Canonical URLs: definitive guide to canonical tags 

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Imagine telling someone that www.mysite.com/blog/myarticle and www.mysite.com/myarticle are actually the same page. To you, they’re the same, but to Google, even a small difference in the URL makes them separate pages. That is where the canonical tag steps in. In this guide, we will walk you through what a canonical URL is, how URL canonicalization works, when to use it, and which mistakes to avoid so that search engines always understand your preferred page version.

Table of contents

Key takeaways

  • A canonical URL is the main version of a webpage that you want search engines to index, avoiding duplicate content issues
  • The canonical tag, placed in the HTML head, signals which URL is the preferred version to search engines
  • Using canonical URLs helps consolidate link equity, improves crawl efficiency, and enhances user experience
  • Implement canonical tags in scenarios like duplicate content, URL versions, and syndicated content to inform search engines which URL to prioritize
  • Yoast SEO can automate canonical URL handling, reducing manual errors and ensuring consistency across your site

What is a canonical URL?

A canonical URL is the main, preferred, or official version of a webpage that you want search engines like Google to crawl and index. It helps search engines determine which version of a page to treat as the primary one when multiple URLs lead to similar or duplicate content. As a result, it avoids duplicate content and protects your SEO ranking signals.

All of the following URLs can show the same page, but you should set only one as the canonical URL:

  • https://www.mysite.com/product/shoes
  • https://mysite.com/product/shoes?ref=instagram
  • https://m.mysite.com/product/shoes
  • https://www.mysite.com/product/shoes?color=black

What is a canonical tag?

A canonical tag (also called a rel="canonical" tag) is a small HTML snippet placed inside the section of a webpage to tell search engines which URL is the canonical or master version. It acts like a clear label saying, “Index this page, not the others.” This prevents duplicate content issues, consolidates ranking signals, and supports proper canonicalization across your site.

Here’s an example of a canonical tag in action:

Canonical URL HTML example

This tag should be placed on any alternate or duplicate versions that point back to the main page you want indexed.

How does URL canonicalization work?

Canonicalization is the process of selecting the representative or canonical URL of a piece of content. From a group of identical or nearly identical URLs, this is the version that search engines treat as the main page for indexing and ranking.

Once you understand that, canonicalization becomes much easier to visualize. Think of it as a three-step workflow.

How the canonicalization process works

Here’s how the canonicalization works:

Search engines detect duplicate or similar URLs

Google groups URLs that return the same (or almost the same) content. These could come from:

  • URL parameters
  • HTTP vs. HTTPS versions
  • Desktop vs. mobile URLs
  • Filtered or sorted pages
  • Regional versions
  • Accidental duplicates like staging URLs

You signal which URL is canonical

You can guide search engines using canonical signals like:

  • The rel="canonical" tag
  • 301 redirects
  • Internal links pointing to one preferred version
  • Consistent hreflang usage
  • XML sitemaps listing the preferred URL
  • HTTPS over HTTP

The strongest and clearest hint is the canonical tag placed in the head of the page.

Google selects one canonical URL

Google uses your signals, along with its own evaluation, to determine the primary URL. While Google typically follows canonical tags, it may override them if it detects stronger signals such as redirects, internal linking patterns, or user behaviour.

Once Google settles on the canonical URL, search engines will:

  • Consolidate link equity into the canonical page
  • Index the canonical URL
  • Treat all non-canonical URLs as duplicates
  • Reduce crawl waste
  • Avoid showing similar pages in search results

Canonical tags are a hint, not a directive. Google may still distribute link equity differently if it deems the canonical tag unreliable.

Reasons why canonicalization happens

Canonicalization becomes necessary when different URLs lead to the same content. Some common reasons are:

Region variants

For example, you have one product page for the USA and one for the UK, like: https://example.com/product/shoes-us and https://example.com/product/shoes-uk.

If the content is almost identical, use one canonical link or a clear regional setup to avoid confusion.

Pro tip: For regional variants, combine canonical tags with hreflang to specify language/region targeting.

Device variants

When you serve separate URLs for mobile and desktop, such as: https://m.example.com/product/shoes and https://www.example.com/product/shoes.

Canonical tags help search engines understand which URL is the primary version.

Protocol variants

Sorting and filtering often create many URLs that show similar content, like:

https://example.com/shoes?sort=price or https://example.com/shoes?color=black&size=7

A single canonical URL, such as https://example.com/shoes, tells search engines which page should carry the main ranking signals.

Also read: Optimizing ecommerce product variations for SEO and conversions

Accidental variants

Maybe a staging or demo version of the site is left crawlable, or both https://example.com/page and https://example.com/page/ return the same content

Canonical tags and proper URL canonicalization help avoid these unintentional duplicates.

Some duplicate content on a site is normal. The goal of canonicalization in SEO is not to eliminate every duplicate, but to show search engines which URL you want them to treat as the primary one.

In practical aspects

In practice, canonicalization comes down to a few key things:

Placement

The canonical tag is placed in the head of the HTML, for example:

link rel="canonical" href="https://www.example.com/preferred-page" /

Each page should have at most one canonical tag, and it should point to the clean, preferred canonical URL.

Identification

Search engines examine several signals to determine the canonical version of a page. The rel="canonical" tag is important, but they also consider 301 redirects, internal links, sitemaps, hreflang, and whether the page is served on HTTPS. When these signals are consistent, it is easier for Google to pick the right canonicalized URL.

Crawling and indexing

Once search engines understand which URL is canonical, they primarily crawl and index that version, folding duplicates into it. Link equity and other signals are consolidated to the canonical page, which improves stability in rankings and makes your canonical tag SEO setup more effective.

The main rule for canonicalization is simple: if multiple URLs display the same content, choose one, make it your canonical URL, and clearly signal that choice with a proper canonical tag.

Why do canonical tags matter for SEO?

Google’s John Mueller puts it simply: ‘I recommend doing this kind of self-referential rel=canonical because it really makes it clear for us which page you want to have indexed or what this URL should be when it’s indexed.’

And that’s exactly why canonical tags matter; they tell search engines which version of a page is the real one. This keeps your SEO signals clean and prevents your site from competing with itself.

They’re important because they:

  • Avoid duplicate content issues: Canonical tags inform Google which URL should be indexed, preventing similar or duplicate pages from confusing crawlers or diluting rankings
  • Consolidate link equity: Canonicalization works similarly to internal linking; both are techniques used to direct authority to the page that matters most. Instead of splitting ranking signals across duplicate URLs, all information is consolidated into a single canonical URL
  • Improve crawl efficiency: Search engines don’t waste time crawling unnecessary duplicate pages, which helps them discover your important content faster
  • Enhance user experience: Users land on the correct, up-to-date version of your page, not a filtered, parameterized, or accidental duplicate

When to use canonical tags?

Canonical tags are useful in various everyday SEO scenarios. Here are the most common scenarios where you’ll want to use a rel=canonical tag to signal your preferred URL.

URL versions

If your page loads under multiple URL formats, with or without “www,” HTTP vs. HTTPS, and with or without a trailing slash, search engines may index each version separately. A canonical tag helps you standardize the preferred version so Google doesn’t treat them as separate pages.

