Ecommerce Management Advisors
Ecommerce Management Advisors
Google AI mode now understands images, allowing you to upload photos and ask questions about them. AI Mode is rolling out to more people.
Microsoft launches Copilot Search in Bing, offering AI-powered summaries with clear links to sources for fast, conversational answers.
Two new studies reveal that most consumers can easily spot AI-generated content, both images and text, which may be more than marketers expected.
The results suggest that brands should be careful when using AI in their marketing materials.
A study by digital marketing consultant Joe Youngblood found that U.S. consumers correctly spotted AI images 71.63% of the time when shown real photos side-by-side with AI versions.
The study surveyed over 4,000 Americans of different ages.
Youngblood states:
“When asking them to determine which photo was real and which one was AI, over 70% of consumers on average could correctly select the AI generated image,”
Detection rates varied by type of image:
However, some images were more challenging to detect. Only 18.05% correctly spotted an AI version of the Eiffel Tower, and 50.89% identified an AI-created painting of George Washington.
A separate report by Hookline& surveyed 1,000 Americans about AI-written content.
Key findings include:
Christopher Walsh Sinka, CEO of Hookline&, stated:
“Writers and brands aren’t sneaking AI-generated content past readers.”
Both studies point to the risks of using AI in content.
From the image study, Youngblood warned,
“If consumers determine that AI images are poor quality or a bad fit they may hold that against your brand/product/services.”
The content study showed:
Older consumers (ages 45–65) were the most critical. Nearly 30% said they did not like AI-written content.
Despite the caution, both studies indicate that some uses of AI are acceptable to consumers.
The content report found that many respondents approved of using AI for:
In the image study, Youngblood noted that consumers might accept AI for fun and informal uses such as memes, video game sprites, cartoons, and diagrams.
However, for important decisions, they prefer real images.
These studies offer guidance for those considering incorporating AI-generated content in marketing material:
Future marketing campaigns should consider how well consumers can detect AI content and adjust their strategies to maintain trust and credibility.
Google DeepMind has shared its plan to make artificial general intelligence (AGI) safer.
The report, titled “An Approach to Technical AGI Safety and Security,” explains how to stop harmful AI uses while amplifying its benefits.
Though highly technical, its ideas could soon affect the AI tools that power search, content creation, and other marketing technologies.
DeepMind believes AGI may be ready by 2030. They expect AI to work at levels that surpass human performance.
The research explains that improvements will happen gradually rather than in dramatic leaps. For marketers, new AI tools will steadily become more powerful, giving businesses time to adjust their strategies.
The report reads:
“We are highly uncertain about the timelines until powerful AI systems are developed, but crucially, we find it plausible that they will be developed by 2030.”
The report focuses on two main goals:
These measures mean that future AI tools in marketing will likely include built-in safety checks while still working as intended.
DeepMind plans to limit certain AI features to prevent misuse.
Techniques like capability suppression ensure that an AI system willingly withholds dangerous functions.
The report also discusses harmlessness post-training, which means the system is trained to ignore requests it sees as harmful.
These steps imply that AI-powered content tools and automation systems will have strong ethical filters. For example, a content generator might refuse to produce misleading or dangerous material, even if pushed by external prompts.
Access to the most advanced AI functions may be tightly controlled. Google could restrict certain features to trusted users and use monitoring to block unsafe actions.
The report states:
“Models with dangerous capabilities can be restricted to vetted user groups and use cases, reducing the surface area of dangerous capabilities that an actor can attempt to inappropriately access.”
This means that enterprise tools might offer broader features for trusted partners, while consumer-facing tools will come with extra safety layers.
Google’s improved safety measures could change how search engines work. New search algorithms might better understand user intent and trust quality content that aligns with core human values.
Advanced AI content generators will offer smarter output with built-in safety rules. Marketers might need to set their instructions so that AI can produce accurate and safe content.
As AI gets more capable, the next generation of ad tech could offer improved targeting and personalization. However, strict safety checks may limit how much the system can push persuasion techniques.
Google DeepMind’s roadmap shows a commitment to advancing AI while making it safe.
For digital marketers, this means the future will bring powerful AI tools with built-in safety measures.
