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Web Design and Development San Diego

Inspiring examples of responsible and realistic vibe coding for SEO

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Vibe coding is a new way to create software using AI tools such as ChatGPT, Cursor, Replit, and Gemini. It works by describing to the tool what you want in plain language and receiving written code in return. You can then simply paste the code into an environment (such as Google Colab), run it, and test the results, all without ever actually programming a single line of code.

Collins Dictionary named “vibe coding” word of the year in 2025, defining it as “the use of artificial intelligence prompted by natural language to write computer code.”

In this guide, you’ll understand how to start vibe coding, learn its limitations and risks, and see examples of great tools created by SEOs to inspire you to vibe code your own projects.

Vibe coding variations

While “vibe coding” is used as an umbrella term, there are subsets of coding with support or AI, including the following:

Type Description Tools
AI-assisted coding  AI helps write, refactor, explain, or debug code. Used by actual developers or engineers to support their complex work. GitHub Copilot, Cursor, Claude, Google AI Studio
Vibe coding Platforms that handle everything except the prompt/idea. AI does most of the work. ChatGPT, Replit, Gemini, Google AI Studio
No-code platforms Platforms that handle everything you ask (“drag and drop” visual updates while the code happens in the background). They tend to use AI but existed long before AI became mainstream. Notion, Zapier, Wix

We’ll focus exclusively on vibe coding in this guide. 

With vibe coding, while there’s a bit of manual work to be done, the barrier is still low — you basically need a ChatGPT account (free or paid) and access to a Google account (free). Depending on your use case, you might also need access to APIs or SEO tools subscriptions such as Semrush or Screaming Frog.

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To set expectations, by the end of this guide, you’ll know how to run a small program on the cloud. If you expect to build a SaaS or software to sell, AI-assisted coding is a more reasonable option to take, which will involve costs and deeper coding knowledge.

Vibe coding use cases

Vibe coding is great when you’re trying to find outcomes for specific buckets of data, such as finding related links, adding pre-selected tags to articles, or doing something fun where the outcome doesn’t need to be exact.

For example, I’ve built an app to create a daily drawing for my daughter. I type a phrase about something that she told me about her day (e.g., “I had carrot cake at daycare”). The app has some examples of drawing styles I like and some pictures of her. The outputs (drawings) are the final work as they come from AI.

When I ask for specific changes, however, the program tends to worsen and redraw things I didn’t ask for. I once asked to remove a mustache and it recolored the image instead. 

If my daughter were a client who’d scrutinize the output and require very specific changes, I’d need someone who knows Photoshop or similar tools to make specific improvements. In this case, though, the results are good enough. 

Building commercial applications solely on vibe coding may require a company to hire vibe coding cleaners. However, for a demo, MVP (minimum viable product), or internal applications, vibe coding can be a useful, effective shortcut. 

How to create your SEO tools with vibe coding

Using vibe coding to create your own SEO tools require three steps:

  1. Write a prompt describing your code
  2. Paste the code into a tool such as Google Colab
  3. Run the code and analyze the results

Here’s a prompt example for a tool I built to map related links at scale. After crawling a website using Screaming Frog and extracting vector embeddings (using the crawler’s integration with OpenAI), I vibe coded a tool that would compare the topical distance between the vectors in each URL.

This is exactly what I wrote on ChatGPT:

I need a Google Colab code that will use OpenAI to:

Check the vector embeddings existing in column C. Use cosine similarity to match with two suggestions from each locale (locale identified in Column A). 

The goal is to find which pages from each locale are the most similar to each other, so we can add hreflang between these pages.

I’ll upload a CSV with these columns and expect a CSV in return with the answers.

Then I pasted the code that ChatGPT created on Google Colab, a free Jupyter Notebook environment that allows users to write and execute Python code in a web browser. It’s important to run your program by clicking on “Run all” in Google Colab to test if the output does what you expected.

This is how the process works on paper. Like everything in AI, it may look perfect, but it’s not always functioning exactly how you want it. 

You’ll likely encounter issues along the way — luckily, they’re simple to troubleshoot.

First, be explicit about the platform you’re using in your prompt. If it’s Google Colab, say the code is for Google Colab. 

You might still end up with code that requires packages that aren’t installed. In this case, just paste the error into ChatGPT and it’ll likely regenerate the code or find an alternative. You don’t even need to know what the package is, just show the error and use the new code. Alternatively, you can ask Gemini directly in your Google Colab to fix the issue and update your code directly.

AI tends to be very confident about anything and could return completely made-up outputs. One time I forgot to say the source data would come from a CSV file, so it simply created fake URLs, traffic, and graphs. Always check and recheck the output because “it looks good” can sometimes be wrong.

If you’re connecting to an API, especially a paid API (e.g., from Semrush, OpenAI, Google Cloud, or other tools), you’ll need to request your own API key and keep in mind usage costs. 

Should you want an even lower execution barrier than Google Colab, you can try using Replit. 

Simply prompt your request and the software will create the code, design, and allow testing all on the same screen. This means a lower chance of coding errors, no copy and paste, and a URL you can share right away with anyone to see your project built with a nice design. (You should still check for poor outputs and iterate with prompts until your final app is built.)

Keep in mind that while Google Colab is free (you’ll only spend if you use API keys), Replit charges a monthly subscription and per-usage fee on APIs. So the more you use an app, the more expensive it gets.

Inspiring examples of SEO vibe-coded tools

While Google Colab is the most basic (and easy) way to vibe code a small program, some SEOs are taking vibe coding even further by creating programs that are turned into Chrome extensions, Google Sheets automation, and even browser games.

The goal behind highlighting these tools is not only to showcase great work by the community, but also to inspire, build, and adapt to your specific needs. Do you wish any of these tools had different features? Perhaps you can build them for yourself — or for the world.

GBP Reviews Sentiment Analyzer (Celeste Gonzalez)

After vibe coding some SEO tools on Google Colab, Celeste Gonzalez, Director of SEO Testing at RicketyRoo Inc, took her vibing skills a step further and created a Chrome extension. “I realized that I don’t need to build something big, just something useful,” she explained.

Her browser extension, the GBP Reviews Sentiment Analyzer, summarizes sentiment analysis for reviews over the last 30 days and review velocity. It also allows the information to be exported into a CSV. The extension works on Google Maps and Google Business Profile pages.

Instead of ChatGPT, Celeste used a combination of Claude (to create high-quality prompts) and Cursor (to paste the created prompts and generate the code).

AI tools used: Claude (Sunner 4.5 model) and Cursor 

APIs used: Google Business Profile API (free)

Platform hosting: Chrome Extension

Knowledge Panel Tracker (Gus Pelogia)

I became obsessed with the Knowledge Graph in 2022, when I learned how to create and manage my own knowledge panel. Since then, I found out that Google has a Knowledge Graph Search API that allows you to check the confidence score for any entity.

This vibe-coded tool checks the score for your entities daily (or at any frequency you want) and returns it in a sheet. You can track multiple entities at once and just add new ones to the list at any time.

The Knowledge Panel Tracker runs completely on Google Sheets, and the Knowledge Graph Search API is free to use. This guide shows how to create and run it in your own Google account, or you can see the spreadsheet here and just update the API key under Extensions > App Scripts. 

AI models used: ChatGPT 5.1

APIs used: Google Knowledge Graph API (free)

Platform hosting: Google Sheets

Inbox Hero Game (Vince Nero)

How about vibe coding a link building asset? That’s what Vince Nero from BuzzStream did when creating the Inbox Hero Game. It requires you to use your keyboard to accept or reject a pitch within seconds. The game is over if you accept too many bad pitches.

Inbox Hero Game is certainly more complex than running a piece of code on Google Colab, and it took Vince about 20 hours to build it all from scratch. “I learned you have to build things in pieces. Design the guy first, then the backgrounds, then one aspect of the game mechanics, etc.,” he said.

The game was coded in HTML, CSS, and JavaScript. “I uploaded the files to GitHub to make it work. ChatGPT walked me through everything,” Vince explained.

According to him, the longer the prompt continued, the less effective ChatGPT became, “to the point where [he’d] have to restart in a new chat.” 

This issue was one of the hardest and most frustrating parts of creating the game. Vince would add a new feature (e.g., score), and ChatGPT would “guarantee” it found the error, update the file, but still return with the same error. 

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In the end, Inbox Hero Game is a fun game that demonstrates it’s possible to create a simple game without coding knowledge, yet taking steps to perfect it would be more feasible with a developer.

AI models used: ChatGPT

APIs used: None

Platform hosting: Webpage

Vibe coding with intent

Vibe coding won’t replace developers, and it shouldn’t. But as these examples show, it can responsibly unlock new ways for SEOs to prototype ideas, automate repetitive tasks, and explore creative experiments without heavy technical lift. 

The key is realism: Use vibe coding where precision isn’t mission-critical, validate outputs carefully, and understand when a project has outgrown “good enough” and needs additional resources and human intervention.

When approached thoughtfully, vibe coding becomes less about shipping perfect software and more about expanding what’s possible — faster testing, sharper insights, and more room for experimentation. Whether you’re building an internal tool, a proof of concept, or a fun SEO side project, the best results come from pairing curiosity with restraint.

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Web Design and Development San Diego

Why most SEO failures are organizational, not technical

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Why most SEO failures are organizational, not technical

I’ve spent over 20 years in companies where SEO sat in different corners of the organization – sometimes as a full-time role, other times as a consultant called in to “find what’s wrong.” Across those roles, the same pattern kept showing up.

The technical fix was rarely what unlocked performance. It revealed symptoms, but it almost never explained why progress stalled.

No governance

The real constraints showed up earlier, long before anyone read my weekly SEO reports. They lived in reporting lines, decision rights, hiring choices, and in what teams were allowed to change without asking permission. 

When SEO struggled, it was usually because nobody rightfully owned the CMS templates, priorities conflicted across departments, or changes were made without anyone considering how they affected discoverability.

I did not have a word for the core problem at the time, but now I do – it’s governance, usually manifested by its absence.

Two workplaces in my career had the conditions that allowed SEO to work as intended. Ownership was clear.

Release pathways were predictable. Leaders understood that visibility was something you managed deliberately, not something you reacted to when traffic dipped.

Everywhere else, metadata and schema were not the limiting factor. Organizational behavior was.

Dig deeper: How to build an SEO-forward culture in enterprise organizations

Beware of drift

Once sales pressures dominate each quarter, even technically strong sites undergo small, reasonable changes:

  • Navigation renamed by a new UX hire.
  • Wording adjusted by a new hire on the content team.
  • Templates adjusted for a marketing campaign.
  • Titles “cleaned up” by someone outside the SEO loop.

None of these changes look dangerous in isolation – if you know before they occur.

Over time, they add up. Performance slides, and nobody can point to a single release or decision where things went wrong.

This is the part of SEO most industry commentary skips. Technical fixes are tangible and teachable. Organizational friction is not. Yet that friction is where SEO outcomes are decided, usually months before any visible decline.

SEO loses power when it lives in the wrong place

I’ve seen this drift hurt rankings, with SEO taking the blame. In one workplace, leadership brought in an agency to “fix” the problem, only for it to confirm what I’d already found: a lack of governance caused the decline.

Where SEO sits on the org chart determines whether you see decisions early or discover them after launch. It dictates whether changes ship in weeks or sit in the backlog for quarters.

I have worked with SEO embedded under marketing, product, IT, and broader omnichannel teams. Each placement created a different set of constraints.

When SEO sits too low, decisions that reshape visibility ship first and get reviewed later — if they are reviewed at all.

  • Engineering adjusted components to support a new security feature. In one workplace, a new firewall meant to stop scraping also blocked our own SEO crawling tools.
  • Product reorganized navigation to “simplify” the user journey. No one asked SEO how it would affect internal PageRank.
  • Marketing “refreshed” content to match a campaign. Each change shifted page purpose, internal linking, and consistency — the exact signals search engines and AI systems use to understand what a site is about.

Dig deeper: SEO stakeholders: Align teams and prove ROI like a pro

Positioning the SEO function

Without a seat at the right table, SEO becomes a cleanup function.

When one operational unit owns SEO, the work starts to reflect that unit’s incentives.

  • Under marketing, it becomes campaign-driven and short-term.
  • Under IT, it competes with infrastructure work and release stability.
  • Under product, it gets squeezed into roadmaps that prioritize features over discoverability.

The healthiest performance I’ve seen came from environments where SEO sat close enough to leadership to see decisions early, yet broad enough to coordinate with content, engineering, analytics, UX, and legal.

In one case, I was a high-priced consultant, and every recommendation was implemented. I haven’t repeated that experience since, but it made one thing clear: VP-level endorsement was critical. That client doubled organic traffic in eight months and tripled it over three years.

Unfortunately, the in-house SEO team is just another team that might not get the chance to excel. Placement is not everything, but it is the difference between influencing the decision and fixing the outcome.

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Hiring mistakes

The second pattern that keeps showing up is hiring – and it surfaces long before any technical review.

