On episode 341 of PPC Live The Podcast, I speak to Andrea Cruz, Head of B2B at Tinuiti, to unpack a mistake many senior marketers quietly struggle with: freezing when clients demand answers you don’t immediately have.
The conversation explored how communication missteps can escalate client tension — and how the right mindset, preparation, and culture can turn those moments into career-defining growth.
From hands-on marketer to team leader
As Cruz advanced in her career, she shifted from managing campaigns directly to leading teams running large, complex accounts. That transition introduced a new challenge: representing work she didn’t personally execute day to day.
When clients pushed back — questioning performance or expectations — Cruz sometimes froze. Saying “I don’t know” or delaying a response could quickly erode trust and escalate frustration.
Her key realization: senior leaders are expected to provide perspective in the moment. Even without every detail, they must guide the conversation confidently.
How to buy time without losing trust
Through mentorship and experience, Cruz developed a practical technique: asking clarifying questions to gain thinking time while deepening understanding.
Examples include:
Asking clients to clarify expectations or timelines
Requesting additional context around their concerns
Confirming what the client already knows about the situation
These questions serve two purposes: they slow down emotionally charged moments and ensure responses address the real issue, not just the surface complaint.
For Cruz, this approach was especially important as a non-native English speaker, giving her space to process complex conversations and respond clearly.
A solutions-first culture beats blame
Cruz emphasized that mistakes are inevitable — but how teams respond defines long-term success.
At Tinuiti, the focus is not on assigning blame but on answering two questions:
Where are we now?
How do we get to where we want to be?
This solutions-oriented mindset creates psychological safety. Teams can openly acknowledge errors, run post-mortems, and identify patterns without fear. Cruz argues that leaders must model this behavior by sharing their own mistakes, not just scrutinizing others’.
That transparency builds trust internally and with clients.
Proactive communication builds stronger client relationship
Rather than waiting for clients to surface problems, Cruz encourages teams to raise issues first. Acknowledging underperformance — even when clients haven’t noticed — demonstrates accountability and strengthens partnerships.
She also recommends tailoring communication styles to each client. Some prefer concise updates; others want detailed explanations. Documenting these preferences helps teams deliver information in ways that resonate.
Regular check-ins about business roadblocks — not just campaign metrics — position agencies as strategic partners, not just media operators.
Common agency mistakes in B2B advertising
Cruz didn’t hold back on recurring issues she sees in audits:
Budgets spread too thin: Running too many channels with insufficient spend leads to meaningless data and weak performance.
Underfunded campaigns: B2B CPCs are inherently high. Campaigns generating only a few clicks per day rarely produce actionable results.
Her advice is blunt: if the budget can’t support a channel properly, it’s better not to run it.
AI is more than a summarization tool
On AI, Cruz cautioned against shallow usage. Treating AI as a simple spreadsheet summarizer misses its broader potential.
Her team is experimenting with advanced applications — automated audits, workflow integrations, and operational efficiencies. She compares AI’s role to medical diagnostics: a powerful assistant that augments expert judgment, not a replacement for it.
For marketers, that means staying curious and continuously exploring new use cases.
The takeaway: preparation and passion drive resilience
Cruz’s central message is simple: mistakes will happen. What matters is preparation, adaptability, and maintaining a solutions-first mindset.
By anticipating client needs, personalizing communication, and embracing experimentation, marketers can transform stressful moments into opportunities to build credibility.
Looking to take the next step in your search marketing career?
Below, you will find the latest SEO, PPC, and digital marketing jobs at brands and agencies. We also include positions from previous weeks that are still open.
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Omniscient Digital is an organic growth agency that partners with ambitious B2B SaaS companies like SAP, Adobe, Loom, and Hotjar to turn SEO and content into growth engines. About this role We’re hiring an SEO Outreach Specialist to partner with high-authority brands and build high-quality backlinks to support our clients’ growth and authority. You will […]
SUMMARY The Digital Marketing Manager is a growth-oriented role that will evolve into a strategic marketing leadership position. You will work closely with the CCO and leadership team to shape our go-to-market strategy while executing high-impact marketing programs today. JOB RESPONSIBILITIES ESSENTIAL FUNCTIONS: Strategic Marketing & Positioning Collaborate with CCO and commercial leadership to evolve […]
Job Description We are a highly motivated bunch who seek to create a space where you know you are going to have a good time. We are known for our art-inspired spaces that are great for social gatherings. Our restaurants are wall to wall with lights, murals, and vignettes. We are the marinara-muddled minds behind […]
Benefits: Bonus based on performance Competitive salary Training & development Fischetti Law Group, a fast-growing Personal Injury and Estate Planning law firm, is seeking a creative, results-driven Digital Marketing Manager to lead our digital presence and community outreach efforts. This is a full-time, in-office position working directly with our Management team to expand our brand […]
Who We Are Oncourse Home Solutions (OHS) is a people-centric, $500M organization that is owned by private equity firm, Apax Partners operating under the brands American Water Resources, Pivotal Home Solutions and American Home Solutions. We do what is right for our people so they can do their best when serving our 1.9+ million customers […]
AppFolio is more than a company. We’re a community of dreamers, big thinkers, problem solvers, active listeners, and multipliers. At every opportunity, we set the pace while delivering innovation built to carry real estate into the future. One in which every experience feels effortless, yet meaningful. Where customers are empowered to take on any opportunity. […]
The Company: VeSync is a portfolio company with brands that cover different categories of health & wellness products. We wouldn’t be surprised if you have one of our Levoit air purifiers in your living room or a COSORI air fryer whipping up healthy and delicious meals for you every night. We’re a young and energetic […]
We’re looking for a Senior SEO Strategist to lead enterprise-level organic growth strategies across traditional search and modern discovery channels, including AI-powered SERPs, Google AI Overviews, and large language models (LLMs). In this role, you’ll own both strategy and execution for a portfolio of enterprise and high-growth clients. You’ll act as a trusted, client-facing advisor—translating complex technical […]
Benefits 401(k) 401(k) matching Company parties Competitive salary introduce open tags Dental insurance Imageno duplicate details Free food & snacks Health insurance Opportunity for advancement Paid time off Parental leave Vision insurance Wellness resources Buzz Franchise Brands i s a fast-growing, multi-brand franchise company headquartered in Virginia Beach, VA. We’re seeking a Digital Paid Media […]
WHY DEPT®? We are pioneers at heart. What this means, is that we are always leaning forward, thinking of what we can create tomorrow that does not exist today. We were born digital and we are a new model of agency, with a deep skillset in tech and marketing. That’s why we hire curious, self-driven, […]
Overview Nutricost is a leading supplement brand known for high-quality nutrition at affordable prices. We proudly partner with Shaquille O’Neal, the Utah Jazz, BYU Athletics, and US Speedskating to promote health and performance nationwide. Job Overview We are seeking a highly experienced and results-driven Paid Search Manager to lead Nutricost’s paid search strategy and execution […]
Paid Search Manager Location: Round Rock, TX – Onsite (5 days a week) Responsibilities Implement and optimize paid search campaigns to meet KPIs and business objectives. Supervise a team of paid search specialists to ensure operational rigor, budget pacing, and campaign quality. Collaborate with creative teams, vendors, and internal stakeholders to develop and execute effective […]
Overview Sam’s Club is hiring a Senior Manager, Marketing Planning & Strategy (Paid Search + Measurement Enablement) to strengthen one of our largest growth engines: paid search and paid channels performance. This role sits in Marketing, but operates at the intersection of paid media, analytics, and technical enablement—ensuring our campaigns are executed flawlessly, measured accurately, […]
You’ll own high-visibility SEO and AI initiatives, architect strategies that drive explosive organic and social visibility, and push the boundaries of what’s possible with search-powered performance.
Every day, you’ll experiment, analyze, and optimize-elevating rankings, boosting conversions across the customer journey, and delivering insights that influence decisions at the highest level.
Cloudflare yesterday announced its new Markdown for Agents feature, which serves machine-friendly versions of web content alongside traditional human-facing pages.
Cloudflare described the update as a response to the rise of AI crawlers and agentic browsing.
When a client requests text/markdown, Cloudflare fetches the HTML from the origin server, converts it at the edge, and returns a Markdown version.
The response also includes a token estimate header intended to help developers manage context windows.
Early reactions focused on the efficiency gains, as well as the broader implications of serving alternate representations of web content.
What’s happening. Cloudflare, which powers roughly 20% of the web, said Markdown for Agents uses standard HTTP content negotiation. If a client sends an Accept: text/markdown header, Cloudflare converts the HTML response on the fly and returns Markdown. The response includes Vary: accept, so caches store separate variants.
Cloudflare positioned the opt-in feature as part of a shift in how content is discovered and consumed, with AI crawlers and agents benefiting from structured, lower-overhead text.
Markdown can cut token usage by up to 80% compared to HTML, Cloudflare said.
Security concern. SEO consultant David McSweeney said Cloudflare’s Markdown for Agents feature could make AI cloaking trivial because the Accept: text/markdown header is forwarded to origin servers, effectively signaling that the request is from an AI agent.
A standard request returns normal content, while a Markdown request can trigger a different HTML response that Cloudflare then converts and delivers to the AI, McSweeney showed on LinkedIn.
The concern: sites could inject hidden instructions, altered product data, or other machine-only content, creating a “shadow web” for bots unless the header is stripped before reaching the origin.
Google and Bing’s markdown smackdown. Recent comments from Google and Microsoft representatives discourage publishers from creating separate markdown pages for large language models. Google’s John Mueller said:
“In my POV, LLMs have trained on – read & parsed – normal web pages since the beginning, it seems a given that they have no problems dealing with HTML. Why would they want to see a page that no user sees? And, if they check for equivalence, why not use HTML?”
And Microsoft’s Fabrice Canel said:
“Really want to double crawl load? We’ll crawl anyway to check similarity. Non-user versions (crawlable AJAX and like) are often neglected, broken. Humans eyes help fixing people and bot-viewed content. We like Schema in pages. AI makes us great at understanding web pages. Less is more in SEO !”
Cloudflare’s feature doesn’t create a second URL. However, it generates different representations based on request headers.
The case against markdown. Technical SEO consultant Jono Alderson said that once a machine-specific representation exists, platforms must decide whether to trust it, verify it against the human-facing version, or ignore it:
“When you flatten a page into markdown, you don’t just remove clutter. You remove judgment, and you remove context.”
“The moment you publish a machine-only representation of a page, you’ve created a second candidate version of reality. It doesn’t matter if you promise it’s generated from the same source or swear that it’s ‘the same content’. From the outside, a system now sees two representations and has to decide which one actually reflects the page.”
Why we care. Cloudflare’s move could make AI ingestion cheaper and cleaner. But could it be considered cloaking if you’re serving different content to humans and crawlers? To be continued…
Google Ads is rolling out a feature that lets advertisers calculate conversion value for new customers based on a target return on ad spend (ROAS), automatically generating a suggested value instead of relying on manual estimates.
The update is designed for campaigns using new customer acquisition goals, where advertisers want to bid more aggressively to attract first-time buyers.
How it works. Advertisers enter their desired ROAS target for new customers, and Google Ads proposes a conversion value aligned with that goal. The system removes some of the guesswork involved in estimating how much a new customer should be worth in bidding models.
The feature doesn’t yet adjust dynamically at the auction, campaign, or product level. Advertisers still apply the value at a broader setting rather than letting the system vary bids based on context.
Why we care. Assigning the right value to a new customer is a weak spot in performance bidding. Many advertisers manually set a flat value that doesn’t always reflect profitability or long-term goals.
By tying suggested conversion values to a target ROAS, advertisers can now optimise towards a more strategy-driven bidding, potentially improving how acquisition campaigns balance growth and efficiency.
What advertisers are saying. Early reactions suggest the feature is a meaningful improvement over static manual inputs. Founder of Savvy Revenue, Andrew Lolk argues the next step would be auction-level intelligence that adjusts values depending on campaign or product performance.
What to watch. If Google expands the feature to support more granular adjustments, it could further reshape how advertisers structure acquisition strategies and value lifetime customer growth.
For now, the tool offers a more structured way to calculate new customer value.
First seen. This update was first spotted by Founder and Digital Marketer Andrew Lolk who showed the new setting on LinkedIn.
SEO is moving out of the marketing silo into organizational design. Visibility now depends on how information is structured, validated, and aligned across the business.
When information is fragmented or contradictory, visibility becomes unstable. The risk isn’t just ranking volatility – it’s losing control of how your brand is interpreted and cited.
For SEO leaders, the choice is unavoidable: remain a channel optimizer or shape the systems that govern how your organization is understood and cited. That shift isn’t happening in a vacuum. AI systems now interpret, reconcile, and assemble information at scale.
The visibility shift beyond rankings
The future of organic search will be shaped by LLMs alongside traditional algorithms. Optimizing for rankings alone is no longer enough. Brands must optimize for how they are interpreted, cited, and synthesized across AI systems.
Clicks may fluctuate and traffic patterns may shift, but the larger change is this: visibility is becoming an interpretation problem, not just a positioning problem. AI systems assemble answers from structured data, brand narratives, third-party mentions, and product signals. When those inputs conflict, inconsistency becomes the output.
In the AI era, collaboration can’t be informal or personality-driven. LLMs reflect the clarity, consistency, and structure of the information they ingest. When messaging, entity signals, or product data are fragmented, visibility fragments with them.
This is a leadership challenge. Visibility can’t be achieved in a silo. It requires redesigning the systems that govern how information is created, validated, and distributed across the organization. That’s how visibility becomes structural, not situational.
If visibility is structural, it needs a system.
Building the visibility supply chain
Collaboration shouldn’t depend on whether the SEO manager and PR manager get along. It must be built into the content supply chain.
To move from a marketing silo to an operational design, we must treat content like an industrial product that requires specific refinement before it’s released into the ecosystem.
