I’ve noticed a shift in how Google is choosing content for its Discover feed, and it seems less tied to traditional search rankings these days.
Yesterday, Andy Almeida from the Google Trust and Safety team shared some insights at the Google Search Central Live event in Zurich. He mentioned that Google Discover isn’t as closely aligned with Google Search rankings as it once was.
Andy presented a slide illustrating how existing systems assist the Google Discover team in addressing challenges. The slide highlighted:
“Minimal alignment to search ranking gives us the tools we need to combat emerging abuse.”
Understanding the Implications. This indicates that Google Discover is moving away from relying heavily on Google’s established search systems, particularly concerning combating platform abuse.
When I asked Andy what this meant for publishers, he explained that Google Discover aims to showcase content from lesser-known and smaller publishers. It seems while Google Search may not always favor them, Discover does, focusing more on its own evaluation systems.
The Challenge with Spam. I’ve been aware of the significant spam issues confronting Google Discover, primarily caused by sites exploiting expired or throwaway domains for spam content. This is a challenge not as prevalent in Google Search.
Back in 2019, Google stated that its core ranking systems affected visibility in Google Discover, especially after a core update. However, this new approach seems to diverge from that stance.
Why This Matters. As Google continues to address these spam problems, it’s balancing the visibility of smaller sites on Discover while curbing spam. This is great news for emerging publishers who focus on niche topics, as long as the spam issue can be effectively managed.
I recently discovered something fascinating about how people interact with AI. It turns out most AI chats don’t have any commercial intent! This insight came from a thorough analysis by Dan Petrovic, the director of AI SEO agency Dejan, who scrutinized millions of conversational turns to shed light on actual AI assistant usage.
Why is this important to us? As someone involved in SEO and marketing, I’m often focused on optimizing for AI. However, Petrovic’s research suggests we might be misunderstanding how people genuinely engage with AI assistants. They don’t typically flood AI with purchase queries. Instead, they explore issues and weigh options.
By the numbers, Petrovic dived into 4.4 billion characters across 613 million words and 3.9 million conversation turns. Here’s what that looks like:
Median chat: Just 2 turns, usually involving a quick question and an immediate response.
While most interactions are short, there are lengthy sessions when users paste documents for summarization or analysis.
Median words per session: 430 words.
Astonishingly, more than 80% of chats contain fewer than 1,000 words.
Only a small fraction, 4.2%, exceed 2,500 words. These are often complex tasks, like editing, coding, or tutoring.
Mean words: 732. This statistic is heavily influenced by long document submissions.
Assistant output: Typically, it’s 1.5 times more than what users contribute.
Median user contribution: Users make up about 16-17% of the conversation.
In exploring how people utilize AI assistants, Petrovic examined 24,259 sessions across 42 intent categories. Surprisingly, 64.6% of chats didn’t align with any purchase funnel. People used AI for writing, brainstorming, planning, learning, analyzing, or just simply chatting. Here’s the breakdown:
Other: 25%
Included are jailbreak attempts, role-playing, and specific requests.
Brainstorming: 7.7%
Planning: 6.5%
Conversation / emotional support: 6.2%
Analysis: 5.7%
Learning: 4.7%
Transformation (summaries, translations): 4.6%
Creation (writing, code, docs): 3.9%
Only 35.4% of chats showed any commercial intent, and most were in the early stages of the buying process. Other insights:
Awareness (10%) and consideration (8.5%) combined to form 18.5%, which Petrovic noted as prime territory for product content.
Post-purchase needs (5.1%) outpaced transactional support (4.8%), discovery (4.1%), and decision support (2.8%), suggesting users seek AI more for ‘How do I use or fix this?’ rather than ‘Should I buy this?’
Bottom line, my takeaway is that AI assistants are utilized far more for creation, cognition, and conversation than for commerce.
I recently came across some eye-opening data highlighting the distinct approaches Google AI and ChatGPT take in citing sources when it comes to retail information. While Google mentions retailers only 4% of the time, ChatGPT cites them 36% of the time. This significant gap of nearly nine times suggests that each platform guides shoppers in noticeably different directions, and this insight comes from the latest BrightEdge data.
Why is this important to us? Nowadays, millions of shoppers are relying on AI to discover deals and gift ideas. However, the process differs greatly between the top AI search platforms. Google tends to focus on what users are saying, while ChatGPT zeroes in on where you can actually purchase items.
Regarding what each AI prioritizes, Google AI Overviews are inclined to reference YouTube reviews, Reddit discussions, and various editorial sites. In contrast, ChatGPT frequently cites retail giants such as Amazon, Walmart, Target, and Best Buy.
