Hey there! Have you ever wondered what GEO is and how it can supercharge your content’s visibility and engagement in AI-based search engines like ChatGPT and Gemini?
I’m excited to share my insights on optimizing your content specifically for these AI platforms. Think of GEO as the key to getting noticed in the digital realm where AI engines are becoming the norm.
By mastering Generative Engine Optimization (GEO), you can pivot your strategy to cater to AI Overviews, boosting your reach by ensuring your content is relevant and easily discoverable. Let’s dive into this transformative journey together!
When I embarked on my journey with Answer Engine Optimization (AEO), I quickly discovered that, unlike traditional SEO, AEO offers a swifter movement toward visible outcomes. However, I needed to adjust my expectations as enduring results might take more time than initially hoped.
Through my personal experience, I’ve learned that even though the pace of progress with AEO is faster, it still requires patience to witness the lasting impact. Here, I’ll share a realistic timeline and some critical markers to monitor along this pathway.
As I continue to navigate this dynamic landscape, I’ve pinpointed crucial elements and strategies that help ensure I’m on the right track. Come along as I break down what I’ve observed and how you too can foster a more predictable and successful AEO journey.
I recently explored Google’s updated guidelines for site moves, specifically about handling all domain variants using their Change of Address tool. This update aims to clarify the process of moving your site from one domain to another, ensuring a smooth transition for all domain variations.
Google’s advice is straightforward: enter every domain variant in their Change of Address tool during a site migration. They emphasize this in their documentation to prevent potential indexing issues.
Google’s Note: They encourage submitting requests for each subdomain and the www and non-www variants of your previous domain. For instance, ensure you submit en.example.com, www.example.com, and example.com if you’re moving to new-example.net, even if these variants aren’t actively used. It’s crucial to have them verified in the Search Console for a seamless migration.
Understanding domain variants is key. These include subdomains and different TLDs, allowing for a comprehensive transition from your old site to the new one without hiccups.
Why It Matters: Proper domain migration ensures that all site variants migrate without issues, which Google confirms as the best practice for SEO. Following Google’s guidelines can significantly mitigate the stress associated with site migrations.
For any SEO practitioner or site owner, site moves can be daunting. However, adhering to these detailed steps can make the transition less overwhelming. The Change of Address tool is designed to expedite this process, so making the most of it is essential.
Recently, I discovered that Google introduced an AI opt-out feature, and it got me thinking.
For as long as I can remember, we’ve been pushing Google for more insight into AI traffic and control over our content’s portrayal in AI settings.
Now, this week, Google answered us with new controls allowing site owners to opt out of AI-powered experiences, like AI Overviews and AI Mode, coupled with fresh AI reporting tools in Google Search Console. Although still in early beta, it signals progress.
Despite this being a step forward, it’s sparked a split. Some are excited about the reporting aspect, while others debate whether opting out is wise.
What intrigued me wasn’t the announcement itself, but how swiftly the conversation pivoted from seeking visibility to potentially forfeiting it.
Let’s clarify what Google really launched with their announcement. The new controls don’t hinder AI Overviews or user engagement with AI Mode, nor do they stall AI’s momentum. Users will continue to engage with AI for searching and queries.
Essentially, publishers have a newfound ability to determine whether their content appears in AI-powered experiences. Was it Google’s plan or a response to external pressure, such as the UK Competition and Markets Authority?
This isn’t a debate about AI itself disappearing. What changes is brand eligibility within AI interactions. If a site like Expedia opts out, people will still plan trips—they’ll just find someone else in the AI-generated responses.
The choice is not about AI’s success, but rather about whether your brand remains present when users turn to AI solutions.
I get it—the appeal to opt out stems from fears around lost traffic and how AI uses our content.
Yet, assuming that opting out changes user behavior is where I disagree. Users aren’t concerned about a brand’s participation; they’re using AI to get quick answers.
Opting out may seem like a decision to curb AI adoption, but it more so enhances your competitors’ visibility. They snag the spotlight and gain trust while yours potentially fades.
The goal isn’t just visibility reduction—it’s about evolving with search behavior changes to remain seen.
