Apr 8 2025
Humans Are Better At Writing Than AI In These Tasks via @sejournal, @Kevin_Indig

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There is something ironic about trying to make AI content more human. But there’s also something exciting about it because our work as writers and content creators changes fundamentally.

This shift reminds me of my time as a DJ – many moons ago.

I came up in the era when DJs would haul eight to 10 crates of vinyl records to every gig. During sets, I’d frantically dig through these crates, searching for the perfect next track.

Like a writer drawing from their mental library of phrases and ideas, I had to remember where specific records were and develop my own tagging system to find them faster.

Then, Serato1 changed everything.

This new technology lets you use two special vinyl records to play your entire digital music collection. No more hauling crates – any song was instantly accessible.

The game changed completely.

While some advantages disappeared (the exclusivity of having rare records), new creative possibilities emerged (like seamless remixing and creating custom edits).

Image Credit: Lyna ™

The same transformation is happening to writing today: LLMs are our Serato.

Instead of laboriously crafting every sentence from scratch or drawing only from our mental archives, we can instantly access diverse expressions and ideas.

Like digital DJing tools, AI writing assistants give us a vast creative palette to work with.

And writers should embrace this shift! There’s no special honor in doing things the hard way.

However, just as a Serato DJ still needs musical knowledge and performance skills, raw AI output still needs human refinement.

Without careful editing, AI-generated content feels sterile and impersonal, making it less likely to resonate with readers or perform well on social platforms.

The key is treating AI-written material as a starting point – raw tracks to be mixed, if you will – and then thoughtfully enhancing it to create something truly compelling and human.

At its core, this piece about editing AI content is really about one question: “What is the unique value humans can still add to content?”

Image Credit: Kevin Indig

After countless nights wrestling with this question both intellectually and emotionally (hello, 3 a.m. anxiety!), I think I’ve finally cracked the code.

Based on my experience and from hours of “mixing” with AI, I’ve identified seven uniquely human writing capabilities that no AI can genuinely replicate:

  1. Patterns: Detecting subtle signals in wording, rhythm, and analogies that resonate with shared human experience.
  2. Topics: Intuitive understanding of what readers will find genuinely interesting or relevant.
  3. Experience: Personal stories and perspectives, especially from individuals with established reputations.
  4. Judgment: Applying nuanced moral reasoning beyond programmed guidelines.
  5. Taste: Making decisions about what works stylistically.
  6. Richness: Describing tastes, smells, textures, and sensations from lived experience.
  7. Secrets: Incorporating insights or data not available in AI training sets.

While humans sometimes overestimate true novelty (most progress is incremental), we remain the essential curators of AI output.

Maybe what matters most isn’t whether AI created something but whether humans recognize its value.

Image Credit: Kevin Indig

To contrast the strengths of AI vs. humans, I want to break it down across four categories:

  1. Technical execution.
  2. Knowledge & information.
  3. Production & adaptation.
  4. Data & emotional intelligence.

1. Technical Execution

AI:

  • Processing and error prevention (grammar, spelling, consistency).
  • Maintaining strict formatting across long documents.
  • Following detailed content guidelines with precision.
  • Producing grammatically accurate content at scale.

Humans:

  • Breaking established rules in meaningful, innovative ways.
  • Creating new styles, formats, and genre-bending approaches.
  • Developing distinctive personal writing styles.
  • Writing with an authentic voice from lived experience.

2. Knowledge & Information

AI:

  • Synthesizing information from vast knowledge bases.
  • Generating factual content when based on data.
  • Creating comprehensive explanations of complex topics.
  • Cross-referencing information from multiple domains.

Humans:

  • Contributing original research and firsthand observations.
  • Developing genuinely novel philosophical insights.
  • Creating work driven by authentic moral conviction.
  • Writing from a deep cultural understanding of specific communities.

3. Production & Adaptation

AI:

  • Generating high volumes of content quickly.
  • Creating variations on existing themes and formats.
  • Translating between languages with high accuracy.
  • Restructuring content for different audiences and platforms.

