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Tag: Content Optimization

How SEO Fundamentals Beat AI in Driving Your Website Traffic

I’ve been observing how AI is transforming search, yet the timeless principles of SEO still seem to bring in the majority of traffic. It’s fascinating to look at data that show which strategies really work.

Generative AI is a huge trend right now. It’s featured in every conference and is all over my LinkedIn. Businesses, mine included, are rethinking organic search.

We’re all in a race to optimize for AI Overviews, work on vector embeddings, and reconfigure content models around LLMs. But what’s less talked about is the simple truth: AI isn’t yet the primary driver of web traffic for most of us.

While AI-driven search is gaining momentum, the LLM platforms collectively account for just a tiny fraction, about 2-3%, of the organic traffic that Google alone provides.

However, I’ve noticed that many teams, maybe even yours, are investing more energy in AI strategies instead of reinforcing essential SEO fundamentals that still deliver tangible results. Focusing too much on the future means we’re not making the most of today’s opportunities.

In my experience, looking closely at proven SEO tactics and real-world data can highlight how they still effectively move the needle today.

Quick SEO Wins Still Deliver Substantial Gains

It’s easy to overlook minor updates when we’re caught up with trends like vector embeddings and semantic SEO. Yet, these small changes can have a significant impact.

Take title tags, for instance. They’re among the simplest and most effective SEO tools. I’ve seen many websites fail to use them effectively, often neglecting to target the right keywords, include key variations, or use any keywords at all.

Just recently, a simple change of adding “& [keyword]” to a client’s homepage title tag resulted in a surge in keyword rankings, clicks, and impressions. No other changes were made, yet the results were significant.

Combining this with other strategies like on-page copy edits, internal linking, and backlinks can lead to ongoing growth. It might sound basic, but these tactics continue to work wonders. Don’t let advanced GEO strategies blind you to simple, impactful tactics.

The Importance of Content Freshness and Authority

The rise of AI might have pushed some tactics like the skyscraper technique into the shadows.

This approach involves crafting superior content for keywords and topics that are already ranking, aiming to outperform existing results. While the internet is flooded with similar content, focusing on keyword authority and freshness can be incredibly effective.

I’ve witnessed this success multiple times. Recently, a client’s article on a well-established topic quickly climbed to the second spot, generating new clicks and impressions almost instantly.

The success was due to the site’s strong authority and because much of the competing content was outdated. Although this strategy may not suit every situation, ignoring it could mean missing out on clear wins.

User Experience: A Key Conversion Lever

Although there’s buzz around AI-driven shopping experiences, the core principles of website optimization remain irreplaceable. Some argue that AI will soon take over interactions and conversions, but this is far from the present reality.

Many websites still rely on traditional search-driven traffic and website-based conversions. Whether visitors come from organic search, paid ads, AI referrals, or direct, what matters is a fast site, an excellent user experience, and a well-defined conversion funnel.

Optimizing these aspects can lead to remarkable performance gains, as I’ve seen through a simple CTR test with a client, which yielded impressive results.

Brands prioritizing user experience and conversion rate optimization will continue to outperform those who don’t. This competitive advantage will only grow if teams delay waiting for AI to perfect conversion mechanisms.

AI’s Role in Search and the Power of Existing Strategies

AI is indeed reshaping search by altering user behavior, influencing SERP appearances, and complicating attribution. Yet, the real risk lies in overreacting to AI at the expense of proven strategies.

For most sites, traditional organic search continues to be the primary traffic source. When well-executed, SEO fundamentals still deliver results. Quick wins and high-quality content are rewarded, and optimizing user experience remains critical.

These efforts support each other, improving organic visibility and complementing paid search and LLM visibility. Staying updated on AI developments is vital but not at the cost of current growth-driving strategies.

Inspired by this post on Search Engine Land.