By understanding these safety plans, you can better plan for a future where AI works quickly, safely, and in tune with business values.
Featured Image: Shutterstock/Iljanaresvara Studio
Social media offers a valuable channel for businesses to connect with their prospects and current customers.
A whopping 63.9% of the world’s population spends time on social media. Yet, social has also become an increasingly difficult forum to stand out in as more businesses continue to seek to capitalize on the opportunity that awaits.
The need for frequent updates, diverse content dependent on social media platforms, high-quality visuals, and compelling copy puts pressure on marketing teams to consistently deliver, often with limited time and resources.
This is where generative AI comes into play.
AI-powered tools can help automate many aspects of social media content creation, helping brands write witty captions, generate unique images, and even produce videos.
However, leaning on AI to help draft social media content comes with one notable dilemma: efficiency versus authenticity.
In this article, we’ll explore how brands can make the most of generative AI while still adhering to brand standards, diving into how AI can be put to work for social media marketers in an ethical and authentic way that captures customer interest.
Generative AI enables social media marketers to quickly and nearly effortlessly generate different types of content, such as text, images, videos, and even audio.
AI learns patterns from vast datasets and can translate this vast amount of information into content that aligns closely with what a human could create.
Unlike traditional automation, which follows pre-set templates, generative AI generates new content based on the prompts the end user provides.
AI is becoming widely adopted, as 75% of marketers are either testing the waters with AI or have fully implemented AI in their operations.
For social media marketers specifically, AI is being used to help generate post copy, reply to comments, create AI images, and much more.
Generative AI has changed the game for content creation and impacted the way social media marketers engage with their followers and analyze insights across their social media channels.
However, it’s important to note that human creativity remains a must. AI is paramount to incorporate into every marketer’s day-to-day efforts, but it must be used wisely.
Here are a few ways social media marketers use AI to automate routine tasks.
Perhaps one of the primary uses for AI-powered language models is to create engaging content for a variety of divergent social media formats, including:
In order to create relevant content that converts, AI tools analyze what content performs best and what gets customers to engage.
As with any AI-generated content, it’s imperative to have human oversight to ensure your message aligns with brand voice and tone and is factual.
Here’s an example of AI in action.
Consider a travel brand that wants to encourage people to travel to a new destination. It might use AI to create a few different versions of a caption. Here are a few different examples of captions AI created:
With a few versions to choose from, brands can quickly tailor their messaging for different audiences and can pick the one that will resonate most with their potential customers.
Visual content is growing increasingly popular, given the rise of TikTok and Instagram.
A recent study found that nearly 22% of marketers reported that over 75% of their content this year was visual content.
The same study found that 34.3% of marketers said that visual content made up at least 20-50% of their overall content marketing strategy.
Given the high demand for engaging visual content, marketers are tasked with finding the resources (and time) to create high-quality visuals.
Bandwidth constraints and a lack of creatives can lead to marketers producing solely text-based content. Enter Generative AI.
Generative AI can now produce high-quality graphics, illustrations, and videos without requiring a human designer. Below are a few ways AI is reshaping how visual content is made:
Consider a beauty brand that plans to launch a new skincare product. The beauty brand could use AI to:
AI visuals help businesses save on labor like production costs and editing fees while allowing brands to generate unique visuals at scale.
Social media marketers know that engagement is key to growing a business’s social media presence.
AI-powered technology can now manage customer engagement, responding to social media comments and direct messages in real-time.
This helps brands boost engagement while also ensuring customers receive timely, thoughtful responses.
For example, a restaurant brand can use AI automation to:
For example, a popular fast-casual restaurant revamped its customer experience by mining through a plethora of customer feedback to identify areas of improvement.
The restaurant found it could improve its ordering and delivery systems by mining for common negative feedback.
By proactively addressing this feedback and making swift changes, the restaurant was able to boost its Google star rating from 4.2 to 4.4.
A recent global survey found that 38% of professionals in marketing, PR, sales, and customer service identified increased efficiency as the top advantage of using generative AI for social media marketing.
The same report found that 34% of respondents highlight easier idea generation as a key benefit of generative AI, showcasing the technology’s growing role in streamlining content creation and strategy.
