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Archilochus, the ancient Greek poet, wrote a line that has traveled through 28 centuries and now belongs to every Navy SEAL training manual and leadership keynote: We don’t rise to the level of our expectations. We fall to the level of our training. That is precisely where most marketers find themselves with artificial intelligence right now. The expectations surrounding AI are enormous. Every marketing software vendor has launched an AI feature, every industry conference has an AI-themed keynote, and every analyst firm has published a new framework. At the same time, CMOs and marketing teams are being asked to deliver more growth, more precise personalization, and greater operational efficiency—all while keeping headcounts flat. Yet, there is a stark divide between the promise of AI and its actual implementation. According to a Gartner report, From Efficiency to Impact: How CMOs Can Achieve Real AI Value, CMOs are now allocating an average of 15.3% of their total marketing budgets to AI initiatives. Despite this massive financial commitment, only 30% of marketing organizations report having a mature or fully developed state of AI readiness. The budget is there, but the operational maturity is not. This imbalance has created a state of “AI overwhelm.” Marketing leaders find themselves asking the wrong questions. Instead of focusing on which new AI tools to purchase, leaders must evaluate whether they are capturing the actual business value of the technology they have already deployed. A study commissioned by Optimove, “Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows,” confirms this gap between ambition and daily execution. The study found that while marketers have high aspirations for AI, their practical adoption remains highly fragmented. Only 39% of marketers currently use AI for content creation, 37% utilize it for campaign workflows, and a mere 14% leverage AI for building complex audience segments. In other words, the highest-impact marketing functions are currently seeing the lowest rates of AI adoption. The McKinsey Diagnosis: Why Organizations Struggle to Scale AI In the book, “Rewired: How Leading Companies Win with Technology and AI,” McKinsey & Company authors outline why corporate digital transformations frequently fail. They argue that most enterprises pursue isolated pilots, confusing technology experimentation with actual organizational transformation. Without rewiring how the business operates, these investments fail to deliver measurable financial value. McKinsey identifies six core capabilities that distinguish companies that successfully capture AI value from those that merely spend money on tools: 1. Transformation Roadmap Organizations must move beyond isolated pilots. Every digital and AI initiative should be directly tied to concrete financial value and strategic business goals. If a marketing team cannot draw a clear line from an AI capability to a specific profit-and-loss (P&L) outcome, that tool is not earning its place in the technology stack. 2. Talent Bench Rather than relying on outsourced agencies or external consultants to handle core technological capabilities, successful companies train the business leaders they already have. Building internal talent who understand both the business context and the application of AI is a primary driver of long-term success. 3. Operating Model Legacy waterfall processes must be dismantled. Modern marketing organizations require product- and platform-based operating models where multidisciplinary teams—comprising data scientists, creative professionals, and campaign managers—work as a single unit rather than passing tasks down a slow corporate relay race. 4. Distributed Technology Environment Monolithic IT systems must be broken down into modular, API-enabled architectures. The primary benefit of this shift is speed: individual business and marketing units gain the ability to build and deploy solutions independently without waiting on a centralized IT department to clear its backlog. 5. Data Everywhere For AI to be effective, high-quality, governed data must be readily accessible across the organization. High-performing companies treat data as an internal product, making it easy for non-technical teams to access. Organizations struggling with AI adoption are often still stuck manually emailing CSV files between departments. 6. User Adoption and Enterprise Scaling The majority of enterprise AI initiatives fail at the adoption phase. True transformation requires active change management and structural process redesign. Simply filming a training video and sending a Slack announcement is not enough to change how employees complete their daily work. Evaluating a marketing organization against these six capabilities often reveals significant gaps. Acknowledging these operational gaps is the first step toward building a mature AI strategy. The Evolution from AI 1.0 to AI 2.0 To understand how to close these gaps, it is necessary to recognize that we are transitioning between two distinct eras of artificial intelligence. AI 1.0 was the productivity era. The focus was on speed and efficiency: tools designed to write copy faster, generate images quickly, summarize reports, and automate manual administrative tasks. For marketing teams that executed this well, AI 1.0 successfully accelerated production times, allowing messages to reach customers more quickly. AI 2.0 is the business outcomes era. This next phase of technology builds on the efficiency gains of the first era but measures success through hard business metrics. AI 2.0 is not measured by hours saved; it is evaluated based on incremental revenue generated, conversion rate uplifts, customer retention improvements, and long-term customer lifetime value. Gartner’s data highlights the risk of staying focused on productivity metrics alone. Currently, only one in three CMOs report seeing the business returns they expect from their AI investments. High-performing marketing leaders are moving past simple time-saving metrics to prioritize business impact, monitoring how AI investments influence customer satisfaction, loyalty, and revenue growth. The correlation between automation and ROI is clear: organizations that automate a higher portion of their marketing workflows are twice as likely to report a positive ROI from their AI investments. However, short-term productivity improvements do not automatically translate into long-term profit unless the organization actively optimizes its workflows for conversion and retention. Gartner predicts that by 2028, only 10% of CMOs who focus primarily on time savings over direct business outcomes will successfully secure the budgets needed to meet their strategic goals. Financial executives are increasingly demanding evidence of revenue generation,

Archilochus, the ancient Greek poet, wrote a line that has traveled through 28 centuries and now belongs to every Navy SEAL training manual and leadership keynote: We don’t rise to the level of our expectations. We fall to the level of our training. That is exactly where most modern marketers find themselves with artificial intelligence today. The expectations surrounding artificial intelligence are staggering. Every technology vendor promises a revolutionary new AI feature, every industry conference features a keynote on generative algorithms, and every analyst firm publishes complex frameworks on cognitive enterprise transformation. Meanwhile, marketing teams are under constant pressure to deliver greater growth, deeper personalization, and higher operational efficiency—all while working with flat or shrinking headcounts. The practical reality of this landscape is far less polished. According to a research study by Gartner, From Efficiency to Impact: How CMOs Can Achieve Real AI Value, Chief Marketing Officers (CMOs) are now dedicating an average of 15.3% of their total marketing budgets to AI initiatives. Yet, despite this significant financial commitment, only 30% of marketing organizations report having a mature or fully developed state of AI readiness. The budget is actively being deployed, but the organizational capability is lagging far behind. This discrepancy has created a widespread sense of “AI overwhelm” across the industry. The critical challenge facing marketing leaders is no longer determining which new AI tool to purchase. Instead, the real challenge is figuring out how to capture actual, measurable value