I’ve realized that AI Overviews are fundamentally changing how users interact with search results. Gone are the days of simple, task-oriented searches. Today, AI Overviews encourage users to dive into comprehensive reading sessions right on the search engine results pages (SERPs).
Let’s talk about some critical insights. AI Overviews merge multiple search intents into a single reading session, disrupting the traditional understanding of search behavior. Winning what I call the ‘second impression’ is crucial for different types of web pages.
Recently, I teamed up with Eric Van Buskirk from Clickstream Solutions to analyze vast amounts of anonymized clickstream data. We discovered that time-on-SERP is no longer solely dependent on search intent when AI Overviews are in play.
Historically, search intent—navigational, informational, etc.—predicted user behavior. But with AI Overviews, now users spend similar amounts of time regardless of their initial intent.
These insights are crucial. Consider Google’s change in approach: it’s less about presenting links and more about providing exact answers. This requires us to think differently about how we engage users.
For operators like me, understanding the significance of the ‘second impression’ helps us adapt our strategy for product, category, and blog pages.
In product detail pages (PDPs), it’s important to manage schemas and compare competitors’ offerings. On category detail pages (CDPs), having visible filters and vast product arrays can make all the difference.
As for blog content, I’m focusing on credibility signals like publication dates and author names within schema markup to gain trust and validation clicks.
Instead of predicting user behavior as before, the new focus is on optimizing my content’s visibility and trustworthiness in an AI-influenced SERP landscape. This shift doesn’t change our core content strategy but adds new layers of intricacy to how we optimize for SERP.
As someone who eagerly follows Google’s updates, I was thrilled to learn about the latest developments in Google Search Console. Recently, Google has started to roll out new Search Generative AI performance reports. These reports, along with a feature to block your content in AI responses, are designed to give website owners more control.
Currently, these features are being introduced to a select group of website owners in the UK, but there are plans to expand access in the near future. This gradual rollout allows us to get accustomed to these changes before they become widely available.
Exploring the Search Generative AI Performance Report
The new AI performance report in Google Search Console is something I’ve been anticipating. Although it doesn’t cover everything, it does provide some important insights into how our content is performing within AI responses, AI Mode, and AI Overviews on Google Search. The report includes data on impressions, pages, countries, devices, and dates. However, a notable omission is click data, so we’re left guessing about the exact number of searchers clicking through to our sites from AI responses.
Google stated:
– We’re rolling out new insights for website owners regarding their pages’ appearances in generative AI Search features. These insights include impressions metrics and information on which pages appear in AI responses and in which countries. We’re working closely with website owners to determine what insights would be most helpful and will expand the metrics available over time.
Additionally, Google shared more details about the metrics we can expect:
– Impressions: Frequency of your site’s URLs appearing in generative AI features in Search and Discover.
– Pages: Identifying URLs that appeared within AI features.
– Countries: Understanding visibility on a country basis.
– Devices: Identifying the devices used to view your website. Available for Search results.
– Dates: Monitoring performance with hourly, daily, weekly, and monthly granularity.
I inquired about click data from a Google representative, who mentioned that they are exploring additional metrics that will help inform our strategies in the future.
Initially, this report is available to a subset of users in the UK, with plans to expand globally in the future.
Another exciting feature Google introduced is the ability to block your content from appearing in AI search features like AI Overviews, AI Mode, or AI Discover. Google described this as a “new toggle” within Google Search Console, allowing us to decide whether or not our site should be part of these AI search features.
Google notes that opting out will prevent your site from receiving traffic or impressions from these features. Importantly, this control won’t affect your ranking in standard search results outside of generative AI Search features, so there’s no risk of negatively impacting core web search visibility.
Again, like the performance report, this toggle is currently available to a subset of UK website owners, with plans to widen access as they complete further testing. Google had promised these controls after facing some backlash from the EU, and it’s promising to see them starting to roll out now.
One study even showed that 1/3rd of SEOs are willing to block Google from showcasing their content in AI search features.
Why It Matters
As site owners and publishers, many of us have been asking for control over how and if our content appears in Google’s AI features. Now, we have just that. Although it’s initially limited, I’m hopeful these features will eventually be available to all.
