His appointment reflects his leadership in higher education digital marketing and Search Influence’s longstanding collaboration with UPCEA, the leading association for online and professional education.
Serving on the UPCEA Board
UPCEA elected new officers and directors in November, with appointments taking effect at the conclusion of the 2026 UPCEA Annual Conference in New Orleans on April 17, 2026. As Corporate Partner Representative, Will will serve as a liaison between corporate partners and institutional members, contributing insight from the evolving landscape of higher education digital marketing.
UPCEA leadership emphasized the importance of strategic, forward-thinking expertise during a pivotal moment for online and continuing education. As institutions adapt to AI-driven search behavior, enrollment shifts, and increased competition, the role of data-informed digital marketing has never been more essential.
About Will Scott, AI SEO Expert
Will Scott is a recognized leader in digital marketing and is credited with coining the phrase “barnacle SEO” in 2008. A founding faculty member of Local U, Will frequently presents at national conferences and contributes to major online marketing publications. With a Master of Architecture from Tulane University, he approaches marketing as a systems problem, solving for visibility, measurement, and long-term impact.
Since launching his first website in 1994, Will has overseen teams that have developed thousands of websites, produced hundreds of thousands of directory pages, and generated millions of visits through search.
Strengthening the Search Influence + UPCEA Partnership
Will’s appointment to the UPCEA Board of Directors reflects more than an individual milestone. It represents the continued alignment between Search Influence and UPCEA’s shared commitment to research-driven innovation in higher education digital marketing.
As a Platinum Partner of UPCEA, Search Influence has worked alongside the association to produce actionable, industry-leading research that helps institutions adapt to shifting search behaviors and evolving enrollment strategies. Together, we have collaborated on three major national studies:
2023 SEO Readiness Research Study, which benchmarked how prepared colleges and universities are for the demands of modern search visibility.
Through conference sessions, webinars, and ongoing thought leadership, the UPCEA x Search Influence partnership delivers practical frameworks for institutions looking to strengthen their visibility, improve ROI, and future-proof their higher education digital marketing strategies.
Learn More About Higher Education Digital Marketing
Will’s appointment reflects the deep alignment between Search Influence and UPCEA’s mission to expand educational access and outcomes.
To learn more about our partnership or to discuss how your institution can strengthen its higher education digital marketing strategy, contact Search Influence today.
AI search is changing how prospective students discover programs. But building an entirely new marketing strategy from scratch isn’t realistic for most higher education teams.
Budgets are tight, staff capacity is limited, and priorities compete for attention.
That’s why we partnered with UPCEA for this spring’s live webinar:
Make Your Existing Marketing Work Harder for AI Search Visibility Tuesday, March 24
12 PM ET | 11 AM CT
Your presenters:
Paula French, Director of Sales and Marketing, Search Influence
Why AI Search Visibility Matters for Higher Education
AI-powered search tools are shaping discovery, oftentimes before a prospective student ever clicks your website. Platforms powered by LLMs evaluate your site, paid campaigns, PR coverage, and social media to determine what information to surface.
When those channels operate in silos, AI tools may pull incomplete or inconsistent details. In some cases, your programs may not appear at all.
For higher education marketers, the opportunity isn’t to rebuild everything. It’s to unify what already exists. When messaging aligns across channels, institutions increase relevance, strengthen credibility, and improve their presence in AI-driven results.
What You’ll Learn in the Webinar
This session is built for teams who want practical guidance they can apply immediately.
In this live webinar, we’ll break down how to:
Create a consistent, credible presence across the marketing channels AI evaluates
Leverage existing assets to improve higher education AI search visibility
Strengthen trust signals so AI tools surface accurate program information
Reduce gaps that limit discoverability in AI-powered search
After the webinar, join one of our small-group Strategy Labs:
Tuesday, March 31 Wednesday, April 1 12 PM ET | 11 AM CT
Led by Will and Paula, these interactive sessions offer hands-on coaching. Bring your questions about specific programs, campaigns, or content gaps. We’ll workshop actionable recommendations to strengthen your AI search visibility and connect strategy to measurable outcomes.
Brand influence now happens before a website visit. Discovery and evaluation increasingly occur inside AI chat interfaces, not on your site or a traditional search engine results page.
Traffic reflects outcomes, not total visibility. Sessions show engagement, but they do not capture upstream exposure.
Presence and citations are leading indicators. Appearing in AI-generated answers and being cited as a source signal authority before traffic occurs.
Brand representation shapes decision-making. How AI systems describe your brand affects perception, trust, and competitive positioning.
Measurement must connect visibility to outcomes. AI tracking works when exposure signals and on-site performance live in the same reporting framework.
For years, organic traffic was the clearest proof that SEO worked.
More sessions meant more visibility. More visibility meant more opportunity. (Rank higher → earn clicks → measure results.)
It was clean, predictable, and measurable.
Today, that proof is less complete.
AI systems increasingly answer questions within their own interfaces. Users compare brands, evaluate options, and form opinions before ever visiting a website.
Traffic still matters. But it no longer reflects the full scope of your visibility.
This post explores:
Why traffic is now an incomplete KPI
What AI search changes about measurement
Which AI SEO KPIs provide clearer insight
How Search Influence’s dashboards report on visibility
TLDR: Traffic tells part of the story. The right AI search KPIs complete it.
Traffic Used to Tell the Truth About SEO Performance
Before generative search reshaped discovery, SEO measurement followed a straightforward assumption: visibility required a click.
When rankings improved, traffic increased. When traffic increased, business outcomes often followed. Organic sessions became the clearest proxy for exposure and performance because users had to visit your site to consume your content.
Why Traffic Worked as a Primary KPI
Historically, traffic has served as a reliable stand-in for:
Search visibility
Content relevance
Audience demand
Business impact tied to on-site behavior
Because outcomes happen on websites, traffic connected search performance to measurable results. That’s why most reporting frameworks still anchor on organic sessions and year-over-year growth.
The structure of search has always supported that model.
Today, however, the structure of search has changed.
AI Search Changed the Journey Before Most Dashboards Changed
The biggest shift isn’t that answers exist inside AI systems. It’s when influence happens.
Consideration now starts earlier and often outside your analytics environment. By the time someone arrives on your website, they may already understand the category, recognize your brand, and have narrowed their options.
That changes the role of the visit.
Instead of initiating discovery, the session often confirms a decision that has already been shaped elsewhere. Users return through branded search, direct navigation, or assisted channels after AI-driven exposure has done part of the persuasion work.
Most reporting systems still assume that influence begins when a session begins.
Increasingly, it does not.
The Visibility–Click Gap (And Why It’s Growing)
The visibility–click gap is the space between being seen and being visited.
Your brand can appear in search results, AI summaries, and comparisons, and still never generate a session. As zero-click behavior continues to rise (roughly 60% of U.S. searches end without a click), that space becomes more visible in your reporting.
You’ve probably noticed the pattern. Impressions stay strong. Click-through rate dips. Traffic slides. Yet conversions hold steady, or even improve. Branded search volume climbs while non-branded sessions level off.
At first, it feels like the data doesn’t line up. It does. Exposure and visits are just no longer moving in lockstep.
Traffic Still Matters, But It’s Not the Lead KPI Anymore
Let’s be clear: traffic didn’t stop being useful.
Sessions still reflect real behavior. They show engagement, interest, and when someone cared enough to act.
What changed is priority.
Traffic used to be the headline metric. In the age of LLMs, it’s now one of several signals. It supports performance analysis, but it no longer defines search success on its own.
What Traffic Still Measures Well
Traffic remains strong at measuring:
Overall demand trends
Whether content resonates enough to earn a visit
Channel efficiency and cost performance
Relative performance across search, paid, referral, and direct channels
If sessions rise, something is working. If they fall sharply, something deserves investigation.
Traffic still provides directional insight. It just doesn’t capture the full environment where influence occurs.
Where Traffic Under-Reports AI Search Impact
Traffic struggles to reflect:
Zero-click discovery and brand exposure
Assisted conversions that begin outside your site pages
Trust-building moments that don’t register as sessions
How your brand appears inside AI-generated summaries
In other words, traffic tells you who arrived.
It doesn’t always tell you who was influenced.
Why “Traffic Loss” Often Gets Misdiagnosed
Today, traffic declines require context.
Traffic can shift for several different reasons, and they don’t all point to the same problem. Before assuming visibility declined, look at the surrounding indicators:
Are impressions holding steady?
Have rankings materially changed?
Is branded search trending upward?
Are conversion rates stable or improving?
If exposure remains strong while sessions dip, the issue may lie in how clicks are distributed rather than how often your brand appears.
There are also cases where fewer visits align with stronger outcomes. A smaller audience arrives with clearer intent. Conversion rates improve. Revenue holds steady.
In that scenario, traffic volume is like counting footsteps in a store. Fewer people may walk in, but if more of them buy, the business hasn’t weakened.
What AI Search Success Looks Like (If You’re Measuring It Correctly)
AI search success expands beyond sessions and rankings.
It reflects how often your brand appears in AI-driven answers, how accurately it’s represented, and how that exposure influences downstream behavior.
To measure that shift, you need a broader set of KPIs alongside traditional SEO metrics.
AI Search KPIs That Belong Next to Traffic in Your Reporting
If traffic shows what happened after someone arrived, these KPIs help you understand what happened before that moment.
Disclaimer: AI search measurement is evolving. AI platforms do not provide flawless attribution, and zero-click exposure often occurs outside traditional analytics reporting. The goal is not perfect precision at the interaction level. It’s consistent trend tracking across visibility and performance metrics to understand directional impact over time.
