Today, I’m excited to share that Yahoo has rolled out MyScout, a new and personalized homepage within its Scout AI platform. This feature transforms Yahoo’s AI search into a daily dashboard tailored just for me.
How MyScout Works. As a logged-in user, I have the power to customize my homepage with tiles that gather information from various Yahoo properties like Mail, News, Sports, Finance, and Games. Here are some of the features I find useful:
Inbox previews from Yahoo Mail.
Live stock updates from my Yahoo Finance watchlists.
The latest news topics and trending stories.
Scores and schedules for my favorite sports teams.
Weather updates, shopping comparisons, and fun games.
I can easily add, remove, reorder, or create tiles to follow topics or queries that interest me.
Certain tiles provide real-time updates, like stock prices.
Others refresh throughout the day with new emails, sports scores, and breaking news.
As the system learns from my activities, it promises a more “agentic and personalized” experience.
New Publisher Features. Yahoo emphasizes supporting the open web by directing users to the original sources of AI-generated answers. With this goal in mind, Yahoo News introduces new publisher features to help grow my recurring audience:
Publisher brand pages that consolidate my articles, videos, and social media feeds on Yahoo.
A follow feature allowing users to subscribe to my content and receive curated newsletters in their inbox.
Availability: MyScout, part of Yahoo Scout, is now in beta for U.S. users at Scout.com and through the Yahoo Search app on iOS and Android.
PPC is becoming an increasingly difficult landscape to navigate, and even though AI provides some help, it doesn’t save the day. Meanwhile, platform transparency continues to decline, leaving us in the dark about budget management.
The latest survey of PPC professionals reveals a challenging environment characterized by less transparent platforms, diminishing effectiveness of traditional measurement methods, and AI tools that have yet to revolutionize our daily routines.
Why I care. As someone deeply invested in PPC, it’s notable that over half of practitioners (53%) believe PPC has become tougher compared to two years ago. The issue isn’t just competition; it’s the increasing number of decisions being made by platforms out of advertisers’ view, which contributes to this growing complexity.
Considering that a whopping 89% of digital ad spend goes to just three companies, those of us who don’t have private measurement tools are essentially navigating without a compass.
By the numbers:
1,306 respondents participated in the survey conducted between November and December 2025, representing agency, freelance, and in-house roles.
62% identified platform opacity as the main reason for increased PPC complexity, with 53% pointing to the loss of effective measurement tools.
5.2 hours/week are saved on average with AI tools, though the majority of us (55%) save only 1–5 hours; almost nobody reports saving over 20 hours.
59% are now using LLMs for ad copy, up significantly from 42% the previous year, marking it as the fastest-growing AI use case.
73% of in-house teams now manage PPC entirely in-house, a significant increase from 44% two years ago.
20% of clients are considering replacing agency work with AI, compared to just 12% planning to switch agencies.
$1 trillion was spent globally on digital ads in 2025, with 89% directed towards Google, Meta, or Amazon.
What they’re saying. Among PPC features, exact match keywords remain the most reliable, with 75% of us using them frequently. However, AI Max for Search sees minimal adoption, with 34% never having used it, possibly due to it being one of Google’s newest updates. Across the board, auto-apply recommendations are viewed with skepticism.
Between the lines. The underlying theme in the report revolves around agency survival. Many of us (62%) highlight the challenges of finding talent and increasing revenue, with the real threat being clients opting to manage PPC internally using AI.
The big picture. We’ve developed a cautious yet practical approach to incorporating AI — leveraging it for tasks like copywriting and research while being wary of its ability to make autonomous decisions. The more pressing issue that remains unaddressed is that platforms are gaining control and giving us less control over visibility, with no easy solution on the horizon.
As a marketing professional, I’ve experienced various identity crises in my journey. Initially, I was just a channel expert, then an integrated marketer, and eventually evolved into roles like growth and performance marketing. And then, AI became a buzzword that sneakily entered everyone’s job description.
Now, I find myself stepping into the era of the full-stack marketer, especially as a media leader. It’s strikingly similar to adopting a product management mindset.
Don’t worry, this doesn’t mean writing Jira tickets for fun (though some of us might enjoy it). It actually signifies that the most successful media leaders will not just focus on campaign optimization. They’ll take ownership of outcomes, foster cross-team connections, and holistically enhance the entire user experience, from first contact to final conversion and beyond.