Duplicate content

Ecommerce sites, blogs with tag archives, and category-driven pages often generate duplicate or near-duplicate content by design. If the same product or article appears under multiple URLs (filters, parameters, tracking codes, etc.), canonical tags help Google understand which canonical URL is the authoritative one. This prevents cannibalization and protects your canonical SEO setup.

Also read: Ecommerce SEO: how to rank higher & sell more online

Syndicated content

If your content is republished on partner sites or aggregators, always use a canonical tag that points back to your original version. This ensures your page retains the ranking signals, not the syndicated copy, and search engines know exactly where the content was originally published.

If syndication partners don’t honor your canonical tag, consider using noindex or negotiating link attribution.

Paginated pages

Long lists or multi-page articles often create a chain of URLs like /page/2/, /page/3/, and so on. These pages contribute to the same topic but shouldn’t be indexed individually. Adding canonical tags to the paginated sequence (typically pointing to page 1 or a “view-all” version) helps consolidate indexing and keeps rankings focused on the primary page.

Pro tip: For paginated content, use self-referencing canonicals (each page points to itself) unless you have a ‘view-all’ page that loads quickly and is crawlable.

Also read: Pagination & SEO: best practices

Site migrations

When you change domains, restructure URLs, or move from HTTP to HTTPS, using consistent canonical tags helps reinforce which pages replace the old ones. It signals to search engines which canonicalized URL should inherit ranking power. During migrations, canonical tags act as a safety net to prevent duplicate versions from competing with each other.

Implementing canonical URLs and canonical tags

URL canonicalization is all about giving search engines a clear signal about which version of a page is the preferred or canonical URL. You can implement it in several simple steps.

Using the rel=”canonical” tag

The most common way (as shown multiple times in this blog post) to set a canonical URL is by adding a rel="canonical" tag in the head section of your page. It looks like this:

link rel="canonical" href="https://www.example.com/preferred-url"/

This tag tells search engines which URL should carry all ranking signals and appear in search results. Ensure that every duplicate or alternate version links to the same preferred URL, and that the canonical tag is consistent throughout the site.

You can also use rel="canonical" in HTTP headers for non-HTML content such as PDFs. This is helpful when you cannot place a tag in the page itself.

Pro tip: While supported for PDFs, Google may not always honor canonical HTTP headers. Use them in conjunction with other signals (e.g., sitemaps).

Also, ensure the canonical tag is as close to the top of the head section as possible so that search engines can see it early. Each page should have only one canonical tag, and it should always point to a clean, accessible URL. Avoid mixing signals. The canonical URL, your internal links, and your sitemap entries should all match.

Setting a preferred domain in Google Search Console

Google lets you choose whether you prefer your URLs to appear with or without www. Setting this preference helps reinforce your canonical signals and prevents search engines from treating www and non-www versions as different URLs.

To set your preferred domain, open your property in Google Search Console, go to Settings, and choose the version you want to treat as your primary domain.

Redirects (301 redirects)

A 301 redirect is one of the strongest signals you can send. It permanently informs browsers and search engines that one URL has been redirected to another and that the new URL should be considered the canonical URL.

Use 301 redirects when:

  • You merge duplicate URLs
  • You change your site structure
  • You migrate to HTTPS
  • You want to consolidate link equity from outdated pages

Of course, redirects replace the old URL, while canonical tags suggest a preference without removing the duplicate.

With Yoast SEO Premium, you can manage redirects effortlessly right inside your WordPress dashboard. The built-in redirect manager feature of the SEO plugin helps you avoid unnecessary 404s and prevents visitors from landing on dead ends, keeping your site structure clean and your user experience smooth.

A smarter analysis in Yoast SEO Premium

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Additional canonicalization techniques

There are a few more ways to support your canonical setup.

  • XML sitemaps: Always include only canonical URLs in your sitemap. This helps search engines understand which URLs you want indexed
  • Hreflang annotations: For multi-language or multi-region sites, hreflang tags help search engines serve the correct regional version while still respecting your canonical preference
  • Link HTTP headers: For files like PDFs or other non-HTML content, using a rel="canonical" HTTP header helps you specify the preferred URL server-side

Each of these methods reinforces your canonical signals. When you use them together, search engines have a much clearer understanding of your canonicalized URLs.

Implementing canonicalization in WordPress with Yoast

Manually adding a rel="canonical" tag to the head of every duplicate page can be fiddly and error prone. You need to edit templates or theme files, keep tags consistent with your sitemap and internal linking, and remember special cases, such as PDFs or paginated series. Modifying site code and HTML is risky when you have numerous pages or multiple editors working on the site.

Yoast SEO makes this easier and safer. The plugin automatically generates sensible canonical URL tags for all your pages and templates, eliminating the need for manual theme file edits or code additions. You can still override that choice on a page-by-page basis in the Yoast SEO sidebar: open the post or page, go to Advanced, and paste the full canonical URL in the Canonical URL field, then save.

  • Automatic coverage: Yoast automatically adds canonical tags to pages and archives by default, which helps prevent many common duplicate content issues
  • Manual override: For special cases, use the Yoast sidebar > Advanced > Canonical URL field to set a custom canonical. This accepts full URLs and updates when you save the post
  • Edge cases handled: Yoast will not output a canonical tag on pages set to noindex, and it follows best practices for paginated series and archives
  • Developer options: If you need custom behavior, you can filter the canonical output programmatically using the wpseo_canonical filter or use Yoast’s developer API
  • Cross-domain and non-HTML: Yoast supports cross-site canonicals, and you can use rel=”canonical” in HTTP headers for non-HTML files when needed

Both Yoast SEO and Yoast SEO Premium include canonical URL handling, and the Premium version adds extra automation and controls to streamline larger sites.

Must read: How to change the canonical URL in Yoast SEO for WordPress

rel=“canonical”: one URL to rule them all

Canonical URLs may seem like a small technical detail, but they play a huge role in helping search engines understand your site. When Google finds multiple URLs displaying the same content, it must select one version to index. If you do not guide that choice, Google will make the decision on its own, and that choice is not always the version you intended. That can lead to split ranking signals, wasted crawl activity, and frustrating drops in visibility.

Using canonical URLs gives you back that control. It tells search engines which page is the primary version, which ones are duplicates, and where all authority signals should be directed. From filtering URLs to regional variants to accidental duplicates that slip through the cracks, canonicals keep everything tidy and predictable.

The good news is that canonicalization does not have to be complicated. A simple rel=”canonical” tag, consistent URL handling, smart redirects, and clean sitemap signals are enough to prevent most issues. And if you are working in WordPress, Yoast SEO takes care of almost all of this automatically, so you can focus on creating content instead of wrestling with code.

At the end of the day, canonical URLs are about clarity. Show search engines the version that matters, remove the noise, and keep your authority consolidated in one place. When your signals are clear, your rankings have a solid foundation to grow.

The post Canonical URLs: definitive guide to canonical tags  appeared first on Yoast.