December 22, 2025
Boosting AI Search Visibility: Avoiding Duplicate Content
Have you ever considered how duplicate content might be impacting your visibility in AI search results? Fabrice Canel and Krishna Madhavan from Microsoft recently discussed how duplicate content complicates AI search systems, reducing the chances of selecting the correct version for summarization.
Much like traditional search engines, AI search platforms such as Bing and Google rely on consistent intent signals. When your content appears in duplicate forms, it can confuse these systems, making it difficult for them to interpret signals accurately.
The Impact of Duplicate Content on AI Search. Here are key takeaways from the Bing blog about the impact of duplicate content:
AI search utilizes traditional SEO signals while also adding layers to understand user intent.
Repeated content across multiple pages weakens intent signals, complicating AI interpretation, and selection.
If several pages contain similar content, AI cannot easily identify which aligns with user intent, reducing preferred page selection chances.
Large Language Models (LLMs) cluster near-duplicate URLs, often selecting outdated versions if variations are minimal.
Campaign pages and localized versions must differ meaningfully; identical content provides less matching signal.
AI favors updates, but duplicates can slow the process of updating system information.
The Challenge of Syndicated Content. Many might not realize syndicated content—articles republished on various sites—can also be problematic. Microsoft considers this duplicate content because identical articles across domains make it difficult for search engines and AI to identify the original source.
Strategies to Minimize Duplicate Content. If you deal with syndicated content, ask partners to:
- Use canonical tags directing to the original version on your site.
- Rework content for uniqueness.
- Noindex republished articles to prevent search engine indexing.
Organizing Campaign Pages for Clarity. Microsoft warns that campaign pages with only minor changes can still be considered duplicates. To manage this:
- Designate a primary campaign page for interaction.
- Apply canonical tags to variations without unique intent.
- Maintain separate pages for distinct intents like seasonal offers or local pricing.
- Redirect outdated or redundant pages to consolidate content.
Handling Localization Pages. Localization can also produce duplicate content if differences are minimal. Microsoft suggests:
- Introduce meaningful local variations with examples, terminology, or regulations.
- Avoid multiple same-language pages for identical purposes.
- Use hreflang to define language and regional targeting accurately.
Addressing Technical SEO Concerns. Technical issues can lead to URL duplication, often managed automatically by search engines. However, it’s best to prevent this by maintaining a single URL per content piece. Common problems include:
- Utilize 301 redirects for URL consolidation.
- Apply canonical tags when accessible versions are necessary.
- Ensure consistent URL structures site-wide.
- Restrict crawler access to staging or archived URLs.
Why This Matters. While duplicate content is not a new issue in SEO, its importance extends into AI search. Familiarity with its impact on indexing and ranking can guide strategies for improved visibility.
For more insights, visit the Bing Webmaster blog.

Inspired by this post on Search Engine Land.
December 19, 2025
Crafting Resilient SEO Plans Amid Constant Change

As I’m deep into the marketing planning season, a familiar tension surfaces that I’ve often heard from CMOs and VPs:

“We build a plan, but the execution never matches the intent.”

If this echoes your experience, know that you’re not alone. The issue isn’t flawed strategies or incorrect goals, but rather that most SEO plans aren’t built to withstand operational hurdles like shifting priorities or unforeseen product launches.

Over the years, after guiding various businesses in developing SEO strategies, I’ve realized that success doesn’t hinge on lavish budgets or cutting-edge tools. Rather, it’s about creating plans that reflect actual workflow realities.

Let me guide you through crafting an SEO annual plan that’s not just aspirational but actionable in the real world. We’ll explore setting clear, actionable goals and establishing quarterly systems to keep us on track even when the unexpected arises.

Why Annual Planning Still Works

It might seem outdated to engage in annual planning when new tools like AI Overviews, ChatGPT, and Perplexity change the landscape overnight. The impulsiveness of frequent algorithm changes can make a 12-month plan seem laughable.

Yet, companies that avoid long-term planning often end up merely reacting, chasing trends without accumulating the assets necessary for sustained growth.

Annual plans should provide guidance and resource allocation frameworks that enable smart decision-making when adjustments inevitably occur.

The Need for Better Planning in a Fragmented Search Landscape

With your audience seeking answers from AI-generated summaries and multiple platforms competing for attention, SEO success involves more than just Google rankings. You need to build brand authority, so AI systems recognize and reference your content.