from the systems and technologies they have already integrated into their stacks. A study commissioned by Optimove, the “Forrester Opportunity Snapshot AI: Accelerating Marketing Impact Through AI And Agile Workflows,” confirms this gap between ambition and execution. The research reveals that while interest in AI is high, actual execution remains limited to a few specific areas. For example, only 39% of surveyed marketers utilize AI for content creation, 37% leverage it to streamline campaign workflows, and a mere 14% use it to build sophisticated audience segments. This data highlights a clear paradox: the highest-impact capabilities, such as automated segmentation and precision targeting, currently have some of the lowest adoption rates in the industry. The McKinsey Diagnosis: Why AI Pilots Stall In their book, “Rewired: How Leading Companies Win with Technology and AI,” the authors from McKinsey & Company argue that many enterprises approach AI through isolated pilots. They often mistake basic experimentation for genuine digital transformation. Without a fundamental rewiring of how an organization operates, these initiatives struggle to deliver measurable financial value. To help companies evaluate their progress, McKinsey outlines six core capabilities that separate organizations achieving concrete value from those merely spending budget on experimental technology: 1. A Value-Driven Transformation Roadmap Successful organizations move beyond disconnected pilot programs. They align every digital and AI initiative directly with clear financial outcomes and strategic business objectives. If a marketing team cannot link a specific AI tool directly to a profit-and-loss (P&L) result, that tool is not proving its worth to the business. 2. An Internal Talent Bench Winning enterprises focus on upskilling their existing business leaders in technology and AI, rather than constantly outsourcing core capabilities. Relying entirely on external consultants or third-party agencies prevents an organization from building the institutional knowledge required for long-term innovation. 3. A Cross-Functional Operating Model Legacy, waterfall-style workflows often struggle to keep pace with modern technology. High-performing organizations shift to product- and platform-based operating models. In this setup, multidisciplinary teams containing developers, data scientists, and marketers work closely together as a single, agile unit rather than passing work off across departmental silos. 4. A Distributed, API-First Technology Environment Rigid, monolithic IT architectures frequently create operational bottlenecks. Modern organizations decompose these older systems into modular, API-enabled components. This modularity allows individual business units to experiment and deploy new capabilities quickly without waiting on central IT approvals. 5. Governed Data Democratization AI models require consistent, high-quality data to function effectively. Leading companies build robust, federally governed data products that give distributed teams direct access to clean information. Organizations that struggle with AI adoption are often still manually emailing CSV files between teams, while leaders have already automated data accessibility. 6. Active Change Management and User Adoption This is where many corporate AI initiatives fail. Overcoming adoption hurdles requires more than just launching a training video or making a company-wide announcement. It demands deep process transformation and ongoing support to change how day-to-day work is actually performed across the enterprise. Most marketing departments will recognize gaps in several of these six areas. Identifying these gaps is a necessary first step toward building a more effective operational structure. Transitioning from AI 1.0 to AI 2.0 The evolution of artificial intelligence in business can be understood as two distinct phases: AI 1.0 and AI 2.0. AI 1.0 focused primarily on productivity. This phase was characterized by tools designed to write copy, generate images, summarize documents, and execute repetitive tasks faster. For early-adopting marketing teams, these speed gains allowed them to ship campaigns quickly and react to customer behaviors with less delay. AI 2.0 focuses on business outcomes. While built on the speed and productivity of the previous phase, AI 2.0 measures success through concrete financial and customer metrics. Instead of tracking time saved, organizations focus on incremental revenue, conversion rate uplifts, customer retention rates, and long-term customer lifetime value (LTV). Gartner’s research indicates that only one in three CMOs are currently seeing their expected returns from AI investments. The majority remain focused on efficiency metrics like production speed. By contrast, high-performing CMOs prioritize bottom-line business outcomes. They focus their optimization efforts on conversion rates, overall customer satisfaction, and repeat purchase behavior. Marketing teams that automate larger portions of their operational workflows are twice as likely to realize a measurable return on investment from AI. However, short-term productivity gains rarely translate into meaningful business results unless the organization deliberately designs its systems to optimize for business impact. By 2028, Gartner projects that only 10% of CMOs who focus primarily on time-saving metrics over hard business outcomes will secure

The digital publishing landscape is currently undergoing one of its most disruptive phases since the inception of the commercial internet. With the explosive rise of generative artificial intelligence (AI) tools like ChatGPT, Claude, and Gemini, the barrier to content creation has effectively dropped to zero. Millions of web pages can now be generated, optimized, and published in a matter of minutes. Yet, as the volume of automated content reaches unprecedented heights, search engines find themselves at a critical crossroads. For years, Google’s underlying mission has remained remarkably consistent: to organize the world’s information and make it universally accessible and useful. This core objective relies heavily on presenting users with high-quality, trustworthy, and original search results. While some marketers believe that the AI revolution has forced Google to fundamentally rewrite its rulebook, the reality is quite different. Google’s core quality standards have not changed. Instead, the rapid proliferation of automated, low-effort content has simply made those standards harder for publishers to ignore, and more crucial than ever for search algorithms to enforce. The tension between automated mass production and authentic, human-driven journalism has reached a boiling point, highlighting a fundamental truth about modern SEO: accountability is the ultimate ranking factor. The Human Element: Sam Sifton’s Philosophy of Journalism To understand the disconnect between automated content and search engine viability, it helps to look at those who are actively championing the opposite approach. In an industry increasingly obsessed with algorithmic shortcuts, figures like Sam Sifton, the Assistant Managing Editor of The New York Times and the founding editor of NYT Cooking, represent a steadfast commitment to human-driven journalism. Sifton’s editorial philosophy is built on a foundation of rigorous testing, personal experience, and unconditional accountability. Under his leadership, every recipe published, every restaurant reviewed, and every story told undergoes a meticulous human vetting process. If a recipe is recommended to readers, it has been cooked, tasted, and tweaked by real people who possess deep culinary expertise. There is a distinct human voice behind the content, and more importantly, a real person who takes responsibility for its accuracy. This dedication to editorial integrity is not just a moral stance; it is a highly successful business model. It builds deep, generational trust with an audience. When a user visits a site backed by this level of rigor, they know they are getting information tested in the real world, not a hallucinated average of existing web data generated by a large language model. As detailed in recent discussions on Search Engine Journal, this human-first approach perfectly mirrors what Google’s search algorithms are desperately trying to identify and reward. The qualities that make Sifton’s work successful in the eyes of readers are the exact same signals that Google