Generative AI has many potential use cases such as allowing brands to seamlessly create, manage, and optimize content at a rapid pace that would be difficult to replicate with a human touch alone.
The following are other major advantages of AI in social media.
AI produces content with just a few clicks. Enter a prompt and users will receive a response nearly in an instant.
Social media marketers have turned to AI to help generate captions, posts, responses, and more to help streamline work.
This reduction in time allows social media marketers to focus on actual strategy, drive revenue, and grow the brand’s social media presence.
Social media marketers no longer need to invest time in brainstorming the perfect hashtags, a catchy caption, or relevant copy as AI can generate multiple diverse content variations in seconds.
Responding to comments is an equally essential task and AI enables personalized responses to customer feedback rapidly.
For some brands, their target audience’s frequent different social media platforms. Each platform requires a different content strategy.
For example, longer-form content typically performs well on LinkedIn, whereas shorter-form content is necessary for X (Twitter) given its character limit.
Lean marketing teams may find value in using AI to scale content production to remove some of the burden of work from the team.
For example, an agency running multiple client accounts might use AI to help generate content or brainstorm potential content ideas without hiring additional writers.
AI is able to review a wealth of information in a matter of seconds, analyzing user behavior and preferences to help create relevant content for different audience segments.
AI-driven audience insights enable brands to understand what type of content resonates most.
It can also translate and adapt messages to fit regional preferences as well, adhering to that region’s unique tone and other popular nomenclature.
For example, a fitness brand creating targeted messaging for individual locations across the country might use AI to adjust language, tone, and services based on regional audience behavior.
Prior to AI, copywriting, graphic design, and video editing were left solely to the professionals.
Now, AI tools can present significant cost savings, reducing the need to rely entirely on professionals, if needed.
AI-generated images and videos can eliminate the need for costly video and photo creation and reduce reliance on external agencies.
For example, a small business that may have previously spent thousands on its creative needs can now use AI tools to create ads with minimal effort or expertise.
One of the biggest concerns surrounding the use of generative AI in social media marketing is the risk of losing the brand’s individuality and unique voice.
In turn, the brand can be seen as disingenuous and inauthentic, both of which greatly erode brand trust.
Consumers have become accustomed to AI and are getting smarter at detecting AI-generated content. This is why it’s essential to have a high level of human oversight.
A human must be tasked with reviewing any and every piece of content that gets published, ensuring content matches brand voice and tone.
While AI-generated content can be a game-changer for streamlining work, over-reliance on it and leaving it unchecked can lead to misinformation, impersonal messaging, and generic content that fails to connect with audiences.
To maintain brand authority in the AI era, brands must be deliberate and strategic in their usage of AI.
Creative storytelling and quality content continue to reign supreme.
Only humans can truly discern whether messaging aligns with brand voice standards and will land right with their audience.
Generative AI can best be seen as an assistant, a tool that helps marketers streamline work but still requires editing and oversight.
Left unchecked, it can lead to false information, poor user experiences, and, in extreme cases, lost sales.
To ensure you’re using generative AI in a way that’s ethical, responsible, and meaningful for your target audience, avoid the following tactics:
The current frontier of AI is exciting, presenting myriad opportunities to scale content at a rapid pace.
However, as exciting an opportunity AI may seem, it doesn’t and can’t replace humans.
Only humans have the expertise and emotion necessary to connect with other humans. Striking a balance between automation and authenticity is a must.
Social media marketers who successfully harness AI will strategically use the technology to assist, rather than replace, human creativity.
Those that can strike a balance will be able to take advantage of AI’s myriad benefits while also maintaining meaningful connections with their audiences.
More Resources:
Featured Image: ImageFlow/Shutterstock
Your site likely suffers from at least some content cannibalization, and you might not even realize it.
Cannibalization hurts organic traffic and revenue: The impact can stretch from key pages not ranking to algorithm issues due to low domain quality.
However, cannibalization is tricky to detect, can change over time, and exists on a spectrum.
It’s the “microplastics of SEO.”
In this Memo, I’ll show you:
I could have never done this without Nicole Guercia from AirOps. I’ve designed the concept and stress-tested the automated workflow, but Nicole built the whole thing.