Your strategy has to unify brand authority and topical depth, applicable across various search situations—from traditional queries to conversational AI.

An effective SEO plan should lead to business results, competitive advantages through authority, and preparedness for market changes.

Setting Action-Driven Goals

It’s common for many SEO plans to falter by prioritizing metrics detached from actual business outcomes, like focusing on rankings or traffic that don’t translate to revenue or conversions.

1. Start with Performance Metrics

Identify what success means for your business—be it ecommerce revenue from organic traffic, SaaS trials, or qualified leads for services.

Analyze these metrics at granular levels, ensuring resource investment is targeted towards high-revenue opportunities.

2. Add Contextual Visibility Metrics

Rather than focusing on isolated keyword rankings, track keyword groups that represent business themes. This offers a comprehensive view of market segment performance.

3. Establish Leading Indicators

Identify metrics that signal future changes, allowing timely interventions to maintain performance. Such metrics might include publication rates or indexation issues.

The Baseline Audit: Know Your Current Position

A thorough assessment of your current stance, focusing on technical health, content gaps, and authority signals, is crucial to prioritize effectively.

Strategy Around Constraints

Most planning falters when it doesn’t account for resource limitations or shifting priorities. Use an effort-versus-impact matrix to prioritize tasks effectively.

Quarterly Execution

Break annual goals into achievable quarterly targets, reserving part of your bandwidth for unexpected challenges. This ensures plans remain actionable, not just theoretical.

Cross-Functional Alignment

SEO isn’t isolated. Regular collaboration with product, content, and PR teams ensures consistency and reinforces shared goals.

Common Pitfalls

Avoid rigidity, competitor mimicry, and neglecting fundamentals in your SEO strategy. Focus on aligning plans with business realities and remaining flexible.

Bridging the Gap Between Planning and Execution

Avoiding execution gaps requires plans that reflect real-world conditions, enabling flexibility and focus on impactful metrics.

Inspired by this post on Search Engine Land.

December 18, 2025
Enhance SEO with AI: Aligning Search Intent Effectively

When I think about improving my website’s visibility, AI comes to mind as a crucial tool. It serves as a second pair of eyes, helping me evaluate intent signals, compare top results, and refocus pages that aren’t performing well.

Despite having well-written content, excellent layout, and robust backlinks, pages can still underperform in rankings. A frequent culprit is misaligned search intent, which can be more elusive than it seems.

Focusing on content optimization and usability sometimes makes it easy to overlook or misjudge intent. This is where AI shines as a reviewing tool, effectively steering things back on course.

Whether I’m working on a new page or revising an existing one, returning to the basics of search intent always sets me up for success.

Starting with a simple AI prompt to outline likely search intents for a keyword offers a solid framework for content creation or optimization.

This comprehensive list isn’t something I strive to cover completely on a single page. Instead, it highlights diverse user types, shifts in intent, and needs I might not have initially considered.

By considering these factors, I aim to create a more useful, well-rounded page that genuinely satisfies user needs.

Dig deeper: There are more than 4 types of search intent

Getting the intent right can be challenging. AI tools help me understand what’s already successful by examining top-ranking pages and what they excel at.

I utilize AI tools for a swift overview of a page’s primary intent. By evaluating this at scale, I can see if top-ranking pages meet the same intent.

It’s crucial to assess the intent of my page with the same rigor, be it a fresh draft or a page I’m optimizing. If the primary intent aligns with what’s succeeding, it’s a strong starting point. If not, it provides clear direction for improvement.

Again, consulting AI tools for improvement suggestions can yield valuable insights into refining intent. Key areas to focus on include:

The language I use can either reinforce or contradict the intended message. For commercial intent, persuasive wording is necessary, while for informational pages, clear and descriptive language is preferred.

The format of a page can also convey intent. For instance, in a sales page, details like product placement and accompanying information matter greatly. Similarly, guides need clear step-by-step labeling and possibly visual aids.

Clearly defined calls to action are essential. They align the user’s actions with the page’s intent, enhancing both engagement and ranking potential. Unclear or generalized calls to action dilute this effect.