Google’s announcement didn’t just focus on opting out but also on the new AI data they’re offering. Though imperfect, it’s a step towards greater transparency in AI search interactions.
Despite demands for more comprehensive reports, reality shows SEO has long dealt with imperfect data. Some of SEO’s big wins came from leveraging imperfect data.
Hence, we shouldn’t be stuck waiting for flawless data. While not perfect, it’s more than what we had before and will likely evolve further.
In my approach, reporting must expand beyond traditional SEO metrics, encompassing a wider discovery landscape, including AI and interaction insights.
We need to assess brand mentions, citation frequency, and how they’re perceived across differing AI platforms. Visibility stretches beyond mere traffic metrics.
Ultimately, we must rethink our questioning. Instead of asking, ‘Should I opt out of AI?’, ask, ‘Can I afford to be absent where users find brands?’ They’re already in these spaces—why shouldn’t we be?
Google’s update isn’t just a feature but a strategic pivot. By choosing to opt out, you aren’t erasing AI; you’re simply amplifying someone else’s presence.
Are you ready to adapt, or will you stay behind, longing for Google’s ‘free clicks’?
I’ve discovered that content businesses flourish when the economic model, systems in place, and editorial insight work harmoniously. However, challenges arise when these vital components begin to operate in silos.
Managing content operations on a small scale can really rely on instincts. When I have a dedicated editorial team, a select few reliable writers, and a solid grasp of our unique voice, everything tends to run smoothly.
However, in larger setups like media rollups or vast affiliate networks, producing vast quantities of content daily becomes not only feasible but essential. For some, content isn’t a mere marketing tool—it is the business model itself.
At these formidable scales, breakdowns often happen not because of the content but due to a disconnect among the economic goals, operational systems, and editorial decision-making.
Not every type of content can handle being scaled like this. In B2B, for instance, if you’re marketing a niche ERP system, such content volume is unnecessary and would ultimately lead to wasteful spending.
Yet, some categories like sports can support high-volume publishing due to the constant and diverse demand for new content—from game insights to player interviews.
For example, a platform like The Athletic thrives under such volume demands thanks to varied revenue streams including subscriptions and advertisements, generating substantial figures like $54 million in a single quarter.
With the bulk of revenue stemming from direct consumer subscriptions, maintaining high editorial standards shifts from being optional to absolutely critical.
In contrast, models heavily reliant on programmatic display ads can be unstable. Such a system drives monetization through shear output of low-production-cost articles.
Here’s the simple breakdown:
Revenue = (Pageviews ÷ 1,000) × RPM
Profit = ((Pageviews ÷ 1,000) × RPM) − Production Cost
When generating $64 per article via 4,000 pageviews at a $16 RPM, tight profit margins necessitate bulk publishing with sustained quality.
Without careful management, these strategies can falter.
As operations scale, there’s a paramount need for robust systems and data analysis, which help prevent operational collapse. Yet, truly sustaining these operations requires not just infrastructure, but judgment too.
As someone deeply invested in improving my SEO processes, I’ve discovered an innovative way to transform my workflows using AI-powered tools that adapt to my unique methods.
By leveraging platforms like ChatGPT and Google’s Gemini, I can get standard on-page SEO reviews. However, these initial responses often feel generic and devoid of specific context related to my business needs.
This generic nature of AI is both its limitation and its potential opportunity. While out-of-the-box AI provides broad solutions, it lacks the personalization that comes from my own business insights.
Fortunately, tools like GPTs, Gems, and Claude Projects allow me to embed my SEO process into custom assistants, making the complex seem straightforward without needing complex coding skills.
I’ve also learned that large language models predict responses from a vast array of internet data, often resulting in average opinions rather than tailored advice for my business specifics.
In SEO, these broad opinions typically revolve around general content improvements and link building, which might not address the unique challenges I face.
What I needed was a tool that factored in my business’s unique landscape, including customer needs and competitive environment. That’s where the personalization of AI tools comes into play.
Contextualizing inputs to AI tools transforms them into powerful assistants that enhance my specific workflow, making it less about generic data and more about strategic insights.