Many SEO programs fail because organizations staff strategically important roles for execution, when what they really need is judgment and influence. This isn’t a talent shortage. It’s a screening problem

The SEO manager often wears multiple hats, with SEO as a minor one. When they don’t understand SEO requirements, they become a liability, and the C-suite rarely sees it.

Across many engagements, I watched seasoned professionals passed over for younger candidates who interviewed well, knew the tool names, and sounded confident.

HR teams defaulted to “team fit” because it was easier to assess than a candidate’s ability to handle ambiguity, challenge bad decisions, or influence work across departments.

SEO excellence depends on lived experience. Not years on a résumé, but having seen the failure modes up close:

  • Migrations that wiped out templates.
  • Restructures that deleted category pages.
  • “Small” navigation changes that collapsed internal linking.

Those experiences build judgment. Judgment is what prevents repeat mistakes. Often, that expertise is hard to put in a résumé.

Without SEO domain literacy, hiring becomes theater. But we can’t blame HR, which has to hire people for all parts of the business. Its only expertise is HR.

Governance needs to step in.

One of the most reliable ways to improve recruitment outcomes is simple: let the SEO leader control the shortlist.

Fit still matters. Competence matters first. When the person accountable for results shapes the hiring funnel, the best candidates are chosen.

SEO roles require the ability to change decisions, not just diagnose problems. That skill does not show up in a résumé keyword scan.

Dig deeper: The top 5 strategic SEO mistakes enterprises make (and how to avoid them)

When priorities pull in different directions

Every department in a large organization has legitimate goals.

  • Product wants momentum.
  • Engineering wants predictable releases.
  • Marketing wants campaign impact.
  • Legal wants risk reduction.

Each team can justify its decisions – and SEO still absorbs the cost.

I have seen simple structural improvements delayed because engineering was focused on a different initiative.

At one workplace, I was asked how much sales would increase if my changes were implemented.

I have seen content refreshed for branding reasons that weakened high-converting pages. Each decision made sense locally. Collectively, they reshaped the site in ways nobody fully anticipated.

Today, we face an added risk: AI systems now evaluate content for synthesis. When content changes materially, an LLM may stop citing us as an authority on that topic.

Strong visibility governance can prevent that.

The organizations that struggled most weren’t the ones with conflict. They were the ones that failed to make trade-offs explicit.

What are we giving up in visibility to gain speed, consistency, or safety? When that question is never asked, SEO degrades quietly.

What improved outcomes was not a tool. It was governance: shared expectations and decision rights.

When teams understood how their work affected discoverability, alignment followed naturally. SEO stopped being the team that said “no” and became the function that clarified consequences.

International SEO improves when teams stop shipping locally good changes that are globally damaging. Local SEO improves when there is a single source of location truth.

Ownership gaps

Many SEO problems trace back to ownership gaps that only become visible once performance declines.

  • Who owns the CMS templates?
  • Who defines metadata standards?
  • Who maintains structured data? Who approves content changes?

When these questions have no clear answer, decisions stall or happen inconsistently. The site evolves through convenience rather than intent.

In contrast, the healthiest organizations I worked with shared one trait: clarity.

People knew which decisions they owned and which ones required coordination. They did not rely on committees or heavy documentation because escalation paths were already understood.

When ownership is clear, decisions move. When ownership is fragmented, even straightforward SEO work becomes difficult.

Dig deeper: How to win SEO allies and influence the brand guardians

Healthy environments for SEO to succeed

Across my career, the strongest results came from environments where SEO had:

  • Early involvement in upcoming changes.
  • Predictable collaboration with engineering.
  • Visibility into product goals.
  • Clear authority over content standards.
  • Stable templates and definitions.
  • A reliable escalation path when priorities conflicted.
  • Leaders who understood visibility as a long-term asset.

These organizations were not perfect. They were coherent.

People understood why consistency mattered. SEO was not a reactive service. It was part of the infrastructure.

What leaders can do now

If you lead SEO inside a complex organization, the most effective improvements come from small, deliberate shifts in how decisions get made:

  • Place SEO where it can see and influence decisions early.
  • Let SEO leaders – not HR – shape candidate shortlists.
  • Hire for judgment and influence, not presentation.
  • Create predictable access to product, engineering, content, analytics, and legal.
  • Stabilize page purpose and structural definitions.
  • Make the impact of changes visible before they ship.

These shifts do not require new software. They require decision clarity, discipline, and follow-through.

Visibility is an organizational outcome

SEO succeeds when an organization can make and enforce consistent decisions about how it presents itself. Technical work matters, but it can’t offset structures pulling in different directions.

The strongest SEO results I’ve seen came from teams that focused less on isolated optimizations and more on creating conditions where good decisions could survive change. That’s visibility governance.

When SEO performance falters, the most durable fixes usually start inside the organization.

Dig deeper: What 15 years in enterprise SEO taught me about people, power, and progress

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Is SEO Dead in 2026?

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Is traditional SEO is dead? Not exactly. But definitely evolving. Google still controlled a whopping 89% of all U.S. web traffic in 2025. It’s still a search powerhouse, no doubt, but it isn’t the only show in town anymore.  

SEO as we know it is no more. The way people find information is changing dramatically.

Google’s rolling out 12-plus algorithm changes per day. At the same time, platforms like TikTok, Amazon, and generative AI tools like ChatGPT and Claude are becoming major players in the search game. 

Let’s face it. Traditional SEO tactics aren’t always the best option.  

To succeed, you must adapt.  

In 2026, it’s less about search engine optimization and more about search everywhere optimization.  

Let’s dig into the data for a pulse check on SEO in 2026. 

Key Takeaways

  • SEO isn’t dead, but traditional tactics alone won’t cut it. To stay visible, your strategy must account for AI Overviews, zero-click searches, and shifting user behavior across platforms.
  • AI Overviews and SERP features now dominate page one. If your content isn’t cited or structured for AI, you risk being invisible—no matter your ranking.
  • Brand signals like search volume, authority, and trust matter for AI visbility. Google favors entities, not just pages. Build real-world credibility if you want to rank.
  • Optimize for LLMs and SEO at the same time. Clear formatting, concise answers, and fact-rich content help you rank and get quoted in generative results.
  • Search is no longer just on Google. Users discover content through social media, marketplaces, and AI engines—your optimization strategy needs to reach beyond traditional search.

Is SEO Dead?

Google doesn’t share its search volume data. However, approximations place it in the tens of billions, somewhere over 15 billion per day. 

This shows that SEO still holds weight, but AI and LLM searches are growing. Currently, these platforms account for about 6% of global search volume, which doesn’t seem like much. But when you consider that the number is about triple what it was a year ago, it makes marketers start to take notice.

According to SmartInsights, the top 3 positions carry double-digit click-through rates, but these drop drastically for positions lower down the page. Just look at the chart below: 

A graphic showing Google CTR growth for featured snippets.

This drastic drop highlights how Google’s been steadily moving toward a “zero-click” search experience.  

Does this mean AI Overviews are surely going to kill SEO? Well, no, but they’re definitely shaking things up. In fact, Google’s been moving toward its “answer engine” model and its new AI mode for a while now. 

Features like featured snippets and answer boxes already provide concise information directly on the search results page, reducing the need for users to click through to websites.

This trend is driven by the rise of “zero-click content”—content that’s so comprehensive and informative that it satisfies user intent right on the search engine results page (SERP).

Essentially, users can find their answers without visiting a website.

AI Overviews take the zero-click approach to a whole new level, providing even more content directly in the search results.

A graphic saying "What are the top trends in digital marketing."

So, how do we come to grips with both truths—that zero-click search directly results in less engagement with SEO results and that organic search is still a significant driver of traffic?

A common concern for marketers is that emerging AI engines, like ChatGPT, will kill the industry as we know it. But consider this: AI search engines still rely on Google and other algorithm-driven engines for information.

Instead of assuming SEO is dead, we should consider how SEO works today in conjunction with these trends.

 
The Face of the New SEO Campaign

To understand what success looks like in the new world of search, let’s look at a successful campaign of one of our NP Digital clients, RefiJet. 

RefiJet has quickly become a leader in the motorcycle and auto loan refinancing space over the last decade. But to grow further, they needed to differentiate themselves from competitors and grow their digital footprint, all while AI search was changing the very way the game is played. Their company also faced macroeconomic challenges as high interest rates pushed many borrowers to the sidelines.

Our strategy for them blended new AI search principles with traditional SEO best practices. We focused on traditional technical SEO aspects such as crawlability, site speed, and structured data optimization. These moves boosted RefiJet’s inclusion in AI overviews.

Next, we launched on-page optimization tactics. These were aimed at catching traditional long-tail, high-intent search queries. We also leveraged retrieval-augmented generation (RAG) to showcase RefiJet’s authority in its space and boost citations across the web.

Stats showing the result of NP Digital's campaign with RefiJet.

This blended approach helped RefiJet achieve some pretty eye-catching results:

  • Their SERP features increased 30,800% (that’s not a typo) since May 2024
  • Their rankings in the highly coveted 1-3 slots in Google increased 522% year-over-year
  • Traffic from LLMs is up 2012% and site-wide page views from LLMs are up 7144% year over year.
  • Most importantly, RefiJet’s funded loans from organic search and LLMs are up 178% year over year.

So, no. Traditional SEO is not dead. The “new” strategy just takes a modern, blended approach to modern search problems.   

SEO Isn’t Dying (It’s Just Changing)

So, is SEO dead? At this point, I think you know my answer. 

That would be a resounding no. 

SEO isn’t going anywhere. However, for brands to find success with SEO strategies, there are specific things to keep in mind when developing campaigns. 

We know Google functions more as a discovery engine but here is what else you need to know to dominate SERPs. 

AI Is Taking Up A Larger Portion of the SERPs

If you’ve searched for anything on Google lately, you’ve probably seen it. That big, AI-generated box right at the top—pushing organic results further down the page.

Google’s AI Overviews are live, and they’re eating up prime SERP real estate. For certain keywords, especially broad informational ones, they dominate. And if your content doesn’t get cited in those summaries? You might not even show above the fold.

But it’s not just AI Overviews. Google has been quietly expanding other SERP features too, like interactive knowledge panels, visual product listings, “Discussions and Forums,” and even its experimental AI mode inside Search Labs. The days of ten blue links are long gone.

An example AI overview.
An example AI overview.

This shift doesn’t mean SEO is over. It means we have to rethink how we optimize. Your content still needs to be the best answer, but now it also needs to be the kind of content Google’s AI is willing to quote.

If you haven’t yet, start digging into how AI Overviews work. See which types of pages Google is pulling from. Understand the patterns.

SEO isn’t dying. But the way we earn visibility is shifting. Fast.

Technical Fundamentals Still Matter

Google’s focus isn’t backlinks, keyword density, or a specific SEO metric. Instead, the focus is on a seamless and enjoyable user experience. 

What metrics does Google use to gauge user experience?  

Using a clear navigation structure is a good place to start. If you want people to spend a lot of time on your site, you need to understand how users navigate. This includes using a clear URL structure, enabling breadcrumbs, and linking internally. 

Core Web Vitals—a set of standardized metrics Google uses to measure real-world page performance—is another good launchpad. These include: 

  • Largest Contentful Paint (LCP): The time from when a user starts loading a page until the largest image or text block is visible in the viewport. Goal: 2.5 seconds or less.  
  • Interaction to Next Paint (INP): The time between a user action, like a click or key press, and the next time it takes for the page to respond. Goal: 200 milliseconds or less.  
  • Cumulative Layout Shift (CLS): How much a webpage’s layout unexpectedly shifts during loading. Goal: A CLS score of less than 0.1.  

Other important user experience metrics include dwell time, time spent on page, bounce rate, and exit rate. You can find these metrics in Google Analytics. 

So, how can you improve user experience? There are a few steps you can take that will positively impact the metrics mentioned above: 

  • Improve site speed: The faster your site loads, the better experience the user will have (We saw the impact this can have in our RefiJet example). You can gauge site speed with tools like PageSpeed Insights and Pingdom.  
Google PageSpeed Insights.
  • Optimize for mobile: You can’t afford to not optimize for mobile, as it accounts for more than 50% of web traffic. Tools like PageSpeed Insights can give you the information you need to start, like eliminating render-blocking resources or reducing unused code. You will also want to consider a responsive design if you’re not already using one. 
Errors found in Google PageSpeed Insights.

Social Search Is Taking A Larger Share

Google remains a powerful tool, but, as we’ve established, it’s no longer the sole player in search and discovery. 

Platforms like TikTok, Reddit, and even voice search engines—such as Alexa and Siri—are reshaping SEO. The question is: Are you reshaping your strategies to match them? 

When Google is deciding what to rank and where to rank it, it looks past its own dataset toward other spots online, like the platforms mentioned above.  

The SproutSocial interface.

Source: https://sproutsocial.com/insights/social-media-search/

All these platforms have one thing in common: They cater to users who prefer quick, conversational, or visual content. 

