The United States Copyright Office released a pre-publication version of a report on the use of copyrighted materials for training generative AI, outlining a legal and factual case that identifies copyright risks at every stage of generative AI development.
The report was created in response to public and congressional concern about the use of copyrighted content, including pirated versions, by AI systems without first obtaining permission. While the Copyright Office doesn’t make legal rulings, the reports it creates offer legal and technical guidance that can influence legislation and court decisions.
The report offers four reasons AI technology companies should be concerned:
The report states that many acts of data acquisition, the process of creating datasets from copyrighted work, and training could “constitute prima facie infringement.”
It challenges the common industry defense that training models does not involve “copying,” noting that the process of creating datasets involves the creation of multiple copies, and that improvements in model weights can also contain copies of those works. The report cites reports of instances where AI reproduces copyrighted works, either word for word or “near identical” copies.
It states that the training process implicates the right of reproduction, one of the exclusive rights granted to emphasizes that memorization and regurgitation of copyrighted content by models may constitute infringement, even if unintended.
Transformative use, where it adds a new meaning to an original work, is an important consideration in fair use analysis. The report acknowledges that “some uses of copyrighted works in AI training are likely to be transformative,” but it “disagrees” with the argument that AI training is transformative simply because it resembles “human learning,” such as when a person reads a book and learns from it.
Copyright Implications At Every Stage of AI Development
Perhaps the most damning part of the report is where it says that there may be copyright issues at every stage of the AI development and lists each stage of development and what may be wrong with it.
“A. Data Collection and Curation
The steps required to produce a training dataset containing copyrighted works clearly implicate the right of reproduction…
B. Training
The training process also implicates the right of reproduction. First, the speed and scale of training requires developers to download the dataset and copy it to high-performance storage prior to training.96 Second, during training, works or substantial portions of works are temporarily reproduced as they are “shown” to the model in batches.
Those copies may persist long enough to infringe the right of reproduction,160 depending on the model at issue and the specific hardware and software implementations used by developers.
Third, the training process—providing training examples, measuring the model’s performance against expected outputs, and iteratively updating weights to improve performance—may result in model weights that contain copies of works in the training data. If so, then subsequent copying of the model weights, even by parties not involved in the training process, could also constitute prima facie infringement.
C. RAG
RAG also involves the reproduction of copyrighted works.110 Typically, RAG works in one of two ways. In one, the AI developer copies material into a retrieval database, and the generative AI system can later access that database to retrieve relevant material and supply it to the model along with the user’s prompt.111 In the other, the system retrieves material from an external source (for example, a search engine or a specific website).181 Both methods involve making reproductions, including when the system copies retrieved content at generation time to augment its response.
D. Outputs
Generative AI models sometimes output material that replicates or closely resembles copyrighted works. Users have demonstrated that generative AI can produce near exact replicas of still images from movies,112 copyrightable characters,113 or text from news stories.114 Such outputs likely infringe the reproduction right and, to the extent they adapt the originals, the right to prepare derivative works.”
The report finds infringement risks at every stage of generative AI development, and while its findings are not legally binding, they could be used to create legislation and serve as guidance for courts.
Takeaways
AI Training And Copyright Infringement: The report argues that both data acquisition and model training can involve unauthorized copying, possibly constituting “prima facie infringement.”
Rejection Of Industry Defenses: The Copyright Office disputes common AI industry claims that training does not involve copying and that AI training is analogous to human learning.
Fair Use And Transformative Use: The report disagrees with the broad application of transformative use as a defense, especially when based on comparisons to human cognition.
Concern About All Stages Of AI Development: Copyright concerns are identified at every stage of AI development, from data collection, training, retrieval-augmented generation (RAG), and model outputs.
Memorization and Model Weights: The Office warns that AI models may retain copyrighted content in weights, meaning even use or distribution of those weights could be infringing.
Output Reproduction and Derivative Works: The ability of AI to generate near-identical outputs (e.g., movie stills, characters, or articles) raises concerns about violations of both reproduction and derivative work rights.
RAG-Specific Infringement Risk: Both methods of RAG, copying content into a database or retrieving from external sources, are described as involving potentially infringing reproductions.
The U.S. Copyright Office report describes multiple ways that generative AI development may infringe copyright law, challenging the legality of using copyrighted data without permission at every technical stage, from dataset creation to model outputs. It rejects the use of the analogy of human learning as a defense and the industry’s broad application of fair use. Although the report doesn’t have the same force as a judicial finding, the report can be used as guidance for lawmakers and courts.
Every day, marketing teams face a crucial decision: Should they rely on Google’s built-in AI tools or invest in custom solutions for specific ad campaign tasks?
I’ve watched this debate play out countless times with clients.
Google continues adding more AI features for tasks like ad copy generation, headline creation, image generation, and product feed optimization.
Meanwhile, specialized tools and custom solutions are thriving, and no real breakthrough for Google AI can be seen.
Recent research supports this tension.
Gherheș et al. (2025) found that while AI-generated content can outperform human-created alternatives in certain contexts, the quality varies significantly depending on implementation and purpose.
Their study revealed that over 50% of users preferred AI-generated informative content over sensationalized approaches, suggesting that how AI is deployed matters more than the technology itself.
But which approach actually delivers better results? And at what cost?
As Pavlik (2024) notes in his analysis of AI in journalism, tools like ChatGPT don’t simply replace human creativity but rather present opportunities for “improving the quality and effectiveness” of creative work when properly integrated into existing workflows.
A recent study by Ameet Khabra compared the performance of human-written versus AI-generated ad copy in Google Ads campaigns.
Over an eight-week period with a $500 budget, human-crafted ads significantly outperformed AI-created content from Copy AI, achieving 60% more clicks, a 1.33% higher click-through rate, and a lower cost per click ($4.85 vs. $6.05).
Researchers attributed human copywriters’ superior performance to their ability to understand audience emotions, employ creativity and emotional appeal, adapt to specific contexts, and leverage cultural nuances that AI still struggles to replicate.
While acknowledging AI’s evolving capabilities and potential value as a supplementary tool, the study emphasizes the enduring importance of human creativity in crafting compelling advertising messages that drive engagement and conversions.
Regardless of these mixed research findings, one thing is certain: AI is increasingly embedded in creative processes across marketing, and its integration is inevitable.
The question isn’t whether AI will play a role in advertising creation, but rather how marketers can best leverage these tools to enhance their campaigns.
As AI capabilities evolve rapidly, today’s limitations may be tomorrow’s strengths. With this inevitability in mind, marketers need practical guidance on navigating the current landscape of available solutions.
This article compares Google Ads integrated AI tools against third-party and custom solutions for creative and optimization tasks specifically.
AI-Generated Ad Copy
Google AI Automatically Created Assets
Google’s AI text generator aims to streamline the ad creation process by converting basic product descriptions into campaign-ready assets.
