AI Search Optimization vs SEO: What You Need to Know in 2025

If you’ve been staring at your Google Search Console and wondering why impressions are climbing but clicks are dropping, you’re not alone. Marketers everywhere are having the same uncomfortable conversation in pipeline review meetings:

“Our traffic is down… but wait, revenue isn’t down. What’s happening?”

It’s a strange moment in marketing history. For decades, the rules of search were straightforward: learn SEO, target the right keywords, publish content that can rank, build backlinks from authoritative sites, and make sure Google can crawl and understand your pages. The model was straightforward: more traffic meant more conversions, which meant more revenue. While the tactics have evolved over the years, the underlying logic of traditional SEO has been consistent.

Today, that logic is breaking down.

It is not because demand has evaporated or buyers have stopped looking for answers. Rather, it is because the way those answers are being delivered is shifting - the very heart of the SEO vs GEO and SEO vs AEO debate.

If you open your Google Search Console, you might notice something strange: impressions are going up, but clicks are going down. In Google Analytics or HubSpot, you may find that non-brand traffic, especially at the top of the funnel is shrinking, while brand searches remain steady. Yet, when you check the pipeline, you see that the bottom-of-funnel conversions that actually lead to revenue have not fallen at the same rate. In some cases, conversion rates are even increasing because the drop in casual, early-stage traffic leaves you with a higher concentration of serious buyers.

Before we dive in, here's a quick look at the difference between the two:

When people talk about AI search optimization vs SEO, one of the first mistakes they make is assuming the recent shifts in their numbers are purely the result of their own marketing choices. The truth is, there are large, organic trends at play: changes in how people search, how platforms surface information, and how the “click” itself is being replaced by the answer. These shifts are happening whether you’ve changed your SEO strategy or not.

Here’s the pattern we’re seeing across B2B: when you open Google Search Console, impressions might still look healthy - they might even be going up. But clicks? That’s where the drop starts to show. And the more you zoom in, the clearer the pattern becomes: the click-through rate is being compressed, not because your meta titles suddenly got worse, but because AI layers are increasingly wedged between the searcher and your site.

Google’s AI Overviews and now AI Mode are big drivers here. For broad, informational queries, AI-generated summaries are giving people the answer before they ever need to visit your site. And since these summaries pull from multiple sources at once, the traditional incentive to click through to “read more” is reduced.

Where the losses hit hardest is at the top of the funnel: early-stage, exploratory searches like “how to manage customer consent” or “best security compliance frameworks.” This is the content marketing bread-and-butter of the last decade. But in this new environment, a searcher can skim an AI-generated paragraph and feel like they have enough context to move on without clicking anything.

The bottom of the funnel, where people are looking for specific vendors, making comparisons, or searching for features, is holding up better. These queries are more transactional by nature, so people are still inclined to click through, dig into details, and vet their options before making a decision.

This is why many companies are experiencing something that feels counterintuitive: total site traffic is shrinking, but their pipeline hasn’t collapsed. In some cases, conversion rates are even rising because the traffic that’s dropping away tends to be the lowest-value, least-ready-to-buy visitors. The folks still making it to your site are, on average, much closer to a purchase decision.

That’s the short-term silver lining. But it comes with a long-term caution. Even if pipeline impact hasn’t cratered yet, the overall potential of traditional SEO is being eroded. Buyers are no longer entering the journey only through Google’s ten blue links. They’re getting answers (and forming impressions) through a whole range of AI-powered environments from Google’s AI Mode to ChatGPT, Perplexity, Gemini, and even domain-specific large language models built into software they already use.

So, while the drop in top-of-funnel traffic might not feel like a crisis yet, it’s the canary in the coal mine. The entry points are multiplying, the decision-making is happening earlier (and off-site), and the brands that adapt to this multi-platform, AI-shaped search reality are the ones that will still be visible when those entry points shift again.

The second big reality check in the AI search optimization vs SEO conversation is just how fast this environment is evolving.