Let’s break down the priorities further. Google AI Overviews tend to cite:
YouTube reviewers and unboxings.
Reddit threads and community consensus.
Editorial reviews and category experts.
Meanwhile, ChatGPT emphasizes:
Major retailer listings.
Brand and manufacturer product pages.
Editorial sources (secondary).
This citation divide is quite telling. On Google, retailers show up only about 4% of the time, as it leans more towards user-generated content and expert reviews—acting more as a research tool rather than a purchase assistant. Top reference sources include:
YouTube
Reddit
Quora
Editorial sites like CNET, The Spruce Eats, and Wirecutter
Conversely, ChatGPT features retailers about 36% of the time, functioning as both an explainer and a shopping assistant, hence why retailer links are far more prevalent. Key sources often cited include:
Amazon
Target
Walmart
Home Depot
Best Buy
About the data: BrightEdge scrutinized tens of thousands of e-commerce prompts across Google AI Overviews and ChatGPT during the 2025 holiday season, identifying and categorizing citation sources. Domains were sorted by type—retailer, UGC/social, editorial, and brand—and directly compared using identical prompts.
I’ve just discovered a game-changer from Google that could simplify our advertising efforts significantly. Their new Data Manager API offers a streamlined way for us to feed our valuable first-party data directly into Google’s sophisticated AI systems.
As an advertiser, utilizing the Data Manager API means I can seamlessly connect our first-party data with Google’s AI-driven ad tools. This connection is poised to elevate our measurement, targeting, and overall performance, eliminating the hassle of managing multiple systems.
Why I care. By leveraging the Data Manager API, I’m able to inject high-quality data into Google’s AI, which optimizes targeting, measurement, and bidding processes. It replaces the need for various APIs, reducing our engineering workload and accelerating insights into our campaigns. With the decline of cookies, this API is crucial for maximizing the data we already have.
Driving the news. This API serves as a single integration point, unifying multiple Google platform APIs. It’s designed for advertisers, agencies, and developers, making our lives a lot easier.
Here’s what I can do with it:
Upload and refresh audience lists
Send offline conversions for improved measurement
Enhance bidding performance by providing Google AI with richer signals
This API expands upon Google’s existing codeless Data Manager tool, which is already in use by thousands of advertisers to activate first-party data.
Partnership push. To speed up adoption, Google is integrating with several partners, including AdSwerve, Customerlabs, Data Hash, and others.
State of play. Starting today, the API is available across Google Ads, Google Analytics, and Display & Video 360, with more integrations to follow.
The bottom line. Adopting the Data Manager API empowers us by enhancing Google’s AI capabilities, improving measurement, reducing technical complexities, and driving better ad performance, all while gearing up for a future that prioritizes privacy.
I’ve always been fascinated by how artificial intelligence is reshaping brand management. From personalization to predictive insights, AI is making waves in areas like content, customer experience, and digital presence.
Discovering how AI can future-proof a brand has been a game-changer for me. The ability to leverage technology for optimal brand positioning and engagement is invaluable in today’s competitive market.
Recently, I stumbled upon claims that Google is planning to introduce ads to its Gemini AI chatbot by 2026. However, Google’s top executive has firmly denied these rumors.
The Buzz: According to AdWeek, Google supposedly informed major advertisers that Gemini is set to feature its own ad slots in 2026—distinct from AI Mode’s current ads. Could this mean something big is on the horizon?
On these calls, advertisers were curious but noted a lack of concrete details or prototypes.
Google’s Rebuttal: Dan Taylor, the VP of Global Ads at Google, took to X, flatly dismissing these claims. He insisted, “There are no ads in the Gemini app and no plans to change that.”
Why This Matters: As advertisers, we are on the edge of our seats, wondering how AI interfaces like Gemini might change the ad landscape. Could an ad-supported chatbot alter advertising strategies and user interactions dramatically?
Discussions are already underway about whether AI chatbots should solely serve as utility tools or evolve into revenue-generating platforms. Even whispers of ads in Gemini drive agencies to consider future possibilities.
Moving Forward: Google’s stance is clear; for now, Gemini remains ad-free. Yet with competitors exploring monetization avenues, we must watch this space closely. The conversation about ads in Gemini is far from over.
I recently learned about a significant ruling that will impact Google’s longstanding agreements with tech giants like Apple and Samsung. This decision means that moving forward, Google will only be able to secure its place as the default search engine on devices for one year at a time. Despite this change, I’m not expecting a major shift in Google’s dominance over the search market anytime soon.
