As I delve further into the world of artificial intelligence, I
Inspired by this post on Search Engine Land.
Month: April 2026
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Unveiling AI Vulnerabilities: The Best-of-N Jailbreaking Threat
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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.
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Evading AI’s ‘Bland Tax’: How to Maintain Brand Visibility
When I think about brand visibility today, it’s clear that being chosen by AI systems is crucial. Authority, unique insights, and consistent signals now determine if my brand makes the cut.
I’ve realized that AI isn’t just reshaping search; it’s deciding which brands are seen and which are ignored.
I learned from Andrew Warden, CMO of Semrush, at the Adobe Summit that visibility is evolving fundamentally, and our brands risk being systematically filtered out by AI systems.
“The idea of standing out is no longer optional. There’s a real risk of sameness,” he pointed out.
With AI systems deciding what to highlight and what to ignore, I know I must compete more fiercely for visibility in AI-generated answers.
AI is Changing How Discovery Works
The change is evident in the data: 60% of Google searches now end without a click to a website. People are still seeking information but aren’t always visiting websites. They’re getting their answers directly from AI systems like Google AI Overviews and ChatGPT.
These AI systems have become, as Warden described, the “new gatekeepers.”
This shift ushers us into the agentic era, where AI systems act as intermediaries, guiding users from inquiry to decision in one seamless interface.
Meanwhile, user behavior is evolving. People engage more in conversational environments, posing follow-up questions, refining queries, and surveying options within the interface, all resulting in fewer clicks but often attracting higher-intent users.
Warden noted that consumers using LLMs convert at least four times higher than those relying solely on search.
SEO is the Foundation
Despite some claims that AI could replace search, Warden reassured us that SEO is not dead.
SEO has become more foundational than ever. It’s essential to ensure my brand exists in the data layer AI systems rely on.
Warden emphasized, “SEO isn’t just for humans anymore. This is a training manual for AI right now.”
This involves ensuring:
- Crawlability
- Indexability
- Structured data
- Authority signals
Without these, my brand won’t appear at all.
Research backs this up: 94% of Google AI Overviews cite at least one top organic result, reaffirming that traditional search signals still support AI outcomes.
The Rise of the ‘Bland Tax’
One striking concept from the session was what Warden dubbed the “bland tax.”
AI conditions itself to overlook blandness, causing generic or repetitive content to vanish.
If I’m generic, Warden warned I’m perceived as average, and if I’m bland, I’m effectively invisible.
AI systems don’t reward sameness. Rather than highlighting my brand, they often condense similar content into a single, attribution-lacking response.
“This is an invisible penalty,” Warden noted.
The consequences manifest in several ways:
- My brand identity gets erased in AI-generated summaries
- My content is filtered out as low-value
- My work becomes training data for AI without offering visibility to my brand
“You also become a free training ground for LLMs,” he said.
What Visibility Depends On
Warden redefined brand visibility as a blend of:
- Discoverability: Can LLMs easily find me?
- Authority: Do they trust my brand enough to include it?
“You absolutely need both,” Warden asserted.
SEO ensures I’m discoverable. Authority determines whether my brand shows up in AI-generated responses.
Without authority, I risk turning into a “commodity that isn’t worth being mentioned.”
How to Win: Three Key Signals
Warden outlined three crucial areas determining whether my brand appears or gets filtered out:
1. Entity Authority
AI systems map entities and relationships, and they must recognize my brand as an authority on a topic.
One key signal is brand demand. If people aren’t seeking out my brand, neither will AI.
Strong brands emphasize their authority across various platforms—owned content, media exposure, and community discussions—demonstrating their niche.
2. Information Density and Originality
AI systems prioritize content that offers new insights. It’s vital to not just publish content but contribute something meaningful.
They emphasize new facts with proprietary data, original research, unique perspectives, and expert insights.
According to Warden, original insights can enhance visibility by 30 to 40%.