Before jumping into the workflow, we must clarify a few guiding principles about content cannibalization that are often misunderstood.
The biggest misconception about cannibalization is that it happens on the keyword level.
It’s actually happening on the user intent level.
We all need to stop thinking about this concept as keyword cannibalization and instead as content cannibalization based on user intent.
With this in mind, cannibalization…
Image Credit: Kevin IndigDifferent types of sites have fundamentally different weaknesses for cannibalization.
My model for site types is the integrator vs. aggregator model. Online retailers and other marketplaces face fundamentally different cases of cannibalization than SaaS or D2C companies.
Integrators cannibalize between pages. Aggregators cannibalize between page types.
An example of content cannibalization (Image Credit: Kevin Indig)Content cannibalization can have one or more of the following symptoms:
Since Google doesn’t give us a clear signal for cannibalization, the best way to measure similarity between two or more pages is cosine similarity between their tokenized embeddings (I know, it’s a mouthful).
But this is what it means: Basically, you compare how similar two pages are by turning their text into numbers and seeing how closely those numbers point in the same direction.
Think about it like a chocolate cookie recipe:
Follow this process to scan your site and find cannibalization candidates:
Ideally, this gives you a shortlist of URLs and titles that are too similar.
In the next step, you can apply the following process to make sure they truly cannibalize each other:
You can use my app script if you’d like to try it out in Google Sheets (but I have a better alternative for you in a moment).
About cosine similarity: It’s not perfect, but good enough.
Yes, you can fine-tune embedding models for specific topics.
And yes, you can use advanced embedding models like sentence transformers on top, but this simplified process is usually sufficient. No need to make an astrophysics project out of it.
Once you’ve identified cannibalization, you should take action.
But don’t forget to adjust your long-term approach to content creation and governance. If you don’t, all this work to find and fix cannibalization is going to be a waste.
The short-term action you should take depends on the degree of cannibalization and how quickly you can act.
“Degree” means how similar the content across two or more pages is, expressed in cosine or content similarity.
Though not an exact science, in my experience, a cosine similarity higher than 0.7 is classified as “high”, while it’s “low” below a value of 0.5.
4 ways to fix cannibalization (Image Credit: Kevin Indig)What to do if the pages have a high degree of similarity:
What to do if the pages have a low degree of similarity:
It’s critical to take long-term action to adjust your strategy or production process because content cannibalization is a symptom of a bigger issue, not a root cause.
(Unless we’re talking about Google changing its understanding of intent during a core algorithm update, and that has nothing to do with you or your team.)
The most critical long-term changes you need to make are:
The last question is: “How do I know when content cannibalization is fixed?”
The answer is when the symptoms mentioned in the previous chapter go away:
And, after working with my clients under this manual framework for years, I decided it’s time to automate it.
Together with Nicole, I used AirOps to build a fully automated AI workflow that goes through 37 steps to detect cannibalization within minutes.
It performs a thorough analysis of content cannibalization by examining keyword rankings, content similarity, and historical data.
Below, I’ll break down the most important steps that it automates on your behalf:
1. Initial URL Processing
The workflow extracts and normalizes the domain and brand name from the input URL.
This foundational step establishes the target website’s identity and creates the baseline for all subsequent analysis.
Image Credit: Kevin Indig2. Target Content Analysis
To ensure that the system has quality source material to analyze and compare against competitors, Step 2 involves:
Image Credit: Kevin Indig3. Keyword Analysis
Step 3 reveals the target URL’s search visibility and potential vulnerabilities by:
Image Credit: Kevin Indig4. Competing Content Analysis (Iteration Over Competing URLs)
Step 4 gathers additional context for cannibalization by iteratively processing each competing URL in the search results through the previous steps.
Image Credit: Kevin Indig5. Final Report Generation
In the final step, the workflow cleans up the data and generates an actionable report.
Image Credit: Kevin Indig
Image Credit: Kevin IndigTry the Cannibalization Detector and check out an example report.
A few things to note:
Please leave your feedback in the comments.
We’d love to hear how we can take the Cannibalization Detector to the next level!
Boost your skills with Growth Memo’s weekly expert insights. Subscribe for free!