The platform encourages advertisers to input unique selling propositions and key product features to generate contextually relevant ad copy.
Upon testing this tool with a simulated video game business specializing in refurbished PlayStation 5 consoles and games, the performance fell notably short of expectations.
The output quality was inconsistent, but more concerning were the significant compliance issues observed.
In one particularly problematic instance, the system generated the phrase “Welcome to the Amazon® Website” as suggested ad text, presenting a clear trademark infringement risk and potential legal exposure for advertisers.
Such critical errors highlight a fundamental limitation in Google’s native AI solution: While offering workflow convenience, it demonstrates inadequate safeguards for brand compliance and legal protection.
The system also produced contextually inappropriate messaging, such as “PlayStation 5 Problems Solved,” which misaligned with sales-oriented campaign objectives by suggesting repair services or technical support rather than product offerings.
Without careful human review, these problems make the tool risky to use, especially for businesses in competitive markets where mistaken identity or inaccurate representations could lead to serious legal issues and damage to your reputation.
Image from author, April 2025
When generating longer headlines, there were much fewer results.
Only three ad suggestions appeared, one of which included free shipping information for orders over $50, which was a hallucination, as this information was never disclosed in the prompt or the landing page.
Image from author, April 2025
Creating descriptions was even worse, as there was only one ad suggestion and not even a good one from a copywriting perspective.
Image from author, April 2025
After trying with different prompts, I was able to get at least five new descriptions out of Google AI.
Still, the results were quite disappointing. The ad copy contained hallucinations like the “free shipping over 100 USD,” as well as the business name “Example Video Games,” instead of using the business name of the account or extracting it from the landing page or URL.
Overall, underwhelming results, considering Google is one of the biggest companies on earth and owns the biggest online advertising platform.
Image from author, April 2025
Third-Party Ad Copy Creation
While Google’s AI text generator struggles with brand accuracy and contextual relevance, several general-purpose AI models offer more sophisticated ad copy creation capabilities that balance automation with quality control.
Leading general AI assistants like Claude, ChatGPT, and Gemini represent compelling alternatives for marketers seeking higher-quality ad copy generation.
Unlike Google’s more constrained system, these platforms offer greater flexibility in handling nuanced prompting and brand-specific requirements.
Image by author, April 2025
In testing with our video game business scenario, we prompted each model to create headlines for refurbished PlayStation 5 consoles.
The results demonstrated significant advantages over Google’s native offering:
Claude 3.7 produced premium-positioned headlines like “Save On Certified PS5 Consoles,” “Quality PS5 | Full Warranty,” and “Premium PS5 | Fast Shipping” that emphasize both value and quality assurance. Claude’s headlines maintained strong brand positioning while highlighting availability (“PS5 Consoles In Stock Now”) and price advantages (“PS5 Consoles 30% Off Retail”) without sacrificing perceived value.
ChatGPT (o3-mini) focused more on emotional appeal and deal framing with options such as “PS5 Deals You’ll Love,” “Get More, Spend Less PS5,” and “Budget PS5, Premium Fun.” ChatGPT’s approach effectively balanced affordability messaging with aspirational elements, potentially appealing to both value-conscious and experience-focused consumers.
Gemini 2.0 took a more direct value-oriented approach with straightforward headlines like “Refurbished PS5 Deals,” “Cheap Used PS5,” and “Discount PS5 Titles.” While less nuanced in positioning, Gemini’s headlines clearly communicate the core offering and may perform well for price-sensitive segments or direct response campaigns.
All three models demonstrated superior context awareness compared to Google’s native tool, with each showcasing different strategic approaches to headline creation.
They successfully avoided the hallucinations and brand confusion issues observed in Google’s Ad tool, while providing greater headline variety tailored to different marketing objectives.
The key advantage these general AI assistants offer is their adaptability and more refined understanding of marketing language.
By providing detailed prompting with brand guidelines, target audience information, and specific messaging requirements, marketers can achieve significantly better results than with Google’s more limited integrated tool.
For businesses where ad copy directly impacts conversion rates, leveraging these more sophisticated AI options can yield higher-quality creative assets that better represent brand positioning and speak more effectively to customer needs.
Despite Gemini’s relevant headline ideas, it struggled to adhere to the 30-character limit for some prompts on Google Ads headlines – a surprising limitation given that Gemini is Google’s own AI model and would be expected to understand Google Ads guidelines inherently – while Claude and ChatGPT consistently produced properly sized headlines without major additional editing or truncation.
Image Generation
Google AI Image Generation
Image generation is another area where AI can really shine and reduce the workload.
Images are a core asset in ecommerce, not only used for product images, but also for category pages, shop banners, display ads, and more.
For our virtual video game business, I tried to create some images matching our PlayStation 5 asset group. The results were interesting to say.
The first created image looks very similar to an Xbox. Specifically, an Xbox One S or an Xbox Series S, which is the latest model.
Now, there are no logos or trademarks seen, and the form factor is a little off.
AI-generated image by author, April 2025
Even more interesting, depending on the exact prompt, Google AI shows an error message related to branded items and content restrictions.
Image from author, April 2025
Another image created looks a little more like a PlayStation, but not as described and advertised as a PlayStation 5, but rather an older PlayStation 4 model.
Again, the content restrictions are most likely responsible for the results.
AI-generated image by author, April 2025
While the image results are somewhat disappointing for those branded items, it is understandable that Google AI follows content restrictions and brand guidelines to avoid any legal issues, as the PlayStation is a trademark of Sony, and the Xbox is a trademark of Microsoft.
It’s interesting to see that Google AI tries to work around this limitation and still creates an image, but in that specific case, the image is more or less useless, as there is little value in showcasing a non-existent video game console.
A question here would be why the content restrictions and guidelines did not apply to text creation when the text asset “Welcome to the Amazon® Website” was created.
To check the image creation quality, I tried a different approach for non-branded items in the dog food category.
The image is good at first glance since multiple products are shown with a dog in the picture, supporting the category, but some things are off.
The text in the image is still a mess for Google AI. Plus, the proportions are wrong. The dog is way too small, considering the cans of dog food displayed, which are small items.
AI-generated image by author, April 2025
Better than video game consoles, but still not good enough to rely solely on Google AI without any backup or alternative.
Third-Party Image Generation
ChatGPT
Using the same prompts to create images, ChatGPT delivers amazing results compared to the Google Ads integrated image creator.
Visually, it was able to recreate a PlayStation video game console with a gaming controller.
ChatGPT even got details right, except the brand logo, which might be due to some brand protection measures.
AI-generated image by author, April 2025
Also, the latest Xbox model was created with in-depth details.
This time, even the Xbox logo was created, maybe because ChatGPT and Microsoft have made a trademark deal of some sort? Or trademark restrictions have some loopholes.
AI-generated image by author, April 2025
At last, the creation of the dog food image was also a success.
