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Category: AI

How AI Revolutionized My Hreflang XML Sitemap Creation
I’ve witnessed AI tools become indispensable in automating complex processes that traditionally demanded a lot of manual effort. However, I’ve also seen them used without any real benefit just because they are available.

That’s why I prefer focusing on AI applications that save time and address genuine challenges.

Recently, I was tasked with aligning the SEO architecture for over a dozen websites across three separate businesses, eight regional domains, and numerous languages, including three English dialects, Italian, Japanese, Spanish, Thai, French, and Korean.

Mapping thousands of URLs to create seamless hreflang XML sitemaps traditionally required specialized software or extensive spreadsheet work. Instead, I used Google Gemini to develop a custom Python script to handle the heavy lifting.

Here’s how an initial prompt evolved into a fully customized automation tool and what it taught me about utilizing AI for technical SEO.

Where AI Delivers the Most Value

I leverage AI primarily for practical, time-saving tasks, including:
- Generating regex patterns when I need quick solutions without researching syntax from scratch.
- Creating complex spreadsheet formulas for reporting workflows that depend on manual data exports.
- Speeding up research and planning for projects requiring competitive analysis across business lines.
- Building custom automation tools for recurring SEO and data-processing tasks.
The hreflang project I discuss here fits perfectly into the last category.

Mapping hreflang at Scale

The challenge was straightforward: accurately map thousands of URLs across multiple multilingual websites into cohesive hreflang XML sitemaps.

I chose not to tackle this manually. Instead, Google Gemini helped me build a custom Python solution.

Here’s a walkthrough of how the process unfolded.

Phase 1: Asking for an Approach, Not Just a Script

One common pitfall of using generative AI for coding is asking it to sprint before understanding the course. Typing, “Write a Python script to create an hreflang sitemap,” will yield generic code prone to breaking with real-world data.

Instead, I started by asking for an approach. I detailed the scenario: multiple regional domains, organic growth over several years leading to mismatched URL slugs, translated subfolders, and appended revision years.

Gemini suggested a multi-step, data-driven approach:
- Crawl the websites to collect live URLs and their metadata.
- Use Python in Google Colab to process the raw data.
- Run an exact match cluster to group identical slugs.
- Use an advanced semantic AI model (like SentenceTransformers) to fuzzy match translated pages based on their titles and normalized URLs.
Phase 2: Crawling and Data Collection

Following the recommended strategy, I used a crawler to spider all regional websites to generate a unified CSV file with live URLs, status codes, title tags, and H1s. Screaming Frog proved ideal for this task.

The quality of AI output relates directly to the quality of your crawl data, so make sure it’s robust.

An AI script can miss an obvious “exact match” if a target URL is a 404 or a 301 redirect. Before feeding data into the script, filter your CSV to include only indexable content.

Dig deeper: International SEO in 2026: What still works, what no longer does, and why

Phase 3: The Google Colab Sandbox

Google Colab offers a free, cloud-based Jupyter notebook environment for coding, bypassing local installations or environment variable issues. I used Google Drive to access it. The free version sufficed for this project.

After uploading the CSV to Colab, Gemini provided an initial Python script that utilized a domain-mapping routine to assign language codes, clean the URLs, and generate an XML tree. The initial results required refinement.

Phase 4: The Iteration (Where the Real Work Happens)

If you expect AI to produce a flawless script on the first try, you’ll be disappointed. Like an intern, AI requires oversight. The true value lies in iteration.

After running the initial script, several unmatched URLs left orphaned pages rather than grouping them with international counterparts. Here’s how I iteratively guided AI through the complexities of human-managed websites.

The Directory Flattening Problem

The U.S. site had recently reorganized its blog into topical folders, unlike the Mexican and Italian sites. I presented these mismatches to Gemini, leading to a script adjustment that flattened directories, allowing slugs to align.

The Aggressive Semantic Trap

Concept traps we implemented were initially strict. A UK article about manufacturing wouldn’t match its Italian counterpart due to a slightly different title. By loosening these traps for general industries and enforcing them for critical terms, the AI became adept at delivering better matches.

The Translated Slug Epiphany

The pivotal insight arrived when examining Mexican blog orphans. A Spanish URL /detras-de-escenas-historias... matched the English /behind-the-scenes-stories..., which I pointed out to Gemini. As a result, Gemini updated the script to create a “Combined Semantic Signature,” dynamically translating slugs and efficiently bridging language gaps.