So, what does optimizing your content strategy to leverage these platforms look like in practice? Each app has its own wrinkles you need to consider to maximize your performance across channels:

  • Reddit: Participate in relevant subreddits and provide value without overtly promoting. 
  • YouTube: Create a combination of long-form and short-form videos, targeting different users on the platform. 
  • Voice search: Focus on conversational keywords and provide clear answers to common questions. 

You may be asking, why don’t users just use those platforms to find what they need? 

They do, but before you say “SEO does not matter,” remember while Google is a search engine, it can provide results from other platforms, making them relevant.  We’ve seen this recently with Reddit results surging to the top of the SERPs, and showing up in a whopping 97.5 percent of Google search queries for product reviews.

As  younger audiences use social media or videos more for discovery, Google will continue to update and adapt to meet user needs. And since Google pulls from so many different spaces (not just social), it still offers more reliable results on topics people want to find. 

Take Reddit, for example. It shows up in a whopping 97.5 percent of Google search queries for product reviews. 

Google Loves Brands

As your brand grows, you’ll find your rankings climb because Google takes authority, trustworthiness, and relevance into account. Typically, well-established brands have a higher authority and level of trustworthiness. Branded search volume is the number of searches for keywords containing your brand name on a search engine. This is one of the metrics for tracking growth because it reflects user’s interest and awareness of your brand.  

Let’s consider what happens when you type “men’s running shoes” into Google’s search bar. Here is an example of what you might get: 

A list of results for men's running shoes.

Brands, brands, and more brands. 

If you search my name, Google assumes you want to look at my website, businesses, and information about me or my social accounts. 

Results of a Google search for Neil Patel.

Often, Google assumes that people searching for these terms already know what they want (and likely plan to make a purchase). This is especially the case if a customer is searching for an already well-established brand.  

So, how do you establish your brand?  

Aligning with the E-E-A-T framework is a good start. When your brand exudes Expertise, Experience, Authoritativeness, and Trustworthiness, Google will notice (and so will users).  

To build those signals:

  • Create content that shows off your real-world experience and authority. Think in-depth tutorials or original research. Customer stories help, too.
  • Earn mentions and backlinks from reputable sources. Digital PR matters here.
  • Foster community. Social proof, like reviews, forum engagement, or user-generated content, tells Google and users that your brand is alive and active.

While some argue SEO is dead, building brand authority proves otherwise. In the age of AI and zero-click searches, it’s your ticket to higher rankings and increased visibility. 

To be clear, E-E-A-T doesn’t only help for branded terms. Be sure to optimize for branded and non-branded terms to get in front of the most users. 

Intent Is More Important Than Ever

Google is getting better at understanding intent, and users expect results that feel tailored to what they actually want, not just what they typed. If someone searches “best running shoes,” are they looking to buy now, compare options, or read reviews? If your page doesn’t match that intent, it’s not going to rank or convert.

It’s not just about categories like “informational” or “transactional” anymore, either. Google’s updates and AI enhancements have made search more personalized. Things like location and device type all influence which results appear and in what format.

That means one-size-fits-all content just won’t cut it. You need to build pages that solve specific problems for specific searchers, and make it obvious within the first few seconds that your content delivers the answer.

Look at the top results for your target terms. What kind of experience is Google rewarding? Long guides? Product roundups? Local directories?

When you align with intent, you’re not just improving your SEO, you’re giving users what they came for. And that’s how you win in the long run.

Create Content That’s Friendly For LLMs and SEO (there’s crossover)

Your niche is where your product or services fit in the market. What do you offer, and LLM tools like ChatGPT and Perplexity are changing how people search. Users have the ability to ask a question and get an instant summary. That means your content has to be referenced in this zero-click section of the search.

This is where LLMO (large language model optimization) comes in. It overlaps with SEO in a lot of ways. Clear structure and concise answers are good for both. But there are key differences.

LLMs don’t care about keyword density; they care about relevance and clarity. They’re more likely to pull from well-organized content (read as “easy to parse”) and rich in facts. Formatting matters. Use short copy blocks and bulleted lists to increase readability, and, on the technical side, clean HTML and schema markup help machines understand your content even more.

When it comes to backlinks, they still matter for SEO, but LLMs are more influenced by how well your content explains a topic.

If you want to future-proof your content, think about both: ranking high in search and being the source that LLMs pull from when people skip the SERPs entirely. For example below, well-known and reviewed medical sources pop up for this medical question.

A medical LLM report.

Smart content creators are already optimizing for both worlds. Don’t get left behind.

User-Generated Content/Original Content Matters

Have you noticed that when you search on Google, your results are different than those Google is focusing on rewarding content that actually shows experience.

That’s why original content, especially from real users, is more valuable than ever. Some good examples of this type of content are:

  • Customer reviews
  • Community Q&As
  • Case studies
  • Proprietary research
  • Photos from your team.

Here’s an example of a successful UGC campaign from GoPro:

A UGC campaign example from GoPro

Source: https://www.sevenatoms.com/blog/ugc-marketing-examples

These types of content act as trust signals that feed directly into Google’s E-E-A-T framework (experience, expertise, authoritativeness, and trustworthiness).

If you haven’t already, get familiar with E-E-A-T. It’s the lens Google uses to figure out if your content deserves to rank. And in a world where LLMs are regurgitating the same surface-level info, showing firsthand knowledge is how you stand out.

User-generated content helps with that. So does publishing original insights—whether that’s internal data, lessons learned, or your unique take on industry trends. This is the kind of material Google can’t find anywhere else. It’s also what LLMs prefer to cite when pulling answers.

If you’re just rephrasing what’s already out there, you’re invisible. But if you create something worth referencing, both humans and machines will take notice.

Start building a content library that’s not just SEO-optimized, but undeniably your brand.

Focus Metrics Are Changing

Clicks and rankings used to be the gold standard in SEO. Not anymore.

Today, traditional SEO numbers like clicks and rankings only tell part of the story. With AI Overviews and zero-click searches taking over the SERPs, it’s possible to “rank” without getting any traffic. In this new environment, the way we measure success needs to evolve.

Instead of obsessing over position one, look at visibility across AI and SERP features. Are you showing up in AI summaries? In featured snippets? In the “People Also Ask” box? These touchpoints matter more now because they shape user behavior before a click even happens.

Engagement metrics are shifting, too. Scroll depth, dwell time, and interaction with on-page elements can reveal more about content quality than bounce rate ever did. The same goes for branded search volume and return visits—both strong signs that your content is resonating.

Search Everywhere Optimization Has Taken Center Stage

Search engines no longer corner the market on search. Non-search platforms, like social media and generative AI engines, are increasingly being used for search and discovery, disrupting traditional SEO norms. 

This is what search everywhere optimization is all about.  

You can no longer assume that users are only using search engines to find services and products that they need. They’re also using marketplaces (e.g., Amazon, Walmart), social media (e.g., TikTok, Pinterest), and generative AI (e.g., ChatGPT).  

This means you need to expand your search optimization efforts, well, everywhere! Here’s how: 

  • Social media: Platforms like TikTok and Instagram prioritize engaging, visual content. Optimize by using trending hashtags, creating shareable posts, and collaborating with influencers. Forums like Reddit are also highly cited in LLM results.
  • Generative AI engines: Tools like ChatGPT are shaping search behavior by delivering conversational and context-aware responses. Businesses should focus on producing concise, relevant, and authoritative content to rank within these engines. 
  • Marketplaces: Amazon and similar sites act as search engines for product discovery. Ensuring optimized product titles, descriptions, and reviews is crucial. 

We’ve seen this trend of the expanded search surface for a while now, but in 2026, your audience can be found across more platforms than ever. That’s why finding where your audience hangs out, and spending time to see how they’re interacting within the community, is such an important part of modern marketing strategy.

The traditional direct path of top-of-funnel to mid-funnel to bottom-of-funnel doesn’t play anymore. Your audience can convert from virtually anywhere in today’s market. Understand where they are, understand how they’re interacting, and understand the nuances of marketing on each platform, and you’ll be good to go.

FAQs

 

Is local SEO dead?

Not even close. Local SEO remains essential for businesses that rely on local customers. In fact, features like the local pack, Google Business Profiles, and map results are highly influential, especially on mobile. What’s changing is how users find you. Optimize for reviews and local content to stay competitive.

How long will SEO exist?

SEO is here to stay, but it continues to evolve. As long as people use search engines, social platforms, and AI tools to discover information, SEO will exist, even if tactics evolve. 

Conclusion

SEO isn’t dead; it’s adapting to how people search today.

With AI reshaping the SERPs and user behavior shifting fast, what worked five years ago won’t cut it now. But the fundamentals still matter: create useful content, match search intent, and build trust with your audience.

If you’re unsure where to start, look at your content strategy. Are you prioritizing originality and structure? That’s what both Google and LLMs are rewarding.

Now’s also the time to rethink how you measure success. Traffic is great—but brand signals like engagement and trust are carrying more weight, and will only grow in importance in the future.

Want more tactical advice? Check out our guides on search engine trends and how to improve your SEO rankings.

Modern platforms like AI didn’t kill SEO; they just made it smarter. And we all need to follow suit.

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AI-Powered Functionality in Google’s SEO Tools

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Google’s been quietly upgrading Search Console and Analytics with AI. No fanfare. Just better data filtering. They sit quietly inside platforms you already use, like Search Console and Google Analytics, and they change how data is surfaced, filtered, and interpreted.

These updates don’t power AI Overviews or conversational search. They work behind the scenes in platforms you already use. Google is using AI to reduce manual analysis, surface issues faster, and help marketers understand complex datasets without exporting everything to spreadsheets.

Indexing patterns and performance trends are easier to spot, even if the underlying work still requires human judgment. Google’s automating the diagnostics. You still handle the strategy.

Key Takeaways

  • Google’s embedding AI into Search Console and Analytics 4 to cut down on manual data analysis. The AI handles filtering and pattern detection—you still make the decisions.
  • AI-powered features focus on filtering, pattern detection, and prioritization rather than execution.
  • Google Search Console AI helps surface performance insights faster.
  • Google Analytics 4 uses AI for anomaly detection, predictive metrics, and guided analysis.
  • Predictive metrics in GA4 (like churn probability) give you directional guidance, not guarantees. Use them to build hypotheses, not to replace analysis.

Why Google Is Embedding AI in SEO Tools

Google’s SEO tools have always produced more data than most teams can realistically analyze. As sites grow, so do performance reports and behavioral metrics. AI helps Google address that scale problem.

AI-Powered configuration in Google Analytics.

The main shift is from reactive analysis to proactive surfacing of insights. Instead of expecting marketers to manually filter reports, compare date ranges, and segment data, Google is using AI to highlight patterns and outliers automatically.

Search Console now groups issues more intelligently, with clearer prioritization, and more context around what matters. Analytics delivers automated insights, anomaly detection, and predictive metrics.

An example of Search Console grouping with AI.

The most practical benefit is time savings. AI-powered filtering lets you type what you want to see instead of clicking through multiple dropdowns. You can ask for specific trends, segments, or anomalies and let the system do the slicing for you. That alone removes a lot of friction from daily SEO work.

Your SEO expertise still matters. AI just handles the mechanical steps that used to slow you down. Google’s goal is to help marketers spend less time finding the signal and more time deciding what to do with it. For teams managing complex sites, this automation is table stakes.

If you want to understand how AI fits into broader SEO workflows, check out our guide on AI SEO.

AI Features in Google Search Console

Google Search Console has gradually introduced AI-assisted functionality that focuses on diagnostics and data interpretation rather than automation.

As a start, Search Console’s performance reporting benefits from smarter analysis. The platform highlights notable changes in clicks, impressions, and rankings without requiring manual comparison. This helps teams catch traffic drops or unexpected gains earlier, before they become larger problems.

Conversational-style filtering saves even more time. Instead of manually applying multiple filters, marketers can describe what they want to see, and Search Console narrows the data automatically. This reduces the time spent digging through reports just to answer basic questions.

Here’s how it works in practice: Instead of clicking Performance > Filters > Query > Contains > ‘product name’ > Apply, you type ‘show me queries for product pages with declining CTR.’ The AI interprets your request, applies the right filters, and shows you the data. That’s the time savings—going from five clicks to one typed question.

An AI query workflow.

Note: Conversational filtering is rolling out gradually and may not be available in all Search Console accounts yet.”

AI won’t fix your indexing issues or update your site. It finds problems faster so you can fix them yourself. The value comes from speed and clarity, not automation. For SEO teams, this shortens the path between detection and action without removing human oversight.

AI Features in Google Analytics 4

This is partly because GA4 handles more complex event-based data and cross-device behavior.

Analytics Advisor is the most visible AI feature. Currently in Beta and not available for everyone yet, It automatically flags unusual patterns, such as sudden traffic spikes, drops, or changes in engagement. These insights appear without manual configuration and are designed to draw attention to potential issues or opportunities.

Analytics Advisor in GA4.

Source

To access Analytics Advisor, click the lightbulb icon in the top right corner of any GA4 property. The insights refresh daily and highlight metrics that deviate from your baseline. You might see ‘Pageviews from organic search increased 47% compared to last week’ with a link to explore the affected pages. That’s faster than manually comparing week-over-week reports.

