For many brands, a GEO agency not seems like an experiment. As generative search becomes embedded in how people discover, compare, and choose, GEO services are moving from “interesting” to operationally relevant.
That shift raises a real query inside marketing teams: is working with a GEO agency now a necessity, or can an in-house SEO team evolve quickly enough to maintain pace?
This isn’t a theoretical discussion anymore. Generative AI is already reshaping how people search, evaluate options, and make decisions. According to research “Generative Engine Optimization (GEO): The Mechanics, Strategy, and Economic Impact of the Post-Search Era,” the digital world is currently “undergoing its most vital structural transformation for the reason that commercialization of the World Wide Web within the mid-Nineteen Nineties.”
The GEO Best Practices Guide by Orange 142 states, “The integration of generative AI into search was inevitable,” as users extend AI tools from productivity into research and buying behavior.
What this implies for brands and agencies is easy but uncomfortable: traditional SEO alone not guarantees visibility. Generative search systems interpret, synthesize, and prioritize information in another way. As a result, GEO services have gotten a part of the strategic conversation for a lot of SEO agencies and digital marketing teams.
In this blog, we discover what’s actually changing, where GEO suits into modern search strategy, and whether investing in a GEO agency is important or if internal SEO teams can realistically adapt fast enough without outside support.
What’s Inside
Generative Search Changes the Rules (And SEO Alone Is No Longer Enough)
Generative search doesn’t just tweak how results are ranked; actually, it fundamentally changes how visibility is granted.
AI-powered serps synthesize information, compress multiple viewpoints into a single response, and surface only a small set of trusted sources. That shift has real consequences for brands which have historically relied on traditional SEO tactics to capture attention.
This change will not be theoretical. McKinsey estimates that generative AI could drive $4.4 trillion in long-term annual productivity gains across corporate use cases, with knowledge work and knowledge retrieval amongst the most important contributors.
Search sits directly inside that value creation layer. When AI systems summarize, evaluate, and cite content on a user’s behalf, visibility becomes less about rating positions and more about being recognized as a reliable source value referencing.
Traditional SEO teams are optimized for crawling, indexing, and rating signals. But generative search introduces latest variables:
- How do large language models interpret authority?
- How is content summarized?
- Which brands are deemed trustworthy enough to be included in a solution in any respect?
User behavior reinforces why these variables have gotten a problem so quickly. 39% of U.S. people have already used AI inside just two years, compared with 20% web adoption in its first two years, signaling how briskly AI-driven interfaces have gotten mainstream.
Users are growing accustomed to asking AI for direct answers, recommendations, and comparisons. And it reduces the variety of touchpoints where traditional SEO tactics once played a role.
So, SEO stays foundational, but by itself, it is not any longer sufficient to ensure visibility inside generative answers. That gap between what SEO teams were built to do and what generative search now requires is why many brands are reassessing their approach and asking where a geo agency suits into the equation.
What Is Generative Search, Really?
Generative search is best understood as a shift from retrieving information to delivering answers.
AI-powered search systems synthesize information from multiple sources and return a single, consolidated response. As the OtterlyAI Generative Engine Optimization Guide puts it plainly:
AI-search engines are answering machines moderately than serps.
According to the identical guide, zero-click searches already account for roughly 60% of searches in each the U.S. and Europe, meaning users often get what they need without ever visiting a website.
As you already know, in traditional search, success was driven by rankings and clicks. In generative search, success relies on whether your brand, product, or expertise is included in the reply in any respect. Regarding that issue, Gartner predicts that organic search traffic will decline by 50% by 2028 as AI-generated answers increasingly replace traditional results.
Generative search (and AI serps like Google AI Overviews, ChatGPT Search, and Perplexity) combines large language models with live web retrieval. A way generally known as Retrieval-Augmented Generation (RAG) is used to generate up-to-date, cited answers.
That’s why citations and mentions matter greater than ever.
In practical terms, generative search is about being recognized as a trusted source. That recognition is what determines whether AI systems reference you, summarize you, or ignore you entirely. And that shift is what sets the inspiration for why brands are actually rethinking SEO, visibility, and the role a geo agency may play going forward.
How AI Search Engines Select Sources (Not Rankings)
Actually, AI doesn’t “rank” content the way in which serps used to.
It decides what to drag into the reply, and that call happens before a user ever sees anything.
In traditional search, your job was to earn a spot on the page and hope someone clicked. In AI search, the system makes that decision for the user. It looks at a pool of knowledge, decides which sources it trusts enough to reference, after which blends them into a single response. If your content isn’t chosen at that stage, it simply doesn’t show up, regardless of how strong your rankings is perhaps elsewhere.