If you’ve been in SEO for a while, you’re used to a certain rhythm. Google rolls out core updates a few times a year, there’s a week or two of panic on SEO Twitter, and then things settle down. The algorithm changes, but the game stays the same: Google still wants to surface the most relevant, highest-quality content while quietly weeding out spam and low-value results. If you were doing things right, you might see a small bump or dip, but the fundamentals of search didn’t suddenly shift out from under you.

That stability is gone.

The launch of AI Overviews was the first true seismic change in Google search in years.It was a fundamental rewrite of the user experience. For the first time, Google wasn’t just pointing you toward information but delivering the answer itself, stitched together from multiple sources, right there on the results page.

Before the industry had even fully processed that change, AI Mode arrived.

If you’ve seen it in action, you know it’s not tucked away in some experimental corner. The UI placement makes it almost irresistible to click. For many users, AI Mode now is the search experience. And because AI-generated answers give the illusion of completeness, they naturally siphon clicks away from traditional organic listings. Even a well-ranked site now has to compete with a dynamic, personalized answer box sitting above it.

And that’s just the pace on the Google side which, historically, has been considered slow compared to the rest of the AI space. In the broader generative search ecosystem, the change cycle moves at a breakneck pace.

Still the dominant LLM search platform by market share recently sunset all of its existing models and shifted entirely to GPT-5 in one move. It happened overnight. On launch day, GPT-5 promptly broke, sending the team scrambling to patch and re-stabilize the product. This kind of “release big, fix later” cycle is jarring if you’re coming from the more deliberate cadence of SEO updates.

Continues to push rapid, substantial updates often introducing entirely new capabilities rather than just tuning performance.

Experimenting aggressively, not just on product features but on distribution tactics. One of their boldest moves? Offering completely free access to every user in India with an active address subscription, effectively seeding the market at a national level.

And it’s not just companies making moves. There are government-scale initiatives reshaping the landscape. Entire sectors are being granted free or heavily subsidized access to specific AI platforms. In the U.S., for example, ChatGPT is reportedly providing full access to the federal government. This means its not just restricted to individual adoption anymore but there's institutional, even geopolitical shifts in how AI tools are deployed.

Compared to the old “search engine wars” : Google vs. Bing vs. Yahoo this is an entirely different competitive dynamic. Those battles were fought over years. What we’re seeing now is change measured in weeks or months. And it’s being fueled by three things the 2000s-era search wars never had: massive late-stage funding rounds, mature digital user behavior, and a global audience already trained to expect instant, conversational search experiences.

For marketers, that means the shelf life of any competitive advantage is shrinking. An approach that works today could be outdated in six months not because your competitors got smarter, but because the platforms themselves reshuffled the deck. And unlike in traditional SEO, where you could often “wait out” a Google update, here the only way to keep pace is to treat adaptation as part of the job.

Before you can really dig into traditional SEO vs AI search optimization, it’s worth slowing down and looking closely at what SEO actually involves. Strip away the jargon, the endless “hacks,” and the sea of tools, and you’ll find that SEO still rests on three main pillars.

This is the most visible part of SEO and the one most people think of first. At its core, it’s a betting game. You identify a keyword you believe your audience is searching for, then you publish a page crafted to rank for it. If you’re right i.e if your page matches the search intent, outperforms competitors, and earns Google’s trust, you win the bet in the form of traffic. The challenge is that every keyword is a different kind of bet. Low-competition, niche keywords might pay off quickly, while highly competitive phrases (“top HRM software” or “best cloud security tool”) might take years of sustained effort before you see a top-ranking position.

In Google’s eyes, every quality backlink is a vote of confidence. The more votes you have, and the more reputable the sites casting them, the more authority your domain carries. This matters because authority determines how big a bet you can take with your keyword targets. If you’ve just launched a new HR tech company, you could write the most comprehensive, insightful guide on “top HRM solutions” and still not rank for it for years, simply because the incumbents have a deep moat of backlinks. Building that authority is slow, sometimes tedious work, but it’s the foundation that lets you compete for keywords that really move the needle.