This is where visibility gates come in: a series of nonnegotiable checkpoints that filter brand data for machine consumption.
Implementing visibility gates
Think of your content moving through a high-pressure pipe. At each joint, a gate filters out noise and ensures the output is pure:
The technical gate (parsing)
The filter: Does the new product page template use valid schema.org markup (product, FAQ, review)?
The goal: Ensuring the raw material is structured so LLMs can ingest the data without friction.
The brand signal gate (clustering)
The filter: Does the PR copy align with our core entities? Are we using terminology that helps LLMs cluster our brand correctly?
The goal: Removing linguistic drift that confuses an LLM’s understanding of who we are.
The accessibility/readability gate (chunking)
The filter: Is the content structured for RAG (retrieval-augmented generation) systems?
The goal: Moving away from fluff and towards high-information-density prose that can be easily chunked and retrieved by an AI.
The authority and de-duplication gate (governance)
The filter: Does this asset create “knowledge cannibalization” or internal noise?
The goal: Acting as a final sieve to remove conflicting information, ensuring the LLM sees only one single source of truth.
The localization gate (verification)
The filter: Is the entity information consistent across global regions?
The goal: Ensuring cross-referenced data points align perfectly to build model trust.
If gates protect what enters the ecosystem, accountability ensures that behavior changes.
Embedding visibility into cross-functional OKRs
But alignment without visibility into results won’t sustain change.
The most sophisticated infrastructure will fail if it relies on the SEO team’s influence alone.
To move beyond polite collaboration, visibility must be codified into the organization’s performance DNA.
We need to shift from SEO-specific goals to shared visibility OKRs.
When a product owner is measured on the machine-readability of a new feature, or a PR lead is incentivised by entity citation growth, SEO requirements suddenly migrate from the bottom of the backlog to the top of the sprint.
What shared OKRs look like in an operational design:
For product teams: “Achieve 100% schema validation and <100ms time-to-first-byte for all top-tier entity pages.”
For PR and communications: “Increase ‘brand-as-a-source’ citations in LLM responses by 15% through high-authority, entity-aligned placements.”
For content teams: “Ensure 90% of new assets meet the ‘high information density’ threshold for RAG retrieval.”
When stakeholders’ KPIs are tied to the brand’s digital footprint, visibility is no longer “the SEO team’s job.” Instead, it becomes a collective business imperative.
This is where the magic happens: the organizational structure finally aligns with the way modern search engines actually work.
Measuring visibility across the organization
The gates ensure the quality of what we put into the digital ecosystem; the unified visibility dashboard measures what we get out. Breaking down silos starts with transparent data.
If the PR team can see which mentions drive AI citations and source links in AI Overviews, they’re more likely to shift toward high-authority, contextually relevant publications instead of chasing any media outlet.
We need to shift from reporting rankings to reporting entity health and Share of Model (SoM). This dashboard is the organization’s single source of truth, showing that when we pass the visibility gates correctly, our brand authority grows with humans and machines.
Systems and incentives matter, but they don’t operate on their own.
Having the right infrastructure isn’t enough. We need a specific set of qualities in the workforce to drive this model. To navigate the visibility transformation, we need to move away from hiring generalists and start hiring for the two distinct pillars of an operational search strategy.
In my experience, this requires a strategic duo: the hacker and the convincer.
Feature
The hacker (technical architect)
The convincer (visibility advocate)
Core mission
Ensuring the brand is discoverable by machines.
Ensuring the brand is supported by humans.
Primary domain
RAG architecture, schema, vector databases, and LLM testing.
Cross-departmental OKRs, C-suite buy-in, and PR/brand alignment.
Success metric
Share of model (SoM) and information density.
Resource allocation and budget growth.
The gate focus
Technical, accessibility, and authority gates.
Brand signal and localization gates.
The hacker: The engine room
Deeply technical, driven, and a relentless early adopter. They don’t just “do SEO.” They reverse-engineer how Perplexity attributes trust and how Google’s knowledge vault weighs brand entities.
They find the “how.” They aren’t just optimizing for a search bar, but are optimizing for agentic discovery, ensuring your brand is the path of least resistance for an LLM’s reasoning engine.
The convincer: The social butterfly of data
This is the visionary who brings people together and talks the language of business results. They act as the social glue, ensuring the hacker’s technical insights are actually implemented by the brand, tech, and PR teams. They translate schema validation into executive visibility, ensuring that the budget flows where it’s needed most.
How AI visibility reshapes in-house and agency roles
As roles evolve, the brand-agency relationship shifts with them. If you’re an in-house SEO manager today, you’re likely evolving into a chief visibility officer, focusing on the “convincer” role of internal politics and resource allocation.
Historically, agencies were the training ground for talent, and brands hired them for execution. That dynamic may flip. In this new era, brands could become training grounds for junior specialists who need to understand a single entity deeply and manage its internal gates.
Meanwhile, agencies may evolve into elite strategic partners staffed by seasoned visibility hackers who help brands navigate high-level visibility transformation that in-house teams are often too siloed or time-constrained to see.
To prepare your team for the shift to SEO as an operational approach, take these steps:
Set the vision: Do you want to be part of the change? Define what visibility-first looks like for your business.
Take stock of talent: Do you have hackers and convincers? Audit your team not just for skills, but for mindset.
Audit the gaps: Where does communication break down? Find friction points between SEO and PR, or SEO and product, and fix them quickly.
Shift the KPIs: Move away from rankings and toward channel authority, impressions, sentiment share, and, most importantly, revenue and leads.
Be radically transparent: Clarity is key. You’ll need new templates, job descriptions, and responsibilities. Data should be shared in real time. There’s no room for siloed thinking.
What the first 90 days should look like:
Days 1-30 (Audit): Map your brand’s entity footprint. Where does your brand data live, and where is it conflicting?
Days 31-60 (Infrastructure): Embed visibility gates into your CMS or project management tool, such as Jira or Asana.
Days 61-90 (Incentives): Tie 10% of the PR and product teams’ bonuses to information integrity or AI citation growth.
The SEO leader as a systems architect
As we move further into the age of AI, the successful SEO leader will no longer be the person who simply moves a page from position four to position one. They’ll be the systems architect who builds the infrastructure that allows a brand to be seen, understood, and recommended by machines and humans alike.
This transition is messy. It requires challenging old thought patterns and communicating transparently and directly to secure buy-in. But by redesigning the structures that create silos, we don’t just “do SEO.” We build a resilient organization that is visible by default, regardless of what the next algorithm or LLM brings.
The future of search isn’t just about keywords. It’s about how your organization’s information flows through the digital ecosystem. It’s time to stop optimizing pages and start optimizing organizations.
For a long time, PPC performance conversations inside agencies have centered on bidding – manual versus automated, Target CPA versus Maximize Conversions, incrementality debates, budget pacing and efficiency thresholds.
But in 2026, that focus is increasingly misplaced. Across Google Ads, Meta Ads, and other major platforms, bidding has largely been solved by automation.
What’s now holding performance back in most accounts isn’t how bids are set, but the quality, volume, and diversity of creative being fed into those systems. Recent platform updates, particularly Meta’s Andromeda system, make this shift impossible to ignore.
Bidding has been commoditized by automation
Most advertisers today are using broadly similar bidding frameworks.
Google Smart Bidding uses real-time signals across device, location, behavior, and intent that humans can’t practically manage at scale. Meta’s delivery system works in much the same way, optimizing toward predicted outcomes rather than static audience definitions.
In practice, this means most advertisers are now competing with broadly the same optimization engines.
Google has been clear that Smart Bidding evaluates millions of contextual signals per auction to optimize toward conversion outcomes. Meta has likewise stated that its ad system prioritizes predicted action rates and ad quality over manual bid manipulation.
The implication is simple. If most advertisers are using the same optimization engines, bidding is no longer a sustainable competitive advantage. It’s table stakes.
What differentiates performance now is what you give those algorithms to work with – and the most influential input is creative.
Andromeda makes creative a delivery gate
Meta’s Andromeda update is the clearest evidence yet that creative is no longer just a performance lever. It’s now a delivery prerequisite. This matters because it changes what gets shown, not just what performs best once shown.
Meta published a technical deep dive explaining Andromeda, its next-generation ads retrieval and ranking system, which fundamentally changes how ads are selected.
Instead of evaluating every eligible ad equally, Meta now filters and ranks ads earlier in the process using AI models trained heavily on creative signals, improving ad quality by more than 8% while increasing retrieval efficiency.
What this means in practice is critical for marketers. Ads that don’t generate strong engagement signals may never meaningfully enter the auction, regardless of targeting, budget, or bid strategy.
If your creative doesn’t perform, the platform doesn’t just charge you more. It limits your reach altogether.
Creative is now the primary optimization input on Meta
Meta has repeatedly stated that creative quality is one of the strongest drivers of auction outcomes.
In its own advertiser guidance, Meta highlights creative as a core factor in delivery efficiency and cost control. Independent analysis has reached the same conclusion.
A widely cited Meta partnered study showed that campaigns using a higher volume of creative variants saw a 34% reduction in cost per acquisition, despite lower impression volume.
The reason is straightforward. More creative gives the system more signals. More signals improve matching. Better matching improves outcomes.
Andromeda accelerates this effect by learning faster and filtering harder. This is why many advertisers are experiencing plateaus even with stable bidding and budgets. Their creative inputs are not keeping pace with the system’s learning requirements.
While Google has not branded its changes as dramatically as Meta, the direction is the same. Performance Max, Demand Gen, Responsive Search Ads, and YouTube Shorts all rely heavily on creative assets to unlock inventory.
Google has explicitly stated that asset quality and diversity influence campaign performance. Accounts with limited creative assets consistently underperform those with strong asset coverage, even when bidding strategies and budgets are otherwise identical.
Google has reinforced this by introducing creative-focused tools such as Asset Studio and Performance Max experiments that allow advertisers to test creative variants directly. As with Meta, the algorithm can only optimize what it is given.
Strong creative expands reach and efficiency. Weak creative constrains both.
Many agencies are seeing the same pattern across accounts. Performance improves after structural fixes or bidding changes. Then it flattens.
Scaling spend leads to diminishing returns. The instinct is often to revisit bids or efficiency targets. But in most cases, the real constraint is creative fatigue.
Audiences have seen the same hooks, visuals, and messages too many times. Engagement drops. Estimated action rates fall. Delivery becomes more expensive.
This isn’t a platform issue. It’s a creative cadence issue. Creative testing is the missing optimization lever in mature accounts.
Most agencies are structurally set up to optimize bids, budgets, and structure faster than they can produce new creative.
Creative takes time. It requires strategy, copy, design, video, approvals, and iteration. Many retainers still treat creative as a one-off or an add-on rather than a core performance input. The result is predictable. Accounts are technically sound but creatively starved.
If your account has had the same core ads running for three months or more, performance is almost certainly being limited by creative volume, not optimization skill.
High-performing accounts today look messy on the surface with dozens of ads, multiple hooks, frequent refreshes, and constant testing. That isn’t inefficiency. That’s how modern PPC works.
Creative testing is a process, not a campaign
One of the biggest mistakes agencies make is treating creative testing as episodic. Launch new ads. Wait four weeks. Review results. Declare winners and losers. That approach is too slow for how fast platforms learn and audiences fatigue.
High-performing teams treat creative like a product roadmap. There’s always something new in development. Always something learning. Always something being retired.
Effective creative testing focuses on one variable at a time: hook, opening line, visual style, offer framing, social proof, or call to action.
It’s not about finding “the best ad.” It’s about building a library of messages the algorithm can deploy to the right people at the right time.
Once you accept that creative is the constraint, the operational implications are unavoidable. If creative is the main constraint, agency processes need to change.
Creative should be planned alongside media, not after it. Retainers should include ongoing creative production, not just optimization time. Testing frameworks should be explicit and documented.
At a minimum, agencies should be asking:
How often are we refreshing creative by platform?
Are we testing new hooks or just new designs?
Do we have enough volume for the algorithm to learn?
Are we feeding performance insights back into creative strategy?
The best agencies now operate closer to content studios than optimization factories. That’s where the value is.
Creative is the performance lever
Bidding, tracking, and structure still matter. But in 2026, those are table stakes.
If your PPC performance is stuck, the answer is rarely another bidding tweak. It’s almost always better creative. More of it. Faster iteration. Smarter testing.
The platforms have told us this. The data supports it. The accounts prove it.
Creative is no longer a nice-to-have. It’s the performance lever. The agencies that recognize that will be the ones that continue to grow.
We’re in a new era where web content visibility is fragmenting across a wide range of search and social platforms.
While still a dominant force, Google is no longer the default search experience. Video-based social media platforms like TikTok and community-based sites like Reddit are becoming popular search engines with dedicated audiences.
This trend is impacting how news content is consumed. Google’s current news SERP evolution is directly influenced by the personalization of query responses offered by LLMs and the rise in influencer authority enabled by social media platforms.
Google has responded by creating its own AI-powered SERP features, such as AI Overviews and AI Mode, and surfacing more content from social media platforms that provide the “helpful, reliable, people-first content” that Google’s ranking systems prioritize.
Now that search and social are more intertwined than ever, a new paradigm is needed – one in which newsroom audience teams made up of social media, SEO, and AI specialists work holistically on a daily basis toward a cohesive content visibility goal.
When optimizing news content for social platforms, publishers should also consider how those posts may perform in the Google SERP. I’ll cover optimizing for specific SERP features below, but first, you’ll want to think about making your news content social-friendly.
Optimize news content for social media platforms
First, a dose of sanity. Publishers should resist the temptation to optimize content for every social media platform.
It’s better to pick one or two social platforms – where an audience is already established and that offer the best opportunity for growth – than to create accounts on every social platform and let them languish.
Review analytics and conduct audience surveys to gain insights into which platforms your audience already consumes news content.