The process of creating a customized AI tool is more about narrating my workflows rather than needing a deep technical background. Tools like GPTs and Gems have become essential as I package my expertise into reusable, intelligent assistants.
Among the various AI platforms, I find GPTs, Gems, and Claude Projects especially user-friendly for most of my SEO tasks. These platforms are intuitive, allowing even non-developers like me to transform repetitive tasks into automated, efficient processes.
However, generic SEO tools, despite their widespread use, don’t pay attention to my company’s unique strategic priorities, unlike the AI applications I’ve tailored to fit my specific needs.
Moreover, crafting personalized AI apps not only aids in SEO but also transforms how I manage and execute marketing strategies, encompassing tasks like keyword research and content strategy more effectively.
My takeaway is that the true value lies not in AI itself but in the expertise I embed into it. My hard-earned industry skills are the real product, and AI simply empowers me to scale my efforts more efficiently.
It’s been enlightening to see how enhancing my AI tools with my knowledge improves productivity, ultimately strengthening my business impact. This process of encoding my SEO knowledge into AI-propelled systems is groundbreaking and transformative.
I’m excited to share how Adobe’s latest tool is changing the game for businesses eager to boost their brand visibility in AI-driven searches.
With the backing of 300 million AI prompts and the comprehensive data of Semrush, this platform is adept at tracking mentions, gauging share of voice, and identifying content gaps across prominent AI platforms.
Adobe introduced a pioneering solution for brands aiming to bolster their visibility and trustworthiness across AI interfaces. As part of the Adobe CX Enterprise, this tool offers an agentic AI system to streamline customer lifecycle management, covering everything from initial acquisition to fostering long-term loyalty.
AI traffic is skyrocketing. The way LLMs are utilized for product and service research represents a major pivot for both marketers and consumers. Recently, Adobe revealed data underlining this massive surge in AI traffic to U.S. retail sites—up by an impressive 1,324% from October 2024 to May 2026. The travel industry saw an even greater increase of 2,215% in the same timeframe.
As Vice President of strategy and product, Loni Stark, remarked to MarTech, “We used to get back the same thing—a SERP page with links. Now results seem random, but aren’t when scaled, and companies lack tools for this.”
Understanding brand visibility in AI search. Adobe Brand Visibility marks Adobe’s first venture into generative engine optimization (GEO), following its acquisition of Semrush. By integrating Adobe LLM Optimizer with Semrush’s AI Optimization tool, it provides unmatched insights.
Drawing from a staggering database of 300 million real-world AI search prompts, Adobe Brand Visibility helps teams pinpoint which prompts lead to brand exposure or loss.
Additionally, utilizing Adobe’s first-party data from owned channels, marketers gain a holistic view of how their brands appear on platforms like ChatGPT, Google AI Mode, Microsoft Copilot, and Perplexity. Metrics encompass mention frequency, reach, competitive share of voice, and content gaps, allowing AI agents to offer prioritized recommendations that teams can rapidly implement and evaluate results.
Competitive intelligence unleashed. Adobe Brand Visibility offers tools for competitive brand analysis, comparison, and trend tracking, enabling marketers to effectively benchmark against competitors.
Featuring advanced SEO intelligence driven by Semrush’s extensive data of 28.5 billion keywords and 43 trillion backlinks, this platform underscores the continued importance of SEO fundamentals for AI search visibility. It shows the potential for existing search authority to yield AI citations and identifies opportunities for content investments across channels.
While there’s still much to learn about leveraging LLMs for brand visibility, Stark is confident in Adobe’s leadership position in this emerging space.
As Stark stated, “Adobe had proprietary data while Semrush offered data and trends. Though we may not have all answers, we possess unrivaled data.”
In 2026, PPC budgeting goes beyond simply setting spending levels. It’s about understanding when to adjust budgets, scaling campaigns effectively, and how data informs Google’s automation in these decisions.
Over the years, Google’s automation has been driven by the signals supplied to it. In 2026, these signals are processed faster and more precisely, making clean signal architecture more crucial than ever.
While the fundamentals of budget management remain constant, the speed at which a poorly structured account can drain your budget has increased significantly.