Common Mistakes Teams Make Measuring AI Search
Even with the right KPIs defined, measurement can still drift off course. AI search introduces new signals, but it also introduces new ways to misread performance.
Before expanding marketing dashboards or shifting budgets, it helps to clarify what strong AI measurement actually requires. Here are some common mistakes and what to do instead.
Mistake
What to Do Instead
Treating AI visibility like traditional rankings
Track consistency of brand mentions across prompts and platforms over time.
Over-reacting to prompt-level volatility1
Focus on directional trends, not single-answer fluctuations.
Measuring visibility without outcomes
Connect exposure to branded search lift, engagement quality, and conversions.
Ignoring third-party and comparison ecosystems
Monitor how your brand appears in listicles, directories, and cited sources.
Making budget decisions based on traffic alone
Evaluate visibility, citations, and influence alongside sessions.
AI search performance requires a broader lens. When teams shift from ranking-based thinking to influence-based measurement, strategy becomes clearer, and decisions become more durable.
¹ Prompt-level volatility refers to natural variation in AI answers. Small shifts in phrasing, user context, model updates, or training data can change which brands appear in a single answer. That does not automatically signal a gain or loss in authority. Individual prompts are snapshots. Trend lines across many prompts and time periods provide a more reliable view of performance.
How Search Influence Tracks AI Search Performance
Impactful measurement works when visibility and outcomes are evaluated together. That requires more than a new metric. It requires a reporting structure that connects exposure inside AI systems to on-site user behavior in a consistent, repeatable way.
Here’s how we approach it.
AI Traffic Report (GA4)
We begin with what is measurable inside analytics.
AI platforms that link to external websites send referral traffic. In GA4, those sessions can be isolated and trended when configured intentionally. Our AI Traffic Report surfaces:
Sessions originating from known AI tools
Engagement quality, including time on site and pages viewed
Top landing pages receiving AI-driven visits
Conversions and downstream actions tied to AI-referred sessions
This layer shows what AI discovery produces once a user leaves an AI interface and engages directly with your content.
AI Visibility Tracker (Scrunch-Powered)
Traffic tells you who arrived. Visibility tracking tells you whether your brand is part of the answer in the first place.
Through our AI visibility tracking powered by Scrunch, we measure how AI platforms surface, cite, and describe your brand across relevant prompts. Scrunch is an enterprise AI visibility tracking platform built specifically to monitor brand presence inside generative search environments like AI Overviews, ChatGPT, Gemini, and Perplexity. It aggregates structured prompt-level data across models to deliver consistent reporting on brand presence, positioning, and competitive context over time.
We use Scrunch to report on:
Prompt-level tracking across major AI platforms
Brand mentions and AI citation count
Sentiment and positioning analysis
Competitive share of voice
Content gaps and citation opportunities
This layer captures exposure that occurs inside AI systems, including interactions that may never generate a direct session.
Why This Lives Beside SEO Reporting
AI visibility does not replace traditional SEO reporting. It extends it.
By placing AI traffic data and AI visibility tracking inside the same dashboard environment, we create context:
Visibility trends can be evaluated alongside engagement trends
Citation shifts can be compared against branded search lift
Traffic patterns can be interpreted with upstream exposure in mind
No single metric defines AI performance. The value comes from evaluating presence and outcomes together, consistently, over time.
That is how AI search becomes measurable in a way that supports real strategy decisions rather than isolated data points.
AI SEO KPI Frequently Asked Questions
Is organic traffic still important for SEO?
Yes. Organic traffic remains among the most important traditional SEO KPIs because it measures demand, engagement, and on-site performance. However, it no longer captures total visibility. Modern AI systems can influence awareness and decision-making before a visit occurs. Traffic should be evaluated alongside AI visibility, citations, and influence metrics for a complete view of SEO performance.
How do AI Overviews affect click-through rates?
AI Overviews can reduce click-through rates for some queries because they provide summarized answers directly in search results. When users receive sufficient information within the AI summary, fewer clicks may occur, even if impressions remain stable. The impact varies by query intent, industry, and whether a brand is prominently featured or cited.
What are the most important AI search KPIs to track?
The most important AI search KPIs measure presence, authority, and influence. These include how often a brand appears in AI-generated answers, how frequently it is cited as a source, how accurately it is represented, and whether exposure correlates with branded search lift, engagement quality, or conversion trends. Together, these metrics provide a broader view of performance than traffic alone.
Can AI search influence conversions without sending traffic?
Yes. AI search can influence awareness, preference, and comparison before a user visits a website. A user may encounter a brand in an AI response, then later return via branded search, direct navigation, or another channel. In this case, AI exposure contributed to the decision even though it did not generate a direct click.
How do you measure brand visibility in AI-generated answers?
Brand visibility in AI-generated answers is measured by tracking relevant prompts across AI models and monitoring how often the brand appears, how it is cited, and how it is described. Measurement focuses on trends over time and competitive context rather than individual responses. This approach provides directional insight into presence and authority within AI-driven search environments.
The Bottom Line: Traffic Is a Signal, Not the Scoreboard
Traffic still matters, and it always will.
But in an AI search pipeline, influence often happens outside your website. Visibility, citations, and brand representation now shape decisions upstream.
Traffic is the outcome. Visibility is the leading indicator.
If your reporting only tracks sessions, you’re only seeing part of the picture. It’s time to measure what happens before the visit.
Paid search and paid social do not compete. They complement each other. Paid social creates demand and brand awareness, while paid search captures high-intent users actively searching for solutions.
AI has compressed the marketing funnel. Users now move fluidly between social media feeds, AI Overviews, and search engine results pages, making an integrated strategy more important than ever.
Paid search is now a validation channel as much as a conversion channel. In AI-influenced SERPs where organic visibility is shrinking, paid placements reinforce credibility and brand trust.
Paid social drives measurable downstream search demand. Strong social campaigns increase branded search queries and high-intent traffic that paid search can convert efficiently.
Full-funnel orchestration drives stronger performance than channel silos. When paid social and paid search share messaging, data, and optimization insights, brands achieve greater efficiency, higher ROAS, and sustained growth.
Search Influence approaches paid search vs. paid social as a unified strategy designed to connect demand creation, intent validation, and conversion across the modern marketing funnel.
The traditional marketing funnel hasn’t just shifted. It has compressed. AI accelerates the speed at which users move from discovery to decision, collapsing awareness, consideration, and conversion into overlapping, nonlinear moments.
Today, influence happens across algorithmic social feeds, AI Overviews in search engine results pages, short-form video content, conversational search experiences, and branded search queries. A user may first encounter a brand through paid social ads, validate it in search results, scan an AI-generated summary, and then click a paid search ad, all within a single session.
One of the biggest misconceptions in digital marketing is that paid search and paid social compete. They don’t.
Paid social creates awareness and demand among targeted audiences. Paid search captures that intent when users actively search for solutions. When aligned, they amplify each other.
This isn’t a search vs paid social debate. It’s a guide to orchestrating both channels together for measurable growth in an AI-influenced world.
AI’s Impact on Digital Advertising
AI compresses the marketing funnel into overlapping micro-moments. Users no longer move predictably from awareness to research to purchase. Instead, they:
Discover brands in social media feeds
Validate through AI-generated summaries
Compare via search engines
Click paid search ads when immediate intent peaks
AI Overviews reduce organic search visibility, pushing organic search results further down search engine results pages. Paid search ads often remain one of the most stable and visible placements.
At the same time, conversational discovery changes when intent forms. Users don’t always start with specific keywords. We’ve shifted from keyword-first journeys to influence-first journeys.
In this environment, channel silos fail. Users move seamlessly between platforms. A digital marketing strategy that isolates paid search advertising from paid social advertising misses the interconnected behavior of modern consumers.
Search and paid social must be planned together to capture qualified traffic at every stage of the entire marketing funnel. Learn more about how AI search affects paid ads.
What Is Paid Search?
Paid search involves paying for ad placement in search engine results pages when users actively search for answers, comparisons, or solutions. Through platforms like Google Ads, advertisers bid on specific keywords and search queries to appear in front of high-intent prospects.
Unlike paid social, paid search captures existing demand. It doesn’t create awareness; it intercepts it at decision moments.
In an AI-powered search environment, the role of paid search has shifted from early discovery to validation and confirmation.
AI feels authoritative but abstract. Users understand that AI aggregates sources, but they can’t always see nuance, depth, or accountability. Paid search ads, by contrast, are explicit and brand-backed. When a recognizable company appears consistently in paid search results, it signals investment and legitimacy.
Repetition builds credibility. Seeing a brand appear in AI summaries, organic search results, and paid search ads reinforces familiarity. And familiarity increases trust.
In AI-influenced SERPs where organic visibility is shrinking, paid search is essential for:
Brand protection
Competitive defense
Capturing demand at the moment of immediate intent
Maintaining immediate visibility in high-competition spaces
Pros of Paid Search
Captures users actively searching with immediate intent
Performs strongly for branded, transactional, and solution-aware search queries
Benefits from AI-enhanced bidding, automation, and cost per click optimization
Provides clear attribution through Google Analytics and conversion tracking
Delivers immediate visibility in competitive search engine results
Functions as a reliable pay-per-click conversion engine when demand already exists
Cons of Paid Search
Limited ability to create demand or introduce new audiences
Dependent on existing awareness and search volume
Rising CPCs as advertisers bid more aggressively using AI automation
Vulnerable to diminishing returns without upper-funnel support
Less effective for shaping early-stage perception
You’re competing over a fixed pool of in-market users. Without channels that increase brand awareness and consideration, you limit audience expansion and eventually cap conversion volume and efficiency.