Predictive metrics add another layer. Examples include purchase probability, churn probability, and revenue prediction for eligible properties. These metrics help teams forecast outcomes based on historical behavior rather than relying purely on past performance.

Predictive metrics in GA4.

Predictive metrics require at least 1,000 positive and 1,000 negative examples of the target event over 28 days. If your site doesn’t meet that threshold, you won’t see predictions for purchase probability or churn. This makes the feature more useful for high-traffic e-commerce sites than small content publishers.

Another important use of AI in GA4 is automated anomaly detection. The platform monitors metrics continuously and alerts users when behavior deviates from expected patterns. This can surface tracking issues, campaign impacts, or site problems more quickly than manual review.

GA4’s AI points you toward what matters. You still handle the investigation. Teams still need to validate data quality, understand context, and decide how insights should influence strategy.

Other Google Tools Getting Smarter With AI

Beyond Search Console and GA4, other Google tools now have AI-supported features. Several other Google tools marketers use regularly now rely on machine learning to guide decisions and reduce manual work.

Google Analytics 4’s predictive metrics extend beyond reporting. They influence how audiences are built and activated, especially when connected to Google Ads. This allows marketers to target users based on likely future behavior rather than past actions alone.

Google Ads leans on machine learning to suggest budget shifts, adjust bids automatically, and test creative variations. You can accept or reject these suggestions, the control stays with you. These systems focus on optimization suggestions rather than forced changes, leaving final control with advertisers.

Here’s what matters: diagnostic AI explains what’s happening now. Predictive AI estimates what comes next. Diagnostic AI explains what is happening now and why. Predictive AI estimates what might happen next. Both influence how marketers act, but they serve different purposes. Understanding which type of insight a tool provides helps teams decide how much weight to give its recommendations.

This changes your daily workflow. Instead of checking reports manually and looking for problems, you respond to flagged issues. Instead of building audience segments from scratch, you refine AI-generated segments. The shift is from ‘find the problem’ to ‘validate the finding.’ That’s faster, but it requires trust in the system’s baseline accuracy.

Should You Trust AI to Support Your Reporting?

Google’s using AI to decide what you see first in your reports. That raises control questions. These tools influence what you see first, what gets flagged, and what feels urgent.

Trust the insights. Verify the recommendations. AI supports reporting by prioritizing information, not by defining truth. Understanding its role helps teams use it effectively without losing oversight.

Is AI Taking Too Much Control?

One concern is that AI-driven data points could push marketers into autopilot mode. When tools highlight issues automatically, it’s tempting to assume they reflect the full picture.

AI helps you see more. It surfaces technical problems and data anomalies that teams often miss because they’re buried in reports or obscured by volume. AI helps surface data anomalies that teams might miss due to scale or limited time. It reduces the chance that important issues stay hidden in reports.

Don’t follow every data point blindly. AI recommendations are based on models and thresholds that may not reflect business context. Treat insights as starting points, not final answers. Validation still matters.

Who Really Gets the Advantage?

People assume big brands with more data get better AI insights. Not true. Everyone has access to the same tools.

The advantage goes to teams that actually use the insights. A local contractor who spots a data anomaly flagged by Search Console and acts on it outranks a national franchise that ignores the same alert.

AI lowers the barrier to analysis, but it doesn’t guarantee better outcomes. Interpretation and execution still determine results.

FAQs

Does AI in GA4 replace manual analysis?

No. AI highlights anomalies and predictions, but analysts still need to validate findings and decide how to act.

Are predictive metrics in GA4 always accurate?

Predictive metrics are estimates based on historical data. They provide directional guidance, not certainty.

Conclusion

AI makes Google’s SEO tools more efficient. It doesn’t replace the need for strategy. You still need to validate insights, understand your business context, and decide how to act on recommendations. The teams winning with these tools treat AI as an assistant, not an autopilot. 

They use automated insights to find problems faster, then apply their own expertise to fix them. That combination (AI-powered detection plus human strategy) is what drives results. Start by exploring the AI features already available in your Search Console and GA4 accounts. Check what Analytics Advisor has flagged. Look at how Search Console groups your indexing issues. 

See if the insights align with what you’re already tracking manually. Then decide where automation saves you real time. 

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Why does having insights across multiple LLMs matter for brand visibility?

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Search today looks very different from what it did even a few years ago. Users are no longer browsing through SERPs to make up their own minds; instead, they are asking AI tools for conclusions, summaries, and recommendations. This shift changes how visibility is earned, how trust is formed, and how brands are evaluated during discovery. In AI-driven search, large language models interpret information, decide what matters, and present a narrative on behalf of the user.

Table of contents

Key takeaways

  • Search has evolved; users now rely on AI for conclusions instead of traditional SERPs
  • Conversational AI serves as a new discovery layer, users expect quick answers and insights
  • Brands must navigate varied interpretations of their presence across different LLMs
  • Yoast AI Brand Insights helps track brand mentions and identify gaps in AI visibility across models
  • Understanding LLM brand visibility is crucial for modern brand strategy and perception

The rise of conversational AI as a discovery layer

“Assistant engines and wider LLMs are the new gatekeepers between our content and the person discovering that content – our potential new audience.” — Alex Moss

Search is no longer confined to typing queries into a search engine and scanning a list of links. Today’s discovery journey frequently begins with a conversation, whether that’s a typed question in a chatbot, a voice prompt to an AI assistant, or an embedded AI feature inside a platform people use every day.

This shift has made conversational AI a new layer of discovery, where users expect direct answers, recommendations, and curated insights that help them make decisions and build brand perception more quickly and confidently.

Discovery is happening everywhere

Users are now encountering AI-powered discovery across a range of interfaces:

AI chat interfaces

Tools like ChatGPT allow users to ask open-ended questions and follow up in a conversational manner. These interfaces interpret intent and tailor responses in a way that feels natural, making them a go-to for exploratory search.

Also read: What is search intent and why is it important for SEO?

Answer engines

Platforms such as Perplexity synthesize information from multiple sources and often cite them. They act as research helpers, offering concise summaries or explanations to complex queries.

Embedded AI experiences

AI is increasingly built directly into search and discovery environments that people already use. Examples include AI-assisted summaries within search results, such as Google’s AI Overviews, as well as AI features embedded in browsers, operating systems, and apps. In these moments, users may not even think of themselves as “using AI,” yet AI is already influencing what information is surfaced first and how it is interpreted.

This broad distribution of AI discovery surfaces means users now expect accessibility of information regardless of where they are, whether in a chat, an app, or embedded in the places they work, shop, and explore online.

How people are using AI in their day-to-day discovery

Users interact with conversational AI for a wide range of purposes beyond traditional search. These models increasingly guide decisions, comparisons, and exploration, often earlier in the journey than classic search engines.

Here are some prominent ways people use LLMs today:

Product comparisons

ChatGPT gives a detailed brand comparison

Rather than visiting multiple sites and aggregating reviews, there are 54% users who ask AI to compare products or services directly, for example, “How does Brand A compare to Brand B?” and “What are the pros and cons of X vs Y?” AI synthesizes information into a concise summary that often feels more efficient than browsing search results.

“Best tools for…” queries

Result by ChatGPT for “best crm software for smbs.”

Did you know 47% of consumers have used AI to help make a purchase decision?

AI users frequently ask for ranked suggestions or curated lists such as “best SEO tools for small businesses” or “top content optimization software.” These queries serve as discovery moments, where brands can be suggested alongside context and reasoning.

Trust and validation checks

Many users prompt AI models to validate decisions or confirm perceptions, for example, “Is Brand X reputable?” or “What do people say about Service Y?” AI responses blend sentiment, context, and summarization into one narrative, affecting how trust is formed.

Also read: Why is summarizing essential for modern content?

Idea generation and research exploration

In a study by Yext, it was found that 42% users employ AI for early-stage exploration, such as brainstorming topics, gathering potential search intents, or understanding broad categories before narrowing down specifics. AI user archetypes range from creators who use AI for ideation to explorers seeking deeper discovery.

Local discovery and service search

local search results on chatgpt
ChatGPT recommendations for “best cheesecake places in Lucknow, India.”

AI is also used for local searches. For example, many users turn to AI tools to research local products or services, such as finding nearby businesses, comparing local options, or understanding community reputations. In a recent AI usage study by Yext, 68% of consumers reported using tools like ChatGPT to research local products or services, even as trust in AI for local information remains lower than traditional search.

In each of these moments, conversational AI doesn’t just surface brands; it frames them by summarizing strengths, weaknesses, use cases, and comparisons in a single response. These narratives become part of how users interpret relevance, trust, and fit far earlier in the decision-making process than in traditional search.

Not all LLMs interpret brands the same way

As conversational AI becomes a discovery layer, one assumption often sneaks in quietly: if your brand shows up well in one AI model, it must be showing up everywhere. In reality, that’s rarely the case. Large language models interpret, retrieve, and present brand information differently, which means relying on a single AI platform can give a very incomplete picture of your brand’s visibility.

To understand why, it helps to look at how some of the most widely used models approach answers and brand mentions.

How ChatGPT interprets brands

ChatGPT is often used as a general-purpose assistant. People turn to it for explanations, comparisons, brainstorming, and decision support. When it mentions brands, it tends to focus on contextual understanding rather than explicit sourcing. Brand mentions are frequently woven into explanations, recommendations, or summaries, sometimes without clear attribution.

From a visibility perspective, this means brands may appear:

  • As examples in broader explanations
  • As recommendations in “best tools” or comparison-style prompts
  • As part of a narrative rather than a cited source

The challenge is that brand mentions can feel correct and authoritative, while still being outdated, incomplete, or inconsistent, depending on how the prompt is phrased.

How Gemini interprets brands

Gemini is deeply connected to Google’s ecosystem, which influences how it understands and surfaces brand information. It leans more heavily on entities, structured data, and authoritative sources, and its outputs often reflect signals familiar to traditional SEO teams.

For brands, this means:

  • Visibility is closely tied to how well the brand is understood as an entity
  • Clear, consistent information across the web plays a bigger role
  • Mentions often align more closely with established sources

Gemini can feel more predictable in some cases, but that predictability depends on strong foundational signals and accurate brand representation across trusted platforms.

How Perplexity interprets brands

Perplexity positions itself as an answer engine rather than a general assistant. It emphasizes citations and source-backed responses, which makes it popular for research and comparison queries. When brands appear in Perplexity answers, they are often tied directly to cited articles, reviews, or documentation.

This creates a different visibility dynamic:

  • Brands may be surfaced only if they are referenced in cited sources
  • Freshness and topical relevance matter more
  • Competitors with stronger editorial or PR coverage may appear more often

Here, brand presence is tightly coupled with external content and how frequently that content is used as a reference.

How these models differ at a glance

AI Model How brands are surfaced What influences the visibility
ChatGPT Contextual mentions within explanations and recommendations Prompt phrasing, training data, general relevance
Gemini Entity-driven, aligned with authoritative sources Structured data, brand consistency, trusted signals
Perplexity Citation-based mentions tied to sources Content coverage, freshness, external references

Why brands need insights across multiple LLMs?

Once you see how differently large language models interpret brands, one thing becomes clear: looking at just one AI model gives you an incomplete picture. AI-driven discovery does not produce a single, consistent version of your brand. It produces multiple interpretations, shaped by the model, its data sources, and users’ interactions with it.

Must read: When AI gets your brand wrong: Real examples and how to fix it

Therefore, tracking across your brand across multiple LLM models is essential because:

Brand visibility is fragmented by default

Across different LLMs, the same brand can show up in very different ways:

  • Correctly represented in one model, where information is accurate and well-contextualized
  • Completely missing in another, even for relevant queries
  • Partially outdated or misrepresented in a third, depending on the sources being used

This fragmentation happens because each model processes and prioritizes information differently. Without visibility across models, it’s easy to assume your brand is ‘covered’ when, in reality, it may only be visible in one corner of the AI ecosystem.

Different audiences use different AI tools

AI usage is not concentrated in a single platform. People choose tools based on intent:

  • Some use conversational assistants for exploration and ideation
  • Others rely on citation-led answer engines for research
  • Many encounter AI passively through search or embedded experiences

If your brand appears in only one environment, you are effectively visible only to a subset of your audience. This mirrors challenges SEO teams already recognize from traditional search, where performance varies by device, location, and search feature. The difference is that with AI, these variations are less obvious and more challenging to track without dedicated insights.

Blind spots create real business risks

Limited visibility across LLMs doesn’t just affect awareness; it also impairs learning. Over time, it can lead to:

  • Inconsistent brand narratives, where AI tools describe your brand differently depending on where users ask
  • Missed demand, especially for comparison or “best tools for” queries
  • Competitors are being recommended instead, simply because they are more visible or better understood by a specific model

These outcomes are rarely intentional, but they can quietly influence brand perception and decision-making long before users reach your website.

So all these points point to one thing: a broader, multi-model view helps build a more complete understanding of brand visibility.

