Automation has shaped PPC for decades, and the landscape keeps shifting.
I’ve seen that evolution firsthand, from helping build the first AdWords Editor to developing early Google Ads scripts and writing about automation layering.
Now we’re entering another major transition.
As AI changes how we search and get answers, it’s also transforming how automation itself gets built.
And this time, the momentum isn’t coming from ad platforms like Google – it’s coming from AI companies like OpenAI.
Until recently, AI mostly helped with human language tasks like writing ad copy, summaries, or reports.
But the latest generation of LLMs can increasingly generate computer language too, including the software and workflows that streamline how we work.
At OpenAI’s DevDay in San Francisco, the company introduced AgentKit, a new way to build AI that can take action.
It marks the start of a phase where the automation mindset that powered PPC optimization can extend far beyond campaigns and into entire workflows.
Imagine if AI could handle your everyday busywork
Picture this:
- A client sends a CSV with weekly results, and before you’ve even opened the email, the file is saved to the right folder and added to your dashboard.
- A client asks for a meeting – AI checks your calendar, drafts an agenda, and schedules it.
- You start writing new ad copy with AI, and the system automatically pulls your brand guidelines and checks for tone and compliance.
This is all possible today, and you don’t need an engineering degree to make it happen.
If you can define how your work is broken down into distinct tasks, you can create an agent that does those steps for you.
Dig deeper: 4 ways to connect your ads data to generative AI for smarter PPC
What agents really are
An AI agent is a smart helper that can figure out what needs to happen and then take action using connected tools.
Software has historically been built around deterministic steps. If X, do Y, else do Z. It’s predictable, but inflexible.
And it requires humans to define every possible scenario that should be covered, which makes writing a helpful program time-consuming and difficult.
But just like an LLM is flexible in how it answers your questions, it can use that flexibility to automatically figure out a reasonable next step to complete a task.
Instead of replying with text, agents can reason through steps, call APIs, and perform tasks.
I’ve explained early versions of this before:
- You ask ChatGPT for restaurant ideas while planning a trip.
- It suggests a few places.
- It then uses an app like Resy to book the reservation.
That’s what an agent does: it can understand your intent and take a real-world step.
This concept builds on earlier OpenAI features, such as GPT Actions and function calling, which gave models controlled access to outside data.
Agents are the next evolution – they combine reasoning with execution, meaning they can plan and act in the same flow.
Now, think about that in PPC terms.
An agent could pull campaign data, summarize results, and even reference brand or policy docs before generating compliant creative.
That’s a big step up from traditional “AI writing assistants.”
Dig deeper: AI agents in PPC: What to know and build today
From coding projects to five-minute builds
AI agents aren’t a new idea.
Many marketers, myself included, have experimented with them for more than a year, but it used to take a lot of technical work.
About a year and a half ago, I built an agent based on the two books I’d written that could answer questions in my tone and reference my ideas.
I used LangChain, one of the first frameworks for connecting large language models to data and tools. It worked, but it wasn’t quick.
I had to learn vector databases, RAG, and several other moving parts to get it working – not something most PPC pros want to tackle on a Monday morning.
Since then, several companies have made it easier to build agents like these, and some even feature them with a digital clone of a person, such as HeyGen.
But when OpenAI introduces a way to create agents, I pay attention – and that’s what they did with AgentKit.
It brings a visual interface for building agents directly on the platform of the most used chatbot.
What used to take hours or days of development can now be done in minutes, and you don’t need to know how to code.
AgentKit: ‘Zapier for AI’
AgentKit is OpenAI’s new toolkit for creating agents that can connect to tools and take actions through those tools.
It’s a visual builder where you link services like Gmail, Dropbox, or Slack, and describe what the agent should do using tools you already use every day.
If you’ve ever used Zapier, n8n, Make, or Rule Engine, the concept will feel familiar: you connect blocks in sequences that represent what you want to happen.
But because a flexible AI model sits at the core of these flows, AgentKit is different – it can use reasoning instead of rigid rules.
If that sounds scary, you can add a simple human-in-the-loop approval step to any flow.
Instead of “If X happens, do Y,” you can say, “If a client sends a campaign report, summarize it and save it to the right folder.”
The AI figures out how to do that by making reasonable requests that help it understand what you mean by vague instructions like “the right folder.”
For PPC marketers, this opens the door to automating work around campaigns (think reporting, documentation, and creative preparation), without waiting for a platform feature or a developer.
The unsung hero: Model Context Protocol (MCP)
Under the hood, much of the power that enables agents to take action comes from the Model Context Protocol, or MCP.
It’s not brand-new, but it’s the key piece that makes all of this work.
MCPs are the connectors that let agents talk to your tools or data in a structured way.