Moreover, we’ve been requesting AI Search reporting from Google from day one. With Google’s announcement following Bing’s release of its own AI performance report, we’re taking a significant step forward. While Google’s report currently targets UK site owners and lacks click data, it holds promise for a global rollout soon.
I’ll be honest; the ongoing discourse around the GEO debate feels like a distraction from a much more significant transformation. AI systems are reimagining how brands, sources, and recommendations are surfaced, demanding our full attention.
It’s both impressive and frustrating how search has managed to spark such passionate debate at a time when it should be becoming more pivotal to clients. Yet, our industry is stuck in arguments that render us irrelevant.
So, who truly owns the future of search? That’s the real question we need to tackle.
Who defines the next phase of search? Who secures the budget? Who articulates the shift from a list of links to a machine-driven recommendation system?
The phrase “it’s just SEO” has caused considerable damage. It sounds like the calm, seasoned wisdom you’d expect from a search veteran. However, it lacks strategic depth. It’s a meme that constrains one of the most substantial commercial opportunities in years.
Why Memes Matter in Search
Memetics isn’t a new concept. Richard Dawkins introduced it in “The Selfish Gene” in 1976, suggesting that ideas spread through culture in a fashion similar to genes. Susan Blackmore expanded on this, claiming we’re essentially ‘meme machines’ built to propagate cultural information. The most resilient ideas aren’t necessarily true; they’re the stickiest.
Take “Happy Birthday to You,” it’s memorable and universally known not because it’s brilliant, but because it’s easy to replicate and emotionally fulfilling. Slogans and professional clichés endure for their simplicity and utility, not their accuracy.
SEO and GEO are entangled in a memetic struggle. This issue is amplified as the phrase “it’s just SEO” became predominant when GEO appeared, driving a wedge into meaningful conversation.
When GEO first came into the discussion, reactions varied. While some recognized the need for new tools and methods, others viewed it as a threat, repelling it with the phrase “it’s just SEO” — turning it into a chant and then a weapon. It was an ideal meme, short and socially protective.
The follow-up meme “GEO grifter” did even more harm, framing advocates of GEO as opportunists and stifling exploration and innovation. This behavior causes harm when consensus forms based solely on repetition, with the algorithms rewarding those repeating the framing, creating a false sense of agreement.
Clients Seek Certainty, Not Acronyms
I’ve observed firsthand at conferences like BrightonSEO that many marketers are already leveraging generative systems. They don’t need debates over terms; they’ve adapted to new processes accordingly.
SEO has always been difficult to sell against paid counterparts due to previous uncertainties and failures. Nonetheless, good SEO generates tangible success. Failing to clarify the changes will see budgets drift elsewhere, especially to paid avenues.
The B2B Institute’s Findings
According to LinkedIn’s B2B Institute, growth for B2B brands stems from being easy to locate. Digital environments now demand visibility across new platforms.
The report views GEO as an extension of SEO and emphasizes establishing authority, relevance, and credibility. Discoverability is altering, yet core principles endure.
The 9 a.m. to 5 p.m. Dilemma
“It’s just SEO” oversimplifies a vast concept. When someone insists GEO is “just SEO,” I must ask — which kind? Each interpretation involves different practices and focuses.
If our response to generative systems is “helpful content,” we’re on the wrong track. The future demands more than vague promises; it requires adopting digital PR, brand strategies, and tactical marketing insights.
No Name, No Funding
Markets can’t invest in what they don’t recognize. Naming GEO is crucial as it turns abstract threats into actionable categories. Without a name and a defined category, the industry will fail to secure the investments needed to thrive in an altered landscape.
Ultimately, whether we call it GEO, AI search visibility, or SEO evolved, defining it ensures survival and growth. Brands that embrace this will capture opportunities that arise as search evolves.
A New Framing for Change
It’s time to acknowledge change and redefine the narrative. The transformation involves becoming the recommended brand — present, visible, and credible. It’s about expanding SEO to embrace the broader spectrum of digital marketing.
Adapting to these shifts will ensure brands maintain their visibility as search continues to evolve. Those clinging to outdated debates are at risk of missing out entirely.
You are not hiring an enterprise SEO agency because your team needs more keyword ideas. You are hiring because something has become difficult to coordinate: technical changes stall, content quality varies across business units, reporting does not connect visibility to revenue, or your brand is missing from AI-generated answers.