In the sectors I’ve engaged with, especially those with extensive consideration cycles and rising acquisition costs, the link between marketing performance and the user experience is evident.
Let’s explore what spurs the rise of the full-stack marketer, what it truly means to “think like a product manager,” and why this mindset is essential for media leaders today.
What is a full-stack marketer, anyway?
From my perspective, a full-stack marketer knows the importance of how various elements mesh together, rather than trying to juggle everything solo, which inevitably leads to burnout.
Reflecting on my career, truly impactful media decisions are never born from expertise in a single channel. Instead, they stem from a broad fluency, inclusive of:
Media and channels: Understanding paid search, paid social, SEO, email, SMS, and staying abreast of upcoming trends and platforms.
Creative and messaging: Grasping what resonates, where, and why.
Data and analytics: Diving beyond dashboards by asking insightful questions.
UX and CRO: Identifying friction, intent, and behavior patterns.
Technology and platforms: Utilizing CRMs, CMSs, automation tools.
The full-stack marketer’s goal isn’t to become an all-knowing expert in every facet. Instead, we aim to gather sufficient knowledge to connect insights and make informed decisions by consistently zooming out and then zooming in whenever necessary.
Why media leaders are evolving into product thinkers
As I reflect on my earlier career, media leadership often revolved around meeting CPA targets and efficiently allocating budgets. These metrics mattered, and they still do.
Yet now, the landscape demands tackling larger, more complex questions like declining conversion rates or mysterious pipeline drop-offs, which oftentimes are product questions by nature.
Product managers focus heavily on the comprehensive experience — the user journey, friction points, trade-offs, and ultimate outcomes. Adopting this mindset encourages media leaders to view campaigns as part of a larger ecosystem, influencing our decision-making significantly.
Media doesn’t live in a vacuum
Marketing performance isn’t isolated. In many sectors, particularly those with extended decision cycles, a click represents merely the beginning of an intricate journey.
Industries such as financial services, healthcare, and education involve buyers moving through nonlinear paths, impacted by numerous interactions. This scenario is where the full-stack mindset becomes crucial.
Example 1: When media isn’t the problem, the experience is
I’ve frequently heard the claim “The platform is getting more expensive” when performance metrics drop. But as a product-minded media leader, I delve deeper into possible reasons, asking:
Has the conversion path recently changed?
Were additional steps or fields introduced?
Is mobile traffic directed to a non-responsive desktop?
In numerous instances, I’ve observed promising intent followed by a sharp decline at the conversion breather, a sign of a flawed product experience rather than a media issue.
For example, in higher education, potential students exhibiting strong intent may encounter roadblocks due to lengthy or unclear application processes. This often has less to do with the marketing campaign and more with the experience provided.
Here, the role of a full-stack marketer is to highlight these challenges, bring data insights to the table, and work cross-functionally to tackle and resolve these issues.
Example 2: Different audiences, different ‘products’
One vital product lesson is that not every user is the same, and thus, shouldn’t be lumped together.
Different audiences possess distinct motivations, risk profiles, and decision timelines. Viewing them as a homogenous group often leads to mediocrity.
I’ve discovered industries like healthcare — where patients, caregivers, and referring providers require individualized approaches — are perfect examples. Similarly, in financial services, decisions vary greatly depending on the individual’s life stage and goals.
A full-stack marketer tailors their media strategy, from messaging to channel selection, understanding that product-market fit is key, not just audience targeting.
Example 3: What happens after the conversion
A common blind spot in media strategies is post-conversion tracking. Product thinkers probe into the depths of:
How prompt and personalized the follow-up is.
Whether the messaging aligns with campaign promises.
I’ve witnessed enhanced performance with simple changes like improving lead response times or ensuring follow-up messages match campaign intentions.
Healthcare stands out in illustrating these principles, showing how vital immediate follow-up and aligned customer experiences can be across workflows.
Thinking in roadmaps
Roadmap thinking — prioritizing initiatives by impact — is another core aspect of product management. Similarly, full-stack media leaders prioritize marketing efforts accordingly.
Instead of pursuing every new shiny channel, we focus on sustainable progress, often by mapping out phases, such as:
Product managers don’t merely view metrics at face value; they challenge them. Being similar in nature, media leaders should mirror this approach, asking:
“Which segments convert faster?”
“How does performance vary across regions or stages?”
“Are engagement signals reflecting readiness or curiosity?”
In higher education, for example, dissecting performance by program or brand intent helps sharpen our strategies, turning data into actionable insights.