This is the behind-the-scenes work that makes your site understandable to search engines. At Spear Growth we always say: good technical SEO does nothing, bad technical SEO holds you back. In other words, you can’t “tech SEO” your way to market dominance, but sloppy technical SEO can definitely choke your growth. It covers everything from site speed, crawlability, and mobile optimization to structured data, canonical tags, and XML sitemaps.

In the traditional SEO model, everything revolves around your own website. Even backlinking which happens on other domains is ultimately about channeling authority back to your domain. The goal is always to make your site stronger, more visible, and more likely to win clicks from Google’s results. Every action, whether it’s writing a blog post, pitching a guest article, or cleaning up broken links, is in service of drawing more people directly onto your site.

This is where the real philosophical gap between SEO and AI search optimization starts to emerge because in AISO, your visibility isn’t confined to the walls of your own site. In fact, the most valuable mentions and recommendations often happen far beyond your domain, in spaces you don’t directly control. But to really understand that shift, you first need to see how traditional SEO has always treated the website as the anchor point: the hub that every tactic, link, and piece of content is ultimately designed to strengthen.

In the traditional SEO playbook, content strategy has always been anchored in one central concept: keywords. You start by identifying search terms that have measurable search volume and that you have a reasonable shot at ranking for. This involves competitive analysis, keyword difficulty scoring, and mapping search intent. Once you’ve picked your targets, you build pages or blog posts tailored to those keywords, making sure the copy, structure, and meta elements match what Google wants to see for that query.

But AI Search Optimization (AISO) blows up that starting point. The question isn’t “Which keyword should I target?” anymore because in the world of LLMs, keywords aren’t the organizing principle. Instead, you have to ask:

“What is a piece of information I can publish that an AI system cannot find anywhere else?”

That’s a radically different mindset. AI systems like ChatGPT, Claude, and Perplexity don’t just look for exact keyword matches; they look for facts, patterns, and context that can be woven into answers. If the AI can already find ten similar pages saying the same thing, your contribution doesn’t matter. But if you publish something unique: proprietary data, fresh case studies, original frameworks, or hard-to-get process explanations you give the model something new to latch onto, cite, and use to shape its recommendations.

This is why AISO content teams need to think less like keyword-focused copywriters and more like investigative journalists. They need to dig for original material:

• Interview subject matter experts within the company
• Extract insights that aren’t published anywhere else
• Surface proprietary data in ways that can be cited and referenced
• Capture detailed process knowledge that competitors aren’t sharing

That kind of work can’t be done by simply Googling and rewriting. It requires real access, real research, and the ability to turn internal expertise into public-facing assets.

Think about the difference between two hypothetical blog posts. The first is “Top 10 Compliance Tools for 2025.” If it’s built the SEO way, it probably just rephrases what’s already in the top five Google results swapping in synonyms, adding a few extra bullet points, maybe embedding a stock graphic. It might rank for a while, but in an AI-driven world, it’s just another data point in a sea of sameness.

Now compare that to “The Seven Most Common Audit Failures in AML Compliance and How to Avoid Them,” built from anonymized real-world examples pulled from your company’s own experience. Suddenly you’re giving the AI model something concrete and rare: scenarios, outcomes, and corrective actions that aren’t in generic compliance blogs. When someone asks an LLM about AML audit pitfalls, it can draw directly from your material not because you ranked for a keyword, but because you’re the source for specific, trustworthy information.

This is the subtle but critical shift in AI SEO vs traditional SEO. In SEO, keywords guide the strategy and originality is optional. In AISO, originality is the strategy, and keywords are often irrelevant. The value isn’t in how well you repackage the known, but in how effectively you become the source of the unknown.

And while it’s possible to create content that works for both, it requires you to consciously separate the two planning processes. One is built around search volume and ranking potential; the other is built around novelty, credibility, and being cited in the generative layer of search.

In traditional SEO, backlinking is one of the most reliable levers you can pull to improve rankings. If you land a backlink from a high-authority domain whether it’s an industry magazine, a popular blog, or even a major news site it’s considered a win almost regardless of what’s actually being said about you.