uses to define “helpful content.” Google’s Unchanged Standards: A History of E-E-A-T For over a decade, Google has published its Search Quality Rater Guidelines—a dense document used by thousands of human evaluators to assess the quality of search results. These guidelines have long relied on the concept of E-A-T: Expertise, Authoritativeness, and Trustworthiness. In December 2022, just as the generative AI wave was starting to swell, Google quietly added a second “E” to the acronym, creating E-E-A-T. The new “E” stands for Experience. This addition was highly strategic. Google anticipated the coming wave of AI-generated content and realized that while an AI can synthesize “expertise” by compiling facts, it cannot possess real-world, first-hand experience. An AI has never traveled to a hotel, tested a laptop, tasted a recipe, or lived through a medical diagnosis. By prioritizing first-hand experience, Google set a benchmark that automated systems simply cannot meet on their own. Google’s Helpful Content System, which has now been integrated into its core ranking algorithms, serves a singular purpose: to filter out content created primarily for search engines rather than humans. The official documentation has consistently asked publishers questions such as: Does the content provide original information, reporting, research, or analysis? Does the content draw on real-world, first-hand experience? Would you trust this content for matters relating to your money or your life (YMYL)? Is the content written by an expert or enthusiast who demonstrably knows the topic well? These questions are not new. They are the same standards Google has championed since the Panda and Penguin updates of the early 2010s. The only difference today is that AI has made the violation of these guidelines so easy and widespread that Google has been forced to deploy increasingly aggressive algorithmic countermeasures. The Core Problem with Scaled AI Automation Why does pure AI automation struggle to rank sustainably in the long term? The issue does not lie in the technology itself, but in how it is used. Many publishers have viewed generative AI as a magic button to produce thousands of articles on every keyword imaginable, aiming to capture long-tail search traffic through sheer volume. This practice, known as scaled content abuse, violates several core tenets of search quality. 1. The Homogenization of the Web Large language models work by predicting the next most likely word based on the vast datasets they were trained on. Because they rely on historical data, they excel at producing average, consensus-based summaries of topics. When hundreds of websites use the same prompts to generate articles on the same topics, the result is a homogenized web where every page looks and sounds identical. Google does not need ten thousand identical articles explaining “How to Boil an Egg.” It needs unique perspectives, fresh data, and original reporting. When an algorithm encounters duplicate or heavily paraphrased information across multiple domains, it will inevitably consolidate ranking signals and favor the source with the highest domain authority and historical trust. 2. The Lack of Information Gain In recent years, patent filings have shown that Google actively calculates an “Information Gain” score for web pages. This system measures how much new, unique information a page brings to a user compared to other pages they have already visited. Purely automated content, by definition, struggle to provide high information gain because

Why LLMs Cite Reddit Instead Of Your Brand: A Practical AI Visibility Audit [Webinar] via @sejournal, @lorenbaker The search landscape is undergoing its most disruptive transformation since the inception of the web. For decades, search engine optimization (SEO) was a straightforward game: optimize your website, build high-quality backlinks, write comprehensive content, and secure a spot on the first page of Google. Today, that playbook is no longer sufficient. With the rapid adoption of Large Language Models (LLMs) and generative search engines—such as Google’s AI Overviews, OpenAI’s SearchGPT, Perplexity, and Claude—the way users retrieve information has fundamentally shifted. Instead of presenting a list of blue links, these engines synthesize answers directly for the user. But if you analyze these generated summaries closely, you will notice a frustrating trend: rather than citing your polished, highly optimized brand website, LLMs frequently cite Reddit threads, forum discussions, and user-generated content. This shift has left digital marketers, CMOs, and SEO professionals asking a critical question: Why do artificial intelligence engines trust a random Reddit user over an established brand with millions of dollars invested in content creation? To survive and thrive in this new ecosystem, brands must understand the underlying mechanics of AI retrieval and learn how to conduct a practical AI visibility audit. The Architecture of Trust: Why LLMs Prefer Reddit To understand why generative engines favor platform discussions over corporate blogs, we have to look at how LLMs are trained, how they retrieve real-time data, and what modern users actually want from their search experience. 1. The Data Licensing Super-Highway The most direct reason LLMs cite Reddit is access. In early 2024, Google secured a landmark $60 million per year licensing deal with Reddit, granting the search giant real-time access to the platform’s data API. Shortly after, OpenAI announced a similar partnership, integrating Reddit content directly into ChatGPT and its downstream search features. These agreements are not just business transactions; they are structural pipelines. By accessing Reddit’s real-time API, LLMs can instantly index and digest the newest trends, consensus opinions, and product feedback. While your brand’s newly published blog post might wait days or weeks to be crawled, parsed, and understood by an LLM, Reddit’s content is fed directly into the training and retrieval loops of these AI models. 2. The Concept of Information Gain Modern search engines, particularly Google, place a high premium on a patent-backed concept known as “Information Gain.” In simple terms, information gain measures how much *new* value a piece of content adds to a user’s search journey compared to what they have already seen. Most corporate blogs suffer from severe content homogeneity. In an attempt to rank for specific keywords, brands analyze top-performing competitor pages and essentially rewrite the same information. The result is a sea of repetitive, sanitized, and predictable content. Reddit, on the other hand, offers highly unique, raw, and diverse perspectives. It contains edge cases, troubleshooting tips, and contrarian opinions that cannot be found on a brand’s official FAQ page. For an LLM seeking to provide a comprehensive, multi-perspective answer, Reddit represents a goldmine of high-information-gain content. 3. The Human Consensus and Sentiment Signals LLMs rely heavily on Reinforcement Learning from Human Feedback (RLHF) during their training processes. Because these models are designed to think and communicate like humans, they gravitate toward content that has already been vetted and approved by real people. Reddit’s upvote and downvote system, nested comment structures, and community moderation act as built-in quality signals. If a specific product recommendation has 500 upvotes and dozens of supportive replies in a dedicated subreddit, the LLM’s algorithm views this as a high-authority consensus. Corporate websites lack these interactive validation signals. A brand can claim its software is the “easiest to use,” but an LLM will cross-reference that claim with community discussions to see if real users agree. 