For some industries, particularly those classified as Your Money or Your Life (YMYL), such as healthcare, finance, and legal, additional compliance layers apply. Without accreditation or verification (like LegitScript), paid search ads may be rejected. These sectors face stricter advertising policies and higher E-E-A-T expectations.
What Is Paid Social?
Paid social is algorithm-driven advertising designed to reach users before intent is fully formed.
Unlike paid search, paid social does not rely on users actively searching specific keywords. Instead, AI-powered social media platforms analyze behaviors, engagement patterns, and demographic signals to place social ads in front of highly targeted audiences.
Paid social shapes perception. It frames problems. It introduces solutions.
Social exposure often plants the initial seed of awareness. Users then conduct branded or category searches later for validation, comparison, and confirmation before converting.
Importantly, social media posts are increasingly included in AI Overviews, further blurring the lines between social and search visibility. This phenomenon, along with the increased number of users searching directly on social channels, is called social search.
Paid social operates earlier in the funnel, but its impact often shows up later in paid search performance.
Pros of Paid Social
Powerful at generating awareness and introducing new brands
Reaches highly targeted audiences without relying on search intent
Leverages AI algorithms to expand reach efficiently
Enables visual storytelling through engaging ads and video ads
Strong performance in early and mid-funnel stages
Influences future search behavior and branded search volume
Cons of Paid Social
Lower immediate conversion intent compared to paid search
Longer path from first touch to measurable conversion
Attribution complexity across devices and platforms
Requires continuous creative testing to stay efficient
Performance can fluctuate as platform AI algorithms evolve
Strategies for Integrating Paid Search and Social
Social-to-Search Funnel: Use highly visual, engaging paid social ads (Meta, TikTok) to create demand and introduce your brand. Users often turn to search engines to learn more after seeing a social ad, which you can capture with branded paid search campaigns.
Search-to-Social Retargeting: Capture high-intent traffic through search, then use platform pixels (like the Meta Pixel) to retarget those visitors on social media with nurturing content, testimonials, or special offers.
Synchronized Messaging: Ensure that ad copy, visuals, and offers are consistent across both platforms to create a seamless, trustworthy user experience.
Data Sharing for Audience Targeting: Use search query data to create targeted interest groups in social campaigns. Conversely, use social data (like Page Insights) to understand the demographics and interests of your audience to refine keyword targeting.
Remarketing Lists for Search Ads (RLSAs): Use social media interaction data to build custom audiences in Google Ads. This allows you to bid higher for users who have already engaged with your brand on social.
Leverage Social for Keyword Insights: Monitor the language, questions, and comments in your paid social ads to identify new high-performing search keywords.
Real-World Example: Hospitality Client Synergy in an AI Environment
One of our hospitality clients provides a clear example of how paid social and paid search work together to drive measurable results in an AI-driven landscape.
Meta Performance: Demand Generation & Efficiency
In January 2026, Meta delivered exceptional efficiency without increasing budget:
Reels-only promoted ads drove higher engagement at lower costs
This performance wasn’t accidental. Highly visual, engaging ads in Reels created awareness among the right audience. AI-driven delivery expanded reach to highly targeted audiences most likely to engage.
Meta served as the demand generator, increasing brand exposure and consideration.
Google Paid Search: Demand Capture & High-Intent Revenue
At the same time, paid search delivered:
Revenue: $80,550.26
Spend: $20,162.22
ROAS: 4.00
CTR: 20.81%
$59,734.49 driven by the “Locals In Market” campaign
173% year-over-year growth in conversions and revenue
As Meta increased brand awareness, branded search queries and high-intent searches increased. Users who first encountered the brand in social media feeds later searched for tickets and local offerings.
Paid search captured that demand when users were ready to book.
Channel Synergy in Action
This is what full-funnel orchestration looks like:
Paid social increased awareness and engagement.
Increased awareness led to measurable increases in high-intent search queries.
Paid search captured those users when they were actively searching.
Consistent messaging across platforms reinforced trust and reliability.
AI-driven optimization improved efficiency on both platforms simultaneously.
In an AI world where users validate across multiple touchpoints, this synergy becomes even more important.
Paid Search vs. Paid Social FAQs
Is paid search the same as paid social?
Paid search and paid social are not the same. Paid search captures existing intent while paid social creates demand before intent exists.
Paid search ads appear when users are actively searching for specific keywords in search engines. Through platforms like Google Ads, advertisers bid on search queries to show up in search engine results pages at the moment of immediate intent. These users are already evaluating solutions.
Paid social advertising works differently. Social ads appear in social media feeds based on user behavior, interests, and engagement patterns, not specific search terms. Instead of responding to explicit queries, paid social shapes perception earlier in the marketing funnel.
Which is better: SEO or SMO?
SEO and SMO are complementary strategies that work best together by reinforcing visibility, authority, and demand across AI-driven discovery.
Search engine optimization builds long-term organic search visibility by aligning content with user intent and search engine algorithms. It drives organic search traffic and strengthens brand authority in search engine results.
Social media optimization amplifies reach and engagement on social media platforms, helping brands connect with highly targeted audiences before intent is fully formed.
As AI-powered search engines blend signals from multiple sources, including website content and social media posts, visibility across organic search and social media increasingly reinforces credibility. Brands that invest in traditional SEO, AI SEO, and social media create multiple touchpoints, increasing familiarity and perceived trust.
How is AI affecting paid search?
AI is reshaping paid search by reducing organic clicks and making paid placements more critical for visibility, validation, and competitive defense.
AI Overviews now answer many search queries directly within search engine results pages. This reduces clicks to organic search results and compresses visible real estate. Paid search ads often remain one of the most prominent placements on the page.
At the same time, AI-driven bidding systems optimize pay-per-click campaigns dynamically based on the predicted likelihood of conversion. Advertisers bid more efficiently, but competition increases, raising cost per click in many industries.
AI also changes user psychology. When users see a brand appear consistently in AI summaries, organic search results, and paid search ads, familiarity increases. That repetition reinforces credibility.
How is AI affecting paid social?
AI is transforming paid social into a primary discovery engine by using algorithms to surface content before users actively search.
Social media platforms rely heavily on artificial intelligence to determine ad placement. Instead of relying on specific keywords, algorithms predict which highly targeted audiences are most likely to engage with particular ad formats, video ads, or messaging.
This means paid social advertising plays a growing role in creating demand. Engaging ads in social media feeds often influence what users search for later in traditional search engines. Social exposure increases brand recall and branded search queries.
AI also introduces volatility. Platforms frequently auto-enable new AI features related to copy generation, image optimization, and targeting. Advertisers must adapt quickly to maintain performance.
In an AI-influenced journey, paid social shapes the early narrative. Paid search captures the resulting intent. When aligned strategically, both channels strengthen performance across the entire marketing funnel.
Talk to Us About a Full-Funnel Paid Media Strategy
At Search Influence, we don’t execute isolated channels. We design integrated digital advertising strategies aligned with real user behavior.
Our AI-enabled digital marketing approach:
Increases campaign efficiency by allocating ad spend where performance is strongest
Reaches the right audience with precision targeting across search engines and social media platforms
Delivers qualified traffic from high-intent prospects
Uses AI to analyze performance in real time and continuously refine campaigns
We combine paid search advertising, paid social advertising, SEO, analytics, and data insights into a unified strategy designed for how users search, scroll, and decide today.
If you’re ready to move beyond search vs paid social and build a performance-driven marketing strategy across the entire marketing funnel, meet with our Director, Paula French.
The message is clear: ranking alone is no longer the finish line.
You now have to win twice — the ranking and the citation.
AI Is Already Influencing Early Trust
The AI Search Research Study surveyed 760 prospective adult learners to understand how AI tools influence program discovery and evaluation. The findings confirm what many marketers are beginning to see in their own analytics.
AI is no longer peripheral. It’s embedded in research behavior.
79% read Google’s AI Overviews when they appear
50% use AI tools at least weekly
56% are more likely to trust a brand cited by AI
Trust is forming earlier. Consideration is being shaped before traditional comparison begins.
Brands aren’t losing visibility because they slipped a few ranking positions. They’re losing it because they were never cited in the AI answer at all.
Discovery No Longer Happens in One Place
Search is now multi-surface.
Prospective students move fluidly between:
Traditional search engines
AI tools
YouTube
Brand-owned websites
Third-party publishers
What they see in an AI summary influences how they read a search result. A YouTube video can establish credibility before a website earns a click.
AI visibility is cumulative. It’s built anywhere your brand appears, not just on the pages you control.
If your strategy treats channels in isolation, your visibility will fragment in the same way.
Awareness Is High. Execution Is Lagging.
To understand the organizational side of the equation, a companion snap poll of 30 UPCEA member institutions examined how teams are adapting.
The most common barriers are familiar: limited bandwidth, competing priorities, and unclear ROI.
AI search may be on the roadmap, but it often lacks clear ownership, defined processes, and measurable accountability.
What Actually Gets Cited
In his article, Will outlines what separates content that ranks from content that gets cited.
AI systems favor optimized content that can be lifted cleanly and reused without interpretation. That typically means content that:
Leads with direct answers
Uses headings aligned to search intent
Separates ideas into self-contained sections
Includes comparison and decision-stage clarity
Higher education offers a useful lens here. Universities bring authority, depth, and long-standing brand recognition. Yet even established institutions are excluded from AI summaries when their content doesn’t match how users ask questions.