The prompt included the brand to be named “Doug’s Best Dog Food,” which was perfectly written on the product, along with a nicely placed bowl full of pellets in front of a golden retriever.
In comparison, Google AI was able to create a decent image, but upon closer look, issues with displaying words were apparent, which ChatGPT could handle perfectly.
AI-generated image by author, April 2025
Qwen
Qwen is an image generation tool based on Deepseek, which is a Chinese-based AI developer.
AI-generated image by author, April 2025
The image from Qwen clearly had an “AI” look compared to the ChatGPT or Google AI image.
However, it got the brand name “Doug’s Best Dog Food” right. With some improvements, Qwen can produce decent images, if you are okay with having a digital image look.
Google AI was able to create a more real-life looking image, with the downside of not being able to display the brand name correctly.
Video Creation Tool
Google Ads Video Creation Tool
Google’s built-in video creation tool aims to make video advertising accessible to marketers without production resources.
The tool covers multiple marketing objectives – from brand awareness and consideration to direct sales and app promotion – accommodating various business categories, including apps, products, and services.
It offers flexibility with vertical, square, and horizontal formats in lengths ranging from quick six-second spots to 15-second and longer videos.
However, the tool’s output quality reveals its limitations.
Most videos are essentially slideshows, stitching together static images, logos, and text overlays rather than fluid video content.
While this approach democratizes video ad creation, the results often lack the polish and engagement power of properly produced video content.
For many marketers, this represents the fundamental tradeoff of Google’s native tools: accessibility and integration vs. creative limitations that might impact performance.
Image from author, April 2025
At best, marketers get a nice-looking slideshow; speaking of a serious advertising video would be too much.
The better templates are mostly for app-related videos, where at least some kind of animation is included with a finger doing some phone touch gestures.
Overall, the native video creation tool serves as a backup for marketers who need a video immediately and don’t have any tools on hand.
In any other case, it’s best to postpone video creation and start with a more decent tool.
Third-Party Video Creation
Canva Video Creation
Screenshot from Canva, April 2025
Canva makes much better videos than Google Ads’ built-in tools with almost the same effort.
Google mostly creates basic slideshows, but Canva gives you thousands of professional templates, animations, and stock videos to use in your marketing.
The simple drag-and-drop design lets you make engaging videos with smooth transitions and text effects that keep viewers engaged.
Unlike Google’s static slideshows, Canva creates flowing video content that looks professionally made.
If you spend just a few more minutes using Canva instead of Google’s tool, your videos will look much more professional and likely perform better with your audience.
Qwen
Alibaba’s Qwen is a strong competitor to Google Ads’ basic video tools, giving marketers better videos without needing special skills.
While Google just makes simple slideshows, Qwen uses AI to turn your images and text into dynamic videos with smooth movements and professional transitions.
The tool is great at automatically creating cohesive visual stories even from minimal input, adding motion to still images in ways that look professional.
What stands out is how Qwen creates animations that actually match your product type and brand style, avoiding the one-size-fits-all look of Google’s templates.
Though not as well-known as Canva in the West, Qwen’s AI approach produces polished videos that look intentionally designed rather than template-made, making it a great choice for marketers who want better videos than what Google offers.
Image by author, April 2025
Image by author, April 2025
For the example of a dog food brand, Qwen delivered exceptional results.
With the first prompt, Qwen created a five-second clip of golden retrievers playing around and going to a human hand to eat dog food from the hand.
Not only did the video look pretty close to real life compared to the Qwen image generation “AI look,” but Qwen also did this as a free tool. No cost involved.
If you compare this to the Google video creation, which is basically a PowerPoint presentation, Qwen makes a really good performance.
Sora
Another great video tool is Sora from OpenAI.
Since Sora is included in the $20 Premium membership, you can generate videos at almost no cost, though with some limitations on video quality and length.
Still, there are a few tools out there that can generate decent AI video output for that cost.
Product Image Improvements
Product Studio
The Product Studio for Merchant Center Next is a beta image optimization tool within the Merchant Center, also accessible via the Google App within Shopify.
It allows for creating product images in various scenes, as well as removing backgrounds and increasing image quality.
Image from author, April 2025
Image from author, April 2025
These are two tries to display a gaming controller in a scene.
Although the quality of the product image has remained reasonably good, the scenes are barely usable.
The image processing prompt was “Showcase this controller in a living room, in front of a TV with neon lighting.”
In practice, the desired scene was not even remotely depicted. The controller in front of notebooks or pens is out of place; the second attempt resulted in three black backgrounds and a fiery background.
Free Alternatives To Google’s Product Studio
Unlike Google’s Product Studio, which struggles with accurate scene generation as shown in the gaming controller example, several free tools offer more reliable image optimization capabilities.
Canva’s free tier includes a background removal tool that produces clean cutouts with remarkable accuracy.
While scene creation is more limited in the free version, you can still place products on various pre-designed backgrounds or use their extensive template library to create more contextually appropriate product displays than what you experienced with Google’s tool.
To remove backgrounds, use remove.bg, which is a specialized tool that focuses exclusively on background removal with impressive results, even for complex products like your gaming controller.
The free version has size limitations but delivers professional-quality cutouts that can then be placed into scenes using other tools.
For everything more complex, GIMP is a free and capable tool. This open-source image editor provides robust capabilities for both background removal and scene composition.
Though it has a steeper learning curve, GIMP offers precise control over image quality enhancement and realistic product placement.
Final Thoughts
Google’s native AI tools, while conveniently integrated into their advertising platform, consistently underperform compared to third-party alternatives.
The evidence is clear and concerning. Google’s AI ad copy generator produced legally problematic content with brand infringement risks and hallucinated product details.
Its image generation produced visually inaccurate representations. The video creation tool delivered little more than basic slideshows rather than engaging video content.
Meanwhile, third-party solutions or Google’s own Gemini model used externally demonstrated superior capabilities across all creative functions:
General-purpose AI assistants like Claude and ChatGPT produced more compelling, accurate, and compliant ad copy.
Specialized tools like Canva, Remove.bg, and Photopea offered vastly superior image manipulation options.
Video creation platforms like Canva and Qwen delivered professional-quality animation and transitions impossible with Google’s basic tools.
This performance gap reveals the fundamental tradeoff marketers face: convenience of integration vs. creative quality and performance.
Google’s in-platform AI tools provide workflow efficiency but at the significant cost of creative limitations, brand safety concerns, and potential legal exposure.
For marketers serious about campaign performance, the path forward is clear: Leverage external AI solutions for creative development, then import these higher-quality assets into Google’s advertising platform.
This hybrid approach maintains the advantage of Google’s targeting and delivery mechanisms while avoiding the substantial limitations of their creative AI tools.
As AI continues to evolve in marketing, successful advertisers will be those who strategically select the right tools for each specific function rather than defaulting to in-platform options for convenience alone.