Here’s what’s driving the news: On Friday, Judge Amit Mehta described this one-year cap as a crucial step in enforcing antitrust measures. This follows his 2024 decision, which concluded that Google was unlawfully monopolizing the realms of search and search advertising. According to Business Insider, the requirement aims to enforce fair competition in the industry.
Additionally, Judge Mehta’s earlier ruling outlined restrictions for Google:
Google must avoid any exclusive contracts regarding the distribution of Google Search, Chrome, Google Assistant, and the Gemini app.
They cannot condition licensing agreements of the Play Store on the preloading of these applications on devices.
Revenue sharing cannot be contingent on placing or maintaining these applications on devices beyond one year.
Partners are free to distribute alternative GSEs, browsers, or GenAI products simultaneously.
Why I care: This landscape shift could mean that user searches originate from a wider array of platforms. If AI-powered competitors like OpenAI, Perplexity, or Microsoft make even modest advances, we could see a more diverse and challenging search terrain emerge.
Reality check: In my view, this is more of a bump in the road rather than a disruption. Google’s financial resources, brand strength, and user habits continue to provide significant leverage in annual negotiations.
In 1997, Apple launched a groundbreaking campaign that I often think about: “Think Different”. It celebrated those who dared to break the mold, challenging norms to change the world. Apple grasped a vital truth: the constraints stifling creativity weren’t real; they were assumed, passed down through tradition.
Fast forward to today, and I see that marketing finds itself in a similar “Think Different” moment. The barriers that once constrained our industry have vanished. Thanks to technology, AI generates countless variations, data platforms provide up-to-the-minute insights, and orchestration tools bridge every channel instantaneously.
Yet, I notice many marketers are still functioning within an outdated paradigm. They wait for others—the data teams, creative teams, or engineers—to move projects along, not realizing technology has already unlocked those doors.
We no longer need to follow a linear, assembly-line process that passes tasks from one department to the next. The box has disappeared, but old habits die hard.
Here’s to the marketers who refuse to wait for approval
I find inspiration in those who see a customer need at 3 p.m. and launch a personalized campaign by 4 p.m., driven by urgency rather than seeking permission.
These are the marketers who don’t send multiple briefs to multiple teams—they pull the data, create content, and execute campaigns independently. Not to sideline experts, but to seize on moments that matter now.
Their constant experimentation, running multiple tests and iterations, proves essential in crafting insights. They know, as I do, that perfection comes from trial and error, not waiting around for analysis.
Here’s to the ones who see campaigns where others see dependencies
For them, it’s not about passing data to an analytics team; it’s about directly accessing and utilizing customer insights instantly.
They bypass traditional creative approvals with AI tools that produce tailored assets swiftly, enabling personalization on a grand scale.
They aren’t beholden to engineering delays but leverage orchestration platforms to automate journeys smoothly, sans tickets.
They’re not reckless nor cowboys
Instead, they work at the speed technology allows, guided by strategic thinking and judgment rather than rigid processes.
This ethos is at the heart of Positionless Marketing: using Data, Creativity, and Optimization powerfully and in tandem, not due to a lack of specialists, but because technology removed those earlier dependencies.
This isn’t just about speed; it’s about potential
In times when marketers managed long processes, their role was merely about coordination. Today, I see it as enabling potential, pushing everyone, including you and me, to do what we’re capable of with unchained boundaries. I no longer see the brief as a roadblock, but a stepping stone to instant creativity and autonomous coordination.
Teach people to think outside the box by showing them there is no longer a box
Now, I can see how the data analyst can transcend report creation to build real-time predictive models. The campaign manager can independently design, test, and optimize entire journeys. The creative strategist can not only craft briefs but execute ideas across platforms.
This is the real impact of technology; not just getting the work done, but dismantling barriers that once held us back, releasing the talents we’ve always possessed.
The Positionless Marketers of today are doing the same thing
They refuse to delay action when immediate responses are needed. They reject the notion that insights take forever when available in seconds. They aren’t bound by bygone constraints.
By thinking differently, not for defiance’s sake, but because the past ways no longer align with the new potential.
Apple once said, “The people who are crazy enough to think they can change the world are the ones who do.” In our era, those who believe they can seamlessly deliver customized experiences and instigate rapid-fire campaigns without relying on dependencies will lead the charge.
The constraints are gone. The assembly-line marketing box can no longer exist.
As a performance marketer, I’ve realized that leveraging AI is crucial for growth and efficiency, especially as we advance into 2026 and beyond.
Anyone not exploring new AI tools to enhance or streamline their PPC efforts is missing out on a significant opportunity for their brand or clients.
