AI Slop: How to Spot (and Fix) AI-Generated Content

It’s easier than ever to create "average content" that doesn't differentiate your brand in any way.

The mainstream term for this is AI Slop, where people can quickly create memes or generic looking images that often combine disparate elements together. Shrimp Jesus is a prime example of it. It's a bizarre AI-generated image of Jesus as a shrimp made from shrimp.

This cookie-cutter content can damage both your brand reputation and search performance, as it is “banal” and “very easy to process” as noted in the Washington Post. 

What are the effects closer to brands? We've seen Google de-emphasize content that deviates too far afield from the core topic clusters and business offerings, much like in the case of Hubspot and their recent organic traffic decline.

Algorithms now actively demote such content, making the short-term productivity gains ultimately self-defeating. What separates effective AI-assisted content from the generic filler clogging search results? 

It goes back to thehuman touch—specifically, your unique expertise and voice, and your genuine understanding of your audience's needs.

This guide shows you how to go from bland AI drafts into helpful materials that also perform well in search. 

What are the problems with generic AI-written content?

AI writing tools create recognizable patterns that you should identify and refine before publishing. 

Large Language Models LLMs like GPT and Claude frequently produce generic-sounding content despite their sophisticated capabilities. Ironically, this generic tone is a feature, not a bug – based on the design of how Large Language Models operate. LLMs generate text by predicting the most probable next word based on training data patterns, naturally favoring common expressions and conventional language structures over distinctive or creative phrasing.

The statistical nature of LLMs leads to predictable, "safe" outputs that lack originality. Because these models have no personal experiences or genuine understanding, they cannot infuse content with authentic emotion or unique perspectives. 

This is why Google prioritizes content that is both relevant, and infuses unique perspective:

Google's automated systems are designed to use many different factors to rank great content. After identifying relevant content, our systems aim to prioritize those that seem most helpful. To do this, they identify a mix of factors that can help determine which content demonstrates aspects of experience, expertise, authoritativeness, and trustworthiness, or what we call E-E-A-T.

Slop problem #1: Generic language patterns

AI writing tools create predictable text with several giveaways.

Tired phrases like:

• "when it comes to"
• “in today’s rapidly evolving landscape"
• "delve" 

Opening paragraphs often contain obvious filler sentences that add no value.

You'll notice monotonous sentence structures following nearly identical structural patterns thta lack the natural variety human writers create. Machine-written content typically repeats a formula: make a claim, expand slightly, then move to the next point without adding depth. This is due to the Large Language Model training and output. 

Trained on internet content as well as formal content from newspapers, academic papers, and official documents, these models default to conventional language patterns and transitions. This problem worsens due to training data limitations. Since AI learns from texts written by a relatively small pool of professional writers, it mimics their formulas and conventions. The end result is what we might call "cohesion without coherence." 

While seemingly well-organized, the content ends up verbose and repetitive with choppy transitions. 

Slop problem #2: Structural problems

While human writers vary their paragraph and sentence lengths for emphasis and pacing, AI content lacks natural variation. For example, AI content paragraphs often contain similar word counts and sentence structures from beginning to end.

They also create overly balanced structures where every section receives equal treatment regardless of topic importance. Other structural red flags to keep an eye out for are predictable section breaks, frequent use of em dashes, and templated conclusions that mirror introductions too closely. 

Slop problem #3: SEO issues

AI often stuffs keywords awkwardly into content. Writers spot these issues immediately—awkward phrases that repeat target terms unnaturally or sentences constructed solely to include a keyword variant. Readers notice too, even if they can't articulate exactly why the content feels "off." 

As a result, engagement metrics drop leading to shorter time on page, fewer pages per session, and higher bounce rates. This negatively impacts readability and triggers Google's detection systems. 

Search engines track these user behavior signals, interpreting them as evidence of low-quality content failing to satisfy searcher intent. Google's BERT algorithm now recognizes natural language patterns with remarkable accuracy. It understands semantic relationships between words rather than just matching exact keywords. This sophisticated language processing penalizes content that reads like it was written primarily for search engines rather than people. Content that tries too hard to rank typically achieves the opposite effect.

Search quality evaluators often flag machine-generated content during manual reviews, providing feedback that further trains algorithms to identify AI patterns.

