I’ve discovered that images aren’t just for human eyes anymore—they are parsed like language by AI. With Optical Character Recognition (OCR), visual context, and pixel-level quality shaping how AI systems interpret content, the game of Image SEO has changed.
For years, Image SEO was all about technical best practices: compressing JPEGs for speedy loading, writing alt text for accessibility, and using lazy loading to enhance page performance. These remain crucial, yet now we must also cater to the needs of advanced multimodal AI models like ChatGPT and Gemini, which present both opportunities and challenges.
Multimodal search embeds diverse content forms into a unified vector space. We are learning to optimize for what I call the “machine gaze.” Generative search technology makes content largely machine-readable by segmenting media and extracting text from visuals via OCR.
It is essential for machine vision to clearly parse images. Low quality or poorly contrasted text on product packaging can lead to misinterpretation or completely missed content by AI systems—a significant problem.
This discussion explores the crucial aspect of improving machine readability, shifting focus from loading speeds to quality and interpretability of images.
Technical hygiene still matters
Before diving into optimization for machine comprehension, I make sure to respect the fundamentals: performance. Images are powerful tools for engagement but can also cause layout issues and slow speeds if not managed properly.
Designing for the machine eye: Pixel-level readability
Large language models view images, audio, and videos as structured data sources. Through visual tokenization, an image is divided into a grid of visual tokens, turning raw pixels into vector sequences.
Poor resolution or compression artifacts can degrade token quality, leading to errors where the AI misreads images or invents details that aren’t there. Ensuring clarity and quality is critical for accurate interpretation.
Reframing alt text as grounding
In today’s context, alt text offers critical grounding for large language models. It provides semantic cues that help the model discern ambiguous visual tokens, improving image interpretation accuracy.
The OCR failure points audit
Technologies like Google Lens and Gemini rely on OCR to read text directly from images, including labels. However, small or low-contrast text often fails this machine gaze.
Character height should be optimized to at least 30 pixels for OCR, and contrast should be clear to prevent errors in text reading. Stylized fonts and reflective packaging can exacerbate these problems.
Originality as a proxy for experience and effort
Original images are vital, serving as canonical signals that enhance page authenticity and origin credibility. Using tools like Google Cloud Vision’s WebDetection can help track duplicate content and boost your visual content’s scoring.
The co-occurrence audit
AI systems analyze the objects in images and their relationships, using these cues to infer brand attributes and audience engagement signals. This makes product placement in images crucial for SEO success.
Tools like Google’s OBJECT_LOCALIZATION feature allow you to audit your media library’s visual entities and ensure that adjacent objects tell the right story to support your brand’s narrative.
Quantifying emotional resonance
Images not only showcase products; they evoke emotions. AI can now quantify these emotions in images, making emotional alignment critical to image SEO.
Tools like Google Cloud Vision provide insight into emotion scores for faceAnnotations, allowing for content adjustments based on detected sentiment to better align with intended search queries.
Closing the semantic gap between pixels and meaning
Images should be curated with intent and precision, given that language models treat them as part of the language sequence. The quality and semantic accuracy of images are as vital as textual content for SEO success.
This past Black Friday and Cyber Monday, I delved into the fascinating insights from our Black Friday Index, crafted from a vast pool of 400 million genuine conversations. It was enlightening to see which brands stood out as AI’s top recommendations, especially as so many of us relied on Answer Engines to hunt down the best deals.
As I explored the data, the impact of AI on shopping trends became crystal clear. The technology not only streamlined how we search for deals but also influenced brand visibility and consumer choices. The excitement of seeing how AI is reshaping shopping habits made this year’s Black Friday and Cyber Monday particularly intriguing for me.
The findings from the Black Friday Index are a testament to the growing importance of AI in retail, showing us how indispensable it has become for both consumers and brands. Being part of this evolution makes me look forward to what future shopping events will bring, especially as technology continues to advance.
I’ve been following the significant regulatory move in which the European Commission launched a formal antitrust investigation into Google.