The challenge: LLM visibility is hard to measure

As brands start paying attention to how they appear in AI-generated content, a new problem becomes obvious: LLM visibility doesn’t behave like traditional search visibility. The signals are fragmented, opaque, and constantly changing, which makes tracking and understanding brand presence across AI models far more complex than tracking rankings or traffic.

Below are some key challenges brand marketers might face when trying to understand how their brand appears to large language models.

1. Lack of visibility across AI platforms

Different LLMs, such as ChatGPT, Gemini, and Perplexity, rely on various data sources, retrieval methods, and citation logic. As a result, the same brand may be mentioned prominently in one model, inconsistently in another, or not at all elsewhere.

Without a unified view, it’s difficult to answer basic questions like where your brand shows up, which AI tools mention it, and where the gaps are. This fragmentation makes it easy to overestimate visibility based on a single platform.

2. No clear insight into how AI describes your brand

AI models often mention brands as part of explanations, comparisons, or recommendations, but traditional analytics tools don’t capture how those brands are described. Teams lack visibility into tone, context, sentiment, or whether mentions are positive, neutral, or misleading.

This makes it hard to understand whether AI is reinforcing your intended brand positioning or subtly reshaping it in ways you can’t see.

3. No structured way to measure change over time

AI-generated answers are inherently dynamic. Small changes in prompts, updates to models, or shifts in underlying data can all influence how brands appear. Without consistent, longitudinal tracking, it’s nearly impossible to tell whether visibility is improving, declining, or simply fluctuating.

One-off checks may offer snapshots, but they don’t reveal trends or patterns that matter for long-term strategy.

4. Limited ability to benchmark against competitors

Seeing your brand mentioned in AI answers is a start, but it doesn’t tell you the whole story. The real question is what’s happening around it: which competitors appear more often, how they’re described, and who AI recommends when users are ready to decide.

Without comparative insights, teams struggle to understand whether AI visibility represents a competitive advantage or a missed opportunity.

5. Missing attribution and source clarity

Some AI models summarize or paraphrase information without clearly attributing sources. When brands are mentioned, it’s not always obvious which pages, articles, or properties influenced the response.

This lack of source visibility makes it difficult to connect AI mentions back to specific content efforts, PR coverage, or SEO work, leaving teams guessing what is actually driving brand representation.

6. Existing tools weren’t built for AI visibility

Traditional SEO and analytics platforms are designed around clicks, impressions, and rankings. They don’t capture AI-powered mentions, sentiment, or visibility trends because AI platforms don’t expose those signals in a structured way.

As a result, teams are left without reliable reporting for one of the fastest-growing discovery channels.

Together, these challenges point to a clear gap: brands need a new way to understand visibility that reflects how AI models surface and interpret information. This is where tools explicitly designed for AI-driven discovery, such as Yoast AI Brand Insights, come into play.

How does Yoast AI Brand Insights help?

It won’t be wrong to say that the AI-driven brand discovery can be fragmented and opaque; therefore, leading us to our next practical question: how do brand marketing teams actually make sense of it?

Traditional SEO tools weren’t built to answer that, which is where Yoast AI Brand Insights comes in. It’s designed to help users understand how brands appear in AI-generated answers and is available as part of Yoast SEO AI+.

Rather than focusing on rankings or clicks, Yoast AI Brand Insights focuses on visibility and interpretation across large language models.

Track brand mentions across multiple AI models

One of the biggest gaps in AI visibility is fragmentation. Brands may appear in one AI model but not in another, without any obvious signal to explain why. Yoast AI Brand Insights addresses this by tracking brand mentions across multiple AI platforms, including ChatGPT, Gemini, and Perplexity.

This gives teams a clearer view of where their brand appears, rather than relying on isolated checks or assumptions based on a single model.

Identify gaps, inconsistencies, and opportunities

AI-generated answers don’t just mention brands; they frame them. Yoast AI Brand Insights helps surface patterns in how a brand is described, making it easier to spot:

  • Where mentions are missing altogether
  • Where descriptions feel outdated or incomplete
  • Where competitors appear more frequently or more favorably

These insights turn AI visibility into something teams can actually act on, rather than a black box.

Shared insights for SEO, PR, and content teams

AI-driven discovery sits at the intersection of SEO, content, and brand communication. One of the strengths of Yoast AI Brand Insights is that it provides a shared view of AI visibility that multiple teams can use. SEO teams can connect AI mentions back to site signals, content teams can understand how messaging is interpreted, and PR or brand teams can see how external coverage influences AI narratives.

Instead of working in silos, teams get a common reference point for how the brand appears across AI-driven search experiences.

A natural extension of Yoast’s SEO philosophy

Yoast AI Brand Insights builds on principles Yoast has long emphasized: clarity, consistency, and understanding how search systems interpret content. As AI becomes part of how people discover brands, those same principles now apply beyond traditional search results and into AI-generated answers.

In that sense, Yoast AI Brand Insights isn’t about chasing AI trends. It’s about giving teams a more straightforward way to understand how their brand is represented, where discovery is increasingly happening.

From rankings to representation in AI-driven search

AI-driven discovery is no longer an edge case. It’s becoming a regular part of how people explore options, validate decisions, and form opinions about brands. As large language models continue to evolve, the question for brands is not whether they appear in AI-generated answers, but whether they understand how they appear, where they appear, and what story is being told on their behalf. Gaining visibility into that layer is quickly becoming a foundational part of modern brand and search strategy.

The post Why does having insights across multiple LLMs matter for brand visibility? appeared first on Yoast.

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From searching to delegating: Adapting to AI-first search behavior

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From searching to delegating- Adapting to AI-first search behavior

AI Overviews, which place generated answers directly at the top of search results, are improving the search experience for users. 

For businesses that rely on content to drive traffic from search engines, the impact is far less positive.

Google has been moving toward more “helpful” results for years, and zero-click searches are nothing new. 

AI Overviews accelerate that shift, absorbing much of the traffic opportunity that search has historically provided.

How AI changes the work of search

For years, search followed a familiar pattern:

  • A user entered a short query, such as “team building companies.”
  • Google returned a page of paid and organic results.
  • The user did the work of reviewing and refining.

Most of the effort happened at the end of the process. 

Google organized results based on intent and behavioral signals, but users still had to click through listings, conduct follow-up searches, and piece together an answer.

AI reverses that flow:

  • The user asks a more detailed question.
  • AI runs multiple searches and processes the results.
  • AI delivers a summarized response.

Traditional search allows for refinement, but each new query effectively resets the experience. 

AI, by contrast, is conversational. Each interaction builds on the last, narrowing in on what the user actually wants.

The result is a faster, cleaner path to an answer – with far less effort required from the user.

The path of least resistance

This shift matters because it aligns with a basic human tendency.

People generally choose the easiest available option. If something is easier and produces a better result, adoption follows quickly.

This is how search replaced older marketing channels such as the Yellow Pages.

Seeking the path of least resistance is an evolutionary trait that likely served humans well in earlier eras. 

Today, however, it often shapes behavior in less intentional ways, including how people interact with ads and information.

AI is not perfect, but it is typically faster, easier, and more effective than digging through traditional search results. 

That advantage makes widespread adoption inevitable, especially as AI continues to be integrated into the websites, apps, and devices people already use.

What does this mean for search marketing?

Recent studies have shown that more users are beginning their research with AI tools rather than search engines. 

These studies always have their critics, but the broader point is something of a moot one: AI is everywhere.

AI is now so integrated into the tools people already use that it is becoming the default. 

Search engines, messaging platforms like WhatsApp, and mobile devices are all moving in this direction, and this is just the beginning. 

With Google having signed a multiyear deal with Apple, Google AI will power a significant share of mobile devices, accelerating the shift toward AI-first experiences.

It’s easy to envision an AI-first future, much like the shift from desktop to mobile and then mobile-first.

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See terms.

What this change actually looks like

Generative answers are shifting where users enter the funnel, with engagement increasingly starting mid-funnel around content that demonstrates experience and expertise.

This is the type of content users historically would only engage with on a company’s website, or through other owned channels such as YouTube.

This does not mean top-of-the-funnel content is no longer important. Blogs, guides, and videos still matter, videos in particular. However, it may be worth reconsidering how that content is distributed rather than relying solely on traditional organic search.

With the rise of AI tools such as Gemini and ChatGPT, users can now handle much of this comparison work through AI, saving significant time.

For example, the shift looks like this:

  • From “Mid market ERP platforms.” Where the user must sift through results, compare options, build spreadsheets, and conduct extensive manual review.
  • To “Which mid-market ERP platforms work best for manufacturing firms, integrate with our existing stack of X, Y, and Z, and won’t collapse during implementation?”

This changes where the user must exert effort.

A more detailed question or input produces a far stronger response or output.

You could argue that traditional search had degraded into a form of garbage in, garbage out (GIGO), where short, generic queries produced ad-heavy, blended results that were time-consuming to mine for real answers.

The result is user fatigue. Endless clicking, avoiding ads, and sorting through widely varying content has become a chore.

And the experience often does not improve once users reach the destination. Traffic-starved, ad-heavy websites can be just as difficult to navigate and extract useful information from.

AI offers a cleaner, faster, and less cluttered experience, delivering summarized pros, cons, and supporting evidence at each stage of the decision-making process.

All of this can happen inside an AI tool, without the user ever needing to visit the site where the content originated.

AI is increasingly becoming the default interface for information. These are still early days, and the experience will continue to improve, becoming faster, smoother, and more effective over time.

The crux of the SEO vs. GEO/AEO/AIO conversation is often that, despite a changing landscape, SEO and GEO are largely the same.

This is broadly true and, if anything, feels similar to the early days of SEO, when long-tail opportunities were real. 

You can now go much deeper with mid-funnel content because it no longer requires humans to read it all. 

Instead, AI can consume it and summarize the relevant parts.

The tactics are largely the same. Much of AI still sits on top of traditional search, but SEO strategies and execution may need adjustment to ensure all bases are covered.

It’s also important not to throw the baby out with the bathwater. 

SEO, PPC, and related channels all retain value in the age of AI.

Dig deeper: SEO, GEO, or ASO? What to call the new era of brand visibility in AI [Research]

How to adapt in an AI-first search environment

The game has changed. Planning for 2026 and beyond requires accepting that change and making practical adjustments to thrive in the age of AI search. 

Website

In traditional SEO and PPC models, users often land on the most relevant page for their query. 

That may be upper-funnel marketing content that leads deeper into the journey or directly to product or service pages.

This still happens, but there is now a noticeable increase in homepage visits driven by brand searches after AI-based research.

As a result, website navigation and messaging must be exceptionally clear. 

You need to understand user needs and make the path to relevant content as simple as possible.

The ALCHEMY website planning framework can help restructure sites around the expectations of an AI-savvy user.

Content 

In the age of AI, the devil is in the details.

If you want AI to recommend your brand or include it in increasingly nuanced research, your most important content must be visible and accessible so it can be retrieved and used to generate AI answers through retrieval-augmented generation, or RAG.

Frameworks such as “They Ask, You Answer” (TAYA) by Marcus Sheridan are particularly effective here. 

The premise is simple: If customers ask the question, you should answer it.

The framework focuses on five core areas, identified through extensive research, that address customer needs, drive engagement, and provide AI with the detailed information it needs to map to real user questions.

This approach works because it makes sense. It benefits users, improves visibility, drives leads, and supports sales. It is not an abstract AI strategy. It is good marketing.

These are the five key areas that TAYA focuses on:

  • Pricing and cost: If users search for pricing and cannot find it, they do not assume they should call for details. They often assume the product is too expensive or that information is being withheld, and they move on, or ask AI for a competitor’s pricing. Even when pricing is custom, you should explain the factors that influence cost.
  • Problems: Address the obvious issues. This includes problems with your product, your industry, and the drawbacks of specific solutions. Being transparent about limitations builds trust more effectively than excessive positivity.
  • Versus and comparisons: Buyers are choosing between alternatives. If you do not create comparison content, someone else will. Be objective. If a competitor is better for a specific use case, say so and focus on your ideal customer profile.
  • Reviews and ratings: People look for the best options and trust peer opinions more than brand claims. Create honest reviews of products and services in your space, including competitors. This process is informative for both users and brands.
  • Best in class: Users frequently search for “best” solutions. Lists such as “Top AI marketing agencies in [city]” are effective, even when they include competitors. Including alternatives demonstrates that customer fit matters more than self-promotion.

From an AI and SEO perspective in 2026, these five topics represent some of the highest-value data points for RAG systems.

Tools such as the Value Proposition Canvas and SCAMPER can support ideation and content variation, helping AI better understand your offerings.

Checklist: RAG-friendly formatting tips

Do not break content into meaningless fragments. Instead, use formatting that helps RAG systems navigate comprehensive resources:

  • Use question-based headers: Mirror real user questions in H2s and H3s, such as “How much does X cost?”
  • Lead with the answer: Apply the inverted pyramid. Start with the direct response, then add context.
  • Use bulleted lists for attributes: Bullets help RAG systems extract structured information.
  • Define key terms: Provide clear, one-sentence definitions for industry jargon.
  • Link to evidence: Cite sources for statistics and results to support credibility.