Collaboration is the new superpower
Full-stack marketers are naturally collaborative. In education, achieving success requires coordination across various departments including admissions and IT. In this role, we don’t just fulfill requests; we help partners navigate choices and establish shared objectives.
Translating data into actionable narratives becomes part of our collaborative toolbox and is essential in breaking down silos.
So, what does this mean for tomorrow’s media leaders?
The rise of the full-stack marketer doesn’t mark the end of specialization. It’s about seeing the broader structure rather than just optimizing single elements.
In my view, tomorrow’s media leaders should:
Understand the business driving their campaigns.
Think beyond their specific channels.
Advocate sincerely for user experiences.
Use data thoughtfully for influence.
Embrace change and unpredictability.
In industries where trust, timing, and transformation are integral, this mindset is vital. Marketing is about more than just campaigns — it’s about guiding pivotal life choices. If you feel like your media leadership role is expanding, that’s because it is — and rightfully so!
Recently, I’ve been diving into McKinsey’s ‘Organize to Value’ strategy, a fascinating blueprint for transforming marketing into a positionless model. According to a comprehensive analysis, it’s not technology that’s holding back operational transformations; it’s unclear objectives, uncommitted leadership, and a stagnant culture that are to blame.
Implementing new AI technologies to drive marketing efforts seems simple. However, the real challenge lies in empowering marketing teams to utilize these tools independently, decisively, and at scale. The primary obstacle? It’s us, the humans.
For as long as I can remember, marketing teams have aimed to keep up with consumers, delivering timely, relevant messages and optimizing customer lifetime value to boost loyalty and ROI. While this goal isn’t new, the AI technologies that help us analyze data and create personalized messages at scale are continuously evolving. Unfortunately, our ability to fully harness this technology has fallen behind.
Despite these challenges, progress is being made. Some marketing teams have overcome these hurdles, yielding remarkable results. Take Caesars Entertainment for example. They reduced campaign execution time from five days to just five minutes. As Asadul Shah, the vice president of player revenue strategy notes, this transformation was ‘a massive game changer.’
Before their transformation, marketers at Caesars manually built targeting lists and coordinated efforts across disconnected systems, often waiting on multiple teams before launching campaigns. This made it difficult to target players with precision and timing. By partnering with Optimove, Caesars combined data, orchestration, and execution into a single platform. This change didn’t just improve efficiency; it allowed the marketing team to react more dynamically to players’ needs.
What truly made this transformation effective wasn’t just the technology—it was the implementation of Positionless Marketing. This framework liberated marketers from fixed roles, empowering every team member to act independently. Optimove provided the platform, while Caesars developed the necessary team structure. This synergy of technology and human ingenuity brought Positionless Marketing to life.
Organizations that achieve such transformation are embodying what McKinsey describes as ‘organizing to value.’ This involves a deep rethinking of structure, decision-making, and accountability, transforming marketing teams into operations that continuously drive value—ultimately optimizing customer lifetime value, fostering loyalty, and delivering measurable ROI.
Yet, McKinsey highlights six pitfalls many teams face when trying to adopt the Positionless model, with only one being technological. The rest involve leadership and organizational issues.
Some key barriers include unclear objectives causing a focus on activity metrics over outcomes, misaligned governance that slows decision-making, and leaders who reinforce silos instead of enabling autonomy. Other obstacles are a stagnant culture resistant to change, muddled execution with no clear accountability, and disconnected technology further compartmentalizing efforts.
This kind of ‘assembly-line’ marketing, where tasks are segmented among different teams, hinders value creation. Peter Drucker famously said, “The purpose of business is to create and keep a customer.” However, when insights, creativity, and activation are siloed, value gets lost in between.
McKinsey’s ‘Organize to Value’ offers a practical path forward. It suggests designing organizations around value creation and impactful outcomes, rather than rigid job titles and processes designed to control.
To truly embrace Positionless Marketing, leaders must apply pragmatic solutions focused on improving marketing execution. This involves starting with a clear purpose, restructuring work to emphasize outcomes, streamlining decision-making processes, and aligning governance, technology, and talent. It empowers marketers to transcend traditional roles and independently deliver results.
This transformation requires commitment but staying with an outdated assembly-line structure is even costlier. Organizations like FDJ United and a major retailer have already seen the benefits: improved execution speed, increased purchase rates, and better use of resources.