The agency landscape becomes easier to navigate when you stop looking for a universal winner. Start with the constraint you need removed, then make each contender prove that its delivery model can work inside your organization.
Read the landscape by operating model, not ranking
A June 1, 2026 evaluation weighted leadership experience at 30%, notable clients at 25%, third-party review averages at 25%, years in business at 12%, and company size at 8%. That lens favors established vendors with recognizable accounts. It does not establish pricing, contract flexibility, technical depth, international coverage, or the quality of the people assigned to your account.
There is another limitation worth keeping visible: First Page Sage produced the ranking and placed itself first. Treat the order as a discovery aid, not an independent verdict. The more useful information is how the firms differ.
A demand team that wants organic and paid search managed as connected acquisition channels
Use the size bands as capacity signals, not quality scores. A larger company may offer more specialists and coverage, but your account can still receive a small delivery team. A smaller firm may provide better access to senior people, but it may have less room to absorb a sudden international rollout. Ask who will actually do the work.
Define the bottleneck before you build the shortlist
An enterprise SEO brief that asks for more traffic invites generic proposals. Replace it with an operating problem. Your brief should name the business outcome, the part of the search system that is failing, and the internal constraint the agency must work around.
If authority is the problem: ask how the agency will extract expertise from executives, product leaders, sales teams, or clinicians without turning every page into a slow approval project. Thought-leadership capability matters more than raw publishing volume.
If technical scale is the problem: describe the platforms, templates, faceted navigation, migrations, international sites, and release process in scope. Look for an agency that can translate crawl and indexation findings into requirements your engineers can ship.
If fragmented channels are the problem: decide which relationship must improve: SEO and paid search, search and social, brand and demand generation, or content and video. Favor the operating model built around that connection.
If AI visibility is the problem: define what you mean by success. It could include accurate brand representation, stronger coverage of customer questions, clearer entity relationships, or visibility in relevant AI answers. Do not accept a promise of guaranteed inclusion.
If market expansion is the problem: require evidence from the actual region, language, search environment, and approval structure involved. A generic global capability claim is not a substitute for local operating knowledge.
This step may remove impressive names from consideration. That is useful. A well-known full-service agency can still be the wrong choice for a technical migration, while a focused specialist can be wrong for a multinational program requiring continuous coverage across several disciplines.
Make every contender prove enterprise readiness
Client logos show that a commercial relationship existed. They do not tell you what the agency owned, whether the work resembled your problem, or whether the people responsible are still there. Ask for evidence that exposes the delivery system behind the pitch.
A named account team: request each person’s role, expected involvement, location, and relevant experience. Clarify which people are committed to delivery and which appear only during sales.
A sample diagnostic: give contenders a bounded scenario from your environment and ask how they would investigate it. You are testing prioritization and reasoning, not collecting free consulting.
Redacted working artifacts: ask to see a technical requirement, content brief, editorial workflow, measurement specification, or executive report. Polished case-study slides reveal less than the documents teams use every week.
A route from recommendation to release: have the agency explain who converts an SEO finding into an engineering ticket, who validates the implementation, and what happens when another team blocks it.
Content governance: ask how subject-matter experts, legal reviewers, brand teams, editors, and local markets participate. The answer should cover ownership and approvals, not merely writing.
Measurement ownership: require a clear distinction between activity, search visibility, qualified visits, conversions, pipeline, and revenue. Confirm who supplies each data set and how disagreements will be resolved.
AI-search methods: ask which work is distinct from established SEO and which work overlaps with technical accessibility, entity clarity, authoritative content, structured data, and off-site reputation. A credible answer should acknowledge uncertainty and avoid guaranteed placements.
Capacity under pressure: present a plausible launch, migration, or reputation issue and ask how staffing and escalation would change. The answer will tell you more than the agency’s total headcount.
References should also be problem-specific. Speak with a client whose organization resembles yours in complexity and ask what slowed the engagement, how senior access changed after the sale, and which promised capability required the most client-side support.
Use a decision scorecard that procurement cannot flatten
Procurement comparisons often make unlike services look interchangeable. Prevent that by marking each criterion as pass, concern, or fail and recording the evidence beside it. Do not average away a failure in an area that can stop the engagement.