The evidence suggests that, for now, the marketing advantage lies decidedly with those willing to look beyond Google’s native AI for their creative development needs.
As brands compete for market share across a whole range of AI platforms, each with its own way of presenting information, brands are on red alert.
The three pillars of presence, perception, and performance that I discuss in this article may help marketers navigate new times. This is especially true as search and AI undergo their biggest make-over ever.
What’s driving this change?
AI isn’t just retrieving information anymore – it’s actively evaluating, framing, and recommending brands before prospects even click a link.
It’s happening now, and it’s accelerating.
Think about it. Today, in many ways, ChatGPT has become just as synonymous with AI as Google was when it launched core search.
More and more users and marketers are experimenting with and utilizing Google AIO, ChatGPT, Perplexity, and more.
According to a recent BrightEdge survey, over 53% of marketers regularly use multiple (two or more) AI search platforms weekly.
AI Is Reshaping How Brands Are Presented And Perceived
Consider how buyers research options today: In Google AIO, a traveler planning a Barcelona vacation once needed dozens of separate searches, each representing an opportunity for visibility.
Now? They ask one question to an AI assistant and receive a complete itinerary, compressing what 50 touchpoints once took into a single interaction.
AI is no longer a passive search engine. It’s an active evaluator, interpreting intent, forming opinions, and determining which brands deserve attention.
In enterprise SEO and B2B contexts, the shift is even more pronounced. AI is effectively writing the request for proposal (RFP), establishing evaluation criteria, and creating shortlists without brands having direct input.
Take enterprise software evaluation, for instance. When a CIO asks an AI about the “best enterprise resource planning solutions,” the AI’s response typically features:
A curated shortlist of vendors.
Evaluation criteria that the AI deems relevant.
Strengths and limitations of each solution.
Recommendations based on various scenarios.
These responses don’t just inform decisions. They frame the entire evaluation process before a vendor’s content is visited.
The question isn’t whether this transformation is happening. It’s whether your brand is prepared for it.
After analyzing thousands of AI search responses using our BrightEdge Generative Parser™, I’ve developed the Triple-P framework (Presence, Perception, and Performance) as a strategic compass for navigating this new landscape.
Let’s break down each component.
Presence: Beyond Traditional Rankings
While Google still commands 89.71% of search market share, the ecosystem is diversifying rapidly:
ChatGPT: 19% monthly traffic growth.
Perplexity: 12% monthly traffic growth.
Claude: 166% monthly traffic surge.
Grok: 266% early-stage spike.
(Source: BrightEdge Generative Parser™ April 2025)
Our research shows that the presence of AI Overviews has nearly doubled since June 2024, with comparison features growing by 70-90% and product visualization features by 45-50% in B2B sectors.
Image from author, May 2025
For enterprise marketers, Google is always your starting point. However, it’s not just about ranking on Google anymore; it’s about showing up wherever AI models showcase your brand.
For example, consider these industry-specific implications:
For CPG brands: When consumers ask about product sustainability, AI doesn’t just list eco-friendly options; it evaluates authenticity based on consistent messaging across digital touchpoints.
For SaaS companies: Buyers researching integration capabilities receive AI-curated assessments that either position you as a compatibility leader or exclude you entirely.
For healthcare providers: Patient questions about treatment options trigger AI responses that cite the most authoritative content, not necessarily the highest-ranking websites.
We are in an era of compressed decision-making. Invisibility equals elimination.
Perception: When AI Forms Opinions
The most revealing insight from our research is that only 31% of AI-generated brand mentions are positive; of those, just 20% include direct recommendations.
Source: BrightEdge AI Catalyst and Generative Parser ™, May 2025
This is a wake-up call for all marketers, especially those managing a brand.
Even when your brand appears in AI results, how it’s framed varies dramatically depending on the AI model, training data, and interpretive logic.
In some AI engines, your brand may appear as the industry leader. In others, you may be completely absent.
What The Data Shows:
Brands with strong pre-existing recognition receive more positive mentions in AI responses.
Consistent messaging across digital touchpoints makes brands more likely to be cited positively.
AI systems appear to “average” brand signals across the web when forming perceptions.
When we analyzed sentiment distribution (April 2025) in AI responses by industry, we saw significant variation, which you could group-match to verticals. For example:
Finance: Positive mentions aligned around good content on regulatory compliance and security.
Healthcare: Positive mentions aligned around good content with accuracy and credibility as key factors.
Retail: Positive mentions aligned around good customer experience and shopping.
Technology: Positive mentions aligned around content on innovation and reliability as primary criteria.
The implications are clear: Perception management is now as crucial as presence.
How does this play out in practice?
When brands implement coordinated perception management strategies across multiple channels, they see improvements in AI sentiment within 60-90 days.
Performance: New Metrics That Matter
The final P (Performance) requires entirely new measurement approaches.
When AI overviews appear in search results, click-through rates often drop by up to 50% according to internal BrightEdge data. Yet, conversion rates typically remain strong, suggesting AI qualifies leads before they reach your site.
We’re entering an era where impressions will be high, click-through rates may drop, but conversions will increase.
I explained at our recent quarterly briefing. AI filters options and delivers buyers who are closer to decisions.
The impact varies dramatically by query type:
Informational queries: Reduction in clicks, minimal conversion impact.
Navigational queries: Reduction in clicks, negligible conversion impact.
Commercial queries: Reduction in clicks, higher conversion rates.
Transactional queries: Reduction in clicks, higher conversion rates.
This pattern suggests AI is most effective at qualifying commercial intent, delivering more purchase-ready traffic.
And impressions matter now – they are a new brand metric.
Five Essential AI Search Metrics:
AI Presence Rate: Percentage of target queries where your brand appears in AI responses.
Citation Authority: How consistently you are cited as the primary source.
Share Of AI Conversation: Your semantic real estate in AI answers versus competitors.
Prompt Effectiveness: How well your content answers natural language prompts.
Response-To-Conversion Velocity: How quickly AI-influenced prospects convert. Brands with strong pre-existing recognition will receive more positive mentions in AI responses.
Position within AI responses matters as much as position in traditional SERPs once did.
Monthly reporting cycles are becoming obsolete. AI-generated results can shift within hours, demanding real-time monitoring capabilities.
The DNA Of AI-Optimized Content
In my experience, content is more likely to be cited by AI with:
Comprehensive coverage: Content addressing multiple related questions outperforms narrow content.
Structured data implementation: Pages with robust schema markup see higher citation rates.
Expert validation: Content with clear expert authorship signals receives more citations.
Multi-format delivery: Topics presented in multiple formats (text, video, data visualizations) earn more citations.
First-party data inclusion: Original research and proprietary data increase citation likelihood.
These patterns suggest AI systems are increasingly sophisticated in their ability to identify genuinely authoritative content versus content merely optimized for traditional ranking factors.
Build consistent messaging: Align key messages across all digital touchpoints.