Here’s a breakdown by platform of which content types work best and how content from each platform can appear on Google.
YouTube
If you’re producing YouTube video content, make sure to follow video SEO best practices. This comprehensive YouTube SEO guide will help you develop a successful video strategy and ensure video titles align with your content.
Per Google, YouTube’s search ranking system prioritizes three elements:
Relevance: Metadata needs to accurately represent video content to be surfaced as relevant for a search query.
Engagement: Includes factors such as a video’s watch time for a specific user query.
Quality: Video content should show topic expertise, authoritativeness, and trustworthiness.
One trend I’ve noticed in YouTube videos on the Google SERP is that older event content can continue to drive visibility rankings long after the event has ended and well after the related article has faded in search rankings.
Explainer videos also demonstrate longevity on the Google SERP. In this government shutdown explainer video, Yahoo Finance includes the expert’s credentials in the description box, further emphasizing the topic expertise element that YouTube’s ranking system prioritizes.
YouTube can also help your visibility in AI Overviews. Nearly 30% of Google AI Overviews cite YouTube, according to BrightEdge. YouTube was cited most often for tutorials, reviews, and shopping-related queries.
While Facebook may not be the cool kid on the block anymore, the social platform has served a diverse set of users over its long history, from its initial audience of college kids to now attracting an older, majority female audience, per Pew Research Center data.
Community-based content and entertainment news that sparks conversation is key to engagement success on Facebook.
While Meta removed the dedicated news tab on Facebook in 2023-2024, leading to cratering Facebook referrals for news publishers, it’s worth noting that Facebook posts have been rising in Google SERP visibility over the last year, so it may be time to reconsider the platform from a search perspective.
In my review of Google search visibility, Facebook posts about holidays and the full moon appear consistently, and the short-form video format is popular.
Since Elon Musk took over the platform in 2022, the audience has shifted to the political right. While the left’s exodus made headlines, usage of X for news is stable or increasing, especially in the U.S., according to the 2025 Digital News Report from the Reuters Institute.
Breaking news, live updates, and political news dominate X feeds and Google visibility, but don’t overlook sports content, where X posts perform well on both the Google SERPs and Discover.
Instagram
This platform emphasizes stylish, visually driven stories and topics, such as red-carpet fashion at award shows. Health topics, especially nutrition and self-care, are also popular.
Sports posts from Instagram, especially game highlights, often surface on the Google SERP as part of a dedicated publisher carousel or in “What people are saying.”
Reddit
A unique aspect of Reddit is that its user base is often not on other social platforms. For news publishers, this can mean a golden opportunity for niche community engagement, but also requires a dedicated strategy that may not translate well to other platforms.
A wide range of news content can perform well on Reddit, from trending topics to health explainers to live sports coverage, but having a deep understanding of the platform’s audience is critical, as is following the Reddit rules of conduct.
Publishers should spend time studying the types of news articles and conversations that drive strong engagement on subreddits before posting anything. Per Reddit, the platform’s largest audiences gravitate toward the following topics:
Technology.
Health.
Direct to consumer (DTC).
Gaming.
Parenting.
The community discussion forum content from Reddit makes it a natural to appear in the Google SERP as part of the “What people are saying” carousel. The Reddit posts I see most often surfaced by Google are related to sports, entertainment, and business.
The TikTok user base leans female and has a greater share of people of color. Approximately half of 18- to 29-year-olds in the U.S. self-report going on TikTok at least once daily, per Pew Research data.
Visual, conversational, and opinion-based content for younger audiences performs best on TikTok. Niche community content also works well; think fashion, #BookTok, etc.
Remember that short-form video requires a dedicated strategy to maximize engagement and reach, and it’s important to keep in mind that TikTok audiences value authenticity over the polish of a professional newsroom production.
Entertainment and shopping content (sales, product reviews) are the categories in which TikTok demonstrates the most Google visibility.
Pinterest
While Pinterest may feel like an old-school social platform, Gen Z is its fastest-growing audience. That being said, Pinterest attracts users from across a wide range of age groups. According to Pinterest’s global data, its audience is 70% women and 30% men.
Don’t overlook the power of Pinterest for lifestyle content niches. Trends around fashion, home decor, DIY, crafts, recipes, and celebrity content are top performers on this visual social platform.
News publishers interested in this platform should have robust lifestyle content that is actionable and delivered with a motivational tone.
How-to and before/after formats are popular. Excellent quality visuals in a vertical format with a 2:3 aspect ratio and text overlays are recommended. Pinterest supports a more relaxed posting schedule compared to other social platforms. Weekly posting is ideal, since much of the content on Pinterest is evergreen.
Similar to Google Trends, Pinterest Trends can help news publishers stay on top of trending topics on the platform.
Social content opportunities by Google SERP feature
If you’re looking to appear in a particular SERP feature, it’s helpful to know how social platform content appears in each type.
Top Stories (or News Box)
The crown jewel of the Google SERP for news publishers, this feature is dedicated to breaking news and developing news stories as well as capturing updates for the big news stories and trends of the moment.
Thumbnail selection is critical for Top Stories. Publishers should pay close attention to the News Box descriptive labels to ensure content is optimized to match the specific intent or angle Google is seeking.
While historically a SERP feature that showcased traditional news publishers, Google is now including relevant social media content in the mix. The Instagram post in Top Stories below is an Instagram Reel from the Detroit Free Press.
Live update articles are often featured in the News Box and are a great format to embed social media posts.
It helps break up walls of texts and serves as a showcase for a news publisher’s live, original reporting from the scene, eyewitness accounts, and related social content that demonstrates a publisher’s subject expertise.
What people are saying
This Google SERP feature is ideal for capturing audience reaction and user-generated content from a variety of social platforms. Short-form video is often featured in this space.
It’s a showcase for any story or topic that drives emotional engagement, including reactions to everything from a celebrity death to a sporting event outcome to a viral trend. Severe weather is also a recurring topic.
Knowledge Panel
There’s a growing interest in this Google SERP feature among news publishers, especially those publishers who produce entertainment content.
Depending on the configuration, publishers have the opportunity to earn a ranking for an image, social post, or article, such as a celebrity biography.
While content opportunities are limited in the Knowledge Panel, they offer more exclusivity, which can increase CTR. YouTube and Instagram are commonly cited here, but X and TikTok have also been growing in visibility.
Google Discover
This social-search hybrid product, which features trending, emotionally engaging content based on a user’s web and app activity, requires a separate optimization strategy.
The keys to Discover visibility are identifying topics that spark curiosity and ensuring articles are formatted for frictionless consumption.
Discover has been considered a “black box” when it comes to content optimization, but there are several basic elements to implement that can increase visibility.
Viral hits may spike a news publisher’s Discover performance temporarily, but as Harry Clarkson-Bennett outlines, publishers need to analyze their Discover performance over time at the entity level to build a smart optimization strategy.
Google’s official Discover optimization tips discourage clickbait practices that actually work quite well on the platform, such as salacious quotes in headlines and content about controversial topics and strong opinion perspectives.
I would never recommend a publisher produce clickbait, but for tabloid publishers, content with a strong, contentious perspective overperforms on Discover, regardless of the official Google guidance.
Headlines and images require serious consideration. While Google is running an experiment in which their AI tool rewrites headlines for Discover, direct, action-oriented, and emotion-driven headlines traditionally perform best. There’s no specific character count recommendation, but at a certain point (typically 100+ characters), the headline will get truncated and an ellipsis will be used.
Images must be formatted to Discover specifications (at least 1,200 pixels wide) and should be eye-catching to make people stop and click. Keep articles short or include a summary box at the top of longer articles. Format articles for scanability.
This Forbes X post featured on my Discover feed nails the elements essential for inclusion.
Politics, sports, and entertainment topics that favor an opinion-driven perspective can drive strong engagement on Discover. For YMYL (Your Money Your Life) content, which can also perform well on Discover, focus on accuracy, expert sources, and lean into the curiosity gap.
YouTube and X are the dominant social platforms featured on Discover, according to a Marfeel study.
This was further confirmed by Clara Soteras, who shared insights from Andy Almeida of Google’s Trust and Safety team as presented at Google Search Central Live in Zurich in December 2025.
Almeida noted that Discover’s algorithm has been updated to “include content from YouTube, Instagram, TikTok, or X published by content creators.”
Instead of feeling dismayed by the increased competition from social media platform content appearing on Google’s SERPs and Discover, news publishers should welcome the additional opportunities for their content to be seen.
In a social and AI-powered search landscape, brand visibility is the key metric. Whether that visibility comes from a news publisher article, video, or social post, it still counts toward brand engagement.
While search strategies have long focused on algorithms, optimizing content for a social-forward SERP requires a different focus. The merging of social and search will spark a holistic audience team revolution in newsrooms, reduce redundant practices, and inspire a content strategy powered by people over algorithms.
As the SaaS market reels from a sell-off sparked by autonomous AI agents like Claude Cowork, new data shows a 53% drop in AI-driven discovery sessions. Wall Street dubbed it the “SaaSpocalypse.”
Whether AI agents will replace SaaS products is a bigger question than this dataset can answer. But the panic is already distorting interpretation, and this data cuts through the noise to show what SEO teams should actually watch.
Copilot went from 0.3% to 9.6% of SaaS AI traffic in 14 months
From November 2024 to December 2025, SaaS sites logged 774,331 LLM sessions. ChatGPT drove 82.3% of that traffic, but Copilot’s growth tells a different story:
SaaS AI Traffic by Source (Nov 2024 – Dec 2025)
Source
Sessions
Share
ChatGPT
637,551
82.3%
Copilot
74,625
9.6%
Claude
40,363
5.2%
Gemini
15,759
2.0%
Perplexity
6,033
0.8%
Starting with just 148 sessions in late 2024, Copilot grew more than 20x by May 2025. From May through December, it averaged 3,822 sessions per month, making it the second-largest AI referrer to SaaS sites by year-end 2025.
Investors erased $300 billion from SaaS market caps over fears that AI agents will replace enterprise software. But this data points to a less dramatic force: proximity.
Copilot thrives because it captures intent inside the workflow. Standalone tools saw a 53% traffic drop while workplace-embedded AI grew 20x.
Software evaluation is work, and Copilot sits where that work happens.
When someone asks, “What CRM should we use for a 20-person sales team?” while building a business case in Excel, that moment is captured—one ChatGPT never sees. The May surge reflects that activation: Microsoft 365 users realizing they could research software without opening a new tab.
41.4% of SaaS AI traffic lands on internal search pages
SaaS AI discovery sends users to internal search results first, not product pages.
Top SaaS Landing Pages by LLM Volume
Page Type
LLM Sessions
% of AI Traffic
Penetration vs Site Avg
Search
320,615
41.4%
8.7x
Blog
127,291
16.4%
8.1x
Pricing
40,503
5.2%
3.2x
Product
39,864
5.1%
2.0x
Support
34,599
4.5%
2.1x
Despite capturing 320,615 sessions — more than blog, pricing, and product pages combined — this dominance likely reflects LLM limitations, not superior content. LLMs route users to search when they lack a specific answer.
For SaaS companies watching their stock crater, that’s useful news: there’s a concrete technical fix. The 41.4% isn’t an existential threat. It’s a crawlability problem.
When an LLM can’t find a direct answer, it defaults to the site’s internal search. The AI treats your search bar as a trusted backup, assuming the search schema will generate a relevant page even if a specific product page isn’t indexed.
At 1.22%, search page penetration is 8.7x the site average. The cause is a “safety net” effect, not optimization.
When more specific pages — like Product or Pricing — lack the data an LLM needs, it falls back to broader search results. LLMs recognize the search URL structure and trust it will return something relevant, even if they can’t predict what.
Blog pages follow with 127,291 sessions and 1.13% penetration. These are structured comparison posts — “best CRM for small teams” or “Salesforce alternatives” — that LLMs cite when they have specific recommendations.
Pricing pages show 0.45% penetration; product pages, 0.28%. When users ask about software selection, LLMs route to comparison surfaces — search and blog — first. Direct product or pricing pages get cited only when the query is already vendor-specific.
The July peak and Q4 decline reflect corporate work cycles
SaaS AI traffic peaked in July at 146,512 sessions, then declined steadily through Q4:
Month
Sessions
Change
July 2025
146,512
Peak
August 2025
120,802
-17.5%
September 2025
134,162
+11.1%
October 2025
135,397
+0.9%
November 2025
107,257
-20.8%
December 2025
68,896
-35.8%
Every platform declined. ChatGPT’s volume was cut in half, dropping from 127,510 sessions in July to 56,786 by year-end. Copilot fell from 4,737 to 2,351. Perplexity dropped from 7,475 to 3,752.
Two factors drove the slide:
People weren’t working. August is vacation season, November includes Thanksgiving, and December is the holidays. Software research happens during work hours; when offices close, discovery drops.
Q4 ends the fiscal “buying window.” Most teams have spent their annual budgets or are deferring contracts until Q1 funding opens. Even teams still working aren’t evaluating tools because there’s no budget left until the new fiscal year.
The July peak reflects midyear momentum: people are working, and Q3 budgets are still available. The Q4 decline reflects both fewer researchers and fewer active buying cycles.
This is where the sell-off narrative breaks down.
Investors treat a 53% traffic drop as proof that AI discovery is stalling. But the data aligns with standard B2B fiscal cycles.
AI isn’t failing as a discovery channel. It’s settling into the same seasonal rhythms as every other B2B buying behavior.
What this data means for SEO teams
Raw traffic numbers don’t show where to invest. Penetration rates and landing page distribution reveal what matters.
Track penetration by page type, not site-wide averages
SaaS shows 0.41% sitewide AI penetration, but that average hides concentration. Search pages reach 1.22%—8.7x higher. Blog pages hit 1.13%. Pricing pages are at 0.45%. Product pages lag at 0.28%.