Humans:

  • Inventing entirely new literary forms and approaches.
  • Crafting narratives that respond to the cultural moment.
  • Creating humor that relies on nuanced cultural context.
  • Developing satire that addresses contemporary issues.

4. Data & Emotional Intelligence

AI:

  • Converting structured data into readable narratives.
  • Summarizing lengthy content while preserving key information.
  • Creating consistent documentation from technical specifications.
  • Adapting content across multiple formats and channels.

Humans:

  • Creating characters with complex, contradictory motivations.
  • Writing dialogue that captures psychological nuance.
  • Conveying subtle emotional states through deliberate word choice.
  • Crafting stories that evoke powerful emotional responses.

Humans should let AI handle the baseline 80% – the beat-matching and tempo control, if you will.

And we should focus our creative energy on that critical 20% where we mix in the samples that nobody else has:our unique perspectives, surprising stories, moral nuance, cultural references, and truly novel ideas.

Now that we understand what makes human content valuable, we need to recognize what makes AI content feel off-putting.

Just as an amateur DJ might technically match beats but still create an awkward, lifeless set, AI writing has specific patterns that signal “something’s not quite right here.”

Remember how even digital DJs still need to read the room?

Similarly, while modern language models can technically string words together beautifully, they still miss crucial human signals.

Through deep research and client work, I’ve identified 11 telltale signs that scream “an AI made this” – patterns that instantly disconnect readers:

  • Sterile language: Overly formal phrasing that no human would actually use.
  • Structural monotony: Predictable sentence patterns that create a hypnotic rhythm.
  • Awkward transitions: Abrupt jumps between ideas without natural connective tissue.
  • Robotic tone: An impersonal voice that keeps readers at arm’s length.
  • Factual shakiness: Assertions that sound plausible but don’t hold up to scrutiny.
  • Personality vacuum: Writing devoid of quirks, humor, or authentic perspective.
  • Generic coverage: Surface-level treatment of predictable topics.
  • Sourceless claims: Data statements without proper attribution.
  • Shallow insights: Ideas that never push beyond the obvious.
  • Brand misalignment: Content that doesn’t match your established voice.
  • Weak bookends: Forgettable openings and conclusions that fail to engage.

The markers really stand out.

In the paper “Linguistic Markers of Inherently False AI Communication and Intentionally False Human Communication,”2researchers were able to detect 80% of AI content accurately by looking for:

  • More emotional/affective language.
  • More analytic writing style.
  • More descriptive (higher use of adjectives).
  • Less readable (more complex sentence structures).

Ironically, many human writers display these same weaknesses.

The difference? Humans can learn to overcome them.

The DJ analogy really comes full circle here.

Just as the best DJs don’t simply play songs in sequence but create something new through their mixing, the most effective content creators don’t just edit AI output – they transform it.

In today’s landscape, the most valuable content comes from creators who:

  1. Understand where AI tools excel (the technical baseline).
  2. Recognize where human input is essential (those seven unique capabilities).
  3. Can identify and eliminate those telltale AI patterns.
  4. Know how to blend the two seamlessly into something greater than the sum of its parts.

We’re not just editing AI content – we’re remixing it with our uniquely human perspective, creating something that no algorithm could generate alone.

Because ultimately, the most compelling content doesn’t come from humans fighting against AI or from AI attempting to replace humans. It comes from the thoughtful collaboration between both.

Next week, I’ll break down my exact workflow for editing AI content – the practical techniques I use daily to transform sterile AI output into content that genuinely resonates, connects, and performs.

You’ll learn how to efficiently leverage these tools while ensuring your content maintains that irreplaceable human touch.

1 Serato

2 Linguistic Markers of Inherently False AI Communication and Intentionally False Human Communication: Evidence From Hotel Reviews

Featured Image: Paulo Bobita/Search Engine Journal

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Apr 7 2025
Shopify CEO’s Memo Marks A Pivotal Moment For AI In The Workplace via @sejournal, @martinibuster

A memo by Shopify’s CEO Tobi Lütke sets a company-wide expectation for the use of AI not just throughout the company but also encourages employees to think about how their end users can use AI. Everyone needs to read this because it marks a pivotal moment in how everyone should be using AI to hundredfold increase what they can accomplish and to visualize how AI can be employed for end users as well.