As I see it, the window to act is narrowing. AI and data technologies are advancing rapidly, and customer expectations for personalized experiences are growing. Those who are quick to adapt will stay ahead, while those who hesitate may fall behind.
McKinsey’s insights confirm that the right structure and technology can unleash human potential, transforming marketing from within. Positionless Marketing is more than a strategy; it’s the future we need to embrace.
I’ve been following the latest updates from OpenAI, and they recently made some significant changes to their privacy policy, especially with the introduction of ads in ChatGPT. These updates are designed to allow advertisers to run personalized ads while ensuring that our chats remain private and secure.
OpenAI shared these updates with ChatGPT users, detailing how ads will function within the platform and clarifying what data is accessible to advertisers. It’s a refreshing assurance that our personal interactions remain confidential.
Why this matters to me. Privacy is paramount, and OpenAI emphasizes that personal chats and histories remain shielded from advertisers. They utilize anonymized engagement signals for ad personalization, ensuring advertisers can target relevant users without accessing sensitive information.
This method allows advertisers to evaluate the performance of their ads within a privacy-first framework, fostering user trust.
Ads in ChatGPT For users like me on Free and Go plans, ads might start appearing, but if you opt for paid tiers like Plus, Pro, Enterprise, Business, and Education, you can enjoy an ad-free experience. OpenAI promises clear labeling and separation of ads from chatbot responses.
Importantly, the content generated by ChatGPT remains unbiased and unaffected by these advertisements.
How ad targeting is handled. OpenAI uses in-platform signals such as ad interactions to personalize ads, but advertisers do not get access to our conversations, chat histories, or personal information.
Advertisers receive only aggregated metrics like total views or clicks, ensuring our personal data stays protected.
Additional privacy updates A new feature allows for optional contact syncing, helping us connect with friends who also use OpenAI services. It’s up to us whether to enable this feature.
They also provided more transparency on data storage durations, processing methods, and user control options, helping us understand our data management better.
Safety and product enhancements. The update encompasses new safety tools and age prediction systems aimed at ensuring a safer environment for teenagers. Documentation for new features like Atlas, Sora 2, and parental controls for teen accounts has also been included.
The bottom line. With the expansion of advertising in ChatGPT, OpenAI is committed to maintaining strict boundaries concerning user privacy, offering advertisers valuable insights without infringing on personal conversations or data.
This update was first spotted by Paid Media expert Arpan Banerjee, who shared insights on LinkedIn. It’s a promising move towards privacy-centric advertising in AI-powered platforms.
Have you heard the news? Google Ads is taking the advertising world by storm with its latest feature: AI voice-over for Performance Max video ads! They’re rolling out this innovative enhancement, automatically narrating video ads with realistic voice-overs, unless, of course, we choose to opt out by March 20.
Google is enhancing viewer engagement and ad performance by utilizing advanced AI voice models. This update will make ads more appealing without any additional creative output on our part. Exciting, isn’t it?
Why this matters to us. If we don’t actively opt out by March 20, our video ads will automatically benefit from Google’s AI voice models. This could transform how our ads sound to viewers, all without any creative effort on our part.
How does it work?
This feature kicks in only when videos lack a voice track.
Google’s AI chooses text from the headlines and descriptions we’ve provided and crafts a realistic voice-over from it.
The voice-over is seamlessly layered onto the original video, transforming it into a new asset.
The catch. This process is set to default, meaning our ads will be automatically eligible for voice enhancements unless we opt out proactively.
Key dates. We have until March 20 to decide if we want to exclude our ads from this feature. To step back from this feature, we need to adjust the video enhancement control settings. After the deadline, any ad with video enhancement control will be open to voice-enhanced updates automatically.
Action steps for us as advertisers. Configuring our video settings is simple. Just visit your Google Ads portal to make any necessary adjustments.
First seen. This update was brought to light by Paid Search specialist Arpan Banerjee in a LinkedIn post. Take a look at his insights here.
Automation and AI are revolutionizing the PPC landscape. Now, PPC teams are transforming into data teams, mastering data infrastructure, measurement, analysis, and experimentation.
Like many people, I worry about AI taking over jobs. Where do my ‘old school’ PPC skills fit in an AI-dominated landscape?
Relax. It’s not a binary situation. The shift is towards data and strategy. Media buying might look automated from the outside, but don’t be misled. The role is simply evolving once more.
Having been in PPC for over 15 years, I’ve learned that there’s nothing to fear. The real question is: am I riding the wave or getting left behind?