Your keyword density might hit statistical targets, but without genuine expertise and natural language flow, those optimizations actively work against your SEO performance.

Slop problem #4: Misinformation

While AI tools often appear authoritative, they can deliver inaccurate or entirely fabricated outputs with unwavering confidence. In a study by the Tow Center for Digital Journalism, generative search tools frequently provided incorrect or incomplete citations—and did so without disclaiming any uncertainty. 

This overconfidence leads to misinformation and erodes reader trust, especially when AI‑generated content includes fabricated references or misattributes sources.

Even premium models demonstrated high error rates, underscoring a critical gap between how AI “sounds” and how accurate it really is. 

The danger here is twofold: first, users may assume the AI’s confident tone implies correctness; second, these inaccuracies can spread quickly, tarnishing your brand’s credibility and leaving readers confused about legitimate sources.

Google's helpful content system penalizes generic AI writing

Google rolled out its Helpful Content Update in August 2022 and fully integrated it into its core algorithm in March 2024. This system continuously evaluates websites, rewarding genuinely useful content while pushing down manipulative material.

The system works through a sitewide assessment that considers:

• Content quality: Articles answering real user questions with original insights rank higher
• AI detection: Generic machine output without expert refinement loses visibility
• Expertise signals: Content created by knowledgeable writers performs better
• Website-wide evaluation: A high percentage of weak content drags down rankings for the entire site

For example, a fitness article about "beginner exercises" will rank higher when a certified trainer writes it with detailed form instructions than when it exists as a generic list without expert input. George Chasiotis from Minuttia expanded on this and how to optimize content for Google search results, as well as other platforms: 

How to take your content beyond AI slop

You don't need to abandon all of your AI writing tools. Instead, you can adopt a strategic approach that satisfies both search engines and readers. Examine your content through these three lenses:

1. How do you offer expertise?

Examine how effectively your content establishes credibility:

• Identify assertions that lack supporting evidence. 
• Assess whether industry terms are used accurately and explained appropriately for your audience. 
• Evaluate whether topics are covered with appropriate depth. Content should explore nuances and implications that only someone with genuine knowledge would recognize.

2. How do you add value? 

Determine whether your content provides genuine value beyond common knowledge:

• Flag sections that merely summarize widely available information and provide no unique value.
• Assess whether your content includes proprietary insights. 
• Evaluate how directly your content addresses specific audience challenges. 

3. How do you drive engagement?

Analyze how well your content connects with readers:

• Examine whether examples and applications match your audience's context. 
• Assess whether content provides clear implementation steps. 
• Evaluate whether the content uses storytelling elements to maintain interest. Compelling content weaves data and insights into narratives that resonate emotionally.

How to fix AI content

Step 1: Add specific examples 

Replace vague statements with concrete details from your experience. 

When you encounter generic AI copy, look for claims that need substantiation through real examples.

Bad: "Email personalization improves conversion rates."

Good: "A regional healthcare provider boosted appointment bookings 43% using personalized email reminders with patient-specific health recommendations."

The difference? 

The second example includes:

• A specific industry (healthcare)
• An exact percentage (43%)
• The precise strategy used (patient-specific recommendations)
• The exact metric that improved (appointment bookings)

This specificity signals genuine expertise. 

Try this test: could a competitor in your industry publish the exact same content? 

If yes, your examples need more detail drawn from your unique experience.

Step 2: Apply your unique expertise

Share original research unavailable elsewhere. Don't rehash the same statistics everyone cites—conduct small studies using your own customer data. 

• Present data from client projects or internal experiments. 
• Track before-and-after metrics for your own content changes. 
• Did strategy X actually increase engagement? By how much? 

These proprietary numbers provide compelling evidence no AI can generate.

Also, include quotes from subject matter experts who offer nuanced perspectives on trends. 

You can also Interview your company's specialists about common misconceptions in your industry as their authentic insights add credibility no AI can replicate:

Step 3: Fix the voice problem

AI defaults to a formal, distant tone that sounds like everyone else's content. Instead of blending in with the AI slop, define a style that captures your brand’s unique personality, style, and messaging.