3. Integrate Real-Time Citation Monitoring
Tracking AI citations regularly is now vital to improve mention rates.
This requires capability beyond traditional rank tracking or Google Search Console analysis.
Implementation Steps:
Deploy continuous monitoring: Track AI responses for priority queries across platforms.
Implement competitor citation alerts: Get notified when competitors gain or lose citations.
Conduct prompt variation testing: Analyze how different user phrasings affect your brand’s inclusion.
Track citation position: Monitor where within AI responses your brand appears.
Measure citation authority: Assess whether you’re positioned as a primary or secondary source.
4. Deploy Cross-Core Search And AI Platforms
Companies that take an integrated approach across traditional search and multiple AI platforms will see higher return on investment (ROI) on search investments.
The future belongs to unified measurement frameworks that connect traditional SEO metrics with emerging AI citation patterns.
Implementation Steps:
Build unified dashboards: Integrate traditional search metrics with AI citation data.
Map keyword-to-prompt relationships: Connect traditional keywords to conversational AI prompts.
Analyze traffic source shifts: Track changing patterns between direct search and AI-referred traffic.
Segment by AI platform: Monitor performance variations across different AI search environments.
Connect to business outcomes: Tie AI presence metrics directly to conversion and revenue data.
5. Use AI To Win At AI
This isn’t theoretical. It’s delivering measurable results:
BrightEdge Autopilot users averaged a 65% performance improvement.
BrightEdge Copilot users saved 1.2 million content research hours.
The brands succeeding most in AI search leverage AI in their workflows.
Implementation Steps:
Automate content research: Use AI to identify comprehensive topic coverage opportunities.
Implement AI-driven schema markup: Systematically structure data for machine interpretation.
Deploy prompt effectiveness testing: Continuously test how well content answers real user prompts.
Create AI-optimized content briefs: Define exactly what comprehensive coverage means for each topic.
Analyze AI citation patterns: Identify what characteristics make competitor content citation-worthy.
Teams using AI for AI optimization will benefit from higher productivity and improved performance to gain that must-have competitive edge in search and AI today.
What’s Coming Next: AI-To-AI Marketing
Looking ahead to two to three years, expect AI to evolve from an information assistant to a trusted advisor that buyers rely on for evaluation, comparison, and vendor selection.
We’re already seeing early indicators of AI-to-AI marketing, where procurement teams use AI agents to automate research and vendor vetting.
Emerging Trends:
Digital twin marketplaces: Buyers will interact with simulated versions of B2B solutions before speaking with vendors
Vertical-specific AI companions: Industry-specialized models for cybersecurity, manufacturing, and healthcare.
AI agent purchasing: Autonomous systems are not just researching but also completing transactions on users’ behalf.
Continuous entity validation: AI systems continuously monitor brand claims against real-world evidence.
Multi-modal search experiences: Voice, image, and text-based AI interactions requiring omnichannel optimization.
Consumers are always more likely to trust brands they already recognize.
AI functions as a trust bridge.
When consumers delegate decision-making to AI, pre-existing brand familiarity becomes disproportionately influential.
The impact is most pronounced in high-consideration purchases.
This creates both a challenge and an opportunity. Established brands must protect their advantage, while emerging brands must strategically build recognition signals detectable by AI.
Organizational Structure For AI Search Success
Leading organizations are already creating “collaborative intelligence” roles – specialists managing the interplay between human creativity and AI amplification.
Successful teams typically include:
AI Search Strategists: Focus on overall presence, perception, and performance.
Prompt Engineers: Specialize in understanding how users phrase requests to AI.
Content Scientists: Develop evidence-based approaches to comprehensive coverage.
AI Citation Analysts: Monitor and optimize for inclusion in AI responses.
Schema Specialists: Ensure that the machine-readable structure enhances entity understanding.
These cross-functional teams integrate with traditional SEO, content marketing, analytics, and business intelligence functions.
The Bottom Line
In this new landscape, the question isn’t whether your website ranks. It’s whether AI recommends your brand when it matters most.
The Triple-P framework gives you the structure to navigate this future with confidence.
Here’s what I recommend getting started:
Conduct an AI presence audit: Understand where your brand appears in AI responses across key platforms.
Analyze sentiment distribution: Assess not just if you’re mentioned, but how you’re portrayed in AI-generated content.
Connect AI metrics to business results: Start tracking the relationship between AI presence and conversion patterns.
Identify entity perception gaps: Compare how AI systems understand your brand versus your desired positioning.
Deploy real-time monitoring: Implement systems to track citation changes as they happen.
The branded AI search revolution isn’t coming – it’s already here.
The brands that embrace the Triple-P framework today will be the ones AI recommends tomorrow.
Note: In March 2025, BrightEdge surveyed over 1,000 of its customers who are marketers. Findings from this survey are referenced above.
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The newest AI tools, built to be smarter, make more factual errors than older versions.
As The New York Times highlights, tests show errors as high as 79% in advanced systems from companies like OpenAI.
This can create problems for marketers who rely on these tools for content and customer service.
Rising Error Rates in Advanced AI Systems
Recent tests reveal a trend: newer AI systems are less accurate than their predecessors.
OpenAI’s latest system, o3, got facts wrong 33% of the time when answering questions about people. That’s twice the error rate of their previous system.
Its o4-mini model performed even worse, with a 48% error rate on the same test.
For general questions, the results (PDF link) were:
OpenAI’s o3 made mistakes 51% of the time
The o4-mini model was wrong 79% of the time
Similar problems appear in systems from Google and DeepSeek.
Amr Awadallah, CEO of Vectara and former Google executive, tells The New York Times:
“Despite our best efforts, they will always hallucinate. That will never go away.”
Real-World Consequences For Businesses
These aren’t just abstract problems. Real businesses are facing backlash when AI gives wrong information.
Last month, Cursor (a tool for programmers) faced angry customers when its AI support bot falsely claimed users couldn’t use the software on multiple computers.
This wasn’t true. The mistake led to canceled accounts and public complaints.
Cursor’s CEO, Michael Truell, had to step in:
“We have no such policy. You’re of course free to use Cursor on multiple machines.”
Why Reliability Is Declining
Why are newer AI systems less accurate? According to a New York Times report, the answer lies in how they’re built.
Companies like OpenAI have used most of the available internet text for training. Now they’re using “reinforcement learning,” which involves teaching AI through trial and error. This approach helps with math and coding, but seems to hurt factual accuracy.
Researcher Laura Perez-Beltrachini explained:
“The way these systems are trained, they will start focusing on one task—and start forgetting about others.”
Another issue is that newer AI models “think” step-by-step before answering. Each step creates another chance for mistakes.
These findings are concerning for marketers using AI for content, customer service, and data analysis.
AI content with factual errors could hurt your search rankings and brand.