The internal memo details a company-wide reflexive AI usage strategy, which means using AI as a matter of course. It sets the stage for reshaping how merchants use Shopify and points toward a future where entrepreneurship on Shopify is AI-native by design. The memo signals how AI is swiftly becoming central to how all businesses will operate, especially yours.

Reflexive Use Of AI

The heart of the memo is the CEOs encouragement of discovering how AI can be applied to every aspect of how work gets done internally, citing his own usage of AI and how he feels he’s only scratching the surface of how it can be integrated into his own workflow. He asks all employees to “tinker” with AI and encourage company-wide adoption so that the usage of AI becomes reflexive.

His use of the word reflexive is important because it means doing something without consciously thinking about it. The express meaning then is that he really wants AI everywhere and the reason for that is because AI has the ability to boost productivity not just ten times but a hundredfold.

Tobias advocates for the transformational qualities of AI as a productivity multiplier, citing the reflexive use of it for unlocking exponential gains in what can be accomplished at Shopify.

He wrote:

“We are all lucky to work with some amazing colleagues, the kind who contribute 10X of what was previously thought possible. It’s my favorite thing about this company. And what’s even more amazing is that, for the first time, we see the tools become 10X themselves.

I’ve seen many of these people approach implausible tasks, ones we wouldn’t even have chosen to tackle before, with reflexive and brilliant usage of AI to get 100X the work done.”

Workplace Expectations and Requirements

What’s important about the Lütke memo is that it sets expectations about the use of AI in the workplace in a way that should serve as an inspiration for how all workplaces may consider following as well.

Using AI effectively is now a fundamental expectation of all Shopify employees and it will be factored into the peer and performance review questionnaires. Employees will be mandated to demonstrate why AI cannot be used to accomplish goals before asking for more resources. The expectations for AI usage is not just about software engineers, it applies to all employees, including all the way to the top at the executive management level.

AI At Every Workflow Step

The memo sets the expectation that AI must be involved during the GSD (Get Sh*t Done) prototype phase and at a “fraction of the time it used to take.” Teams are also encouraged to envision their projects as if AI were also a part of the team.

He writes:

“What would this area look like if autonomous AI agents were already part of the team? This question can lead to really fun discussions and projects.”

And elsewhere:

“In my On Leadership memo years ago, I described Shopify as a red queen race based on the Alice in Wonderland story—you have to keep running just to stay still. In a company growing 20-40% year over year, you must improve by at least that every year just to re-qualify. This goes for me as well as everyone else.

This sounds daunting, but given the nature of the tools, this doesn’t even sound terribly ambitious to me anymore. It’s also exactly the kind of environment that our top performers tell us they want. Learning together, surrounded by people who also are on their own journey of personal growth and working on worthwhile, meaningful, and hard problems is precisely the environment Shopify was created to provide. This represents both an opportunity and a requirement, deeply connected to our core values of Be a Constant Learner and Thrive on Change. These aren’t just aspirational phrases—they’re fundamental expectations that come with being a part of this world-class team. This is what we founders wanted, and this is what we built.”

Learning, Collaboration, and Community

The other exciting part of Lütke’s memo for AI usage in the workplace is that he encourages employees to share their discoveries and breakthroughs with each other so that all employees can benefit from new and creative ways of getting things done with AI, to share all of their wins with each other.

“We’ll learn and adapt together as a team. We’ll be sharing Ws (and Ls!) with each other as we experiment with new AI capabilities, and we’ll dedicate time to AI integration in our monthly business reviews and product development cycles. Slack and Vault have lots of places where people share prompts that they developed, like #revenue-ai-use-cases and #ai-centaurs.”

Takeaways

Lütke’s memo shows how AI is radically changing the workplace at Shopify and how it can spread across every workforce, including your own.