Dig deeper: Cultural SEO: A practical framework for Spanish markets in AI search

Lessons from Building an AI-Assisted SEO Tool

This project reinforced a simple truth: AI excels as a collaborator rather than a shortcut.
- Be the strategist, let AI be the coder: Rather than demanding a finished product, discuss architecture and logic first, treating AI as a junior developer needing guidance.
- Provide concrete examples: Don’t simply state, “It’s broken.” Give specific failed URL examples or mismatches to help AI refine its logic.
- Embrace the iterative loop: Run the code, identify issues, and iterate. Each iteration enhances the tool’s intelligence.
- Leverage Google Colab: You don’t need to be a Python guru to apply Python in SEO. Colab bridges the gap, providing access to complex data science libraries in your browser.
In the end, I had a fully customized Python script capable of processing a massive CSV to generate a cross-referenced hreflang XML sitemap in minutes.

Though AI isn’t replacing technical SEOs, those who collaborate with AI to build scalable tools will have a significant edge.

Dig deeper: How AI search defines market relevance beyond hreflang

Inspired by this post on Search Engine Land.
June 16, 2026
Transforming Content Strategy: AI Search & Engagement Unveiled

I often find myself pondering how AI is changing the landscape of content strategy, especially in the realm of SEO and citations. It’s fascinating to see this shift from merely retrieving information to creating engaging and citation-worthy content.

As I delve deeper into the evolving AI search mechanisms, it’s clear that content needs to provide a stellar user experience to earn citations from LLMs like Claude and ChatGPT. The focus should be on understanding where our readers and potential customers are in their journey.

My strategy now includes considering how third-party platforms perceive our brand. It’s all about consistent messaging, ensuring that AI systems like Google’s understand our brand identity, target audience, and the right moments to highlight our offerings.

Transitioning from traditional SEO to what I call “experience-based GEO” offers exciting opportunities. Instead of prioritizing SEO, I focus on creating content that speaks directly to our desired audience, ensuring our brand emerges in relevant queries.

I’ve learned that while some SEO fundamentals remain, LLMs emphasize customized user experiences. This means our content marketing should aim to resonate with individual preferences, not just optimize for search engines.

Consider this: although the client’s CEO and I share similar demographics, our wine preferences differ, indicating how personalized AI interactions have become. When I’m seeking wine suggestions from an LLM, the results are tuned precisely to my tastes, showing how AI can truly understand consumer desires.

Google is shifting too, leaning towards AI-driven personalized results. This means that I need to adapt my content, both on my site and on external platforms, to align with these new AI paradigms.

Creating a content strategy extending beyond just our website is crucial. RAG (retrieval-augmented generation) depends on authoritative sources, which means featuring our brand in trusted platforms is key.

For instance, ensuring our wine retailer clients get mentioned in niche articles with relevant talking points can help them stand out in this AI-driven content realm. I emphasize using media buys or PR for placements that matter to our buyer personas.

As an individual brand, focusing on listicles and strategic mentions where our unique selling points are highlighted is vital. This ensures our brand is noticed for the solutions we provide.

AI systems crave expertise. By continually positioning ourselves as thought leaders and reliable retailers, we enhance our reputation, allowing LLMs to recognize and trust our brand over time.

It’s clear that traditional SEO techniques aren’t obsolete; they’re evolving. Schema, server-side rendering, and appropriate content structure remain essential, helping AI systems fully grasp who we are and what we offer.

In essence, my focus is on making our site an easy-to-navigate space for both human visitors and AI systems. By surveying customers and understanding their needs, I can tailor content to align with what they truly seek.

Creating a seamless customer experience ensures that our offerings are clear to both users and search engines, potentially improving our conversions.

I’m committed to keeping up with the evolving landscape of LLMs and SEO. By maintaining consistency and adapting our strategies, we can ensure our brand remains relevant and ready for whatever technological advancements come our way.

Inspired by this post on Search Engine Land.

June 15, 2026
Unlocking AI’s Potential: How Unique Prompt Patterns Boost SEO
I’ve always been fascinated by the evolving nature of SEO, especially in an era dominated by artificial intelligence. For over twenty years, SEO heavily relied on keywords. But with the rise of generative AI and conversational tools like ChatGPT, we’re now seeing a shift toward prompts as the backbone of search visibility.

