I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.
You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.
The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.
When I attended Google Marketing Live 2026, I witnessed firsthand how Gemini is reshaping the world of Search, advertising, commerce, and measurement. The event highlighted the move towards a more conversational, AI-driven ecosystem.
This year, the focus was on agentic AI, conversational Search, automated creative production, and AI-assisted shopping. Google rolled out tools across Search, YouTube, Merchant Center, and Analytics aimed at making campaigns more autonomous, predictive, and interconnected.
Let me take you through the biggest announcements from Google Marketing Live 2026.
Google Introduces a New Generation of AI-Powered Search Ads
Google rolled out new Gemini-powered ad formats that enhance AI Mode and conversational Search experiences.
The updates include:
Conversational Discovery ads
Highlighted Answers
AI-powered Shopping ads
Business Agent for Leads
These innovative formats are crafted to be more contextual and interactive by embedding AI-generated explanations and conversational experiences directly into Search journeys.
Plus, Google expanded its Direct Offers pilot with AI-generated bundles, native checkout, and travel promotions seamlessly integrated into AI-assisted Search experiences.
Google Launches Ask Advisor Across Ads, Analytics, and Merchant Center
At the event, Google introduced Ask Advisor, a Gemini-powered AI collaborator that bridges Google Ads, Analytics, Merchant Center, and the Google Marketing Platform.
It functions as a unified assistant to help marketers:
Build campaigns
Analyze performance
Receive recommendations
Automate operational tasks
Google assures that Ask Advisor expedites the process from planning to optimization by pulling insights across platforms.
Google Upgrades Measurement with Meridian and Predictive AI Tools
Google announced new tools for measurement and forecasting within Google Analytics 360.
Meridian, an open-source marketing mix model, is being integrated directly into Analytics 360, along with Qualified Future Conversions (QFCs), a predictive reporting metric powered by Gemini.
These tools will assist advertisers in:
Improving media mix modeling
Forecasting campaign outcomes
Measuring incrementality
Linking current ad activity with future revenue signals
I’m excited to share that Google is testing new conversational ad formats, powered by Gemini, across AI Mode and Search. This development is aimed at making ads more contextual and engaging, bringing a fresh approach to advertising.
The introduction of these Gemini-powered formats was revealed at Google Marketing Live 2026. With these new ad experiences, ads are intended to feel more conversational, contextually relevant, and genuinely helpful to users like you and me.
Driving the news: Google announced exciting additions to AI-powered Search ads. These include Conversational Discovery ads, Highlighted Answers, AI-powered Shopping ads, and the Business Agent for Leads. All these are part of Google’s strategy to integrate Gemini deeper into its Search and advertising framework.
Conversational Discovery ads are really innovative! Imagine asking a question about making your home smell like a spa, and right there in AI Mode, you see creative solutions generated with Gemini that perfectly match your query.
How it works: Google’s Gemini models analyze what you’re really asking and create ad content that fits the conversation. These ads come with an AI explainer that helps you understand the product or service better, integrating it with what the advertiser wants to tell you.
I’m particularly intrigued by the Highlighted Answers, where relevant ads pop up right within AI-generated recommendations. It feels like a natural extension of the conversation!
Additionally, Google is rolling out AI-powered Shopping ads for significant purchase decisions like buying a new TV or home appliance. Gemini steps in to create unique explainers that highlight why a product might be perfect for your needs.
Business Agent for Leads takes interactivity to a new level by embedding an AI chat experience in lead generation ads. Instead of completing static forms, you can chat with a Gemini-powered agent to learn more, directly informed by the sponsor’s website.
Moreover, Google is expanding its Direct Offers pilot, bringing features like promotion bundling, native checkout for UCP merchants, and AI-generated offer recommendations to the table. This ensures offers are tailored to what you might actually be shopping for!
Why we care: These updates represent a paradigm shift in how ads are rendered in AI-powered Search ecosystems. By focusing on conversational discovery and intent-rich interactions, I believe Google is paving the way for advertisers to better connect with their audiences.
It’s crucial for advertisers, who adapt quickly to these new ad formats, to optimize experiences that resonate better, potentially gaining an edge as user search habits evolve.