Let’s explore what the current PPC landscape looks like with ad network automation, and more importantly, where today’s PPC teams truly add value.
The Return of the Technical PPC Team
A decade ago, technical PPC agencies distinguished themselves by developing scripts, managing data on a large scale, and overseeing complex structures. As automation matured, many teams pivoted towards strategy and creativity.
Now, with AI’s help, producing quality creatives or analyzing massive datasets to create strategies is easier than ever. However, these outputs aren’t flawless.
From a client’s perspective, the typical creative-centric or strategy-focused agency might be out of the game. Therefore, rejoice, PPC folks: the technical edge is back, albeit in a different form. It’s time to bring back the spreadsheet enthusiasts from the 2010s who can now drive the PPC industry forward.
Still skeptical? Let’s rewind and get a clearer view of the necessary skill sets.
The PPC Edge: From Spreadsheet Skills to Data Nerds
Today, successful PPC agencies sell something vastly different than a decade ago, though the core mindset remains the same.
Why? Let’s consider the key performance drivers nowadays:
Integrating down-funnel data into strategy.
Building a data infrastructure to support strategy.
Providing accurate signals to ad algorithms.
Building systems to scale operations, including creative tasks.
See the pattern? A broken data model can’t be solved just by prompts. This is your advantage, what clients value most. Automation enhances the value of technical literacy rather than diminishing it.
Who do you turn to for technical literacy? The seasoned PPC marketers who thrived on manipulating paid search ads using custom Excel macros or managing extensive product feed items. They have the mindset: a love for automation, data, and math.
1. Data Engineer
The data engineer builds and maintains the infrastructure. Although they might come after the tracking specialist in the data chain, they are central, which is why we mention them first.
In today’s multi-platform world, think of CRM integration with Google Ads or blending online and offline data sets to strategize effectively.
Without a comprehensive data model, strategies become vague gut feelings needing constant reality checks. The data engineer’s role is to set a strong foundation to prevent such situations.
Without this role, you face repetitive manual exports and inconsistent numbers across teams, leading to sluggish decision-making.
What is the Data Engineer’s Scope?
Building a data infrastructure follows an ETL process: extract data, manipulate it, and make it usable in tools like Looker Studio, Power BI, or Tableau.
Build data pipelines from ad platforms, analytics, or CRM tools into the warehouse for data like spend and revenue.
Structure tables for these sources and merge them for specific use cases.
Maintain datasets and perform automated QA, including refresh schedules.
What Skill Sets and Tools Does the Data Engineer Use?
In a Google-centric world, we often hear about BigQuery, but there are alternatives like Microsoft Azure. The essential skills are coding, particularly SQL and Python.
These languages are used to structure tables within the data warehouse (using SQL) and to create data pipelines (using Python).
2. Tracking and Measurement Architect
Some might think this role overlaps with data engineers, but I strongly disagree. This person focuses solely on maintaining signal quality within tight deadlines when issues arise.
Tracking failures mean lost conversion data, impacting ad platforms’ performance because they’re built on conversion data insights.
Notice this when CPAs fluctuate unexpectedly or in-platform data varies drastically from your ‘source of truth’ (GA, CRM, others). These architects help stabilize bidding and improve event match quality for better data in Google Ads.
What is the Tracking Architect’s Scope?
They design comprehensive, regulation-compliant data collection mechanisms, making sure everything is aligned with privacy compliance.
Align tracking with privacy regulations.
Design client- and server-side tracking.
Implement GTM and server containers.
Co-manage Conversions API integrations with the data engineer.
Co-ensure deduplication logic with the media buyer.
What Skill Sets and Tools Does the Tracking Architect Use?
While many PPCs have used Google Tag Manager, few have set up server-side tagging. This role needs a deep understanding of Consent Mode frameworks, CAPI, among other tools.
3. Data Analyst
If data engineers build the pipes and tracking architects secure the signals, data analysts interpret what the data implies. It’s a role quite affected by AI, yet crucial due to the risk of misinterpretation.
Wrong interpretations can lead to costly errors. Fully relying on AI over data analysts could be a grave mistake, as misinterpreted metrics like ROAS versus actual contribution margins or CPA disparities can derail strategies.
What is the Data Analyst’s Scope?
While outsiders might think they only build dashboards, data analysts handle much more, like designing models aligned with KPIs and rigorous analysis, all while questioning platform narratives.