Understanding the prompts my audience uses with large language models is crucial. Otherwise, my content might never see the light of day in search results. Let’s explore how prompts vary by industry and their impact on search visibility.

How Prompts Differ by Vertical

It’s clear to me that the context holds paramount importance in the responses generated by large language models (LLMs). Different industries have specific patterns that dictate how users construct their prompts. I need to tailor my content to these unique frameworks to ensure maximum relevance.

Healthcare: Symptom-driven and Cautious Language
- In the healthcare sector, I’ve observed users leveraging AI as an initial triage tool. Instead of a vague term like “chronic fatigue,” detailed prompts narrate specific symptoms.
- The prompt pattern: These healthcare prompts are rich in personal context, symptom mapping, and cautious constraints. Questions often revolve around symptom lists and safety considerations linked to age or medication.
- Anatomy of a healthcare prompt: Consider a prompt like: “I’m a 45-year-old female experiencing sudden joint pain and a rash after starting [Medication X]. What side effects should I monitor, and when is it critical to seek medical help?”
- The content shift: To stand out here, my content cannot simply define medical terms. It must align with a patient’s decision-making process.
- The action: I focus on structured FAQs, clear risk factors, and headers addressing specific symptoms combinations to engage effectively.
B2B: Comparison-heavy and ROI-driven
- In B2B contexts, I see users turning to AI for detailed comparisons and ROI evaluations, bypassing traditional marketing materials.
- The prompt pattern: B2B prompts are analytical, featuring deep dives into financial justifications. Requests often include data for presentation-ready tables or matrices.
- Anatomy of a B2B prompt: Typical requests might be like: “Compare CRM ‘Brand A’ and ‘Brand B’ for a 500-user company, with implementation timelines and ROI over three years formatted in a table.”
- The content shift: Without transparent, data-rich content, my B2B efforts remain invisible to LLMs.
- The action: I need to publish open comparison pages with hard data, ensuring technical details are structured in an easily extractable format for AI systems.
Ecommerce: Intentional Clusters of ‘Best,’ ‘Cheap,’ and ‘Reviews’

The ecommerce landscape, as I see it, is an interactive shopping experience with AI-driven, personalized recommendations.
- The prompt pattern: Queries often combine quality markers like “best reviewed” with budget constraints like “under $150” within specific contexts.
- Anatomy of an ecommerce prompt: An example might be: “What are the best-reviewed running shoes for overpronators under $150, excluding brands with poor durability?”
- The content shift: Beyond simple keyword targeting, I must infuse my content with the semantic depth necessary for LLM validation.
- The action: I optimize my merchant feeds with conversational attributes, ensure crawlable user reviews, and connect product specs to consumer value.
Why Prompt Structure Impacts Your Search Visibility

Understanding why prompt structures matter is key for me. They shape whether my site appears in LLM responses, based on how a user constructs their inquiry.

The Power of ‘Reasoning Lift’ and Direct Citations

By optimizing for direct citations and structured data, I could boost the visibility of my content by up to 40%, according to research from Princeton and the Allen Institute for AI.

It’s intriguing how more than 80% of links in AI-driven searches come from domains not ranking in traditional top searches. This emphasizes the importance of content quality and structure over legacy backlinks.

Operationalizing Prompt Research

Shifting my focus from keywords to prompts is crucial. I need to revamp my content strategy to align with conversational search trends, ensuring my brand stays visible.
- Stop tracking isolated keywords: Instead, I’ll search for conversational data within search logs and consumer interactions.
- Audit for LLM readability: My content must be easily parseable by AI, underpinned by modern standards and structured data.
- Write for the follow-up: Rather than focusing solely on initial queries, I’ll anticipate and address follow-up questions within the same content.
To stay ahead, aligning my content with AI interaction patterns is non-negotiable.

Inspired by this post on Search Engine Land.
June 12, 2026
Harness Claude Code: Build a Second Brain for Agencies
Understanding how memory, search, MCP integrations, and AI skills come together to streamline agency workflows and eliminate context-switching.

If you work in an agency or manage clients, you probably know how quickly your morning can disappear into Gmail, Slack, and CRMs just to recall what mattered yesterday.

In the past, I would juggle decisions like pricing for my team, roadmap calls for our app, Slack threads, and urgent sales follow-ups, all before my first coffee.