4. The Quest for Authentic Experience (E-E-A-T) Google’s quality rater guidelines place a massive emphasis on E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness. The addition of the first “E” (Experience) was a direct response to the influx of sterile, AI-generated content flooding the web. Users and AI engines alike are suffering from “marketing fatigue.” When a user searches for the “best project management tool for small teams,” they know that a brand’s landing page will be biased. A Reddit thread under the r/projectmanagement subreddit, however, features real practitioners debating the pros and cons of various tools based on their actual daily workflows. This lived experience is highly valuable to LLMs striving to deliver objective, helpful answers. What is an AI Visibility Audit? If you do not know how your brand is being perceived, summarized, or cited by artificial intelligence, you cannot optimize for it. An AI Visibility Audit is a systematic process used to evaluate your brand’s footprint across major LLMs and generative search engines. The goal of this audit is to identify where you are being mentioned, where you are losing citations to competitor platforms or community forums, and what sentiment the AI associates with your brand name. Step 1: Map Your AI Touchpoints Begin by identifying the primary AI engines your target audience uses to find information. While Google’s AI Overviews will capture the largest share of general searchers, other platforms are highly influential depending on your industry: ChatGPT (OpenAI): The market leader for general conversational queries, brainstorming, and product discovery. Perplexity AI: A search-first generative engine favored by tech-savvy users and professionals seeking real-time citations. Google Gemini: Integrated directly into the Google ecosystem and highly influential in search-driven summaries. Claude (Anthropic): Widely used for deep analysis, comparison, and technical evaluation. Step 2: Query the Engines Using Persona-Based Prompts To audit your visibility, you must move away from traditional keyword searches and adopt conversational, intent-based queries. Draft a list of prompts that represent different stages of your customer’s journey: Informational/Discovery: “What are the best methods for automating inventory management in 2025?” Commercial Investigation: “Compare Brand A and Brand B for a mid-sized marketing agency.” Direct Brand Queries: “What are the common complaints about Brand

The Next Frontier of SEO: Personal Intelligence and Brand Visibility Search engine optimization has historically been defined by how brands optimize their public-facing web assets to rank on public search engine results pages (SERPs). However, the rapid integration of artificial intelligence into daily productivity tools is shifting the search paradigm. With the introduction of personalized AI assistants, search is no longer confined to the open web. Instead, it is increasingly moving toward private data ecosystems, where artificial intelligence analyzes a user’s personal files, emails, and photos to deliver highly customized answers. To understand how these emerging personal AI models interact with private user data, the technical SEO and digital marketing agency iPullRank conducted a series of tests. Their research focused specifically on opted-in “AI Mode” Personal Intelligence accounts, examining how different private data signals influence what the AI displays. The study analyzed signals from both Google Photos and Gmail to determine which platform has the greatest impact on brand recognition and visibility within personalized AI ecosystems. The findings from the iPullRank study reveal a massive opportunity for forward-thinking digital marketers: Gmail content showed the strongest brand visibility lift in personal AI environments. This discovery suggests that the integration of artificial intelligence into email is fundamentally changing the role of email marketing, turning the user’s inbox into a critical database for personal search engines. What is Personal Intelligence and “AI Mode”? To fully grasp the implications of the iPullRank study, it is essential to understand what Google’s Personal Intelligence and “AI Mode” entail. Modern large language models (LLMs) like Google Gemini are no longer limited to the information they were trained on or the public web search results they can retrieve. Through integrations and extensions, these AI models can now access a user’s personal digital workspace, including Google Docs, Google Sheets, Google Drive, Google Photos, and Gmail. When users opt into these advanced AI capabilities, they grant the AI assistant permission to parse their private data to answer highly contextual queries. For instance, instead of searching through dozens of emails to find a flight confirmation number, a user can simply ask the AI, “When is my flight to Chicago, and what hotel am I staying at?” The AI then queries the user’s Gmail inbox, extracts the relevant details from confirmation emails, and presents a concise, structured answer. This process relies on a technology known as Retrieval-Augmented Generation (RAG). Instead of relying on general knowledge, the AI uses RAG to fetch specific, authoritative information from the user’s personal data repository to construct its response. This means that private emails are now functioning as a highly trusted, hyper-personalized index for the user’s personal AI search engine. The iPullRank Study: Gmail vs. Google Photos In their testing of opted-in Personal Intelligence accounts, the research team at iPullRank sought to determine how different data sources within Google’s ecosystem influence the AI’s output. Specifically, they looked at Gmail and Google Photos to see how effectively the AI could recognize, process, and surface brands based on the content stored in these accounts. The testing process involved feeding various brand signals into these private accounts and then prompting the AI with brand-related queries. The results were clear: Gmail content provided a significantly stronger brand visibility lift than Google Photos. While Google Photos does possess advanced image recognition capabilities that allow the AI to identify objects, locations, and even brand logos within a user’s photo library, the structured text and direct context found within Gmail proved to be far more powerful. The text-heavy nature of emails, combined with transactional data and clear brand identifiers, makes Gmail the primary source of truth for personal AI models when generating brand-related responses. Why Gmail Content Boosts Brand Visibility in AI Mode There are several technical and structural reasons why Gmail content exerts such a powerful influence on Personal Intelligence models. Understanding these factors is key to understanding how brands can optimize their email communications for the age of AI search. 1. High-Quality, Structured Text and Context AI models are fundamentally language processors. They excel at understanding context, sentiment, and relationship entities within text. Gmail messages are packed with high-quality, descriptive text. Unlike a social media post or an image, an email often contains a complete narrative of a consumer’s interaction with a brand. Whether it is a newsletter discussing new product releases, a customer service interaction, or a detailed product review, Gmail provides the semantic depth that AI engines require to understand the relationship between a user and a brand. 2. Transactional and Behavioral Data Emails are home to highly authoritative transactional records, including order confirmations, receipts, shipping updates, and booking details. This data represents a confirmed, financial relationship between the consumer and the brand. When an AI parses this information, it recognizes that the user is an active customer of that brand. Consequently, if the user asks the AI for recommendations or inquiries about past purchases, the AI will prioritize the brands found in these transactional emails, as they represent verified user preferences. 3. Use of Schema Markup and Metadata Many transactional and promotional emails utilize structured data markup (such as Schema.org) to help email clients display interactive features, like RSVP buttons or package tracking trackers. Personal AI engines are highly sensitive to this structured metadata. When an email contains clean JSON-LD or Microdata, the AI can effortlessly extract key entities, dates, prices, and brand names without needing to interpret unstructured text. This dramatically increases the likelihood that the brand will be accurately cataloged and surfaced by the AI. 4. Longevity and Retrieval Value Unlike search queries or social media feeds, which are highly ephemeral, emails are rarely deleted immediately. Most users archive their emails, allowing messages from months or even years ago to remain in their personal index. This means that a well-optimized email sent by a brand today can continue to drive brand visibility within a user’s AI assistant indefinitely, acting as a permanent reference point for the AI’s personal retrieval system. The Convergence of SEO and Email

