During a recent presentation, I was thrilled to learn about Microsoft’s latest tease regarding new AI reporting features in Bing Webmaster Tools. These updates aim to enhance the existing AI performance reports, offering fascinating insights into citation share, query intent grounding, and GEO-focused recommendations.
I stumbled upon shared screenshots from this intriguing presentation delivered by Krishna Madhavan at SEO Week in the bustling city of New York. Azeem Ahmad captured the essence of this moment, highlighting the growing transparency gap between Bing and Google.
Intriguing Details: The presentation shared several slides showcasing these promising new features. One can feel the excitement building within the SEO community as these innovations hint at a more insightful way to track AI interactions.
Bing Webmaster Tools just dropped some VERY COOL stuff at #SEOWeek 2026
Stay Tuned: While these features aren’t live just yet, catching a glimpse of them was very promising. It seems Microsoft is ramping up to offer more ways to navigate AI-driven search results.
Why This Matters: Gaining more transparency on how our content performs in AI search results is invaluable. I eagerly anticipate the day when these tools go live, promising greater clarity and control over AI interactions.
At the moment, details on the exact functionality and release timeline remain vague. I will certainly keep my eyes peeled for further updates to better understand their full potential.
I’ve noticed that AI systems are improving in generating Spanish language content, but they’re not quite grasping the nuances of Spanish markets.
In fact, we often see a familiar trend: over 20 Spanish-speaking nations reduced to a single standard. Spain is typically the default, and Mexico might as well be interchangeable with any other country. The rest get simplified into statistical norms.
The root of this problem is structural, involving dialect defaulting, format contamination, and regulatory hallucination. These issues are more pronounced in a generative search setup where one synthesized response replaces several search results.
This misinterpretation acts as a barrier to visibility. Generative AI seeks clarity, and if my content doesn’t specify its market context, it defaults to an average—leading to missed opportunities and misapplication.
To tackle this, I’ve developed a framework that ensures market context is clear across content, technical indicators, and retrieval systems, so AI systems don’t have to assume.
What is Cultural SEO?
Cultural SEO goes beyond mere multilingual support or localization. Its foundation is firm on locale precision—ensuring the market context is clear in retrieval and generation practices so that your Spanish content is associated with the specific country it was intended for.
Here’s a framework that proves effective when working around Spanish and Latin American markets.
You can’t effectively optimize for a market you aren’t serving. Cultural SEO isn’t an afterthought; it’s the backbone of a strategic decision to genuinely operate within a market, encompassing logistics, customer service, compliance, and product-market alignment.
If you ship from Spain to Mexico with unrealistic delivery times or lack local support, even the best hreflang configuration won’t suffice. Users will abandon such experiences, and as AI learns from these interactions, it will deprioritize similar content.
Speaking the market’s language goes beyond spoken words—it’s about conveying trust, ensuring payment and delivery expectations are met, and adhering to regulatory standards.
Assuming you’re committed to these standards, here are the four pillars: segmentation, transcreation, retrieval constraints, and entity reinforcement. Before applying any framework, ensure this commitment.
Pillar 1: Market Segmentation at the Entity Level
International SEO often considers segmentation as a mere folder structure: /es-es/, /es-mx/, /es-ar/, but that’s merely scratching the surface.
In generative search, the challenge is ensuring the AI associates a page with a specific country like Mexico, and accumulates enough market-specific signals to prefer it over a general alternative. If the architecture simplifies differences, visibility diminishes equally.
Pillar 2: Transcreation, Not Just Translation
Translation is about converting words, while transcreation is about interpreting meaning. Given two pages with 95% similar content, the AI merges them into one representation—defaulting to one perceived as standard. Therefore, differentiating with local examples or unique terminologies is essential.
Pillar 3: Retrieval Constraints
In constructing AI experiences like RAG (Retrieval-Augmented Generation), it’s crucial to establish clear boundaries about what content should be sourced for specific markets to avoid defaulting to “Global Spanish.”
Pillar 4: Market Authority Through Entity Reinforcement
AI models learn from both your site’s content and external perceptions. Thus, building location-specific authority through local media presence, partnerships, and consistent regional knowledge graph reinforcement is vital to establish market-specific authority.