Pratik Verma, CEO of Okahu, tells the New York Times:
“You spend a lot of time trying to figure out which responses are factual and which aren’t. Not dealing with these errors properly basically eliminates the value of AI systems.”
Protecting Your Marketing Operations
Here’s how to safeguard your marketing:
Have humans review all customer-facing AI content
Create fact-checking processes for AI-generated material
Use AI for structure and ideas rather than facts
Consider AI tools that cite sources (called retrieval-augmented generation)
Create clear steps to follow when you spot questionable AI information
The Road Ahead
Researchers are working on these accuracy problems. OpenAI says it’s “actively working to reduce the higher rates of hallucination” in its newer models.
Marketing teams need their own safeguards while still using AI’s benefits. Companies with strong verification processes will better balance AI’s efficiency with the need for accuracy.
Finding this balance between speed and correctness will remain one of digital marketing’s biggest challenges as AI continues to evolve.
The latest numbers show ChatGPT’s market share rebounding to 80.1%, up from 77.6% a month ago.
Here’s how the competition stacks up:
DeepSeek: 6.5% (down from 7.6% last month)
Google’s AI tools: 5.6% (up slightly from 5.5% last month)
Perplexity: 1.5% (down from 1.9% last month)
Grok: 2.6% (down from 3.2% last month)
These numbers are part of Similarweb’s bigger “AI Global” report (PDF link).
Traditional Search Engines Losing Ground
The most important finding may be that traditional search engines aren’t growing:
Google: -2% year-over-year
Bing: -18% year-over-year (a big drop from +18% in January)
Yahoo: -11% year-over-year
DuckDuckGo: -6% year-over-year
Baidu: -12% year-over-year
Traditional search shows a steady decline of -1% to -2% compared to last year. It’s important to note, however, that Google has seven times the user base of ChatGPT.
Which AI Categories Are Growing Fastest
While AI is changing search, some AI categories are growing faster than others:
DevOps & Code Completion: +103% (over 12 weeks)
General AI tools: +34%
Music Generation: +12%
Voice Generation: +8%
On the other hand, some AI areas are shrinking, including Writing and content Generation (-12 %), Customer Support (11%), and Legal AI (70%).
Beyond Search: Other Affected Industries
AI’s impact goes beyond just search engines. Other digital sectors facing big changes include:
EdTech: -28% year-over-year (with Chegg down 66% and CourseHero down 69%)
Website Builders: -13% year-over-year
Freelance Platforms: -19% year-over-year
Design platforms are still growing at +10% year over year, suggesting that AI might be helping rather than replacing these services.
What This Means
Traditional SEO still matters, but it isn’t enough. As traditional search traffic drops, you need to branch out.
Similarweb’s data shows consistent negative growth for traditional search engines alongside ChatGPT’s dominant market position, indicating a significant shift in information discovery patterns.
The takeaway for search marketers is to adapt to AI-driven search while keeping up with practices that work in traditional search. This balanced approach will be key to success in 2025 and beyond.
Organizations adopting schema markup at scale often see a boost in non-branded search queries, signaling broader topic authority and improved discoverability.
It has also become a powerful answer to a pressing executive question: “What are we doing about generative AI?” One smart answer is, “We’re implementing schema markup.”
In March 2025, Fabrice Canel, principal program manager at Bing, confirmed that Microsoft uses structured data to support how its large language models (LLMs) interpret web content.
Just a day later, at Google’s Search Central Live event in New York, Google structured data engineer Ryan Levering shared that schema markup plays a critical role in grounding and scaling Google’s own generative AI systems.
“A lot of our systems run much better with structured data,” he noted, adding that “it’s computationally cheaper than extracting it.”
This is unsurprising to hear since schema markup, when done semantically, creates a knowledge graph, a structured framework of organizing information that connects concepts, entities, and their relationships.
A 2023 study by Data.world found that enterprise knowledge graphs improved LLM response accuracy by up to 300%, underscoring the value structured data brings to AI initiatives.
With Google continuing to dominate both search and AI – most recently launching Gemini 2.5 in March 2025, which topped the LMArena leaderboard – the intersection between structured data and AI is only growing more critical.
With that in mind, let’s explore the four key factors to consider when implementing schema markup at scale.
1. Establish Your Goal For Implementing Schema Markup
Before you invest in doing schema markup at scale, let’s explore the business outcomes you can achieve with the different schema markup implementations.
There are three different levels of schema markup complexity:
Basic schema markup.
Internal and external linked schema markup.
Full representation of your content with a content knowledge graph.
Level Of Schema Markup
Outcome
Strategy
Basic Schema Markup
Rich results with higher click-through rates.
Implement schema markup for required properties.
Internal and external linked entities within schema markup
Increase in non-branded queries.
Entities can be fully understood by AI and search engines.
Define key entities within the page and add them to your schema markup. Link entities within the website and to external knowledge bases for clarity.
Content knowledge graph: A full representation of your content as a content knowledge graph.
Content is fully understood in context.
A reusable semantic data layer that enables accurate inferencing and supports LLMs.
Define all important elements of a page using the Schema.org vocabulary and elaborate entity linking to enable accurate extraction of facts about your brand.
Basic Schema Markup
Basic schema markup is when you choose to optimize a page specifically to achieve a rich result.
You look at the minimum required properties from Google’s Documentation and add them to the markup on your page.
The benefits of basic schema markup come from being eligible for a rich result. Achieving this enhanced search result can help your page stand out on the search engine results page (SERP), and it typically yields a higher click-through rate.
Internal And External Linked Entities Within The Schema Markup
Building on your basic schema markup, you can use the Schema.org vocabulary to clarify the entities on your website and how they connect with each other.
An entity refers to a single, unique, well-defined, and distinguishable thing or idea. Examples of an entity on your website include your organization, employees, products, services, blog articles, etc.
You can clarify a topic by linking an entity mentioned on your page to a corresponding external entity definition on Wikidata, Wikipedia, or Google’s knowledge graph.
This enables search engines to clearly understand the entity mentioned on your website, which results in measurable increases in non-branded queries related to that entity or topic.
You can also provide context on how entities on your site are connected by using the appropriate property to link your entity and its identifier.
For example, if you had a page that outlined your product geared toward women, you would use external entity linking to clarify that the audience is women.
If the page also lists related products or services, your schema markup would be used to point to where those related products and services are defined on your site.
When you do this, you provide a holistic and complete view of the content on your page.
With these internal and external entities fully defined, AI and search engines can understand and contextualize your entities accurately.
Full Representation Of Your Content As A Content Knowledge Graph
The final level of schema markup involves using Schema.org to define all page content. This creates a content knowledge graph, which is the most strategic use case of schema markup and has the greatest potential impact on the business.
The benefit of building a content knowledge graph lies in providing an accurate semantic data layer to both search engines and AI to fully understand your brand and the content on your website.
By defining the relationships between things on the website, you give them what they need to get accurate, clear answers.