If you think of APIs as the connectors of the web, MCPs are similar, but built as a standard that any LLM can use.
Some are built by OpenAI, like the connectors for Dropbox or Gmail.
Others come from third-party developers, like Box.
And you can create your own to connect private data or internal systems.
You can think of it this way: MCPs are the plumbing. AgentKit is the faucet.
The plumbing defines what data can flow where. The faucet is how you turn that into something usable.
Without MCPs, an agent would be like a brilliant intern with no logins to any of the systems they need.
With them, the agent can safely use your data and tools with clear permissions.
Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing
MCPs in plain terms
If this still sounds abstract, think of an MCP as a menu of what an AI can do inside a given flow.
For example, the Google Ads MCP currently includes actions like:
- Search for entities.
- List connected customers.
That’s it for now. It can read data, but it can’t change bids or create ads yet.
That limitation is a good illustration that MCPs don’t open the door to entire systems for an LLM to go wild.
Instead, they provide a defined set of capabilities created by the MCP developer.
It’s an important guardrail. And even with MCPs that offer broader capabilities, you still control exactly which actions your agent can access when you integrate them into a flow.
Even in this early state, it’s a clear preview of how AI might eventually interact with Google Ads data through well-defined, secure interfaces.
Example: A ‘brand-safe ad assistant’
Here’s what this looks like in practice.
Imagine you want an AI assistant that writes Google Ads while automatically following your brand voice and legal disclaimers.
In AgentKit, you could create an agent with two connected tools:
- Dropbox, where your brand guidelines live.
- A vector store with your agency’s tone and policy docs.
You could then ask the agent to “write new RSA headlines for our fall campaign using our style and disclaimers,” and it would connect with the right data to complete the task.
Behind the scenes, it reads the files, extracts the rules, and generates compliant ad copy. You still approve the final version, but the prep work is automated.
It may sound simple, especially since you can already do this with a custom GPT, but it shows how these building blocks can be expanded.
For example, you could integrate an MCP for your email platform and have the agent send a client an approval request for the creatives it generated.
Connecting data sources in AgentKit
Here are the steps to create an agent connected to the two data sources mentioned above.
In Agent Builder, click the + icon next to Tools to give your agent a new capability, such as connecting it to an MCP.
Choose an existing MCP, like the ones shown here, or connect a custom MCP by clicking + Server.
You can also add a file search capability and select the files to include directly in the pop-up dialog.
Now you can interact with the agent to see how it uses its new abilities to produce better answers and, where enabled, how it uses other tools to take actions.
Dig deeper: How to get smarter with AI in PPC
Why this shift matters for PPC
If you’ve been in PPC for a while, you’ve seen this script before.
We went from manual optimizations, to automated rules, to scripts, to automation layering – and each wave changed the skill set needed to stay ahead. Agents are the next wave.
Instead of writing scripts or building workflows with APIs, we’ll soon describe them in plain English and let AI generate the logic.
That amplifies what marketers can do.
The core skills stay the same – strategy, measurement, and judgment – but the way we build automation is about to get much faster, more flexible, and far more accessible.
The current tools for building AI agents are still early.
Setting up an MCP takes some configuration, and the Google Ads connector is limited to reading data.
But the potential is clear: AI will move beyond generating text to running workflows, checking rules, and getting work done.
If you want to stay ahead of this shift, start small.
Experiment with simple automations that connect your email, files, or reports.
Learn what agents can and can’t do yet.
Just as marketers who adopted scripts early will be the ones setting the standard later, those who learn this now will be the ones setting the standard later.
Dig deeper: Agentic PPC: What performance marketing could look like in 2030
How AI Drives Local SEO Strategies for Regional Markets in 2025 – Analytics Insight
/in AI Search, website SEO, Website Traffic/by Team ZYTAs artificial intelligence continues to transform the landscape of search optimization, businesses are discovering new ways to connect with regional audiences. In 2025, the intersection of AI tools and local SEO is unlocking opportunities for brands to stand out in competitive markets, offering tailored approaches that drive measurable growth and engagement.
AI-powered platforms are now essential for businesses aiming to improve their presence in specific regions. These tools analyze search patterns, competitor tactics, and consumer intent with remarkable precision. For example, companies operating in Florida’s vibrant sports entertainment sector can leverage AI-driven keyword research to identify niche opportunities. By focusing on industry-specific terms such as sports betting florida, marketers can craft content that resonates with local audiences and meets the unique demands of their market. This targeted approach not only boosts visibility but also enhances relevance, helping businesses connect authentically with potential customers.