Shopify is envisioning the next stage of ecommerce entrepreneurship, AI-everything, where AI is an ubiquitous presence for merchants. This is an example of the kind of leadership all entrepreneurs and small businesses should have, to start thinking of how they can integrate AI for themselves and their customers instead of lowering the window blinds to spy across the street to see what competitors are doing.

Read the entire memo:

Featured Image by Shutterstock/TarikVision

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Apr 3 2025
Studies Reveal Consumers Easily Detect AI-Generated Content via @sejournal, @MattGSouthern

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.

Consumers Identify AI-Generated Images

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:

  • Celebrity images (Scarlett Johansson as Black Widow): 88.78% identified correctly
  • Natural landscapes (Italian countryside): 88.46% identified correctly
  • Animal photos (baby peacock): 87.97% identified correctly
  • Space images (Jupiter): 83.58% identified correctly

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.

Similar Skepticism Toward AI-Written Content

A separate report by Hookline& surveyed 1,000 Americans about AI-written content.

Key findings include:

  • 82.1% of respondents can spot AI-written content at least some of the time.
  • Among those aged 22–34, the rate rises to 88.4%.
  • Only 11.6% of young people said they never notice AI content.

Christopher Walsh Sinka, CEO of Hookline&, stated:

“Writers and brands aren’t sneaking AI-generated content past readers.”

Reputational Risks for Brands and Writers

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:

  • 50.1% of respondents would think less of writers who use AI.
  • 40.4% would view brands more negatively if they used AI-generated content.
  • Only 10.1% would view the brands more favorably.

Older consumers (ages 45–65) were the most critical. Nearly 30% said they did not like AI-written content.

Acceptable Use Cases for AI

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:

  • Brainstorming ideas (53.7%)
  • Conducting research (55.8%)
  • Editing content (50.8%)
  • Data analysis (50.1%)

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.

What This Means

These studies offer guidance for those considering incorporating AI-generated content in marketing material:

  1. Be Transparent: Since many consumers can spot AI-generated content, honesty about its use may help maintain trust.
  2. Focus on Quality: Both studies suggest that genuine, professionally produced content is seen as more reliable.
  3. Use AI Wisely: Save AI for tasks like research and editing, but let people handle creative decisions.
  4. Know Your Audience: Younger consumers may be more accepting of AI than older groups. Tailor your strategy accordingly.

Future marketing campaigns should consider how well consumers can detect AI content and adjust their strategies to maintain trust and credibility.

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Apr 2 2025
Google DeepMind’s AGI Plan: What Marketers Need to Know via @sejournal, @MattGSouthern

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.

Google’s AGI Timeline

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.”

Two Key Focus Areas: Preventing Misuse and Misalignment

The report focuses on two main goals:

  • Stopping Misuse: Google wants to block bad actors from using powerful AI. Systems will be designed to detect and stop harmful activities.
  • Stopping Misalignment: Google also aims to ensure that AI systems follow people’s wishes instead of acting independently.

These measures mean that future AI tools in marketing will likely include built-in safety checks while still working as intended.

How This May Affect Marketing Technology

Model-Level Controls

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.

System-Level Protections

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.

Potential Impact On Specific Marketing Areas

Search & SEO

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.

Content Creation Tools

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.

Advertising & Personalization

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.

Looking Ahead

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

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Apr 2 2025
Generative AI And Social Media: Redefining Content Creation via @sejournal, @rio_seo

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.

What Is Generative AI In Social Media?

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.

How AI Works For Social Media Content

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.

AI For Text Generation

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:

  • Photo and Reels Captions: AI can generate catchy, concise, and engaging captions tailored to different platforms.
  • Responses: AI can suggest follow-up replies to comments and feedback, helping brands create engaging threads that encourage participation and further conversation.
  • Thought Leadership: AI can help businesses discover ideas and angles for longer-form content like blog posts or content for LinkedIn, helping brands establish and position themselves as thought leaders.
  • Ad Copy: AI can help generate ideas for succinct yet meaningful ad copy, providing several options for A/B testing to see what resonates best.
  • Hashtags: Need help crafting the right hashtags? AI can help generate hashtags related to your business and what people are looking for to help boost visibility.