In addition to how search engines use this robust schema markup, internal AI initiatives can use it to accelerate training on your web data.
Now that you have decided what kind of schema markup you need to achieve your business goals, let’s talk about the role cross-functional stakeholders play in helping you do schema markup at scale.
2. Cross-Departmental Collaboration And Buy-In
The SEO team often initiates Schema markup. They define the strategy, map Schema.org types to key pages, and validate the markup to ensure it’s indexed by search engines.
Successful schema markup implementation at scale requires alignment across multiple departments that can all derive business results from this strategy.
To maximize the value of your schema markup strategy, consider these key stakeholders before you get started:
Content Team
Whether it’s your core content team, lines of business, or a center of excellence, the teams who own the content on the website play a critical role.
Your schema markup is only as good as the content on the page. If you want to achieve a rich result and gain visibility for a specific entity, you need to ensure your page has the required content to make it eligible for this result.
Help your content team understand the value of structured data and how it helps them achieve their goals, so they’ll be motivated to make the content adjustments needed to support your schema markup strategy.
IT Team
No matter how you plan to implement schema markup, whether internally or through a vendor, your IT team’s buy-in is essential.
If you’re working with a vendor, IT will support setting up integrations and enforce security protocols. Their support is critical for enabling deployment while protecting your infrastructure.
If you’re managing schema markup in-house, IT will be responsible for the technical implementation, building advanced capabilities such as entity recognition, and ongoing maintenance.
Without their partnership, scaling and creating an agile, high-value schema markup strategy will be a challenge.
Either way, securing IT’s support early on ensures smoother implementation, stronger data governance, and long-term success.
Executive Team
Your executive leadership team ultimately determines where you should put your dollars to get the best return on investment (ROI).
They want to see the ROI and understand how this strategy helps them prepare for AI, and also stay competitive in the market.
Clear reporting on the outcomes of your structured data efforts will help secure ongoing executive support.
Educating them on how schema markup can help their brand visibility, AI search understanding, and accelerate internal AI initiatives can often help get them on board.
Innovation Team
As mentioned earlier, you can use schema markup to develop a semantic data layer, also known as a content knowledge graph.
This can be useful for your innovation or AI governance team as they could use this data layer to ground their LLMs and accelerate internal AI programs.
Your innovation team will want to understand this potential, especially if AI is a priority on the roadmap.
Pro tip: Communicate early and often. Sharing both the why and the wins will keep cross-functional teams aligned and invested as your schema markup strategy scales.
3. Capability Readiness For Doing Schema Markup At Scale
Now that you know what type of schema markup you want to implement at scale and have the cross-functional team aligned, there are some technical capabilities you need to consider.
When looking to do schema markup at scale, here are key capabilities required from either your IT team or vendor to achieve your desired outcomes.
Basic Schema Markup Capabilities
For basic schema markup for rich results, the capabilities required to implement at scale are the ability to map content to required properties to achieve a rich result and integrate it to show up on page load to be seen by Google. The key factor that simplifies this process is having a well-templated website.
Your team or vendor can map the schema markup and required properties from Google to the appropriate content elements on the page and generate the JSON-LD using these mappings.
Internal And External Entity Linking Capabilities
If you want to do internal and external entity linking within your schema markup at scale, you require more complex capabilities to identify, define, and nest entities within your schema markup.
To identify your internal and external entities and nest them within your schema markup to showcase their relationships, your team or vendor will need the ability to do Named Entity Recognition (NER).
NER extracts named entities and disambiguates the terms.
In addition to extracting proper nouns, you will want the technology to be able to recognize your business terms, your products, people, and events that perhaps aren’t notable yet to warrant a Wikipedia page.
Once the entity is identified, you will need the capability to look up the Entity Definition in a reference knowledge base. This is often done with an API to Wikidata or Google’s knowledge graph.
Now that the entity is defined, you will need the capability to dynamically insert the entity with the appropriate relationship within your schema markup.
To ensure accuracy and completeness on entity identification and relationship mapping, you want controls for the human in the loop to fine-tune matches in your domain.
Full Content Knowledge Graph Representation
For a full representation of your content knowledge graph, which can scale and update dynamically with your content, you will need to add further natural language processing capabilities.
Specifically, your vendor or IT will need to have the ability to identify the semantic relationship between entities in the text (relation extraction) and the ability to identify the concepts within sentences (semantic parsing).
Alternatively, you can do these three functions (NER, relation extraction, and semantic parsing) with a large language model.
LLMs dramatically improve this functionality with some caveats, which include high cost, lack of explainability, and hallucinations.
Once the semantic schema markup is created, your IT or vendor will store the schema markup in a database or knowledge graph and monitor the data to ensure business outcomes.
Finally, depending on the business case, you’ll want the capability to re-use your knowledge graph, so ensure that your knowledge graph data is available to be queried by other tools and systems.
4. The Maintenance Factor
Schema markup isn’t a “set it and forget it” strategy.
Your website content is constantly evolving, especially in enterprise organizations, where different teams may be publishing new content daily.
To remain accurate and effective, your schema markup needs to be dynamic and stay up to date alongside any content changes.
Apart from your website, the broader search landscape is also rapidly shifting.
Between Google’s frequent updates and the growing influence of AI platforms that consume and interpret your content, your schema markup strategy needs to be agile and adaptable.
Consider having someone on your team focused on evolving your schema markup in alignment with business goals and desired outcomes.
Whether it’s an internal resource or a vendor partner, this individual should be adaptable and bear a growth mindset.
They’ll measure the impact of your schema markup, as well as test and measure new strategies (like those mentioned above) to help you thrive in search and AI-driven experiences.
In this ever-changing search landscape, agility is key. The ability to iterate quickly is critical to staying ahead of your competitors in today’s fast-moving digital environment.
Finally, don’t overlook the importance of ongoing monitoring.
Ensuring your markup remains valid and accurate across all key pages is where long-term value is realized.
Many organizations forget this step, but it’s often where the biggest gains in performance and visibility happen.
Schema Markup Is A Business Growth Lever
Schema markup is not just an SEO tactic to achieve rich results. It’s a business growth lever that can drive discoverability, support AI readiness, and fuel long-term business growth.
Depending on the business outcome your organization is targeting – whether it’s improved search visibility, AI initiatives, deeper content intelligence, or all of the above – different factors will take priority.
That’s why CMOs and digital leaders must treat structured data as a core component of their marketing and digital transformation strategy and carefully consider how they will scale it for the best outcomes.
BrightEdge Generative Parser™ detected an expansion of AI Overviews beginning April 25th, covering a larger quantity of entertainment and travel search queries, with noteworthy growth in insurance, B2B technology and education queries.
Expanded AIO Coverage
Expansion of AIO coverage for actor filmographies represented the largest growth area for the entertainment sector, with 76.34% of new query coverage focused on these kinds of queries. In total, the entertainment sector experienced approximately 175% expansion of AI Overview coverage.