Authority alone does not guarantee inclusion.
Clarity increasingly determines visibility.
Where Things Stand
AI search hasn’t replaced SEO.
It has expanded the battlefield.
Discovery is happening earlier. Trust is assigned sooner. Visibility is often shaped before rankings ever come into play.
The brands that adapt now will shape how they’re represented.
The ones that wait may find themselves summarized by someone else.
Want to go deeper? Will Scott will expand on these findings in a Generative Engine Optimization Master Class with Search Engine Land on April 14, 2026. The online training covers AI visibility strategy, entity optimization, and measurement techniques for evolving search environments. View session details.
Search is evolving fast. But that doesn’t mean the foundation disappears.
On February 6, Paula French, Director of Sales and Marketing at Search Influence, joined the SEO On-Air podcast to unpack one of the biggest questions in digital marketing right now: what is the real difference between foundational SEO and AI SEO, and which do businesses actually need?
As AI search tools, large language models (LLMs), and Google’s AI-driven experiences reshape discovery, many organizations are racing toward “AI-first” strategies.
Chasing the future is smart. Forgetting the basics is not.
Technical health, crawlability, structured content, internal linking, entity clarity, and topical authority still determine whether a brand earns visibility in the first place. AI tools may interpret and surface information, but they rely on those existing signals.
If a site struggles with thin content, weak authority, or technical issues, shifting budget into AI-focused tactics will not fix the underlying gaps. The fundamentals remain the starting point.
SEO Maturity Should Guide Strategy
Another key insight from the discussion was the concept of SEO maturity.
Not every organization needs the same next move. Businesses with limited organic traction often benefit most from strengthening foundational SEO first. Brands with established authority and structured content systems may be ready to refine for AI-driven citation and visibility.
Instead of asking, “How do we optimize for AI?” a better question is, “Are our fundamentals strong enough to support AI visibility?”
That shift in thinking prevents reactive decision-making and keeps strategy aligned with measurable outcomes.
Avoiding the “AI-First” Rush
There is growing pressure across industries to pivot immediately toward AI search optimization. The episode explored the risk of chasing trends without diagnosing readiness.
AI search is changing how users interact with information. It’s influencing evaluation, comparison, and brand perception before a click happens. But abandoning core SEO practices in favor of hype-driven tactics creates instability.
Foundational SEO builds durable visibility. AI optimization refines how that visibility is interpreted and surfaced.
The most effective strategy isn’t either-or. It’s layered.
Tune In for the Full Conversation
For SEOs, founders, marketing leaders, and digital strategists navigating this evolving landscape, the full episode of “Foundational SEO vs. AI SEO: What Businesses Actually Need” provides a grounded, practical perspective.
If you’re evaluating your 2026 search strategy, wondering whether to double down on fundamentals or invest in AI optimization, this conversation offers clarity without trend-chasing.
Listen to the February 6 episode of SEO On-Air featuring Paula French to explore how foundational SEO and AI SEO work together, and how to determine what your business actually needs next.
On Tuesday, April 14, 2026, Will Scott, Co-Founder and CEO of Search Influence, returns to SMX Online to teach his Generative Engine Optimization (GEO) Master Class, the best-selling Master Class in SMX Online history.
The live, online session runs from 11:00 a.m. to 4:45 p.m. ET and is available both live and on demand for $199.
Designed for experienced marketers navigating AI-driven search, this intensive training delivers actionable, real-world strategies on how to stay visible as platforms like Google AI Overviews, ChatGPT, and Perplexity reshape how content is discovered.
What You’ll Learn in the GEO Master Class
This is not a theoretical overview of AI SEO. It’s a hands-on, tactical course that focuses on how generative engines actually retrieve, evaluate, and cite content today.
Attendees will learn how AI platforms differ from traditional search engines and what that means for content structure, keyword strategy, and authority signals. The course dives into creating content that works for humans and machines alike, including entity optimization, formatting for AI extraction, and writing in a way that earns citations in AI-generated answers.
Will also explores how keyword strategy has evolved in an AI-first world, shifting from static phrases to conversational, intent-driven language. Competitive analysis also plays a key role, with practical exercises that show how to evaluate which brands are winning AI visibility and why.
Rounding out the day are sessions on measuring AI visibility, tracking performance across platforms, and future-proofing your content strategy as generative search continues to evolve.
Who Should Attend
The Generative Engine Optimization Master Class is built for SEO professionals, content strategists, and digital marketers with 2–5 years of experience who are ready to expand beyond traditional optimization. It’s especially valuable for agency marketers, in-house teams managing complex websites, and leaders responsible for long-term content strategy.
If you’re already strong in SEO fundamentals but need clarity on how AI is changing rankings, visibility, and brand authority, this course is designed for you.
Meet AI SEO Expert Will Scott
Will Scott is a recognized authority in SEO and AI-driven content strategy and a longtime advocate for adapting marketing to how search actually works. He is widely known for coining the term “barnacle SEO” and has been a featured speaker at industry events, including PubCon, SMX, and Local U.
Will leads the Search Influence team in delivering AI-enhanced, data-driven SEO strategies for industries such as higher education, healthcare, and hospitality. With a degree in architecture from Tulane University, he blends strategic systems thinking with practical execution, making complex concepts actionable for marketers.
Save Your Seat
Generative search is no longer optional knowledge. It’s the foundation of future visibility. If you want to understand how AI platforms select sources, summarize content, and decide which brands get cited, the GEO Master Class is built to give you that edge.
Bing launched an AI Performance report inside Webmaster Tools earlier this month. We pulled our data the same day.
91 days of Copilot citation data. 19,717 total citations across 86 pages. One page accounting for 69% of all of them.
We’ve been tracking AI search visibility for clients using Scrunch and our AI Grader for months. But this is different. This is Microsoft showing us exactly how often — and why — Copilot pulls our content as a source when generating answers.
Microsoft released this as a public preview in February 2026. Anyone with a verified site in Bing Webmaster Tools can access it.
You get three data exports:
Daily overview — total citations and number of unique pages cited, by day
Page-level stats — which URLs get cited and how often
Grounding queries — the retrieval queries that triggered citations
No API access yet. Fabrice Canel from Microsoft confirmed on X that API support is on their backlog but didn’t give a timeline. For now, it’s CSV exports from the dashboard.
Our Numbers
We pulled 91 days of data for searchinfluence.com, covering November 12, 2025 through February 10, 2026.
The timeline tells a simple story: citations spiked hard in early December, then fell off.
December 7 hit 5,804 citations in a single day. That spike almost certainly corresponds to our AI SEO Tracking Tools 2026 analysis gaining traction in Copilot’s retrieval index. By late January, daily citations had dropped below 50.
The period breakdown makes the decline even clearer. Dec 1-8 averaged 1,520 citations per day. February: 34. That’s a 97% drop in two months.
A few possible explanations: the analysis was written for a specific moment in time and may be aging out of Copilot’s freshness window, new competing content entered Bing’s index, or Microsoft changed how Copilot’s retrieval weights sources. We’re still looking into it.
One Page Captures Almost Everything
Of the 86 pages Copilot cited across the full period, one captured 69% of all citations.
The top four pages — all AI SEO content — accounted for 90% of total citations. Everything else on the site combined makes up the remaining 10%.
That concentration is more extreme than what we see in traditional search. Google distributes traffic across many pages because users click through a list of results. AI search works differently — it picks one or two sources to ground its answer, and those sources absorb almost everything.
Building deep authority on your strongest topics matters more than spreading thin across many. In AI search, being the second-best resource on a topic might mean getting zero citations.
The Grounding Queries Are the Most Useful Part
The third export — grounding queries — is where we found the most actionable data. It also revealed something about how Copilot’s retrieval system works under the hood.
These queries aren’t what users typed into Copilot. They’re what Copilot’s retrieval system searched for internally when it needed a source to ground its answer.
Look at these examples. Nobody types queries like this into a search box:
“accuracy of AI SEO GEO platforms tracking position in AI shopping guides”
“AI search optimization GEO platforms competitor tracking pricing features positioning”
“push data to analytics platforms or tag managers from AI search optimization GEO platforms”
Those read like machine-generated retrieval queries — Copilot decomposing a user’s conversational question into keyword-dense search queries optimized for Bing’s index.
Then there’s query fanout. Same user question, multiple retrieval variants:
The “optimize content for AI search” cluster shows five variations of the same query. “Track AI model versions” shows four. Same intent, rephrased to catch different documents in the index.
This matters for interpreting the numbers. One user conversation likely generates 3-5 citation events through this fanout process. So our “19,717 citations” probably represents closer to 4,000-6,000 actual user conversations. The raw numbers are inflated by the retrieval architecture itself.
But the query themes are accurate. Over 400 unique grounding queries, clustered into clear topic areas:
AI SEO tool comparisons dominate — pricing, features, platform coverage, specific vendor evaluations. Higher ed marketing shows up as a secondary cluster. Both line up exactly with the content areas where we’ve invested the most over the past year.
What This Means for Content Strategy
Four things stood out from the data.
Structured comparison content earns citations. The page capturing 69% of all citations is a detailed tool-by-tool comparison with pricing, features, trade-offs, and named vendors. AI retrieval systems need specific, structured data to ground their answers. High-level overviews without specifics don’t get pulled in.
Grounding queries are a new form of keyword research. These aren’t the same queries that show up in Google Search Console. They represent what AI retrieval systems search for when answering user questions — a different target than traditional SEO keywords. If you have access to this data, use it to find content gaps and understand exactly what people are asking AI about your topic areas.