Personalization is at the heart of successful regional marketing. AI tools enable brands to segment audiences based on location, preferences, and behavior, ensuring that messaging feels timely and relevant. For instance, sports organizations in Florida can tailor their outreach to reflect local interests, using real-time data to adjust campaigns as trends shift. This level of customization fosters stronger engagement and encourages repeat interactions, as audiences receive information that aligns with their passions and needs. The integration of AI in business analytics is revolutionizing the way companies make decisions, enabling them to extract actionable insights from vast amounts of data in real time through solutions like AI in Business Analytics.
Predictive analytics are becoming a revolution for regional marketing plans. Using consumer behavior modeling and demand forecasting, companies can preempt changes in local markets and take measures accordingly. In sports, which is not static in Florida, predictive statistics are used to know fan behavior, to promote events efficiently, and to manage resources in the best way possible. This approach, which is entirely based on data, eliminates the need for guessing and ensures the highest return on investment. It also enables the brands to be in the leading position of course, but at the same time to provide the community with value.
The development of AI-based SEO is going to raise the bar for regional marketing in the year 2025. Through the use of sophisticated analytics, unique content, and forecasting insights, companies will be able to confidently handle the difficult aspects of the local markets. It will be the case that the companies using AI strategical approach will be the ones that will win the race for attracting, keeping and growing their customer base in the areas of their choice as the tech gets better and better.
Analytics Insight is an award-winning tech news publication that delivers in-depth insights into the major technology trends that impact the markets. The content produced on this website is for educational purposes only and does not constitute investment advice or recommendation. Always conduct your own research or check with certified experts before investing, and be prepared for potential losses. Opinions expressed herein are those of the authors and not necessarily those of Analytics Insight, or any of its affiliates, officers or directors. © Analytics Insight 2025. All rights reserved.
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SEO.co Expands SEO Audit Services to Include Advanced AI & LLM Visibility Audits – FinancialContent
/in AI Search, website SEO, Website Traffic/by Team ZYTNew SEO audit framework measures brand accuracy, authority, and visibility across leading AI search and generative platforms
Seattle-Tacoma, WA, Washington, United States, November 20, 2025 — SEO.co, a leader in enterprise search engine optimization and digital growth strategy, today announced a major expansion of its SEO audit services to include a comprehensive suite of AI and Large Language Model (LLM) Visibility Audits. This new framework is designed to help companies understand exactly how they are represented, referenced, and ranked inside the world’s most influential AI-driven platforms—including ChatGPT, Gemini, Claude, Copilot, Perplexity, and emerging models shaping the future of search.
“AI-driven search is no longer optional—it’s foundational,” said Nate Nead, CEO of SEO.co. “Every brand is already being indexed, interpreted, and summarized by LLMs, whether they’re aware of it or not. Our role is to ensure the information being generated about them is accurate, trustworthy, and competitive. This expansion represents the biggest shift in SEO since the rise of mobile search.”
AI is Reshaping How Consumers Discover Brands
Consumers and decision-makers now rely heavily on AI systems to research, compare, and validate products and services. Unlike traditional search results, these systems generate direct, authoritative answers, creating both unprecedented opportunity—and significant risk—for companies.
Generative AI tools may:
“The companies winning today aren’t just optimizing for Google,” said Timothy Carter, Chief Revenue Officer at SEO.co. “They’re optimizing for every system that influences buying decisions. AI models now sit upstream from the customer journey, shaping perception before a prospect ever reaches your website. Our expanded audit gives clients the intelligence they need to correct misrepresentations, improve recommendations, and strengthen authority across AI-powered platforms.”
Inside the Expanded SEO.co AI & LLM Visibility Audit
SEO.co’s audit expansion introduces a highly detailed evaluation of brand presence across generative AI systems. Key components include:
AI Mention Frequency Analysis
How often a brand is mentioned or recommended across the major AI tools, and how mention share compares to competitors.
Brand Accuracy & Risk Review
A systematic check for factual correctness regarding services, pricing, locations, leadership, policies, compliance statements, and industry positioning.
Competitive Recommendation Benchmarking
Identifies which competitors AI platforms recommend instead—and why.
LLM Training Data Footprint Assessment
Analyzes the strength and depth of a brand’s representation across public sources commonly used in model training, including news websites, publisher networks, knowledge bases, and open data repositories.
E-E-A-T & Entity Optimization Audit
Evaluates structured data, schema markup, entity relationships, knowledge graph visibility, and authority signals that influence AI-generated content.
AI Safety & Hallucination Exposure Testing
Pinpoints where AI models are hallucinating or providing misleading or damaging brand statements.
Remediation & Optimization Roadmap
A detailed, prioritized plan to enhance AI visibility, improve accuracy, increase citation frequency, and strengthen LLM trust signals.