Geographic specific travel queries experienced substantial coverage growth of approximately 108%, showing up in greater numbers for people who are searching for activities in specific travel destinations within specific time periods. These are complex search queries that are difficult to get right with the normal organic search.
B2B Technology
The technology space continues to experience steady growth of approximately 7% while the Insurance topic has a slightly greater expansion of nearly 8%. These two sectors bear a little more examination because they mean that publishers increasingly shouldn’t rely on keyword search results performance but instead focus on growing mindshare in the audience that are likely to be interested in these topics. Doing this may also assist in generating the external signals of relevance that Google may be looking for when understanding what topics a website is authoritative and expert in.
According to BrightEdge:
“Technical implementation queries for containerization (Docker) and data management technologies are gaining significant traction, with AIOs expanding to address specific coding challenges.”
That suggests that Google is stepping up on how-to type queries to help people understand the blizzard of new technologies, services and products that are available every month.
Education Queries
The Education sector also continues to see steady growth with a nearly 5% expansion of AIO keyword coverage, wiwth nearly 32% of that growth coming from keywords associated with online learning, with particular focus on specialized degree programs and professional certifications in new and emerging fields.
BrightEdge commented on the data:
“Industry-specific expansion rates directly impact visibility potential. Intent patterns are unique to each vertical – success requires understanding the specific query types gaining AI Overviews in YOUR industry, not just high-volume terms. Google is building distinct AI Overview patterns for each sector.”
Jim Yu, CEO of BrightEdge, observes:
“The data is clear, Google is reshaping search with AI-first results in highly specific ways across different verticals. What works in one industry won’t translate to another.”
Takeaways
Entertainment Sector Sees Largest AIO Growth
Actor filmographies dominate expanded coverage, making up over 76% of entertainment-related expansions.
Entertainment queries in AIO expanded by about 175%.
Travel AIO Coverage Grows For Location-Specific Queries
Geographic and time-specific activity searches expanded by roughly 108%.
AIO is increasingly surfacing for complex trip planning queries.
Steady AIO Expansion In B2B Technology
About 7% growth, with increasing coverage of technical topics.
Google appears to target how-to queries in fast growing technology sectors.
Insurance topics coverage by AIO grew by nearly 8%.
Education Sector Growth Is Focused On Online Learning
5% increase overall, with nearly one-third of new AIO coverage tied to online programs and professional certifications in emerging fields.
Sector-Specific AIO Patterns Require Tailored SEO Strategies
Success depends on understanding AIO triggers within your vertical and not relying solely on high-volume keywords, which means considering a more nuanced approach to topics. Google’s AI-first indexing is reshaping how publishers need to think about search visibility.
Google has removed the waitlist for AI Mode in Search. This Gemini-powered search tool is now available to all US users.
The update introduces new features, including visual cards for places and products, shopping integration, and a history panel for desktop users.
This growth aligns with Google’s recent earnings reports, which indicate that investments in AI are yielding financial returns.
AI Mode Now Available to All US Users
Previously, AI Mode was only available to participants in Google Labs. Now, anyone in the United States can access it.
Google reports that early users provided “incredibly positive feedback” about the tool.
The announcement reads:
“Millions of people are using AI Mode in Labs to search in new ways. They’re asking longer, harder questions, using follow-ups to dig deeper, and discovering new websites and businesses.”
New Visual Cards for Places and Products
The update adds visual cards to AI Mode results. These cards help users take action after getting information.
For local businesses, cards show:
Ratings and reviews
Opening hours
How busy a place is right now
Quick buttons to call or get directions
Here’s an example of a local business query in Google’s AI mode:
Image Credit: Google
Image Credit: Google
For products, cards include:
Current prices and deals
Product images
Shipping details
Local store availability
Google’s announcement reads:
“This is made possible by Google’s trusted and up-to-date info about local businesses, and our Shopping Graph — with over 45 billion product listings.”
It’s worth noting this expansion comes days after OpenAI announced an upgrade to ChatGPT’s shopping capabilities.
History Panel for Continuous Research
Google has added a new left-side panel on desktop that saves your past AI Mode searches. This helps with ongoing research projects. You can:
Return to previous search topics
Pick up where you left off
Ask follow-up questions
Take the next steps based on what you found earlier
Here’s an example of what it looks like:
Image Credit: Google
Limited Test Outside of Labs
Google plans to test AI Mode beyond the Labs environment. The company says:
“In the coming weeks, a small percentage of people in the U.S. will see the AI Mode tab in Search.”
This indicates that Google is moving cautiously toward broader integration.
AI Mode Capabilities
Google’s AI Mode utilizes a technology called “query fan-out.” This means it runs multiple searches at once across different topics and sources. It then combines this information into a comprehensive answer, providing links to sources.
The system also supports image search. You can upload pictures and ask questions about them. It combines Google Lens, which identifies objects, with Gemini’s reasoning abilities to understand and explain what’s in the image.
AI Investment Reflected in Earnings
The expansion of AI Mode follows strong financial results from Google.
Despite concerns that AI might harm traditional search, Google Search revenue increased 10% to $50.7 billion in Q1 2025. This suggests AI is helping, not hurting, their core business.
Google plans to invest $75 billion in capital improvements in 2025, including infrastructure to support its AI features.
7 new undersea cable projects to improve global connectivity.
Alphabet’s spending on infrastructure jumped 43% to $17.2 billion in Q1 2025.
Pichai claims that modern data centers now deliver four times more computing power using the same amount of energy.
For marketers, this financial context matters. Google’s investment in AI search isn’t just a tech experiment. It’s a core business strategy that’s already showing positive returns.
As these AI-powered search experiences continue to grow, marketing strategies must evolve to remain visible.
What This Means for Digital Marketers
For SEO and marketing professionals, these updates signal the following trends:
Visual content is becoming increasingly important as Google improves its ability to understand and display images in search results.
Local SEO remains critical, with business details appearing directly in AI Mode responses.
As AI Mode pulls from Google’s Shopping Graph, product data feeds must be accurate and complete.
Long-form content addressing complex questions may become more valuable, as AI Mode is better equipped to handle longer, more nuanced queries.
Google’s success with AI search, resulting in 10% revenue growth in Q1 2025, indicates that these features will continue to expand.
Availability
To access AI Mode, you need:
To be in the United States
To be at least 18 years old
The latest Google app or Chrome browser
Search history turned on
You can access AI Mode through google.com/aimode, the Google.com homepage (tap AI Mode below the search bar), or the Google app.
Join Shannon Vize, Sr. Content Marketing Manager at Conductor, and Pat Reinhart, VP of Services & Thought Leadership, as they walk through the biggest search and content shifts shaping 2025. From Google’s AI Overviews to new content strategies that actually convert, you’ll get clear guidance to help you move forward with confidence.
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