Featured Image: Paulo Bobita/Search Engine Journal
A report from the Association for the Advancement of Artificial Intelligence (AAAI) reveals a disconnect between public perceptions of AI capabilities and the reality of current technology.
Factuality remains a major unsolved challenge for even the most advanced models.
The AAAI’s “Presidential Panel on the Future of AI Research” report draws on input from 24 experienced AI researchers and survey responses from 475 participants.
Here are the findings that directly impact search and digital marketing strategies.
Despite billions in research investment, AI factuality remains largely unsolved.
According to the report, even the most advanced models from OpenAI and Anthropic “correctly answered less than half of the questions” on new benchmarks like SimpleQA, a collection of straightforward factual questions.
The report identifies three main techniques being deployed to improve factuality:
However, these techniques show limited success, with 60% of AI researchers expressing pessimism that factuality issues will be “solved” in the near future.
This suggests you should prepare for continuous human oversight to ensure content and data accuracy. AI tools may speed up routine tasks, but full autonomy remains risky.
The report highlights a concerning perception gap, with 79% of AI researchers surveyed disagreeing or strongly disagreeing that “current perception of AI capabilities matches the reality.”
The report states:
“The current Generative AI Hype Cycle is the first introduction to AI for perhaps the majority of people in the world and they do not have the tools to gauge the validity of many claims.”
As of November, Gartner placed Generative AI just past its peak of inflated expectations and is now heading toward the “trough of disillusionment” in its Hype Cycle framework.
For those in SEO and digital marketing, this cycle can provoke boom-or-bust investment patterns. Decision-makers might overcommit resources based on AI’s short-term promise, only to experience setbacks when performance fails to meet objectives.
Perhaps most concerning, 74% of researchers believe research directions are driven by hype rather than scientific priorities, potentially diverting resources from foundational issues like factuality.
Dr. Henry Kautz, chair of the Factuality & Trustworthiness section of the report, notes that “many of the public statements of people quite new to the field are out of line with reality,” suggesting that even expert commentary should be evaluated cautiously.
The pressure to adopt AI tools can overshadow their limitations. Since issues of factual accuracy remain unresolved, marketers should use AI responsibly.
Conducting regular audits and seeking expert reviews can help reduce the risks of misinformation, particularly in industries regulated by YMYL (Your Money, Your Life) standards, such as finance and healthcare.
AI-based content generation can lead to inaccuracies that can directly harm user trust and brand reputation. Search engines may demote websites that publish unreliable or deceptive material produced by AI.
Taking a human-plus-AI approach, where editors meticulously fact-check AI outputs, is recommended.
Beyond content creation challenges, leaders must adopt a clear-eyed view to navigate the hype cycle. The report warns that hype can misdirect resources and overshadow more sustainable gains.
Search professionals who understand AI’s capabilities and limitations will be best positioned to make strategic decisions that deliver real value.
For more details, read the full report (PDF link).
Featured Image: patpitchaya/Shutterstock
For over a decade, E-A-T (expertise, authoritativeness, and trustworthiness) has played a role in search rankings, first introduced in Google’s Search Quality Rater Guidelines in 2014.
But with the rise of AI-generated content and AI-synthesized answers, E-E-A-T (now including experience) is no longer just a good idea. It has become the defining factor in determining which sources AI-driven search results consider authoritative enough to cite and include in their synthesized narratives and responses.
AI Overviews and other AI-generated search features don’t just favor sites that “align with E-E-A-T principles” – they favor recognized experts.
To be cited in AI-driven answers, a brand needs to demonstrate undeniable expertise and establish itself as the authority in its field.
This means consistently producing original research, providing real-world insights, and gaining industry-wide (or broader) recognition.
In this article, we’ll explore how E-E-A-T determines visibility in AI-driven search and AI-generated answers, what challenges brands face in maintaining credibility, and strategies for ensuring that AI models and search engines rely on your content as a trusted source.
The rise of AI-generated search results presents both opportunities and challenges for brands.
AI-powered features like Google’s AI Overviews, ChatGPT search integrations, and Perplexity AI are synthesizing answers instead of just returning traditional blue links.
This means that appearing in AI-driven answers requires more than just good SEO – it requires E-E-A-T-backed authority.