At the heart of this issue is Google’s use of publisher content to develop AI Overviews and other generative AI features, potentially diverting traffic from original publishers.
As someone involved in SEO or content strategy, I’m immediately affected by these developments.
The question I’m pondering is whether Google is overstepping by using publisher content for AI answers, or if it’s just part of being in an open web environment.
With regulators stepping in, I’m seeing the industry reevaluate how we use, manage, and value machine-readable content. It raises questions about the cost to brands, publishers, and agencies if regulation doesn’t catch up with innovation.
Here’s what’s going on, why it’s significant, and how the industry is already responding.
What’s Actually Happening: Core Allegations in the Complaint
This move from the EU is unfolding alongside other legal challenges, like those from publishers taking a stand against OpenAI and Penske Media’s recent antitrust suite targeting Google’s AI offerings.
Many publishers see Google’s actions as a no-choice situation: allow the use of their content for AI, or face losing vital search traffic.
At the same time, I notice how technical tools like robots.txt, Google-Extended, and new noai/nopreview conventions are reflecting an industry that’s striving to reclaim control.
The crux of the issue is whether AI training and answer generation stretch the bounds of traditional indexing and require licensing or proper attribution.
The Big Debate: ‘Google Doesn’t Owe You’ vs. ‘It’s Not Their Content’
I often see the assumption that control of web content lies in our hands.
Yet, without search engines, their reach is quite limited.
This tension fuels an ongoing debate dividing SEO perspectives.
On one side is the belief that ‘Google doesn’t owe you anything’.
Many argue that the web is open, allowing search engines to crawl freely grants implicit permission for content use.
Google facilitates discovery, but clicks or backlinks aren’t guaranteed.
On the flip side, there’s the perspective that ‘It’s not their content’.
Publishers argue against unlicensed use of content for LLM training and AI responses.
They see generation without attribution or compensation as disruptive.
This debate is active across social media and discussion forums.
Some suggest focusing on generative engine optimization, or GEO, replacing traditional rankings with AI quotes.
Nonetheless, that approach keeps publishers reliant on Google’s linking decisions.
In practice, there’s validity to both arguments.
Yet, the broader trend reveals the trajectory.
Even if Google faces consequences, search is unlikely to return solely to blue links.
The zero-click conversion is advancing.
The Dark Future of a Web Without Unique Content
Before diving into potential outcomes of the complaint, consider the impact on information itself.
As creators feel their work is reused without reward, the drive for original content wanes.
Simultaneously, AI-generated content is growing, often with minimal human input.
Entire sites now rely heavily on generative systems for content.
This often involves reworking existing text, with occasional inaccuracies.
As this cycle continues, the risk is declining informational quality due to a lack of truly fresh inputs.
The debate over AI training isn’t just about traffic or monetization.
It questions how the web can sustain unique knowledge creation and why protecting publishers is crucial to prevent information quality degradation.
What Can Happen if Google Loses
The traditional Google-publisher agreement was straightforward: “I let you crawl, you give me clicks.”
Generative AI disrupted this balance.
If the EU finds Google’s actions anticompetitive, we could witness major shifts:
Mandatory opt-out mechanisms: Effective changes could enforce a granular system that protects against AI summaries without sacrificing rankings.
The licensing economy: Following the music industry model, licensing could become compulsory, splitting organic search into free and premium sectors.
AEO formalization: Attribution could be legally required, turning source citations into a ranking factor.
Ads and the Shifting Economics of Visibility
While this primarily concerns AI and content rights, ads still significantly impact SERP dynamics.
As organic space shrinks due to AI summaries, paid ads remain a strong visibility tool.
Even if EU pressures curb AI answers, the space for blue links is unlikely to grow.
The landscape will continue to favor revenue-driven Google products.
If AI Overviews reduce organic visibility, CPCs could rise, affecting ad positions.
Whatever the AI outcome, one truth is apparent: the cost of visibility is on the rise.
How to Adapt Your SEO and Content Strategy
Before any EU decision, I see top teams already shifting their strategies from merely ranking for keywords to ensuring they are the main entity answer wherever an AI model scans.