Introduction: The Evolution of Search and the AI Overview Era Google’s transition from a traditional search engine to an AI-powered answer engine has been one of the most significant shifts in the history of the internet. With the rollout of AI Overviews—previously known during its testing phase as the Search Generative Experience (SGE)—Google aimed to redefine how users interact with information. Instead of presenting a simple list of blue links, Google now attempts to synthesize complex web data into a single, cohesive, and conversational answer at the very top of the search engine results pages (SERPs). However, this transition has not been without its hurdles. From factual hallucinations to bizarre recommendations, the AI-generated summaries have faced intense scrutiny from users, journalists, and search engine optimization (SEO) professionals alike. In a recent candid discussion, Google CEO Sundar Pichai addressed these concerns head-on, reviewing a live AI Overview and admitting that the system was, in some cases, “more opinionated than it should be.” This admission sheds light on the complex balancing act Google must perform: delivering fast, direct answers while maintaining neutrality and preserving the fragile ecosystem of publishers and content creators who fuel the web. Sundar Pichai’s Admission: “More Opinionated Than It Should Be” The controversy surrounding AI Overviews often centers on how the underlying Large Language Models (LLMs) interpret search queries. Unlike standard search algorithms that match keywords and rank pages based on authority and relevance, generative AI attempts to construct a narrative. In doing so, it sometimes crosses the line from summarizing objective facts to taking a definitive, subjective stance. During a live review of the search feature, Sundar Pichai observed an AI Overview that took a surprisingly firm stance on a subjective topic. Pichai openly acknowledged the flaw, stating that the output was indeed “more opinionated than it should be.” This comment highlights a fundamental challenge in generative AI development: teaching machines the nuance of human opinion versus objective reality. For decades, Google’s primary objective has been to remain an impartial gatekeeper of information. When a user searches for a controversial topic, a subjective question, or a comparison between two products, Google’s traditional algorithm presents diverse perspectives from various sources. By contrast, an AI Overview that adopts a specific opinion risks alienating users, presenting biased viewpoints as absolute truth, and misrepresenting the consensus of the web. Why AI Bias and Subjectivity Pose a Threat to Google’s Core Mission To understand why an “opinionated” AI is problematic for Google, one must look at the foundation of search user experience. Google’s dominance is built on trust. Users trust that when they input a query, the search engine will return the most accurate, reliable, and unbiased resources available. The Danger of Single-Source Answers In a traditional search layout, if a user searches for “Is a low-carb diet healthy?” they are presented with articles highlighting both the benefits and the potential risks from medical journals, fitness blogs, and news outlets. The user is left to synthesize this information and form their own opinion. When an AI Overview takes the lead, it often synthesizes these viewpoints into a single paragraph. If the model leans too heavily on one subset of training data or poorly weighs the consensus, it may declare definitively that low-carb diets are either universally good or universally bad. This lack of nuance is not just a minor annoyance; for “Your Money or Your Life” (YMYL) queries—which cover health, finance, and safety—it can have serious real-world consequences. The Challenge of Neutrality in LLMs Large Language Models are trained on vast datasets consisting of human-written text from books, articles, websites, and social media. Because human writing is inherently filled with bias, opinions, and subjective arguments, LLMs naturally inherit these traits. Google’s engineering teams work continuously to implement guardrails, safety filters, and alignment techniques to keep the AI neutral, but Pichai’s admission proves that these guardrails are still a work in progress. The Publisher Dilemma: Traffic, Citations, and the Concept of Bounce Clicks Beyond the philosophical questions of AI neutrality lies a very practical, financial concern for the digital publishing industry. If Google provides the answer directly on the search results page, why would a user ever click through to a publisher’s website? For over twenty years, a symbiotic relationship existed: publishers created high-quality content, and Google sent them traffic in exchange for indexing that content. AI Overviews threaten to disrupt this balance. Pichai addressed these anxieties by discussing user behavior, publisher traffic, and the phenomenon of “bounce clicks.” Understanding “Bounce Clicks” in the AI Era In web analytics, a “bounce” traditionally occurs when a user visits a page and leaves without interacting further. In the context of AI Overviews, the term takes on a slightly different nuance. It refers to situations where users click on a citation link within an AI summary, quickly realize the AI had already extracted the exact piece of information they needed, and immediately bounce back to the SERP. While Google maintains that AI Overviews actually drive high-quality traffic to websites because the users who do click are highly motivated, many publishers remain skeptical. The fear is that informational search queries—the bread and butter of many content sites—will see a massive decline in organic click-through rates (CTR). If a user can see the recipe, the coding syntax, or the historical date directly in the Google interface, the publisher who wrote the original content loses the page view, the ad impression, and the potential conversion. Pichai’s Stance on Publisher Traffic Despite these industry fears, Pichai defended Google’s implementation of AI in search, asserting that the company remains committed to sending valuable traffic to the web ecosystem. Google’s internal data suggests that the links featured within AI Overviews receive higher click-through rates than standard search listings would in the same position, because the AI contextualizes the link for the user. However, the SEO community remains watchful. The consensus among digital marketers is that while high-intent, transactional queries might still yield valuable traffic, purely informational websites must adapt to a

The digital advertising landscape is undergoing another seismic shift as Google continues to streamline its ad formats and lean heavily into artificial intelligence and multi-channel automation. In a move that signals the end of an era for traditional pay-per-click (PPC) marketing, Google is retiring standalone Display campaigns and transitioning Google Display Network (GDN) inventory directly into Demand Gen campaigns. This transition marks a fundamental change in how advertisers purchase, manage, and optimize banner and visual ads across Google’s vast ecosystem. For over a decade, standalone Display campaigns served as a cornerstone of digital marketing, offering advertisers a way to secure cheap impressions, build brand awareness, and run retargeting campaigns across millions of third-party websites and mobile apps. Now, that inventory is being consolidated under the umbrella of Demand Gen, altering control mechanics, audience targeting, brand safety exclusions, and performance reporting. For search engine marketing (SEM) professionals and brands alike, adapting to this change is crucial. To navigate this transition successfully, advertisers must understand the mechanics of Demand Gen, how it differs from traditional Display, and how to adjust their creative and targeting strategies for an AI-first ad environment. The Evolution of Google Ads: From Discovery to Demand Gen To understand why Google is retiring standalone Display campaigns, it is helpful to look at the trajectory of Google’s ad products over the last few years. In late 2023, Google officially introduced Demand Gen campaigns as the direct successor to Discovery campaigns. Designed to compete with social media advertising platforms like Meta, TikTok, and Pinterest, Demand Gen was built to capture consumer attention during the exploratory phases of the buying journey. Unlike