Align data models with business KPIs.
Analyze performance cohorts, churn rates, and profitability.
What Skill Sets and Tools Does the Data Analyst Use?
Think of data analysts as translators; understanding numbers doesn’t mean you’re ready to interpret them correctly. They need SQL for warehouse queries and modeling skills for strategic planning, along with strong statistical reasoning.
4. CRO and Experimentation Lead
Once data is cleaned and analyzed, CROs leverage insights to enhance visitor economics. A low conversion rate can mean higher CPA, which no one wants. Their expertise helps scale operations efficiently rather than throwing money at inefficient processes.
What is the CRO’s Scope?
CRO roles are not just about landing pages but full-funnel optimizations, identifying friction points, structuring tests, and working with creative teams to position offers effectively.
Navigate from impression to revenue.
Utilize heat maps to locate friction points.
Use proper methodologies instead of random experiments.
Coordinate with creative and product teams for best offer placements.
What Skill Sets and Tools Does the CRO Lead Use?
Core tools include GA4 and heat mapping software, with options to scale based on needs. Critical skills involve a firm grasp of statistical reasoning and translation of business metrics into actionable insights.
From Media Buyers to Data Teams
Today’s PPC teams resemble hybrids of marketing, data, and product roles rather than mere media buyers. Successful teams deliberately build capabilities around understanding algorithms, data dynamics, and economics, enabling AI to become a strategic asset rather than a threat.
With Google referrals declining and LLM usage on the rise, I’ve discovered that successful discoverability now hinges on metrics, structure, and authority—not just rankings.
If your organic traffic is decreasing while impressions rise, AI might be citing your content without generating clicks. If both metrics are down, it’s likely your content is being overlooked. Either way, the conventional search behavior that shaped your marketing strategy has transformed, and merely waiting for traffic to rebound is not a viable strategy.
The year 2026 presents a new reality. According to KEO Marketing, 73% of B2B websites faced significant traffic declines between 2024 and 2025, averaging a 34% year-over-year drop.
These drops aren’t uniform. Websites with predominantly informational content have been more adversely affected, experiencing declines between 15% and 64% since AI Overviews emerged.
News publishers, in particular, have been vulnerable, with Google referrals decreasing globally by 33% in the year leading up to November 2025.
These aren’t typical fluctuations; they signify a fundamental shift in how information is discovered online, posing a threat to business models reliant on site traffic.
Organic clicks are diminishing due to two intersecting reasons, each necessitating a different approach:
Google has fostered zero-click behavior through features like featured snippets and knowledge panels. These provide answers directly on the search results page, often eliminating the need to click on search results. While 25% of searches concluded without clicks ten years ago, today it’s over 65%. This trend has rapidly accelerated with AI Overviews, now found in about 16% of desktop searches and 41% of mobile searches.
On top of that, a growing number of users are bypassing traditional searches entirely. Nearly 52% of U.S. adults now frequently use AI tools, and approximately 28% of employed Americans incorporate AI at work. When they seek answers from ChatGPT or other LLMs, they often get responses without visiting any websites. While your content might contribute to that answer, it doesn’t translate to traffic or attribution.
Traditional metrics such as impressions, clicks, and page views no longer accurately reflect discoverability. They measure site behavior without informing how your brand performs in AI-mediated interactions, impacting upstream traffic.
Here are the five key metrics for AI visibility:
Citations in AI responses indicate how often your content is directly referenced when an LLM responds to a query. A citation suggests your content is valuable, well-structured for AI parsing, and authoritative.
Brand mentions differ from citations. LLMs may mention your brand without citing your content, often pulling data from review sites, forums, and third-party articles. A mention absent a citation implies your brand is recognized but not sourced from your content, guiding where to focus investments.
Share of voice measures your frequency of citations and mentions relative to competitors within specific categories.
Brand sentiment evaluates whether AI-generated responses portray your brand positively, neutrally, or negatively.
AI-influenced traffic gauges the proportion of traffic generated from LLM referrals. Initial data indicates this traffic has a conversion rate 3-5 times higher than other sources, making it valuable to track even if minor in volume.
Modern tools can track these metrics at scale, eliminating the necessity for manual LLM prompts. However, even conducting basic benchmarks by querying major LLMs with your target questions and tracking mentions is advantageous over not measuring at all.
Achieving visibility in AI-driven search doesn’t involve rewriting your content strategy but instead requires shedding ineffective practices and pivoting towards lasting principles.