Decision area
Question to settle
Evidence to retain
Strategic fit
Does the proposed program address the bottleneck in your brief?
Problem statement, priorities, exclusions, and expected business outcome
Technical execution
Can recommendations survive your CMS, engineering, security, and release constraints?
Sample requirements, validation process, and ownership map
Content operations
Can the agency obtain expertise and move work through your approvals?
Workflow, role definitions, briefs, and quality controls
SEO, AEO, and GEO scope
Are conventional search and AI discovery connected without vague claims?
Defined activities, measurement limits, and reporting examples
Measurement
Can the agency connect its work to outcomes your leadership recognizes?
Metric definitions, data dependencies, attribution assumptions, and reporting cadence
Team quality
Are the proposed specialists the people who will serve the account?
Named staffing plan, responsibilities, availability, and escalation path
Commercial clarity
Can you tell what is included and what triggers more cost?
Deliverables, dependencies, change process, renewal terms, and exit provisions
Treat access to the delivery team, measurement ownership, and implementation responsibility as gates. A strong brand name or attractive review average should not compensate for ambiguity in those areas. Record concerns during the pitch process; memory becomes generous once polished proposals arrive.
Key takeaways
Choose an operating model that fits your bottleneck, not the agency with the highest overall rank.
Use company size as a capacity clue, then verify the people and time assigned to your account.
Replace client-logo proof with relevant artifacts, named team members, and problem-specific references.
Define AI-search success before buying GEO or AEO services, and reject guaranteed-inclusion claims.
Make technical execution, measurement ownership, and delivery-team access non-negotiable gates.
Your next move is to write a brief around the constraint that is costing your organization the most. Send the same scenario and evidence requests to every contender. The right agency will make the work, ownership, and tradeoffs clearer before the contract is signed.
If you’re wondering whether AI makes your SEO program obsolete, the useful answer is no. It changes where discovery happens, how answers are assembled, and what success looks like. It doesn’t remove the need for accessible pages, clear information, credible evidence, or a recognizable brand.
Your job is expanding. You still need to help a page rank, but you also need to make its information easy for an answer engine to retrieve, interpret, trust, and represent accurately.
Key takeaways
SEO is evolving from ranking pages alone to making a brand and its knowledge retrievable across search and AI interfaces.
Technical access, search intent, useful content, internal links, and authority remain the foundation.
AI optimization adds clearer answer structure, stronger entity signals, supported claims, and structured data that matches visible content.
Clicks are no longer a complete scorecard. Track visibility, citations, brand representation, qualified visits, and conversions together.
Start with one commercially relevant topic cluster and improve the full path from question to evidence to action.
SEO has changed before, but the target is broader now
Early search optimization often focused on exploiting visible ranking signals. Practices such as keyword stuffing and cloaking could influence engines that were easier to manipulate. The landscape included names such as Excite, AltaVista, and Northern Light, and much of the discipline was learned through experimentation and informal community knowledge.
That model became less dependable as search systems improved. Panda and Penguin became major milestones because they forced site owners to confront content quality and manipulative promotion. The durable lesson wasn’t that optimization had stopped working. It was that tactics built around weaknesses in a system had a shorter life than work built around users.
AI is another shift in the interface, but it is not a clean break from search. A conventional results page gives a user several candidates to evaluate. A generative interface can combine information into a response before the user visits a website. Your page may influence that response, earn a citation, receive a click, or remain invisible even when it ranks well elsewhere.
This widens the optimization target. You are no longer working only for a blue-link position. You are working to become a reliable candidate whenever a system needs information about your topic, product, organization, or expertise.
What remains essential and what AI adds
It helps to separate enduring SEO work from the additional demands of answer-driven discovery. If the foundation is weak, adding schema or rewriting a few headings won’t rescue it.
Area
Enduring SEO requirement
Additional AI-era requirement
Access
Pages must be crawlable, indexable, and internally connected.
Important facts must be available in readable page content rather than hidden behind an interaction.
Intent
A page should satisfy the reason behind a query.
It should also answer the follow-up questions a synthesized response is likely to combine.
Content
Information should be useful, original, and easy to navigate.
Definitions, distinctions, conditions, and conclusions should be explicit enough to extract without losing context.