3. Signal Alignment
AI evaluates not just what I convey but also what others say about my brand.
This includes reviews, discussions on platforms like Reddit and YouTube, media mentions, and customer conversations.
Warden warned that conflicting signals could prompt AI to flag my brand as unreliable.
Consistency across these channels creates what he called a “consensus signal” that AI systems can trust.
Why Most Organizations Aren’t Ready
One of our biggest challenges is organizational, as visibility isn’t just a channel issue; it’s an organizational one.
Currently, responsibilities are fragmented. SEO teams focus solely on rankings, PR and brand teams manage messaging, and growth teams conduct experiments. This leaves no one clearly owning AI visibility.
This fragmentation leads to inconsistent signals and missed opportunities for us.
To truly compete, we need alignment across teams, working on a shared strategy about how my brand appears wherever LLMs gather data.
The Measurement Problem
Meanwhile, traditional performance metrics are unraveling.
Many marketers, including myself, notice a gap where rankings hold steady, but traffic declines. Meanwhile, leads might increase, yet attribution remains murky.
Warden explained that demand remains, but traffic no longer serves as its proxy. Our content is utilized, but not in ways directing users back to us.
This creates a growing disparity between impact and the ability to measure that impact accurately.
From Rankings to Relevance
The nature of competition has evolved. I’m no longer vying for a mere position; instead, I’m competing to be featured in a synthesized AI answer.
Authority, once easier to influence, now hinges on external validation—emphasizing what others say over what I publish.
Algorithms have shifted from being my allies to arbiters of meaning, marking a significant change in search dynamics since Google itself emerged.
The New Rules of Brand Visibility
AI has not altered what makes a brand strong but has transformed how that strength is measured and rewarded. The brands that win today will build real authority in a focused niche, publish original and high-value content, and ensure consistent messaging across every platform.
The need for consistent third-party validation across an ecosystem is paramount.
As Warden urged, I must make it impossible for LLMs to ignore my brand.
Inspired by this post on Search Engine Land.
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Discover the Top eCommerce ERP Connectors of 2026
nIn March 2026, I, along with my research team, delved into the world of solutions used by B2B distributors, manufacturers, and enterprise commerce businesses. Our goal was simple: to find the best tools to connect ERP systems to eCommerce platforms. We studied 34 products spread across three categories: dedicated middleware connectors, ERP-native proprietary storefronts, and general-purpose iPaaS platforms. Each type made our list because they
Inspired by this post on First Page Sage Blog.
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Boost Conversions with Google’s New AI-Qualified Call Leads
I’ve discovered that Google has enhanced the Google Ads call campaign measurement with a new AI-qualified call leads feature. This upgrade focuses on boosting lead quality, moving beyond just measuring call length.
What’s new. Through machine learning, AI-qualified call leads analyze calls to determine if they represent valuable business opportunities. The system seamlessly integrates this data into bidding and reporting for improved results.
Zoom in. As an advertiser, I now receive AI-generated call summaries and tags, providing clearer visibility into each interaction. This transparency allows smart bidding to prioritize leads of higher value instead of relying solely on call duration.
Why I care. Call campaigns have traditionally depended on call duration to gauge value. With this update, I can shift the focus to actual lead quality, filtering out low-value interactions, including spam and robocalls. This change means better ROI, reduced wasted spend, and a clearer understanding of which calls really make a difference.
How it works. Recording calls is a default feature for most advertisers, allowing AI to evaluate call quality effectively. However, sectors like healthcare and financial services are exceptions. Advertisers, including myself, can adjust call length thresholds or opt to disable recording in account settings.
The fine print. Currently, this feature is available only for calls within the U.S. and Canada.
Bottom line. Google is revolutionizing call tracking by shifting the focus to call qualification, enabling advertisers to hone in on leads more likely to convert.
Inspired by this post on Search Engine Land.