Those hectic days are now behind me. About six months ago, I rebuilt my workflow using Claude Code as my second brain, and my Monday morning catch-up now takes just a minute.

Let me share what I built, why it’s been transformative, and how you can do the same.

Why Most Second-Brain Setups Break Down

The concept of a “second brain” isn’t new. Tiago Forte’s “Building a Second Brain,” PARA method, Notion, and Obsidian all capitalize on the same idea: externalizing memory.

Catching information is effective. The recall? Mostly. The real value lies in transforming recalled data into actionable tasks.

Most implementations fail in three ways:
- Passive storage. Information enters but doesn’t exit without a manual search and personal memory, especially meeting notes.
- Context-switching tax. Finding the right note involves copy-pasting and additional prompting before it becomes useful.
- No action layer. Without drafting or executing tasks, it becomes a burden of excess notes, leading to cognitive overload.
The issue isn’t documenting tasks but having those scattered in myriad apps without a unifying layer to read across them.

What truly saves time is a layer that can amalgamate all of this and turn it into action.

Dig deeper: How to turn Claude Code into your SEO command center

How Claude Code Changes the Equation

General AI assistants can answer queries but aren’t seamless with file systems or past interactions. Claude Code changes this with:
- Native file system access: It reads and writes within project folders, accessing local files directly.
- Persistent, structured memory: Remembers session data stored in curated Markdown files.
- MCP integrations: Directly connects with Gmail, Slack, Google Drive, HubSpot, Scoro, without altering workflows.
- An action layer: Drafts documents, analyzes data, and handles repeatable tasks in my workflow.
The most advantageous aspect is moving from mere storage to actionable insights, saving immense time.

The Four Layers of an AI Second Brain

I structured my second brain using four fundamental layers.

1. Memory

Stored in a small collection of Markdown files. They cover my work details, client preferences, decision-making data, and my desired AI persona.

These automatically load, eliminating the need to reintroduce context every session.

Memory self-expands, converting daily logs into long-term memory selectively for accurate client models.

2. Search

Minimizing memory size keeps daily logs indexed in a local database for quick retrieval of past conversations with full context.

3. Skills

Focused capabilities like drafting a brief or proposal, replying in my voice, or summarizing meetings. Small, purposeful, and memory-inherited.

Not an all-encompassing agent, but an adaptable assistant, growing daily with specific skills.

4. A Heartbeat

An hourly process checks emails, calendar, Slack, and pipeline activities, alerting me if intervention is needed with a summarized Slack ping and draft.

Dig deeper: How a ‘client brain’ gives AI the context SEO work needs

Where It Pays Back Hours Every Week

Here’s how it saves time:

Faster Context-Gathering for Client Work

When clients request updates, my second brain already compiles all relevant transcripts, threads, and notes, reducing my prep time dramatically.

Faster Data Analysis

From analytics to rank-tracking data, the second brain swiftly compiles the necessary context for review.

Discovery to Scope

New engagements once required lengthy exchanges. Now, the second brain formulates a scope based on past discoveries, reducing my workload.

Overall, this system enhances efficiency and service by ensuring critical information isn’t overlooked.

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The Guardrails That Make This Work

Such powerful tools need proper guidelines to prevent unintended actions by the agent.

Read-only by Default

Integrations begin read-only, seeing and drafting in tools like Slack and Gmail, without sending or committing.

Write access is carefully granted after evaluating its performance, reducing the risk of undesirable actions.

Memory Hygiene Matters

Resist storing everything. Long-term memory should affect agent actions—like pricing or preferred workflows.

Trust the Draft, Verify the Action

Always review drafts before sending them out. It’s not about removing yourself from the process but leveraging a head start with your expertise.

Dig deeper: How to train Claude to sound like your brand

How to Build Your Own Second Brain

You can customize your setup with preferred tools. Here’s the process I followed:
- Identify key decision-making tools—email, calendar, messaging, CRM, task tool.
- Incorporate a transcript layer for calls where essential context is discussed.
- Create a memory foundation with a ‘this is me’ file and a distilled daily log. Communicate until it feels familiar with your business.
- Add skills incrementally, starting with the most repetitive task.
- Integrate the heartbeat once retrieval and skills are working, starting with notification capabilities, then slowly adding write permissions.
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This is a Second Brain, But Don’t Let It Replace Your Actual One

The aim is not to replace your brain but to enhance efficiency in daily operations, creating more value for teams and clients.