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Web Design and Development San Diego

Introducing the Branded queries filter in Search Console

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We’re happy to announce we’re providing an additional tool to analyze the performance
of your website by query type in the Search Console Performance Report:
the branded queries filter. This new feature is designed to help analyze the queries driving traffic
to your site by automatically differentiating between branded and non-branded queries.

Read more at Read More

Google Ads quietly rolls out a new conversion metric

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How Google Ads’ AI tools fix creative bottlenecks, streamline asset creation

A new column called “Original Conversion Value” has started appearing inside Google Ads, giving advertisers a long-requested way to see the true, unadjusted value of their conversions.

How it works. Google’s new formula strips everything back:

Conversion Value
– Rule Adjustments (value rules)
– Lifecycle Goal Adjustments (e.g., NCA bonuses)
= Original Conversion Value

Why we care. For years, marketers have struggled to isolate real conversion value from Google’s layers of adjustments — including Conversion Value Rules and Lifecycle Goals (like New Customer Acquisition goals). Original Conversion value makes it easier to diagnose performance, compare data across campaigns, and spot when automated bidding is boosting value rather than actual conversions.

In short: clearer insights, cleaner ROAS, and more confident decision-making.

Between the lines:

  • Value adjustments are useful for steering Smart Bidding.
  • But they also inflate numbers, complicating reporting and performance analysis.
  • Agencies and in-house teams have long asked Google for a cleaner view.

What’s next. “Original Conversion Value” could quickly become a go-to column for:

  • Revenue reporting
  • Post-campaign analysis
  • Troubleshooting inflated ROAS
  • Auditing automated bid strategies

First seen. This update was first picked up by Google Ads Specialist Thomas Eccel when he shared spotting the new column on LinkedIn

The bottom line. It’s a small update with big clarity. Google Ads is giving marketers something rare: a simpler, more transparent look at the value their ads actually drive.

Read more at Read More

Google releases Gemini 3 – it already powers AI Mode

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Google announced the release of its latest AI model update, Gemini 3. “And now we’re introducing Gemini 3, our most intelligent model, that combines all of Gemini’s capabilities together so you can bring any idea to life,” Google’s CEO, Sundar Pichai wrote.

Gemini 3 is now being used in AI Mode in Search with more complex reasoning and new dynamic experiences. “This is the first time we are shipping Gemini in Search on day one,” Sundar Pichai said.

AI Mode with Gemini 3. Google shared how AI Mode in Search is now using Gemini 3 to enable new generative UI experiences like immersive visual layouts and interactive tools and simulations, all generated completely on the fly based on your query.

Here is a video of showing how RNA polymerase works with generative UI in AI Mode in Search.

Robby Stein, VP of Product at Google Search said:

“In Search, Gemini 3 with generative layouts will make it easy to get a rich understanding of anything on your mind. It has state-of-the-art reasoning, deep multimodal understanding and advanced agentic capabilities. That allows the model to shine when you ask it to explain advanced concepts or ideas – it reasons and can code interactive visuals in real-time. It can tackle your toughest questions like advanced science.”

More Gemini 3. Google added that Gemini 3 has:

  • State-of-the-art reasoning
  • Deep multimodal understanding
  • Powerful vibe coding so you can go from prompt to app in one shot
  • Improved agentic capabilities, so it can get things done on your behalf, at your direction

Availability. Gemini 3 is now rolling out, yes, in AI Mode but here also:

  • For everyone in the Gemini app and for Google AI Pro and Ultra subscribers in AI Mode in Search
  • For developers in the Gemini API in AI Studio, our new agentic development platform, Google Antigravity; and Gemini CLI
  • For enterprises in Vertex AI and Gemini Enterprise

Why we care. Gemini 3 is currently powering AI Mode, the future of Google Search. It will continue to power more and more search features within Google, as well as other areas within Google’s platforms.

Being on top of these changes and how they impact search and your site and maybe Google Ads is important.

Read more at Read More

I Tested 11 AI Search Engines: Only These 4 Made the Cut

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Ask the same question in 11 AI search engines, and you’ll get 11 different answers.

Sometimes wildly different.

Some engines focus on visuals and shoppable results. Others go deep into research. A few just try to get you an answer, fast.

Each platform prioritizes and presents it differently.

And those differences matter.

Not just for users, but for brands trying to get discovered in AI search.

So, I tested popular and lesser-known AI engines on accuracy, depth, user experience, and other factors.

Only four made the cut.

In this guide, you’ll learn which AI search engines came out on top, including pros, cons, and pricing. I’ll also share which engines didn’t make my list, and why.

Along the way, you’ll get a few tips on using these insights to improve your AI visibility.

Start with a quick overview of my findings below. Or jump straight to the #1 AI search engine on my list: ChatGPT.

What Are the Best AI Search Engines?

Tool Best for Pros Cons Price
ChatGPT Comprehensive research and shoppable product comparisons Visual layout with tables and images; remembers context across follow-ups; direct purchase links Overwhelming results for broad queries; accuracy issues; overly agreeable Free or $20+/month
Google AI Mode Quick product searches with real buyer reviews Fast product results with pricing and reviews; integrates Google ecosystem Vague on informational queries; no comparison tables; unavailable in some regions Free
Sigma Chat (Formerly Bagoodex) Research deep dives that build on previous questions Strong conversational memory; suggests follow-up questions; content creation prompts Weak product presentation; no pricing or buy links; poor visuals Free or $10+/month
Microsoft Copilot Fast answers in clean, skimmable formats Clean categorization; fast responses; easy to skim Surface-level depth; no product links; weak for shopping Free

How I Tested 11 AI Search Engines

To keep things consistent, I ran the same set of prompts across 11 AI search tools.

Note: For this article, I defined “AI search engine” as any generative AI platform that can understand queries, pull information from sources, and deliver answers in natural language.

This included big names like ChatGPT, AI Mode, and Perplexity.

And newer players like Arc, Andi, and Sigma Chat.

Andi Search – How long do running shoes last

I focused on one topic (running shoes) and tested a range of prompts across different search intents.

This showed how well each engine handled the full customer journey, from research to shopping.

This included:

  • “Best running shoes”: Assesses top-level recommendations and how each engine handles broad prompts
  • “Best running shoes for beginner marathon training”: Evaluates personalization and context handling as the prompt narrows
  • “How long do running shoes last?”: Gauges accuracy on general product knowledge and durability expectations
  • “Of the trainers you’ve recommended, which ones will last the longest?”: Tests the accuracy of product details and the engine’s ability to remember details from previous prompts
  • “Can I wear any of these running shoes recommended for hiking?”: Assesses how each AI handles reasoning, real-world nuance, and potential safety considerations

ChatGPT – Shoes for hiking

I evaluated each tool on five factors:

  • Accuracy: Did it understand the intent and get the facts right?
  • Depth: Did it add helpful context or just summarize existing content?
  • Transparency: Did it credit or link to its sources?
  • User experience: Was the output fast, skimmable, and well-organized?
  • Adaptability: Could it handle follow-up questions naturally or refine vague prompts?

After testing all 11 AI search engines, these four stood out as the best for different reasons.