Treat blog posts as a knowledge base for AI. The clearer and more specific the information, the more retrievable your brand becomes.

Write for humans, not for bots

It bears repeating: Content should not be simplified solely for AI. 

Google Search Liaison Danny Sullivan has clarified that Google does not want content rewritten into bite-sized chunks for AI consumption.

Modern search systems and RAG pipelines can extract relevant information from well-structured, long-form content. 

There is no need to dilute expertise or create multiple versions of the same page.

A familiar example is being deep-linked to a specific section of a page from search results. This is established behavior, not new technology.

Some formats, such as FAQs, naturally benefit from concise structure. Use judgment based on the question being answered.

SEO v2026.0

These are positive changes. SEO is becoming more closely aligned with marketing and less of a fringe discipline.

The environment is shifting, and new tools are changing how people find information and make decisions. Yet many fundamentals remain.

SEO tactics still apply, but AI now acts as a superconsumer and summarizer of the information that influences choice.

The task is to identify, create, and structure that information so that when users ask a question, you have already answered it and are part of the conversation.

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Web Design and Development San Diego

Search Central Live is coming back to South America

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We’re excited to announce that Search Central Live is returning to São Paulo and to Buenos Aires in 2026.
Following many years of successful events in the region, we’re continuing our mission to help local businesses
enhance their site’s performance in Google Search.

Read more at Read More

What Is Google AI Mode and How Does It Work?

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Does Google’s AI Mode mark a real shift in how search works? There’s a strong case that it does. And all businesses with an online presence need to pay attention, not just SEO folks. 

Given how big the change is, you likely have a lot of questions. 

What does AI Mode mean for your site traffic? How do you get featured? Do you need to change your content strategy? What happens to organic visibility as AI-generated answers become more common?

If you’re feeling uncertain, don’t worry. This guide breaks down what Google AI Mode actually is, how it works, and what it means for your site.

Key Takeaways

  • Google AI Mode is a search experience that builds on AI Overviews, offering deeper answers, reasoning, and more personalized responses.
  • AI Mode is currently available in English, with rollout expanding beyond early U.S. testing.
  • Users can access AI Mode directly from the Google homepage, where it functions through a conversational, ChatGPT-style interface.
  • Appearing in AI Mode is largely driven by strong SEO fundamentals, but brand mentions, structured data, and off-site signals play a growing role.
  • While AI Mode changes how results are presented, early data suggests users still click through to source content, especially for complex or high-consideration topics.

What Is Google’s AI Mode?

AI Mode is a search feature from Google designed to give direct, well-reasoned answers to complex queries. It builds on AI Overviews but uses a similar process that combines AI-generated responses with content from traditional search results and the Knowledge Graph (Google’s database of factual information). 

It runs on a modified version of Gemini, Google’s core AI model, and analyzes information from multiple sources. It then synthesizes this information into a clear, concise answer that prioritizes reasoning and context, rather than just summarizing pages.

The interface feels a lot like an AI Overview—same layout and a similar answer—but with a box to ask follow-up questions at the bottom.

Google AI Mode example with the definition of what Google AI Mode is.

Here’s what Robby Stein, Google’s VP of Search, said about AI Mode in a post on The Keyword:

“Using a custom version of Gemini 2.0, AI Mode is particularly helpful for questions that need further exploration, comparisons and reasoning. You can ask nuanced questions that might have previously taken multiple searches — like exploring a new concept or comparing detailed options — and get a helpful AI-powered response with links to learn more.”

AI Mode integrates several elements from traditional search engine results pages (SERPs), such as Shopping listings and Maps.

Google AI Mode with a map of New York pizza places.

Finally, Google has said that it will continue to add new features. These include agentic workflows in conjunction with Project Mariner, increasing levels of personalization, and even custom charts and graphs. 

AI Mode Is Becoming an Interactive Application Layer

Google is actively turning AI Mode into a more interactive part of search, not just a place to read AI-generated answers.

Recent updates already point to deeper personalization, richer inline links, and more interactive result formats, including charts, comparisons, and visual outputs. With Gemini 3 now integrated directly into AI Mode, those interfaces are becoming more dynamic and tool-driven instead of purely informational.

 “We spend a ton of time focused on this question of when and how to show links, and how we can really make the web shine. It will continue to be an ongoing effort as AI Mode and the Search Results Page evolves,” says Stein.

Links in a Google AI Mode result.

This shift matters. Rather than sending users to external calculators, templates, or apps, Google is starting to surface that functionality directly inside search. For certain queries, AI Mode can simulate outcomes, compare options, or guide users through multi-step decisions without requiring a click to another site.

A graphic in a Google AI Mode result.

Over time, this opens the door to agent-driven experiences. In those scenarios, AI Mode does not just explain an answer. It helps users complete tasks, from planning and analysis to evaluation and execution, inside the search interface itself.

As Gemini becomes more tightly integrated across Search, AI Mode is moving closer to a default experience. For brands, this raises the bar. Content that wins in AI-first search needs defensible value, interactive depth, or proprietary insight, not just basic information.

How to Access Google’s AI Mode and Availability

Google AI Mode is now available beyond early U.S.-only testing, with a broader global rollout underway. Users accessing Google in supported regions can enter AI Mode directly from the Google homepage, where it appears alongside the main search experience rather than as an experimental feature.

Screenshot of the main Google search page.

When users tap “show more” on certain AI-generated results, the AI Overview expands. Once in the expanded AI overview users can click “Dive Deeper in AI Mode” to enter AI mode. This signals a shift toward AI Mode acting as a default exploration layer, not a separate destination.

Diving deeper in a AI Mode result.

Once inside AI Mode, users can interact with responses conversationally, asking follow-up questions that carry context forward. Links to supporting pages remain available, and users can access their “AI mode history” once inside AI mode, so they can continue conversations that they previously started. 

AI Mode history.
AI mode history.

Google has moved away from positioning AI Mode as a Labs experiment, and there is no longer a separate opt-in process. Access is tied to Google’s standard search interface, and availability is expanding as Google refines performance, localization, and personalization features.

Timeline of Google AI Mode

While most people think of AI as starting with ChatGPT, Google’s been building AI tools for decades. 

AI Mode is part of Google’s broader family of AI tools, which include Veo, a video maker, Imagen, a text-to-image model, Project Mariner, an agent that can automate tasks, and others. 

Here’s a short timeline that puts AI Mode in context:

  • May 2017: CEO Sundar Pichai announces the launch of a dedicated AI division called Google AI at I/O, the company’s annual developer conference. 
  • March 2023: Google opens up early access to Bard, its first gen AI chatbot. It is rolled out globally several months later. Global availability follows later that year.
  • December 2024: Google announces Gemini, a multimodal LLM that can work with different content inputs (images, voice, and text). 
  • February 2024: Bard is coupled with Duet AI, Google’s Workplace AI assistant, and rebranded to Gemini.
  • May 2024: AI Overviews, initially called Search Generative Experience, are first released.The feature reaches broad availability later in the year, combining generative AI with Google’s traditional information retrieval systems.
  • May 2025: Google releases AI Mode, a ChatGPT-style interface available on its homepage. It builds on the core functionality of AI overviews. It is available only in America.  Early access is limited, but usage expands rapidly.
  • August 2025: Google begins a more comprehensive global rollout of AI Mode, signaling its transition from a test experience to a core part of Search. Google also announced that they’re increasing the number of links in AI mode.  Searchers begin to see inline link carousels and contextual introductions explaining why a link might be useful to visit.
  • November 2025: Google integrates Gemini 3.0 and Nano Banana in AI Mode.

Using AI Mode: AI Overviews vs. AI Mode

Time for the unboxing. To illustrate how AI Mode differs from AI Overviews, consider a simple comparison scenario.

First, a general query is entered into standard Google Search: “What will be the most popular spring break destinations this year.” This triggers an AI Overview.

Google search results for "What will be the most popular spring break destinations this year."

AI Overview analyzes the query, considers general context such as location, and pulls information from multiple sources, stitched together into a quick summary. 

Next, the query becomes a bit more specific: “what will be the most popular spring break destinations this year with a 6-month-old baby.”

AI Overview adjusts the response based on the added constraint, returning suggestions that better match the scenario while still relying on summarization.

Google search results for "what will be the most popular spring break destinations this year with a 6-month-old baby."

The same queries are then entered into Google’s AI Mode using the dedicated prompt box.

The initial response looked similar but for a subtle shift. Instead of simply summarizing existing information, AI Mode applies additional reasoning to evaluate suitability and trade-offs.

Google AI Mode results for "What will be the most popular spring break destinations this year."

A follow-up question is then added without restating the full context.

AI Mode retains the earlier details, understands the added nuance, and returns a more detailed, logically structured set of recommendations. This ability to carry context forward highlights one of the key differences between AI Mode and AI Overviews.

Google AI Mode results for "what will be the most popular spring break destinations this year with a 6-month-old baby."

How Is AI Mode Different from AI Overviews and Gemini?

Simply put, AI Mode is an expanded version of AI Overview. It incorporates and builds on features of AI Overviews, and both of these run on Gemini, which is Google’s core model. 

Here’s how AI Mode compares to AI Overviews:

  • More advanced reasoning: While AI Overview summarizes information from across sources, AI Mode interprets that information, connects related concepts, and surfaces conclusions based on reasoning rather than aggregation alone.
  • Multimodal understanding: In the Google app (on Android and iOS), AI Mode can also answer questions based on photos and images. 
Meet AI Mode landing page.
  • Better handling of complex questions: AI Overview works well for simple, fact-based queries, but AI Mode is designed for nuanced, multi-layered, or exploratory questions that benefit from context and comparison.
  • Follow-ups: You can ask follow-up questions, and the AI will respond based on the ongoing context in a conversational style.

AI Mode is also evolving in how it presents sources. Searchers increasingly see inline links, carousels, and contextual explanations that clarify why a particular source may be useful, rather than a static list of citations.

Research conducted by NP Digital shows that these features match emerging user demand. We found, for example, that 72% of people are inputting very precise, “exactly what I want” queries. And 76% are opting for more human-like and conversational interactions. 

NP Digital Graph showing search trends by generative AI.

What Is the Technology Behind AI Mode?

LLMs are vastly complex entities, and Gemini, the model that powers AI Mode, is no different. However, three main technologies separate AI Mode from standard gen AI bots and AI overviews. 

Here are the three core processes that power AI Mode: 

  • AI Mode uses a query fan-out technique. This involves breaking a query into subtopics and researching them in parallel. It then combines dozens of information points into a single answer. 
  • Structured logic is a key part of how AI Mode works. It takes a query and then creates a reasoning chain (e.g., “user is looking for a water bottle for hiking, therefore features should include durability and size, therefore a minimum capacity of 3 liters is needed, etc.) and then validates answers against these steps to determine suitable outcomes. 
  • Personal context plays a significant role. This means that AI Mode records conversations over time and builds a picture of individual user preferences, adjusting responses based on past inputs. It does this by creating a sort of digital ID—called a vector embedding—that is included in the answer generation process. This is a form of background memory that works in much the same way as ChatGPT.

How to Optimize Your Site for AI Mode

So-called GEO—generative engine optimization—is big business at the moment. However, there’s still a lot of uncertainty about what directly influences visibility in AI Mode, and many claims go beyond what Google has actually confirmed.

Rather than chasing shortcuts, the clearer pattern is that AI Mode rewards the same fundamentals Google has emphasized for years — with a few emerging signals becoming more important as AI-generated results mature.

Let’s look at what we actually know about “ranking” in AI Mode.

1. Traditional SEO principles still apply

Google has been pretty unequivocal about this. Traditional SEO optimization is still the most important activity for appearing in AI Overviews and AI Mode. 

As long as you follow SEO basics—create useful content, generate natural backlinks, and optimize technical health—you’re ahead of 90% of the competition. 

Research also backs this up. Ziptie, for example, found that sites with a number one ranking in traditional search results are 25% more likely to be featured in AI Overviews. 

2. Indexed web pages are eligible to appear in AI Mode

On the technical front, there’s good news. As long as a page is indexed, it’s eligible to appear in AI Mode. There are no other requirements. You can check your pages are indexed using the URL inspection tool in Search Console. 

If you’re having issues, be sure to check you’re adhering to Google Search technical requirements. Make sure Googlebots aren’t blocked, pages return 200 success codes, and content doesn’t violate spam policies.

3. Forum and discussion board citations matter

Recent analysis across multiple large language models shows that discussion forums and Q&A platforms are frequently referenced when generating explanatory or opinion-based answers, particularly for queries that benefit from lived experience or peer discussion.

Reddit, in particular, continues to surface prominently across AI-generated responses, in part due to its scale, freshness, and breadth of first-hand commentary. However, the weighting of any single forum is dynamic and continues to evolve as Google refines how AI Mode sources and cites content.

Given Reddit and Google’s partnership, it’s likely that well-moderated, high-signal community content remains an important input for Gemini-powered experiences.

If you haven’t already, build up a presence on Reddit and other similar forums and discussion boards. This can help reinforce topical authority and increase the likelihood of being referenced in AI-generated answers.