That’s why for decades, SEO link-building strategies have focused on quantity and authority. PR coverage? Great, ask for a link. Guest posting? Perfect, link to your own resource. Broken link outreach? Swap it with your content. The nuance of how you’re framed rarely factored into the backlink’s value.

AI Search Optimization (AISO) changes that calculus entirely. In the LLM-driven world, it’s not just about whether you’re linked, it’s about what’s written about you in that context. Large Language Models like ChatGPT, Claude, and Perplexity “read” the full text surrounding your brand mentions. They parse the descriptors, the positioning, and the factual accuracy before deciding whether you’re a fit to recommend for a given prompt.

Here’s the kicker: the difference between being mentioned and being chosen often comes down to your position and description in third-party content.

If you’re buried in tenth place on a “Top Tools” list with a vague, outdated blurb something like “Company X offers various solutions for compliance needs” your chances of surfacing in an AI-generated shortlist are slim. But if you’re in the top three with a crisp, benefit-driven description that matches the user’s query “Company X helps mid-market fintechs achieve SOC 2 compliance in half the usual time, with automated evidence collection” you’re giving the AI both a reason and the confidence to pick you.

That’s why backlinking in AISO starts to look less like the old SEO “authority chase” and more like ongoing brand management. It’s not enough to be linked. You have to be represented in a way that aligns with the prompts your ICP is actually using.

In practice, that can mean:

• Reviewing and updating your profiles on high-authority review sites like G2, Capterra, or Clutch, making sure the descriptions, categories, and customer quotes reflect your current positioning.
• Reaching out to authors of listicles and industry roundups to request edits that make your entry more specific, accurate, and relevant to your best-fit audience.
• Seeding high-value talking points: your strongest differentiators, key integrations, compliance credentials, or ROI stats into trusted third-party content that LLMs are already scraping and citing.

In SEO, an unoptimized or outdated mention was still worth something because the link itself passed authority. In AISO, that same unoptimized mention could be a missed opportunity or worse, an active blocker to being recommended. If an AI finds a weak, generic description of you in its trusted sources, it may conclude that you’re not the right fit for high-intent prompts, even if you actually are.

That’s why in AI SEO vs traditional SEO, the backlinking pillar has evolved from a largely mechanical exercise into a much more editorial, PR-driven discipline. When AI is pulling its facts from third-party sites, you need those sites to describe you the way you want to be seen. If you haven’t read it yet, our piece PR for AI Search Visibility: Why It Matters & How to Do It Right walks through exactly how to make that happen.

In traditional SEO, “technical” work is often the unglamorous but essential foundation that allows everything else to function. It’s a long, often intimidating checklist of tasks: implement schema markup so Google understands your content’s structure; create and maintain XML sitemaps so it knows where to find everything; use canonical tags to prevent duplicate content issues; ensure mobile responsiveness for a seamless user experience; optimize page speed to reduce bounce rates; and the list goes on.

Large Language Models (LLMs), however, operate in a very different way. They aren’t just matching queries to pages and ranking them; they’re synthesizing answers from multiple sources. And they can often interpret content without the rigid, structured markup that Google demands. For example, a missing schema.org tag won’t necessarily stop ChatGPT from understanding that your page contains a list of SOC 2 compliance tools.

But here’s the catch: while LLMs are more forgiving about formatting, they’re far less forgiving when it comes to accessibility of the content itself.

If key facts about your product, service, or expertise are locked behind JavaScript rendering maybe your site uses dynamic loading for critical information like pricing tables, feature lists, or compliance certifications there’s a very real chance those details will never make it into the model’s “knowledge.” Unlike Google, which can sometimes execute JavaScript to see that content, many AI models simply skip it because rendering is computationally expensive and often outside their crawling patterns.

That’s one of the core differences in generative engine optimization vs SEO: for AI visibility, it’s not just about making your content well-structured for a crawler, it’s about ensuring the most important, reputation-defining facts about you exist in plain, easily indexable HTML somewhere on the web. That “somewhere” doesn’t even have to be your own site. It could be a partner’s site, a well-trafficked industry directory, a review platform like G2, or a press release hosted on a major newswire.