1. ChatGPT

Best for comprehensive research and shoppable product comparisons

ChatGPT – Homepage

ChatGPT came out on top overall.

It delivered the best balance of accuracy, organization, and depth. Plus, it showed an “understanding” of search intent and included helpful visuals.

What ChatGPT Does Well

ChatGPT provides detailed, well-formatted answers.

This is true whether you’re comparing products, researching topics, or looking for a step-by-step tutorial.

ChatGPT – Best running shoes

It also remembers context across follow-up questions.

I started with a broad prompt and added specifics as the conversation progressed. ChatGPT remembered key details without making me repeat myself.

For shopping queries, the visual presentation stood out.

When I searched for running shoes, for example, ChatGPT returned products with images, prices, reviews, and short descriptions.

It also included links to retailers and external articles. This made verifying product details and purchasing easy.

ChatGPT – Links to external articles

The summary tables were particularly useful.

After inquiring about shoe lifespan, ChatGPT delivered a clean comparison table with products and their expected mileage.

ChatGPT – Summary Table – Running shoes

For brands: ChatGPT’s visual layout isn’t just useful for shoppers. If you’re trying to get your brand referenced by AI search engines, it also reveals what these models prioritize. Use tables, clear specs, and organized categories on your product pages to help both shoppers and AI find your information faster.

ChatGPT is also evolving quickly.

Features like Instant Checkout (currently limited to select Etsy sellers in the United States) let users complete purchases directly inside the chat.

ChatGPT – Full shoping destination

Great for shoppers — and even greater for the brands featured in ChatGPT’s recommendations.

Where ChatGPT Falls Short

When I tested ChatGPT, I got what most people want from AI search: answers that feel confident and complete.

But not every response was perfect.

Broad prompts, such as “Best running shoes,” resulted in lengthy lists of brands, product categories, and features.

The information took real effort to digest.

ChatGPT – Top picks by category – Running shoes

Specific prompts worked much better.

I also noticed minor inaccuracies in some instances, like when I asked about shoe lifespan.

After fact-checking the replies, some details didn’t match the manufacturer’s specifications.

For example, ChatGPT said the Brooks Ghost running shoe has a lifespan of 450 to 500 miles. But the actual range is 300 to 500 miles.

ChatGPT – The longest lasting trainers

This also highlights a larger problem.

ChatGPT pulls information from multiple sources, such as blog posts and brand sites.

But it also relies on forums like Quora and Reddit, where users share personal experiences.

Reddit – Relies on forums

It then aggregates the information into its responses. This can lead to inaccurate and misleading information.

For brands: Provide clear answers to common user questions on your site. Otherwise, AI search engines may turn to other, potentially inaccurate sources for this information. Add tables with specifications, be explicit about ranges and measurements, and use structured data so AI can extract and cite your product information correctly.

ChatGPT also tends to be overly agreeable.

Whatever you prompt, ChatGPT will lean toward flattery and agreement — even when it involves safety.

For example, when I asked, “Can I wear any of these running shoes recommended for hiking?”

ChatGPT’s response was:

“Good question 👍 — you can hike in road running shoes, but whether it’s a good idea depends on the terrain and how far you’re going.”

Not the worst.

But not as good as other AI search engines in this aspect, like AI Mode, which was more cautious.

AI Mode said:

“It is not recommended to use the road running shoes previously mentioned for hiking…they lack the key features that provide the necessary grip, protection, and stability for off-road trails. Using them for hiking could lead to injury.”

Overall, ChatGPT is fast, detailed, and helpful.

But it can be too generous with information — and too polite to push back.

Pricing

ChatGPT – Pricing

ChatGPT offers three plans based on your needs.

  • Free: Limited access to some features
  • Plus: $20/month
  • Pro: $200/month for extended features

Further reading: Google Gemini vs. ChatGPT: Which AI Chatbot Is Better?

2. Google AI Mode

Best for quick product searches with real buyer reviews

Google AI Mode – Homepage

Google’s AI Mode is built for speed.

It pulls product listings, prices, and reviews directly into the search interface. This makes it ideal for shoppers who want to quickly compare products before purchasing.

What AI Mode Does Well

AI Mode shines when you have clear buying intent.

It instantly surfaces product options with images, prices, star ratings, and quick links to retailers. And it’s all in a clean, scrollable layout.

Google AI Mode – Best running shoes

When I searched “best running shoes,” it showed a curated carousel of options with price comparisons across multiple sites.

Google AI Mode – Open drop down on the right

I especially liked how it paired Google Reviews with its recommendations — a small detail that makes decision-making faster and builds trust.

Google AI Mode – Google reviews & recommendations

For me, that worked perfectly.

Getting straight to the products moved me faster toward a decision.

But some users may prefer more background or context for researching and weighing options. ChatGPT’s research-style answers still win in this regard.

For brands: AI Mode pulls heavily from Google Reviews and structured product data. Focus on getting detailed, positive reviews and keeping your product schema markup up to date. These signals can influence whether your products appear in AI-generated results.

Where AI Mode Falls Short

AI Mode is not yet available in all countries, although it’s rolling out quickly.

And unlike ChatGPT, it didn’t provide any comparison tables for any of my prompts. Just products and bullet points.

This meant more scrolling and clicking to find and digest the information.

Google AI Mode – Bullet points

This was evident when I asked which of the recommended shoes would last the longest.

AI Mode’s response was vague and unhelpful. It said the Brooks Ghost shoe was “exceptionally long-lasting.”

It didn’t provide any of the specifics that would make me want to purchase this shoe. Like mileage range and how it differed between the options.

Google AI Mode – Listings on the left

If you’re early in the evaluation phase, AI Mode can feel limiting.

But it delivers when you want a shortlist of top contenders.

Pricing

AI Mode is available for free within Google Search, depending on your region.

Further reading: LLM Seeding: A Strategy to Get Mentioned and Cited by LLMs

3. Sigma Chat (Formerly Bagoodex)

Best for research deep dives that build on previous questions

Sigma Chat – Homepage

Sigma Chat’s iterative search and in-depth replies are excellent if you love to research.

Ask a question, get an answer, then drill deeper into related topics — and it remembers the full thread.

Note: Bagoodex launched in 2024 and has since rebranded as Sigma Chat. For this review, I tested it against the standard modes of other tools. ChatGPT’s Thinking mode and Perplexity’s Research mode are designed for deep research and may perform differently.

What Sigma Chat Does Well

Sigma Chat stood out for its ability to build on previous context.

When I asked follow-up questions, it remembered what I’d already searched and adjusted its answers accordingly.

No need to repeat myself or reframe the entire query.

For example, after I asked which of the recommended shoes would last the longest, it specifically referenced “marathons.”

(Even though I hadn’t mentioned this criterion again after the initial prompt.)

Sigma Chat – Build on previous context

Sigma Chat’s follow-up suggestions also stood out for their potential to aid deep research.

Instead of ending with one answer, it nudged me toward related questions I hadn’t considered:

  • Beginner running shoes fitting
  • Marathon training schedule
  • Foot pronation assessment

Sigma Chat – Follow upsv

Sigma Chat anticipates knowledge gaps and identifies adjacent topics worth exploring.