How to maintain a distinct brand voice

A Brand Kit ensures that AI-generated content stays on-brand by standardizing key elements like tone, structure, and messaging. 

AirOps Brand Kits centralizes these attributes and integrates them directly into AI workflows. This allows teams to maintain brand consistency while leveraging AI for content creation.

For example, it enables teams to define:

• Tone of Voice (e.g., conversational, authoritative, playful)
• Author Persona (who is speaking—an expert, a peer, or a guide?)
• Brand Point of View (core beliefs and differentiators)
• Writing Samples & Structures (examples of ideal content for AI to reference)

By using variables like {{brand_kit.writing_tone}} and {{brand_kit.brand_point_of_view}}, teams can embed these attributes into AI-generated content, ensuring that messaging stays aligned with the brand’s unique voice.

Instead of letting AI dictate style, the Brand Kit controls how AI applies brand tone, structure, and customer-specific messaging. This eliminates generic, templated AI content and ensures that every output reflects the brand’s expertise and positioning. Kill the AI Slop! 

How one company grew organic traffic by 20x

Before working with AirOps, Wyndly faced a common challenge: they had a wealth of expert knowledge in video format but struggled to efficiently repurpose it into written content that would drive organic search traffic. 

Their SEO team and writers were producing 40 articles per month, but scaling further was difficult due to manual content workflows, resource limitations, and the need for strict medical accuracy.

By leveraging AI-powered workflows, Wyndly transformed their content production, scaling to 200+ articles per month while maintaining their brand’s credibility and expertise. Key improvements included:

• Automated research and content briefs, allowing writers to focus on high-value content creation
• Brand-consistent, medically reviewed content, ensuring articles met both SEO and accuracy standards
• Integrated SEO optimization, including AI-generated meta descriptions and internal linking
• Streamlined publishing workflows, automating distribution across platforms

The impact was massive: Wyndly achieved a 20x growth in organic traffic, a 5x increase in content production, and a 28% boost in organic customer acquisition.

"We're now consistently outranking major medical sites like WebMD and Healthline on key allergy terms – something that seemed impossible before."
— Aakash Shah, CEO and co-founder of Wyndly

This success underscores how AI-driven workflows speed up content creation while maintaining quality and authority in their industry, an industry that where any AI slop would torpedo credibility. 

If you're only using AI for text generation, you're doing it wrong

AI can do much more than text generation. It can transform your processes. In our post about quality vs quantity, we talked about how AI workflows can help you do manual tasks and receive more comprehensive insights: 

Simplify the complex:

• In the past: Tasks like keyword research, identifying internal linking opportunities, and updating meta descriptions were manual and time-consuming. Teams often spent hours sifting through spreadsheets, tracking edits, and coordinating updates by email.
• Now: AI-powered workflows streamline these processes. Repetitive tasks are automated, freeing up time for strategic planning and creativity.

Create standards at scale:

• In the past: Maintaining consistent quality required time-intensive reviews, often leading to bottlenecks.
• Now: Modern tools allow teams to apply predefined quality benchmarks automatically across all content, ensuring every piece meets high standards without constant oversight.

Make better decisions:

• In the past: Relied on intuition and sporadic performance reviews. Analytics were often inaccessible or siloed, making it hard to identify what worked.
• Now: Faster experimentation with AI provides actionable insights, helping teams prioritize content that drives results while refining underperforming assets.

Centralize your assets:

• In the past: Managing content often meant navigating chaotic folder systems or outdated assets scattered across platforms.
• Now: Centralized systems make it easy to update, repurpose, and organize content, ensuring nothing goes to waste and everything stays relevant.

The easiest path to stand out

As AI-generated content floods online channels, distinctive material becomes increasingly valuable. Here's how to create content no algorithm can replicate.

Create original research

Run your own surveys with a clear methodology. Even a simple 100-person survey within your industry yields insights no one else has. Design your surveys to challenge common assumptions and ask questions competitors aren't asking. 

As an example of a proprietary study, here is a great example from Mutiny:

You can analyze proprietary data from your customer base and share insights from real company experiences instead of recycling common statistics. 

Don’t be afraid to document your failures as well as successes as readers value honest accounts of what didn't work as much as what did.