Authority
Relevant links, reputation, and subject expertise support trust.
Consistent entity information and independent corroboration help systems identify who you are and why your claims matter.
Structured data
Valid markup can clarify page type and important attributes.
Connected, accurate entities can reduce ambiguity, but markup must agree with what a visitor can see.
Measurement
Rankings, impressions, clicks, engagement, and conversions show search performance.
Answer inclusion, citations, brand mentions, representation accuracy, and assisted discovery provide additional signals.
Do not treat the right-hand column as a replacement checklist. It is an extension of the left-hand column. A fast, well-linked, authoritative page with a precise answer is useful in either environment.
Build an AI-ready SEO workflow around real questions
You don’t need to rebuild your entire site at once. Choose a topic connected to revenue, retention, or a recurring customer problem, then work through the following sequence.
Collect the language your audience uses. Pull questions from sales calls, support conversations, on-site search, keyword data, and Search Console. Group them by discovery, comparison, decision, and post-purchase intent. This prevents you from creating a disconnected page for every wording variation.
Choose one primary page for the topic. Decide which URL should carry the clearest, most complete answer. Merge overlapping material where it creates confusion, and use supporting pages only when a subtopic deserves separate treatment.
Put the answer before the expansion. State the central answer near the beginning. Then explain conditions, exceptions, evidence, examples, and next steps. A reader should not have to cross several promotional paragraphs to learn whether the page addresses the question.
Make important relationships explicit. Use consistent names for your company, products, services, people, and locations. Connect relevant author biographies, About information, policy pages, and supporting resources with descriptive internal links. Do not expect a machine to infer that two inconsistent labels refer to the same entity.
Add only defensible structured data. Select schema types that describe the visible page. Keep names, authorship, dates, offers, and organizational details aligned with the content. Validate the syntax, but also inspect whether the markup tells the truth. Technical validity does not correct a false or unsupported claim.
Strengthen the evidence layer. Replace vague assertions with demonstrations, documented methods, primary references, or clearly attributed expertise. Seek relevant third-party mentions because a claim repeated only across your own pages is not independent confirmation.
Design the next action. Match the call to action to the question’s stage. An educational query may need a related explainer or checklist. A comparison query may need specifications, constraints, or pricing context. A decision query may justify a demo, trial, purchase, or contact option.
Review the finished page as if its paragraphs might be separated from the layout. Check whether a definition still makes sense without the heading above it, whether a recommendation names its conditions, and whether a quoted fact remains connected to its evidence. This is good editing for people and useful preparation for machine retrieval.
Measure visibility without mistaking mentions for results
AI answers can change the relationship between visibility and traffic. A user may learn your name without clicking, or an assistant may cite your page while sending few visits. The opposite can also happen: a small amount of highly qualified traffic can produce meaningful business results.
Use a scorecard with four layers:
Search presence: impressions, relevant rankings, indexed URLs, click-through behavior, and the mix of branded and non-branded discovery.
AI presence: whether your brand appears for a stable set of important questions, whether it receives a citation, and whether the description is accurate.
On-site behavior: landing-page engagement, progression to another useful page, leads, sales, subscriptions, or other outcomes tied to the page’s purpose.
Business quality: lead relevance, conversion value, sales feedback, and the customer questions that remain unanswered.
Treat AI visibility checks as sampled observations, not permanent rankings. Responses can vary with phrasing and context. Keep a consistent set of questions, record the wording you used, and compare patterns over time. A single favorable response is not a strategy, and a citation that misrepresents your company is not a clean win.
Start with the strongest page in one valuable topic cluster. Clarify its answer, repair its evidence and entity signals, align its structured data, and give the reader a sensible next step. That work improves your odds across traditional search and emerging answer interfaces without betting your entire program on one platform.
I’ve noticed that TikTok Shop creators excel by tapping into the psychology that drives people to act. Let me share how we can leverage these persuasive principles in our writing.
SEO content is often designed to rank, but conversion can sometimes fall by the wayside when we’re caught up in the technical checklist. In light of AI Overviews and falling click-through rates making visibility more challenging, I believe it’s time to focus on whether our content encourages action once someone engages with it.
Take a cue from TikTok Shop creators—they don’t just thrive because of large followings. They master persuasion by understanding consumer psychology and scaling actions. This insight can transform how we approach our written content.