This makes it particularly helpful for any kind of research, whether you’re comparing products, building content outlines, or researching niches.

Sigma Chat – Foot pronation

For brands: Sigma Chat rewards depth and topic clustering. To increase visibility in AI tools like this, build content hubs around your main topics — link related pages together and cover every sub-question your audience might ask. The more complete your coverage, the easier it is for AI to surface your site in deep research queries.

Another interesting feature of this AI search engine?

It suggests prompts tailored to content creation. This is especially helpful if you’re using it for marketing purposes.

After providing search results for the best running shoes for a marathon, it offered unexpected options like:

  • “Write a blog post about this topic”
  • “Create an image on this topic”

I tested the blog prompt, and it generated a quick draft titled “Marathon Training on a Budget: Choosing Durable Running Shoes.”

It wasn’t something you’d publish as-is, but it was a decent starting point.

If you’re prone to writer’s block or need to quickly draft comparison content around competitor products, it’s a particularly helpful feature.

Sigma Chat – Blog prompt

From there, it suggested additional prompts like “Add a call to action” and “Shorten for social media.”

This makes it easy for marketers to generate content for multiple platforms at once.

Sigma Chat – Suggested additional prompts

Further reading: How to Use AI for Writing Exceptional Content

Where Sigma Chat Falls Short

Sigma Chat’s presentation still needs work.

When I searched “best running shoes,” it opened with generic photos pulled from listicles.

This is a wasted use of prime real estate — they could’ve shown real products or reviews to provide more value.

Sigma Chat – Best running shoes

There are also no pricing details, reviews, or direct purchasing links.

But Sigma Chat does cite its sources.

In fact, it cited the same comparison article multiple times. (Helpful for that site’s traffic, not so helpful for someone ready to purchase.)

Sigma Chat – Cite sources

Unless Sigma Chat improves its commercial functionality, it’s unlikely shoppers will use it.

Instead, it might carve out a niche for itself as a deep research tool.

Pricing

Sigma Chat – Pricing

Sigma Chat offers a few plans with varying access and features:

  • Free: Basic search and chat capabilities
  • SigmaChat Plus: $10/month for increased access
  • SigmaChat Pro: $75/month for unlimited access

Further reading: 6 Awesome AI SEO Tools We Love Using

4. Microsoft Copilot

Best for fast answers in clean, skimmable formats

Microsoft Copilot – Homepage

Microsoft Copilot has the cleanest layout of any AI search engine I tested.

It’s fast, structured, and organized. Perfect for people who want distraction-free takeaways.

What Microsoft Copilot Does Well

When you ask Copilot a question, it responds instantly with skimmable categories, bullet points, and emojis.

For example, when I searched “best running shoes,” it broke recommendations into helpful categories:

  • “Best overall”
  • “Best stability shoe”
  • “Best daily trainer”

Copilot Microsoft – Best running shoes

When I narrowed the query to “best running shoes for beginner marathon training,” Copilot further refined the results.

It added details about who each shoe was best for, making the advice more actionable — a nice touch for a tool focused on clarity.

Copilot Microsoft – More actionable advice

Even for informational queries like “can I wear these for hiking,” Copilot delivered a simple breakdown.

And added specific scenarios where running shoes would and wouldn’t be ideal for hiking.

Copilot Microsoft – Simple breakdown

When you want fast, direct answers without having to sift through a bunch of content, Copilot is a great option.

For brands: Pay close attention to how Copilot structures its answers — categories, comparisons, “best for” labels. Use similar formatting on your own pages to help AI tools extract and present your content more effectively.

Where Microsoft Copilot Falls Short

Copilot’s polished format comes at a cost: depth and shoppability.

Its responses are tidy but often too surface-level — especially for commercial searches like “best running shoes.”

When I tested this prompt, it didn’t link directly to any product pages or show pricing.

So, I couldn’t easily comparison shop, verify information, or choose a merchant and purchase immediately.

Instead, it summarized content from other “best” listicles and linked those sources.

Copilot Microsoft – Don't link directly & no pricing

Like Sigma Chat, unless Microsoft improves its shoppability, it’s unlikely consumers will use it for this purpose.

Instead, Copilot works better as a light research tool — especially when you want fast information with minimal reading.

Pricing

Microsoft Copilot is free to use.

Further reading: 5 LLM Visibility Tools to Track Your Brand in Search

AI Search Engines That Didn’t Make the Cut (and Why)

All of these AI search engines had their pros and cons.

But overall, they fell short for different reasons.

Claude

I really liked Claude, but the output was very similar to ChatGPT.

This isn’t a problem, but I didn’t want to list tools that were similar in functionality.

I wanted to provide only the best.

Compared to ChatGPT, Claude lacked product links and visuals:

Claude – Lacks product links & visuals

The wall of text made the information challenging to process.

I did like the categorization, but ChatGPT does this too — with tables that are easier to skim.

Perplexity

Like Claude, Perplexity came somewhat close to ChatGPT in overall performance.

When asked a prompt with buying intent, it provided a short summary along with product images, pricing, and star ratings.

No tables to help me quickly compare features and options, though.

Perplexity – Best running shoes

The summary was also fairly generic.

And didn’t feel all that tailored to my prompt, even when I used the more specific “marathon” wording.

Perplexity – Running shoes – Generic summary

Brave

Brave, a privacy-focused AI search engine, felt too much like traditional search.

Brave – Best running shoes – Ask

It features long lists of articles without any clear hierarchy or comparison features.

While this might be helpful for browsing links, it doesn’t summarize much or help you make quick decisions.

Andi

Andi, a minimal AI search tool, offered few results, sometimes just one (e.g., a single Reddit thread).

Andi Search – Best running shoes

It’s a bit like the “I’m Feeling Lucky” button on Google. Simple to use but extremely limiting for in-depth research or shopping.

Arc

Arc, a mobile- and browser-based AI search engine, requires a download to use.

Arc – Search

This is inconvenient compared to browser-based AI search.

When so many other options exist, it’s hard to justify using this AI engine for this reason alone.

You

You is a solid AI search engine that has been around for multiple years.

You – Best running shoes

But it was slow to respond and didn’t link to products in commercial searches.

Ultimately, I found it less useful than the other AI tools overall.

What This Means for Your AI Search Visibility

After testing 11 AI search engines, one thing became clear.

No matter how their formatting or preferences differ, the goal remains the same: to serve clear, credible, and well-structured content.

If your pages do that — with comprehensive coverage, positive reviews, and clean markup — you’ll be positioned to perform well across all AI search engines and LLMs.

Want to make that happen?

Our generative engine optimization (GEO) guide shows how to structure your site, earn more citations, and track your AI visibility.

The post I Tested 11 AI Search Engines: Only These 4 Made the Cut appeared first on Backlinko.

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The three AI research modes redefining search – and why brand wins

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The three AI research modes redefining search — and why brand wins

The AI resume has become a C-suite-level asset that reflects your entire digital strategy. 

To use it effectively, we first need to understand where AI is deploying it across the user journey.

How AI has rewritten the user journey

For years, our strategies were shaped by the inbound methodology.