Finally, be sure to present your research professionally:

• Clearly explain your methodology and sample size
• Include visualizations that highlight key findings
• Compare your results with industry benchmarks when available
• Draw specific, actionable conclusions

This approach differentiates your content and establishes you as a thought leader with unique perspectives backed by data no one else has.

Show real expertise

Share actual challenges you've overcome and your solutions. Instead of generic advice, it’s more useful to your audience if you detail the specific problem you faced, why standard approaches failed, and the unconventional solution that worked.

Include specific details only someone with hands-on experience would know. This is where the “build in public” movement and founder/influencer marketing succeeds.‍

Here's an example in the SEO space with Nick Jordan. Yes, he is sharing good SEO tips, but think about the larger picture -- he's sharing first-hand experience of how he implemented changes for a brand and then shared the results. His expertise is on display.
‍

Consider sharing your professional decision-making framework and explain not just what you do but how you decide which approach to take in different scenarios. This demonstrates the nuanced thinking that comes with true expertise. These authentic perspectives establish credibility and make your content significantly more engaging than generic AI output. Readers will immediately recognize and value this content because it has been created by someone who has actually done the work and faced the challenges they describe.

AirOps has helped founders like Will Leatherman generate new business with founder-led content. 

Build actual conversations

To create engaging content, focus on fostering genuine dialogue rather than simply broadcasting information. Instead of ending with a generic conclusion, pose thought-provoking questions that challenge industry assumptions. This invites deeper engagement, signaling to your audience that their perspectives matter.

Engagement shouldn’t stop once the content is published. 

Thoughtful responses to reader comments can transform a single post into an ongoing conversation. Instead of a generic acknowledgment, reference specific points, expand on ideas, and incorporate audience insights into future content. Paying attention to recurring questions allows you to create material that directly addresses your audience’s needs, keeping your content fresh and relevant.

SEO that works with modern search

Effective content pairs technical optimization with qualities AI cannot replicate: deep analysis, original insights, and targeted solutions addressing specific user needs. Executing on these strategies has helped hockeystick.io founder Chang Chen scale her organic traffic 4x. 

Satisfy search intent

You no longer need to obsess over keyword density. Instead, create comprehensive resources that answer questions from multiple angles. Try to weave related keywords naturally through your content and focus on building connected content clusters that demonstrate your expertise across entire topics.

Balance keywords and reliability

Again - focus on context rather than keyword density here. Use semantically related phrases instead of repeating the same terms and be sure to integrate technical terminology naturally within conversational writing. Maintain clear heading hierarchies and descriptive meta titles to support rather than overshadow your content.

Keep content fresh

Keeping your content fresh signals value to both search engines and readers so develop a structured refresh strategy that focuses first on your highest-performing pages. 

When updating, replace outdated statistics and incorporate new industry developments. 

Don’t forget to reference performance metrics when deciding which changes to make to create the greatest impact.

Use AirOps for your unique insights and voice

AI writing tools will continue to evolve and transform content creation, becoming increasingly sophisticated in mimicking human writing patterns. Rather than avoiding these tools, take a strategic approach and find ways to enhance efficiency while sharing your brand's distinctive voice, expertise, and authenticity. Successful content that ranks well will depend on original insights drawn from real-world experience and clear industry perspectives that only human expertise can provide.

AirOps transforms content teams into high-efficiency growth engines with AI-powered workflows that automate research, simplify production, and refresh content dynamically.

With AirOps, you have tools for:

• Deeper Research – AI-powered editorial briefs that align with search intent, uncover AEO opportunities, and drive smarter content strategies.
• Enriched Articles – EEAT-optimized, dynamically updated content that ranks for long-tail keywords and maintains authority.
• Programmatic Content – Data-driven pages that capture long-tail traffic and high-intent queries without manual effort.
• Automated Refresh – Keep content fresh, competitive, and high-ranking with automatic updates to links, structure, and metadata.
• Brand Kits and Knowledge Bases – Maintain consistency, train AI models, and align content with your unique voice and expertise.
• Built-in integrations with your CMS and SEO stack—no extra setup required.
• Human review checkpoints to ensure AI-driven content meets brand and quality standards.

Want to see how it works? Request a demo and start building smarter content workflows today.