This involves several key actions:
Enhancing entity clarity with schema and consistent data for accurate AI association.
Auditing brand representation in AI Overviews and tracking emerging visibility KPIs.
Reconsidering robots.txt strategies to manage IP protection versus AI visibility.
Educating leadership that visibility extends beyond traffic, incorporating citation and AI source value.
The strategic goal is remaining readable and rights-conscious while ensuring brand presence where AI answers are most trusted.
I’m excited to share that Semrush has launched the new AI Visibility Awards, highlighting which brands are excelling in AI-generated search results.
As AI chatbots increasingly become our go-to for travel plans and product recommendations, I often wonder how we can ensure our brands feature prominently in their answers.
Semrush seems to have found the solution and has introduced this award program to celebrate the trailblazers in this field.
The AI Visibility Awards honor brands frequently mentioned and recommended in AI-generated responses, assessed using Semrush’s AI Visibility Index—a dataset crafted from over 2,500 real prompts processed through ChatGPT and Google’s AI Mode.
Andrew Warden, Semrush’s CMO, notes:
“This year marks a turning point in how visibility is achieved. It’s driven by actual user behavior rather than submissions or panels. These awards spotlight those marketers who have mastered AI interaction and earned significant trust inside the answers.”
What the AI Visibility Awards Measure
The awards recognize three performer types within four major industries:
Category Leaders: Brands with the biggest presence in AI searches
Growth Engines: Brands rapidly gaining visibility
Challengers: Emerging brands gaining AI traction
To illustrate, Google tops the Business & Professional Services category, while Rippling stands out as a Challenger. In Consumer Electronics, Samsung leads, with Logitech and Nothing Technology recognized as a Growth Engine and Challenger, respectively.
Other notable winners include:
Microsoft, named Category Leader for Digital Tech & Software
UNIQLO as a Growth Engine in Fashion & Apparel
Anthropic as a Challenger in Digital Tech & Software
AI Search Trends Marketers Should Watch
The award insights reveal some emerging truths about AI-powered discovery:
Stability among leaders: Top brands display less than 20% monthly volatility in AI share-of-voice, suggesting AI platforms tend to “lock in” trusted names.
Niches break through: Brands with niche relevance—like Patagonia in ethical fashion or Logitech in gaming accessories—prove advantageously positioned.
Challengers can compete: Newer players, like Nuuly and Anthropic, gain traction with robust positioning and strategic momentum.
Verticals behave differently: While some sectors, such as Business & Professional Services, stay fiercely competitive, others benefit from consistency or unique specialization.
These awards highlight a significant message for marketers: gaining AI visibility is turning into a crucial part of the competitive landscape. For certain brands, it’s already reshaping strategies.
Upon evaluating a whopping 10,000 keywords, I’ve discovered an intriguing insight: pages that successfully rank for Google AI Overview ‘fan-out’ queries are significantly more likely to be cited. In fact, they account for more than half of all citations on these platforms.
From my analysis, it’s clear that pages leveraging these queries dramatically increase their chances of being referenced. As data from Surfer SEO suggests, these pages offer more citation opportunities compared to those focusing solely on the main search query.
An analysis of these 10,000 keywords revealed a strong correlation—precisely, a Spearman of 0.77—between the volume of fan-out queries a page ranks for and its likelihood of citation in Google’s AI Overviews.
Diving into the numbers. I found that pages ranking for fan-out queries are 161% more likely to be cited than those ranking exclusively for the main query. Consider this:
76% of the keywords evaluated triggered AI Overviews.
Through Gemini, I extracted 33,000 fan-out queries.
Pages ranking for both the main query and at least one fan-out constituted 51% of AI Overview citations.
In contrast, pages ranking solely for the main query accounted for just under 20%.
Fan-outs outshine the main query. Recognizing the power of ranking for fan-out queries, I noticed such rankings were 49% more likely to earn citations than merely ranking for the main term. When the AI Overviews chose to reference organic results, here’s what stood out:
Approximately 20% of cited pages ranked only for the main query.