Two Budget Mechanics You Must Grasp Now
Before tweaking targets, audiences, or bid strategies, it’s essential to comprehend how these two budget controls operate.
The Ad Scheduling Pacing Change
Google now paces campaigns with ad scheduling towards the full 30.4x monthly billing cap, regardless of how many days your ads run. Previously, a $100 daily budget targeted around $2,200 across 22 weekdays. Now, it targets $3,040 in the same period, and the billing ceiling remains unchanged.
If your campaigns utilize ad scheduling, you need to recalibrate your daily budget based on your total monthly spend rather than active days, setting it by dividing your monthly target by 30.4. For example, a $2,200 monthly target becomes a $72 per day budget if calculated this way. However, 24/7 campaigns remain unaffected.
Available for Demand Gen, Search, Standard Shopping, Performance Max, and YouTube campaigns, campaign total budgets let me set a fixed spending ceiling over a defined period instead of managing a daily limit. This window is from three to 90 days for some campaigns, while others can extend up to a year.
While there is no daily spend cap, allowing flexibility, it’s crucial to monitor these closely, especially when running alongside ongoing campaigns. Additionally, the budget type cannot be altered post-campaign creation, making committed decisions at setup vital.
What Actually Governs Google Ads Budget Spending
Efficiency Targets Usually Constrain Spend Before Budgets
In Smart Bidding strategies, efficiency targets often restrict spending before budget caps do. With a set tCPA of $50, if leads cost $80, the system reduces bids to avoid surpassing your target. It appears as if there’s a budget problem, but it’s actually a target problem.
I must initially set targets closer to the market conversion rates and then fine-tune them to align with my true goals. When close, the 10%-20% margin aids in navigating those final conversion opportunities effectively.
Performance Max Decides Where Your Budget Goes
Performance Max automatically allocates budget across various channels like Search, Shopping, and YouTube, with Google determining the split, not me. Excluding my brand can prevent paying for redundant conversions from Search campaigns.
Checking my negative keyword lists ensures clarity in branding and budget allocation. This helps avoid misallocation and focuses resources effectively.
AI Max Expands Ad Appearances
AI Max, available since April, expands query matching beyond my keyword list, generates ad copy from existing assets, and dynamically targets landing pages. Monitoring the initial spend distribution closely helps maintain alignment with intended strategies.
An insurance broker using Smart Bidding faced a disconnect: a 416% rise in conversion volume didn’t reflect in revenue due to form starts mistaken for completions. The system optimized for interactions, but the alignment with Cyrillic-language spam was costly without benefiting the pipeline.
This reflects a broader issue in lead generation: equal weight is assigned to all form fills, leaving Smart Bidding unable to distinguish high-value leads from irrelevant submissions.
Primary conversions must be meaningful actions that properly guide Smart Bidding. Secondary engagements belong in reports to avoid skewing bidding data.
For accounts outside the current beta, extending conversion windows to 90 days and assessing performance over these periods can help counteract issues arising from longer sales cycles.
Using First-Party Data for Budget Guidance
Customer Match, with a 540-day max membership duration, remains crucial in guiding automation toward valuable traffic. For effective budget allocation, I focus on exclusion before expansion, targeting acquisition budgets toward new prospects.
Retention strategies should be run separately to maintain consistency in conversion goals. It’s vital that exclusions, available from the start, streamline acquisition efforts effectively.
For ongoing daily budget campaigns, weekly increases of 10-20% are still relevant. For scheduled campaigns, I focus on monthly targets divided by 30.4 instead of daily adjustments.
Using Smart Bidding Exploration in open beta for Performance Max can increase unique conversions by exploring new queries. I evaluate results over 60-day windows to make informed decisions.
Demand-led pacing, complementing daily management, tracks predicted high demand periods to optimize spend within budgetary limits. For B2B accounts, longer evaluation periods safeguard against undervaluing long cycle campaigns.
Recently, I’ve been delving into an intriguing study by Lily Ray, which reveals some unexpected findings about Google’s AI Overviews. Apparently, these Overviews frequently reference brands’ own listicles but tend to recommend their competitors.