What to watch: As the rollout continues, I’ll be keeping an eye on how these conversational placements impact metrics like click-through rates and conversions. The broader implications for monetizing search with AI are enormous!
For those wondering when they can see these innovations: Conversational Discovery ads and Highlighted Answers are currently in testing phases in the U.S. on both mobile and desktop platforms. Meanwhile, AI-powered Shopping ads and the Business Agent for Leads feature are expected to unfold soon, starting in open beta for U.S. businesses.
Dig deeper: If you’re interested in more groundbreaking updates from Google Marketing Live 2026, check out these stories:
Entity optimization might sound like a complex term, but trust me, it’s incredibly powerful when you’re trying to make AI understand your brand better. Essentially, my goal is to help AI see exactly who I am and what I’m about. Let me share more about how you can do the same.
When I optimize entities related to my brand, I start by clarifying what my brand represents. This means ensuring that all my online content clearly reflects my brand’s identity and core values. By creating a strong, consistent message, AI can better understand and categorize my content.
Next, I focus on strengthening associations. This involves connecting my brand with relevant entities and concepts within my industry. When AI detects these connections, it increases my brand’s relevance in related searches.
Finally, driving accurate AI citations is crucial. I make sure that any references to my brand on different platforms are correct and consistent. This helps in building trust with AI, ensuring that it can reliably reference my brand in the right contexts.
I’m thrilled to share that Google has just unveiled Ask Advisor, a new AI-driven tool designed to transform the way we approach campaign management, analytics, and optimization. Announced at Google Marketing Live 2026, this Gemini-powered AI is here to integrate seamlessly across Google Ads, Google Analytics, Merchant Center, and the Google Marketing Platform.
Making Waves. Ask Advisor is set to be a game-changer, acting as a unifying force that weaves together insights, workflows, and recommendations across Google’s vast marketing ecosystem.
For those of us in marketing, this means we can launch campaigns, analyze performance, and uncover optimization recommendations all without having to juggle between different tools.
Imagine asking Ask Advisor to “find new customers for my hair care products.” It would seamlessly pull details from the Merchant Center and assist in crafting a campaign right in Google Ads.
Understanding the Process. Ask Advisor connects the dots between Google Ads, Analytics, the Merchant Center, and the Marketing Platform via a Gemini-powered interface. This connectivity allows it to access a range of data to create recommendations, automate tasks, and offer insights that align with marketing goals.
It doesn’t stop there. The integration of insights from Google Ads and Google Analytics helps explain campaign performance and suggests subsequent steps.
The aim, Google states, is to democratize advanced campaign management, enabling even those without extensive technical expertise to make the most out of their advertising strategies.
This launch supports Google’s expanding lineup of AI-driven in-product agents, positioning Gemini as a fundamental layer in advertising and measurement tools.
Why This Matters to Us. Ask Advisor symbolizes one of Google’s most direct steps into agent-based advertising workflows.
Instead of interacting manually with separate reporting dashboards, campaign tools, and optimization settings, AI agents are being poised to handle operational tasks and present strategic insights.
The more substantial evolution is structural: Google is anchoring Gemini as the core across its advertising platform, potentially redefining how campaigns are developed, optimized, and evaluated.
Keep an Eye On. The biggest discussion point will be how much control advertisers are willing to cede to AI agents. Transparency over recommendations, automation choices, and reporting accuracy will be under scrutiny as Ask Advisor rolls out.
When You Can Get It. Currently in beta, Ask Advisor is available for English-language accounts, with more features anticipated later this year.
Want to Learn More? Here’s additional news from Google Marketing Live 2026:
Today, I want to share some exciting news. Google has unveiled its most significant change to the search box in 25 years. This new feature, known as the “Intelligent Search Box,” is designed to provide us with an easier way to access AI search capabilities.
This innovation is powered by the latest technology, the Gemini 3.5 Flash.
Here’s How It Looks. Google completely redesigned the search box to give us more space for longer and deeper queries. As I type my search, the box will expand, supported by an AI-powered suggestion tool that goes beyond simple autocomplete, according to Google’s Head of Search, Liz Reid.