traditional search campaigns that capture active intent, or legacy Display campaigns that often serve passive banners on peripheral websites, Demand Gen focuses on highly visual, immersive, and native ad placements. Until now, Demand Gen lived alongside standalone Display. However, by merging legacy GDN inventory into the Demand Gen ecosystem, Google is pushing advertisers away from fragmented, single-network campaigns and toward unified, audience-first, multi-format media buying. What is Demand Gen and Where Does It Serve? Demand Gen campaigns are designed to reach users across Google’s most visually engaging, high-traffic properties. With the integration of legacy Display Network inventory, Demand Gen now spans an incredibly broad and diverse footprint: YouTube Shorts: Vertical, short-form video content that has seen explosive growth and offers direct competition to TikTok and Instagram Reels. YouTube Home and Watch Next Feeds: Premium placements on the YouTube homepage and alongside recommended videos where users go to discover new content. Google Discover: The highly personalized content feed on mobile devices that serves news, articles, and videos based on user interests. Gmail: Social and Promotions tabs inside Google’s email client, offering native, expandable ad placements. Google Display Network (GDN): The millions of partner websites, blogs, news outlets, and mobile apps that previously comprised standalone Display targeting. By folding standalone Display into this mix, Google allows its machine learning models to dynamically shift budget between these placements based on where a user is most likely to engage or convert. Key Differences Between Standalone Display and Demand Gen The transition from standalone Display to Demand Gen is not merely a rebranding; it represents a complete overhaul of how visual campaigns are targeted, bid on, and measured. Advertisers accustomed to the precise, manual controls of legacy Display will notice several key differences. 1. Target Audience vs. Contextual Placement In traditional Display campaigns, advertisers could target ads contextually—placing banners on specific websites, blogs, or forums using keywords, topics, and manual placement exclusions. If a brand sold hiking boots, they could choose to show their ads exclusively on outdoor recreation blogs. Demand Gen moves away from this granular, site-specific targeting. Instead, it prioritizes audience-centric targeting. Advertisers leverage first-party data, custom segments, and Google’s unique “Lookalike segments” to find users who match their ideal customer profile, regardless of what website or app they happen to be browsing at that moment. 2. Bidding and Optimization Strategies Standalone Display campaigns offered a variety of bidding models, including manual Cost-Per-Click (CPC), viewable Cost-Per-Thousand-Impressions (vCPM), and target CPA. Demand Gen is entirely automated and conversion-focused. It relies heavily on Smart Bidding, offering options such as: Maximize Conversions Target Cost-Per-Acquisition (tCPA) Maximize Conversion Value Target Return on Ad Spend (tROAS) Maximize Clicks (often used to drive high-quality traffic to the top of the funnel) 3. Asset Variety and Creative Requirements Legacy Display campaigns primarily relied on static image banners (GIF, JPEG, PNG) in standardized dimensions like 300×250, 728×90, or 160×600. While Responsive Display Ads eventually added some automation, the creative demands remained relatively basic. Demand Gen is a creative-first campaign type. It requires a diverse mix of high-quality assets, including landscape and square images, vertical videos for YouTube Shorts, and long-form horizontal videos. Google’s AI dynamically compiles these assets into the optimal format for each specific placement, making video a critical element of success where it was previously optional in standard Display. How Exclusions, Reporting, and Controls Are Changing The consolidation of GDN into Demand Gen significantly impacts how digital marketers manage brand safety, view performance data, and exercise control over their ad spend. Adjusting Brand Safety and Placement Exclusions One of the biggest concerns for advertisers transitioning from legacy Display is placement control. On the Google Display Network, ads can occasionally appear on low-quality mobile apps or controversial websites. Historically, advertisers managed this by uploading extensive lists of placement exclusions. With Demand Gen, the application of placement exclusions works differently. Because Demand Gen serves across premium Google-owned properties (like YouTube and Discover) alongside third-party sites, traditional, granular placement exclusions are treated differently. Content suitability settings, digital content labels, and account-level placement exclusions still apply, but advertisers have less direct control over where individual impressions land on the wider web. Google’s algorithm plays a larger role in determining brand-safe, high-performing environments. Modernized Reporting and Analytics Reporting in Demand Gen campaigns is designed to provide a holistic view of the customer journey across multiple touchpoints, rather than isolated last-click metrics on a

How To: Optimize Your Small Business For AI-Powered Search via @sejournal, @lorenbaker The search engine landscape is undergoing its most significant transformation since the invention of the crawler. Traditional search, which relied on users scrolling through a list of blue links, is rapidly evolving into a conversational, generative model. Today, search engines do not just point users toward answers—they generate those answers directly using advanced artificial intelligence. With the rise of Google’s AI Overviews (formerly SGE), Bing Copilot, ChatGPT Search, and Perplexity AI, the way consumers find local services and products has fundamentally changed. If your small business relies on search engine optimization (SEO) to drive traffic and leads, adapting to AI-powered search is no longer optional. It is a necessity for survival. To keep your brand visible and trusted by these sophisticated AI engines, you must transition your strategy from traditional search engine optimization to Generative Engine Optimization (GEO). Here is a comprehensive, actionable guide on how to optimize your small business for the new era of AI search. Understanding AI-Powered Search: How LLMs Find Information Before optimizing your website, it is crucial to understand how Large Language Models (LLMs) and conversational AI engines retrieve information. Unlike traditional search algorithms that crawl keywords and index pages based primarily on backlink authority, AI engines use a process known as Retrieval-Augmented Generation (RAG). When a user asks a question, the AI engine performs a real-time search of its index, retrieves the most relevant and high-quality documents, and synthesizes that information into a cohesive, conversational response. Along with the generated text, the AI provides citations, links, or cards pointing to its sources. AI search engines prioritize three primary factors when selecting sources to cite: Factuality and Accuracy: AI engines avoid hallucinating by pulling data from highly structured, consistently verified, and trusted sources. Brand Authority and Sentiment: LLMs read across the web to understand public consensus about your business. They look at reviews, news articles, and forum discussions to gauge your reputation. Contextual Relevance: AI excels at matching long-tail, conversational queries with highly specific solutions. For a detailed breakdown of this shifting paradigm, you can read the full recap of the industry discussion on Search Engine Journal. 1. Focus on Entity-Based SEO and the Knowledge Graph In traditional SEO, you optimize for keywords. In AI search, you must optimize for “entities.” An entity is a well-defined person, place, thing, or concept that a search engine can uniquely identify. AI models understand the world through a web of connected entities (a Knowledge Graph). To ensure an AI engine recognizes your small business as a prominent, trusted entity in your industry and geographic area, you must build a strong digital footprint. Claim and Populate Major Digital Directory Listings AI engines train on public data. If your business information is inconsistent across the web, AI engines will view your brand as less reliable. Ensure your Name, Address, and Phone Number (NAP) are identical across all major platforms: Google Business Profile Apple Maps and Apple Business Connect Yelp, Bing Places, and Yahoo Local Niche directories specific to your industry (e.g., TripAdvisor, Houzz, or Avvo) Build a Presence on Wiki Databases While small businesses may not qualify for a Wikipedia page due to strict notability guidelines, AI models heavily rely on structured open databases. Creating a profile on Wikidata or DBpedia can help search engines establish your business as a recognized entity. 