Conversely, around 30% ranked exclusively for fan-out queries.
Most AI citations skip top ranks. Fascinatingly, about 68% of cited pages didn’t appear among Google’s top 10 results for either their main or fan-out queries. However, for the top three most prominent citations, this figure dropped to roughly 46%.
But there’s more. It’s crucial to understand that correlation doesn’t equate to causation. Additionally:
Achieving a ranking for fan-out queries alone won’t guarantee an AI Overview citation.
User context and personalization affect fan-outs, with only about 27% remaining constant across test runs.
Normal SEO practices don’t fully determine citation selection.
Why this matters to us. If your goal is to be cited in AI Overviews, striving for broader topic authority might be the answer. Surfer SEO advises crafting extensive topical content around core subjects, creating content that naturally responds to a variety of related questions, and allowing AI Overviews to recognize your pertinence across different fan-outs.
When I think about improving my website’s visibility, AI comes to mind as a crucial tool. It serves as a second pair of eyes, helping me evaluate intent signals, compare top results, and refocus pages that aren’t performing well.
Despite having well-written content, excellent layout, and robust backlinks, pages can still underperform in rankings. A frequent culprit is misaligned search intent, which can be more elusive than it seems.
Focusing on content optimization and usability sometimes makes it easy to overlook or misjudge intent. This is where AI shines as a reviewing tool, effectively steering things back on course.
Whether I’m working on a new page or revising an existing one, returning to the basics of search intent always sets me up for success.
Starting with a simple AI prompt to outline likely search intents for a keyword offers a solid framework for content creation or optimization.
This comprehensive list isn’t something I strive to cover completely on a single page. Instead, it highlights diverse user types, shifts in intent, and needs I might not have initially considered.
By considering these factors, I aim to create a more useful, well-rounded page that genuinely satisfies user needs.
Getting the intent right can be challenging. AI tools help me understand what’s already successful by examining top-ranking pages and what they excel at.
I utilize AI tools for a swift overview of a page’s primary intent. By evaluating this at scale, I can see if top-ranking pages meet the same intent.
It’s crucial to assess the intent of my page with the same rigor, be it a fresh draft or a page I’m optimizing. If the primary intent aligns with what’s succeeding, it’s a strong starting point. If not, it provides clear direction for improvement.
Again, consulting AI tools for improvement suggestions can yield valuable insights into refining intent. Key areas to focus on include:
The language I use can either reinforce or contradict the intended message. For commercial intent, persuasive wording is necessary, while for informational pages, clear and descriptive language is preferred.
The format of a page can also convey intent. For instance, in a sales page, details like product placement and accompanying information matter greatly. Similarly, guides need clear step-by-step labeling and possibly visual aids.
Clearly defined calls to action are essential. They align the user’s actions with the page’s intent, enhancing both engagement and ranking potential. Unclear or generalized calls to action dilute this effect.
Listing accurate pricing, VAT elements, and currency signals is vital in conveying commercial intent. They guide users accurately at critical decision points.
Availability of support is another crucial factor. I make sure that pre- or post-sale queries can be easily addressed by ensuring my contact details and support options are clearly visible.
Trust signals, like product guarantees, return policies, and customer reviews, make a big difference in user decisions. Including these details serves to strengthen user trust.
When clear comparisons are needed, laying out products side by side can assist users in their decision-making process, moving them closer to making a purchase.
In my experience with working pages centered around user intent, I’ve seen that excess information can sometimes bloat a page.
Previously, this depth might have worked, but now clarity and a focus on intent are what truly resonate.
I’ve learned to reassess where content performs best within the user journey, often seeking AI’s guidance to refocus content structure wisely.
For instance, if I notice my sales page for internal French doors isn’t performing, I consult AI, along with competitor analysis, to uncover key insights.
Competitors might be focusing on selling first, while my page addresses user concerns, which means I need to reposition my content priorities.
By reordering sales-driven content and addressing pain points concisely, I better align with user intent, letting supporting pages deal with detailed post-sale information.
AI isn’t here to replace expertise but to guide my strategic intent, enhancing my understanding of user behavior for better conversion.