AI systems are filtering for things like:
- Is this source credible and widely trusted?
- Do multiple sources agree on this point?
- Is the knowledge clear, factual, and simple to summarize?
- Does this source fit naturally throughout the platform’s ecosystem?
This shift also explains why visibility feels harder to predict. As we mentioned earlier, a large share of searches now end without a click in any respect, since the answer is delivered directly within the interface.
The paper The Mechanics, Strategy and Economic Impact of the Post-Search Era, looks at how different AI search platforms actually select their sources. What it found is that there’s no single rulebook. Each system has its own preferences and biases:
Source: Generative Engine Optimization (GEO): The Mechanics, Strategy, and Economic Impact of the Post-Search Era
Seen together, this paints a clear picture: there isn’t one generative search algorithm to optimize for anymore. There are multiple systems, each deciding trust in barely other ways.
Why Traditional SEO Teams Struggle With Generative Search
Most SEO teams aren’t failing; they’re operating under assumptions that not hold.
Traditional SEO was built around a clear goal: improve rankings, drive clicks, and optimize pages for traffic.
Generative search breaks that model. AI systems don’t reward pages for rating well; they reward sources for being useful to the reply. That subtle difference is where many SEO teams begin to feel friction.
- The first challenge is misaligned incentives.
SEO teams are typically measured on metrics like impressions, clicks, and keyword positions. Generative search, nonetheless, often produces answers without clicks in any respect.
When success looks like being cited or referenced, classic KPIs stop telling the total story. To bridge the gap between output and influence, teams need a GEO KPI because you’ll be able to’t optimize what you aren’t set as much as measure.
- The second issue is how content is created.
SEO workflows are inclined to prioritize keyword coverage, page templates, and incremental optimization.
Generative search favors something else entirely: clear explanations, defensible facts, strong sourcing, and content that may be easily summarized by a model. Pages written to “rank” don’t at all times translate into content that an AI system wants to drag from.
- There’s also a tooling gap.
Most SEO platforms are still designed to observe SERPs, backlinks, and on-page signals. They don’t show:
- Whether a brand is appearing in AI answers,
- How often it’s being cited,
- Which competitors have gotten preferred sources in generative results?
Without visibility into those systems, teams are effectively optimizing at midnight.
- Another point is organizational structure.
Generative search cuts across SEO, content, PR, brand, and even product teams. Traditional SEO functions often sit in silos, focused narrowly on search performance.
AI systems, then again, draw from your complete information ecosystem, earned media, thought leadership, community platforms, structured data, and authoritative references. Coordinating across those inputs isn’t something most SEO teams were designed to do.
- Finally, there’s a mental model gap.
SEO has at all times been about competing for positions. Generative search is about earning trust. That requires pondering less like a tactician and more like a publisher, educator, or source of record. For teams trained on algorithm updates and rating aspects, that shift doesn’t occur overnight.
None of this implies SEO teams are obsolete.
In fact, lots of the fundamentals they manage, technical health, structured content, and authority, are still essential. The struggle comes from the transition. Generative search asks SEO teams to maneuver upstream, away from rankings and toward source credibility. And without latest processes, metrics, and mandates, that’s a difficult leap to make alone.
SEO Teams Are Trained for Pages, Not Answers
Most SEO agencies & teams are excellent at optimizing pages. That’s what they were built to do, what they’re measured on, and what their tools are designed to support.
Generative search, nonetheless, changes the unit of value. AI systems don’t judge success by page performance: actually, they judge whether a source helps them construct a clear, trustworthy answer. That gap is where traditional SEO starts to feel strained.
Large language models interpret a prompt, resolve which sources are credible enough to make use of, after which synthesize a response. What the user sees is a solution, not a page. And that distinction changes how visibility is earned.
This is where many teams run into friction. SEO professionals are trained to ask, “How can we rank this page?” AI systems are asking, “Which sources can we trust to elucidate this?” Those are different problems, requiring different inputs.
The table below shows why this transition is greater than a small adjustment and why some brands begin exploring support from a GEO agency as generative search matures:
Source: Ottlerly.AI guide
So, updating a page or earning a backlink doesn’t at all times change whether an AI system chooses to reference that content. Visibility depends more on clarity, authority, consistency, and the way easily information may be summarized and reused.
This can also be why some organizations look beyond their existing SEO function. A generative engine optimization company approaches the issue from a broader angle: it focuses on how a brand appears across the knowledge ecosystem that AI systems draw from.