2. Leverage Advanced Schema Markup Schema markup is a form of structured data code that you add to your website. It helps search engine crawlers understand the context of your content, rather than just reading raw text. For AI-powered search, schema is the ultimate translator. By using structured data, you tell AI engines exactly what your business does, what products you sell, where you are located, and how customers feel about you. Ensure you implement the following schema types on your website: LocalBusiness Schema This code specifies your business hours, location, contact details, payment accepted, and service area. It ensures that local conversational queries like “find a family-owned Italian restaurant near me open after 10 PM” pull up your establishment. Product and Price Schema If you sell physical goods or specific services, use Product schema. This allows AI search tools to pull your pricing, availability, and product features directly into search results, making your business more competitive in comparative AI prompts. FAQ and Review Schema AI platforms frequently generate summaries based on user questions. Using FAQ schema helps AI engines easily parse your answers and attribute them to your brand. Review schema displays your star ratings, signalling authority and customer satisfaction to the AI crawler. 3. Optimize for Conversational and Long-Tail Queries The way people search is shifting from fragmented keywords to natural, conversational dialogue. Instead of typing “best HVAC repair Denver,” a user might voice-search: “My AC is leaking water in my basement; what should I do, and who can fix it today in Denver?” To capture this conversational traffic, your content strategy must adapt: Adopt a Question-and-Answer Format Structure your blog posts, service pages, and FAQs to directly address common customer pain points. Use clear headers (H2 and H3) that phrase questions exactly as a customer would ask them, followed immediately by a direct, concise answer in the first paragraph. This structure is highly scannable for LLMs looking to pull quick quotes or bullet points for search overviews. Create Comparison and Direct-Answer Guides AI search engines are often used to help users make decisions. They generate comparison tables and pros-and-cons lists. Write unbiased comparison articles (e.g., “Tankless vs. Traditional Water Heaters”) and detail-rich guides to position your website as the definitive source of information the AI uses to build these summaries. 4. Cultivate Consistent, High-Quality Reviews AI search engines are designed to give users the best possible recommendations. When a user asks an AI tool, “Who is the most reliable commercial plumber in Atlanta?” the AI does not just look at who has the best keywords. It actively reads and analyzes customer reviews

The landscape of search and digital publishing is undergoing its most radical transformation since the invention of the hyperlink. For decades, the primary objective of web design and search engine optimization (SEO) was clear: build visually appealing websites for human eyes, and use basic technical optimizations to help search engines catalog those pages. We focused heavily on visual aesthetics, conversion rate optimization (CRO), and user interface (UI) design, treating search bots as secondary, passive observers. Today, we are entering an era dominated by artificial intelligence, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and autonomous AI agents. Modern platforms like ChatGPT, Claude, Gemini, Perplexity, and Google’s AI Overviews do not simply crawl and index your pages; they actively read, synthesize, summarize, and cite them to answer user queries directly. If these automated systems cannot efficiently parse your website, your brand risks becoming entirely invisible to a rapidly growing segment of the digital population. To survive and thrive in this new ecosystem, digital publishers, web developers, and SEO strategists must adopt a new paradigm: Machine-First Architecture (MFA). By designing your digital properties for the most constrained consumer—the machine—you build an incredibly robust, high-performance foundation that naturally elevates the experience for every human visitor. The Concept of the “Most Constrained Consumer” To understand why Machine-First Architecture is so powerful, we must first look at the concept of the most constrained consumer. Human visitors are remarkably adaptable. We possess highly sophisticated cognitive abilities that allow us to instantly filter out visual noise, recognize page layouts, ignore intrusive ads, and understand contextual nuances even when a page’s code is poorly written or disorganized. A machine, on the other hand, has none of these intuitive advantages. Whether it is an SEO crawler, an AI scraping bot, or an accessibility screen reader, a machine is a highly literal, highly constrained consumer. It relies entirely on the structural integrity of your code, the clarity of your metadata, and the logical organization of your content. If a machine encounters a convoluted DOM (Document Object Model) tree, broken semantic tags, or heavy client-side JavaScript rendering blocks, it will struggle to extract the core meaning of your content. When you architect a website to satisfy the rigorous, structured needs of a machine, you solve the fundamental issues of web performance, accessibility, and crawlability. In essence, optimizing for the machine forces you to build a clean, fast, and highly logical website, which ultimately provides a superior experience for human users as well. Pillar 1: How Machines Identify Your Brand and Content The first step in Machine-First Architecture is establishing a clear, unambiguous digital identity. Before a machine can read or cite your content, it must be able to verify who you are, what authority you hold, and whether your website is a trusted source of truth. Entity Resolution and Schema Markup In the age of the semantic web, search engines and AI models do not just look at keywords; they look at entities and the relationships between them. An entity is a distinct, well-defined concept, such as a person, place, organization, or object. To help machines identify your brand as a trusted entity, you must implement comprehensive JSON-LD structured data. This goes far beyond basic metadata. You should utilize specific schemas to construct an interconnected knowledge graph of your brand: Organization Schema: Explicitly define your brand’s name, official logo, contact details, and physical address. SameAs Properties: Use the sameAs attribute within your schema to link your website to verified external profiles, such as your Wikipedia page, Wikidata entry, official social media profiles, and industry directories. This helps machines resolve your brand’s identity across the web. Author and Publisher Schema: Connect every piece of editorial content to a verified author entity (using Person schema) and publisher entity (using Organization schema), proving that your content is created by real, authoritative experts. DNS Security and Domain Trust Machines evaluate the security and legitimacy of your domain before choosing to trust your data. Implementing robust Domain Name System (DNS) protocols is a critical aspect of machine-first identity. Make sure your domain is fully secured with: HTTPS/TLS: Secure, encrypted connections are mandatory for machine trust. SPF, DKIM, and DMARC: These email authentication protocols verify that your domain cannot be easily spoofed, protecting your brand’s reputation in automated trust networks. Security.txt: Adding a standardized security.txt file to your server’s /.well-known/ directory tells automated security systems how to report vulnerabilities, signaling that your platform is actively managed and secure. Pillar 2: How Machines Read Your Content Once a machine has identified and trusted your domain, it needs to read and comprehend your content. Traditional search