AI systems cite a narrow set of pages. Even on days with 5,000+ citations, only 15-18 unique pages got referenced. Copilot picks a small number of authoritative sources rather than pulling from a wide set. Depth beats breadth.
Citation decay is real and fast. Our 97% decline from December to February suggests either content freshness matters in AI retrieval, competitive content displaced us, or both. Publish-and-forget doesn’t work for AI visibility, just like it doesn’t work for traditional SEO. Probably more so.
What We Can’t See Yet
An honest look at the gaps, because there are several.
This is Copilot only. No equivalent data exists yet from ChatGPT, Perplexity, Gemini, or Google AI Overviews. The query themes likely transfer across platforms — people ask similar questions regardless of which AI they use — but citation volumes could look very different elsewhere.
No click-through data. Citations don’t equal traffic. We don’t know how many users clicked through from a Copilot answer to our site versus just reading the AI-generated response. Microsoft may add this metric later, but right now we can measure AI visibility without measuring engagement.
No competitive view. We can see our own citations but not what other sites Copilot cited alongside ours for the same queries. Knowing who else gets cited — and for which queries — would make this data significantly more useful.
The data is still in preview. Microsoft has said more data is coming throughout 2026. What we have now is a starting point.
What We’re Doing With This
We’re using the grounding queries to map content gaps. 400+ queries show us exactly what Copilot users are asking about our topic areas. Where our existing content doesn’t fully answer those queries, that’s where we’re focusing next.
For clients, we’re adding Copilot citation metrics to monthly reports. “Your site was cited X times in AI search this month across Y pages” is a concrete number. Most of the industry is still guessing about AI visibility. This is actual measurement, even if it’s limited to one platform.
And we’re layering this data alongside what we already track through Scrunch (AI visibility across ChatGPT, Perplexity, and other platforms) and our AI Grader (content readiness scores). Three data sources covering three layers: content quality, AI visibility, and actual citations. Together, they give us the closest thing to a full picture of AI search performance that exists right now.
Check Your Own Data
If you want to see your Copilot citation numbers, verify your site in Bing Webmaster Tools and look for the AI Performance section. The report is available for all verified sites.
Want to see how your content scores for AI search readiness right now? Try the AI Grader — it takes about 30 seconds.
The AI traffic plateau is real and expected. The experimental growth phase is over; we’ve entered an optimization and efficiency phase.
AI-referred traffic is smaller but higher quality. Engagement time and intent consistently outperform traditional organic sessions.
Visibility ≠ measurability. AI Overviews and AI Mode remain partial black boxes, making citation trends more meaningful than raw rankings.
On-site optimization alone isn’t enough anymore. Third-party comparison and aggregator content increasingly shape AI understanding.
Winning brands build citation networks, not just pages. Presence across AI-trusted domains now drives long-term visibility.
Success metrics must evolve. Citation momentum, brand sentiment in AI responses, and AI-assisted conversions matter more than impressions.
If you’ve been tracking AI-driven traffic, you’ve probably noticed something: the growth curve is flattening.
That’s not a bug. It’s a feature.
The Inflection Point Is Here
Here’s my working theory: We’ve hit the point where AI presence in search has largely stabilized. The industry has shifted from rapid, experimental rollout to deep infrastructure integration. AI Overviews aren’t new anymore — they’re baked in. The dramatic expansion phase is behind us.
Unless there are global increases in total search traffic or dramatic expansion of AI features, we should expect:
Organic traffic stops declining
AI-referred traffic stops growing
Everything settles into a new equilibrium
This isn’t necessarily bad news. It’s just… news. The land grab phase is ending. Now comes the optimization phase.
The Visibility Gap We Can’t Ignore
Here’s the piece we don’t have visibility on: AI Overviews and AI Mode as traffic drivers.
We’re still relying on tracking URL parameters — UTM sources, page anchors, the little breadcrumbs platforms leave behind. But that’s incomplete. Google’s AI Overviews, in particular, represent a black box of citation-driven traffic we can’t fully measure yet.
What we can see: citations are increasing even as AI Overview rankings plateau. That’s encouraging. It suggests presence is building even when ranking positions stay flat.
Google Is Refining the AI Overview Experience
One thing that explains the plateau: Google is getting smarter about when to show AI Overviews.
According to recent reports, Google is now stripping AI Overviews from searches where users aren’t interacting with them. They’re figuring out what people actually engage with and putting AI Overviews there.
What this means: You’re not ranking for random, low-intent searches anymore. The pie has shrunk, but it’s a more qualified pie.
Less visibility in aggregate, but potentially more valuable visibility where it matters.
The data backs this up. Looking at recent numbers across several higher ed clients, AI-referred traffic consistently shows stronger engagement than traditional organic:
SEO Engagement Time
AI Engagement Time
SEO Engagement Rate
AI Engagement Rate
Client A
1:05
3:14
32%
71%
Client B
2:07
3:17
65%
45%
Client C
2:27
6:03
67%
46%
AI traffic isn’t just smaller — it’s more qualified. These users are arriving with higher intent and spending more time with the content.
What’s Actually Working: Lessons from the Field
Looking at clients who’ve maintained or grown their AI presence during this plateau period, a few on-site tactics stand out:
1. Semantic Header Optimization
Not just “put keywords in H2s” — but structuring headers to reflect how AI models organize information. Think entity relationships, not keyword density.
2. AI-Friendly Language
Shift from salesy, marketing-speak to fact-based, outcome-based content. LLMs are trained on informational content. They don’t respond well to “Schedule your free consultation today!”
What they do respond to: clear statements of fact, specific outcomes, data points.
3. Structured Data with Linked Entities
Schema markup matters more than ever, but it’s not just about having schema. It’s about connecting your entities to the broader knowledge graph. Make sure your Course, Organization, and Person entities reference established identifiers.
4. FAQ Optimization
Still a consistent win. LLMs love well-structured Q&A content. It’s easy to parse, easy to cite.
The Comparison Content Problem
On-site optimization only gets you so far. AI models give weight to what other authoritative sources say about you. If you’re only optimizing your own site, you’re playing with one hand tied behind your back.
Here’s an uncomfortable truth: AI Overviews are increasingly citing off-site aggregator and list-style content.
“Top 10 medical billing programs,” “Best car service providers in Chicago,” “Construction management software comparison.”
This content format is showing up everywhere in AI responses. And for many clients, it’s content they can’t or won’t create.
Brand compliance teams get nervous about comparing themselves to competitors. Legal wants to vet every claim. By the time approvals come through, the opportunity has moved on.
The workaround? Third-party placements.
We’ve had success getting comparison content placed on external sites — parenting blogs, industry directories, and niche publications. It’s not scalable, but it works.
One example: A comparison article we placed on a regional parenting site now ranks 7th organically for a competitive local service query. Not in the Map Pack, not in the AI Overview, but it’s in the ecosystem. That content is feeding the AI’s understanding of the market.
The Path Forward: Building Your Citation Network
So where do we go from here?
I’m working on building a list of 50-100 article placement opportunities. Sites that:
Accept guest content
Are indexed by Google
Are cited by AI (both Google AI and ChatGPT)
That third point is key. Being in Google News isn’t enough. The question is: are these domains showing up in AI responses?
How to verify:
DataForSEO has metrics for Google AI and ChatGPT indexing
Ahrefs shows indexed pages and citations in their main view
Or build your own tool using SERP APIs and LLM APIs (I’m working on this now)
The hypothesis: if a domain is already cited by AI platforms, content you publish there has a higher chance of feeding those same AI responses.
Tracking the Right Metrics
Given the plateau, what should you actually be measuring?
Stop obsessing over:
Prompt-by-prompt rankings (too volatile)
Total AI impression counts (too noisy)
Start focusing on:
Citation trends over time (up and to the right)
Brand sentiment in AI responses (does the model understand what you do?)
Conversion attribution from AI-referred traffic (when trackable)
Third-party mentions in AI responses
All the data is wrong. The question is: how wrong is it? Pick your metrics, track consistently, and look for directional movement.
What This Means for Your Strategy
If AI traffic has plateaued, the response isn’t to panic — it’s to shift from growth tactics to optimization tactics.
Priority 1: Technical Foundation
AI engines are less patient about crawl than traditional search. If they can’t see your content quickly and cleanly, they won’t cite it.
Fix crawlability issues
Improve site speed
Verify AI bot access in robots.txt
Priority 2: Content Format
Structure content for AI ingestion:
Clear heading hierarchy
FAQ sections
Definition lists for key terms
Schema markup that connects entities
Priority 3: Third-Party Footprint
Build presence on sites that AI already trusts:
Industry publications
Authoritative directories
Comparison content (even if you’re not creating it yourself)
Priority 4: Measurement Infrastructure
Set up tracking for AI-referred traffic now, before you need it:
Monitor URL parameters (UTM sources, anchors)
Track citation trends in AI monitoring tools
Document brand mentions in AI responses
The Monetization Wildcard
There’s one variable we can’t predict yet: how will future monetization of AI answers affect referral behavior?
Google hasn’t fully figured out how to make money from AI Overviews. Neither has OpenAI, Perplexity, or anyone else. When they do, the incentive structures will shift.
A few scenarios to watch:
Scenario 1: Ads in AI responses. If Google inserts sponsored content into AI Overviews (they’re already testing this), organic citations become less prominent. Your content might still inform the answer, but the click goes to an advertiser.
Scenario 2: Premium AI tiers. Paid AI modes could behave differently than free ones — deeper research, more citations, different source preferences. Optimization strategies might need to account for which tier your audience uses.