Experience, Expertise, Authoritativeness, and Trustworthiness (E-E-A-T) continue to form the foundation of content credibility. LLMs give precedence to sources that demonstrate real expertise and are trusted by authoritative figures.
By earning citations from reputable sites, producing content authored by subject matter experts, and delving into topics thoroughly, you can outshine content that fails to meet these criteria, regardless of optimization efforts for other factors.
Structure and clarity are essential because LLMs extract content by pinpointing passages that effectively answer questions. Structuring content around clear questions and answers, utilizing bullet point summaries, and avoiding dense paragraphs enhance retrievability over embedding answers in narrative prose.
Your information architecture should be comprehensible to both human readers and LLM systems. Introducing a Q&A section or reorganizing posts around clear question-and-answer pairs provides significant improvements.
Human-written, human-led content has a distinct advantage. After Google’s recent core update, AI-generated content saw an 87% drop in rankings and citation frequency, with keyword-optimized content seeing a 63% fall. LLMs are becoming adept at detecting AI-created content and rank it lower.
The 2025 demand for AI-produced content has highlighted a quality issue now evident in performance data. Prioritizing quality over quantity is essential. Use AI for drafting and editing, but not for generating final content. Implement a review process to catch generic phrasing or a synthetic tone, either through AI-detection tools or human editors.
Recency is crucial for AI citations. AI systems consider both the publication and update dates when selecting sources. A high-quality piece from 2022 can be dismissed for a newer version from 2025.
Audit your high-traffic pages and key assets for outdated data, refreshing them with recent examples and data. It’s a quick yet often overlooked strategy.
Promotional language will not get cited. If your writing appears too commercial—emphasizing product claims and brand-forward language—answer engines may deprioritize it over more neutral sources.
This doesn’t mean you should avoid mentioning your product; rather, write about it like an impartial party by acknowledging trade-offs, providing context, and letting facts speak for themselves. Listicles and comparison articles excel here.
LLMs respond best to organized, objective comparisons—even when one option is clearly preferred.
If my presence is limited to my own blog, I’m at a disadvantage against a brand with less expressive assets but more robust third-party coverage.
That is why cultivating an external content ecosystem is critical. Reviews on sites like G2, Capterra, and Google are frequently used in AI curation. User-generated content on forums like Reddit is heavily indexed. Third-party articles, tutorial videos, and newsletter mentions build the multi-source consensus essential for AI citations.
Content partnerships also deserve focused effort. Sponsoring articles or placing newsletters in relevant publications not only drives referral traffic but also earns trusted, external citations that elevate AI visibility. With a growing readership, newsletters — offering curated, human-authored content — are vital, with YouTube citations becoming increasingly influential. ChatGPT favors authoritative video creators for citations.
The goal isn’t to merely generate mentions but to consistently express your brand’s narrative through credible external sources so LLMs consistently recognize that narrative. Consistency across partners, review platforms, and third-party content strengthens your AI share of voice.
With organic traffic plummeting by 30% or more, the visitors arriving at your site are more deliberate and valuable than before, making conversion optimization on landing pages crucial.
Focus on simplicity: one offer, one message, minimal text.
Each landing page should focus on a single call to action and a singular argument. If there are multiple conversion goals, develop separate landing pages rather than a single page attempting everything.
Ensure the header conveys the full value proposition succinctly, with supporting points kept brief. Visitors should instantly grasp the offer and know how to act without needing to scroll.
This approach contrasts with blog and thought leadership content, which should be detailed, well-sourced, and designed for LLM retrieval. Each serves different objectives and requires varied standards. Conversion-centric landing pages are not the place for nuance or elaborate prose.
The decline in traffic isn’t a temporary issue that will resolve itself. Users increasingly get answers directly from AI, bypassing websites, and this trend will only intensify. A strategy focused solely on ranking for clicks is now insufficient.
The new strategy involves a dual focus: optimizing for citations by AI answer engines and cultivating an external brand presence that offers LLMs compelling reasons to consistently mention you. These objectives align with longstanding best practices: crafting clear, authoritative content grounded in expertise.
AI-driven discovery favors brands excelling in the fundamentals: building real credibility, securing trusted external mentions, and writing for audiences rather than algorithms.
This approach was always the best, and now AI search makes it essential.
It’s fascinating to see the evolution of Google’s AI Mode and how it increasingly cites Google itself. In fact, almost one out of every five sources in its AI-generated answers now originates from Google, often guiding users back to more Google searches.