If you’re only tracking total AI sessions, you’re measuring the wrong metric. AI traffic could grow 50% while penetration on high-value pages declines. Volume hides what matters: where AI users concentrate when they arrive with intent.
Action:
Segment AI traffic by page type in GA4 or your analytics platform.
Track penetration (AI sessions ÷ total sessions) by page category monthly.
Identify pages with elevated concentration, then optimize those surfaces first.
Search results pages are now a primary discovery surface
Internal search captures 41.4% of SaaS AI traffic. If those results aren’t crawlable, indexable, or structured for comparison, you’re invisible to the largest segment of AI-driven buyers.
Most SaaS sites treat internal search as navigation, not content. Results return paginated lists with minimal product detail, no filter signals in URLs, and JavaScript-rendered content LLMs can’t parse.
Action:
With 41.4% of traffic hitting internal search, treat your search bar as an API for AI agents.
Make search pages crawlable (check robots.txt and indexability).
Add structured data using SoftwareApplication or Product schema.
Surface comparison data — pricing, key features, user count — directly in results, not just product names.
Make your data legible to LLMs — pricing and content both
The sell-off is pricing in obsolescence, but for most SaaS companies the real risk is invisibility. Pricing pages show 0.45% AI penetration—below the 0.46% cross-industry average. Blog pages captured 127,291 sessions at 1.13% penetration, but only when content directly answered selection queries. The pattern is clear: LLMs cite what they can read and parse. They skip what they can’t.
Many SaaS sites still gate pricing behind contact forms. If pricing requires a sales conversation, AI won’t recommend you for “tools under $100/month” queries. The same applies to blog content. When someone asks, “What CRM should I use?” the LLM looks for posts that compare options, define criteria, and explain tradeoffs. Generic thought leadership on CRM trends doesn’t get cited.
Action:
Publish pricing on a dedicated, crawlable page. Include representative examples, seat minimums, contract terms, and exclusions.
Keep pricing transparent. Transparent pages get cited; gated pages don’t.
Replace generic blog posts with structured comparison pages. Use tables and clear data points.
Remove fluff. Provide grounding data that lets AI verify compliance and integration capabilities in seconds, not minutes.
Workplace-embedded AI is growing 10x faster than standalone LLMs
Copilot grew 15.89x year over year. Claude grew 7.79x. ChatGPT grew 1.42x. The fastest growth is in tools embedded in existing workflows.
Workplace AI shifts discovery context. In ChatGPT, users are explicitly researching. In Copilot, they’re asking questions mid-task—drafting a proposal, building a comparison spreadsheet, or reviewing vendor options with their team.
Action:
Track Copilot and Claude referrals separately from ChatGPT. Monitor which pages these sources favor.
Recognize intent: these users aren’t browsing — they’re mid-task, deeper in evaluation, and closer to a purchase decision.
Show up in workplace AI discovery to support real-time purchase justification.
Survival favors the findable
The 53% drop from July to December reflects AI usage settling into the software buying process. Buyers are learning which decisions benefit from AI synthesis and which don’t. The remaining traffic is more deliberate, concentrated on complex evaluations where comparison matters.
For SaaS companies, the window for early positioning is closing. The $300 billion sell-off is hitting the sector broadly, but the companies that survive the repricing will be those buyers can find when they ask an AI agent, “Should we renew this contract?”
Teams investing now in transparent pricing, crawlable data, and comparison-focused content are building that findability while competitors debate whether AI discovery matters.
In Google AI Overviews and LLM-driven retrieval, credibility isn’t enough. Content must be structured, reinforced, and clear enough for machines to evaluate and reuse confidently.
Many SEO strategies still optimize for recognition. But AI systems prioritize utility. If your authority can’t be located, verified, and extracted within a semantic system, it won’t shape retrieval.
This article explains how authority works in AI search, why familiar SEO practices fall short, and what it takes to build entity strength that drives visibility.
Why traditional authority signals worked – until they didn’t
For years, SEOs liked to believe that “doing E-E-A-T” would make sites authoritative.
Author bios were optimized, credentials showcased, outbound links added, and About pages polished, all in hopes that those signals would translate into authority.
In practice, we all knew what actually moved the needle: links.
E-E-A-T never really replaced external validation. Authority was still conferred primarily through links and third-party references.
E-E-A-T helped sites appear coherent as entities, while links supplied the real gravitas behind the scenes. That arrangement worked as long as authority could be vague and still rewarded.
It stops working when systems need to use authority, not just acknowledge it. In AI-driven retrieval, being recognized as authoritative isn’t enough. Authority still has to be specific, independently reinforced, and machine-verifiable, or it doesn’t get used.
Being authoritative but not used is like being “paid” with experience. It doesn’t pay the bills.
Search no longer operates on a flat plane of keywords and pages. AI-driven systems rely on a multi-dimensional semantic space that models entities, relationships, and topical proximity.
In that semantic space, entities function much like celestial bodies in physical space, discrete objects whose influence is defined by mass, distance, and interaction with others.
E-E-A-T still matters, but the framework version is no longer a differentiator. Authority is now evaluated in a broader context that can’t be optimized with a handful of on-page tasks.
In AI Overviews, ChatGPT, Claude, and similar systems, visibility doesn’t hinge on prestige or brand recognition. Those are symptoms of entity strength, not its source.
What matters is whether a model can locate your entity within its semantic environment and whether that entity has accumulated enough mass to exert influence.
That mass isn’t decorative. It’s built through third-party citations, mentions, and corroboration, then made machine-legible through consistent authorship, structure, and explicit entity relationships.
Models don’t trust authority. They calculate it by measuring how densely and consistently an entity is reinforced across the broader corpus.
Smaller brands don’t need to shine like legacy publishers. In a semantic system, apparent size and visibility don’t determine influence. Density does.
In astrophysics, some planets appear enormous yet exert surprisingly weak gravity because their mass is spread thinly. Others are much smaller, but dense enough to exert stronger pull.
AI visibility works the same way. What matters isn’t how large your brand appears to humans, but how concentrated and reinforced your authority is in machine-readable form.
The problem with E-E-A-T was never the concept itself. It was the assumption that trustworthiness could be meaningfully demonstrated in isolation, primarily through signals a site applied to itself.
Over time, E-E-A-T became operationalized as visible, on-page indicators: author bios, credentials, About pages, and lightweight citations.
These signals were easy to implement and easy to audit, which made them attractive. They created the appearance of rigor, even when they did little to change how authority was actually conferred.
That compromise held when search systems were willing to infer authority from proxies. It breaks down in AI-driven retrieval, where authority must be explicitly reinforced, independently corroborated, and machine-verifiable to carry weight.
Surface-level trust markers don’t fail because models ignore them. They fail because they don’t supply the external reinforcement required to give an entity real mass.
In a semantic system, entities gain influence through repeated confirmation across the broader corpus. On-site signals can help make an entity legible, but they don’t generate density on their own. Compliance isn’t comprehension, and E-E-A-T as a checklist doesn’t create gravitational pull.
In human-centered search, these visible trust cues acted as reasonable stand-ins. In LLM retrieval, they don’t translate. Models aren’t evaluating presentation or intent. They’re evaluating semantic consistency, entity alignment, and whether claims can be cross-verified elsewhere.
Applying E-E-A-T principles only within your own site won’t create the mass that machines need to recognize, align with, and prioritize your entity in a retrieval system.
AI doesn’t trust, it calculates
Human trust is emotional. Machine trust is statistical.
They reward clean extraction. Lists, tables, and focused paragraphs are easiest to reuse.
They cross-verify facts. Redundant, consistent statements across multiple sources appear more reliable than a single sprawling narrative.
Retrieval models evaluate confidence, not charisma. Structural decisions such as headings, paragraph boundaries, markup, and lists directly affect how accurately a model can map content to a query.
This is why ChatGPT and AI Overview citations often come from unfamiliar brands.
It’s also why brand-specific queries behave differently. When a query explicitly names a brand or entity, the model isn’t navigating the galaxy broadly. It’s plotting a short, precise trajectory to a known body.
With intent tightly constrained and only one plausible source of truth, there’s far less risk of drifting toward adjacent entities.
In those cases, the system can rely directly on the entity’s own content because the destination is already fixed. The models aren’t “discovering” hidden experts. They’re rewarding content whose structure reduces uncertainty.
The semantic galaxy: How entities behave like bodies
LLMs don’t experience topics, entities, or websites. They model relationships between representations in a high-dimensional semantic space.
That’s why AI retrieval is better understood as plotting a course through a system of interacting gravitational bodies rather than “finding” an answer. Influence comes from mass, not intention.
Over time, citations, mentions, and third-party reinforcement increase an entity’s semantic mass. Each independent reference adds weight, making that entity increasingly difficult for the system to ignore.
Queries move through this space as vectors shaped by intent. As they pass near sufficiently massive entities, they bend. The strongest entities exert the greatest gravitational pull, not because they are trusted in a human sense, but because they are repeatedly reinforced across the broader corpus.
Extractability doesn’t create that gravity. It determines what happens after attraction occurs. An entity can be massive enough to warp trajectories and still be unusable if its signals aren’t machine-legible, like a planet with enough gravity to draw a spacecraft in but no viable way to land.
Authority, in this context, isn’t belief. It’s gravity, the cumulative pull created by repeated, independent reinforcement across the wider semantic system.
Entity strength vs. extractability
Classic SEO emphasized backlinks and brand reputation. AI search desires entity strength for discovery, but demands clarity and semantic extractability to be included.
Entity strength – your connections across the Knowledge Graph, Wikidata, and trusted domains – still matters and arguably matters more now. Unfortunately, no amount of entity strength helps if your content isn’t machine-parsable.
Consider two sites featuring recognized experts:
One uses clean headings, explicit definitions, and consistent links to verified profiles.
The other buries its expertise inside dense, unstructured paragraphs.
Only one will earn citations.
LLMs need:
One entity per paragraph or section.
Explicit, unambiguous mentions.
Repetition that reinforces relationships (“Dr. Jane Smith, cardiologist at XYZ Clinic”).
Precision makes authority extractable. Extractability determines whether existing gravitational pull can be acted on once attraction has occurred, not whether that pull exists in the first place.
Structure like you mean it: Abstract first, then detail
LLM retrieval is constrained by context windows and truncation limits, as outlined by Lewis et al. in their 2020 NeurIPS paper on retrieval-augmented generation. Models rarely process or reuse long-form content in its entirety.
If you want to be cited, you can’t bury the lede.
LLMs read the beginning, but then they skim. After a certain number of tokens, they truncate. Basically, if your core insight is buried in paragraph 12, it’s invisible.
To optimize for retrieval:
Open with a paragraph that functions as its own TL;DR.
State your stance, the core insight, and what follows.
Expand below the fold with depth and nuance.
Don’t save your best material for the finale. Neither users nor models will reach it.
Stop ‘linking out,’ start citing like a researcher
The difference between a citation and a link isn’t subtle, but it’s routinely misunderstood. Part of that confusion comes from how E-E-A-T was operationalized in practice.
In many traditional E-E-A-T playbooks, adding outbound links became a checkbox, a visible, easy-to-execute task that stood in for the harder work of substantiating claims. Over time, “cite sources” quietly degraded into “link out a few times.”
A bad citation looks like this:
A generic outbound link to a blog post or company homepage offered as vague “support,” often with language like “according to industry experts” or “SEO best practices say.”
The source may be tangentially related, self-promotional, or simply restating opinion, but it does nothing to reinforce your entity’s factual position in the broader semantic system.
A good citation behaves more like academic referencing. It points to:
Primary research.
Original reporting.
Standards bodies.
Widely recognized authorities in that domain.
It’s also tied directly to a specific claim in your content. The model can independently verify the statement, cross-reference it elsewhere, and reinforce the association.
The point was never to just “link out.” The point was to cite sources.
Engineering retrieval authority without falling back into a checklist
The patterns below aren’t tasks to complete or boxes to tick. They describe the recurring structural signals that, over time, allow an entity to accumulate mass and express gravity across systems.
This is where many SEOs slip back into old habits. Once you say “E-E-A-T isn’t a checklist,” the instinct is to immediately ask, “Okay, so what’s the checklist?”
But engineering retrieval authority isn’t a list of tasks. It’s a way of structuring your entire semantic footprint so your entity gains mass in the galaxy the models navigate.
Authority isn’t something you sprinkle into content. It’s something you construct systematically across everything tied to your entity.
Make authorship machine-legible: Use consistent naming. Link to canonical profiles. Add author and sameAs schema. Inconsistent bylines fragment your entity mass.
Strengthen your internal entity web: Use descriptive anchor text. Connect related topics the way a knowledge graph would. Strong internal linking increases gravitational coherence.
Write with semantic clarity: One idea per paragraph. Minimize rhetorical detours. LLMs reward explicitness, not flourish.
Use schema and LLMS.txt as amplifiers: They don’t create authority. They expose it.
Audit your “invisible” content: If critical information is hidden in pop-ups, accordions, or rendered outside the DOM, the model can’t see it. Invisible authority is no authority.
E-E-A-T taught us to signal trust to humans. AI search demands more: understanding the forces that determine how information is pulled into view.
Rocket science gets something into orbit. Astrophysics navigates and understands the systems it moves through once there.
Traditional SEO focused on launching pages—optimizing, publishing, promoting. AI SEO is about mass, gravity, and interaction: how often your entity is cited, corroborated, and reinforced across the broader semantic system, and how strongly that accumulated mass influences retrieval.
The brands that win won’t shine brightest or claim authority loudest, nor will they be no-name sites simulating credibility with artificial corroboration and junk links.
They’ll be entities that are dense, coherent, and repeatedly confirmed by independent sources—entities with enough gravity to bend queries toward them.