These tools were non-existent 18 months ago, but now, they pay off setup efforts quickly.

Dig deeper: How to build custom SEO reports with Claude Code and Google Search Console

Inspired by this post on Search Engine Land.
June 12, 2026
Transforming Alexa: Your New Shopping Ally and Ad Hub

I recently discovered how Amazon is revolutionizing shopping with Alexa by turning it into a powerful tool for both purchasing and advertising. It’s fascinating to see how they are threading advertising into AI-driven shopping chats, opening fresh channels for brands to connect with customers.

Amazon is demonstrating that conversational and agentic shopping aren’t just futuristic ideas. They’re already changing the landscape of how we discover, compare, and purchase products today. It’s a compelling shift that makes shopping more interactive.

As AI assistants evolve into shopping hubs, advertisers can seize the moment when I express buying intent, rather than depending solely on traditional search methods or passive sites. This is a game-changer for both consumers and brands.

What’s happening: Amazon has cleverly merged its AI shopping assistant, Rufus, with Alexa+ to create an enhanced shopping experience named Alexa for Shopping. This service aids us in researching products, comparing options, tracking prices, building carts, and even automating purchases.

Advertising is a key component of this new experience. Integrated directly into our shopping dialogues are sponsored products, brands, and conversational ad formats, making it easier for brands to capture our attention.

What advertisers get: If you’re an advertiser, good news — your existing sponsored ad campaigns are automatically enabled to appear in Alexa for Shopping. The conversational ad formats also give brands a unique way to engage us throughout our buying journey. Tools like closed-loop measurement, first-party data signals, and AI-driven campaign optimizers make ad management more efficient.

Why we care: The integration of advertising into Alexa for Shopping provides advertisers with access to rich conversations filled with intent, from the moment of product discovery all the way to purchase. This means a potentially shorter path to conversion and enhanced metrics tracking.

The update also shows us how commerce and advertising are blending within AI assistants. This blend could potentially make our journey from product discovery to purchase smoother, while also offering advertisers comprehensive measurement abilities from the initial impression to the final purchase.

By the numbers: In 2025, more than 300 million customers used Rufus, according to Amazon. They also reported that nearly 20% of us engage in ongoing conversations about brands when prompted by Sponsored Brands, and those prompts lead to a 6% increase in conversions.

Between the lines: Amazon’s offering to advertisers is that conversational AI generates richer intent signals than traditional methods. Instead of decoding our needs from clicks or searches, Alexa can respond directly to our expressed requests, preferences, and purchase goals in our natural language.

The bottom line: As shopping turns more conversational, Amazon is integrating advertising within the same platforms we use for product research, option comparison, and purchase finalization.

Inspired by this post on Search Engine Land.

June 11, 2026
Google Held Accountable for False AI Claims in Germany

Recently, a German court ruling caught my attention because it asserts that Google can be directly liable for false claims made in their AI Overviews. The Regional Court of Munich’s decision highlights a significant shift, considering AI-generated summaries as Google’s own content rather than just protected search results.

This ruling emerged from a case where AI Overviews mistakenly linked two Munich publishers to scams and dubious practices, despite the linked pages containing no such evidence, as reported by The Decoder.

AI Overviews are not just search tools. According to the court, these Overviews go beyond merely assisting users in finding third-party content. They actually process and present information in their own distinctive manner.

What struck me was the court’s findings that the AI Overview allegedly made standalone accusations regarding questionable business practices, which were not substantiated by the linked sources. Because Google crafts and controls these features and their algorithms, the court ruled these statements to be Google’s own content.

Traditional search protections didn’t apply here. Google argued that they should be protected by German case law, which generally shields search engines as indirect infringers. However, the court disagreed, emphasizing that AI Overviews are distinct as they generate new statements from multiple sources.

The court also dismissed Google’s argument that users could verify claims by reviewing linked content. They highlighted that AI Overviews offer claims that stand as complete answers without needing verification.

Why does this matter to me? The court’s stance implies that AI Overviews aren’t neutral links. If they issue incorrect claims about a company, Google may bear direct responsibility for these words.

Mismatched connections and misinformation. The court determined that misinformation resulted from AI conflating data about other entities with that concerning the publishers.

Given that the contested claims weren’t present on the linked sites, the publishers lacked a clear third party to target legally, should Google be considered only as an intermediary.