Scenario 3: Publisher revenue sharing. If platforms start compensating publishers for citations (the way some news partnerships work), the economics of content creation change. Sites that currently can’t justify AI-focused content might suddenly have a business case.
None of this is certain. But the fact that AI monetization is still being figured out means the referral dynamics we’re seeing today aren’t permanent.
Build for the current reality, but stay flexible.
The Bottom Line
The AI traffic plateau isn’t the end of growth — it’s the end of easy growth.
The early adopters who were showing up everywhere just by existing have hit their ceiling. What comes next is more intentional: optimizing for how AI models understand and cite your content, building presence on the sites that feed those models, and measuring what actually matters.
Traditional search isn’t going anywhere. AI is additive, not a replacement. The brands that win are the ones that show up in both.
What are you seeing with your AI traffic trends? I’m curious whether this plateau is showing up across industries or if it’s specific to certain verticals.
This post was based on a conversation among the Search Influence SEO team, Will, Cory, and Chuck, with input from Jess, the account manager for a couple of the cited clients.
The question we were tasked to discuss was how to explain the plateau in AI traffic.
Half of prospective students now use AI search tools weekly to research programs. If your institution isn’t showing up in ChatGPT, Claude, Perplexity, or Google AI Overviews, you’re invisible to half your audience. In 2026, success is measured by AI citations and brand mentions within generative summaries, not just clicks. This guide covers what actually works for AI search visibility, based on testing, not theory. (Data source: UPCEA/Search Influence 2025 AI Search in Higher Education study)
The Shift in Student Search You Can’t Ignore
Half of prospective students now use AI-powered search tools at least weekly, and 79% read Google’s AI Overviews before clicking any result. That’s according to the 2025 AI Search in Higher Education study by UPCEA and Search Influence, which surveyed 760 adults actively researching programs.
Source: UPCEA/Search Influence AI Search in Higher Education Study, 2025
While your team optimizes for Google rankings, half of your prospective students are also asking ChatGPT:
“What are the best nursing programs near me?”
“Which universities have strong data science programs?”
“Should I go to [Your University] or [Competitor]?”
The uncomfortable truth: traditional SEO rankings don’t automatically translate to AI search results. Your brand is no longer just what you say about yourself, or even what others say about you. It’s what AI believes about you and shares with millions of prospective students.
I’ve been tracking this space since late 2022. Higher education institutions with strong Google rankings often get completely left out of AI-driven search results. While smaller schools with better-structured content show up consistently.
Traditional search engines still drive most organic traffic. That’s not changing soon. But AI search is a new channel growing fast, and it’s where a third of your prospective students are already researching. The catch: AI-generated search results often summarize information without requiring users to click through, which means even sites with strong search engine optimization can see declining traffic from AI-driven queries.
The universities that appear in AI-driven search results now will have a head start that the rest can’t easily catch up to.
What actually works?
How AI “Decides” What to Recommend
To make these SEO strategies work, you need to understand how these systems operate. It’s different from traditional search engines.
Large language models like ChatGPT, Claude, and Perplexity don’t crawl your site in real-time and rank web pages. They operate on different principles:
They draw from training data
Content that existed when the model was trained becomes part of its “knowledge.” This is why outdated information persists. The model learned it months or years ago.
They reference recent web crawls
Some models (like Perplexity and ChatGPT with browsing enabled) pull fresh content. But the freshness varies by platform and query type.
They cite authoritative sources
AI systems prefer content that appears to know what it’s talking about. They’re pattern-matching on what “good sources” look like — structure, depth, and credibility signals.
They match search intent, not just keywords
AI understands concepts and entities through natural language processing, not keyword matching. You don’t need “best MBA program for working professionals near Chicago” repeated verbatim. You need content that actually covers the topic in depth and with specificity. Traditional search engines match keywords; AI systems match user intent and search intent. This is why traditional keyword research alone isn’t enough anymore. You need to understand what prospective students actually want to know, not just what phrases they type.
They prioritize E-E-A-T signals
AI systems, like traditional search engines, favor content that demonstrates Expertise, Experience, Authoritativeness, and Trustworthiness. Faculty credentials, institutional accreditation, specific outcomes data, and cited sources all signal that your content is worth recommending. Generic marketing copy doesn’t cut it.
What this means for you:
Your content needs to be structured so AI can understand it, not just index it. With Google, you’re trying to rank. With AI, you’re trying to be the source that gets cited when AI generates its response. Different goal, different tactics.
SEO fundamentals still apply—but the emphasis shifts.
SEO fundamentals still apply. Sites that rank well in Google tend to get cited more by AI, but it’s not automatic. Backlinks from authoritative sites signal to search engines that your website is trustworthy and valuable, and AI systems pick up on these same credibility signals. You need to optimize for both traditional search and AI platforms.
One principle remains constant: creating exceptional, high-quality content is the best way to boost SEO performance and satisfy prospective students. Content should prioritize people over bots. If it genuinely helps your target audience, it will perform well with AI systems too.
When students ask about programs you offer, competitors show up, and you don’t. This is the most painful finding, but it’s the most actionable.
Missing differentiators
AI can describe your university in generic terms, but doesn’t mention what makes you unique. Your $50M new engineering building? Your unique co-op program? Your 95% nursing board pass rate? If AI doesn’t know about it, AI can’t recommend you for it.
Outdated information
Programs that no longer exist, old leadership names, incorrect tuition figures, former campus locations. AI models don’t always have up-to-date information, and even when they do, they may have ingested outdated pages from your site.
Generic descriptions
AI says you’re “a comprehensive university offering undergraduate and graduate programs in a variety of fields.” That’s true. It’s also useless. Nobody chooses a university based on that description.
Step 2: Create Content That AI Wants to Cite
AI systems prefer citing website content that appears authoritative and thorough. They’re trained on high-quality content, so they pattern-match on what those sources look like. Your content creation strategy needs to account for this.
Create content that answers the specific questions students ask during their research process. That means your content needs to:
Be structurally parseable
AI reads differently from humans. Clear heading hierarchies (H2, H3, H4) help AI understand the relationship between concepts. Dense paragraphs of text are harder to parse than structured lists.
Formats that work well:
FAQ sections that mirror natural language questions
Definition lists for key terms
Comparison tables
Bulleted lists with specific data points
Step-by-step numbered processes
Include specific, citable data
Vague claims get ignored. Specific data gets cited.
Include:
Enrollment numbers (total, by program, by format)
Graduation and retention rates
Employment outcomes (percentage employed, average salary, top employers)
Program rankings and accreditations
Tuition costs (total and per credit hour)
Financial aid statistics (percentage receiving aid, average package)
Student-to-faculty ratios
Research funding and grants
Answer the questions prospective students actually ask
Look at your website chat logs. Look at your admissions email inbox. Look at your campus visit Q&A sessions. What do prospective students actually want to know? This is better than any keyword research tool for identifying relevant keywords and topics.
Create structured content that directly answers those questions, and format it so AI can find and cite those answers.
Create multimedia content
Creating multimedia content (videos, infographics, virtual tours) enhances engagement and helps students envision themselves on campus. Video testimonials, program overviews, and campus walk-throughs give AI systems additional content to index. YouTube content especially matters; it’s owned by Google and feeds directly into AI training data.
Same content, restructured for AI visibility.
Step 3: Make Your Brand “Like Fluoride in the Water”
You want your brand to be so present across the web that AI just… knows you.
Think about Kleenex. Or Xerox. Or Google (as a verb). Nobody has to explain what these brands are. AI models have seen so many references across so many contexts that the brand is baked into their understanding.
Obviously, you can’t become Kleenex overnight. That takes decades. But you can systematically increase your brand’s presence in the sources AI learns from:
When journalists write about trends in nursing education, they quote someone. Why not your nursing dean? When publications list “top programs for X,” they source from somewhere. Why not your outcomes data?
Publish research that others cite
Original research gets cited. Surveys, studies, white papers, data analyses. Your institutional research office has data that would be valuable to others. Package it and publish it.
Maintain active, consistent social presence
AI models train on social media content. LinkedIn, Twitter/X, YouTube. Your consistent presence builds brand recognition in the training data. Video SEO matters here too; YouTube is owned by Google and feeds into AI training data. Optimizing content for YouTube (with strong titles, descriptions, and transcripts) improves visibility across both traditional search and AI platforms.
Show up in industry rankings and lists
Rankings aren’t just for prospective students. They’re for AI training data. When AI learns “best X programs,” it learns from published lists.
Create content that other institutions reference
Thought leadership content that other universities link to and cite. Best practices guides. Innovative program design. This creates a citation network that AI follows.
AI learns about your brand from everywhere—not just your website.
The goal isn’t any single mention. The goal is to be so present across the web that when AI thinks about your program area, your institution naturally comes to mind. Like fluoride in the water, invisible but everywhere.
Step 4: Don’t Neglect Local SEO for Regional Student Search
Local SEO is critical for attracting regional students, especially for institutions with multiple campus locations. For higher education institutions serving regional markets, local SEO directly impacts AI search results and recommendations.
When a prospective student asks, “What are the best nursing programs near me?” or uses voice search for “colleges in [city],” AI pulls from local signals. These natural language queries are increasingly common as generative AI tools encourage students to ask more conversational questions.