The rapid pace at which these tools evolve can make it feel like a full-time job to keep up, which is why my agency prioritizes AI in our knowledge-sharing sessions.
As a team, we’ve distilled our top picks for creative, campaign management, and AI search measurement tools.
This article will guide you through essential tools in each category, providing quick reviews and highlighting my current favorite.
One key piece of advice before diving in: Be cautious about long-term contracts with AI tools, as today’s sensation might be tomorrow’s obsolete tool.
AI Creative Tools for Paid Social Campaigns
With numerous tools available for generating creative assets, each offers unique benefits but also carries the risk of creating subpar AI content.
Whatever tool you choose, ensure it undergoes rigorous testing and is backed by a robust human-in-the-loop process to maintain quality, accuracy, and brand alignment.
Here’s a summary of tools we’ve evaluated:
AdCreative.ai: Generates images, video creatives, ad copy, and headlines efficiently, with data-backed scoring.
Creatify: Excels in video ads and supports diverse formats.
WASK: Merges AI creative generation with campaign optimization and competitor analysis.
Revid AI: Suited for story format creation.
ChatGPT: A familiar, free tool that gives marketers an edge in crafting effective prompts.
Currently, I favor AdCreative.ai. It simplifies brainstorming creative concepts and testing variations quickly.
It offers significant advantages like:
Creating multiple variants swiftly to keep creative fresh and combat ad fatigue.
Reducing dependence on external designers for repetitive or template-based content.
Experimenting with various creative elements rapidly to determine winning combinations.
Providing data-driven insights such as creative performance predictions.
However, always ensure you establish:
Guardrails to prevent off-brand outputs with clear voice guides and style rules.
Verification processes to catch errors in technical claims or data.
AI Campaign Management and Workflow Tools for Performance Campaigns
In the realm of workflow automation tools like Zapier, Workato, and Microsoft Power Automate, our agency prefers n8n for its flexibility in creating agentic workflows and integrations.
Our primary n8n use cases include:
Lead management: Enhance and route leads automatically with the integration of Clearbit and CRMs.
UTM cleanup: Normalize UTM parameters automatically for accurate CRM entry.
Data reporting: Fetch metrics, structure data, and use AI to summarize insights, sharing them via Slack or collaborative tools.
Be aware of potential challenges with n8n:
Requires technical knowledge of APIs, JSON, and authentication methods.
Security setups are necessary to protect data; misconfigured systems can pose risks.
Lacks some ad platform integrations which require more manual work.
While I strive for my content to remain relevant over time, I’m aware that this piece may become more of a historical reference.
Nonetheless, the need to:
Stay updated on AI developments.
Vigorously test new capabilities and features.
Foster strong knowledge-sharing within the team remains essential.
Though AI in performance marketing has grown rapidly, there’s still room for teams that adapt quickly, conduct strategic testing, and pivot effectively to differentiate themselves.
As I navigate the ever-evolving world of SEO, evaluating tools in 2026 has become a complex task. Rising costs and the AI frenzy often make it difficult to justify the investment in new platforms.
The challenge lies in demonstrating the business value of these tools to leadership, who are more interested in results than in the number of keywords we can track or the speed of content optimization.
Most tools fail to meet the demand to connect SEO work directly to business outcomes. The offerings often come bundled in convoluted packages, further complicating the decision-making process.
This article offers a framework to approach SEO tool evaluation in 2026, focusing on must-have features, efficient tool comparison methods, and effective vendor conversations.
Understanding the forces reshaping SEO tools can help. Many platforms lag in connecting SEO to measurable business value, complicating budget approvals.
AI advancements are setting new expectations. Whether to train a custom AI agent or invest in a ready-made tool is a key question every team faces.
Small teams need automation that truly saves time. Without context, many tools only generate noise, failing to deliver tailored insights for specific markets or businesses.
Technical SEO tools remain relatively stable, yet the assumption that AI can solve all problems presents a budgeting challenge.
Real impact in tool evaluation lies in focus areas like advanced data analysis, SERP intelligence, meaningful automation, robust multilingual support, and transparent pricing.
To avoid wasting time comparing tools, start with clear pricing, align tests with typical weekly tasks, and ensure you always secure a free trial.
When it comes to vendor interactions, concise goals and informed questions can streamline discussions and facilitate more productive evaluations.
Business considerations should include presenting a range of options, avoiding overpromising, and ensuring that proposed tools align with strategic business objectives.
As we look to the future of SEO tools, connecting searches to tangible business outcomes will define premium offerings, though such solutions remain rare.