The Gap Between “Optimized Content” and “Citable Knowledge”
For years, “optimized content” meant content that ranked well. If a page hit the suitable keywords, earned backlinks, and followed SEO best practices, it was considered successful.
Generative search introduces a different standard. AI systems don’t just search for optimized pages; they appear for citable knowledge.
When citable knowledge appears in Google results as an AI summary, users click an organic result only 8% of the time. In that environment, being “optimized” is not any longer enough.
AI engines prioritize information they will confidently reuse. The report titled How to Optimize Content for GEO and AEO in an AI-Native World defines generative engine optimization because the practice of designing content so LLMs usually tend to cite it directly.
So, optimized content is usually written to satisfy algorithms. Citable knowledge is written to satisfy models.
From an operational standpoint, that is where a GEO agency often becomes relevant. Building citable knowledge requires original data, third-party validation, consistent brand presence across trusted platforms, and content structured for AI parsing. And all these require a daring strategy and cross-functional coordination.
What a GEO Agency Actually Does (That SEO Teams Usually Don’t)
At a glance, a GEO agency can appear to be an extension of SEO. In practice, the work is fundamentally different.
As we mentioned before, where SEO teams deal with making pages rank, a GEO agency focuses on making knowledge travel out of your brand into AI-generated answers.
Again, generative search doesn’t reward effort on the page level alone. It rewards brands that consistently show up as credible inputs across the broader information ecosystem.
Let’s be more specific, a GEO agency:
- Designs content for citation, not traffic.
- Coordinates visibility across those environments. So a brand appears consistently wherever AI systems search for consensus.
- Builds content so it sounds less like promotion and more like something an AI would trust and reuse.
- Focuses on how a brand shows up across the broader information ecosystem that AI systems depend on. That might include turning internal expertise into data-backed explainers, placing insights in credible third-party publications, or structuring content so it’s easier for AI models to extract and reuse.
For example, many GEO agencies within the USA work with brands to remodel product knowledge into reference-style content (definitions, benchmarks, or research summaries) that look less like marketing and more like something an AI would confidently cite.
Entity Authority and Source Credibility Engineering
So far, we’ve explored what a GEO agency really does. Now, it’s time to say entity authority and source credibility.
Entity authority (or E.E.A.T.) refers to how strongly an AI system recognizes and understands a brand. In generative search, entities usually are not pages. They are conceptual objects with attributes: what they’re known for, which topics they consistently appear in, and the way often they’re validated by other trusted sources.
The research paper we previously cited, Generative Engine Optimization (GEO): The Mechanics, Strategy, and Economic Impact of the Post-Search Era, explains entities as follows:
The fundamental unit of understanding in GEO is the entity, not the keyword. LLMs understand the world through a vast Knowledge Graph of entities (people, places, concepts) and the relationships between them.
And a brand that’s frequently linked to certain entities within the training data forms a strong association within the vector space.
For example, if Salesforce ceaselessly co-occurs with CRM and Enterprise across hundreds of documents, the model learns this relationship as a fundamental truth. GEO involves strengthening these associations through consistent messaging and schema markup.
In easy terms: if an AI doesn’t clearly “know who you’re,” it won’t reference you.
In its YouTube video, SMA marketing says that in relation to generative optimization, the largest thing we’re attempting to do is make our brand surface in AI responses.
We wish to ensure that our brand is an element of the AI conversation, so meaning our content needs to be approached a little bit in another way from an SEO standpoint. Within these large language models, we’re educating those models with our content in order that we’ll include our entity in relevant results. We wish to ensure that we’re known for certain topics, niches, problems, and answers to questions. That’s a barely different view of content and the metrics we use are going to be different traditionally.
Source credibility, then again, is about risk.
AI systems are designed to avoid hallucinations and misinformation. To do this, they favor sources that show reliability through evidence, attribution, and third-party validation. Generative engines prioritize sources that show:
- Clear authorship and provenance,
- Verifiable facts and data,
- Independent corroboration across trusted platforms.
When combined, entity authority answers “Who is that this?” and source credibility answers “Can we trust them?” Generative serps need each before including a brand in a solution. One without the opposite isn’t enough.
That’s why this work is increasingly described as source credibility engineering. It’s not accidental. Brands must make clear their identity. In a post-search world, being visible isn’t about rating higher; it’s about being recognized and trusted as an entity value citing.