Ultimately, Cultural SEO ensures that content not only serves the market but resonates with it. By embracing these pillars, I can ensure my brand isn’t just another “Spanish” entity but a recognized authority in each targeted market.
This journey isn’t about merely adapting your website but architecting systems to reflexively consider the market’s dynamics from the ground up.
I’m excited to introduce you to the innovative iteration nodes in Profound Agents, designed to revolutionize the way we manage complex workflows.
The beauty of the iteration node lies in its ability to encapsulate a series of steps within your Agent. By setting up these steps just once, I can easily pass in a list of items, and watch as each item seamlessly progresses through the specified sequence, simultaneously.
About a year ago, I found myself walking out of a meeting with engineers focused on enhancing automations for content briefs. Just days after that encounter, someone from the analytics team — who hadn’t even been part of those conversations — surprised me with a tool they’d developed. This tool could generate content briefs using various data pipelines and APIs.
That moment was a revelation for me. Encouraging people to adopt AI isn’t the real challenge; it’s the actual implementation and seamless integration that pose difficulties.
I frequently observe that most SEO teams, including mine, aren’t short on tools. What we struggle with is prioritizing high-impact efforts and achieving alignment within the organization.
In our team, one group might experiment with prompts while another auto-generates briefs, and yet another constructs dashboards no one requested — often resulting in us overlapping each other’s work. Each team contributes something valuable, but duplication tends to dilute the efforts, and everyone races toward execution.
Leadership demands speed; legal teams push for caution; developers need clarity.
The result is often fragmentation, which is not the transformation AI marketing teams require. For AI to have a significant impact on SEO performance, it must be well-structured before scaling; otherwise, this fragmentation only grows.
Through my experience working with large, complex organizations transitioning in this space, I have identified three frameworks that consistently prevent chaos and create momentum. When applied together, they help us align our vision, clarify what we automate, and transition prioritization into execution.
The biggest barrier to adopting AI is coordination. SEO already resides at the crossroads of engineering, content, analytics, products, and branding. With the inclusion of AI and the emergence of social search, we now have to factor in organic social, conversion rate optimization, affiliates, and creativity.
AI spans all these areas, but it’s too extensive for any single person or team. Without a shared mental model, teams tend to drift apart, duplication seeps in, and accountability becomes vague, transforming AI into a race rather than a productivity enhancer.
In leading large teams and collaborating with numerous Fortune 100 executives, I learned how analogies help teams grasp complex ideas quickly. Research supports that analogies improve understanding and the transmission of ideas across different domains. When teams map new concepts onto familiar structures, alignment accelerates.
Introducing: the AI SEO City. Instead of describing AI as a series of tools and experiments, envision your SEO ecosystem as a bustling city.
Think of your website as an SEO house that no longer operates in isolation. Technical SEO creates the foundation. Content hubs define the interior. Off-site SEO offers the curb appeal. User experience provides the staging.
With AI search, this house is now more integrated with a broader city. Platforms like TikTok, Reddit, YouTube, and Amazon shape the responses AI systems deliver.
To thrive in AI search, this city requires a strong planner to advocate for budgets, plan future steps, and maintain effective strategies. Here, the SEO team acts as the planner, while other teams build and manage their respective “buildings.”
The transition from analogy to action centers on ownership. Every major platform becomes a building.
Each of these buildings has a leader, performance indicators linked to business outcomes, AI-enhanced workflows, and a roadmap, making AI projects tangible, accountable, and coordinated.
After aligning our vision, many teams make the mistake of trying to automate everything. This indiscriminate automation creates fragility.
If your go-to person for automation leaves, you risk losing both business processes and valued work. That’s why I use the SOAR framework to navigate smart adoption.
To truly integrate AI, streamlining the basics is crucial. Having robust, standardized processes before incorporating AI can significantly enhance its effectiveness. According to McKinsey’s 2023 State of AI report, organizations that have already digitized and standardized core workflows gain the most from AI.
In my own experience, the easiest and most valuable automations accelerate predefined manual processes. Therefore, my team’s policy has always been to engage in manual tasks before attempting automation.