If that means publishing your integration list in HTML on a partner’s marketplace page, or ensuring your compliance badges appear in text (not just in image format) on your press releases, that’s worth doing because if the LLMs can’t “see” it, they can’t use it to recommend you.

In practical terms, this means:

• Audit your site for JavaScript-heavy sections and convert critical details into static HTML wherever possible.
• Make sure your high-value talking points: integrations, certifications, pricing models, ROI metrics exist on multiple authoritative, crawlable sources.
• Avoid hiding essential information behind gated PDFs or logged-in customer portals if you want it to influence AI recommendations.

If you’ve ever worked in SEO, you already know it’s a team sport, but it’s a weird kind of team sport. The SEO team might own the strategy, but they don’t directly control all the moving parts. They need the content team to create articles and landing pages, the web development team to fix technical issues or deploy new templates, and sometimes a PR or outreach specialist to secure backlinks. Product marketing might step in occasionally if a core product page needs an update, but for the most part, the SEO touchpoints across teams are episodic.

That in itself makes SEO unusual compared to other marketing channels. If you want to run more paid ads, your entire team: copywriters, designers, media buyers- all sit in the same pod and share KPIs. In SEO, you often have to convince people outside your direct team to take actions that help your goals but don’t obviously affect theirs. Infact a lot of SEO success comes down to building goodwill with other departments: buying the web dev team samosas or hosting pizza Fridays so they’ll prioritize your ticket.

Now, AI Search Optimization (AISO) inherits that same cross-team dependency but takes it several steps further because the scope of influence moves well beyond the website.

In AISO, they’re not just working on positioning for ads or sales decks, they’re defining the narratives that need to appear inside AI-generated answers. When a buyer asks an LLM, “What’s the best compliance platform for fintech startups?” the product marketing team’s positioning choices determine whether the answer describes you as “best for enterprise security” or “affordable for small businesses.” That framing is critical, and it’s driven by PMM-owned messaging.

In SEO, PR might have been useful for generating backlinks through news announcements, but in AISO, they’re deliberately placing key facts and differentiators into authoritative third-party sources: industry publications, review sites, analyst reports etc that AI tools already trust and scrape. It’s no longer enough to announce “Company X raises $20M Series B.” Now the release also needs to communicate your unique capabilities, compliance credentials, or customer proof points, because that’s the copy the LLM will ingest.

On platforms like G2, Capterra, or Clutch, it’s not just your star rating that matters. Your category selection, the wording of your product descriptions, and even the phrasing in customer reviews can shape how an AI perceives and recommends you. If those fields are outdated, incomplete, or inconsistent, the LLM might fill in the blanks with competitor-friendly information instead.

In AISO they are tasked with plugging the gaps AI models might otherwise fill with someone else’s narrative. That means producing the kind of original, fact-rich material the model can cite with confidence, not just keyword-optimized blog posts for Google, but deep resources that answer the exact high-intent prompts your ICP might type into ChatGPT or Perplexity.

This is why AISO can’t live in isolation within one team. It’s inherently cross-functional. You need product marketing shaping the story, PR distributing it to the right channels, review managers keeping the official records straight, and content teams creating net-new sources of truth. You might still need technical support from web developers, but unlike SEO, your success in AISO hinges on how well your company can coordinate off-site narratives as much as on-site assets.

One of the most compelling differences between AI Search Optimization and traditional SEO is how quickly you can start to see results. In a well-structured, bottom-of-funnel SEO program, you might begin generating a small trickle of MQLs within four to five weeks, perhaps two or three qualified leads a month to start. But even in those faster-than-average scenarios, major gains in rankings and traffic take time to mature. It’s not unusual for full results to take a quarter or more, especially if you’re targeting competitive keywords.