Dig deeper: How to master user intent with SEO personas

Listing accurate pricing, VAT elements, and currency signals is vital in conveying commercial intent. They guide users accurately at critical decision points.

Availability of support is another crucial factor. I make sure that pre- or post-sale queries can be easily addressed by ensuring my contact details and support options are clearly visible.

Trust signals, like product guarantees, return policies, and customer reviews, make a big difference in user decisions. Including these details serves to strengthen user trust.

When clear comparisons are needed, laying out products side by side can assist users in their decision-making process, moving them closer to making a purchase.

In my experience with working pages centered around user intent, I’ve seen that excess information can sometimes bloat a page.

Previously, this depth might have worked, but now clarity and a focus on intent are what truly resonate.

I’ve learned to reassess where content performs best within the user journey, often seeking AI’s guidance to refocus content structure wisely.

For instance, if I notice my sales page for internal French doors isn’t performing, I consult AI, along with competitor analysis, to uncover key insights.

Competitors might be focusing on selling first, while my page addresses user concerns, which means I need to reposition my content priorities.

By reordering sales-driven content and addressing pain points concisely, I better align with user intent, letting supporting pages deal with detailed post-sale information.

AI isn’t here to replace expertise but to guide my strategic intent, enhancing my understanding of user behavior for better conversion.

Inspired by this post on Search Engine Land.

December 18, 2025
How AI Transforms Marketing: Solve Key Challenges Effortlessly

Have you ever felt the pressure of rising expectations in marketing while budgets stay flat? It’s certainly a dilemma I face regularly. In 2025, marketing budgets have plateaued, averaging 7.7% of company revenue. However, our goals continue to grow, prompting us to seek efficient solutions. Enter AI – not as a futuristic possibility, but as the answer to today’s challenges.

Let me walk you through how AI, particularly tools like Artlist AI, is revolutionizing our workflow by cutting down costs and speeding up production, all while maintaining our brand’s creative integrity.

1) Video Production Challenges

As a marketer, I know how pivotal video content is, yet it often becomes a bottleneck. We’re looking to deliver more, faster, without breaking budgets. Luckily, with AI, we’re finding ways to do just that.

By utilizing Artlist AI, our team rapidly converts scripts into screen reality, making video production cycle much less burdensome. From quick concept storyboards to instant variations and voiceovers, AI is a game-changer.

2) Consistent Brand Voice

Maintaining a uniform brand voice across different markets and languages is daunting. AI voiceover allows us to deliver a steady tone and pacing, ensuring our brand’s message is consistent and recognizably ours.

Artlist gives us the tools to tailor our tone for different cultural contexts without losing brand integrity, refining messaging quickly and at scale.

3) Agile Creative Testing

Today’s social media landscape demands rapid creative cycles. With AI, producing and testing multiple versions becomes feasible, allowing us to adapt quickly and maintain engagement.

I’ve seen firsthand how using AI to test creative variations leads directly to improved ad performance and deeper insights into what resonates with audiences.

4) Meaningful Metrics and Feedback

The true impact of creative elements can often feel elusive. AI provides real-time analytics, correlating creative inputs with engagement metrics. For me, this means turning subjective creative decisions into data-driven strategies.

By leveraging these insights, I can ensure that every marketing move is not only creative but also grounded in real effectiveness.

The Takeaway

Integrating AI doesn’t require an overhaul of our processes. Instead, it enhances what we already do, offering efficiencies that allow us to meet increasing demands without expanding budgets.

If you’re keen to elevate your marketing game, Artlist’s suite of AI tools might just be the solution you need. I’ve experienced the difference they make, turning what once seemed like bottlenecks into seamless workflow elements.

Inspired by this post on Search Engine Land.

December 17, 2025
Mastering Your 2026 Content Strategy: Proven Steps to Success

Every week, I sift through fresh data that showcases both the common ground and the differences in effective organic search techniques. These insights span traditional SEO methods on Google SERPs and newer practices like GEO for platforms such as ChatGPT and AI-driven overviews.