AI adoption necessitates cross-functional collaboration, making it essential for SEOs to orchestrate teams efficiently across the organization. Revisiting AI SEO City ownership insights can help clarify review processes, QA ownership, and publishing governance.
Establishing regular checkpoints, such as weekly SEO syncs with diverse teams, monthly performance reviews, and quarterly roadmap alignments, encourages consistency and diminishes resistance.
AI has the potential to save people approximately four hours a week, which equates to about 200 hours a year — roughly five weeks.
It’s crucial to utilize AI for tasks like metadata drafting, monthly report insights, FAQ expansion, internal linking suggestions, keyword clustering, and SERP analysis, thus freeing time for executing high-impact tasks.
AI implementation should eventually free up strategists to coordinate across teams, bridge the gap between strategy and business impact, map out enhanced customer search journeys, and anticipate AI search trends.
Google has announced billions of monthly AI Overview users, which has fundamentally altered how queries are presented. Now is not the time to manually write metadata; instead, it’s time to build your AI SEO City.
Even with smart automation and alignment, the chaos resurfaces when prioritization becomes lax. RISE helps pressure-test whether an initiative deserves investment by focusing on reach, intent, scale, and execution.
The RISE framework helps me assess whether an initiative truly warrants resources.
Reach requires you to quantify potential upsides before building anything. You must move beyond gut feelings or trending topics to focus on modeled opportunities based on specific questions.
If positive business impact isn’t numerically clear, it shouldn’t proceed. This approach discourages vanity projects mistakenly labeled as innovative developments and focuses on your leadership and strategic instincts instead of mere tinkering.
Intent drove AI search systems to reward depth over generic content. You need to be able to ask the right questions to ensure each strategy serves the correct purpose.
Scale involves verifying whether an idea can become part of the operating system without repeated effort. In AI-driven SEO, scale is about creating structural efficiencies.
Finally, embedding strategic initiatives into workflows where work actually happens transforms great ideas into real results. Defining acceptance criteria and assigning ownership are crucial steps towards successful execution.
By rigorously applying the RISE framework, the number of AI ideas may decrease, but the quality improves exponentially. Instead of debating which tool is better, the conversation shifts to identifying the right opportunities.
Ultimately, structure matters more than speed when integrating AI into SEO strategies. The winning teams won’t be those generating the most content through AI, but those constructing the strongest systems.
As someone deeply interested in how technology shapes our interactions, I found Google’s new AI developments in search particularly fascinating. Google’s VP of Search, Liz Reid, recently delved into how AI is transforming search intent, monetization, and content visibility. In a new Bloomberg podcast, she explained how these changes are reshaping our search behavior.
Reid assured us that AI is not diminishing Search but altering its usage. AI Overviews now help filter low-value clicks while encouraging more frequent searches. Reid highlighted how AI reduces “bounce” clicks, those quick visits to a page for a single fact. It’s an interesting evolution—sometimes we only have seconds to spare, while other times, we aim to immerse ourselves for longer periods.
People Want AI and the Web Together
Reid debunked the myth that users desire AI over the web. Instead, she notes, people want AI integrated into their web experience. I see this pattern in my own browsing habits, where I might search for a quick fact one moment and dive deeply into an article the next. She emphasized that people still crave human perspectives and diverse insights.
AI Overviews: Adapting to User Needs
Liz Reid explained that AI Overviews aren’t activated for every search. Google’s strategy is user-centric, providing AI support only when it’s beneficial. This selective approach ensures we get the best possible answer for our queries. The system evolves as user behaviors change, and Google continually refines which queries deserve an AI Overview.
Changing Search Habits
It’s intriguing to note the shift in how we query Google. Searches have become longer and more conversational, moving away from terse keywords. In my own searching, I now use full sentences to express my needs, which aligns with Reid’s insights. She reiterated that users now articulate their problems more clearly, allowing Google to provide comprehensive responses.
Ads and AI: A New Dynamic
Even with AI-enhanced answers, Google can still generate revenue from Search, assuring us that the commercialization of queries largely remains unaffected. When I’m on the hunt for products, such as buying shoes, I still rely on ads to guide my purchasing decisions. Reid also highlighted that detailed queries offer potential for more targeted ads.