In an AI-driven search landscape, authority isn’t declared. It’s built, reinforced, and made impossible for machines to ignore.
AI search visibility in beauty is increasingly shaped before a prompt is ever entered.
Brands that appear in generative answers are often those already discussed, validated, and reinforced across social platforms. By the time a user turns to AI search, much of the groundwork has been laid.
Using the beauty category as a lens, this article examines how social discovery influences brand visibility – and why AI search ultimately reflects those signals.
Discovery didn’t move to AI – it fragmented
Brand discovery has fragmented across platforms. AI tools influence mid-funnel consideration, but much discovery happens before a user enters a prompt.
The signals that determine AI visibility are formed upstream. By the time a user reaches generative search, preferences and perceptions may already be set. If brands wait until AI search to influence demand, the window to shape consideration has narrowed.
That upstream influence is increasingly social. Roughly two-thirds of U.S. consumers now use social platforms as search engines, per eMarketer research.
This shift extends beyond Gen Z and reflects how people validate information and discover brands. These same platforms consistently appear among the top citation sources in AI results. The dynamic is especially visible in the beauty category.
In a study our agency conducted with a beauty brand partner, we found that Reddit, YouTube, and Facebook ranked among the top cited domains in both AI Overviews and ChatGPT.
While Reddit is often viewed as an anti-brand environment, YouTube appears nearly as frequently in citation data, making it a logical and underutilized target for citation optimization.
The volume reality: Social behavior still outpaces AI
It’s easy to focus on headline figures around AI usage, including the billions of prompts processed daily. But when measured against business outcomes such as traffic and transactions, the scale looks different.
Social platforms are already embedded in mainstream search behavior. For many users, search-like activity on platforms such as TikTok and YouTube is habitual. Nearly 40% of TikTok users search the platform multiple times per day, and 73% search at least once daily.
Referral data reinforces the contrast. ChatGPT referral traffic accounted for roughly 0.2% of total sessions in a 12-month analysis of 973 ecommerce sites, a University of Hamburg and Frankfurt School working paper found. In the same dataset, Google’s organic search traffic was approximately 200 times larger than organic LLM referrals.
AI search is growing and strategically important. But in terms of repeat behavior, measurable sessions, and downstream transactions, social platforms and traditional search continue to operate at a substantially larger scale.
The validation loop: Why AI needs social
The most critical contrarian point for 2026 is that optimizing for social is also optimizing for AI. Large language models are not primary sources of truth. They function as mirrors, reflecting the consensus formed through human conversations in the data they are trained on.
AI systems also demonstrate skepticism toward brand-owned properties. One study found that only 25% of sources cited in AI-generated answers were brand-managed websites.
At the same time, AI engines prioritize third-party validation. Up to 6.4% of citation links in AI responses originated from Reddit, an analysis by OtterlyAI found. This outpaces many traditional publishers.
There’s also a measurable relationship between sentiment and visibility. Research shows a moderate positive correlation between positive brand sentiment on social media and visibility in AI search results.
Treating video as a “brand channel” or a social-first effort rather than a search surface is a strategic failure.
On platforms such as TikTok and YouTube, ranking signals are shaped by spoken language, on-screen text, and captions – signals AI crawlers increasingly use to “triangulate trust.”
In the beauty category, for example, ChatGPT accounts for about 4.3% of searches, while Google processes roughly 14 billion searches per day. However, for “how-to” and technique-based queries, consumers favor the detailed, personalized guidance of social-first video content.
Science-backed brands such as Paula’s Choice and CeraVe dominate AI-generated results because they publish deep, structured educational content. Meanwhile, more traditional marketing-led brands are significantly less visible.
The phrase “dermatologist recommended” correlates with high visibility in AI results because large language models treat expert social proof as a primary ranking signal, according to the same report.
Breaking the high-production barrier: Creating content at scale
One of the biggest hurdles brands cite is budget. Many believe they need a Hollywood production crew to compete in video environments. That is a legacy mindset.
In today’s environment, high-gloss production can be a deterrent. The current landscape rewards authenticity over polish. Consumers are looking for real people with real skin concerns, not highly filtered commercials.
Optimizing for video discovery doesn’t require filmmaking expertise. Brands can leverage internal talent without adding headcount.
Partner with creator platforms: Platforms such as Billow or Social Native allow brands to work with creators for as little as $500 per video. When mapped to a high-intent query, that investment can drive measurable search visibility outcomes.
Leverage social natives on staff: Often, the strongest asset is internal. Identify team members who are active on platforms such as TikTok and understand platform dynamics. Creating internal incentives or challenges to produce content can generate a steady stream of authentic assets while contributing to culture.
Make strategy the differentiator: A large following is not a prerequisite for visibility. In one case, a TikTok profile built from scratch with one part-time creator at $2,500 per month generated hundreds of thousands of views within 90 days. The focus was not on viral trends, but on meaningful transactional terms that drive revenue.
If a new profile can reach more than 100,000 views per video within three months on a limited budget, the barrier isn’t equipment. It’s clarity on the business case and disciplined execution.
The data is clear. Brands can’t win the generative engine if they’re losing the social conversation.
AI models function as mirrors, reflecting web consensus. If real users on Reddit, YouTube, and TikTok aren’t discussing a brand, AI systems have little to surface.
If marketers wait until a user reaches a ChatGPT prompt to shape perception, the opportunity has already narrowed.
Discovery happens upstream. Validation occurs in the loop between social proof and algorithmic citation.
Translating this into action requires rethinking team structure and priorities:
Stop the silos: Your SEO and social teams shouldn’t speak different languages. Both must focus on search surfaces.
Prioritize the “why” before the “what”: Don’t just fix a technical tag. Build the business case for how social sentiment and expert validation drive market share.
Embrace scrappy execution: Whether through $500 creator partnerships or internal social-native talent, start building authentic assets now.
We’re witnessing a shift from algorithm-driven discovery to community-driven discovery.
It’s agile and multidisciplinary, and when executed well, it can meaningfully impact the bottom line.
Local search remains one of the strongest drivers of consistent lead flow for service businesses.
Outdated SEO tactics are losing impact as Google’s algorithm updates reshape local visibility. Success now depends on disciplined tracking and consistent execution.
This 90-day sprint plan shows how to do both.
Why local visibility is more volatile in 2026
Many service businesses aren’t current on how local search has changed or how Google Maps now determines visibility. They have a Google Business Profile (GBP) and a website, yet the phone is quiet.
If a GBP isn’t visible, local prospects won’t find the business when they search for its services. That may sound obvious, but the rules behind that visibility have changed.
Much of that shift traces back to Google’s 2025 spam updates, which significantly cleaned up map results and tightened enforcement.
Review spam, keyword-stuffed business names, fake addresses, and profiles that don’t match real-world details are being filtered more aggressively. At the same time, Google is testing sponsored placements in the map pack, and AI-driven features are shaping how results appear.
The result? Volatility.
Rankings move even when nothing obvious has changed on the site. Business owners and SEOs regularly report drops in GBP impressions and map visibility in public forums. One thread doesn’t prove causation, but it reinforces a broader pattern: local search is less stable than many assume.
Shortcuts that once produced temporary lifts now carry long-term risk. Buying reviews, stuffing keywords into a business name, or stretching service areas beyond reality can lead to suspensions or lost visibility — often just as momentum begins to build.
If local visibility feels unstable, one of three core levers is usually weak. These levers form the foundation of any effective sprint plan and must work together.
Fix only one, and results will be inconsistent. Strengthen all three, and you create stability and sustained lead flow.
Lead lever
What it means
What it changes
Relevance
Google clearly understands your services and service area.
More map pack visibility.
Prominence
Reviews, links, mentions, and local trust signals.
More stability, more clicks.
Conversion
Your site and GBP make contacting you frictionless.
More leads from the same traffic.
Google evaluates local businesses across multiple signals, from proximity and service clarity to reputation and user behavior.
Durable relevance comes from real local authority – accurate categories, consistent citations, strong service pages, and steady review growth.
The 90-day sprint plan
Here’s a structured way to strengthen each of the three lead levers.
Sprint warm-up (Days 1-3): Establish your measurement baseline
If you don’t track from day one, local SEO becomes guesswork — and guesswork doesn’t generate consistent leads. Without clear attribution, you can’t fix what’s broken or scale what’s working.
When you begin working with a service business, start with attribution. Can you trace every call, form fill, and booking to its source? If not, optimization becomes trial and error.
Use the table below as a stop sign. If the core tracking elements aren’t in place, pause and fix them before moving forward.
Tracking checklist: Mark “yes” or “no.” This is your baseline.
Item
What “done” means
Yes / No
Notes
GA4 setup
GA4 installed and collecting data.
Search Console
Verified and connected.
GBP Insights
Baseline saved.
UTM on GBP link
UTM added in GBP website field.
Call tracking
Tracking number. Source known.
CallRail is a solid option
Form tracking
Form submit tracked. Source captured.
Booking tracking
Bookings tracked and attributed.
Weekly numbers
Weekly tracking routine set.
Monthly numbers
Monthly summary routine set.
Baseline snapshot: Complete the table below before making any changes. Save a monthly screenshot as a clear baseline as you run your 90-day sprint.
Metric
Last 7 days
Last 28 days
GBP calls
GBP website clicks
Form submissions
Booked jobs
GSC impressions
GSC clicks
Phase 1 (Days 4-10): Fix GBP fundamentals
Start by fixing issues with your GBP. It’s where Google gathers local signals and evaluates what your business offers. If your profile lacks clarity, even a strong website won’t compensate.
One basic element people often get wrong is the primary category. If you’re an HVAC contractor, your primary category should be “HVAC contractor,” not “Furnace repair service” or “Contractor.” Be exact.
Secondary categories should reflect allied services only. Many businesses add long lists of secondary categories, believing it will generate more calls. In reality, it can dilute relevance and weaken the primary category.
What about posts, geotagged images, inflated service areas, or keyword-stuffed business names? These tactics create activity, not impact.
GBP area
What to do
What to avoid
Primary category
Pick the closest match to your main money service
Picking a vague category “because it ranks”
Secondary categories
Only true supporting services
Adding everything under the sun
Services
Add real services you sell
Made up services to chase traffic
Description
Keep it simple. Service + areas + proof
Keyword soup
Photos
Real photos. Real jobs
Stock images and fake “before after”
Address and service area reality
Don’t try to cover an entire metro area if you can’t serve it. Set service areas based on reality and Google’s rules. If you’re not compliant, your profile faces a higher risk of suspension and video verification.
If you’re a service area business, be conservative. Focus on the radius you can serve well. It’s better to rank and convert strongly within your true radius than to look “bigger” on paper and struggle to build real signals.
Phase 2 (Days 11-35): Build service and location pages
This is core relevance work. Your GBP can be perfect, but if your website is thin, you’ll struggle to hold positions long term.
Many businesses have only a homepage and a contact page, yet expect Google to understand everything about what they offer.
Google needs clear service pages, and so do customers. Each page should focus on one service and explain the process, benefits, and expectations in depth. These pages aren’t just for rankings—they answer questions, reduce hesitation, and drive calls.
Start with your highest-value pages:
Top 2-3 services you sell most.
Top 2-4 areas you truly serve within a two-hour drive.
Focus on your actual location and radius. That’s where you can build the right signals.
For example, if you’re a plumber in Mississauga, Ontario, and you create thin location pages for every city in the Greater Toronto Area, you may get impressions. But without real proof, real jobs, and real conversion strength, those pages rarely hold. You end up with a bloated site that’s hard to maintain and easy for Google to ignore.
What a money service page must include: This isn’t “SEO copy.” This is how you win calls.
Block
What to include
Pricing range
A range. Not “call for quote.” Explain why your pricing differs.
Process
How do you do the service, step-by-step?
Proof
Licenses. Accreditations. Awards. Local reviews.
FAQs
Real answers to real questions customers ask.
CTA
Call. Form. Booking. Make it easy for your potential customers.
On pricing, don’t overthink it. You don’t need a perfect quote on the page — just a range and a reason for that range.
Why is your pricing different?
What is included?
What changes the price?
What does “emergency” mean?
These details turn tire-kicking visitors into qualified calls.
Location pages: Do them right or don’t do them at all
Copy-paste location pages are a common mistake. You can’t just swap the city name and call it a strategy.
Use this checklist to ensure each location page is unique and robust:
Location page element
What makes it real
Local proof
Photos. Projects. Neighborhood references you actually serve
Service fit
Only services you provide in that area
Local FAQs
“Do you serve X.” “What’s the travel fee.” “Same-day service”
Contact
Phone and booking paths that work on mobile
A simple and effective internal linking structure
Build internal links on your site like they are a map. Because they are, for both site visitors as well as Google. If you leave pages disconnected, you waste the work you put into them. Check that:
Service pages link to relevant location pages.
Location pages link to top services.
Relevant blog posts link to money pages.
Phase 3 (Days 36-70): Strengthen reviews and local authority
Phase 3 is about cadence. Continuity beats bursts. At this point, many feel tempted to “go hard for two weeks” and then move on to something else.
That’s the wrong pattern for reviews and trust signals. A steady flow is safer and more believable.
Reviews. Weekly. Forever.
Collect reviews every week, not all at once and then radio silence. Put into place practices that regularly solicit reviews from recent customers.
Also, make customers aware of what they can mention in reviews.
The service you provided.
Their location (neighborhood/city).
Joy Hawkins has published case studies on review recency and performance, and continues to reinforce the idea that fresh reviews matter. But the bigger point is that this means utilizing a complete review strategy, not just a one-time push.
Clear, consistent citations won’t fix a bad business. But they reduce confusion and strengthen local trust signals. The goal here is not “more listings.” The goal is “no contradictions.”
Your name, address, and phone number (NAP) should match across:
GBP.
Website.
Local citations.
Local links that make sense
Don’t buy backlinks. Build local authority that is real. What might this look like?