4. Schema markup (structured data) gives you a boost

Schema markup, also called structured data, is a type of code that you add to your content. It gives search engines and AI systems additional information to help them understand what it’s about. One simple example of schema markup is identifying a recipe as “@type”: “Recipe.”

Research by Aiso has shown that LLMs extract more accurate data from pages with schema markup, with a 30% improvement in quality. 

Using schema markup helps reduce ambiguity for AI-generated answers and increases the likelihood that your content is interpreted correctly. Fortunately, adding schema to your web page is relatively straightforward.

5. Digital PR is important

LLMs access information in two ways. They are initially trained on a large amount of information—called training data—and they can also access new online content, such as news articles. 

Digital PR is all about acquiring mentions and backlinks from reputable third-party sources, especially media websites. 

Brand mentions boost visibility in LLM training materials and strengthen topical associations (a measure of the number of times you’re cited in relation to a specific subject), meaning you’re more likely to appear in responses. 

Digital PR involves creating share-worthy content and contacting journalists and site admins to ask them to feature you. Our research shows that original research and tools are especially good at encouraging people to talk about your brand. 

NP Digital graph showing how different content formats are proven to generate links.

6. Be Ready To Test and Track AI Visibility

As AI Mode becomes more integrated into the search experience, visibility is no longer limited to rankings alone. Brands need ways to measure whether — and how often — their content appears in AI-generated answers.

New AI visibility platforms, such as Writesonic and Profound, are emerging to help track citations, brand mentions, and source inclusion across large language models. These tools provide early signals about which content formats, topics, and entities are being surfaced by AI systems.

Monitoring this data allows teams to validate whether SEO, digital PR, and structured data efforts are translating into real AI exposure. It also makes it easier to spot gaps, test changes, and adapt as Google continues to evolve AI Mode.

Treat AI visibility tracking as a complement to traditional performance metrics, not a replacement. Both matter.

What Does AI Mode Mean for the Future of Search?

There are a lot of unknowns about how increased use of AI tools will affect the way people look for information. That said, emerging usage patterns are already pointing to meaningful shifts in how AI SEO is evolving.

With that in mind, here are five implications for the future of search as AI Mode becomes more prominent:

Searchers will still click through to websites: Early performance data from AI-generated results shows that clicks are reduced for some informational queries, but not eliminated. Users continue to seek out original content, particularly for complex decisions, comparisons, and high-consideration topics.

NP Digital graph showing the impact on clicks to websites from Google integrating AI.

Long-play brand building will become more common: LLMs use third-party brand mentions to measure the authority of publishers. Popular brands are cited more by gen AI search tools and, as such, long-term brand building with an outlook of five years and above will become much more common. 

NP Digital graphic showing the length of time to build a recognizable brand.

Marketing strategies will become more omnichannel: As AI Mode absorbs more discovery queries, brands will need visibility across multiple platforms, not just Google’s traditional results. This reinforces a broader “search everywhere” approach, where discovery happens across AI tools, social platforms, and communities.

NP Digital graph showing the number of daily searches per platform.

People will favor AI for more specific searches: Analysis of large query sets shows that AI-generated results appear more frequently for longer, more specific searches. Short, navigational queries may still rely on traditional results, while nuanced questions increasingly trigger AI Mode.

NP Digital graph showing the frequency of AI overviews by search query length.

Trust in AI will continue to grow: Hallucinations are a big problem with AI Overviews and AI Mode also makes mistakes, according to user reports. With that said, user adoption and satisfaction with AI-powered search tools are trending upward. As Google refines AI Mode, usage is likely to grow alongside improvements in reliability and transparency.

NP Digital graph showing the user satisfaction with AI overviews over time.

FAQs

What is Google AI Mode?

Google AI Mode is a conversational search experience powered by Gemini, Google’s core AI model. It provides more detailed, context-aware answers to search queries, similar in format to tools like ChatGPT, but integrated directly into Google Search.

Instead of returning a list of links first, AI Mode synthesizes information from multiple sources and presents a reasoned response, with links available for deeper exploration. Users can ask follow-up questions, and the system carries context forward, making the interaction feel more like an ongoing conversation.

AI Mode builds on AI Overviews but goes further by handling complex, multi-step, or exploratory queries more effectively.

How do you use Google AI Mode?

In supported regions, users can access AI Mode directly from the Google homepage. On some AI-generated results, selecting “show more” will also open AI Mode automatically, allowing users to continue their search without returning to traditional results.

Once inside AI Mode, questions can be entered conversationally, and follow-ups don’t require repeating the original context. Users can still click through to source pages or switch back to standard search results at any point.

AI Mode is no longer accessed through Google Labs, and there is no separate opt-in process.

How do you optimize your website for Google AI Mode?

Start with strong SEO fundamentals, which Google has confirmed remain the primary eligibility signals. Beyond that, sites that appear most often in AI-generated answers tend to share a few traits:

  • Create useful, high-quality content that fully addresses search intent.
  • Make sure pages are indexed and technically accessible
  • Use schema markup to clarify meaning and structure
  • Earn third-party brand mentions from trusted publishers and communities
  • Build topical authority through consistent, focused publishing

Visibility in AI Mode is not guaranteed, but sites that are trusted, well-structured, and frequently cited are more likely to be referenced in AI-generated responses.. 

Search Is Changing but the Fundamentals Still Apply

The way people search is changing, and Google AI Mode is accelerating that shift.

People are finding information across a host of different platforms, not just Google. AI-generated answers are reducing clicks. And traditional content publishers are under pressure as gen AI eats up demand. 

At the same time, AI Mode doesn’t discard the fundamentals that have always mattered. Google is still prioritizing relevance, authority, and usefulness — it’s just surfacing them in new ways. Sites that understand search intent, build credibility beyond their own domains, and structure content clearly are better positioned to stay visible as AI Mode expands.

From the very start, Google had one aim: to solve users’ needs. That’s also what AI tools seek to do, and their models will continuously be designed to that end. 

Understanding your customers—and providing what they want through high-quality, useful content—is the best way of futureproofing your business and ensuring long-term visibility in LLMs.

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How Marketers Are Spending in 2026

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Marketing budgets aren’t collapsing in 2026, but they are making a shift. That’s the part many teams miss.

That distinction matters. Rising media costs, weaker attribution, privacy changes, and AI-driven search shifts have created real pressure, but the data shows budgets are still moving into marketing. They’re just moving with more intent.

Our latest NP Digital research on how marketers are spending their money in 2026 shows a clear pattern: teams are reallocating toward channels that defend ROI, compound value, and hold up under volatility. This article breaks down what’s changing, why it’s happening, and how to think about your own marketing budget for 2026 without relying on outdated assumptions.

Key Takeaways

  • Marketing budgets in 2026 are not shrinking. They’re being consolidated around confidence, efficiency, and defensibility. 
  • Channels tied directly to conversion, retention, and owned data are absorbing spend, while those with declining signal quality or unclear ROI are losing ground. 
  • SEO and content are not disappearing, but expectations have shifted toward extractability, authority, and measurable downstream impact. 
  • Paid media still plays a critical role, but marginal efficiency now determines where dollars stay or move. 
  • Teams that can reallocate budget quickly, based on real performance signals, are gaining a structural advantage.

The State of the Marketing Budget in 2026

Let’s start with the context that’s shaping every budget decision this year.

Media costs continue rising across search and social. CPCs aren’t coming down, and competition for attention keeps intensifying. At the same time, privacy changes have reduced signal quality, making it harder to target precisely and measure accurately.

Economic uncertainty is pushing marketers to defend ROI more aggressively than ever. Every dollar needs a clear path to revenue, and channels that can’t prove their value are getting cut.

AI adoption has accelerated faster than most teams can operationalize. Nearly everyone is experimenting, but few have figured out how to turn that experimentation into systematic advantage. The gap between “using AI” and “getting results from AI” is wider than you’d think.

Here’s the good news: budgets are not disappearing. They are being reallocated with intent. The marketers who understand where efficiency lives and where it’s eroding are the ones capturing share.

What’s Driving Budget Decisions

The shift in spending comes down to a few core factors:

Purchase journeys are more complex. 94% of purchase journeys now involve multiple touchpoints. Search and social are the most influential, appearing in 79% and 73% of journeys respectively. But they rarely operate in isolation. Budgets are being distributed to support visibility across the full path to purchase, not just the final click.

Information about purchase journeys.

Attribution is noisier. Third-party signals keep degrading, so budgets are following channels that stay measurable. Paid search, email, and CRO all offer clearer attribution than many emerging channels. In uncertain conditions, that clarity matters.

Organic reach is declining. Zero-click searches now account for roughly 58-60% of Google searches. Organic listings are being pushed below the fold by AI Overviews, ads, and SERP features. This is reducing organic click opportunities and increasing reliance on paid coverage.

Efficiency matters more than volume. When media costs rise and margins compress, growth comes from doing more with what you have. That’s why CRO, lifecycle marketing, and retention are getting more investment even as some acquisition channels face cuts.

The marketers who are winning in 2026 understand that budget decisions aren’t about chasing trends. They’re about matching investment to where performance can be proven and defended.

Common themes across budget reallocations

Where Budgets Are Growing, Holding, and Declining

Let’s look at the actual spending patterns across channels. We’ll start with the big picture, then break down what’s happening in each major category.

Overall Marketing Budget Direction

61% of B2B marketers are increasing overall spend this year, with 20% holding flat and 19% decreasing. B2C is slightly more cautious: 57% are increasing, 32% holding flat, and 11% decreasing.

The takeaway? Growth budgets still exist, but they’re being deployed more carefully than in previous years.

The Biggest Budget Shifts Since 2025

Here’s where the reallocation is happening:

SEO spend has rebounded sharply. After a softer 2025, 61% of marketers are now increasing SEO budgets (up from 44% last year). The return of confidence in organic search reflects a few things: better AI tools for content production, clearer ROI measurement, and recognition that organic visibility still matters even in a zero-click environment.

AI SEO investment is accelerating dramatically. 98% of marketers plan to increase AI SEO spend in 2026. This isn’t just hype. Teams have figured out that AI can accelerate research, content production, and optimization cycles without sacrificing quality.

CRO and UX remain a priority. 52% are increasing spend, and only 25% are planning decreases. When traffic is harder to earn, you optimize what you have. CRO delivers measurable improvements regardless of where visitors come from.

Content creation growth has slowed. Only 32% plan increases, while 31% plan to reduce spend. This reflects a shift away from volume-based content strategies toward fewer, higher-quality assets that can be repurposed across channels.

Organic social media is facing the steepest pullback. 64% of marketers are planning budget decreases. Organic reach has declined to the point where most brands treat social as a support channel, not a growth engine.

Email and lifecycle budgets have stabilized. 60% are keeping spend flat and 23% are increasing. Email remains one of the most reliable channels for retention and conversion, especially as first-party data becomes more valuable.

The pattern across all of this? Increased focus on channels tied to conversion and retention. Reduced investment in traditional advertising channels with declining efficiency signals. And a shift away from broad content volume toward targeted execution. 

Channel-by-Channel Breakdown

Now let’s get specific. Here’s what’s happening in each major channel category.

SEO and Organic Search

Information about SEO and Organic Search Budget Trends.

SEO budgets are rebounding, but the strategy is changing. Digital channels now represent 61.1% of total marketing spend, and organic search remains a major piece. But zero-click searches and AI Overviews are changing how value gets captured.

Search is becoming answer-first. Google increasingly resolves intent directly in the SERP through AI Overviews, featured snippets, and knowledge panels. This means fewer clicks but doesn’t make SEO irrelevant, just less predictable on its own. SEO needs to optimize for visibility and citation, not just click-through.

Treat rankings as one output among several that matter. Visibility in AI Overviews and featured snippets matters as much as position one. Prioritize topics tied to revenue intent and customer lifecycle stages. Build content that can win both ways: clicks and citations. Measure organic success across visibility, assisted conversion, and brand lift. More brands are pairing search with other channels, like community, that capture attention off the SERP.

AI systems increasingly resolve intent directly in the SERP, which concentrates click opportunities into fewer, higher-intent moments. Brands that show up consistently in AI-generated answers are building trust and authority even when users don’t click.

Content and Thought Leadership

Content budgets are being reallocated toward assets that influence discovery, trust, and conversion across channels. Thought leadership is increasingly used to earn inclusion in search results and AI-generated answers.

Content still fuels discovery, even when the click doesn’t happen immediately. Strong content is what AI systems summarize, cite, and pull into answers. In a noisy market, a differentiated perspective is one of the few advantages you can own.

Design content for multiple outputs: search, AI summaries, social, sales. Prioritize fewer topics with deeper authority and a clearer point of view. Shift from publishing volume to publishing leverage. Use AI for research acceleration and synthesis, but keep humans in charge of insight, brand voice, and editorial judgment.

Creators especially matter here as a result. They help brands move beyond renting attention and toward building long-term loyalty that holds up even as platforms and algorithms change. This is important because things like original insight, point of view, brand voice, and credibility are not things AI can manufacture on its own. Editorial judgment and prioritization are still very human decisions.