Why This Matters to Modern Marketing Teams
With the rapid adoption of AI assistants—whether embedded in mobile devices, browsers, search engines, or enterprise software—brands must now “optimize for AI” with the same rigor they once applied to traditional SEO.
“AI visibility is now a core pillar of digital marketing,” said Samuel Edwards, Chief Marketing Officer at SEO.co. “If an AI system misrepresents your brand—or worse, doesn’t know your brand exists—customer trust erodes instantly. Marketers now have to manage not just their search presence, but their AI presence. Our enhanced audit suite empowers teams to take control of their brand narrative across platforms used by millions of consumers every day.”
This shift is especially significant for industries where trust, accuracy, and expertise are foundational to decision-making, including:
SEO.co’s expanded audits give these organizations the visibility and insights required to correct misinformation, reinforce authority, and capture competitive advantage across AI-driven discovery channels.
Availability and Implementation
The expanded AI & LLM Visibility Audit offerings are available immediately to:
SEO.co also provides optional monitoring to track how a brand’s AI presence evolves over time as models update, retrain, and incorporate new public datasets.
About SEO.co
SEO.co is an industry-leading digital marketing firm specializing in advanced SEO strategy, enterprise content development, technical optimization, link acquisition, and audit-based performance improvement, including LLM SEO services. Since 2009, the firm has helped Fortune 500 companies, mid-market organizations, and high-growth startups accelerate search performance and digital visibility. SEO.co also offers an expanding suite of SEO tools and white-label solutions deployed by agencies worldwide.
Contact Info:
Name: Samuel Edwards
Email: Send Email
Organization: PR Digital
Website: https://pr.digital
Release ID: 89176522
If you come across any problems, discrepancies, or concerns related to the content contained within this press release that necessitate action or if a press release requires takedown, we strongly encourage you to reach out without delay by contacting error@releasecontact.com (it is important to note that this email is the authorized channel for such matters, sending multiple emails to multiple addresses does not necessarily help expedite your request). Our committed team will be readily accessible round-the-clock to address your concerns within 8 hours and take appropriate actions to rectify identified issues or support with press release removals. Ensuring accurate and reliable information remains our unwavering commitment.
source
From scripts to agents: OpenAI’s new tools unlock the next phase of automation
/in SEO/by Team ZYTAutomation has shaped PPC for decades, and the landscape keeps shifting.
I’ve seen that evolution firsthand, from helping build the first AdWords Editor to developing early Google Ads scripts and writing about automation layering.
Now we’re entering another major transition.
As AI changes how we search and get answers, it’s also transforming how automation itself gets built.
And this time, the momentum isn’t coming from ad platforms like Google – it’s coming from AI companies like OpenAI.
Until recently, AI mostly helped with human language tasks like writing ad copy, summaries, or reports.
But the latest generation of LLMs can increasingly generate computer language too, including the software and workflows that streamline how we work.
At OpenAI’s DevDay in San Francisco, the company introduced AgentKit, a new way to build AI that can take action.
It marks the start of a phase where the automation mindset that powered PPC optimization can extend far beyond campaigns and into entire workflows.
Imagine if AI could handle your everyday busywork
Picture this:
This is all possible today, and you don’t need an engineering degree to make it happen.
If you can define how your work is broken down into distinct tasks, you can create an agent that does those steps for you.
Dig deeper: 4 ways to connect your ads data to generative AI for smarter PPC
What agents really are
An AI agent is a smart helper that can figure out what needs to happen and then take action using connected tools.
Software has historically been built around deterministic steps. If X, do Y, else do Z. It’s predictable, but inflexible.
And it requires humans to define every possible scenario that should be covered, which makes writing a helpful program time-consuming and difficult.
But just like an LLM is flexible in how it answers your questions, it can use that flexibility to automatically figure out a reasonable next step to complete a task.
Instead of replying with text, agents can reason through steps, call APIs, and perform tasks.
I’ve explained early versions of this before:
That’s what an agent does: it can understand your intent and take a real-world step.
This concept builds on earlier OpenAI features, such as GPT Actions and function calling, which gave models controlled access to outside data.
Agents are the next evolution – they combine reasoning with execution, meaning they can plan and act in the same flow.
Now, think about that in PPC terms.
An agent could pull campaign data, summarize results, and even reference brand or policy docs before generating compliant creative.
That’s a big step up from traditional “AI writing assistants.”
Dig deeper: AI agents in PPC: What to know and build today
From coding projects to five-minute builds
AI agents aren’t a new idea.
Many marketers, myself included, have experimented with them for more than a year, but it used to take a lot of technical work.
About a year and a half ago, I built an agent based on the two books I’d written that could answer questions in my tone and reference my ideas.
I used LangChain, one of the first frameworks for connecting large language models to data and tools. It worked, but it wasn’t quick.