What’s even more impressive is the ability to search with text, images, files, videos, and even my Chrome tabs. It’s truly versatile!
Let me show you what this looks like:
This innovation puts Google’s most powerful AI tools right at our fingertips, enabling us to ask questions more easily, as explained by Liz Reid from Google.
Seamless Transition to AI Mode. Google also made it easier to switch to AI Mode with their new AI Overviews feature, which is now available globally on both desktop and mobile. Initially launched to many in January, it’s now fully operational.
Here’s how it works:
Why It Matters to Us. The transformation of the Google Search Box impacts how we search and potentially changes the type of traffic Google sends our way. It may encourage more users like me to switch to AI Mode for deeper answers, possibly leading to fewer direct clicks on our websites.
While change can be challenging, it’s also inevitable. Google’s CEO Sundar Pichai emphasized how our expectations from Google Search evolve—from individual queries to ongoing conversations and now to agentic workflows. As the world’s most-used product, Google is determined to stay ahead of our needs.
An analysis of 200 GPT-5.2 responses revealed that enhanced reasoning increases the citation of sources, deepens research, and boosts early-stage funnel visibility.
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I’ve explored how AI provides a conversational experience through large language models (LLMs) and chatbots. However, I’ve noticed that no one has thoroughly examined the evolution of citations and mentions within these conversations.
By examining data from the Semrush AI Visibility Toolkit, I reviewed 20 buyer journeys across four industries, comparing the high and low reasoning of ChatGPT5.2.
In this analysis, you’ll discover:
How high reasoning cites a vastly different web with only 25.6% domain overlap and which source types gain or lose prominence.
The renewed importance of TOFU content: Brands cited at the Problem stage tend to persist through to the Selection stage under high reasoning.
How to differentiate your prompt tracking by reasoning modes, ensuring your AI visibility reports reflect two distinct systems instead of an average.
Methodology
Data collection utilized the Semrush AI Visibility Toolkit to capture prompts, citations, and fan-out queries generated by ChatGPT for each response.
We executed 100 prompts twice through GPT-5.2, once with minimal reasoning and once with high reasoning, totaling 200 responses.
Prompts covered 20 buyer journeys across four sectors (B2B SaaS, Finance, Consumer Tech, Health/Lifestyle), each consisting of 5 stages: Problem, Exploration, Comparison, Validation, Selection.
The citation rate represents the proportion of prompts where the response cited at least one external source.
The average citation quantifies the sources per cited response.
Fan-out queries are sub-queries the model generates internally for research before responding, surfaced via the Semrush API.
High Reasoning in GPT 5.2 Leads to More Citations and Searches
Activating high reasoning elevates the citation rate from 50% to 68%, nearly doubles the average sources per response (from 2.6 to 4.5), and multiplies fan-out queries by 4.6 times. High reasoning also draws from 173 unique domains versus 127 with minimal reasoning, with 99 domains appearing exclusively under high reasoning.
*Citation Rate signifies the share of prompts where at least one external source is cited.
This grounding is essential. When the model thinks more critically, it increasingly depends on web-based research, significantly impacting brand visibility, although user activation of reasoning remains uncertain.
Query intent provides a clearer indication than user demographics. Even free-tier users can access reasoning, albeit at limited rates, and ChatGPT automatically routes challenging prompts to Thinking mode. The critical question isn’t about affordability but about which prompts trigger reasoning automatically.
Complex comparisons, evaluation frameworks, compliance inquiries, and intricate shopping setups are most likely to invoke reasoning across all users. It’s crucial to categorize your audience by query type rather than paywall status.
High Reasoning Launches More Fan-out Queries in Later Stages
Users navigate problem-solving and purchasing decisions through stages, often within the same conversation. The distinction between minimal and high reasoning is not static; it varies based on the user’s journey stage.
For instance, consider a buyer evaluating CRM software:
Problem: “How do I know if my sales team needs a CRM?”
Exploration: “What types of CRM software exist for B2B SaaS?”
Comparison: “HubSpot vs. Salesforce vs. Pipedrive for a 50-person sales team.”