The formula that successful TikTok Shop creators follow isn’t random. It relies on consumer psychology principles, not on celebrity status or follower count. I’ve realized that 99% of my own video views come from non-followers. Therefore, it’s the understanding of the psychology behind actions that matters.
By focusing on visual hooks, psychological triggers, storytelling, and relentless experimentation, we can apply these elements to written content to drive similar results.
People often buy based on emotions, justifying their decisions rationally later. It’s crucial to connect with their motivations rather than just presenting facts.
Persuasive content succeeds because it targets human desires like protecting loved ones, enjoying life, feeling safe, and seeking social approval.
Understanding these motivations allows me to craft content that resonates more deeply with my audience, ultimately leading to better engagement and conversion rates.
Your shortlist may be full of agencies that claim to know your industry. The difficult part is telling genuine operating knowledge from a few client logos and a newly written service page.
You need evidence that an agency understands how your customers search, what an accurate answer requires, and which actions produce qualified business. The framework below will help you test that evidence before you sign a contract.
Key takeaways
Industry specialization matters only when it improves research, content decisions, technical execution, and lead quality.
Set pass-or-fail requirements before scoring agencies so a polished presentation cannot hide a missing capability.
Use the same weighted scorecard for every candidate and record the evidence behind each score.
Evaluate the people, workflow, deliverables, and reporting model you will actually receive, not just the agency brand.
Verify industry expertise through decisions, not labels
A specialist should get beyond your industry’s basic vocabulary quickly. Its team should understand who buys, what triggers demand, which questions delay a decision, and what evidence helps a prospect trust an answer.
That knowledge should be visible in three areas:
Customer and query fluency: The agency can separate informational questions from comparison, qualification, and purchase-intent searches. It recognizes that different buyers may use different language for the same problem.
Accuracy and risk awareness: The team knows which claims require careful review, where subject-matter expertise is necessary, and which details cannot be replaced with generic AI-generated copy.
Commercial understanding: Recommendations reflect service areas, margins, sales cycles, lead quality, and the conversions that matter to your business.
Relevant client work is useful evidence, but it is not a verdict. A 2026 evaluation of 72 pest-control GEO agencies assigned notable clients 25% of its scoring model. That is a sensible reminder to check direct experience while still examining leadership, capacity, longevity, and client feedback.
The specialization question also applies to aerospace and aviation SEO, where audiences, terminology, buying journeys, and evidence requirements differ sharply from local consumer services. An agency’s experience in one demanding vertical does not automatically transfer to another.
Give every candidate the same short brief about a real offering. Ask which search questions it would prioritize, what evidence the existing site lacks, which pages it would improve, and how it would connect that work to a business outcome. A specialist should make sharper distinctions than a generalist without pretending to know facts that only your internal experts can supply.
Require SEO and GEO to operate as one system
SEO helps people and search engines find, understand, and trust your pages. GEO extends that work to the environments where generative systems assemble answers and recommendations. The disciplines overlap, but they are not interchangeable.
Capability
What a capable agency should demonstrate
Warning sign
Technical SEO
A method for finding crawl, indexing, rendering, internal-linking, and page-template problems
Content production begins before the site can reliably expose and support that content
Search strategy
A topic and query model tied to buyer needs, search intent, and commercial priorities
A keyword list with no explanation of audiences, decisions, or conversions
Answer readiness
Clear answers, useful supporting detail, identifiable entities, and appropriate structured data
Schema markup is presented as a shortcut that can compensate for weak content
Authority development
A plan for credible mentions, citations, expert contributions, and consistent brand information beyond your own domain
GEO is treated as publishing more pages on your site
Measurement
Defined search, AI-visibility, engagement, lead, and revenue indicators with stated limitations
A single visibility score is offered without query-level or business context
Ask the agency to trace a priority topic through its full workflow: demand analysis, page selection, content creation, expert review, internal linking, structured data, external corroboration, visibility monitoring, and conversion measurement. If separate teams own those steps, ask how information moves between them.
Pay particular attention to JSON-LD and entity work. The agency should be able to explain what each schema type communicates, where the underlying information appears on the page, and how it validates the implementation. It should never promise that markup alone will make an AI system cite or recommend your brand.