engines used simple text parsers. Modern AI search engines, however, utilize vector databases and chunking strategies to split your content into digestible pieces for RAG systems. If your content layout is overly complex, the machine’s “chunks” will be filled with useless noise, leading to poor comprehension and a lack of citations. Semantic HTML5: Beyond the “Div Soup” Many modern websites are built using complex frontend frameworks that generate nested layers of generic <div> tags. This is often referred to as “div soup,” and it is a major obstacle for machines. To make your site highly readable, you must utilize native, semantic HTML5 markup to define the structural hierarchy of your content: <header> and <footer>: Clearly demarcate the global navigational and administrative elements of your website. <nav>: Isolate primary navigation menus so crawlers can understand your site’s structure without getting confused by internal links. <main>: Tell the machine exactly where the primary, unique content of the page begins and ends. <article>: Wrap your core editorial content, blog posts, or news reports in an article tag, signaling that this text can stand alone as a valuable resource. <aside>: Place secondary information, sidebars, and advertisements within an aside tag, telling the machine that this content is non-essential and can be ignored during core analysis. Eliminating JavaScript Obstacles While search engines like Google have advanced capabilities to render JavaScript, doing so is highly resource-intensive and expensive. Many

The landscape of search marketing is shifting at a rapid pace. As search engines evolve into generative AI engines, the demand for forward-thinking professionals is reaching historic highs. From local search optimization and paid media strategy to cutting-edge Answer Engine Optimization (AEO), companies are actively looking for experts who can navigate these disruptive changes. Whether you are a seasoned pay-per-click (PPC) specialist, a technical SEO lead, or an organic growth strategist, the career opportunities available today reflect the industry’s focus on modern platforms, AI-driven visibility, and measurable revenue generation. Below, you will find a curated breakdown of the latest search marketing roles open at leading brands and specialist agencies, along with expert insights on the skills needed to secure them. Newest SEO and Organic Growth Jobs The organic search landscape is no longer just about optimizing for traditional blue links. Emerging specialties like Generative Engine Optimization (GEO) and Answer Engine Optimization (AEO) are redefining what it means to build an organic marketing footprint. The following open positions are provided to us by SEOjobs.com. Marketing Lead — Blissbook (Remote) Date Posted: May 29, 2026 | Apply Here Blissbook is on a mission to help HR teams design and deliver high-quality, digital employee handbooks and policy experiences. Having built a profitable, bootstrapped, and steadily growing business with hundreds of happy clients, they are now looking for an entrepreneurial, highly execution-oriented marketer to take full ownership of their marketing ecosystem. This role is ideal for modern marketers who understand how to leverage AI agents to scale outreach, content generation, and operations. Instead of managing a massive, traditional team, you will design automated workflows, utilize cutting-edge AI marketing tooling, and lead the brand’s growth strategies directly. SEO, GEO & Content Strategist — Stratabeat Date Posted: May 28, 2026 | Apply Here Stratabeat is seeking an innovative strategist who wants to help transform mid-market and enterprise B2B brands. This position is built for an ego-free, collaborative professional who is insatiably curious and wants to see their strategic ideas brought to life. The inclusion of “GEO” (Generative Engine Optimization) in this title indicates Stratabeat’s commitment to optimizing content for AI-powered engines like ChatGPT, Google Gemini, and Claude. If you are comfortable looking beyond traditional keyword volumes to build authoritative, query-focused content frameworks, this is an excellent environment to showcase your expertise. Growth SEO Associate — Solace Date Posted: May 26, 2026 | Apply Here In the United States, healthcare navigation remains a profound challenge. Statistics show that roughly 88% of American adults lack the fundamental health literacy to navigate the medical system on their own. Solace is solving this problem by pairing patients with dedicated professional advocates. As a Growth SEO Associate, you will help make these life-saving advocacy services more discoverable online. The role demands a balance of high-empathy content development, local search optimizations, and strong technical SEO foundations to ensure that people searching for complex medical answers find Solace’s resources. SEO Content Writer — Property Restoration & Disaster Recovery — Compose.ly Date Posted: May 25, 2026 | Apply Here This specialized writing opportunity is tailored for those with hands-on experience in property restoration, disaster recovery, or closely related service industries. High-quality content in this sector requires technical precision, empathy, and strict adherence to industry regulations. Your writing will need to align with local SEO signals and on-page optimization practices, addressing real-world concerns like water damage, mold mitigation, structural restoration workflows, and customer concerns during crisis situations. It is a fantastic opportunity to combine industry-specific domain expertise with modern SEO writing methodologies. Director, Earned and Owned Strategy (SEO and Content) — Wpromote Date Posted: May 24, 2026 | Apply Here Wpromote is looking for a senior-level Director of Earned & Owned Strategy to act as an individual contributor overseeing organic programs across enterprise accounts. This high-impact role focuses heavily on local search strategies and multi-location businesses. If you have a strong background in scale-focused local SEO, programmatic technical setups, and mapping out content architecture for consumer brands with hundreds of brick-and-mortar storefronts, this role offers a prominent platform within one of the nation’s premier independent performance agencies. Director, Organic Growth (SEO & GEO) — SKIMS Date Posted: May 22, 2026 | Apply Here SKIMS has taken the fashion and apparel world by storm, crafting technically innovative, design-forward underwear, shapewear, and loungewear. To maintain their massive cultural and economic momentum, they are searching for a Director of Organic Growth to run their global SEO and Generative Engine Optimization strategy. This is a defining role for an experienced growth leader. You will determine how a premier e-commerce brand targets visibility inside modern platforms, ensuring SKIMS remains highly recommended by consumer AI applications, lifestyle search engines, and traditional search crawlers alike. SEO Service Delivery Manager — SEO Services — Gannett Date Posted: May 22, 2026 | Apply Here Gannett is seeking an SEO Service Delivery Manager to bridge the gap between technical execution and elite client management. You will serve as the core operational leader overseeing performance tracking, campaign setups, and client satisfaction metrics. This position requires a leader with deep technical SEO knowledge paired with refined operational discipline. You must be comfortable managing diverse delivery teams, serving as a primary escalation point for complex programmatic challenges, and reporting clear ROI metrics to internal and external partners. Digital Marketing Assistant — Remote — F5 Logistics Marketing Consultants Date Posted: May 21, 2026 | Apply Here If you are looking for a hands-on, execution-focused operational role, this remote position with F5 Logistics is an ideal entry point. This is not a strategy-level planning role; instead, it focuses on daily implementation across multiple client accounts. Key responsibilities include publishing content updates, managing B2B prospect lists, auditing local directory listings, and assisting with general agency administration. Working directly with the founder, this role requires exceptional English communication skills and a meticulous eye for detail. Growth Marketer, Pipeline Development — Samba Date Posted: May 21, 2026 | Apply Here Samba is a media intelligence powerhouse tracking real-time consumer viewing behavior across