Validation: “Is HubSpot worth the price for mid-market B2B?”
Selection: “How do I get started with HubSpot Sales Hub?”
The following patterns are consistent across all 20 buyer journeys:
The citation rate increases as users progress through the funnel in both reasoning modes, but early-stage gaps close faster in high reasoning: +35pp at the Problem stage, only +5pp at Validation.
Fan-out queries peak during the Comparison stage, with high reasoning triggering 24 sub-queries per response compared to 5.5 in minimal reasoning. For Selection, these numbers are 15.4 and 2.6, respectively.
Average citations per response culminate during the Comparison stage (9.8 high, 5.8 minimal) and narrow during the Selection stage (4.7 high, 2.6 minimal). The citation pattern resembles an hourglass throughout the funnel.
Aggregately, minimal reasoning triggers 245 search queries over 100 prompts, while high reasoning triggers 1,130. In high reasoning, the model conducts thorough investigations for each prompt, with most research occurring during the Comparison and Selection phases.
What does fan-out look like?
A B2B SaaS prompt that requires high reasoning, like comparing Salesforce, HubSpot, and Pipedrive for a 50-person sales team, breaks down into different queries regarding API rate limits, compliance standards, support tools, pricing tiers, and more. Each aspect requires specific retrieval. The brand that succeeds here will be the one with clean, accessible documentation for each sub-query, not merely ranking for the initial prompt.
The Selection stage features a remarkable variance in per-response queries: between 0 and 40 fan-out queries with the same five-stage cohort. This variance is driven mainly by the specificity of prompts.
Bounded prompts (like “should I finance through the dealer at 0% APR or use a bank?” or “draft an RFP to 3 SEO agencies”) run zero queries since the answer’s structure is predefined. On the other hand, open-ended tasks (“shopping list for a $3,000 home gym” or “which travel card system matches our grocery spending?”) prompt 28 to 40 queries. With no single query type dominating the Selection stage, the model’s research intensity correlates with the degrees of freedom left by the prompt.
For marketers: Capturing early-funnel visibility is highly dependent on reasoning mode. If buyers engage with ChatGPT in reasoning mode, your Problem-stage and Exploration-stage content become more relevant. Otherwise, visibility might only surface during the Comparison stage.
How Reasoning Alters Brand Representation in Conversations
A session with an LLM is more conversational than transactional. Does an initially cited brand endure till the concluding stage? If yes, early-funnel visibility multiplies. If no, each step is an independent battleground.
For minimal reasoning, persistence from the Problem stage to the Selection stage rarely happens. With high reasoning, however, continuous brand presence was recorded in 4 journeys across all 5 stages.
Within individual responses, high reasoning strongly relies on specific sources, with 51 out of 100 high-reasoning responses citing the same domain multiple times versus 26 in minimal reasoning. When committed, high reasoning cites a source repeatedly.
Analyzing brand names mentioned in the text provides a broader perspective. With a relaxed test criterion, persistence was noticeable in 3 high-reasoning sessions and 2 in minimal reasoning: HubSpot through CRM Selection, American Express in Business Credit Cards, and prominent mentions of Sony and Canon in Mirrorless Cameras. Consumer Tech again emerges, albeit without citation persistence, showing dominance through continuous conversation presence.
High reasoning establishes a consistent perception of the solution landscape throughout a session. Crucially, TOFU prompts possess enormous value. A brand appearing at the Problem stage is likely to be present at the Selection stage. Top-of-funnel content transcends mere brand awareness for AI visibility—it’s a predictor of where the model’s reasoning lands at decision-making points.
There are two more significant insights:
All four persistent journeys occur within Finance, indicating persistence thrives on authoritative-source content like regulatory pages and official brand sites, echoing the +28pp lift in Finance.
For marketers focusing on account-based strategies or market creation, visibility in reasoning mode is paramount as it’s the sole mode turning early funnel efforts into selection-stage citations.
Reasoning Mode: A Distinct Search Paradigm
The champions under minimal reasoning differ from those under high reasoning: Three out of four cited domains diverge. The diversity in source types and citation stages is unmistakable.