Monitoring User Retention
Reid highlighted that a key metric for Google is whether users return to Search more frequently. This is more than just increased search volume; it’s about building a loyal user base that turns to Google consistently because it meets their needs effectively.
AI Slop: Addressing Content Quality
Interestingly, AI hasn’t introduced new content quality issues but rather increased its volume. Reid assured us that Google’s aim is to spotlight quality content while minimizing the visibility of “slop.” It’s a challenge, but one that Google is committed to tackling by continually enhancing its ranking systems.
As I dive deeper into the world of AI, I’ve come across something truly fascinating about how query language is changing the landscape of AI citations. In our analysis, Profound looked at an astounding 3.25 billion citations spread across seven AI models and fourteen countries. What the data revealed was mind-blowing: the language used in queries is the main catalyst reshaping citation rates across different AI platforms.
Interestingly, I noted that AI tools like Google AI Overviews and ChatGPT handle non-English prompts in uniquely distinct manners. This variation has far-reaching consequences for brand visibility on a global scale, especially within the realms of AI search. The differences in response patterns not only highlight the power of language but also impact how brands are perceived worldwide.
Have you ever wondered how AI manages to stay grounded in reality? As I delve into the fascinating world of LLM grounding, I uncover how AI models maintain their accuracy, and why this is crucial for your brand’s visibility and success across platforms like ChatGPT and Gemini.
Understanding how AI functions in this way is not just about technical curiosity; it’s about knowing how to leverage these tools to enhance your brand’s presence and credibility online. Join me as I explore the role of LLM grounding in shaping AI’s effectiveness and reliability.
I’m thrilled to share that Yelp has just introduced a powerful AI update, bringing a new level of ease to local searches and bookings with their conversational “Yelp Assistant.” This tool is designed to help me move from searching to booking, ordering, and scheduling—all in one seamless experience.
Discover What’s New. At the heart of this innovation is the Yelp Assistant, a chatbot capable of answering complex questions, recommending businesses, and even making reservations or appointments without me ever needing to leave the app.
How It Works. The assistant taps into Yelp’s vast database of user reviews and photos to offer tailored recommendations, explain why a business is a good fit, and allow me to refine results in a conversational manner. It takes things to the next level by letting me book a table, order food, or request a quote without needing to switch platforms.
What Else Is New. Yelp is also enhancing integrations with platforms like Vagaro, Zocdoc, and Calendly, which streamlines bookings in categories such as beauty, healthcare, and home services. Plus, they’re strengthening their partnership with DoorDash for smoother delivery options.
Spotlight on Menu Vision. Another exciting feature is the revamped “Menu Vision,” which uses AI and visual overlays to display dishes, reviews, and photos in real time while I’m browsing a menu. This makes deciding what to order quicker and easier.
Why This Matters. For someone like me looking for convenience, Yelp is transforming from a simple discovery platform into a full-fledged transaction experience powered by AI. This means that while visibility remains important, businesses need to ensure they’re optimized for conversions right within the platform.
The Bigger Picture. By focusing on AI not just for discovery but also conversion, Yelp is turning intent into transactions without redirecting me elsewhere.
Looking Ahead. The assistant is now live on iOS and Android, with plans to expand further across more categories and desktop later this year.
When it comes to Google Ads management, I’ve always followed the same routine: logging in, evaluating the performance, making updates, and crossing my fingers for success.
Despite advances in technology, from spreadsheets to automated bidding, the fundamental process hasn’t changed—until now.
Today, groas is shaking things up with a new, fully autonomous model for managing campaigns. The aim? To seamlessly handle the entire advertising process without constant manual input.
This revolutionary system has been in the making for years. Our company has developed an AI-driven approach that runs 24/7, matching or even exceeding industry benchmarks in PPC performance.
From building a campaign to managing bids, creating ad copy, and expanding keywords, this AI network takes care of everything autonomously.
When we first launched groas as a lightweight platform, it primarily provided optimization tips. But the true game-changer came from real-world data.
Early adopters joined from various industries, providing invaluable data that shaped groas into the powerhouse it is today.
Thanks to this diverse data from real campaigns, our AI has become skilled at understanding what truly works.