Structuring Knowledge So AI Can Trust and Reuse It
Once an AI system recognizes an entity and believes it’s credible, the subsequent query becomes practical: Can this information actually be reused?
In generative search, trust alone isn’t enough. Knowledge must be structured in a way that AI systems can clearly interpret and extract.
In human terms, AI systems don’t “read” content the way in which people do. They break it down into pieces after which reassemble those pieces into latest answers. Content that’s vague, overly promotional, or poorly organized creates friction.
Then, what kind of knowledge are AI systems more more likely to reuse?
- Key concepts are clearly defined,
- Claims are separated from opinions, with evidence attached,
- Relationships between ideas are explicit.
This can also be where many brands struggle. Traditional content is usually written to influence or rank. It blends messaging, context, and conclusions in a way that works for humans skimming a page but creates ambiguity for AI systems attempting to extract a clean answer.
So, structuring knowledge for AI means being deliberate. What’s more?
- Explanations must stand on their very own.
- Facts need clear attribution.
- Data needs context that travels with it.
When AI systems encounter this type of content repeatedly from the identical source, trust compounds.
Before closing that section, let’s keep in mind that some content types, like questions and detailed search queries, usually tend to be processed by AI, as Pew Research Center stated:
Source: Pew Research Center
Testing, Tracking, and Iterating AI Visibility Signals
GEO work treats visibility as an ongoing feedback loop, not a one-time optimization.
The GEO Best Practices Guide states:
As AI serps develop into more prevalent, success can’t just be measured by website traffic anymore. What matters now’s how accurately and favorably AI systems present your brand when answering user queries. GEO is about ensuring AI systems understand and represent your brand appropriately when synthesizing information for users, not nearly appearing high in search results.
Once brands accept that generative search visibility can’t be measured by rankings alone, the subsequent challenge is knowing what to trace as an alternative.
Actually, AI visibility requires latest, model-native metrics and signals that reflect how brands actually appear inside AI-generated answers. Generative Engine Optimization (GEO): The Mechanics, Strategy, and Economic Impact of the Post-Search Era highlights several indicators that GEO agencies use to check and track AI systems:
This looks at how ceaselessly a brand appears across a defined set of prompts inside a category. For instance, when AI is asked a broad range of questions on enterprise CRM software, SoM compares how often one brand is surfaced relative to others.
It distinguishes between a brand being casually referenced and being explicitly linked or named as a source. The research shows that sources receiving formal citations usually tend to be reused in future responses.
This signal looks at how a brand is described, whether the tone suggests endorsement, neutrality, or concern. The paper emphasizes that sentiment matters because AI summaries can shape perception quickly and at scale.
- Conversational Engagement Rate (CER)
This measures what happens next. When an AI response includes a brand, does it prompt the user to ask follow-up questions on it? The next engagement rate suggests that the brand is relevant enough to sustain the conversation.
So, GEO agencies test how brands show up across prompts, improve structure and clarity, and reinforce authority signals.
Over time, they track how those changes influence SoM, citation behavior, sentiment, and engagement, measuring GEO success by whether the brand earns consistent visibility. In a generative search landscape, success comes from becoming a part of the conversation.
When an In-House SEO Team Can Handle Generative Search
So far, we’ve explored that GEO is a different story from SEO. Visibility works in another way. Content is evaluated in another way. Even success is measured in another way.
Generative search introduces latest surfaces, latest expectations, and latest types of influence that don’t map neatly to rankings, traffic, or keyword performance. And that affects how brands take into consideration geo pricing and the worth of appearing in these environments.
That sometimes means brands need outside help.
In the early stages, an in-house SEO team can handle parts of generative search. Teams that already produce high-quality content, maintain strong technical foundations, and understand authority signals are ranging from a good place. With effort and time, they will experiment with generative formats.
However, traditional SEO dashboards weren’t built for measuring GEO KPIs. Rankings don’t explain whether a brand is being cited. Traffic doesn’t reveal how often it appears in AI answers. Even impressions fall short when AI systems summarize information without sending users anywhere. Once teams try to maneuver beyond surface-level statement and into real GEO performance measurement, the gaps develop into obvious.
One of the GEO agencies we listed in our blog titled “What is a GEO Agency? Top 7 GEO Agencies Leading AI Search Optimization,” Propeller, showcases its solutions as follows:
From zero-click optimization to LLM tracking and entity optimization, there are numerous services GEO agencies cover. It seems that in-house SEO teams need to handle these areas, one after the other.
When You Actually Need a GEO Agency
One of the clearest signals that GEO is moving from “nice to have” to “strategic requirement” is how user behavior is shifting.