It can feel overwhelming. One moment, we read how traditional SEO methods suit ChatGPT; the next, discussions highlight how one platform favors Reddit while another favors a different approach.

As this landscape rapidly evolves, I’m eager to share the approach, process, and resources my team is utilizing to craft content for 2026.

Our strategy stretches beyond a mere content calendar. It involves merging insights about our audience with the dynamics of organic platforms, alongside our brand’s unique perspective, to create a content system that truly adds value.

The goal is to create high-quality content that stands out. E-E-A-T principles remain core to our strategy, applicable to both AI search discoverability and traditional SEO.

Understanding the audience is the foundation of strong content creation. I constantly ask myself: Who are they? What do they need? What type of content will guide them?

Content, like any product or service, requires identifying a need and addressing it, understanding the involved emotions, and demonstrating credentials through third-party brand mentions, a leading factor in AI search visibility.

For content to be effective in both Google and LLM search realms, it should be crafted as an authoritative source with structured data, prioritizing clarity, depth, and a consistent brand voice AI models will quote.

In a world teeming with AI content, what sets us apart are original insights and data. Therefore, our content systems incorporate a step for “original proof” like data, interviews, or unique commentary.

I’m also focusing on how our content fits into AI experiences, placing value on summaries, bullet points, and explainers that address complexity effectively.

Optimizing for retrieval and credibility rather than just ranking is critical. This approach ensures our content is impactfully represented by AI systems through schema, structured data, and a consistent brand voice.

The content strategy process I recommend starts with empathy, acknowledging the audience’s problem, and providing objective solutions, thus establishing trust. The goal is to transform this understanding into a modular engine, creating multiple media forms aligned to a central theme.

Adaptation is crucial, and my team utilizes a range of resources to achieve a detailed, audience-focused content strategy. This includes qualitative interviews and audience analysis from AI tools, helping shape informed structural decisions.

Social media platforms are instrumental for real-time audience insights and increasing brand mentions, signaling relevance to AI platforms.

Competitor analysis has shifted focus too, evaluating content depth and originality, and identifying opportunities to showcase the expertise our brand brings to the table.

Our KPIs must now reflect the evolution in search, weighing brand mentions alongside traditional metrics to capture content’s full impact on conversions and cross-channel engagement.

In the end, continually adapting to trends ensures we don’t rest on past successes. The real-time changes in user behavior driven by ChatGPT and similar platforms require us to stay vigilant and prepared.

Inspired by this post on Search Engine Land.

December 15, 2025
Transforming Content Ops with AI: Profound Workflows Unleashed

As someone who deeply values efficiency in my digital marketing strategies, I’m excited to introduce Profound Workflows—a revolutionary automation layer designed specifically for the AI search era. This innovative tool is set to reshape how we manage content operations, offering a significant leap in productivity.

With Profound Workflows, I can now audit, analyze, and optimize content on a large scale with ease. Thanks to its automated processes, it takes the heavy lifting out of content management, enabling me to focus on strategic decisions rather than getting bogged down by manual tasks.

The integration of research-backed insights ensures that every piece of content I work with is not only optimized for search but also tailored to meet user needs. This streamlined approach reduces my workload while enhancing our growth trajectory.

For marketers like me, using Profound Workflows means embracing a seamless transition into the future of AI-enhanced content management—where manual effort is minimized, and operational growth is expedited.

Inspired by this post on Try Profound Blog.

December 11, 2025
Elevate Your Content and Research with Humanizing LLMs
See how collaborating with LLMs can transform your content by converting customer, expert, and competitor data into actionable insights.

When I think about large language models (LLMs), one major discussion point is their ability to scale content creation. It’s a tool we’re all tempted to lean on heavily. However, balancing efficiency with creativity is key.

With our busy schedules, boosting productivity is essential. Imagine using tools like Claude and ChatGPT not just for speeding up processes, but also for adding a personal touch to your website and making your day-to-day tasks easier, all without sacrificing creativity.