Your City’s Chamber of Commerce membership and listing.
Supplier and partner pages (real ones).
Sponsoring local teams and events.
Local causes.
PR-worthy local stories.
Partner pages built through real value.
Spammy link tactics might give your site a short boost. But they’re harmful in the long run.
Also, make certain that links are geographically sensible. If you’re a business in Canada, focus on links from Canada and not from random overseas sites. Relevance matters, and locality matters the most.
Phase 4 (Days 71-90): Scale what’s working and report results
By the end of Month 3, your GSC queries should start to look up. Higher impressions. Better clicks.
If not, take a look at your pages that are in Positions 6-20. That’s where you’re getting impressions, but you’re not getting clicks.
This is where many businesses make mistakes. A big one is that they keep publishing new pages instead of improving pages that are already close to winning.
When you see queries and pages with Positions 6-20 in GSC
If you have pages that are ranking in these positions, here are some things you can fix to help them move up:
Update page titles to make certain that are relevant.
Add answers on those pages to the questions your customers usually ask.
Chunk the Q&A so that it’s easier for the crawler to scan.
This matches how people consume information today: fast, on mobile, and looking for direct answers.
Simple reporting dashboard
Here’s a simple dashboard to help you keep track of how you’re doing during the 90-day sprint and beyond. Use it consistently to track growth.
An ongoing local SEO plan outperforms one-time optimization
Local SEO is no longer something you “set up” and revisit later. Rankings shift. Reviews age. Competitors publish new pages. Google adjusts the map pack. One-time optimization fades faster than most teams expect.
A 90-day sprint enforces consistency—tracking before changing anything, fixing core GBP issues, building real service pages, collecting reviews weekly, and improving pages already close to ranking instead of chasing new ones. The gains compound.
IIt also keeps you away from the shortcuts that create problems in the first place. No:
Keyword-stuffed business names.
Fake addresses.
Bought reviews.
Copy-paste location pages.
Random secondary categories.
Purchased backlinks.
Just as important, no operational gaps. If calls go unanswered or booking paths break, prospects move to the next listing. Over time, that lost engagement shows up in performance.
Local SEO in 2026 rewards businesses that operate like real businesses—clear, consistent, responsive. A 90-day sprint builds that rhythm. One-time optimization doesn’t.
The Wild West of web scraping is changing, due in large part to OpenAI’s deal with Disney. The deal allows OpenAI to train on high-fidelity, human-verified cinematic content – intended to combat AI slop fatigue.
This is how most of us feel when dealing with AI slop. Video production by Impolite.
This deal opens up new opportunities to reinforce your brand’s visibility and recall. AI models are hungry for high-quality data, and this shift turns video into an essential asset for your brand.
Here’s a breakdown of why video is the new source of truth for AI and how you can use it to protect your brand’s identity.
How AI brand drift happens
When a large language model’s training set lacks data on a specific brand, the LLM doesn’t admit that it doesn’t know. Instead, it interpolates, filling the gaps in your brand’s story. It makes guesses about your brand identity based on patterns from similar brands or general industry information.
This interpolation can lead to brand drift. Here’s what it looks like when an AI model narrates an inaccurate version of your business.
Say you represent a SaaS company. A user asks ChatGPT about one of your product’s features. But the model doesn’t have information about that specific feature.
So, the model constructs elaborate setup instructions, pricing tiers, and integration requirements for the phantom feature.
This has surfaced for companies like Streamer.bot, where users regularly arrive with confidently wrong instructions generated by ChatGPT – forcing teams to correct misinformation that the product never published.
A Streamer.bot team member describing how AI-generated setup instructions regularly misrepresent product behavior, creating confusion and additional support burden.
AI brand drift happens to local businesses, too. As one restaurant owner told Futurism, Google AI Overviews repeatedly shared false information about both specials and menu items.
To correct brand drift and prevent AI from distorting your brand message, your company must provide a canonical source of truth.
By producing authoritative videos (e.g., a demo that explicitly clarifies pricing), you provide strong semantic information through the transcript and visual proof. The video becomes the canonical source of truth that makes things clear, overriding opinions from Reddit and other sources.
In contrast, a text file contains low entropy. A statement like “50% off” is identical whether it was written in 2015 or 2025. Text often lacks the timestamp of reality, making it easy for AI to manipulate or lose the context of the real world.
To fix this, you need a medium with more data packed into every second. A five-minute video at 60 frames per second contains 18,000 frames of visual evidence, a nuanced audio track, and a text transcript.
Video enables LLMs to capture non-verbal, high-fidelity cues, creating a validation layer that preserves the visual evidence often flattened or lost in written content.
Creative studios like Berlin-based Impolite specialize in high-production-value video that provides the chaotic, non-repetitive entropy that AI needs to verify. The studio’s work for global brands serves as the high-density data source that prevents brand drift.
For example, Karman’s “The Space That Makes Us Human” project is a masterclass in creating a canonical source of truth, using high-fidelity, expert-led video to anchor brand identity.
As deepfakes proliferate, authenticity is shifting from a vague moral concept to a hard technical signal. Search engines and AI agents need a way to verify the provenance.
Is this video real? Is it from the brand it claims to be?
For AI models, real-world human footage is the ultimate high-trust data source. It provides physical evidence, such as a person speaking, a product in motion, or a specific location. In contrast, AI-generated video often lacks the chaotic, non-repetitive entropy of real-world light and physics.
The Coalition for Content Provenance and Authenticity (C2PA) is developing a new provenance standard to verify authenticity. The organization, which includes members such as Google, Adobe, Microsoft, and OpenAI, provides the technical specifications that enable this data to be cryptographically verifiable.
Together, the two organizations go beyond simple watermarking. They allow brands to sign videos the moment they begin recording, providing a signal that AI models can prioritize over unverified noise.
How media verification works: From lens to screen
Ever notice that tiny “CR” mark in the corner of certain media on LinkedIn? This label stands for content credentials. It appears on images and videos to indicate their origin and whether the creator used AI to produce or edit them.
When you click or hover over the “CR” icon on a LinkedIn post, a sidebar or pop-up appears that shows:
The creator: The name of the person or organization that produced the media
The tools used: Which software (e.g., Adobe Photoshop) the creator used to edit or generate the media
AI disclosure: A specific note if the content was generated with AI
The process: A history of edits made to the file to ensure the image hasn’t been deceptively altered
Some creators are already looking to circumvent the icon. Some have shared tips to hide the tag.
While some call it LinkedIn shaming, its presence signals authority. It’s also gaining traction.
For content marketers, adopting C2PA is a defensive moat against misinformation and a proactive signal of quality.
If a bad actor deepfakes your CEO, the absence of your corporate cryptographic signature acts as a silent alarm. Platforms and AI agents will immediately detect that the content lacks a verified origin seal and de-prioritize it in favor of authenticated assets.
Here’s how it works in practice.
1. Capture: The hardware root of trust
Select Sony cameras use the brand’s camera authenticity solution to embed digital signatures in real time. The signature uses keys held in a secure hardware chipset. Sony uses 3D depth data alongside the C2PA manifest rather than a 2D screen or a projection to verify that a real 3D subject was filmed.
Similarly, select Qualcomm’s products support a cryptographic seal that proves the photo’s authenticity. In addition, apps like Truepic and ProofMode can sign footage on standard devices.
2. Edit: The editorial ledger
C2PA-aware software, such as Adobe Premiere Pro, integrates content credentials. This allows brands to embed a manifest listing the creator, edits, and software.
Think of it as a content ledger. Content credentials act as a digital paper trail, logging every hand that touches the file:
When an editor exports a video, the software preserves the original camera signature and appends a manifest of every cut and color grade.
If generative AI tools are used, relevant frames are tagged as AI-generated, preserving the integrity of the remaining human-verified footage.
3. Verify: Tamper-proof evidence in action
If the content is altered outside of a C2PA-compliant tool, the cryptographic link is severed.
When an AI model performs an evidence-weighting calculation to decide which information to show a user, it will see this broken signature.
Information overload is constant nowadays. Traditional gatekeepers are struggling because AI generates content faster than humans can verify it. Authenticity becomes scarce online as Audiences increasingly seek out authenticity and strive to distinguish signal from noise.
From LLMs to search engines like Google, AI systems struggle with the same challenge. Verified subject matter experts (SMEs) are emerging as critical differentiators and as guarantors of credibility and pertinence.
An SME is a human anchor point of credibility for both humans and machines. When brands pair expertise with verifiable video documentation, they create something AI can’t replicate: authentic authority that audiences can see, hear, and trust.
Why expert video should be the source material
A video transcript of an expert explaining a complex topic often captures colloquial, nuanced details that polished, static blog posts miss. Here’s how to use expert-led videos as the starting point of your content flywheel:
Text stream: Extract the transcript to create authoritative, long-form blogs, FAQs, and social captions. This provides the semantic foundation for text-based retrieval.
Visual stream: Pull high-quality frames for infographics and thumbnails. This provides visual proof that anchors the text.
Audio stream: Repurpose the audio for podcast distribution, capturing your expert’s tonal authority.
Discovery stream: Cut vertical TikTok and YouTube clips. These act as entry points that lead AI agents back to your canonical source.
By repurposing a single high-density video asset across these formats, you create a self-reinforcing loop of authority.
This increases the probability that an AI model will encounter and index your brand’s expertise in the format that the model prefers. For example, Gemini might index the video, while Perplexity might index the transcript.
It doesn’t have to be fancy, as this clip from Search with Sean shows:
Before you hit record, identify where your brand is most vulnerable to AI drift. To maximize the surface area for AI retrieval, proceed this way:
Identify the gap: Where is AI hallucinating elements of your story? Find the topics where your brand voice is missing or being misrepresented by outdated Reddit posts or competitor noise.
Anchor with verified experts: Use real people with verifiable credentials. AI agents now cross-reference experts against LinkedIn data and professional knowledge graphs to weigh the authority of the content.
Preserve the nuance: Marketing and legal departments often strip it from blog posts, making them generic. Video preserves the colloquial, detailed explanations that signal true expertise.
With infinite, low-cost AI slop cropping up, it’s going to get harder and harder to fight deepfakes. But it’s harder for an AI to hallucinate a real physical event than a sentence.
The most valuable asset a brand owns is its verifiable expertise. By anchoring your brand in expert-led, multimodal video, you ensure that your identity remains consistent, protected, and prioritized.
A clear hierarchy of data is emerging: high-fidelity, cryptographically signed video is the premium currency. For every other brand, the mandate is simple: Record reality. If you don’t provide a signed, high-density video record of your business, the AI will hallucinate one for you.
Generative engine optimization (GEO) is the practice of positioning your brand and content so that AI platforms like Google AI Overviews, ChatGPT, and Perplexity cite, recommend, or mention you when users search for answers.
If that sounds abstract, the results aren’t.
For bootstrapped form builder tool, Tally, ChatGPT became the #1 referral source.
They’re not alone. Across industries, the shift is already measurable.
ChatGPT reaches over 800 million weekly users. Google’s Gemini app has surpassed 750 million monthly users. And AI Overviews are appearing in at least 16% of all searches (significantly higher for comparison and high-intent queries).
The question isn’t whether AI is changing discovery. It’s whether your brand is showing up when it happens.
So GEO is real. But is it stable enough to invest in seriously?
That’s a fair question.
When we tracked 2,500 prompts across Google AI Mode and ChatGPT through the Semrush AI Visibility Index, the first thing we noticed was volatility.
Between 40 and 60% of cited sources change from month to month.
But underneath the variances, patterns emerged.
The brands showing up consistently shared specific structural characteristics. Entity clarity, content extractability, multi-platform presence made them easier for AI systems to find, trust, and reference.
In this guide, I’ll share what we’ve found about what GEO requires, how it differs from SEO, and the framework for increasing your visibility in AI-driven discovery.
GEO helps your brand appear in AI-generated answers.
For example, when someone asks an AI tool “What is the best whey protein powder for a mom in their 50s,” the response typically evaluates brands and recommends options based on ingredients, reviews, and credibility signals.
If your content or brand is included in that response, it’s an example of GEO in action.
Getting there requires coordinated effort across several areas:
Content strategy: Publishing information that AI systems can discover, understand, and extract for answers
Brand presence: Establishing your authority across platforms where AI tools pull information (not just your website)
Technical Optimization: Ensuring AI crawlers can access and process your content
Reputation Building: Earning mentions and associations that signal credibility to AI systems
These activities overlap with traditional SEO, but the emphasis shifts.
How GEO differs from traditional SEO
GEO builds on the same SEO fundamentals you already use. But it shifts the focus from rankings and clicks to how your brand is mentioned and cited inside AI-generated answers.
Here’s a snapshot of some key differences between GEO and traditional SEO:
What Changes
Traditional SEO
GEO
Primary goal
Rank in top search positions
Be referenced or mentioned in AI answers
Success metrics
Rankings, clicks, traffic
Citations, mentions, share of voice
How users find you
Click through to your site
AI includes you in generated responses
Key platforms
Google, Bing
Google AI Overviews and AI Mode, ChatGPT, Perplexity
How you optimize content
Title tags, keywords, site speed, content quality
Self-contained paragraphs, clear facts, structured data
Positive mentions across trusted platforms and communities
Use this table to update your mental model.
Traditional SEO fundamentals still matter. We’re just adapting how we apply them as AI systems change how people discover information.
Now, let’s break down what this means in practice.
What stays the same
The core principles behind effective SEO still apply to GEO.
You still need to publish high-quality, authoritative content for real users. Your site still needs to be technically accessible. You still need credible signals of trust and expertise. And you still need to understand user intent and deliver clear value.
AI systems tend to reference content that is authoritative, well-structured, and easy to interpret. Those are the same qualities that support strong SEO performance.
If you already have a solid SEO foundation, GEO builds on it rather than replacing it.