I had to learn vector databases, RAG, and several other moving parts to get it working – not something most PPC pros want to tackle on a Monday morning.
Since then, several companies have made it easier to build agents like these, and some even feature them with a digital clone of a person, such as HeyGen.
But when OpenAI introduces a way to create agents, I pay attention – and that’s what they did with AgentKit.
It brings a visual interface for building agents directly on the platform of the most used chatbot.
What used to take hours or days of development can now be done in minutes, and you don’t need to know how to code.
AgentKit: ‘Zapier for AI’
AgentKit is OpenAI’s new toolkit for creating agents that can connect to tools and take actions through those tools.
It’s a visual builder where you link services like Gmail, Dropbox, or Slack, and describe what the agent should do using tools you already use every day.
If you’ve ever used Zapier, n8n, Make, or Rule Engine, the concept will feel familiar: you connect blocks in sequences that represent what you want to happen.
But because a flexible AI model sits at the core of these flows, AgentKit is different – it can use reasoning instead of rigid rules.
If that sounds scary, you can add a simple human-in-the-loop approval step to any flow.
Instead of “If X happens, do Y,” you can say, “If a client sends a campaign report, summarize it and save it to the right folder.”
The AI figures out how to do that by making reasonable requests that help it understand what you mean by vague instructions like “the right folder.”
For PPC marketers, this opens the door to automating work around campaigns (think reporting, documentation, and creative preparation), without waiting for a platform feature or a developer.
Get the newsletter search marketers rely on.
The unsung hero: Model Context Protocol (MCP)
Under the hood, much of the power that enables agents to take action comes from the Model Context Protocol, or MCP.
It’s not brand-new, but it’s the key piece that makes all of this work.
MCPs are the connectors that let agents talk to your tools or data in a structured way.
If you think of APIs as the connectors of the web, MCPs are similar, but built as a standard that any LLM can use.
Some are built by OpenAI, like the connectors for Dropbox or Gmail.
Others come from third-party developers, like Box.
And you can create your own to connect private data or internal systems.
You can think of it this way: MCPs are the plumbing. AgentKit is the faucet.
The plumbing defines what data can flow where. The faucet is how you turn that into something usable.
Without MCPs, an agent would be like a brilliant intern with no logins to any of the systems they need.
With them, the agent can safely use your data and tools with clear permissions.
Dig deeper: How Model Context Protocol is shaping the future of AI and search marketing
MCPs in plain terms
If this still sounds abstract, think of an MCP as a menu of what an AI can do inside a given flow.
For example, the Google Ads MCP currently includes actions like:
That’s it for now. It can read data, but it can’t change bids or create ads yet.
That limitation is a good illustration that MCPs don’t open the door to entire systems for an LLM to go wild.
Instead, they provide a defined set of capabilities created by the MCP developer.
It’s an important guardrail. And even with MCPs that offer broader capabilities, you still control exactly which actions your agent can access when you integrate them into a flow.
Even in this early state, it’s a clear preview of how AI might eventually interact with Google Ads data through well-defined, secure interfaces.
Example: A ‘brand-safe ad assistant’
Here’s what this looks like in practice.
Imagine you want an AI assistant that writes Google Ads while automatically following your brand voice and legal disclaimers.
In AgentKit, you could create an agent with two connected tools:
You could then ask the agent to “write new RSA headlines for our fall campaign using our style and disclaimers,” and it would connect with the right data to complete the task.
Behind the scenes, it reads the files, extracts the rules, and generates compliant ad copy. You still approve the final version, but the prep work is automated.
It may sound simple, especially since you can already do this with a custom GPT, but it shows how these building blocks can be expanded.
For example, you could integrate an MCP for your email platform and have the agent send a client an approval request for the creatives it generated.
Connecting data sources in AgentKit
Here are the steps to create an agent connected to the two data sources mentioned above.
In Agent Builder, click the + icon next to Tools to give your agent a new capability, such as connecting it to an MCP.
Choose an existing MCP, like the ones shown here, or connect a custom MCP by clicking + Server.
You can also add a file search capability and select the files to include directly in the pop-up dialog.
Now you can interact with the agent to see how it uses its new abilities to produce better answers and, where enabled, how it uses other tools to take actions.
Dig deeper: How to get smarter with AI in PPC
Why this shift matters for PPC
If you’ve been in PPC for a while, you’ve seen this script before.
We went from manual optimizations, to automated rules, to scripts, to automation layering – and each wave changed the skill set needed to stay ahead. Agents are the next wave.
Instead of writing scripts or building workflows with APIs, we’ll soon describe them in plain English and let AI generate the logic.
That amplifies what marketers can do.