Score the shortlist with evidence you can audit
Apply pass-or-fail gates before assigning scores. A candidate should fail the gate if it cannot support your required market, produce technically sound work, follow your review obligations, or report against agreed business outcomes. Scoring an agency that cannot meet a non-negotiable requirement only creates false precision.
For the remaining candidates, a defensible vertical-agency weighting uses the following proportions:
Criterion
Weight
Evidence to record
Average review score
30%
Ratings and repeated client feedback across review platforms and testimonials
Notable industry clients
25%
Relevant organizations, comparable engagements, and the actual work performed
Leadership experience
20%
Experience in GEO, industry marketing, and digital strategy, plus involvement in your account
Year founded
15%
Operating history and evidence of adapting as search behavior and platforms changed
Company size
10%
Enough capacity and role coverage to deliver the proposed program consistently
Do not let the percentage become a substitute for judgment. A high average rating can hide feedback unrelated to SEO or GEO. A famous client logo does not prove the agency handled the same work you need. Longevity shows operating history, not automatic competence in generative search. Company size indicates capacity, not attention.
Have stakeholders score candidates independently, attach evidence to every rating, and then discuss the largest differences. This exposes assumptions that disappear when a group jumps straight to a consensus score.
Run the sales interview around your actual work
A good sales presentation can describe a credible process without proving that the delivery team can apply it. Turn the interview into a working session.
Bring a real revenue problem. Use an offering, location, audience, or sales objection that matters. Remove confidential details if necessary, but keep the business decision realistic.
Ask for diagnosis before tactics. Strong candidates will ask about customers, competitors, sales qualification, current visibility, subject-matter experts, analytics, and technical constraints before prescribing content.
Inspect representative deliverables. Review a technical finding, content brief, finished page, schema recommendation, reporting view, and authority-building output. Anonymized examples are sufficient if they show the depth of the work.
Define measurement in plain language. Ask which changes will be monitored across conventional search, generative answers, brand citations, qualified leads, and revenue. Require the agency to separate observed results from estimates and directional indicators.
Pressure-test the promise. Ask what the agency cannot guarantee, which dependencies belong to your team, and what it would do if visibility improves without lead quality improving.
Be cautious when a candidate guarantees rankings or AI citations, proposes large-scale generic content before examining your site, treats structured data as the entire GEO strategy, or cannot show how its reporting leads to a decision. GEO is still optimization work under uncertainty. Honest limits are a sign of a usable partner, not a weakness.
Choose the delivery team, not just the agency name
Industry expertise has little value if the knowledgeable people disappear after the sales call. Ask for the names or role profiles of the people who will research, write, review, implement, analyze, and make strategic decisions.
Confirm who leads strategy and how often that person reviews the account.
Identify who writes and who verifies industry claims before publication.
Clarify whether developers implement changes or only send recommendations.
Ask who investigates measurement changes and turns them into the next action.
Map your own approvals, data access, expert input, and development support into the workflow.
A smaller specialist may provide direct senior attention, while a larger firm may offer broader execution capacity. Neither structure is inherently better. Choose the one whose named team, communication rhythm, and implementation responsibilities match the way your organization can work.
Start by writing your non-negotiable requirements and a short real-world brief. Send both to every candidate, score the responses with the same evidence standard, and hire only after you know who will do the work and how success will change the next decision.
I find it fascinating that users interact differently when faced with AI Overviews compared to AI Mode. New clickstream data reveals that AI Overviews significantly alter user behavior—from reverse scrolling to extended evaluation of search results across various intents.
Take Netflix, for example. The average user spends about 18 minutes just browsing. They skim through tiles, watch trailers, and often circle back. It turns out, searching isn’t much different these days, thanks to new insights.
This week, I’m diving into:
Four notable behavioral shifts observed with AI Overviews, gathered from over 846,000 Google sessions.
The evolving role of brand-name searches and why they no longer offer the same shortcuts.
An insight that might change how you craft title tags and meta descriptions this quarter.
Eric Van Buskirk from Clickstream Solutions mined anonymized clickstream data supplied by Surfer SEO. The study analyzed around 846,000 U.S.-based Google searches from February and March of 2026.