Interestingly, the court insisted that Google could compare AI-generated content against primary sources, at least in analogous situations.

Action required from Google. The injunction demands that Google refrains from repeating the disputed claims, which include allegations of scams and nonexistent business practices.

Furthermore, Google is instructed to bear 80% of the legal costs, while each publisher covers 10%. Despite Google’s lack of a cease-and-desist declaration with a penalty clause, the potential for repeat violations was noted, emphasizing the importance of this ruling for future similar claims.

Inspired by this post on Search Engine Land.

June 10, 2026
Mastering Prompt Tracking: Strategies for Accurate AI Insights

I’ve come to realize that prompt tracking is often misunderstood as mere noise, but it’s actually a golden opportunity to refine AI interactions through a structured approach.

AI responses can be unpredictable. However, by utilizing repeated runs, establishing fixed sampling rules, and calculating confidence intervals, we can transform variance into a trustworthy metric.

By embarking on this journey with me, you’ll soon be equipped to create a reliable AI tracking system.

You’re already ahead if you’ve embraced persona-based prompt design as discussed in Synthetic Personas for Better Prompt Tracking.

For those immersed in AI SEO strategies, understanding the true trajectory of your efforts over the noise is crucial. Explore more with How Much Can We Influence AI Responses.

While many have dismissed prompt tracking due to its variability, I’ve discovered that it mirrors the unpredictability seen in weather forecasts and credit scoring, which are still meticulously tracked.

Reflecting on keyword tracking’s evolution, I see a parallel path for prompt tracking, which requires adapting its methodology to account for the numerous platforms now at play.

At pivotal industry events, experts speak of a shift from single search queries to a conversational model, emphasizing the changing landscape we must adapt to.

The shortcomings of current prompt-tracking tools are evident in their lack of innovation, yet I believe we can rise above with a more strategic approach.

Although single-turn prompts provide limited insight, constructing full conversational sequences reveals persistence, a vital metric often overlooked.

Imagine tracking a B2B SaaS CRM journey through defined stages, extending prompts to capture decision-making across multiple touchpoints to truly gauge influence.

HubSpot’s visibility across platforms like ChatGPT and Perplexity illustrates the nuanced understanding needed to strategize investments in brand-centric content.

The future of prompt tracking resembles opinion polling, employing systematic and repeatable methodologies to extract meaningful data amidst variability.

This piece first appeared on the author’s website and is shared with permission here.

Inspired by this post on Search Engine Land.

June 10, 2026
Publishers Demand Halt in AI Data Collection by Common Crawl
Could AI be losing a crucial source of its training data? As a major shift looms, significant publishers are urging Common Crawl to pause its collection and distribution of their content for AI training.

Digital Content Next (DCN) has sent a cease-and-desist letter to the Common Crawl Foundation, asking them to stop scraping and sharing protected publisher content.

Representing leading digital publishers like the AP, the New York Times, NBC Universal, Bloomberg, NPR, and Fox, DCN is also insisting that Common Crawl remove its members’ content, including paywalled and subscriber-only news articles, from its datasets.

Concerns Over Opt-Outs: Questions arise regarding Common Crawl’s adherence to publisher opt-out requests. Specifically, DCN’s lawyers are scrutinizing whether previous statements about compliance—often citing technical costs and delays—were perhaps misleading.
- The registry maintained by Common Crawl does list sites opting out, including several prominent news organizations.
Claims of Infringement: DCN firmly holds that copyright isn’t an opt-out system. They allege Common Crawl has been “flagrantly infringing” on publisher copyrights by distributing protected content without authorization or compensation.
- The group further critiques how Common Crawl shares this content with AI developers.
- DCN’s CEO, Jason Kint, signifies this legal action is a stance against the notion that online content is available for unrestricted collection, storage, and reuse.
Common Crawl’s Defense: Rich Skrenta, the Executive Director, denies allegations of bypassing paywalls and misleading publishers. He references a prompt and technical response to remove previously crawled content upon request.
- “Our removal process aligns with our dataset’s technical framework,” Skrenta explains.
Importance of This Battle: The outcome of this dispute could drastically influence the scope of publisher content that AI search engines use without explicit permission. Should there be heightened consent requirements, licensed sources may prevail, reducing reliance on openly available web content.