Our founder, David Pourquery, once shared the frustration of valuable recommendations sitting idle, awaiting approval. Now, our system makes those changes automatically.
We recently overhauled our system, creating interconnected AI agents that process mountains of data every hour, lifting the limits of manual management.
Ads management tasks are automated, allowing human professionals to focus on bigger strategic goals. groas delivers dynamic landing pages through a single JavaScript line, enhancing conversion rates continuously with A/B testing.
I don’t have to check in daily. Weekly reports summarize the autonomous progress while a human PPC manager supervises it all.
Starting off with groas is quick and easy. My personal account manager handles the setup, providing a detailed action plan within a day.
groas now autonomously manages significant monthly ad spends, all through word-of-mouth and direct referrals—without a dime spent on advertising.
Our client base includes businesses seeking consistent results and agencies leveraging groas for streamlined campaign execution.
With Google’s lean towards automated ads, groas offers a unique, fully autonomous solution that maintains strategic involvement through a dedicated manager.
The industry has long debated automation degrees in PPC. groas answers by fully automating while managing extensive ad spend.
groas has transcended traditional approaches; we’ve reduced the need for recommendation engines entirely.
Our services start at $999 per month, scaling as needed. This model requires a minimum $2,000 monthly ad spend to optimize data effectively.
As I look around, it seems like everyone is scrambling to harness AI’s power. However, I’m realizing that fundamental identity gaps and issues like fraud and unreliable inputs are not getting resolved, but rather they are magnified by AI models.
AI has quickly become one of the most confidently discussed items in our modern marketing strategies. Budgets are reallocated, teams restructured, and vendors evaluated primarily by how “AI-powered” they appear. The belief is strong that once the right AI models are in place, performance metrics—such as targeting, segmentation, and conversion—will simply fall into place.
Yet, I’ve discovered a quieter truth. While organizations aren’t necessarily struggling with using AI, they face challenges feeding it adequate data. And often, the data they are supplying AI isn’t nearly as reliable as assumed.
This realization leads me to the uncomfortable truth about inputs. AI doesn’t produce truths; it magnifies what’s provided. If data is fragmented, outdated, or manipulated, AI doesn’t correct it—it scales it confidently.
Marketers have invested heavily in data infrastructures, only to find that an abundance of data and signals doesn’t necessarily equate to readiness. Large volumes do not guarantee validity. For instance, customer profiles built from various identifiers don’t assure a unified identity, and AI models are not inherently designed to question these flawed inputs.
Identity is at the core of this issue. Every AI-driven marketing effort assumes accurate identity for analysis and targeting, yet identity remains a fluctuating component in our data stacks. Consumers frequently move across devices and change profiles, making it tricky to track accurately over time. However, most systems treat a snapshot identity as a constant, and AI inherits this flawed assumption.
Additionally, not all data issues stem from outdated sources. Some are intentionally deceptive due to evolving fraud tactics, becoming more challenging to distinguish without additional context. Fraudulent behavior can significantly distort model outputs and performance metrics, creating a feedback loop where AI unintentionally perpetuates the very issues it should mitigate.
Traditional data strategies often focus on structure over substance, and clean data doesn’t equate to accuracy. AI demands an in-depth understanding of identity validity, activity authenticity, and risk awareness, which traditional strategies may overlook.
The illusion of AI readiness becomes apparent when dashboards show excellent match rates and models yield seemingly precise outputs. However, metrics of identity reachability and engagement accuracy become crucial yet often disregarded questions.
True AI readiness starts with ensuring that our data inputs are trustworthy. It focuses on verifying identity accuracy, validating meaningful activities, and acknowledging risks rather than simply accumulating data records.
By addressing these foundational elements, organizations can suppress low-value identities, optimize outreach, and mitigate misuse before it skews results. Over time, this creates a structural advantage for AI operations, leading to more reliable predictions and efficient campaigns.
I’ve come to understand that AI’s impact on marketing is undeniable, yet it cannot independently resolve inherent data challenges. Organizations need to prioritize and invest in understanding the integrity of their data systems.
The real question isn’t about applying AI but assessing whether our data is worthy of AI. This deeper level of scrutiny defines true readiness and distinguishes the truly prepared from those merely rushing ahead.