The core skills stay the same – strategy, measurement, and judgment – but the way we build automation is about to get much faster, more flexible, and far more accessible.
The current tools for building AI agents are still early.
Setting up an MCP takes some configuration, and the Google Ads connector is limited to reading data.
But the potential is clear: AI will move beyond generating text to running workflows, checking rules, and getting work done.
If you want to stay ahead of this shift, start small.
Experiment with simple automations that connect your email, files, or reports.
Learn what agents can and can’t do yet.
Just as marketers who adopted scripts early will be the ones setting the standard later, those who learn this now will be the ones setting the standard later.
Dig deeper: Agentic PPC: What performance marketing could look like in 2030
Contributing authors are invited to create content for Search Engine Land and are chosen for their expertise and contribution to the search community. Our contributors work under the oversight of the editorial staff and contributions are checked for quality and relevance to our readers. Search Engine Land is owned by Semrush. Contributor was not asked to make any direct or indirect mentions of Semrush. The opinions they express are their own.
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Introducing Nano Banana Pro
/in Google, Online Marketing, website SEO/by Team ZYTHow Nano Banana Pro helps you bring any idea or design to life
Nano Banana Pro can help you visualize any idea and design anything – from prototypes, to representing data as infographics, to turning handwritten notes into diagrams.
With Nano Banana Pro, now you can:
Generate more accurate, context-rich visuals based on enhanced reasoning, world knowledge and real-time information
With Gemini 3’s advanced reasoning, Nano Banana Pro doesn’t just create beautiful images, it also helps you create more helpful content. You can get accurate educational explainers to learn more about a new subject, like context-rich infographics and diagrams based on the content you provide or facts from the real world. Nano Banana Pro can also connect to Google Search’s vast knowledge base to help you create a quick snapshot for a recipe or visualize real-time information like weather or sports.
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Google issues emergency Chrome update: Billions at risk if you don’t act now | Technology News – Hindustan Times
/in AI Search, website SEO, Website Traffic/by Team ZYTSubscribe Now! Get features like
Google has started pushing an urgent security fix for Chrome after detecting a serious browser flaw that attackers have already begun to exploit. The company has asked all desktop users to install the new update at the earliest, as the vulnerability places more than two billion users at immediate risk. The rollout has reached Windows, macOS, and Linux, and Google has stressed that users and organisations must confirm their browser is updated without delay.
The issue involves a zero-day flaw that attackers are currently using in real-world conditions. Google identified the bug as CVE-2025-13223 and linked it to Chrome’s V8 JavaScript engine. The flaw stems from a “type confusion” error, which can lead the browser to mishandle memory when it loads content crafted by an attacker. This gap could allow a harmful webpage to push unwanted code into the system or cause Chrome to crash.
Also read: iQOO 15 pre-booking window now live ahead of launch: Price, features, availability and more
Google has chosen not to share complete technical details yet. The company said it will release more information only after most users receive the patch. This step aims to reduce the risk of further attacks, as detailed insights could help threat actors target unpatched systems.
Users who rely on the stable version of Chrome across major operating systems remain exposed until they install the update. The alert also covers third-party browsers built using Chromium. These browsers typically adopt Chrome’s core technology, which means they could face the same risk until developers issue matching updates. The warning is especially relevant for users in India, where Chrome is widely used on personal devices, office systems, and shared computers in educational environments.
Also read: ChatGPT’s AI browser Atlas gets major upgrade with new features on Mac
Also read: Your Apple Watch can do more: 5 Features you probably never tried
People using other Chromium-based browsers should also check for updates. Until the patch is installed, users should avoid unfamiliar websites and links from unknown sources, as exposure to malicious content may increase during this period.
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Funkymedia from Lodz Honoured for Outstanding SEO Campaigns in Spain and the Canary Islands – openPR.com
/in AI Search, website SEO, Website Traffic/by Team ZYTPermanent link to this press release:
All 5 Releases
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AI Mode Is Growing — Here’s How Marketers Can Still Win Search – CMSWire
/in AI Search, website SEO, Website Traffic/by Team ZYTAI Mode Is Growing — Here’s How Marketers Can Still Win Search CMSWire
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Google Search with Gemini 3: Our most intelligent search yet
/in Google, Online Marketing, website SEO/by Team ZYTThanks to Gemini 3’s advanced reasoning, Google Search’s query fan-out technique is getting a major upgrade. Now, not only can it perform even more searches to uncover relevant web content, but because Gemini more intelligently understands your intent it can find new content that it may have previously missed. This means Search can help you find even more credible, highly relevant content for your specific question.
And in the coming weeks, we’re also enhancing our automatic model selection in Search with Gemini 3. This means Search will intelligently route your most challenging questions in AI Mode and AI Overviews to this frontier model — while continuing to use faster models for simpler tasks. This will be rolling out to Google AI Pro and Ultra subscribers in the U.S.