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Why AI Is Revolutionizing Acquisition with a Bottom-Up Approach
AI has reshaped how we think about acquisition strategy. It’s no longer about starting at the top of the funnel with broad awareness campaigns. Instead, we begin at the bottom, focusing on building understanding, credibility, and reach in the right sequence.
For the past 30 years, the industry followed a top-down model: raising awareness, gaining visibility, and then guiding potential customers through the purchase funnel. This approach made sense during the broadcast era and was somewhat effective in the search era, but today, in AI-driven environments, it’s outdated.
Today’s search engines and AI-powered assistants build brand recommendations from the ground up. They need to grasp who we are before they can evaluate our credibility. Only after establishing credibility can they recommend us. If we prioritize top-down strategies, we’re essentially wasting budget on awareness without a strong foundational understanding for AI to work with.
AI systems hold the key to successful brand recommendations — if they don’t understand our brand, or find us less credible compared to our competitors, they’ll likely recommend someone else. This AI-led shift is what I call the ultimate zero-sum game: the unseen recommendation to prospects we might not even know about.
The acquisition funnel hasn’t altered for users. They still journey from awareness to consideration to decision. Essentially, Elias St. Elmo Lewis’s model from 1898 still applies. All marketing models have been based on this, although channels have evolved. The mantra remains: reach first, relationship second, commitment third.
In my experience, the digital landscape changed with Google’s Knowledge Graph in 2012. It allowed machines to form independent opinions about brands, highlighting the need for brand understanding and reputation over mere awareness. Since then, my focus has centered on these aspects because AI-driven engines and agents rely on it to direct users towards credible destinations.
This marks a structural shift in marketing since 1898. While the user still travels from awareness to decision, in AI engines and agents, it’s our understanding and credibility that position us at the top of their funnel, achieved by training AI to guide users to us.
The coexistence of top-down and bottom-up strategies is real. We can still build awareness through controlled channels—paid media, broadcasts, and direct outreach. However, in the realm of organic engines, we must start from the bottom of the funnel, building a foundation for AI to guide users efficiently.
Every algorithm, AI engine, and agent operates based on entity and brand signals. Social media reach, too, hinges on brand recognition and engagement. Therefore, investing in a solid brand understanding orients us favorably within the AI framework, where roadmaps to our brand are increasingly machine-built.
This content reflects my approach to developing robust brand presences that resonate with both AI systems and human audiences.
Inspired by this post on Search Engine Land.
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Master YouTube Analytics with Data Studio for Clear Insights
Have you ever wondered about the performance of your YouTube videos? With the time and resources invested in creating content, it’s crucial to track its success.
While YouTube Studio offers robust analytics, accessing the data can be tricky, especially for sharing with others. Here’s where Google Data Studio (previously Looker Studio) comes in handy, offering an easier way to analyze and share YouTube data.
With Data Studio, I can seamlessly integrate YouTube data, schedule updates for stakeholders, customize dashboards, and monitor performance without needing direct access to the backend.
Let me guide you on integrating YouTube analytics into a Data Studio report.
Using a template or starting from scratch
Setting up a report in Data Studio offers two paths. Google’s YouTube Analytics template is a quick start, presenting a clean report with foundational metrics. But be prepared to fix some common issues, which I’ll help you navigate. Alternatively, if you’re up for a challenge, creating a report from scratch can deepen your understanding of Data Studio.
This guide covers both options.
If you’re not the YouTube account owner
For those creating a report without owning the YouTube account, you may find the account isn’t showing as a source in Data Studio. Don’t worry; there’s a workaround. First, access YouTube Studio settings, navigate to Permissions, and grant Manager permissions to the email associated with your Data Studio. Then, obtain the Channel ID from the YouTube URL, add a YouTube connector in Data Studio, and paste the Channel ID under Advanced settings to access the account.