The High Stakes of AI Training: Established in 2008, Common Crawl has amassed billions of webpages to form a free public repository, a vital tool for training AI models. Notably, The New York Times’ lawsuit against OpenAI in 2023 cited that Common Crawl comprised 60% of GPT-3’s training data, as reported by Press Gazette.
- A 2024 Mozilla Foundation paper found generative AI would scarcely exist today without Common Crawl.
- Common Crawl’s ongoing efforts to create AI crawling standards indicate a willingness to adapt, yet DCN calls for decisive action—fully halting the scraping of protected content.
Inspired by this post on Search Engine Land.
June 10, 2026
Unlocking AI Search and Ads: Insights from Ginny Marvin

After Google Marketing Live, I’m still left with a lot of questions, and I’m sure I’m not the only one. Thankfully, Ginny Marvin, Google Ads Liaison, joined a comprehensive Q&A with Julie Bacchini and the PPC Chat community to tackle big topics like AI Max, AI Search ads, first-party data, and more.

The discussion was enlightening, bringing clarity to AI Search eligibility, reporting challenges, and Google’s increasing focus on data quality.

AI Max: Not a Must-Have for AI Search Ads

A major revelation was that AI Max isn’t required for participating in AI-driven search experiences. This surprised many of us, as we’d assumed AI Max was crucial for tapping into Google’s AI search surfaces.

Ginny highlighted that campaigns with broad match keywords are still eligible for AI Overviews and AI Mode. Even so, AI Max does broaden possibilities by treating phrase and exact match keywords with broad match behavior and enabling keywordless matching.

This means there are still multiple avenues available for us to access AI Search inventory.

AI Search Reporting is Still on Hold

Many of us were eagerly hoping for detailed reporting on AI-powered search results. However, Ginny confirmed that current ads in AI Overviews and AI Mode are reported like other top-of-page ads, with no distinct breakdown. Google’s still figuring out what these reports should eventually look like.

This leaves us with limited insights into how much AI-driven traffic and performance we’re actually seeing.

Google’s AI Brief: A New Layer of Control

A significant part of the discussion circled around AI Brief, set to become the control layer for AI Max campaigns. Advertisers like me will soon be able to provide specific guidance such as “never mention prices” or define target audiences, message themes, and search intents to prioritize.

The rollout will start with English Search campaigns and eventually spread to Performance Max and Shopping campaigns.

For those of us worried about automation reducing our control, AI Brief offers a promising solution.

The Core of Effective Advertising: First-party Data

If there’s anything I walked away with, it’s the emphasis on data quality, particularly first-party data. Google’s focus is what they call “Data Strength,” and tools like Enhanced Conversions and Google Tag Gateway are pivotal.

It’s clear: better data enhances AI performance and outcomes.

Exploring New Metrics: Qualified Future Conversions

Another fascinating development is Qualified Future Conversions (QFC). This metric estimates potential conversions occurring within 180 days post-ad interaction. It’s especially useful if you’re in B2B or lead generation sectors with lengthy sales cycles.

Currently, it’s in testing with select advertisers, and I’m keen to see it roll out further later this year.

Key Areas of Excitement at Google

When asked about her personal highlights from GML, Ginny shared three areas: the new ad formats for AI Search, measurement innovations like QFC, and YouTube Creator Partnerships.

This truly illustrates where Google is investing: AI discovery, advanced measurement, and creator-driven advertising.

Putting It All Together

This Q&A has definitely filled in some gaps left by the GML presentations. I’ve realized that broad match terms still provide a pathway to AI Search, AI-specific reporting is evolving, and Google’s vision continues to be centered on automation, powered by first-party data.

Most importantly, it’s about balancing automation with new controls like AI Brief to shape Google’s AI systems to our advantage.

Inspired by this post on Search Engine Land.

June 10, 2026
Unlocking AI Power: Conductor’s AEO Meets Optimizely

I’ve been truly amazed at how Conductor’s AEO intelligence is now seamlessly integrated into Optimizely, providing a powerhouse of pre-built agents that are all set to take quick action.

The fusion of these two technologies feels like having an AI ally in my corner, transforming visibility into actionable insights with remarkable efficiency. It’s a game-changer for anyone serious about leveraging AI in their optimization strategies.

The integration is not just powerful; it’s incredibly user-friendly, making it easier than ever to harness the full potential of AI-driven insights directly within Optimizely’s platform.

Inspired by this post on Conductor Blog.

June 10, 2026

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