Where GEO diverges is in how that foundation is applied.
1. Where you need presence
Traditional SEO focuses primarily on your owned properties, i.e. your website and blog.
GEO benefits from strategic presence across platforms where AI tools discover information, including:
Reddit threads where your target audience asks questions
YouTube videos demonstrating your expertise
Industry publications that establish your authority
Review sites where customers discuss solutions
Social platforms where conversations happen
2. How you structure information
AI systems extract specific passages from your content to construct answers. They pull a paragraph here, a statistic there, and weave them together.
This changes how you need to structure information.
When you’re explaining a concept, defining a term, or sharing data, that paragraph should ideally work on its own. AI systems often extract these substantive passages without the conversational setup around them. (We’ll cover the mechanics of how this works in the strategic framework later.)
You need clear headings to help AI identify which section answers which question.
Also, putting answers early in sections may make them easier for AI to find and extract.
Traditional SEO often rewards comprehensive coverage. GEO places more emphasis on content that’s easy to extract and reassemble. We’re still learning exactly how different AI systems prioritize structure, but clarity consistently helps.
3. What you measure
Traditional SEO metrics like rankings, clicks, and bounce rate tell part of the story.
GEO adds new measurements, like:
AI visibility score: A benchmark of how often and where your brand appears in AI-generated answers
Share of voice: Your visibility compared to competitors in AI responses
Sentiment: Whether mentions are positive, neutral, or negative
Context or prompt: What questions or topics trigger mentions of your brand
Together, these metrics help you understand not just whether you’re visible, but how your brand is being positioned inside AI-generated responses.
You need both traditional SEO metrics and AI visibility metrics to understand your full organic search presence in 2026.
Note: You can track these metrics using Semrush’s Enterprise AIO, which monitors your brand’s visibility across AI platforms like ChatGPT, Google AI Mode, and Perplexity.
It provides granular tracking of mentions, sentiment, share of voice, and competitive benchmarking to help you optimize your AI visibility strategy.
5 principles for AI visibility: A strategic framework
An effective GEO strategy rests on five connected principles that work together to maximize your AI visibility.
(As AI systems evolve, specific patterns may shift, but these underlying principles provide a stable foundation.)
Each one addresses how AI systems discover, evaluate, and reference your brand.
Let’s look at them in detail.
1. SEO fundamentals are the foundation
SEO fundamentals still matter for GEO, but for a different reason than in traditional search.
In AI-driven discovery, these fundamentals still function as optimization levers, but they influence retrieval, interpretation, and attribution rather than rankings alone.
They create the baseline conditions that allow AI systems to retrieve information, interpret it accurately, and attribute it to a source with confidence.
For instance, AI-generated answers are assembled from content that is accessible, readable, and attributable.
When accessibility, readability, or clear attribution are weak, even strong content becomes harder for AI systems to surface or reference reliably.
This is why many sources cited by AI platforms share characteristics long associated with solid SEO foundations.
The overlap exists because clarity and reliability still matter across discovery systems, even as the surfaces change.
Technical accessibility plays a role here.
Content that cannot be consistently crawled, indexed, or rendered introduces uncertainty at the retrieval layer.
Page performance has a similar effect. Slower or unstable experiences don’t block inclusion outright. But they reduce how dependable a source appears when answers are assembled.
JavaScript-heavy implementations highlight this dynamic.
Many AI crawlers still struggle to consistently process client-side rendered content, which can make core information harder to extract or interpret.
When that happens, AI systems have less certainty about using the content as a reference point.
But technical setup is only part of the equation.
AI systems also assess content quality and credibility. Information that reflects real experience, clear expertise, and identifiable authorship is easier to contextualize and trust.
Signals associated with E-E-A-T (Experience, Expertise, Authoritativeness, and Trust) influence not just whether content is referenced, but how it is framed within an answer.
Taken together, these foundations explain why SEO still underpins GEO. Not as a ranking system, but as the infrastructure that makes AI visibility possible.
Entities help AI systems understand and categorize information on the web. This includes distinguishing your brand from similar names, identifying what category you belong to, and understanding which topics you’re credible for.
AI systems don’t just read words. They interpret structure.
Before schema ever comes into play, they look for clear signals about:
What your brand is
What category it belongs to
What it offers
What it’s authoritative for
The most reliable way to provide those signals is through well-structured information.
If those signals are unclear or inconsistent, AI systems have less confidence when deciding whether and how to reference you.
Take monday.com as an example. When AI systems crawl websites and process information, they see “monday” mentioned in many different contexts.
Clear, consistent descriptions across the site and supporting sources help AI understand that monday.com refers to project management software. Not the day of the week.
The same principle applies to category clarity. If you sell organic dog food, AI needs to categorize your brand under pet nutrition, not general groceries or pet accessories.
When someone asks “what’s the best grain-free dog food,” AI is more likely to consider brands it can clearly place in the correct category.
On a product page, it should be unambiguous what each element represents — the product name, the description, the price, the attributes, availability and variants.
That clarity needs to exist in the visible page content first.
Schema markup can then mirror that structure in a machine-readable format (typically JSON-LD). And that same structured understanding should also be reflected in downstream systems, like your product feed submitted to Google Merchant Center.
In other words, the page structure, the schema markup, and the commerce feed should all describe the same thing in the same way.
The goal isn’t to “add schema.” The goal is to make your information logically structured so machines can consistently understand it across systems.
This is important because we don’t know how structured data is used inside large language models. Or how exactly schema influences training, retrieval, or real-time answer generation.
But we do know this: AI systems cross-reference signals from multiple sources and formats.
Your brand description on LinkedIn should align with what appears on your site. Profiles on Crunchbase, review platforms, or industry directories should reinforce the same category, positioning, and value proposition.
When these signals are consistent across sources, AI systems can categorize and reference your brand with greater confidence. When they conflict, confidence drops, and your brand is less likely to be mentioned.
This is why entity clarity isn’t just about a single markup tactic. It comes from designing your content and presence so machines can reliably understand who you are, what you offer, and where you belong wherever your brand appears.
Tip: You can check if your site has missing structured data that makes entity relationships unclear — along with other issues that could potentially be hurting your AI search visibility — using Semrush’s Site Audit.
3. Content must be easy to extract and reuse
If entity clarity determines whether AI systems consider your content at all, extractability determines which specific parts get pulled into AI-generated answers.
This principle operates at the retrieval layer.
AI systems don’t consume pages the way humans do. When generating answers, they retrieve specific passages from across the web and assemble them into a response.
Here’s how it works mechanically:
LLMs break content into chunks, convert those chunks into numerical representations (vectors), and retrieve the most relevant passages when assembling an answer.
Those retrieved chunks are then synthesized into a response — often without the surrounding context from your original page.
This has practical implications.
Based on what we’ve observed, passages that retain meaning when read in isolation are more likely to be retrieved and used accurately. Passages that rely on conversational setup or references like “as mentioned above” or “this is why” tend to lose clarity when extracted.
Now this may not apply to every paragraph on a page.
But paragraphs that contain definitions, explanations, comparisons, or key facts should ideally stand on their own. These are the passages AI systems are most likely to extract without the surrounding narrative.
So what makes content extractable?
Self-contained paragraphs: Each paragraph expresses one complete idea that makes sense on its own, without vague references to surrounding text
Specific facts and statistics: Concrete numbers and clear statements are easier for AI to extract than vague generalizations
Clear, descriptive headings: Headings signal what each section covers, helping AI understand content organization
Front-loaded information: The main point appears at the start of paragraphs rather than at the end
One important distinction: This principle mainly applies to retrieval-augmented systems — like Google AI Mode and Perplexity with grounding, and ChatGPT with browsing enabled. These systems get content in real-time.
For base model knowledge (what the LLM learned during training), content structure is less important. That knowledge comes from training, not from retrieving per-query. Building presence in training data takes time and requires consistent, authoritative publishing.
Below is an example of self-contained content that AI systems can easily extract and reference.
It answers a single, well-defined question: which sources AI platforms rely on for finance-related queries
The main takeaway is stated immediately, without setup
Supporting context (platforms, percentages, category) is included within the same frame
The insight makes sense on its own, even if quoted or summarized elsewhere
The same extractability principle shows up in everyday writing as well.
For example, compare these two ways of explaining the same cooking technique:
Hard to extract: “There are several reasons this method works. After trying it, most people find their eggplant tastes better. That’s why many chefs use it.”
Easy to extract: “Salting eggplant for 15 minutes before cooking removes bitterness and excess moisture. This technique improves the final texture.”
Both explain the same idea. But the second version states the technique, timing, benefit, and result clearly, which makes it easy for AI to extract as a standalone passage.
Here are other examples:
When content is structured this way, AI systems can reliably retrieve relevant passages and include them in answers.
Over time, that increases the likelihood that your expertise is surfaced accurately when users ask questions related to your domain.
4. AI visibility extends beyond your website
AI systems don’t just pull from your website when building answers. They gather information from YouTube, Reddit, review sites, industry publications, social platforms, and more.
This creates two opportunities for visibility:
Your owned presence
Owned presence is content you or your team create on platforms beyond your website.
Your YouTube channel showing product features gives AI video content to reference
Your company’s participation in relevant subreddit discussions shows expertise in action
Your executives’ LinkedIn newsletters establish thought leadership
Podcasts, webinars, conference presentations, and educational platforms provide additional long-form content AI systems can extract from.
These platforms often play an important role in AI discovery.
In fact, Reddit, Linkedin, and YouTube were among the top cited sources by the top LLMs in October 2025.
When your brand creates valuable content on these platforms, you give AI systems more material to draw from.
But the key is creating substantive, helpful content that addresses real problems in your industry.
Earned mentions
Earned mentions are references to your brand that you don’t directly control.
Customer reviews on G2, Capterra, or Trustpilot describe real experiences with your product
Industry journalists mentioning your company in news articles provide third-party validation
Community discussions on Reddit or Quora where users recommend your solution show authentic sentiment. Like this:
When multiple independent sources discuss your brand in relevant contexts, AI systems have clearer signals to interpret your credibility.
Side note: Tools like Semrush’s AI PR Toolkit make this easier to evaluate at scale. Beyond counting earned mentions, it shows how your brand is framed across sources, including whether mentions skew positive, neutral, or negative.
This metric can be very important as you work to extend brand visibility beyond your website. Because sentiment influences how AI systems frame your brand in answers, not just whether they mention you at all.
Why both matter
Owned presence and earned mentions work together.
Your owned content demonstrates expertise and provides detailed information AI can reference. Earned mentions from customers and industry sources validate your credibility.
When AI systems encounter both, they build a comprehensive understanding of what you offer.
This owned and earned content may also become part of LLM training data in the future, shaping how AI systems learn about and reference your brand long-term.
5. Visibility Is measured differently in AI search
Traditional SEO metrics (like rankings, clicks, and traffic) only tell part of the story. But they had one major advantage: the attribution path was clear.
A user clicked, landed on your site, and either converted or didn’t. You could tie that traffic directly to revenue.
AI search breaks that path. When an AI tool recommends your product to a user, they might never click through to your site. The conversion may still happen — they Google your brand name later, sign up the following week — but your analytics won’t connect it back to the AI mention that started it.
That’s the real measurement challenge. It’s not just that the metrics are different. It’s that the link between visibility and revenue becomes harder to trace.
The value here isn’t just the click. It’s being part of the answer.
This requires measuring your visibility differently.
Here are the key metrics to consider:
Citation frequency: This measures how often AI platforms mention your brand when answering questions
Share of voice: Your mention rate compared to competitors. If an AI answers 100 questions about “best CRM,” how many times do you appear vs. your rivals? This reveals your true competitive position.
Context tracking: Where do you appear? Understanding which specific prompts or topics trigger your brand mentions helps you identify the subjects you own versus where you’re invisible.
Sentiment: Are the mentions positive, neutral, or negative? A high share of voice means nothing if the AI is telling users your product is “overpriced” or “buggy.”
The challenge is that traditional analytics platforms (like GA4 or Google Search Console) cannot track these signals. They only see what happens after a click.
This creates a “measurement blind spot.” You might be the most mentioned brand in ChatGPT, but your standard dashboards would show zero activity.
Platforms like Semrush’s AI Visibility Toolkit are built to solve this specific problem. They help quantify these “invisible” GEO metrics, turning qualitative data (like sentiment and mention frequency) into trackable numbers.
Its Brand Performance report shows how visible your brand is in AI answers, how you compare to competitors, and whether mentions skew positive, neutral, or negative.
The toolkit also highlights AI visibility insights, helping you understand how your brand is currently interpreted in AI answers and where adjustments may improve visibility.
Ultimately, a modern search strategy requires monitoring two distinct dashboards:
One for your website’s performance (rankings and traffic) in traditional search. And one for your brand’s mentions across AI search
You need both to see the full picture.
What this framework doesn’t guarantee
These principles increase your probability of appearing in AI answers. They don’t guarantee it.
The volatility in AI citations means even well-optimized brands experience fluctuation.
Different AI platforms weigh signals differently. User context and conversation history affect what gets cited. And AI systems are evolving rapidly — what works today may shift as models update.
Think of GEO like brand building: you’re increasing your odds across many moments of potential visibility, not securing a fixed position.
The brands that do this well show up more often, more accurately, and in better context. But there’s no “rank #1” equivalent to chase.
That realism isn’t a reason to ignore GEO. It’s a reason to approach it as an ongoing discipline. Showing up consistently, across surfaces, over time, is how you build trust with AI systems.
What’s the biggest misconception about GEO right now?
The biggest misconception is that AI-generated answers are too volatile to optimize for.
While individual responses change, the underlying inputs do not. AI systems consistently rely on durable signals like authority, clarity, and trust. Brands with strong entity clarity and credible sources appear repeatedly, even as surface-level outputs fluctuate. The patterns are stable enough to act on.