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:

  • Casual: “Looking for your next adventure? 🏝️ Book your dream trip today!”
  • Luxury: “Indulge in an unforgettable getaway at our exclusive beachfront resort. 🌊✨”
  • Call to Action (CTA) Focused: “Flights are filling up fast! Book now and escape to paradise. ✈️🌴”

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.

AI For Image And Video Creation

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:

  • AI-Generated Images: Tools like DALL·E, MidJourney, and Canva AI allow marketers to create custom graphics based on text prompts.
  • AI Video Generation: Platforms like Runway ML and Synthesia allow marketers to create short promotional videos, product showcases, or AI-powered explainer videos without the need for a videographer or video editing.
  • Smart Image Editing: AI tools can make images look better by boosting certain elements like brightness and saturation, removing backgrounds, and enhancing low-resolution graphics. This helps to ensure that every visual your business publishes is high-quality and up to brand standards.

Consider a beauty brand that plans to launch a new skincare product. The beauty brand could use AI to:

  • Generate realistic AI-created images of the product, such as on a bathroom counter, on different skin tones, or in a model’s hand.
  • Create a short promotional video that introduces the product, explains its benefits, and gives tips for application.
  • Modify user-generated content (UGC) by removing cluttered backgrounds or enhancing lighting for a more professional look.

AI visuals help businesses save on labor like production costs and editing fees while allowing brands to generate unique visuals at scale.

AI For Automated Engagement And Customer Interaction

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.

  • AI Chatbots: Platforms like Drift and ManyChat allow businesses to automate FAQs, product recommendations, and customer service questions through social media messaging.
  • AI-Driven Comment Moderation: AI can analyze and respond to user comments, helping brands respond quickly to customer feedback.
  • Real-Time Sentiment Analysis: AI tools track user sentiment, identifying positive engagement opportunities and potential PR risks.

For example, a restaurant brand can use AI automation to:

  • Respond instantly to frequently asked questions like “Are you open for brunch on the weekends?” with pre-programmed answers.
  • Automatically direct users to a reservation link when they ask, “How can I book a table?”
  • Flag and escalate negative reviews or complaints for human customer service intervention.

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.

The Benefits Of AI In Social Media

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.

Speed And Efficiency

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.

Scalability

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.

  • AI-generated content allows brands to post frequently without the need to come up with fresh ideas for each channel.
  • AI-driven scheduling tools automatically determine when the ideal posting times and dates are based on engagement trends.
  • AI can adjust content formats dependent on the channel, such as lengthening a short-form Instagram caption into a long-form LinkedIn post.

For example, an agency running multiple client accounts might use AI to help generate content or brainstorm potential content ideas without hiring additional writers.

Personalization

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.

Cost Savings

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.

Maintaining Brand Authenticity With AI Content

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.

How To Use AI Ethically And Effectively In Social Media Marketing

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:

  • Overreliance: Avoid using AI excessively and look at it more as a tool for idea generation.
  • Lack of Human Editing: Ensure AI-generated content has human oversight. The future of AI will still require a level of human intervention to ensure authenticity and accuracy.
  • Generic Content: Humans crave connection. AI models, while sophisticated, can lack human emotion. This can lead to less engaging content or content that relies heavily on clichés, buzzwords, or generic phrasing that every other brand is using. Use AI-generated content as a starting point and refine it with human expertise.
  • Inconsistent Voice: AI finds information from a variety of sources, which can translate to diverse tones and voices in the content it returns. Train AI tools to understand your brand’s unique voice and tone by sharing past content with them. Have a human editor review each piece of content to ensure it aligns with brand guidelines and standards.
  • Forgetting the Power of UGC: Brand content is great, but the power of user-generated content can’t be forgotten. UGC can help tell your brand’s story from a customer’s point of view. Potential customers often rely on testimonials to convince them to convert.
  • Lack of Transparency: The future of AI will call for even greater transparency for disclosing when brands are using AI. Ethical concerns have already been raised about what’s real and what’s artificially created, and these concerns will only continue to grow in the future.
  • Only Using AI Visuals: AI-generated visuals can be high-quality and cost-effective, but brands should try to incorporate their own images and UGC as well. Customers are growing to accept AI visuals, but in the future, they’ll likely still welcome company-owned and produced images and videos.

