crucial.

Dig deeper: How each Google Ads bid strategy influences campaign success

Mistake 6: Underfunding campaigns, keeping them in learning mode

Underfunding during the learning phase results in indefinite stalled progress. Adequately funding new campaigns from the outset fosters quicker, more accurate results.

Expanding beyond Meta to include Google is a strategic move, accessing actively expressed demand. These pitfalls aren’t deterrents but guideposts for smoother transitions and optimized strategies.

For early adopters, start with my guide on expanding from Meta to Google Ads. If seeking further optimization, learn how to sidestep Google’s automation traps.

Inspired by this post on Search Engine Land.
April 2, 2026
Unlocking Google AI Max: Insights from 23 Tests Revealed
Over the past nine months, I’ve put Google AI Max to the test, conducting 23 in-depth analyses with 16 well-established advertisers across diverse sectors. My goal? To truly harness the capabilities of this campaign for optimal outcomes.

Of course, your own tests and insights might differ, and that’s where the real conversation begins. I’m eager to engage in a dialogue about AI Max, encourage replication of my analyses in your accounts, and explore outcomes unique to your data.

Before you dive into your AI Max tests, consider some critical elements. Two stand out:

Your campaigns must bid on crucial conversion actions relevant to your business. Utilize tools like Enhanced Conversions to polish your conversion strategy. Aim for value-based bidding when possible. Additionally, ensure your campaigns are not restricted by budget limitations. This is particularly important with AI Max as it opens up new targeting opportunities.

Let’s delve into some key insights I’ve gathered from testing AI Max.

AI Max can reach its full potential when you activate all three core features:
- Search term matching.
- Text customization.
- URL optimization.
Campaigns that leveraged all three features saw a 40% higher success rate compared to those that only used search term matching.

Text customization can significantly enhance performance, increasing return on ad spend and extracting more value per impression. While it’s more frequently applied to headlines than descriptions, the benefits are clear.

One exciting outcome of text customization is the observable boost in Quality Score. Our analysis showed that enabling this feature improved Quality Score from 6.8 to 7.3, with ad relevance seeing the most significant rise.

Given these findings, I encourage testing all three features if possible, especially since our tests showed that only half of the campaigns utilized text customization and even fewer activated URL optimization.

If you’re testing AI Max, consider implementing it across your entire account rather than selectively. This approach facilitates a more comprehensive assessment of its impact.

Not all new AI Max traffic will be completely new to your account, with 54% of queries having been previously captured by other campaigns. Despite this, AI Max still provides an additional uplift in conversion value.

Ensure you evaluate AI Max by looking at overall account performance rather than isolated campaign tactics. Additionally, monitor how AI Max interacts with other campaigns, notably Dynamic Search Ads (DSA), since overlapping capabilities can sometimes hinder performance.

Once you’re comfortable with AI Max, explore additional testing opportunities such as partnering it with Search Bidding Exploration (SBE) for achieving even greater customer reach.

Finally, it’s crucial to experiment beyond AI Max’s current scope. Consider alternative strategies and the evolving balance between segmentation and consolidation within your account structure.

Inspired by this post on Search Engine Land.
March 12, 2026
Unlocking SEO Success: AI’s Role in Authority Building

As someone deeply immersed in the world of SEO, I’ve witnessed a fascinating evolution. In the early 2000s, if you were like me, you probably focused on gaming PageRank with enough links and keywords to achieve visibility. It was a mechanical process, and frankly, relatively simple to exploit.

Fast forward two decades, and the search landscape has radically transformed. Algorithms have become sophisticated, mirroring Google’s deeper understanding of brands, individuals, and reputations. This transformation, driven by AI-powered discovery, means authority is now the cornerstone of search rankings. The journey culminates in an era where brand legitimacy is sustained through genuine visibility.

From links to legitimacy: How authority evolved

I witnessed Google’s first significant stand against manipulation with the Penguin update, prompting many of us to rethink our link-building strategies. “Digital PR” began to replace traditional notions, while Google’s experiments with entity-based understanding introduced innovations like author photos in search results and knowledge panels.

Although Google eventually phased out some features like authorship, the message was clear: authority assessment was being redefined. Instead of asking, “Who links to this page?” Google’s algorithms started considering “Who authored this content, and how is this author recognized?” This shift, propelled by AI-driven search enhancements over the past year, is now impossible to ignore.

Helpful content and the end of synthetic authority

When Google integrated the helpful content system into its core algorithm, it marked a turning point for us in SEO. Sites that once thrived on over-optimization saw their performance crumble. In contrast, brands demonstrating authentic expertise and brand authority began to rise.

It’s now vital that search systems accurately evaluate whether content reflects true expertise. As someone who’s navigated the core updates, I’ve seen larger brands with robust reputations consistently outperform technically proficient but less well-known sites. Authority has evolved from being a differentiator to a necessity.

Authority in an AI‑mediated search world

In diving into resources about large language models (LLMs), I’ve learned that they source their information from diverse platforms—journalism, forums, reviews, and video transcripts. It’s through these platforms that reputation is built, highlighting the power of consistent, positive mention of your brand.

This revelation has profound implications for our SEO strategies. Platforms like Reddit, Quora, LinkedIn, YouTube, and trusted review platforms such as G2 are regularly cited in AI search responses. These platforms organically reflect what people genuinely think about brands, rather than what we aim to claim.

This doesn’t mean the end of Google

Despite AI’s growing integration, Google continues to dominate with over 90% of global search usage. Even among frequent AI platform users, reliance on Google persists. Google’s interfaces now absorb AI-style answers, meaning users experience AI directly within Google platforms. This hybrid presence offers an exciting opportunity for building cross-platform authority.

Brand building is the new SEO multiplier

As someone who bridges the gap between paid and organic strategy, I’ve seen that effective authority signals often emerge from outside traditional search channels. Digital PR, brand advertising, events, and offline activities increasingly shape organic performance. This sphere where paid and organic strategies converge enhances your brand’s legitimacy.

Brand awareness significantly boosts click-through rates, with familiar names drawing references across various media. I’ve noticed mentions in YouTube videos or long-form journalism reinforcing topical authority that simple links cannot. The digital ecosystem now validates authority externally, and this multiplication effect is constantly evident in the results I oversee.

A practical framework: The three pillars of authority

Building enduring authority requires an integrated approach. Drawing from my experience, I’ve devised a framework focusing on three core areas: Category, Canonical, and Distributed authority. Each pillar strengthens your position as an industry leader, beyond mere SEO tactics.

1. Category authority: Owning the truth, not just the traffic

It begins with shaping how the category is defined. Instead of chasing keywords, the focus is on establishing your brand as the reference point others turn to for clarity. This strategy cultivates an authentic authority that search engines and AI increasingly reward.

2. Canonical authority: Creating the definitive explanations

This involves crafting explanation-focused content that thoroughly answers queries, becoming the go-to resource cited across various platforms. The content serves as the backbone across the digital landscape, ensuring enduring visibility through AI and future technologies.

3. Distributed authority: Proving legitimacy beyond your website

Genuine authority thrives through widespread credibility on platforms outside your control, including PR coverage, social media mentions, and product experiences. These elements amplify your brand’s presence and solidify trustworthiness.

Ultimately, focusing on brand authority ensures durability amidst evolving algorithms. It’s about becoming the undisputed leader in your niche, where authority extends beyond traditional SEO into the realm of comprehensive digital engagement.

Inspired by this post on Search Engine Land.

February 19, 2026

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