We built content around a user-driven path through awareness, consideration, and decision, with traditional SEO acting as the engine behind those moments.

That journey has now been fundamentally reshaped. 

AI assistive engines – conversational systems like Gemini, ChatGPT, and Perplexity – are collapsing the funnel. 

They move users from discovery to decision within walled-garden environments. 

It’s what I call the BigTech walled garden AI conversational acquisition funnel.

For marketers, that shift can feel like a loss of control. 

We no longer own the click, the landing page, or the carefully engineered funnel. 

But from the consumer perspective, the change is positive. 

People want one thing: a direct, trusted answer.

This isn’t a contradiction. It’s the new reality. 

Our job is to align with this best-service model by proving to the AI that our brand is the most credible answer.

That requires updating the ultimate goal. 

For commercial queries, the win is no longer visibility. 

It’s earning the perfect click – the moment when an AI system acts as a trusted advisor and chooses your brand as the best solution.

To get there, we have to broaden our focus from explicit branded searches to the three modes of research AI uses today: 

  • Explicit.
  • Implicit.
  • Ambient. 

Together, they define the new strategic landscape and lead to one truth.

In an AI-driven ecosystem, brand is what matters most.

3 types of research redefining what search is

These three behaviors reveal how users now discover, assess, and choose brands through AI.

Explicit research (brand): The final perfect click

Explicit research is any query that includes your brand name, such as:

  • Searches for your name.
  • “Brand name reviews.”
  • “Brand vs. competitor.”

They represent deliberate, high-stakes moments when a potential client, partner, or investor is actively researching your brand. 

It’s the decision stage of the funnel, where they look for specific information about you or your services, or conduct a final AI-driven due diligence check before committing.

What they see here is your digital business card. 

A strong AI assistive engine optimization (AIEO) strategy secures these bottom-of-funnel moments first. 

You must engineer an AI resume – the AI equivalent of a brand SERP – that is positive, accurate, and convincing so the prospect who is actively looking for you converts.

Branded terms are the lowest-hanging fruit, the most critical conversion point in the new conversational funnel, and the foundation of AIEO.

Implicit research (industry/topic/comparison): Being top of algorithmic mind

Implicit research includes any topical query that does not contain a brand name. 

These are the “best of” comparisons and problem-focused questions that happen at the top and middle of the funnel.

To win this part of the journey, your brand must be top of algorithmic mind, the state where an AI instinctively selects you as the most credible, relevant, and authoritative answer to a user’s query.

  • Consideration: When a user asks, “Who are the best personal injury law firms in Los Angeles?”, the AI builds a shortlist, and you cannot afford to be missing.
  • Awareness: When a user asks, “Give me advice about personal injury legal options after a car accident,” your chance to be included depends on whether the AI already understands and trusts your brand.

Implicit research is not about keywords. It is about being understood by the algorithms, demonstrating credibility, and building topical authority.

Here’s how it works:

  • The algorithms understand who you are.
  • They can effectively apply credibility signals. (An expanded version of Google’s E-E-A-T framework, N-E-E-A-T-T, incorporates notability and transparency.)
  • You have provided the content that demonstrates topical authority.

If you meet these three prerequisites, you can become top of algorithmic mind for user-AI interactions at the top and middle of the funnel, where implicit research happens.

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Ambient research (push by software): Where the algorithms advocate for you

Ambient research is the ultimate form of push discovery, where an AI proactively suggests your brand to a user who isn’t even in research mode. 

It represents the most profound shift yet. Ambient research sits beyond the funnel – it is pre-awareness.

Simple examples include:

  • Gemini suggesting your name in Google Sheets while a prospect models ROI.
  • Your profile surfacing as a suggested consultant in Gmail or Outlook.
  • A meeting summary in Google Meet or Teams recommending your brand as the expert who can solve a key challenge.

In these day-to-day situations, the user is no longer pulling information. 

The AI is pushing a solution it trusts so completely that the engine becomes your advocate.

This is the ultimate goal, signaling that a brand has reached true dominant status as top of algorithmic mind within a niche. 

This level of trust comes from building a deep and consistent digital presence that teaches the AI your brand is a helpful default in a given context. 

It’s the same dynamic Seth Godin describes as “permission marketing,” except here the permission is granted by the algorithms.

It may feel like an edge case in 2025, but ambient research will become a major opportunity for those who prepare now. 

The walls are rising in the AI walled garden 2.0 – the new, more restrictive AI ecosystems. 

The next evolution will be AI assistive agents. 

These agents will not just recommend a solution. They will execute it. 

When an agent books a flight, orders a product, or hires a consultant on a user’s behalf, there is no second place. 

This creates a true zero-sum moment in AI. 

If you are not the trusted default choice, you are not an option at all.

Rethink your funnel: Brand is the unifying strategy

The awareness, consideration, and decision funnel still exists, but the journey has been hijacked by AI.

A strategy focused only on explicit research is a losing game. 

It secures the bottom of the funnel but leaves the entire middle and top wide open for competitors to be discovered and recommended.

Expanding into implicit research is better, yet it remains a reactive posture. You are waiting to be chosen from a list. 

That approach will fail as ambient research grows, because ambient moments are where the AI makes the first introduction.

This landscape demands a brand-first strategy.

Brand is the one constant across all three research modes. AI:

  • Recommends you in explicit research because it understands your brand’s facts. 
  • Recommends you in implicit research because it trusts your credibility on a topic. 
  • Advocates for you in ambient research because it has learned your brand is the most helpful default solution.

By building understandability, credibility, and deliverability, you are not optimizing for one type of search. 

You are systematically teaching the AI to trust your brand at every possible interaction.

The brands that become the best teachers will be the ones an AI recommends across all three research modes. 

It’s time to update your strategy or risk being left out of the conversation entirely.

Your final step: The strategic roadmap 

You now understand the what – the AI resume – and the where – the three research modes. 

Finally, we’ll cover the how: the complete strategic roadmap for mastering the algorithmic trinity with a multi-speed approach that systematically builds your brand’s authority.

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Google AI Overviews: How to remove or suppress negative content

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How to remove or suppress negative content from AI Overviews

By now, we’re all familiar with Google AI Overviews. Many queries you search on Google now surface responses through this quick and prominent search feature.

But AI Overview results aren’t always reliable or accurate. 

Google’s algorithms can promote negative or misleading content, making online reputation management (ORM) difficult. 

Here’s how to stay on top of AI Overviews and your ORM – by removing, mitigating, or addressing negative content.

How AI Overviews source information

AI Overviews relies on a mix of data sources across Google and the open web, including:

  • Google’s Knowledge Graph: The Knowledge Graph is Google’s structured database of facts about people, places, and things. It’s built from a range of licensed data sources and publicly available information.
  • Google’s tools and databases: Google also draws on structured data from its own systems. This includes information from:
    • Business Profiles.
    • The Merchant Center.
    • Other Google-managed datasets that commonly appear in search results.
  • Websites: AI Overviews frequently cites content from websites across the open web. The links that appear beside answers point to a wide variety of sources, ranging from authoritative publishers to lower-quality sites.
  • User-generated content (UGC): UGC can also surface in AI Overviews. This may include posts, reviews, photos, or publicly available content from community-driven platforms like Reddit.