The Future AI For Social Media

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

Ecommerce MGMT 0 Comments
Apr 1 2025
An AI-Powered Workflow To Solve Content Cannibalization via @sejournal, @Kevin_Indig

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:

  1. How to identify and fix content cannibalization reliably.
  2. How to automate content cannibalization detection.
  3. An automated workflow you can try out right now: The Cannibalization Detector, my new keyword cannibalization tool.

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.

How To Think About Content Cannibalization The Right Way

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…

  • Is a moving target: When Google updates its understanding of intent during a core update, suddenly two pages can compete with each other that previously didn’t.
  • Exists on a spectrum: A page can compete with another page or several pages, with an intent overlap from 10% to 100%. It’s hard to say exactly how much overlap is fine without looking at outcomes and context.
  • Doesn’t stop at rankings: Looking for two pages that are getting a “substantial” amount of impressions or rankings for the same keyword(s) can help you spot cannibalization, but it is not a very accurate method. It’s not enough proof.
  • Needs regular check-ups: You need to check your site for cannibalization regularly and treat your content library as a “living” ecosystem.
  • Can be sneaky: Many cases are not clear-cut. For example, international content cannibalization is not obvious. A /en directory to address all English-speaking countries can compete with a /en-us directory for the U.S. market.
Image Credit: Kevin Indig

Different 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.

  • With aggregators, cannibalization often happens when two page types are too similar. For example, you can have two page types that could or could not compete with each other: “points of interest in {city}” and “things to do in {city}”.
  • With integrators, cannibalization often happens when companies publish new content without maintenance and a plan for the existing content. A big part of the issue is that it becomes harder to keep an overview of what you have and what keywords/intent it targets at a certain number of articles (I found the linchpin to be around 250 articles).

How To Spot Content Cannibalization

An example of content cannibalization (Image Credit: Kevin Indig)

Content cannibalization can have one or more of the following symptoms:

  • “URL flickering”: meaning at least two URLs alternate in ranking for one or more keywords.
  • A page loses traffic and/or ranking positions after another one goes live.
  • A new page hits a ranking plateau for its main keyword and cannot break into the top 3 positions.
  • Google doesn’t index a new page or pages within the same page type.
  • Exact duplicate titles appear in Google’s search index.
  • Google reports “crawled, not indexed” or “discovered, not indexed” for URLs that don’t have thin content or technical issues.

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:

  • Tokenization = Break down each recipe (e.g., page content) into ingredients: flour, sugar, chocolate chips, etc.
  • Embeddings = Convert each ingredient into numbers, like how much of each ingredient is used and how important each one is to the recipe’s identity.
  • Cosine Similarity = Compare the recipes mathematically. This gives you a number between 0 and 1. A score of 1 means the recipes are identical, while 0 means they’re completely different.

Follow this process to scan your site and find cannibalization candidates:

  • Crawl: Scrape your site with a tool like Screaming Frog (optionally, exclude pages that have no SEO purpose) to extract the URL and meta title of each page
  • Tokenization: Turn words in both the URL and title into pieces of words that are easier to work with. These are your tokens.
  • Embeddings: Turn the tokens into numbers to do “word math.”
  • Similarity: Calculate the cosine similarity between all URLs and meta titles

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:

  • Extract content: Clearly isolate the main content (exclude navigation, footer, ads, etc.). Maybe clean up certain elements, like stop words.
  • Chunking or tokenization: Either split content into meaningful chunks (sentences or paragraphs) or tokenize directly. I prefer the latter.
  • Embeddings: Embed the tokens.
  • Entities: Extract named entities from the tokens and weigh them higher in embeddings. In essence, you check which embeddings are “known things” and give them more power in your analysis.
  • Aggregation of embeddings: Aggregate token/chunk embeddings with a weighted averaging (eg, TF-IDF) or attention-weighted pooling.
  • Cosine similarity: Calculate cosine similarity between resulting embeddings.