What to do:
Claim and optimize Google Business Profile for each campus location
Ensure NAP (name, address, phone) consistency across all web pages
Create location-specific content for each campus
Incorporate keywords naturally for regional search intent (“nursing program in [city],” “[state] MBA programs”)
Encourage and respond to Google reviews. They’re credibility signals for both traditional search engines and AI
Build citations in local directories and regional publications
Local SEO isn’t separate from AI SEO; it feeds it. AI systems learn about your regional presence from these same signals. Higher ed marketers often overlook local SEO because they’re focused on national rankings, but for most higher education institutions, regional search visibility is where enrollment actually happens.
Optimizing Academic Program Pages for AI-Driven Search Results
Program pages are where enrollment happens, or doesn’t. When a student asks ChatGPT, “What are the best MBA programs for working professionals?”, AI scans the web, evaluates sources, and generates an answer. Your program page either contains everything AI needs to recommend you, or it doesn’t. There’s no second impression.
Institutions should create dedicated landing pages for each academic program with detailed information. Most university program pages fail this test. They’re designed for humans who already know about the institution and are browsing to learn more. AI doesn’t browse. It extracts, evaluates, and cites, or moves on.
Students now expect instant, personalized answers to their questions during their college search. Your program pages need to deliver.
The Anatomy of an AI-Optimized Program Page
1. Clear Program Identity (Above the Fold)
Start with unambiguous program identification:
Exact degree name and type (BS, BA, MS, MBA, MEd, PhD, etc.)
Program format (on-campus, fully online, hybrid, evening/weekend)
Duration (credit hours required, typical time to completion)
Accreditation status and accrediting bodies
Department and college affiliation
Why this matters: AI needs to correctly categorize your program. If your page title says “Business Administration” but doesn’t specify MBA vs. undergraduate, AI may miscategorize you.
2. Outcomes Data (Make It Prominent)
Universities are often reluctant to publish employment data — worried about liability, or not confident in the numbers. But students make decisions based on outcomes, and AI cites specifics.
Include:
Employment rate within 6 months and 1 year of graduation
Average and median starting salary
Salary range (10th to 90th percentile)
Top employers hiring your graduates (named companies)
Job titles graduates hold
Career paths and advancement trajectories
Professional licensure/certification pass rates (nursing boards, CPA exam, bar exam, etc.)
Graduate school acceptance rates (for undergrad programs)
If you have strong outcomes, show them. If you don’t have this data, start collecting it.
3. Curriculum Overview (Structured for Scannability)
Don’t just link to a PDF catalog. Present curriculum information directly on the page:
Core/required courses with brief descriptions
Elective options and specialization tracks
Unique program features (capstone projects, internship requirements, study abroad, lab experiences)
Sample course sequence or suggested schedule
Total credit hours and breakdown by category
Format this as a table or structured list, not paragraphs.
4. Admission Requirements (Be Specific)
Prospective students ask AI-specific questions: “What GPA do I need for X program?” Make sure AI can find the answer on your page.
Test score requirements or policies (GRE, GMAT, test-optional status)
Prerequisite courses
Required application materials
Application deadlines (early, regular, rolling)
International student requirements
5. Cost and Financial Information (Don’t Hide It)
Tuition is one of the top questions students ask. AI will answer it. The question is whether AI gets the answer from your site or somewhere else.
Include:
Total program cost
Per-credit-hour rate
Fee breakdowns
Scholarship opportunities specific to this program
Graduate assistantship availability
Employer tuition reimbursement partnerships
Financial aid statistics for this program
ROI calculations, if available
6. FAQ Section (Mirror How Students Ask)
FAQ sections structured as question-and-answer pairs are exactly what AI systems are looking for. Easy to implement, high impact.
Address questions students actually ask:
“Can I complete this program while working full-time?”
“What’s the difference between the online and on-campus versions?”
“Is this program accredited?”
“What kind of support services are available for online students?”
“Can I transfer credits into this program?”
“What technology/software will I need?”
“Are there networking or career services?”
Use the exact phrasing students use. That’s what they’ll type into ChatGPT.
7. Student Testimonials and Success Stories
Real stories from real students are citation gold. AI systems recognize authentic student testimonials as credibility signals, and prospective students find them compelling. Student testimonials provide the social proof that influences user behavior during the decision-making process.
Include named testimonials (with permission), specific outcomes, and career trajectories. “Sarah graduated in 2023 and now works as a data analyst at IBM” is more citable than “Our graduates go on to great careers.”
Video testimonials work even better. They’re harder to fake and more engaging. If you have them, embed them on the page with transcripts for AI to parse. This combines video SEO with powerful conversion content.
Common Mistakes I See
Mistake 1: Content buried in PDFs
AI can’t easily parse PDF content. If your program details live in a downloadable brochure or catalog PDF, they might as well not exist for AI purposes. Extract that content and put it on the page.
Mistake 2: Fragmented information across multiple pages
If students (or AI) have to click through five pages to understand your program (overview, curriculum, admissions, financial aid, outcomes), AI won’t piece it together. Consolidate essential information into a single page, with links to deep dives.
Mistake 3: Missing or hidden outcomes data
If you have good outcomes, show them prominently. If you have mediocre outcomes, at least show the data you’re proud of. Something specific beats nothing every time.
Mistake 4: Generic marketing copy
“Prepare for success in a dynamic global economy” means nothing. Literally nothing. It’s filler text that adds no information.
Compared to: “92% of graduates employed in their field within 6 months, with an average starting salary of $68,000. Top employers include Mayo Clinic, Cleveland Clinic, and Johns Hopkins.”
Which one would you cite? Which one would AI cite?
Mistake 5: No FAQ section
If your program page doesn’t have an FAQ section, you’re leaving AI citations on the table. This is the easiest win. Just add it.
Structured Data and Schema for Higher Education
This section gets technical. Schema markup is how you explicitly tell AI what your content means — metadata that machines read. It’s becoming increasingly valuable for AI visibility.
Why Schema Matters for AI
When AI systems encounter structured data, they don’t have to guess what your content means. You’re telling them directly:
This is an educational organization
This is a course/program
This is an FAQ
This is an event
These are the properties (name, cost, duration, requirements)
Think of it as the difference between handing someone a box of puzzle pieces versus handing them the completed puzzle. Same information, wildly different usability.
AI systems can extract information from unstructured text. But structured data is unambiguous. It removes interpretation. It’s machine-readable by design.
Schema removes ambiguity
Schema Types That Matter for Higher Ed
If you’re not technical, share this section with your developer. If you are technical, here are the four schema types to prioritize:
EducationalOrganization Schema
Your foundation tells AI who you are at the institutional level.
This is especially important for entity disambiguation. If your institution shares a name with another (e.g., multiple “Trinity” universities, multiple “State” schools), schema helps AI understand which one you are. The same applies to Google’s Knowledge Graph. That information panel that appears when someone searches your name. Claim and optimize your Knowledge Panel through Google’s verification process. When AI systems reference knowledge graphs, they’re pulling from that same entity data.
{
“@type”: “EducationalOrganization”,
“name”: “University Name”,
“alternateName”: “Common Abbreviation”,
“description”: “Full description of the institution”,
“url”: “https://www.university.edu”,
“logo”: “https://www.university.edu/logo.png”,
“address”: {
“@type”: “PostalAddress”,
“streetAddress”: “123 Campus Drive”,
“addressLocality”: “City”,
“addressRegion”: “State”,
“postalCode”: “12345”
},
“telephone”: “+1-555-123-4567”,
“foundingDate”: “1890”,
“accreditedBy”: [
{
“@type”: “Organization”,
“name”: “Higher Learning Commission”
}
]
}
Course Schema
For each academic program. This is where the detail matters.
{
“@type”: “Course”,
“name”: “Bachelor of Science in Nursing”,
“description”: “Four-year nursing program preparing students for RN licensure”,
“provider”: {
“@type”: “EducationalOrganization”,
“name”: “University Name”
},
“hasCourseInstance”: [
{
“@type”: “CourseInstance”,
“courseMode”: “onsite”,
“courseWorkload”: “PT120H”
},
{
“@type”: “CourseInstance”,
“courseMode”: “online”
}
],
“occupationalCredentialAwarded”: “BSN”,
“numberOfCredits”: 120,
“educationalLevel”: “Bachelor’s Degree”,
“timeRequired”: “P4Y”
}
FAQPage Schema
For those FAQ sections. This makes your Q&A pairs directly extractable.
{
“@type”: “FAQPage”,
“mainEntity”: [
{
“@type”: “Question”,
“name”: “Can I complete this program while working full-time?”,
“acceptedAnswer”: {
“@type”: “Answer”,
“text”: “Yes, our evening and weekend format is designed for working professionals…”
}
}
]
}
Event Schema
For open houses, information sessions, and application deadlines.
EducationalOrganization schema on your homepage — Define who you are
FAQPage schema on key program and admission pages — Quick win, high impact
Course schema on each academic program page — The biggest lift, but most valuable
Event schema on recruitment event pages — Good for search and AI
Full disclosure: implementing this well usually requires developer resources. Your marketing team can specify what needs to be marked up, but implementation typically needs IT involvement. It’s not a quick win, but it compounds over time. Once it’s in place, it keeps working.
Technical Foundations for AI Visibility
Technical SEO and Site Performance Still Matter
Technical SEO is essential for maintaining a website’s backend health and ensuring it can be identified by search engines. Site speed, mobile responsiveness, crawlability, and security (HTTPS) still matter. AI systems may not rank web pages the way traditional search engines do, but they do learn from sites that meet basic technical standards. Search engine optimization fundamentals haven’t gone away; they’re table stakes for any higher education SEO strategy.