Search engines still dominate overall volume, however the direction of travel matters greater than absolutely the numbers.
Source: AI Search Optimization / GEO Geo Tracker: Your Brand’s AI Visibility
According to the info shown above, traditional serps still drive roughly 1.6 trillion visits, yet that traffic is declining yr over yr. At the identical time, chatbot-driven traffic sits closer to 50 billion visits, nevertheless it’s growing at an accelerated pace of greater than 80% yr over yr.
As long as most discovery happens through classic search, SEO performance can mask weaknesses in generative visibility. Brands still get traffic, still rank, still convert. But underneath that stability, attention is slowly migrating to AI-native interfaces. These interfaces that don’t reward rankings, don’t guarantee clicks, and don’t surface ten options without delay.
This is often while you need a GEO agency becomes essential.
You need a GEO agency when growth is occurring somewhere your dashboards don’t fully cover. In-house teams may notice traffic holding regular while brand mentions inside AI answers lag behind competitors. Or leadership may start asking why certain competitors keep appearing in AI-generated recommendations despite similar SEO performance.
The traffic split above also highlights one other inflection point: AI traffic compounds in another way. Chatbot sessions are conversational. Once a brand appears inside a solution, it may possibly influence multiple follow-up questions in the identical session. That dynamic doesn’t exist in traditional search, and it’s rarely captured by SEO tooling.
GEO agencies are built to observe those early signals, track generative search performance before it shows up in revenue reports, and strengthen visibility where momentum is clearly constructing.
In short, you don’t hire a GEO agency because search is dead. You hire one because the subsequent layer of discovery is growing faster than SEO alone can explain or control.
If AI Answers Ignore Your Brand Completely
One of the clearest warning signs within the generative era is easy: Your brand doesn’t appear in any respect. When AI answers ignore your brand, it’s often not a content quality issue.
In traditional search, absence was easy to diagnose. You checked rankings. You reviewed impressions. You adjusted pages. In generative search, the story is different. As we mentioned before, AI systems can deliver complete answers without ever touching your site, which implies SEO metrics alone not reveal whether you’re visible or invisible.
Brands often assume they’ve visibility because traffic is stable. But when teams start measuring AI search visibility, they often discover that competitors are being cited, really useful, or discussed. At that time, the difficulty is the AI answer inclusion rate.
As you’ll be able to predict, the inclusion rate measures how often a brand appears across a defined set of prompts inside its category. If that number is “consistently” near zero, it indicates that AI systems don’t yet see the brand as a reliable or essential source (no matter how well its pages perform in search results).
In those circumstances, a GEO agency asks:
- Are we included in any respect?
How often do AI systems select competitors as an alternative? - Are we invisible across informational, comparison, and advice prompts?
This can also be where the difference between GEO vs SEO metrics becomes clear.
If You Can’t Explain Why AI Picks Certain Sources
The inability to offer a clear explanation for why AI uses particular sources is a common red flag in generative search work. When selection feels arbitrary, it often means the brand remains to be evaluating AI behavior through an SEO lens that not suits.
This is where the pondering behind a generative engine optimization company differs from traditional SEO services. GEO work begins with reverse-engineering source selection: understanding what makes information reusable for giant language models.
As previously explained, AI systems favor content that’s structured, evidence-based, and explicit. And, in practice, this explains why AI often cites research reports, neutral explainers, or third-party articles over branded content. So, the difficulty is interpretability.
This is the purpose many brands begin exploring leading GEO agencies or a specialized AI GEO agency. Not because internal teams lack skill, but because explaining AI behavior requires a different operating model. GEO specialists spend less time optimizing individual pages and more time understanding why certain sources develop into default references across prompts.
Let’s remember a fact at that time: The Identifying and Scaling AI Use Cases report highlights that only 1% of organizations consider their AI efforts fully mature, largely because teams struggle to interpret and operationalize AI decision-making.
That gap shows up clearly in search; brands might even see AI answers change, competitors appear, or messaging shift, but without the frameworks to elucidate those outcomes, optimization becomes guesswork.
This is why top GEO SEO agencies deal with explainability as much as execution. They help brands understand:
- Why are certain sources chosen repeatedly?
- Why are others ignored despite strong SEO?
- Which signals actually influence AI reuse?
Until a team can confidently explain why AI picks one source over one other, visibility will proceed to feel unpredictable. GEO doesn’t eliminate uncertainty, nevertheless it replaces intuition with patterns and guesswork with mechanisms.
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