I’m particularly intrigued by these findings:
Firstly, measurement. It’s imperative to differentiate low and high reasoning in our prompt trackers to avoid oversimplification, as their functions are distinct.
This endeavor may seem costlier, but it significantly enhances prompt tracking accuracy.
Secondly, the relevance of funnel stages. In the latest AI Mode user behavior study, it was observed that users heavily rely on shortlists, much like they do with Google’s top results. It initially appeared that focusing on BOFU prompts to generate shortlists was most strategic.
Nonetheless, TOFU prompts carry substantial benefits due to their persistence potential. Brands entering the buyer journey early can remain present throughout. Mapping buyer journeys and tracking persistence offer the best insights.
This post originally appeared on the author’s website and is reproduced here with permission.
In my journey to optimize AI search visibility, I’ve discovered some of the best tools in Generative Engine Optimization (GEO). These tools not only boost citations in platforms like ChatGPT and Gemini but also guide me in selecting the most effective GEO platform for my needs.
Let me show you how you can measure AI search visibility effectively. It’s all about understanding how your content interacts with these advanced systems and using the right tools to enhance your reach.
Choosing the right GEO platform can be a game-changer. It’s essential to select a system that aligns perfectly with your goals and optimizes your AI-driven content for maximum impact.
Reflecting on my journey, it all began with a simple student side hustle, not a meticulously crafted career path in the world of PPC.
Back in 1998, as a Stanford student, I stumbled upon an opportunity to resell used Blockbuster video cassettes, prompting me to seek a way to connect with buyers. That quest introduced me to GoTo, an early search engine where I first experienced the power of paid search through keyword bidding.
Over two decades later, I find myself recognized as a prominent voice in PPC, having journeyed from Google to founding Optmyzr, shaping the landscape of Google Ads.
In this reflective interview, I delve into the transformation of Google Ads from its inception to the current era of automation, and I urge marketers to stay mindful as we transition from keywords to AI-driven prompts.
Paid Search’s Humble Beginnings
My initial ‘aha’ moment with paid search occurred before Google Ads became an advertising giant.
GoTo revealed to me the potential of reaching audiences without an enormous budget; buying a keyword allowed me to test and refine my strategies easily, a stark contrast to traditional advertising requiring hefty budgets with less measurable results.
This accessibility fundamentally changed the game.
Google Ads: Pioneering Measurable Success
Joining Google in 2002 marked a new chapter as I helped launch Google Ads in Dutch, expanding its reach as the sixth supported language.
Back then, a significant advertiser was spending about $30,000 monthly—a figure that, while modest by today’s standards, was groundbreaking at the time.
Google’s unique selling point wasn’t merely traffic; it provided proof through tools like Urchin (Google Analytics) and conversion tracking, offering insights into post-click activity, transforming paid search into a provably effective strategy.
Search Engine Land’s Influence
By 2006, when Search Engine Land emerged, paid search had already solidified its position as a serious advertising channel.
The platform became more than just news for me; it was a community for idea exchange, learning, and connection-building that significantly impacted my career.
It even inspired Optmyzr’s creation after connecting with my future co-founders through a published article on quality score, catalyzing a collaborative venture.
Understanding Quality Score
Google Ads’ quality score has always centered around relevance. Initially, it was primarily about click-through rate, ensuring ads were not only high-bid but also relevant to the user.
This necessity for a balance between bid and quality formed the auction’s cornerstone, a balance managed by machine learning, which humans like myself initially handled, sometimes reviewing keywords to ensure relevance.
Cyclical Nature of Search
The evolution of paid search is undeniably cyclical. Initially, advertisers had limited data, but Google empowered them with analytics, conversion tracking, and search query reports, only for visibility to be obscured again by privacy changes.
Products like Performance Max followed this pattern, launching with restricted features and expanding as demanded. Yet, the industry often views such ‘black box’ phenomena as novel, overlooking the pendulum swing between simplicity, control, automation, and transparency.
The Impact of Smart Bidding
A pivotal moment was when Smart Bidding became highly effective. It shifted advertisers’ roles and compelled tool providers like Optmyzr to redefine their contribution.