Using the Data Studio YouTube Analytics template
Getting started is simple. On the Data Studio home page, click on Templates followed by Template Gallery. Select YouTube Analytics from the dropdown menu. This template comes preloaded with sample data, which you can replace with your own by clicking “Use my own data.”
During setup, you’ll need to authorize your data by choosing the connected Google Account. Your YouTube channels will then be selectable from a dropdown menu. Note: the dropdown controls settings, not the charts. To update the charts, use the Edit and Share button, which allows you to adjust data sources and metrics.
Copying a template into an existing report
While Data Studio doesn’t directly support importing templates into existing reports, copying a page is an option. After setting up a report with the template, you can transfer it by selecting everything, copying, and then pasting into an existing report’s new page. Although the initial imported charts might show errors, you can reassign the correct data sources using the Properties sidebar.
![```json
{
"alt": "Menu options in a [Sample] YouTube Channel Report interface, highlighting 'Current page settings'.",
"caption": "Navigating through a [Sample] YouTube Channel Report, the 'Page' menu option is highlighted, focusing on 'Current page settings'.",
"description": "This image shows a dropdown menu within a [Sample] YouTube Channel Report interface. The 'Page' menu is opened, highlighting 'Current page settings' in red, indicating it as a selected option. Options like 'New page', 'Duplicate page', and others are visible. The interface appears to be part of a reporting tool for YouTube channels, used for managing and customizing report pages."
}
```](https://crushpress.ai/wp-content/uploads/2026/04/gap-69e792797170b.png)
Customizing your report
The YouTube template offers a solid starting point, but Data Studio allows for extensive customization. While some metrics like revenue and specific audience insights aren’t available, there’s plenty to explore. Adding new charts involves expanding the canvas and leveraging a variety of metrics and dimensions to tailor reports to specific needs.
By following these steps, we’ve crafted a report that’s both functional and informative, ready for sharing performance insights. Automating report exports as PDFs ensures easy distribution, facilitating informed decisions for all stakeholders.
Inspired by this post on Search Engine Land.
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Unlocking the Power of Google’s New AI Safety Features in Ads Advisor
I’ve recently discovered that Google has introduced some exciting AI safety features in their Ads Advisor, which could really transform how we manage campaigns. This update promises to automate policy fixes, enhance security, and expedite certifications, all to help us run our campaigns more efficiently.
As someone who spends a lot of time tackling policy issues and managing certifications, this news is music to my ears. With advertising campaigns becoming increasingly complex, having AI handle these time-consuming tasks could significantly boost our productivity and performance.
What’s New. The latest update brings proactive troubleshooting, continuous security monitoring, and immediate certifications. Thanks to AI and Google’s Gemini capabilities, these features promise to be a real game-changer.
Zoom In:
Ads Advisor can now automatically flag and resolve policy violations before they even catch our attention. This proactive approach ensures we stay ahead of potential issues.
The new security dashboard is always on the lookout for risks such as suspicious domains or dormant users. It’s like having an ever-vigilant guard protecting our accounts 24/7.
Imagine getting certifications that used to take weeks, approved instantly with just a click. This means we can focus on strategy rather than paperwork.
How It Works. Ads Advisor proactively scans accounts and sites, offering up fixes and confirming resolutions without the need for manual intervention. On the security front, it continuously checks account health and even supports passkey use, reducing our dependency on passwords.
Why We Care. These features save us hours that were once spent fixing issues, upping our security game, and dealing with certifications. This proactive system reduces delays and risks, ultimately enhancing campaign speed and efficiency.
What to Watch. Google plans to roll out these features for English-speaking accounts over the coming months, with additional languages to follow.
Bottom Line. Google is transforming Ads Advisor into an active operator, making ad management safer, quicker, and far less labor-intensive. I’m eager to see how these changes will impact the way we work.
Inspired by this post on Search Engine Land.
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Unveiling Microsoft
Microsoft has just rolled out a suite of updates across Microsoft Advertising, and I couldn
Inspired by this post on Search Engine Land.