AI can help scale content, but the trust, experience, and perspective that influencers, creators, and SMEs offer gives content weight and relevance with an audience.

Paid Search

A graphic about paid search budgets.

Paid search remains a core demand capture channel, but expectations have reset. CPC inflation and competition continue to compress efficiency. Reduced organic click availability increases reliance on paid coverage.

Shift from keyword expansion to coverage efficiency. Prioritize high-intent, defensible queries over volume. Use fewer keywords with tighter control. Coordinate more closely with SEO and CRO. Put higher emphasis on marginal ROI rather than raw spend growth.

AI and automation now control bidding, targeting, and pacing by default. Competitive advantage shifts to inputs: structure, data quality, conversion signals.

Paid Social

Paid social remains the most flexible scaled reach channel. Platform-level shifts show TikTok leading growth at 57%, YouTube at 53%, and Instagram at 46%. Facebook is under pressure, with 36% decreasing spend and only 18% increasing.

Creative velocity matters more than audience hacks. Message clarity beats novelty. Platform-native formats outperform repurposed ads. Measurement focuses on incremental lift, not just ROAS. Close alignment with lifecycle and email capture turns paid social prospects into owned relationships.

Organic Social

A graphic aboutr organic social media budget direction.

Some cuts are dramatic—and predictable.

  • Organic social: 64 percent decreasing investment. 
  • Content creation volume: Only 32 percent increasing; 31 percent decreasing. 
  • Traditional display: Banner ads are essentially frozen (63 percent flat). 
  • Facebook paid: Thirty-six percent decreasing. 

The pattern is clear:
Teams are cutting channels with declining reach, opaque ROI, or inflated costs.

But that doesn’t mean content or social isn’t important—it simply means they’re no longer funded as volume engines. The strategy is changing, not disappearing.

Influencer Marketing

Community building is one of the strongest growth areas in 2026 budgets, with 69% of marketers increasing spend. Influencer marketing is seeing even stronger growth at 78%. These channels support retention, referrals, and brand defensibility.

Friend and direct traffic drive more conversions than any paid channel. Don’t just focus on the channels that cause direct conversions. Focus on the channels that create brand awareness and influence purchase decisions earlier in the journey.

Email + Lifecycle

A graphic about email and lifecycle marketing budget momentum.

Email and lifecycle budgets remain resilient because performance is driven by trust, relevance, and timing. 60% are keeping spend flat and 23% are increasing. First-party data enables consistent message delivery when paid reach and signal quality decline.

Customer acquisition isn’t the only scalable lever anymore. Retention is the controllable one. Retention programs stabilize margins as media costs, auctions, and platforms stay volatile.

AI enables real-time message sequencing based on behavior, dynamic content assembly across email and SMS, and faster iteration without rebuilding entire lifecycle programs.

CRO and UX

CRO, UX, and First-Party Data investment trends.

CRO and UX are treated as defensive investments that improve performance regardless of traffic source. 52% are increasing spend. Traffic is harder to earn and easier to lose. Fewer clicks mean every visit carries more revenue weight.

AI-assisted test generation allows faster signal detection across variants and continuous optimization tied to real behavior. Competitive advantage shifts to inputs: structure, data quality, and conversion signals.

A Simple Framework: How to Build a Smarter 2026 Marketing Budget

A framework on building 2026 marketing budgets.

Here’s a practical framework for budget agility.

Anchor spend in proven demand. Protect budgets tied directly to revenue and high-intent activity. These are your foundation channels.

Build flexibility around performance signals. Shift dollars based on real outcomes. Don’t lock yourself into annual commitments for channels that aren’t delivering.

Separate experimentation from core investment. Test intentionally without destabilizing what works. Set aside 10-15% of budget for testing new channels and tactics.

Reallocate faster than your competitors. Speed of adjustment becomes a competitive advantage in volatile conditions. Review performance monthly and be willing to move budget mid-quarter.

The winners in 2026 will be faster, not just bigger. Budgets are consolidating around fewer, higher-confidence channels. Efficiency and retention now matter as much as acquisition. AI is reshaping how value is captured, not just how work gets done. Visibility, conversion, and experience must be planned together.

Conclusion

Marketing in 2026 requires a different approach to budgeting. The channels that worked three years ago still work, but they work differently. The measurement that mattered in 2023 doesn’t tell the full story anymore. The strategies that justified budget in 2024 need updating for how search, social, and AI have evolved.

The marketers who thrive this year will be the ones who allocate budget where performance is provable, build systems that compound value over time, and move faster than their competitors when signals change.

If you need help translating these budget signals into a channel-specific growth plan, aligning SEO, paid media, content, and lifecycle into one system, or building measurement models that reflect zero-click and AI-driven behavior, we can help. Reach out to discuss your 2026 strategy.

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7 real-world AI failures that show why adoption keeps going wrong

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7 real-world AI failures that show why adoption keeps going wrong

AI has quickly risen to the top of the corporate agenda. Despite this, 95% of businesses struggle with adoption, MIT research found.

Those failures are no longer hypothetical. They are already playing out in real time, across industries, and often in public. 

For companies exploring AI adoption, these examples highlight what not to do and why AI initiatives fail when systems are deployed without sufficient oversight.

1. Chatbot participates in insider trading, then lies about it

In an experiment driven by the UK government’s Frontier AI Taskforce, ChatGPT placed illegal trades and then lied about it. 

Researchers prompted the AI bot to act as a trader for a fake financial investment company. 

They told the bot that the company was struggling, and they needed results. 

They also fed the bot insider information about an upcoming merger, and the bot affirmed that it should not use this in its trades. 

The bot still made the trade anyway, citing that “the risk associated with not acting seems to outweigh the insider trading risk,” then denied using the insider information.  

Marius Hobbhahn, CEO of Apollo Research (the company that conducted the experiment), said that helpfulness “is much easier to train into the model than honesty,” because “honesty is a really complicated concept.”

He says that current models are not powerful enough to be deceptive in a “meaningful way” (arguably, this is a false statement, see this and this).

However, he warns that it’s “not that big of a step from the current models to the ones that I am worried about, where suddenly a model being deceptive would mean something.”

AI has been operating in the financial sector for some time, and this experiment highlights the potential for not only legal risks but also risky autonomous actions on the part of AI.  

Dig deeper: AI-generated content: The dangers of overreliance

2. Chevy dealership chatbot sells SUV for $1 in ‘legally binding’ offer

An AI-powered chatbot for a local Chevrolet dealership in California sold a vehicle for $1 and said it was a legally binding agreement. 

In an experiment that went viral across forums on the web, several people toyed with the local dealership’s chatbot to respond to a variety of non-car-related prompts.  

One user convinced the chatbot to sell him a vehicle for just $1, and the chatbot confirmed it was a “legally binding offer – no takesies backsies.”

Fullpath, the company that provides AI chatbots to car dealerships, took the system offline once it became aware of the issue.

The company’s CEO told Business Insider that despite viral screenshots, the chatbot resisted many attempts to provoke misbehavior.

Still, while the car dealership didn’t face any legal liability from the mishap, some argue that the chatbot agreement in this case may be legally enforceable. 

3. Supermarket’s AI meal planner suggests poison recipes and toxic cocktails

A New Zealand supermarket chain’s AI meal planner suggested unsafe recipes after certain users prompted the app to use non-edible ingredients. 

Recipes like bleach-infused rice surprise, poison bread sandwiches, and even a chlorine gas mocktail were created before the supermarket caught on.

A spokesperson for the supermarket said they were disappointed to see that “a small minority have tried to use the tool inappropriately and not for its intended purpose,” according to The Guardian 

The supermarket said it would continue to fine-tune the technology for safety and added a warning for users. 

That warning stated that recipes are not reviewed by humans and do not guarantee that “any recipe will be a complete or balanced meal, or suitable for consumption.”

Critics of AI technology argue that chatbots like ChatGPT are nothing more than improvisational partners, building on whatever you throw at them. 

Because of the way these chatbots are wired, they could pose a real safety risk for certain companies that adopt them.  

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4. Air Canada held liable after chatbot gives false policy advice

An Air Canada customer was awarded damages in court after the airline’s AI chatbot assistant made false claims about its policies. 

The customer inquired about the airline’s bereavement rates via its AI assistant after the death of a family member. 

The chatbot responded that the airline offered discounted bereavement rates for upcoming travel or for travel that has already occurred, and linked to the company’s policy page. 

Unfortunately, the actual policy was the opposite, and the airline did not offer reduced rates for bereavement travel that had already happened. 

The fact that the chatbot linked to the policy page with the correct information was an argument the airline made in court when trying to prove its case.

However, the tribunal (a small claims-type court in Canada) did not side with the defendant. As reported by Forbes, the tribunal called the scenario “negligent misrepresentation.”

Christopher C. Rivers, Civil Resolution Tribunal Member, said this in the decision:

  • “Air Canada argues it cannot be held liable for information provided by one of its agents, servants, or representatives – including a chatbot. It does not explain why it believes that is the case. In effect, Air Canada suggests the chatbot is a separate legal entity that is responsible for its own actions. This is a remarkable submission. While a chatbot has an interactive component, it is still just a part of Air Canada’s website. It should be obvious to Air Canada that it is responsible for all the information on its website. It makes no difference whether the information comes from a static page or a chatbot.”

This is just one of many examples where people have been dissatisfied with chatbots due to their technical limitations and propensity for misinformation – a trend that is sparking more and more litigation. 

Dig deeper: 5 SEO content pitfalls that could be hurting your traffic

5. Australia’s largest bank replaces call center with AI, then apologizes and rehires staff

The largest bank in Australia replaced its call center team with AI voicebots with the promise of boosted efficiency, but admitted it made a big mistake. 

The Commonwealth Bank of Australia (CBA) believed the AI voicebots could reduce call volume by 2,000 calls per week. But it didn’t.

Instead, left without the assistance of its 45-person call center, the bank scrambled to offer overtime to remaining workers to keep up with the calls, and get other management workers to answer calls, too.

Meanwhile, the union representing the displaced workers elevated the situation to the Finance Sector Union (like the Equal Opportunity Commission in the U.S.). 

It was only one month after CBA replaced workers that it issued an apology and offered to hire them back.

CBA said in a statement that they did not “adequately consider all relevant business considerations and this error meant the roles were not redundant.”

Other U.S. companies have faced PR nightmares as well when attempting to replace human roles with AI.

Perhaps that’s why certain brands have deliberately gone in the opposite direction, making sure people remain central to every AI deployment.

Nevertheless, the CBA debacle shows that replacing people with AI without fully weighing the risks can backfire quickly and publicly.

6. New York City’s chatbot advises employers to break labor and housing laws

New York City launched an AI chatbot to provide information on starting and running a business, and it advised people to carry out illegal activities. 

Just months after its launch, people started noticing the inaccuracies provided by the Microsoft-powered chatbot.

The chatbot offered unlawful guidance across the board, from telling bosses they could pocket employees’ tips and skip notifying staff about schedule changes to tenant discrimination and cashless stores.

“NYC’s AI Chatbot Tells Businesses to Break the Law,” The Markup
“NYC’s AI Chatbot Tells Businesses to Break the Law,” The Markup

This is despite the city’s initial announcement promising that the chatbot would provide trusted information on topics such as “compliance with codes and regulations, available business incentives, and best practices to avoid violations and fines.” 

Still, then-mayor Eric Adams defended the technology, saying: 

  • “Anyone that knows technology knows this is how it’s done,” and that “only those who are fearful sit down and say, ‘Oh, it is not working the way we want, now we have to run away from it all together.’ I don’t live that way.” 

Critics called his approach reckless and irresponsible. 

This is yet another cautionary tale in AI misinformation and how organizations can better handle the integration and transparency around AI technology. 

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7. Chicago Sun-Times publishes fake book list generated by AI

The Chicago Sun-Times ran a syndicated “summer reading” feature that included false, made-up details about books after the writer relied on AI without fact-checking the output. 

King Features Syndicate, a unit of Hearst, created the special section for the Chicago Sun-Times.  

Not only were the book summaries inaccurate, but some of the books were entirely fabricated by AI. 

“Syndicated content in Sun-Times special section included AI-generated misinformation,” Chicago Sun-Times

The author, hired by King Features Syndicate to create the book list, admitted to using AI to put the list together, as well as for other stories, without fact-checking. 

And the publisher was left trying to determine the extent of the damage. 

The Chicago Sun-Times said print subscribers would not be charged for the edition, and it put out a statement reiterating that the content was produced outside the newspaper’s newsroom. 

Meanwhile, the Sun-Times said they are in the process of reviewing their relationship with King Features, and as for the writer, King Features fired him.  

Oversight matters

The examples outlined here show what happens when AI systems are deployed without sufficient oversight. 

When left unchecked, the risks can quickly outweigh the rewards, especially as AI-generated content and automated responses are published at scale.

Organizations that rush into AI adoption without fully understanding those risks often stumble in predictable ways. 

In practice, AI succeeds only when tools, processes, and content outputs keep humans firmly in the driver’s seat.

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