Key considerations for ensuring visibility in AI search features:
Read More: A Candid Assessment Of AI Search & SEO
Google’s recent post on AI Overviews and AI Mode highlights how AI-generated search experiences are evolving and underscores the importance of E-E-A-T in shaping AI-driven responses.
Here are key takeaways that reinforce the role of E-E-A-T:
This reinforces the need for brands to proactively establish E-E-A-T authority to maintain visibility in AI-driven search features.
Read More: AI Search Optimization: Data Finds Brand Mentions Improve Visibility
Despite its benefits, AI-driven search presents several challenges for brands trying to maintain visibility:
1. AI Prioritizes Recognized Authorities: Simply optimizing for E-E-A-T is not enough. Brands must become the trusted source that AI search engines consistently reference.
It’s easy to optimize for or align with E-E-A-T in principle, but much more difficult to achieve in reality because some of the requirements simply aren’t within your control.
2. Potential For Misinformation: AI-generated search results can fabricate statistics, misquote sources, or create misleading narratives. Brands must actively monitor AI-generated mentions for accuracy.
3. Duplicate And Unoriginal Content: AI often pulls from widely cited knowledge bases, meaning brands that don’t produce original insights and research risk being ignored.
4. Algorithmic Bias And Filtering: AI search models prioritize widely referenced sources, which can disadvantage emerging brands. Overcoming this requires strategic partnerships, citations, and broad industry engagement.
A March 2025 study by the Columbia Journalism Review found that AI-powered search tools frequently provide incorrect answers with “alarming confidence.”
The study tested eight major AI search engines and found that chatbots collectively provided inaccurate answers more than 60% of the time, nearly always without acknowledging uncertainty.
Most interesting finding: Premium AI models were even more prone to confidently incorrect responses than their free counterparts, contradicting the assumption that paid AI services are more reliable.
ChatGPT, in particular, only indicated uncertainty in its wrong answers 7.5% of the time. Which means that 92.5% of the times it was wrong, it was confident it was correct.
If ChatGPT’s success rate at indicating uncertainty were a batting average, it would be .075.
John Vukovich, known for recording the lowest ever MLB batting average (for non-pitchers with more than 500 at bats), had a career BA of .161 – which is still 100% better than ChatGPT’s ability to acknowledge it might not be right.
The findings in this report only underscore the need for careful, attentive human oversight when producing content and active reputation management to ensure accuracy in AI-generated search environments.
Read More: The Impact Of AI And Other Innovations On Data Storytelling
To ensure visibility in AI-generated search results, brands must prioritize establishing true authority, not just optimizing content.
1. Own And Optimize Your Knowledge Graph
2. Demonstrate Real-World Expertise
3. Become The Primary Source Of Industry Insights
4. Monitor And Influence AI Search Results
5. Leverage Thought Leadership Beyond Your Website
Read More: What 7 SEO Experts Think About AI Overviews And Where Search Is Going
E-E-A-T is the key to visibility in AI-driven search – but it’s not just about optimization.
Brands must become the expert sources AI models trust, reference, and cite.
Those who invest in credibility, expertise, and real-world authority will survive in AI-powered search landscapes, and those who don’t will fade into irrelevance.
More Resources:
Featured Image: insta_photos/Shutterstock
Google has released its annual summer travel trends report alongside several AI-powered updates to its travel planning tools.
The announcement reveals shifting travel preferences while introducing enhancements to Search, Maps, Lens, and Gemini functionality.
Google announced five major updates to its travel planning ecosystem.
Google has enhanced its AI Overviews in Search to generate travel recommendations for entire countries and regions, not just cities.
You can now request specialized itineraries by entering queries like “create an itinerary for Costa Rica with a focus on nature.”
The feature includes visual elements and the ability to export recommendations to various Google products.
Image Credit: GoogleFollowing its flight price tracking implementation, Google has extended similar functionality to accommodations.
When browsing google.com/hotels, you can now toggle price tracking to receive alerts when hotel rates decrease for selected dates and destinations.
The system factors in applied filters include amenity preferences and star ratings.
Image Credit: GoogleA new Google Maps feature can help organize travel plans by automatically identifying places mentioned in screenshots.