This journey explores how to:
- Analyze customer feedback and questions comprehensively.
- Streamline the gathering of detailed insights from subject matter experts.
- Conduct competitive analysis effectively.
These tasks, often done manually, can be remarkably enhanced with automation, giving you an edge by rooting your approach in customer and market realities instead of working in a vacuum.

By tapping into this information, I can better connect with my audience, avoiding the pitfalls of an echo chamber.

Analyzing Customer Feedback at Scale

One outstanding feature of LLMs is their scalability in processing data, identifying patterns, and uncovering trends—tasks that might otherwise take me or a colleague days or even weeks to complete.

If you’re not part of a global enterprise with a dedicated data team, LLMs are your next best ally to substitute those capabilities. Focusing on customer feedback, for instance, could mean the difference between success and redundancy. The thought of sifting through thousands of NPS surveys doesn’t sound appealing to me, and I doubt it does to you either.

Utilizing raw data uploads into a project knowledge space and having my LLM of choice run its analysis is one way to go. However, I prefer uploading this data into something like BigQuery, using LLMs to write relevant SQL queries for in-depth analysis, ensuring integrity and accuracy.

This approach not only lets me peek behind the analytical curtain, learning SQL by osmosis but also serves as a safeguard against potential inaccuracies or hallucinations often seen with direct LLM data uploads.

The separate handling of data fosters a more reliable, accurate, and actionable insight, preventing the wild goose chases that could arise from misleading automated responses.

Practically speaking, unless overwhelmed by enormous datasets, BigQuery is a free resource (setup might require a credit card, though). And fear not if SQL is new to you; with an LLM, you’re set for success with full query support in place.

Here’s a glimpse into my workflow:
- Generate SQL functions using the LLM.
- Debug and validate data entries.
- Feed LLM with results from SQL queries.
- Create visualizations either with the LLM or via further SQL queries.
- Iterate as necessary.
Dig deeper: 7 focus areas as AI transforms search and the customer journey in 2026

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Automating Subject Matter Expert Interviews

Frustrations abound when attempting to secure time with subject matter experts, whose schedules often leave them stretched thin.

Why would they want to regurgitate information they’ve already discussed ad nauseam with the manufacturing team? Yet, for marketing purposes, I still need this information to clearly present new features on our platform, offering customers precise details beyond mere specifications.

How to get this coveted expertise? By crafting a customized GPT that can assume the role of interviewer, asking the right questions.

Be advised: customization may vary depending on the launch, product, or service in question. A ChatGPT Plus subscription should suffice for this task.

The guidelines should entail the following:
- Role and tone: Define the interviewer’s persona.
- Context: Clarify learning objectives and rationale.
- Interview structure: Outline initial topics and follow-ups.
- Pacing: Implement a structure of query-response dynamics.
- Closing: Craft a concluding summary or call to action.
Testing it myself, I pretended to be a subject matter expert to refine this tool, always seeking to fit within their limited downtime.

The responses provided can then be further analyzed or converted into draft articles thanks to an LLM.

Dig deeper: SEO personas for AI search: How to go beyond static profiles

Analyzing Competitors for Strategic Insights

While potentially tricky, the strategic examination of competitors can yield profound insights regarding the competitive landscape and personal business gaps.

Here’s a few things I’ve found valuable when dissecting competitor data:
- Aggregating competitors’ reviews helps identify common themes, benefits, and problem areas.
- An analysis of their web copy gives clues into the type of audience they’re targeting and their unique positioning. Combine this with the Wayback Machine to track how messages have evolved over time.
- Job postings can highlight strategic priorities or areas of potential experimentation.
- Social media engagement data can provide insight into customer satisfaction and desire, revealing potential gaps in their customer service.
Dig deeper: How to use competitive audits for AI SERP optimization

Scaling Research Without Losing the Human Thread

Using LLMs alongside extensive datasets allows me to remain grounded in customer realities while being swift in delivering specific, actionable insights through pair programming.

The methods explored within are just starting points. Consider other useful data sources you might already have access to:
- Call transcripts from sales teams.
- Query data from Google Search Console.
- Insights from on-site searches.
- Heatmaps tracking user interactions.
A note of caution—while analytics data is tempting, sticking to qualitative, customer-focused data rather than quantitative metrics leads to richer insights.