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.

How To Fix Cannibalization

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.

Solving Cannibalization In The Short Term

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:

  • Canonicalize or noindex the page when cannibalization happens due to technical issues like parameter URLs, or if the cannibalizing page is irrelevant for SEO, like paid landing pages. In this case, canonicalize the parameter URL to the non-parameter URL (or noindex the paid landing page).
  • Consolidate with another page when it’s not a technical issue. Consolidation means combining the content and redirecting the URLs. I suggest taking the older page and/or the worse-performing page and redirecting to a new, better page. Then, transfer any useful content to the new variant.

What to do if the pages have a low degree of similarity:

  • Noindex or remove (status code: 410) when you don’t have the capacity or ability to make content changes.
  • Disambiguate the intent focus of the content if you have the capacity, and if the overlap is not too strong. In essence, you want to differentiate the parts of the pages that are too similar.

Solving Cannibalization In The Long Term

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:

  1. Create a content roadmap: SEO Integrators should maintain a living spreadsheet or database with all SEO-relevant URLs and their main target keywords and intent to tighten editorial oversight. Whoever is in charge of the content roadmap needs to ensure there is no overlap between articles and other page types. Writers need to have a clear target intent for new and existing content.
  2. Develop clear site architecture: The pendant of a content map for SEO Aggregators is a site architecture map, which is simply an overview of different page types and the intent they target. It’s critical to underline the intent as you define it with example keywords that you verify on a regular basis (”Are we still ranking well for those keywords?”) to match it against Google’s understanding and competitors.

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:

  • Indexing issues resolve.
  • URL flickering goes away.
  • No duplicate titles appear in Google’s search index.
  • “Crawled, not indexed” or “discovered, not indexed” issues decrease.
  • Rankings stabilize and break through a plateau (if the page has no other apparent issues).

And, after working with my clients under this manual framework for years, I decided it’s time to automate it.

Introducing: A Fully Automated Cannibalization Detector

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 Indig

2. Target Content Analysis

To ensure that the system has quality source material to analyze and compare against competitors, Step 2 involves:

  • Scraping the page.
  • Validating and analyzing the HTML structure for main content extraction.
  • Cleaning the article content and generating target embeddings.
Image Credit: Kevin Indig

3. Keyword Analysis

Step 3 reveals the target URL’s search visibility and potential vulnerabilities by:

  • Analyzing ranking keywords through Semrush data.
  • Filtering branded versus non-branded terms.
  • Identifying SERP overlap with competing URLs.
  • Conducting historical ranking analysis.
  • Determining page value based on multiple metrics.
  • Analyzing position differential changes over time.
Image Credit: Kevin Indig

4. 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 Indig

5. Final Report Generation

In the final step, the workflow cleans up the data and generates an actionable report.

Image Credit: Kevin Indig

Try The Automated Content Cannibalization Detector

Image Credit: Kevin Indig

Try the Cannibalization Detector and check out an example report.

A few things to note:

  1. This is an early version. We’re planning to optimize and improve it over time.
  2. The workflow can time out due to a high number of requests. We intentionally limit usage so as not to get overwhelmed by API calls (they cost money). We’ll monitor usage and might temporarily raise the limit, which means if your first attempt isn’t successful, try again in a few minutes. It might just be a temporary spike in usage.
  3. I’m an advisor to AirOps but was neither paid nor incentivized in any other way to build this workflow.

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

Ecommerce MGMT 0 Comments
Mar 31 2025
AI Researchers Warn: Hallucinations Persist In Leading AI Models via @sejournal, @MattGSouthern

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.

Leading AI Models Fail Basic Factuality Tests

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:

  • Retrieval-augmented generation (RAG): Gathering relevant documents using traditional information retrieval before generating answers.
  • Automated reasoning checks: Verifying outputs against predefined rules to cull inconsistent responses.
  • Chain-of-thought (CoT): Breaking questions into smaller units and prompting AI to reflect on tentative conclusions

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 Reality Gap: AI Capabilities vs. Public Perception

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.