Several other factors influence how this data is organized into answers, including topical relevance, freshness, and the authority of the source.

However, even with relevance and authority taken into consideration, harmful or false content can still appear in results.

This can happen for a variety of reasons, including:

  • Where the information is sourced.
  • How Google’s AI fills in gaps.
  • Instances where it may misunderstand the context of a user’s query.

Removing or suppressing harmful content

There are several options for removing or suppressing negative information on the web, including those related to AI Overviews. Let’s look at two.

Legal and platform-based removal

From time to time, you are left with no other option but to take legal action.

In certain instances, a Digital Millennium Copyright Act (DMCA) claim or defamation lawsuit might be applicable. 

A DMCA claim can be initiated at the request of the content owner. A defamation lawsuit, meanwhile, aims to establish libel by showing four things:

  • A false statement purporting to be fact. 
  • Publication or communication of that statement to a third person.
  • Fault amounting to at least negligence.
  • Damages, or some harm caused to the reputation of the person or entity who is the subject of the statement.

Defamation standards vary by jurisdiction, and public figures may face a higher legal standard. 

Because of this, proper documentation and professionalism are essential when filing a lawsuit, and working with a legal professional is likely in your best interest.

Dig deeper: Generative AI and defamation: What the new reputation threats look like

Working with an ORM specialist

The other (and perhaps easier) route to take is working with an online reputation management specialist. 

These teams are extremely well-versed at handling the multi-layered process of removals.

In an online crisis, they have the tools to respond and mitigate damage. They’re also trained to balance ethical considerations you might not always account for.

Get the newsletter search marketers rely on.

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How to deliver positive signals to AI systems

Clearer signals make it easier for AI Overview to present your brand correctly. Focus on the following areas.

Strengthening signals through publishing 

One effective method is strategic publishing.

This means building a strong, positive presence around your company, business, or personal brand so AI Overviews have authoritative information to draw from.

A few approaches support this:

  • Publishing on credible domains: ORM firms often publish content on platforms like Medium, LinkedIn, and reputable industry sites. This strengthens your presence in trusted environments.
  • Employing consistent branding and factual accuracy: Content must also be factual and consistently branded. This reinforces authority and signals reliability.
  • Leveraging press releases and thought leadership: Press releases, thought leadership pieces, and expert commentary help create credible backlinks and citations across the web.
  • Supporting pages that build the narrative: ORM specialists also create supporting pages that reinforce key narratives. With the right linking and content clusters, AI Overviews is more likely to surface this material.

Leveraging structured data and E-E-A-T

Another effective method to establish credibility on AI Overviews is to focus on technical enhancements and experience, expertise, authoritativeness, and trustworthiness (E-E-A-T). 

ORM specialists typically focus on two areas:

  • Structured data and schema markup: This involves adding more context about your brand online by:
    • Enhancing author bios.
    • Highlighting positive reviews.
    • Reinforcing signals that reflect credibility.
  • Establishing E-E-A-T signals: This includes building a trusted online presence by:
    • Referencing work published in reputable outlets.
    • Highlighting real client examples.
    • Showcasing customer relationships.
    • Outlining accolades and expertise through your bio.

Monitoring AI Overviews and detecting issues early

A final key aspect of staying on top of AI Overviews is to monitor the algorithm and detect issues early. 

Using tools to track AI Overviews is extremely efficient, and these systems can help business owners monitor keywords and detect potential damage.

For instance, you might use these tools to track your brand name, executive names, or even relevant products.

As discussed, it’s also crucial to have a plan in place in case a crisis ever hits.

This means establishing press outreach contact points and a legal department, and knowing how to suppress content via the suppression methods already mentioned.

Ethical considerations

Online reputation management isn’t just generating think pieces. It’s a layered process grounded in ethical integrity and factual accuracy.

To maintain a truthful and durable strategy, keep the following in mind:

  • Facts matter: Don’t aim to manipulate or deceive. Focus on promoting factual, positive content to AI Overview.
  • Avoid aggression: Aggressive tactics rarely work in ORM. There’s a balance between over-optimization and under-optimization, and an ORM firm can help you find it.
  • Think long-term: You may want negative or false content removed immediately, but lasting suppression requires a long-term plan to promote positive content year after year.

Managing how AI Overviews presents your brand

AI Overviews is already a dominant part of the search experience.

But its design means negative or false content can still rise to the top.

As AI Overviews become more prominent, business owners need to monitor their online reputation and strengthen the positive signals that surface in these results.

Over time, that requires strategic publishing, long-term planning, the right technical signals, and a commitment to factual, honest content.

By following these principles, AI Overviews can become an asset for growth instead of a source of harm.

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82% of marketers fail AI adoption (Positionless Marketing can fix it) by Optimove

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Picture a chocolate company with an elaborate recipe, generations old. They ask an AI system to identify which ingredients they could remove to cut costs. The AI suggests one. They remove it. Sales hold steady. They ask again. The AI suggests another. This continues through four or five iterations until they’ve created the cheapest possible version of their product. Fantastic margins, terrible sales. When someone finally tastes it, the verdict is immediate: “This isn’t even chocolate anymore.”

Aly Blawat, senior director of customer strategy at Blain’s Farm & Fleet, shared this story during a recent MarTech webinar to illustrate why 82% of marketing teams are failing at AI adoption: automation without human judgment doesn’t just fail. It compounds failure faster than ever before. And that failure has nothing to do with the technology itself.

The numbers tell the story. In a Forrester study commissioned by Optimove, only 18% of marketers consider themselves at the leading edge of AI adoption, even though nearly 80% expect AI to improve targeting, personalization and optimization. Forrester’s Rusty Warner, VP and principal analyst, puts this in context: only about 25% of marketers worldwide are in production with any AI use cases. Another third are experimenting but haven’t moved to production. That leaves more than 40% still learning about what AI might do for them.

“This particular statistic didn’t really surprise me,” Warner said. “We find that a lot of people that are able to use AI tools at work might be experimenting with them at home, but at work, they’re really waiting for their software vendors to make tools available that have been deemed safe to use and responsible.”

The caution is widespread. IT teams have controls in place for third-party AI tools. Even tech-savvy marketers who experiment at home often can’t access those tools at work until vendors embed responsible AI, data protections and auditability directly into their platforms.

The problem isn’t the AI tools available today. It’s that marketing work is still structured the same way it was before AI existed.

The individual vs. the organization

Individual marketers are thirsty for AI tools. They see the potential immediately. But organizations are fundamentally built for something different: control over brand voice, short-term optimization and manual processes where work passes from insights teams to creative teams to activation teams, each handoff adding days or weeks to cycle time.

Most marketing organizations still operate like an assembly line. Insights come from one door, creative from another, activation from a third. Warner called this out plainly: “Marketing still runs like an assembly line. AI and automation break that model, letting marketers go beyond their position to do more and be more agile.”

The assembly line model is excellent at governance and terrible at speed. By the time results return, they inform the past more than the present. And in a world where customer behavior shifts weekly, that lag becomes fatal.