Using Gemini AI capabilities, the system recognizes venues from saved images and allows users to add them to dedicated lists.
The feature is launching first on iOS in English, with Android rollout planned.
Google’s Gemini AI assistant now offers enhanced travel planning support, allowing users to create “Gems” – customized AI assistants for specific travel needs.
Now available at no cost, these specialized assistants can help with destination selection, local recommendations, and trip logistics.
Google Lens continues evolving, offering enhanced AI-powered information delivery when pointing your camera at landmarks or objects.
The feature is expanding beyond English to include Hindi, Indonesian, Japanese, Korean, Portuguese, and Spanish, complementing its existing translation capabilities.
Image Credit: GoogleAccording to Google’s Flights and Search data analysis, travelers are increasingly drawn to coastal destinations for the Summer of 2025.
Caribbean islands, including Puerto Rico, Curacao, and St. Lucia, are seeing significant search growth, along with other beach destinations like Rio de Janeiro, Maui, and Nantucket.
The data also reveals continued momentum for outdoor adventure travel within the U.S.:
An unexpected trend in luggage preferences was also noted, with “checked bags” queries now exceeding historically dominant “carry on” searches.
Supporting this shift, space-saving solutions like vacuum bags and compression packing cubes have become top trending travel accessory searches.
These updates signal Google’s continued investment in controlling the travel research journey within its own ecosystem.
The expansion of AI-generated itineraries and information potentially reduces the need for users to visit traditional travel content sites during the planning phase.
Travel brands and publishers may need to adapt their SEO and content strategies to account for these changes, focusing more on unique experiences and in-depth content beyond what Google’s AI tools can generate.
The trend data also provides valuable insights for travel-related keyword targeting and content development as summer vacation planning begins for many consumers.
OpenAI has rolled out a new image generation system directly integrated with GPT-4o. This system allows the AI to access its knowledge base and conversation context when creating images.
This integration is said to enable more contextually relevant and accurate visual outputs.
OpenAI’s announcement reads:
“GPT‑4o image generation excels at accurately rendering text, precisely following prompts, and leveraging 4o’s inherent knowledge base and chat context—including transforming uploaded images or using them as visual inspiration. These capabilities make it easier to create exactly the image you envision, helping you communicate more effectively through visuals and advancing image generation into a practical tool with precision and power.”
Here’s everything else you need to know.
OpenAI highlights the following capabilities of its new image generation system:
OpenAI states in its announcement:
“Because image generation is now native to GPT‑4o, you can refine images through natural conversation. GPT‑4o can build upon images and text in chat context, ensuring consistency throughout. For example, if you’re designing a video game character, the character’s appearance remains coherent across multiple iterations as you refine and experiment.”
To demonstrate character consistency, here’s an example showing a cat and then that same cat with a hat and monocle.
Screenshot from: openai.com/index/introducing-4o-image-generation/, March 2025.Here’s a more practical example for marketers, demonstrating text generation: a full restaurant menu generated with a detailed prompt.
Screenshot from: openai.com/index/introducing-4o-image-generation/, March 2025.There are dozens more examples in OpenAI’s announcement post, many of which contain several prompts and follow-ups.
OpenAI admits:
“Our model isn’t perfect. We’re aware of multiple limitations at the moment which we will work to address through model improvements after the initial launch.”
The company notes the following limitations of its new image generation system:
This update changes AI image generation from mainly decorative uses to more practical functions in business and communication.
Websites can use AI-generated images but with important considerations.
Google’s guidelines do not prohibit AI-generated visuals, focusing instead on whether content provides value regardless of how it’s produced.
Following these best practices is recommended:
Google Search Advocate John Mueller has expressed a negative opinion regarding AI-generated images. While his personal preferences don’t influence Google’s algorithms, they may indicate how others feel about AI images.
Screenshot from: bsky.app/profile/johnmu.com, March 2025.Note that Google is implementing measures to label AI-generated images in search results.
The feature is now available to ChatGPT users with Plus, Pro, Team, or Free plans. Access for Enterprise and Edu users will be available soon.
Developers can expect API access in the coming weeks. Because of higher processing needs, image generation takes about one minute on average.
Featured Image: PatrickAssale/Shutterstock