As I dive into optimizing content for Meta AI, it’s crucial to enhance brand visibility across its various platforms, including Instagram, Facebook, and the Meta AI chatbot.
Understanding the unique dynamics of each platform helps me tailor my content strategy effectively. Meta AI offers a vast ecosystem, and by leveraging its tools, I can easily increase my brand’s reach and engagement.
From structuring content to optimizing performance, I am committed to exploring the best practices that ensure my brand shines within this innovative environment. Join me as I uncover the secrets to mastering content optimization for Meta AI.
As I dive into the evolving landscape of search, I’ve noticed a shift from traditional keywords to more conversational prompts. In today’s digital world, searchers are replacing shorter queries with detailed prompts, seeking comprehensive answers rather than a mere list of links.
Until we’re equipped with an AI-specific Google Search Console or Bing Webmaster Tools, understanding our audience’s behavior on AI platforms feels like a guessing game. But fear not, as we can still trace their journey using data proxies. By leveraging these proxies, I can uncover how my audience might be searching and track those prompts with my preferred AI Tracking Tool.
One invaluable tool is the ‘People Also Ask’ feature on search engines. This well-known SERP component can help transition from keywords to questions. Introduced in 2014, it suggests related questions, allowing me to explore queries that echo conversational prompts.
Using platforms like AlsoAsked, I can extract these questions at scale, finding long conversational queries that closely resemble AI prompts.
Another avenue I explore is through Userbots such as ChatGPT-User and Perplexity-User. These bots offer insights into how my content is utilized in AI search, highlighting pages that are frequently cited without needing to guess the relevance of prompts.
The process, called RAG (Retrieval-Augmented Generation), effectively grounds language models in factual data. It’s fascinating to consider how my content can play a role in shaping user responses, even if it doesn’t result in a direct click.
Gaining insights from long queries through tools like Google Search Console is another method I employ. By utilizing innovative techniques like Ziggy Shtrosberg’s complex regex filters, I can unearth queries that simulate AI search behavior.
It’s essential to approach this data cautiously, as some patterns might stem from automated trackers rather than genuine human interaction. For instance, high-appearance queries with zero clicks could indicate non-human usage.
Engaging with Perplexity AI’s follow-up feature is also enlightening. This feature can hint at how users might prompt AI systems, aiding my understanding of expected human interaction.
Finally, the Semrush AI Visibility Tool provides an ingenious way to manage the scaling challenge of unique prompts. By merging prompts into broader topics and using AI to distill their meanings, I gain valuable insights into intent and brand mentions across different regions.
In a rapidly changing tech environment, staying grounded in data is vital. Not all prompts engage Retrieval-Augmented Generation (RAG), which means those needing answers already in training data may bypass linking to new page sources.
However, when users seek recommendations (for example, dining options or attractions), page visibility within AI-generated answers can still convert offline interactions, benefiting brand exposure.
Checking the background operations of ChatGPT reveals search prompts within Chrome Dev Tools. By identifying searches and their relevancy to RAG, I can strategize to optimize this invisible layer of search behavior.
The quest to master AI search dynamics is ongoing. New AI models and evolving user behaviors necessitate continuous adaptation to comprehend and leverage audience interactions effectively.
Have you ever felt the pressure of rising expectations in marketing while budgets stay flat? It’s certainly a dilemma I face regularly. In 2025, marketing budgets have plateaued, averaging 7.7% of company revenue. However, our goals continue to grow, prompting us to seek efficient solutions. Enter AI – not as a futuristic possibility, but as the answer to today’s challenges.
Let me walk you through how AI, particularly tools like Artlist AI, is revolutionizing our workflow by cutting down costs and speeding up production, all while maintaining our brand’s creative integrity.
1) Video Production Challenges
As a marketer, I know how pivotal video content is, yet it often becomes a bottleneck. We’re looking to deliver more, faster, without breaking budgets. Luckily, with AI, we’re finding ways to do just that.