Happy exploring!

Inspired by this post on Search Engine Land.
December 11, 2025
How Google Discover’s Shifts Impact Content Visibility

I’ve noticed a shift in how Google is choosing content for its Discover feed, and it seems less tied to traditional search rankings these days.

Yesterday, Andy Almeida from the Google Trust and Safety team shared some insights at the Google Search Central Live event in Zurich. He mentioned that Google Discover isn’t as closely aligned with Google Search rankings as it once was.

Andy presented a slide illustrating how existing systems assist the Google Discover team in addressing challenges. The slide highlighted:

“Minimal alignment to search ranking gives us the tools we need to combat emerging abuse.”

Understanding the Implications. This indicates that Google Discover is moving away from relying heavily on Google’s established search systems, particularly concerning combating platform abuse.

When I asked Andy what this meant for publishers, he explained that Google Discover aims to showcase content from lesser-known and smaller publishers. It seems while Google Search may not always favor them, Discover does, focusing more on its own evaluation systems.

The Challenge with Spam. I’ve been aware of the significant spam issues confronting Google Discover, primarily caused by sites exploiting expired or throwaway domains for spam content. This is a challenge not as prevalent in Google Search.

Back in 2019, Google stated that its core ranking systems affected visibility in Google Discover, especially after a core update. However, this new approach seems to diverge from that stance.

Why This Matters. As Google continues to address these spam problems, it’s balancing the visibility of smaller sites on Discover while curbing spam. This is great news for emerging publishers who focus on niche topics, as long as the spam issue can be effectively managed.

Inspired by this post on Search Engine Land.

December 10, 2025
Unleashing PESO Power: Enhance AI Search Visibility

As I delve into the evolving world of AI and brand discovery, I’ve noticed how AI is transforming the way people find and perceive brands.

More and more, users are leaning towards AI-driven platforms like ChatGPT, Perplexity, and Google’s AI Overviews, rather than traditional search engines to get their answers.

These AI tools provide synthesized summaries instead of regular search results, prompting me as a marketer to rethink how we can achieve visibility.

SEO remains important but now extends far beyond on-page strategies. It’s about how frequently I’m able to ensure our brand is mentioned and discussed across various digital arenas.

This is where the PESO model comes into play. PESO, which stands for paid, earned, shared, and owned media, is becoming increasingly critical in my strategy for generative search visibility.

By balancing these media types, I can create a ‘visibility engine’ that fuels trust signals and contextual cues, enabling AI to include our brand in its summaries.

Generative search visibility is about ensuring our brand’s presence in AI-generated responses on various platforms.

These AI systems pull from a wealth of data, ranging from news to forums, and being consistently cited in recent and reliable content increases our chances of being noticed.

With PESO, I’m reminded that AI doesn’t see our marketing silos. It’s about reinforcing our brand across these channels to enhance our presence in AI results.

Let’s explore how each PESO component influences AI visibility.

Paid media, albeit indirect in AI summaries, boosts the authority and engagement signals AI systems recognize by driving traffic to well-crafted content.

Earned media is crucial as up to 89% of AI citations come from such sources. Being featured in high-authority articles can elevate our brand’s credibility and reach.

Shared media’s role cannot be overlooked. Engagement across platforms like LinkedIn influences AI by indicating trending and credible topics.

Owned media remains a stronghold, with structured data and clear formatting ensuring our web content is AI-accessible, responding to major queries effectively.

Applying PESO towards generative engine optimization includes understanding audience inquiries, reinforcing messages, monitoring content appearance, and auditing for trust signals, which are essential steps for me to enhance our brand’s AI visibility.

The PESO model is far beyond just media balance. It’s a strategic lever allowing me to build trust and visibility, adapting as AI systems change how users discover information.

Through consistency and meaningful content across PESO channels, I can ensure our brand isn’t left out of these vital AI-driven conversations.

Inspired by this post on Search Engine Land.

December 4, 2025

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