This marks the fifth study on user behavior with Google’s AI features over the past year. Earlier, a UX study on 70 users in May 2025 utilized think-aloud and screen recording methods, while a study from October 2025 examined AI Mode specifically. This research trades depth for scale, uncovering patterns too subtle for smaller studies.
For a bit of context, previous SERP mouse-tracking studies involved only a handful of people—this one, however, evaluates queries from tens of thousands of users.
A fascinating contrast surfaces: User behavior in AI Overviews starkly opposes that in AI Mode, where AI Mode is akin to autoplay, while AI Overviews replicate the browsing experience.
This article outlines four major findings from this recent study and how they might influence your title tags and meta descriptions in 2026. Full methodology available here.
With groundbreaking insights, like how nearly half of AI Overview interactions involve reverse scrolling and how search types no longer reliably predict behavior, this data is invaluable. It challenges traditional assumptions and has meaningful implications for e-commerce and decision-heavy categories.
Surprising findings include brand searches losing their shortcut advantage, implying even users searching specifically for brands might pause to consider adjacent content on the SERP.
Read more intriguing insights on how the AI landscape shifts user engagement and strategy in SEO.
I’ve just delved into Goodie’s enlightening AI search traffic report for early 2026, covering the period from January to April, and I’m excited to share my insights with you. This report dives into trends in usership, referral traffic, and marketing considerations, offering a comprehensive view of the shifting landscape.
You’ll want to pay particular attention to how ChatGPT’s dominance is starting to wane, with some surprising contenders like Claude and Gemini making waves. This shift could significantly impact how marketers strategize their efforts in AI-driven search optimization.
The data reveals fascinating patterns in user habits and referral traffic, which could inform future marketing strategies and the allocation of resources. For a full dive into these emerging trends and what they might mean for businesses, I encourage you to explore the detailed findings of the report.
I’ve recently discovered that Google has introduced a new feature in Chrome Lighthouse to check for llms.txt files. Though Google mentions that llms.txt isn’t necessary for AI search visibility, Lighthouse has started flagging sites based on their presence.
Google’s latest Lighthouse audits, under the “Agentic Browsing” category, now focus on a site’s usability for machine interaction. I find this interesting as it aligns with Google’s push towards better machine readability.
The new audits are part of Chrome’s evolving “Agentic Browsing” features, which analyze if sites are prepared for automated interaction. This concept came soon after Google issued guidance on AI search optimization, debunking the necessity of llms.txt files in their new guide on generative AI features.
What Lighthouse Evaluates Now. Lighthouse’s Agentic Browsing tests focus on how well my site is built for machine interactions, incorporating various deterministic audits as per Google’s documentation. These checks include:
– WebMCP integration.
– Accessibility tree integrity.
– Layout stability through CLS.
– Presence of an llms.txt file.
These audits help ensure that there’s a machine-readable summary at the site’s domain root. Google explains that without llms.txt, agents might take longer to understand a site’s main structure.
The impact of these audits doesn’t translate into a traditional Lighthouse score but into a fractional pass ratio related to agentic readiness signals.
The Tension. Interestingly, while these audits don’t directly affect SEO rankings, their mention in Google’s readiness checks could make SEOs reconsider their stance on llms.txt files.
Agentic Engine Optimization. Google’s approach aligns with insights shared by Addy Osmani from Google Cloud AI about Agentic Engine Optimization. Osmani emphasizes creating web content that is semantically structured, token-efficient, and easy for AI to process.
SEO vs. llms.txt. According to Google, creating llms.txt or similar files isn’t necessary for AI search success, as outlined in the guide on Mythbusting generative AI search. The AI systems can discover, crawl, and index a variety of file types encountered on the internet.
John Mueller from Google responded to concerns about the role of llms.txt in a discussion with Lily Ray on Bluesky, stating that the use of these files is more for functionality and not directly linked to search engine optimization.
Google’s Take on AI Agents. Besides llms.txt, Google’s Lighthouse guidelines place strong emphasis on accessibility and interface stability. The insight I gained is that AI agents heavily rely on the accessibility tree as their core data model, focusing on integrity and proper layout.
Ultimately, while Google indicates llms.txt isn’t needed for search, including such files might be beneficial for adapting to Google’s evolving tools that prioritize machine readability.