Why This Matters for SEOs & Digital Marketing

Adopting New Tools

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.

The Impact On Content Quality

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.

Navigating the Hype

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

Ecommerce MGMT 0 Comments
Mar 31 2025
The Role Of E-E-A-T In AI Narratives: Building Brand Authority For Search Success via @sejournal, @cshel

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 Intersection Of E-E-A-T And AI-Generated Answers

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:

  • Experience: AI models favor content backed by first-hand knowledge. Brands that demonstrate real-world expertise through case studies, original research, and hands-on experience have a greater chance of being cited.
  • Expertise: AI-generated answers prefer sources with clear subject matter expertise. Author bylines, credentials, and expert contributions all signal trustworthiness to AI-driven search.
  • Authoritativeness: AI Overviews and LLM-generated answers prioritize brands that own their knowledge graph, are widely referenced, and are recognized leaders in their industry.
  • Trustworthiness: AI-generated content is acceptable to use (in that it is not inherently “bad” or penalized) but must be factually accurate and verifiable. Content backed by reliable sources, citations, and transparent authorship is more likely to surface in AI-generated search features.

Read More: A Candid Assessment Of AI Search & SEO

AI Overviews And E-E-A-T: What Google’s Latest Research Reveals

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:

Google Integrates E-E-A-T Into AI Overviews

  • AI Overviews leverage Google’s ranking systems and Knowledge Graph to determine which sources are most authoritative. (Hint: Ensure your Knowledge Graph exists and is accurate!)
  • E-E-A-T signals directly influence which websites AI Overviews pull from, reinforcing the need for brands to establish themselves as leading authorities.

High-Quality Sources Are A Requirement

  • AI Overviews corroborate AI-generated summaries with top-ranked content, (theoretically) ensuring the information is reliable.
  • For Your Money or Your Life (YMYL) queries, the bar for trustworthiness is even higher, emphasizing the importance of expert-driven content. (This is why author biographies with CVs, other credentials, and proof of expertise are necessary.)

AI Overviews Increase Engagement With High-Quality Content

  • Google reports that users who interact with AI Overviews visit a greater diversity of websites and that click-throughs from AI Overviews are of higher quality.
  • This presents an opportunity for brands with strong E-E-A-T signals to attract engaged visitors who trust the AI-curated results (but click through to verify).

Manual And Algorithmic Safety Checks Reinforce E-E-A-T’s Importance

  • Google’s Search Quality Raters, adversarial testing, and fact-checking systems ensure AI Overviews prioritize reliable information.
  • Brands that lack E-E-A-T credentials (specifically Knowledge Graphs and other key indicators that your brand is considered authoritative) may struggle to appear in AI-generated search experiences.

Future AI Search Innovations Will Reward E-E-A-T Signals

  • Google’s experimental AI Mode in Search expands AI-generated responses using multimodal data and real-time corroboration with authoritative sources.
  • Brands with verified expertise, structured citations, and widespread recognition will have an advantage in AI-driven search.

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

Challenges In Applying E-E-A-T To AI-Generated Search

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.

AI’s Tendency To Be “Confidently Wrong”

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

Strategies For Strengthening E-E-A-T In AI-Driven Search

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

  • Ensure Google’s Knowledge Graph accurately represents your brand.
  • Claim your entity in Google Search and establish schema markup for credibility.

2. Demonstrate Real-World Expertise

  • Publish original research, case studies, and expert insights.
  • Engage in media interviews, guest contributions, and speaking engagements.

3. Become The Primary Source Of Industry Insights

4. Monitor And Influence AI Search Results

  • Actively track how AI-generated answers represent your brand.
  • Engage with AI search models via feedback loops and corrections.

5. Leverage Thought Leadership Beyond Your Website

  • Be featured on authoritative platforms, podcasts, and news outlets.
  • Participate in peer-reviewed research and industry collaborations.

Read More: What 7 SEO Experts Think About AI Overviews And Where Search Is Going

    Becoming The Source AI Can’t Ignore

    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

    Ecommerce MGMT 0 Comments