By utilizing Artlist AI, our team rapidly converts scripts into screen reality, making video production cycle much less burdensome. From quick concept storyboards to instant variations and voiceovers, AI is a game-changer.
2) Consistent Brand Voice
Maintaining a uniform brand voice across different markets and languages is daunting. AI voiceover allows us to deliver a steady tone and pacing, ensuring our brand’s message is consistent and recognizably ours.
Artlist gives us the tools to tailor our tone for different cultural contexts without losing brand integrity, refining messaging quickly and at scale.
3) Agile Creative Testing
Today’s social media landscape demands rapid creative cycles. With AI, producing and testing multiple versions becomes feasible, allowing us to adapt quickly and maintain engagement.
I’ve seen firsthand how using AI to test creative variations leads directly to improved ad performance and deeper insights into what resonates with audiences.
4) Meaningful Metrics and Feedback
The true impact of creative elements can often feel elusive. AI provides real-time analytics, correlating creative inputs with engagement metrics. For me, this means turning subjective creative decisions into data-driven strategies.
By leveraging these insights, I can ensure that every marketing move is not only creative but also grounded in real effectiveness.
The Takeaway
Integrating AI doesn’t require an overhaul of our processes. Instead, it enhances what we already do, offering efficiencies that allow us to meet increasing demands without expanding budgets.
If you’re keen to elevate your marketing game, Artlist’s suite of AI tools might just be the solution you need. I’ve experienced the difference they make, turning what once seemed like bottlenecks into seamless workflow elements.
I’ve been captivated by how Google AI Overviews shifted the search landscape in 2025. Since then, I’ve delved into a detailed analysis by Semrush, which evaluated over 10 million keywords, revealing significant volatility, an increase in ads, stronger click-through rates (CTRs), and AI Overviews venturing beyond purely informational searches.
The year witnessed a rapid expansion of AI Overviews in Google’s search functions, which eventually tapered off as they began appearing in commercial and navigational inquiries. Between January and November, Semrush’s analysis identified these dynamic changes.
AI Overviews surged, then retreated. The deployment of AI Overviews was far from linear. Google introduced them at a rapid pace, peaking mid-year, then scaled back based on user data and feedback:
January: AI Overviews appeared in 6.5% of all queries.
July: Their presence peaked, appearing in nearly 25% of searches.
November: By this time, their appearance was retracted to less than 16%.
Zero-click behavior defied expectations. Contrary to initial beliefs, I noticed that click-through rates for searches with AI Overviews have increased steadily. It seems that rather than reducing clicks, AI Overviews may actually encourage them.
AI Overviews are more common on searches that generally lead to no clicks.
But when examining the same keywords pre and post-introduction of an AI Overview, the zero-click rates decreased from 33.75% to 31.53%.
Informational queries no longer dominate. At the start of 2025, AI Overviews predominantly served informational purposes:
January: 91% informational
October: 57% informational
Eventually, I observed AI Overviews appearing in commercial and transactional searches:
Commercial queries: Jumped from 8% to 18%
Transactional queries: Increased from 2% to 14%
Navigational queries are rising fast. Interestingly, there’s a noticeable increase in AI Overviews intercepting brand and destination searches:
Navigational AI Overviews rose from under 1% in January to over 10% by November.
Google Ads + AI Overviews. Earlier this year, ads rarely appeared next to AI Overviews. Now, their presence is much more common:
Ads alongside AI Overviews grew from about 3% in January to around 40% by November.
Roughly 25% of AI Overview SERPs now show ads at the bottom.
Science is the most impacted industry. In terms of keyword saturation, Science tops the list with AI Overviews appearing in 25.96% of searches. This is followed by Computers & Electronics at 17.92%, and People & Society at 17.29%.
Since March, Food & Drink has experienced the fastest growth among all categories in AI Overview usage.
In contrast, sectors like Real Estate, Shopping, and Arts & Entertainment see AI Overviews in less than 3% of queries.
Why we care. With AI Overviews persistently reshaping click behaviors, commercial visibility, and ad placements, I believe it’s important to keep a close eye on these shifts and adapt accordingly.