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Mastering SEO Reporting: Move Beyond Data Studio
As I delve into the world of SEO reporting, I realize just how much we’ve outgrown platforms like Data Studio. Let me share what I’ve discovered and the exciting changes on the horizon that promise more efficient workflows powered by AI and APIs.
Imagine this scenario: Our team depends on Data Studio for delivering SEO reports. Just as we’re gearing up for a crucial meeting, Data Studio unexpectedly crashes, leaving us with nothing to showcase. It’s frustratingly common and incredibly embarrassing.
Just last year, I was praising Looker Studio (now Data Studio) for its advantages in SEO reporting. Fast forward, and it seems outdated compared to the dynamic coding tools I’m now utilizing. Here’s why rigid dashboards are holding us back and why transitioning to code-driven SEO reporting is essential.
Data Studio once reigned supreme for customizing SEO reports, but technology advanced, revealing its limitations. From dataset crashes to tedious manual interfaces, let me take you through some challenges I’ve faced with Data Studio.
We’re all familiar with the struggle: vast datasets in Data Studio are prone to breaking, often due to the low limits on rows and fields. Hasn’t it been just one too many times when a minor data addition causes everything to crash?
Manual updates in a slow interface make any iteration seem endless. Even the introduction of AI features addresses only a fraction of report-building issues.
Debugging Data Studio reports feels like a never-ending click maze. Unlike code-based systems where agents breeze through files, I’m often left clicking mindlessly within the interface.
Data Studio’s weak API is another stumbling block. It’s representative of Google’s missed opportunities for API-centric platforms. This flaw severely limits external management capabilities.
Despite recent rebranding efforts, these platforms lag behind modern SEO reporting technologies. Let me show you how everything is shifting with AI, APIs, and coding.
The evolution we’re witnessing is astounding. AI-driven coding tools like Claude Code and OpenAI Codex have changed the game. I describe my SEO reporting needs, and these tools take over, executing multi-step workflows efficiently.
Without needing deep coding expertise, I’m able to set up programmatic report workflows from beginning to end. Tools generate code that directly connects to data sources, eliminating reliance on cumbersome dashboard connectors.
Within minutes, comprehensive reports appear as I get accustomed to these tools. Each offers unique advantages, from reasoning to integration speed, transforming manual, rigid processes into infinitely flexible options.
AI coding tools usher in new possibilities for SEO teams by removing barriers between data management and reporting.
Speed is an unmistakable upside. Coding assistants enable SEOs to achieve in hours what once took days, and what took hours, now takes minutes.
Interacting with data directly through coding instead of dashboard interfaces drastically cuts down wait times for refreshes and modifications.
I’m no longer bound by rigid templates. Alongside on-demand data plotting and diverse frameworks, I can tailor reports to perfectly match needs and provide insightful visualizations.
Setting up these tools requires some initial effort but soon transforms the team’s efficiency, offering clearer data constraints and enhanced process transparency.
I’ve discovered how agentic coding assistants can revolutionize real-world SEO applications, from pre-meeting reports to ad hoc stakeholder requests, reducing late-night work and ensuring quick, reliable data access.
AI is reshaping the landscape for all professionals, not just us in SEO. As we adopt this technology, especially in SEO reporting, studies from Stanford and MIT show increased productivity. The shift isn’t optional; it’s imperative.
Teams leveraging AI tools in SEO witness faster iterations and can tackle complex issues more robustly, transforming analysts into strategists with unprecedented capabilities.
Begin this transformation with a small, repeatable project, connect data sources, and slowly expand your use of code-driven reporting. Early adopters are set to lead in SEO efficiency and results.
Traditional SEO reporting tools no longer meet the fast-paced demands of today’s analytics and strategic needs. Through AI and coding, we can leap ahead in reporting accuracy and timeliness, securing a competitive edge.
Inspired by this post on Search Engine Land.