Generative UI: Visual layouts, interactive tools and simulations in AI Mode
Gemini 3’s unparalleled multimodal understanding and powerful agentic coding capabilities are also unlocking more bespoke generative user interfaces. Now, Gemini 3 in AI Mode can dynamically create the ideal visual layout for responses on the fly — featuring interactive tools and simulations — tailored to your query.
To do this, Gemini 3 analyzes your question and creates the most helpful layout, building a custom response with visual elements — like images, tables and grids — so the final output isn’t just informative, but clear and actionable. When the model detects that an interactive tool will help you better understand the topic, it uses its generative capabilities to code a custom simulation or tool in real-time and adds it into your response.
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Governance: The New SEO Battleground – WebProNews
/in AI Search, website SEO, Website Traffic/by Team ZYTIn the rapidly evolving world of search engine optimization, a seismic shift is underway. As artificial intelligence reshapes how content is created and consumed, industry experts argue that the future of SEO lies not in churning out more articles, but in robust governance frameworks. This transformation is driven by the need to manage AI-generated content, ensure data integrity, and comply with emerging regulations, positioning governance as the cornerstone of sustainable SEO strategies.
According to a recent analysis, the proliferation of AI tools has democratized content creation, making high-quality output accessible to all. However, this abundance has commoditized content, forcing SEO professionals to focus on governance to differentiate their efforts. ‘SEO’s future isn’t content – it’s governance,’ states an in-depth piece from Search Engine Land, highlighting how governance encompasses data management, AI ethics, and compliance to maintain trust and visibility in search results.
The AI-Driven Content Explosion
The rise of large language models like those from OpenAI and Google has flooded the internet with generated content, challenging traditional SEO paradigms. Experts note that while AI can produce vast quantities of material quickly, it often lacks the nuance and accuracy that human oversight provides. This has led to concerns over content quality and authenticity, prompting search engines to prioritize sites with strong governance practices.
Recent trends indicate that Google’s algorithms are increasingly favoring entities that demonstrate expertise, authoritativeness, and trustworthiness—collectively known as E-E-A-T. A report from Exploding Topics emphasizes that in 2025, AI Overviews and E-E-A-T will dominate SEO strategies, underscoring the need for governance to ensure content aligns with these criteria.
Redefining Data Management in SEO
Governance in SEO now extends to data stewardship, where organizations must curate and validate datasets used to train AI models. Poor data quality can lead to hallucinations or biased outputs, damaging a brand’s reputation and search rankings. Industry insiders point out that effective governance involves establishing protocols for data sourcing, verification, and updates to maintain relevance.
Drawing from posts on X, SEO expert Matt Diggity predicts that ‘AI isn’t killing SEO. It’s creating the biggest opportunity gap,’ emphasizing entity optimization and structured data as key to thriving in AI-driven search. This aligns with insights from Semrush, which discusses zero-click searches and conversational keywords as part of the 2025 landscape, where governance ensures content is optimized for these new paradigms.
Ethical AI and Regulatory Compliance
As AI becomes integral to SEO, ethical considerations are paramount. Governance frameworks must address issues like transparency in AI usage, avoiding plagiarism, and ensuring fair representation. Regulatory bodies are stepping in, with potential laws requiring disclosure of AI-generated content, making compliance a critical SEO factor.
A Forbes Council post via Forbes notes that ‘the artificial intelligence revolution is transforming search engine optimization in ways we couldn’t have imagined,’ stressing the role of governance in navigating this change. Similarly, Neil Patel’s observations on X highlight the expansion of SEO beyond Google to multiple platforms, necessitating unified governance strategies.
Case Studies in SEO Governance Success
Leading companies are already reaping benefits from prioritizing governance. For instance, enterprises implementing AI governance have seen improved search visibility by ensuring content authenticity. One example involves a major e-commerce platform that adopted strict data validation processes, resulting in a 30% uplift in organic traffic, as reported in industry analyses.
From ClearVoice, experts affirm that ‘SEO is not dying as long as people are searching,’ but it requires adapting to AI and LLMs through governance. Connor Gillivan’s X post outlines a ‘2025 Winning SEO Strategy’ that includes technical foundations like optimized speed and URL structures, all underpinned by governance to sustain long-term gains.
Navigating Zero-Click and Conversational Search
The advent of zero-click searches, where answers appear directly in search results, demands that SEO governance focuses on snippet optimization and structured data. This shift reduces traditional click-through rates, making it essential for brands to govern content for direct visibility.