If your higher ed website is slow, broken on mobile, or has crawl errors, fix that first. No amount of schema markup or AI-friendly content will overcome a site that doesn’t load. Run technical SEO audits before diving into the AI-specific optimizations. AI tools can automate tasks like competitor analysis, backlink monitoring, and technical SEO audits. Tools like Screaming Frog, Sitebulb, or AI-powered platforms like Semrush can streamline this analysis.
Managing AI Crawlers
AI systems like ChatGPT, Claude, and Perplexity use their own crawlers (GPTBot, ClaudeBot, PerplexityBot) to index content. You can control their access through robots.txt. Same as traditional search engines.
Most universities should allow these crawlers. If AI can’t access your content, AI can’t recommend you. But if you have gated content or specific sections you want to exclude, you can block specific bots:
User-agent: GPTBot
Disallow: /internal-documents/
User-agent: ClaudeBot
Disallow: /internal-documents/
There’s also a newer standard emerging: llms.txt. This file (placed at your domain root, like robots.txt) tells AI systems how to interpret your site—what’s most important, how content relates, and what context matters. It’s not universally adopted yet, but worth watching as AI crawling matures.
Using AI to Support Student Recruitment
Everything above is about getting *found* by AI. But AI can also be a tool you use directly in recruitment. This section is optional reading (the core work is in the previous sections), but worth considering if you’re building out your digital strategy.
AI Chatbots for Enrollment
A lot of colleges and universities are implementing AI chatbots now. Some are doing it well. Most are not.
My take:
Do:
Use chatbots for high-volume, repetitive questions (office hours, application deadlines, document requirements, program listings)
Train them on your actual FAQ data — real questions from real students
Have clear handoff protocols to human staff for complex questions
Track what questions come up most often — this is gold for content strategy
Set appropriate expectations (tell users they’re talking to a bot)
Expect them to replace human connection — they augment, not replace
Use generic chatbot responses — customize for your institution
Forget to update the knowledge base as information changes
An important distinction: The 50% of students using AI search tools weekly? They’re not looking to talk to a bot on your website. They’re using ChatGPT and Google AI Overviews because they perceive these as unbiased, aggregated answers.
Your institutional chatbot serves a different purpose. Convenience and availability, not research.
A student at 11 pm who wants to know if their transcript was received?
Chatbot territory.
A student trying to decide between your program and a competitor?
That needs a human.
AI-Powered Personalization
Some colleges and universities are using AI tools to create more personalized digital experiences:
Homepage personalization
Showing different content based on visitor signals — location, referral source, previous visits, stated interests. A visitor from Texas sees Texas-specific information and regional alumni. A visitor who previously looked at nursing programs sees nursing content prominently.
Program recommendations
“Based on your interests, you might also consider…” recommendations powered by AI analysis of similar student paths.
Dynamic financial aid estimates
AI-powered calculators that provide personalized estimates based on student-provided information.
Email campaign personalization
Content customization within email campaigns based on recipient behavior and preferences.
AI personalization in action.
The caveat: privacy matters. FERPA applies to student records. GDPR may apply to international visitors. State privacy laws are evolving. Be thoughtful about what data you collect, how you use it, and how you communicate that to visitors.
The line between “helpful personalization” and “creepy surveillance” is real. Stay on the right side of it.
Measuring AI SEO and Search Engine Optimization Performance
You’ve audited, optimized, and implemented. How do you know if any of this is working?
Measuring AI visibility is nothing like measuring traditional SEO. It’s messier, less precise, and still evolving. And the metrics that matter are different. You’re not just tracking organic traffic, website traffic, and keyword rankings anymore. AI-driven search features are changing how students discover information, and AI-generated search results often summarize information without requiring users to click through to your website. You need new metrics for a new search strategy.
What You Can Track
Brand mentions across LLMs
AI SEO tracking tools like Scrunch, Profound, RankScale, and others now track how often your brand appears in AI responses across ChatGPT, Claude, Perplexity, and Google AI Overviews.
Full disclosure, we use Scrunch at my agency, and I think it’s the most thorough option for agencies and enterprises. But there are others at different price points:
Scrunch: Enterprise-focused, full-stack tracking, API access
Profound: Enterprise-focused, detailed insights across 10+ AI engines, custom pricing
RankScale: Budget-friendly, credit-based pricing
The tracking piece is becoming a commodity. Most tools can tell you if you’re showing up. The differentiation is in what they do with that data.
Example AI visibility dashboard—showing metrics that matter.
Position in AI-generated lists
When someone asks “best X programs,” where do you show up? First? Fifth? Not at all? This is trackable and meaningful.
Citation rate
How often does AI cite your content as a source? This is particularly important for Perplexity and Google AI Overviews, which show their sources. Being cited is different from being mentioned; it’s a stronger signal.
Sentiment and accuracy
What does AI say about you? Is it positive, neutral, or negative? More importantly, is it accurate? Inaccuracies need to be addressed.
Competitor share of voice
How do you compare to competitors in AI recommendations? If students ask about your program category, who gets mentioned most?
What You Can’t (Easily) Track
Individual user conversations with AI (privacy and access limitations)
Exactly how AI weighs different factors (black box)
Real-time changes to AI recommendations (there’s always a lag)
Causal attribution (did they enroll because AI recommended you?)
Direct impact on website traffic from AI-driven search results (unlike Google Analytics for traditional search)
The “Windsock” Approach
I’ve said this before, and I’ll say it again: all AI tracking data is imperfect. Analytics aren’t an absolute truth. They’re opinions with decimal points.
AI tracking tools are a windsock, not a GPS. They tell you direction, not precise position.
You’re looking for directional trends:
Are mentions increasing over time?
Is share of voice improving vs. competitors?
Are inaccuracies getting corrected after you update content?
Is sentiment trending positive?
Don’t obsess over precision. Don’t argue about whether you’re mentioned in 47% or 52% of relevant queries. Pick your tool, track consistently, and look for trends up and to the right over time.
Example AI visibility dashboard—showing metrics that matter.
What This Means for Higher Ed Marketers and Marketing Teams
Where do you actually start? These higher education SEO strategies need to fit into your broader web strategy. My recommendations, scaled to your marketing teams and resources:
If You Have Limited Resources (Marketing Team of 1-3)
Start here:
Audit what AI currently says about your institution. This takes 30 minutes and costs nothing. Open ChatGPT, Claude, and Perplexity. Ask the questions we covered. Document what’s wrong.
Fix factual inaccuracies on your website. If AI is saying something wrong, it probably learned it from your site (or from outdated information). Update your site.
Restructure your top 3-5 program pages. Pick your highest-priority programs. Add clear headings, FAQ sections, and outcomes data. This is manual work, but high impact.
Add FAQ sections to key pages. If you do nothing else, do this. FAQs are the easiest content for AI to cite.
If You Have Moderate Resources (Marketing Team of 4-10)
Add:
Implement basic schema markup. Start with EducationalOrganization on your homepage and FAQPage schema on key pages. This requires developer time but pays dividends.
Create a thorough “About” page optimized for AI. A single page that fully answers “What is [University Name]?” with specific data points, history, differentiators, and programs.
Set up tracking with an AI visibility tool. Pick one, commit to it, and track monthly. RankScale is affordable for smaller teams.
Train your content team on AI-friendly formatting. Share this guide. Make it part of your content standards.
If You’re Ready to Go Deep (Dedicated Digital Team)
Then:
Full schema implementation across all program pages. This is a project. Scope it, resource it, execute it systematically.
Competitive analysis based on AI presence. What are competitors doing that you’re not? Where are they getting cited and you’re not?
Ongoing optimization and monitoring program. Monthly reviews of AI visibility data. Quarterly content updates based on findings.
Integration with broader GEO strategy. AI SEO doesn’t exist in isolation. Connect it to your overall search strategy, content creation strategy, and brand strategy. Your SEO strategies should address both traditional search engines and AI platforms.
PR and content strategy aligned with AI visibility. Proactive outreach to get mentioned in publications AI learns from.
The Bottom Line: Adapting Higher Education SEO Strategies for AI
What this all comes down to:
Brand used to be what you said about yourself. You controlled the message.
Then it became what others said about you. Reviews, social media, word of mouth.
Now it’s what AI understands and believes about you. AI synthesizes everything (your content, others’ content, structured data, citations) and forms a representation of your institution that it shares with millions of users.
Universities that move early get the edge. The rest play catch-up.
The tactics here work. I’ve tested them. I’ve seen universities go from invisible in generative search results to consistently recommended. But tactics change. AI changes fast. What won’t change is the need to help AI systems understand who you are, what you offer, and why you matter.
Ultimately, that’s not so different from what we’ve always done in higher ed marketing. We’re just speaking to a new kind of audience. One that never sleeps, has perfect memory, and is advising a third of your prospective students.
The question isn’t whether to adapt. It’s how fast.
What’s Next
Ready to see where you stand?
Start with our free AI Website Grader at ai-grader.searchinfluence.com. It analyzes your site’s AI visibility and gives you a baseline to work from. Then schedule a conversation with our team to walk through the results and identify your highest-impact opportunities.
UPCEA/Search Influence: “AI Search in Higher Education” (2025 research study)
SparkToro/Datos: AI Search Usage Data reports
Google Search Central: AI Overviews documentation
Tools Mentioned:
ChatGPT
Claude
Perplexity
Google AI Overviews (in Google Search)
*Will Scott is cofounder of Search Influence, a digital marketing agency specializing in higher education. He teaches the SMX Masterclass on Generative Engine Optimization (GEO) and has been tracking the AI search space since late 2022. Connect with him on LinkedIn.*
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