With Google automating bidding so competently, the focus shifted from bid adjustments to offering ‘PPC insurance,’ monitoring automation, setting limits, and diagnosing system errors, a critical part of Optmyzr’s strategy.
AI: The Next Shift
The launch of ChatGPT marked a significant shift, propelling Google to advance its Gemini technology and prompting the industry to move beyond keyword-based advertising.
With Google’s foundation built on keywords, we’re now seeing a shift towards interactions through prompts, conversations, and AI assistants, raising fundamental questions about whether the existing system should be overhauled or if an entirely new framework is needed.
AI Search: More Than Just Information
AI is transforming mere search queries into actionable tasks. People now not only seek information but also request AI tools to produce content, solve issues, and deliver solutions.
This evolution demands advertisers understand user goals profoundly, thus enhancing opportunities to assist at crucial junctures rather than simply matching a keyword to an ad.
Enhancing AI with Context
One prevalent mistake is treating AI like traditional search, asking limited questions and dismissing tools based on unsatisfactory answers.
My advice? Empower AI with true objectives. Don’t just ask for the ‘best mattress’ if your goal is health improvement; outline the broader context and let AI explore the solutions.
The same principle applies to marketing strategies, emphasizing goal clarity whether it’s lead generation, recruitment, education, or brand growth.
Future Opportunities for Problem Solvers
Marketers must evolve beyond past mechanisms. If my role was solely ‘keyword manager,’ the future might seem uncertain. But focusing on customer engagement and problem-solving signifies that while tools might change, the mission remains intact.
The future of search will favor those adept at understanding consumer needs and communicating value, adjusting to innovative discovery methods.
Advice to My Younger Self
Looking back, the simplest advice would have been to invest in more Google stocks.
Beyond that, I’m content with my journey’s trajectory. My guidance? Be intentional, think systematically, and join communities offering significant insights.
Communities like Search Engine Land, SMX, and Silicon Valley introduced worthwhile problems to solve.
What I’m Most Proud Of
I’m proud of becoming part of Google’s early team, contributing to digital infrastructure that defines the modern digital ecosystem.
While Optmyzr is a significant achievement, Google’s broad impact—from Ads to Maps, and Drive—is what I regard as monumental, funding products that revolutionize access to information and daily activities.
PPC Marketers’ Secret
We often joke that PPC experts never confess ignorance. Instead, we say, ‘It depends.’
Though humorously put, it’s mostly accurate.
The intricacies of paid search abound with caveats and evolving contexts, which is why continuous learning is essential for longevity in this industry.
Recently, I’ve discovered that Google is stepping up its game in AI tools for advertisers and retailers.
They’re testing something quite futuristic called Merchant Advisor, an AI assistant integrated directly into the Merchant Center. This tool aims to simplify the process of setup, troubleshooting, and optimization for us all.
What’s happening. As someone who watches Google’s every move, I’ve noticed them testing Merchant Advisor, a cutting-edge AI-powered chatbot right within Google Merchant Center. Although in beta, its purpose is clear: to offer personalized recommendations and support, making my experience smoother than ever.
How it works. The Merchant Advisor acts like a proactive assistant, offering tasks and suggestions like setting up a returns policy or finalizing account setup steps. It feels like having an assistant who is always available to enhance my feed quality and account health.
The bigger trend. This development is part of Google’s strategy to weave AI assistants throughout its marketing products, reminding me of earlier launches like Google Ads Advisor and Analytics Advisor. The AI co-pilots are evidently becoming the norm for managing campaigns and analytics.
Between the lines. Let’s face it, Merchant Center can be a technical labyrinth, especially for smaller retailers juggling feeds, policies, and diagnostics. But now, with an embedded AI guide, I’m finding it less daunting to get onboarded quickly and spot optimization opportunities I might have overlooked.
Spotted by. This feature first caught the eye of Tamara Hellgren during a Google Ads Decoded podcast episode that focused on retail innovations.
The bottom line. It’s clear to me that Google is transforming the Merchant Center into a more intuitive, AI-assisted environment, which reflects a larger trend towards automation within its advertising landscape.