Why does this matter to us? As someone deeply involved in the world of digital content and SEO, I’m aware that AI search should highlight the best online sources. If Google prioritizes its own content, there’s a risk that we might encounter fewer direct links and see a reduction in traffic as users remain within Google’s ecosystem.
So let’s delve into the details. Research by SE Ranking reveals that Google.com is the most cited source within AI Mode responses, making up 17.42% of all references. This makes Google more mentioned than even the combined total of the next six well-known platforms: YouTube, Facebook, Reddit, Amazon, Indeed, and Zillow.
In an accelerated trend, back in June 2025, Google referenced itself in only 5.7% of AI-generated answers, but now that figure has tripled.
Almost one out of five AI citations is from Google. When considering YouTube, Google-owned properties account for about 20% of all sources.
This self-referencing is quite pronounced, with AI Overviews linking heavily to Google properties such as Maps, Images, and YouTube. AI Mode expands on this by further embedding users within the Google environment, often through presenting additional search results rather than directing them to external sites.
This strategy keeps users engaged with Google platforms where monetized content such as ads and reviews can be found.
What’s changed? Previous research showed that Google was mostly citing Google Business Profiles. However, this trend has shifted:
Travel: 53.18% of citations
Entertainment & hobbies: 48.74% of citations
Real estate: 30.54% of citations
Interestingly, the one area where Google is not the top source is Careers and Jobs, where Indeed appears more than three times as often as Google.
The data supporting these findings were gathered by SE Ranking, who analyzed 68,313 keywords across 20 industries, reviewing over 1.3 million AI Mode citations to determine how frequently Google.com was referenced.
59% of citations now direct to conventional Google search results.
36.1% still reference Google Business Profiles.
A smaller portion links to Google Support (1.7%), Google Flights (0.1%), and other Google services.
Often, these AI citations are accompanied by a mini search results panel beside the answer, effectively creating a new search opportunity.
Industry differences are also evident. Google dominates citations across several topics, but some sectors show a stronger dependency on Google:
Travel: 53.18% of citations
Entertainment & hobbies: 48.74% of citations
Real estate: 30.54% of citations
Interestingly, the one area where Google is not the top source is Careers and Jobs, where Indeed appears more than three times as often as Google.
The data supporting these findings were gathered by SE Ranking, who analyzed 68,313 keywords across 20 industries, reviewing over 1.3 million AI Mode citations to determine how frequently Google.com was referenced.
As I delve into the world of Google, I’m fascinated by Liz Reid’s insights on Google Search and Gemini. While these might eventually converge or further diverge, the journey remains equally captivating.
The big picture Reid painted is compelling. Search mainly helps us connect with the web, while Gemini leans towards enhancing productivity and creativity. But with the rapid evolution of AI, the boundaries feel almost fluid to me.
What she’s saying. Reid clarified that despite sharing tech, Search and Gemini follow different “north stars.” It’s intriguing to think about whether they might overlap more as time progresses or if their paths will widen further. Here are Reid’s thoughts from her interview:
“I don’t know the answer is the short answer.”
“Some areas they’re converging more and some areas they’re diverging more, right?”
“Are they getting closer or further apart? I think we’ll see.”
“Maybe a third product emerges altogether.”
Gemini vs. Search. Reid’s distinction piqued my interest:
On Gemini: “Focused on being an assistant, leaning towards productivity and creation.”
On Search: “Information-based, fostering connection and engagement with the web.”
Agents and the web’s future. Reid’s vision of increased agent activity on the internet is enthralling. Imagine a world where not just people, but agents interact online.
“Agents are doing a lot of interaction, not just people.”
“Agents communicating with each other as we evolve.”
Google vs. ChatGPT. Contrary to popular belief, Reid believes we won’t end up with only one dominant AI product, which is enlightening.
“Not just one product will dominate the landscape.”
“Tech advances allow more questions and tool adoption.”
Trusted sources. Reid’s emphasis on highlighting trusted or paid sources resonates with me. Google’s Preferred Sources and subscription-aware features are steps in the right direction.
“How do you enhance relationships with trusted sources?”
“Content from loved or paid-for sources should surface easily.”
Why we care. Reid’s insights remind us that Google’s long-term role in an AI-centric world is still being defined. It’s an exciting time to follow these developments as AI assistants and search dynamics shift.