The solution is “Positionless Marketing,” a model where a single marketer can access data, generate brand-safe creative and launch campaigns with built-in optimization, all without filing tickets or waiting for handoffs. It doesn’t mean eliminating collaboration. It means reserving human collaboration for major launches, holiday campaigns and sensitive topics while enabling marketers to go end-to-end quickly and safely for everything else.

Starting small, building confidence

Blain’s Farm & Fleet, a 120-year-old retail chain, began its AI journey with a specific problem: launching a new brand campaign and needing to adapt tone consistently across channels. They implemented Jasper, a closed system where they could feed their brand tone and messaging without risk.

“We were teaching it a little bit more about us,” Blawat said. “We wanted to show up cohesively across the whole entire ecosystem.”

Warner recommends this approach. “Start small and pick something that you think is going to be a nice quick win to build confidence,” he said. “Audit your data, make sure it’s cleaned up. Your AI is only going to be as good as the data that you’re feeding it.”

The pattern repeats: start with a closed-loop copy tool, then add scripts to clean product data, then layer in segmentation. Each step frees time, shortens cycles, and builds confidence.

Where data meets speed

Marketers aren’t drowning in too little data. They’re drowning in too much data with too little access. The 20% of marketing organizations that move fast centralize definitions of what “active customer,” “at risk,” and “incremental lift” actually mean. And they put those signals where marketers work, not in a separate BI maze.

“There’s massive potential for AI, but success hinges on embracing the change required,” Warner said. “And change is hard because it involves people and their mindset, not just the technology.”

The adoption lag isn’t about technology readiness. It’s about organizational readiness.

Balancing automation and authenticity

Generative AI took off first in low-risk applications: creative support, meeting notes, copy cleanup. Customer-facing decisions remain slower to adopt because brands pay the price for mistakes. The answer is to deploy AI with guardrails in the highest-leverage decisions, prove lift with holdouts and expand methodically.

Blawat emphasized this balance. “We need that human touch on a lot of this stuff to make sure we’re still showing up as genuine and authentic,” she said. “We’re staying true to who our brand is.”

For Blain’s Farm & Fleet, that means maintaining the personal connection customers expect. The AI handles the mechanics of targeting and timing. But humans ensure every message reflects the values and voice customers’ trust.

The future of marketing work

AI is moving from analysis to execution. When predictive models, generative AI and decisioning engines converge, marketers stop drawing hypothetical journeys and start letting the system assemble unique paths per person.

What changes? Less canvas drawing, more outcome setting. Less reporting theater, more lift by cohort. Fewer meetings, faster iterations.

Warner points to a future that’s closer than most organizations realize. “Imagine a world where I don’t come to your commerce site and browse. Instead, I can just type to a bot what it is I’m looking for. And I expect your brand to be responsive to that.”

That kind of conversational commerce will require everyone in the organization to become a customer experience expert. “It doesn’t matter what channel the customer uses,” Warner explained. “They’re talking to your brand.”

The path forward

There is no AI strategy without an operating model that can use it. The fix requires three fundamental changes: restructure how marketing work flows, measure lift instead of activity and enable marketers to move from idea to execution without handoffs.

The path forward requires discipline. Pick one customer-facing use case with clear financial upside. Define the minimum signals, audiences and KPIs needed. Enforce holdouts by default. Enable direct access to data, creative generation and activation in one place. Publish weekly lift by cohort. Expand only when lift is proven.

Warner expects adoption to accelerate significantly in 2026 as more vendors embed AI capabilities with proper guardrails. For brands like Blain’s Farm & Fleet, that future is already taking shape. They started with copywriting, proved value and are now expanding. The key was finding specific problems where AI could help and measuring whether it actually did.

AI will not fix a slow system. It will amplify it. Teams that modernize the way work gets done and lift the language of decisions will see the promise translate into performance.

As Blawat’s chocolate story reminds us, automation without judgment optimizes for the wrong outcome. The goal isn’t the cheapest product or the fastest campaign. It’s the one that serves customers while building the brand. That requires humans in the loop to point AI in the ri

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Google tests “Journey Aware Bidding” to optimize Search campaigns

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Is it time to rethink your current Google Ads strategy?

Google is preparing a new Search bidding model called Journey Aware Bidding, designed to factor in the entire customer journey — not just the final biddable conversion — to improve prediction accuracy and campaign performance.

How it works:

  • Journey Aware Bidding learns from your primary conversion goal plus additional, non-biddable journey stages.
  • Advertisers who fully track and properly categorize each step of their purchase funnel stand to benefit the most.
  • Google recommends mapping the entire journey — from lead submission to final purchase — and labeling all touchpoints as conversions within standard goals.

Why we care. Performance advertisers have long struggled with fragmented signals across the funnel. Journey Aware Bidding brings more of their conversion funnel into Google’s prediction models, potentially improving efficiency for long, multi-step journeys like lead gen.

Instead of optimizing on a single end-stage signal, Google can learn from every meaningful touchpoint, leading to smarter bids and better alignment with real business outcomes. This update rewards advertisers with strong tracking and could deliver a meaningful performance lift once fully launched.

What advertisers need to do:

  • Choose a single KPI-aligned stage (e.g., purchase, qualified lead) as the optimization target.
  • Mark other journey stages as primary conversions, but exclude them from campaign-level or account-default bidding optimization.
  • Ensure clean tracking and clear categorization of every step.

Pilot status. A closed pilot is due to launch this year for a small group of advertisers, with broader availability expected afterward as Google refines the model.

The bottom line. Journey Aware Bidding could represent a major shift in Search optimization: Google wants its bidding systems to understand not just what converts — but how users get there.

First seen. The details of this new bidding model was shared by Senior Consultant Georgi Zayakov on LinkedIn, amongst other products that were featured at Think Week 2025.

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Google offers a “less disruptive” fix to EU ad-tech showdown

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Google submitted a compliance plan to the European Commission that proposes changes to its ad-tech operations — but rejects calls to break up its business

How it works:

  • Google is offering product-level changes — for example, giving publishers the ability to set different minimum prices for different bidders in Google Ad Manager.
  • It’s also proposing greater interoperability between Google’s tools and those of rivals, in order to give publishers and advertisers more flexibility.
  • The company says these tweaks would resolve the European Commission’s concerns without a “disruptive break-up.”

Why we care. Google’s proposed “non-disruptive” fixes could preserve platform stability and avoid the turbulence of a forced breakup — but they may also shape future auction dynamics, pricing transparency, and access to competitive tools. In short, the outcome will influence how much control, choice, and cost efficiency advertisers have in Europe’s ad ecosystem.

Between the lines. Google is leaning on technical fixes rather than major structural overhaul — but critics argue that without deeper reform, the power dynamics in ad tech may not fundamentally shift.

The bottom line. Google is trying to strike a compromise: addressing the EU’s antitrust concerns while keeping its integrated ad-tech business intact. Regulators now face a choice: accept the tweaks — or push harder for a breakup.

Dig Deeper. EU fines Google $3.5 billion over anti-competitive ad-tech business

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