Insights from Exposure Ninja detail seven critical trends for 2025, including adapting to AI changes, where governance plays a pivotal role in maintaining relevance. Posts on X from Aaron Zhu warn that ‘Generative Engine Optimization will eat SEO,’ urging a pivot to governance for AI-queried content.
Building Trust Through E-E-A-T Governance
Enhancing E-E-A-T requires governance mechanisms that document expertise and build trust signals. This includes author bios, source citations, and regular content audits to align with search engine guidelines.
A WordStream analysis lists eight trends shaping 2025 search, emphasizing user intent and AI integration, all reliant on strong governance. Usman Usman’s X post advocates a ‘2025 SEO Growth Formula’ combining AIO, digital PR, and outreach, centered on authority and trust via governance.
Future-Proofing SEO Strategies
To stay ahead, SEO professionals must integrate governance into their core operations, investing in tools for AI monitoring and compliance tracking. This proactive approach mitigates risks from algorithm updates and positions brands as leaders in ethical digital practices.
From Backlinko, a guide to 2025 SEO trends covers voice search and new on-page techniques, stressing governance for intent matching. Jonathan Berthold’s X post reflects on 2025’s chaos, predicting ‘Relevance Engineering’ in 2026, where governance will define SEO success.
Industry Voices on Governance Evolution
Quotes from thought leaders underscore this shift. ‘SEO in 2025 will look nothing like what you’re used to,’ says Matt Diggity on X, compiling predictions that highlight governance’s role. Similarly, SA News Channel’s thread on X describes SEO copywriting as a blend of strategy and optimization, with governance ensuring people-first planning.
Recent news from Future Tech Solution provides 2025 insights on algorithm updates and ranking strategies, reinforcing governance’s importance. Aaron Cort’s X post advises focusing on human intent over algorithms, a governance principle that maps real user needs.
Challenges and Opportunities Ahead
Despite its promise, implementing SEO governance faces hurdles like resource allocation and skill gaps. Organizations must train teams in AI ethics and data management to overcome these, turning challenges into competitive advantages.
A deep dive from Harmukh Technologies explores SEO’s evolution toward AI-driven reasoning, where governance ensures structured, meaningful content. Yerain Abreu’s X post notes AI’s disruptive force, urging adaptation through governance frameworks.
Strategic Implementation of Governance
Practical steps include auditing current practices, establishing governance committees, and leveraging analytics for continuous improvement. This holistic approach not only boosts SEO but enhances overall digital resilience.
From WebProNews, an article delves into hidden forces like AI overviews and user intent, advocating governance for resilient strategies. Another from the same publication on ‘Decoding SEO Mastery’ provides a blueprint for 2025, integrating ethical practices and adaptability.
Emerging Trends in AI Governance for SEO
Looking forward, trends like generative engine optimization (GEO) will demand advanced governance to optimize for AI responses. This involves entity-based SEO and knowledge graphs, ensuring content is discoverable in non-traditional searches.
Posts on X from Web Directory highlight search intent’s crucial role, with AI changing content creation. Hiilite’s news on 2026 trends via Hiilite discusses LLMs and GEO, positioning governance as essential for ranking improvements.
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Cloudflare outage hits X Perplexity ChatGPT Canva Google Cloud – theweek.in
/in AI Search, website SEO, Website Traffic/by Team ZYTNew Delhi, Nov 18 (PTI) A widespread internet outage struck several global digital platforms on Tuesday, leaving millions of users unable to access services like X, OpenAI’s ChatGPT, Perplexity AI, Google Cloud, and Canva.
Popular gaming titles League of Legends and Valorant were also impacted.
The disruption has been traced back to a critical network failure at Cloudflare, a web infrastructure company that powers a significant portion of the internet.
"Cloudflare is experiencing an internal service degradation. Some services may be intermittently impacted. We are focused on restoring service. We will update as we are able to remediate. More updates to follow shortly," Cloudflare’s system status site showed, identifying the problem as a "global network issue" causing widespread API and dashboard failures.
Outage tracking website Downdetector saw a massive spike in reports, logging over 10,000 complaints for various service disruptions before the site itself struggled to load – ironically, also becoming a victim of the Cloudflare outage.
As screens went blank, users flocked to the few surviving platforms, such as Reddit and Threads, to vent their frustration and crack jokes about the "internet meltdown".
"My entire staff portal and sites are using Cloudflare, so everything is down," a netizen said, while another took a dig at how the outage happened just after maintenance.
"Cloudflare being down, and almost all of my most visited websites and mobile banking are also down with them, means that it’s probably not smart for these companies to put all of their cybersecurity eggs in one basket," another user quipped.
(This story has not been edited by THE WEEK and is auto-generated from PTI)
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Why were X, ChatGPT, Canva and other sites down? Cloudflare says it’s working on restoring service
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