Is GEO replacing SEO?
No, GEO builds on SEO fundamentals.
Traditional SEO optimizes for rankings and clicks. GEO optimizes for mentions, citations, and recommendations inside AI-generated answers.
They work together. Strong SEO creates the foundation (technical accessibility, quality content, credibility signals) that AI systems rely on when deciding which brands to reference.
How should we think about GEO in the bigger AI search shift?
The clearest way to frame it is as a hierarchy.
AI search is the environment
AI SEO is the practice
AI visibility is the outcome
GEO sits inside AI SEO as one way to improve visibility within generative systems. The goal is not optimizing for a single model or interface. The goal is being seen, trusted, and reused wherever people search for answers.
What types of content are more likely to appear in generative AI responses?
Content that is easy for AI systems to retrieve, understand, and reuse is most likely to appear in generative AI responses.
In practice, this means clear, direct answers to specific questions, self-contained explanations, fact-based comparisons, and concise definitions that make sense without surrounding context. AI systems tend to pull individual passages, not entire pages, so structure and clarity matter more than length.
Does AI search favor large, well-known brands, or does GEO level the playing field?
Well-known brands often start with more authority, but they don’t automatically win. Smaller publishers can compete when they own a clearly defined topic, show up consistently across platforms, and are easy for AI systems to understand and trust.
In practice, focused niche sites may outperform larger brands when their expertise is clearer, better structured, and tightly aligned with specific audience needs.
What’s the right way to think about GEO moving forward?
The right way to think about GEO is as a long-term visibility discipline, not a short-term optimization tactic.
Success comes from making your expertise clear, consistent, and reusable wherever AI systems look for answers. That requires strong alignment across content, SEO, brand, PR, product, and customer touchpoints.
AI search does not change the goal of helping users. It raises the standard for coherence, accuracy, and trust across the entire web.
Google today announced an early preview of WebMCP, a new protocol that defines how AI agents interact with websites.
“WebMCP aims to provide a standard way for exposing structured tools, ensuring AI agents can perform actions on your side with increased speed, reliability, and precision,” wrote André Cipriani Bandarra from Google.
WebMCP lets developers tell large language models exactly what each button or link on a website does. WebMCP allows websites to explicitly publish a clear “Tool Contract” that defines available actions.
It runs on a new browser API, navigator.modelContext. Through that API, the website shares a structured list of tools — such as buyTicket(destination, date). The AI can then call those functions directly, making interactions faster, more accurate, and far more reliable.
Structured interactions for the agentic web. WebMCP introduces two new APIs that let browser agents act on a user’s behalf:
Declarative API: Handles standard actions defined directly in HTML forms.
Imperative API: Supports complex, dynamic interactions that require JavaScript execution.
These APIs act as a bridge, making your website agent-ready. They enable faster, more reliable agent workflows than raw DOM manipulation.
Use cases. Google shared use cases that show how an AI agent can handle complex tasks for your users with speed and confidence:
Travel: Users can get the exact flights they want. Agents can search, filter results, and complete bookings using structured data that delivers accurate results every time.
Customer support: Users can create detailed support tickets faster. Agents can automatically fill in the required technical details.
Ecommerce: Users can shop more efficiently. Agents can find products, configure options, and move through checkout with precision.
How to access the preview. You can apply for the preview to WebMCP here.
Why we care. Agentic experiences are shaping the future of search—and possibly SEO. Dan Petrovic called it the biggest shift in technical SEO since structured data. Glenn Gabe called this a big deal. It’s worth exploring these new protocols now.
Google is redesigning shopping and advertising around AI-powered, agent-driven experiences, and said speed and certainty will converge for consumers and brands in 2026.
In her third annual letter, Vidhya Srinivasan, Google’s VP and GM of Ads and Commerce, outlined how Search, YouTube, and its shopping infrastructure are being rebuilt for the agentic era — where AI doesn’t just surface information but actively assists, recommends, and completes transactions.
Key trends. Google is redefining commercial intent across Search, YouTube, and AI interfaces. Ads are moving deeper into conversational experiences like AI Mode, creative production is becoming AI-native, and checkout is embedding directly into Search. Here are key takeaways from Srinivasan’s letter:
Creators to commerce: YouTube remains a discovery hub, with creators serving as trusted tastemakers. AI helps match brands with the right creators, turning influence into measurable business impact.
Search ads evolve: As conversational and visual queries rise, AI Mode reimagines ads as part of the discovery journey. New formats (e.g., sponsored retail listings, Direct Offers), aim to help users find products and services while giving brands meaningful ways to convert interest into sales.
Agentic commerce arrives: Google is standardizing AI-driven shopping through the Universal Commerce Protocol (UCP), enabling consumers to browse, pay, and complete purchases seamlessly in AI Mode. Early rollouts include Etsy and Wayfair, with Shopify, Target, and Walmart to follow.
AI-powered creative and performance: Gemini 3 powers ad tools that automate creative production and campaign optimization. Generative tools like Nano Banana and Veo 3 help advertisers create studio-quality assets in minutes, while AI Max expands reach and drives performance.
Why we care. Adapting to AI-mediated commerce is increasingly necessary to stay competitive. Buying decisions are shifting — more often happening inside AI-driven search, creator content, and agent-powered checkout flows that could reshape traffic and conversion paths. These changes may create new ways to reach high-intent shoppers, but they also signal growing platform control over discovery, measurement, and transactions, potentially affecting competition, costs, and brand visibility.
As consumers lean into AI search, the industry has focused on the technical “how” – tracking everything from Agentic Commerce Protocols (ACP) to ChatGPT’s latest shopping research tools. In doing so, it often misses the larger shift: conversational search, which is changing how visibility is earned.
There’s a common argument that big brands will always win in AI. I disagree. When you move beyond the “best running shoes” shorthand and look at the deep context users now provide, the playing field levels. AI is trying to match user needs to specific solutions, and it’s up to your brand to provide the details.
This article explains how conversational search changes product discovery and what ecommerce teams need to update on product detail pages (PDPs) to remain visible in AI-driven shopping experiences.
How conversational search builds on semantic search
While semantic search is critical for understanding the meaning and context of words, conversational search is the ability to maintain a back-and-forth dialogue with a user over time.
Semantic search is the foundation for conversational visibility. Think of it like a restaurant: If semantic search is the chef who knows exactly what you mean by “something light,” conversational search is the waiter who remembers that you’re ordering for dinner.
Feature
Semantic search
Conversational search
Goal
To understand intent and context
To handle a flow of questions
How it thinks
It knows “car” and “automobile” are the same thing
It knows that when you say “how much is it?”, “it” refers to the car you just mentioned
The interaction
Searching with a phrase instead of keywords
Having a chat where the computer remembers what you were asking about before
Example
Asking “What is a healthy meal?” and getting results for “nutritious recipes.”
Asking “What is a healthy meal?” followed by “give me a recipe for that.”
AI blends them together. It uses semantic understanding to decode your complex intent and conversational logic to keep the thread of the story moving. For brands, this means your content has to be clear enough for the “chef” to interpret and consistent enough for the “waiter” to follow.
What conversational search and AI discovery mean for ecommerce
I recently shared how my mom was using ChatGPT to remodel her kitchen. She didn’t start by searching for “the best cabinets.” Instead, she leveraged ChatGPT as her pseudo-designer and contractor, using AI to solve specific problems.
Product discovery happened naturally through constraint-based queries:
“Find cabinets that fit these dimensions and match this specific wood type.”
“Are these cabinets easy for a DIY installation?”
Her conversations were piling up, allowing her to reach multiple solutions at once. Her discovery journey was layered. When ChatGPT recommended products to complete her tasks, she simply followed up with, “Where can I buy those?”
Brands and marketers need to stop optimizing for keywords and start optimizing for tasks. Identify the specific conversations where your product becomes the solution. If your data can’t answer the “Will this fit?” or “Is this easy?” questions, you won’t be part of the final recommendation.
“Recommend products” is the top task users trust AI to handle, highlighting a clear opportunity for brands, according to Tinuiti’s 2026 AI Trends Study. (Disclosure: I am the Sr. Director of AI SEO Innovation at Tinuiti.)
For your brand to be the one recommended, your PDPs must provide the “ground truth” details these assistants need to make a confident selection.
Step away from the keyword research tools and stop asking for “prompt volumes.” In an AI-driven world, intent is more important than volume. Before changing a single page, you need to understand the high-intent journeys your personas are actually taking.
To identify your high-intent semantic opportunities:
Audit your personas: Who is your buyer, and what are their non-negotiable questions? If you haven’t mapped these lately, start there.
Bridge the team gap: Talk to your product and sales teams. They know the specific attributes and “deal-breaker” details that actually drive conversions.
Listen to the market: Use sentiment analysis and social listening to find hidden use cases or brand problems. How are people actually using, or struggling with, your product in ways your brand team hasn’t considered?
Map constraints, not keywords: Identify the specific constraints (size, compatibility, budget) that AI agents use to filter recommendations.
How to build PDPs for AI search with decision support
Your PDP should operate like a product knowledge document and be optimized for natural language. This helps an AI system decide whether to recommend the product for a specific situation.
Name your ideal buyer and edge cases
Content should support better decision-making. Audit your PDPs to determine whether they provide enough detail on who the product is best for – and not for. Does the page explicitly name your ideal buyer, their skill level, lifestyle constraints, and deal-breakers?
AI shopping queries often include exclusions, and clearly outlining the important parts of your user search journey will help you understand where your products fit best.
Cover compatibility and product specifications
Compatibility feels synonymous with electronics (e.g., “Will my headphones connect to this computer?”). But think beyond one-to-one compatibility and expand into lifestyle compatibility:
Is this laptop bag waterproof enough for a 20-minute bike ride in the rain, and does it have a clip for a taillight?
Can I fit a Kindle and a book in this purse?
Will this detergent work with my HE washer?
Will this carry-on suitcase fit in the overhead compartment on every airline?
Is this “family-sized” cutting board actually small enough to fit inside a standard dishwasher?
People are searching for how products fit into their lifestyle needs. Highlight and emphasize the features that make your products compatible with their lifestyle.
Breaking down your customer search journey and listening to your customers’ concerns, either through AI sentiment analysis, social listening, or product reviews, will help you understand what you need to be specific about.
Apparel brands should add sizing and fit guidance. Maybe you’re comparing your size 10 jeans to competitors’ sizing, or considering sizing changes based on the cut or style of your other jeans.
Beauty or skincare brands need ingredient combination details. Is this product compatible with other common formulas? Can I layer it over a vitamin C serum?
Toy brands could include important details for parents. Does your product need to be assembled, and how long will it take? Can they assemble it the night before Christmas?
If your biggest customer complaint is understanding when and how to use your products, you’re likely not making it easy enough for them to buy. Better defining your product attributes helps users and LLMs alike better understand your products.
Write for constraint matching instead of browsing
AI shopping discovery is driven by constraints instead of keywords. Shoppers aren’t asking for “the best laptop bag.” They’re asking for a bag that fits under an airplane seat, survives a rainy commute, and still looks professional in a meeting.
PDPs should be written to reflect that reality. Audit your product pages to see whether they answer common “Can I …?” and “Will this work if …?” questions in plain language. These details often live in reviews, FAQs, or support tickets, but rarely surface in core product copy where AI systems are most likely to pull from.
Here’s what transforming your content can look like:
Traditional PDP copy
Laptop backpack
Water-resistant polyester exterior.
Fits laptops up to 15″.
Multiple interior compartments.
Lightweight design.
USB charging port.
PDP copy written for constraints
Laptop backpack
Best for: Daily commuters, frequent flyers, and students who need to carry tech in unpredictable weather.
Not ideal for: Extended outdoor exposure or laptops larger than 15.6″.
Weather readiness: Water-resistant coating protects electronics during short walks or bike commutes in light rain, but is not designed for heavy downpours.
Travel compatibility: Fits comfortably under most airplane seats and in overhead bins on domestic flights.
Capacity and layout: Holds a 15-15.6″ laptop, charger, and tablet, with room for a book or light jacket – but not bulky items.
Lifestyle considerations: Integrated USB port supports charging on the go (power bank not included).
LLMs evaluate how well a product satisfies specific constraints in conversational queries or based on predetermined user preference information.
PDPs that clearly articulate those constraints are more likely to be selected, summarized, and recommended. This type of copy should also help your on-site customers better understand your products.
Just because search platforms change doesn’t mean we should abandon everything we’ve learned in traditional optimization.
Technical SEO fundamentals still heavily apply in AI search:
Can crawlers access and index your site?
Are your product listing pages (PLPs) and PDPs clearly linked and structured?
Do pages load quickly enough for crawlers and users?
Is your most critical content accessible?
In conversational shopping, structured data is playing a different role than it did in traditional SEO strategies. In conversational shopping, it’s about verification.
AI systems use your schema to validate facts before they risk reusing them in an answer. If the AI can’t verify your price, availability, or shipping details through a merchant feed or structured data, it won’t risk recommending you.
Variant clarity is just as important. When differences like size, color, or configuration aren’t clearly defined, AI systems may treat variants as separate products or merge them incorrectly. The result is inaccurate pricing, incompatible recommendations, or missed visibility.
Most importantly, structured data must match what’s visibly true on the page. When schema contradicts on-page content, AI systems avoid recommending uncertain information.
Success on the digital shelf has moved beyond high-volume keywords. In this new era, your visibility depends on how well you satisfy the complex constraints users can provide in a single search. AI models are scanning your pages to see if you meet specific, nuanced requirements, like “gluten-free,” “easy to install,” or “fits a 30-inch window.”
The shift to conversational discovery means your product data must be ready to sustain a dialogue. The goal is simple: provide the density of information necessary for an AI to confidently transact on a user’s behalf. Those who build for these multi-layered journeys will own the future of discovery.
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