The study highlighted that Google AI Overviews cited these self-promotional listicles in a whopping 69% of B2B software-related queries. Yet, they favored rival brands in their recommendations. This got me thinking about the strategies brands employ to influence AI search outcomes.
Detailed Findings. I discovered that the analysis was quite comprehensive. Ray reviewed 100 B2B queries spanning categories like “best [category] software.” She gathered data across three specific periods: April 15, May 15, and June 8.
The study found that out of 80 queries that triggered an AI Overview, self-serving listicles were referenced 323 times, yet in 224 instances, Google didn’t actually recommend those brands. This mismatch intrigued me.
Analysis of Recommendations. While examining specific cases, it became evident that Google sometimes cited a brand’s listicle but opted to recommend more renowned competitors instead. For instance, in the search for “best LMS for selling courses,” Oasis LMS was mentioned, yet Kajabi and others were pushed forward as the preferred options.
This pattern wasn’t just isolated to LMS software; it appeared in multiple domains like help desk tools, task management, and more. It made me ponder over the dominance of stronger brands in recommendations.
Observing Organic Declines. An interesting trend noted was a drop in organic visibility for websites heavily leaning on self-promotional listicles. I noticed beginnings of these declines back in January and observed further drops post-Google’s May 2026 core update.
Interestingly, these sites also seemed to have expanded into AI-generated content and other “best” pages prominently featuring their own brands.
Rise of Third-party Citations. Ray’s analysis also showed an upsurge in Google comprising third-party content for “best” queries. Platforms like Reddit, Forbes, and YouTube gained traction in citations.
Understanding Impact. I believe it’s crucial to realize that merely having your content cited doesn’t equate to a recommendation. This situation offers competitors the chance to snag attention and, ultimately, valuable visibility.
Keeping Up with Changes. Previously, Search Engine Land shared insights on how some SaaS and B2B businesses witnessed visibility losses after banking on self-ranked “best” lists. The risks are significant when company-driven content doesn’t transparently disclose material relationships as mandated by the FTC’s Consumer Review Rule.
About Ray’s Data. To reach her conclusions, Ray employed Ahrefs Brand Radar to examine numerous AI Overview responses. Her analysis spanned 100 B2B software queries, focusing on citations versus actual recommendations.
I recently came across an intriguing development regarding Google and its operations in the UK. The UK’s Competition and Markets Authority (CMA) has taken a proactive stance, requiring Google to not only allow site owners a way to opt out of AI Overviews but also to clarify how they rank search results.
In addition, Google is required to enable users to port their search data to specific third-party services, a move aimed at increasing data portability.
Transparency on search rankings. The CMA’s demand for Google is to enhance transparency and fairness in ranking search results, with an implementation deadline of six months.
Many UK businesses have voiced concerns to the CMA, claiming that Google’s ranking practices lack fairness and transparency. They argue that changes are implemented without sufficient notice, impacting their operations without providing them with adequate avenues to express their concerns.
Yes, we cover Google search updates frequently, and it’s evident that Google is constantly refining its algorithms to make search results more relevant and to deter manipulation attempts.
According to the CMA, Google must:
Establish clear processes for businesses to voice concerns about Google’s ranking methods, ensuring these concerns are addressed effectively.
Use objective and non-discriminatory criteria to rank ‘organic’ search results, which includes AI Overviews but excludes sponsored results.
Offer businesses greater transparency on ranking mechanics and provide advance notice of significant changes.
Data portability. The CMA also seeks Google’s cooperation to “Allow users to port their search data to authorized third parties, such as rewards platforms or businesses offering personalized offers or discount codes”, aiming for this within three months.
The potential for third-party companies to access Google’s search data could open new avenues for personalized services, such as tailored travel suggestions and more relevant shopping deals, enhancing consumer experiences.
Why we care. Despite these orders, I’m skeptical that Google will comply, as doing so might compromise its highly valued search ranking algorithm, risking exposure to competitors and potential manipulation.
This isn’t the first time such demands have been made and undoubtedly won’t be the last. Google is likely to resist these orders firmly.