AISO, at least in its current stage of adoption, can be much more immediate. Because AI-generated answers update dynamically based on the sources they pull from, even a small change to the right citation can influence results in days, sometimes even within a week. For eg, after just one week of targeted work: updating content, fixing how the company was represented in key places, and improving certain citations our team closed $100,000 in revenue within a month from AI-sourced leads.

However, this advantage isn’t static.The platforms are evolving. Early on, tools like ChatGPT seemed to refresh their knowledge of live web content almost instantly: you could make a change to a page and see it reflected in generated answers the same day. But more recently, there are clear signs of cache mechanisms coming into play.

This means some models are relying on stored snapshots of content rather than fetching live updates every time, creating a lag between the moment you publish a change and the moment it appears in AI-generated answers.

This lag is still much shorter than SEO’s typical timeline, but it’s lengthening. What might have been a same-day result a few months ago could now take several days or even a couple of weeks to propagate. The takeaway? Early adoption matters. Right now, targeted, high-value actions in AISO: correcting a misrepresentation, inserting a new differentiator into a cited source, or publishing a fact-rich page that fills a knowledge gap can yield outsized visibility gains with relatively little effort. As more brands catch on and the AI platforms mature, that window for “quick wins” will narrow.

The smart play is to start now, while the feedback loop is still fast and responsive, and use that early momentum to build a stronger long-term position before the environment stabilizes and competition for those same AI-visible spots intensifies.

If you’ve been in SEO for a while, you already know the drill. The SEO toolkit is a well-stocked garage: Ahrefs, Semrush, Screaming Frog, Moz, and dozens of other shiny gadgets. Each one has its specialty: tracking keyword rankings, analyzing backlinks, running technical audits, finding content gaps. They’re built for a world where success is measured in keyword positions and page rankings. And they’re good at it. Decades of refinement have made them powerful, predictable, and easy to slot into a workflow.

AISO, though? Totally different ballgame. The tooling is younger, messier, and if we’re being honest - still figuring itself out. AI search tracking isn’t like Google rank tracking where you get neat charts and consistent positions. There’s no “Google Search Console for ChatGPT.” Instead, prompts change constantly, answers shift from one day to the next, and AI models even tailor responses to the user’s profile and chat history. That means your ideal customer might see something completely different from what you see in your own test.

As we broke broke this down in our Best AI Rank Tracking Tools for 2025 , most AISO tools fall into three big buckets:

These are the scrappy, quick-build tools that let you paste in prompts you think your buyers might ask. The tool runs those prompts through ChatGPT’s API, captures the answer, and highlights if your brand is mentioned. It’s basically like running a Google search for yourself except you’re guessing the exact phrasing your buyers might use, and there’s no guarantee they’re asking those questions at all. They’re cheap, easy, and kind of fun to play with… but also unreliable. One day you’re “visible” for 24% of your prompts, the next day you’re not. Still, they’re a decent way to scratch that curiosity itch if you just want to see if you pop up at all.

Think big SEO platforms like Ahrefs’ Brand Radar that have jumped into AI search tracking. Instead of asking you for prompts, they claim to have built a massive library of queries in your category and can tell you if you show up. On paper, this feels more automated and data-rich. The catch? Their coverage might be great for big, generic terms but if you’re in a niche B2B market, there’s a decent chance your real buying queries aren’t even in their index. And because you can’t see why you showed up, it’s easy to get stuck at “cool chart, now what?”

This is where Chosenly comes in. These tools don’t stop at “Did you appear in this AI answer?” They dig into why you showed up, what you need to fix, which content gaps to fill, and how to make sure you’re the top choice instead of your competitor. They account for real-world complexity like prompt variations, mid-conversation follow-ups, and model personalization. The result is playbook for how to win more recommendations in AI search.

Right now, the majority of AISO tools on the market are still stuck in those first two categories: they either make you guess prompts or give you a static index of generic ones.

The third category: actionable, context-aware platforms that integrate into your GTM workflow is still rare, which is exactly why early adopters of tools like Chosenly are gaining a disproportionate share of AI search visibility

If you want to see how each type stacks up, check out our Top 10 AI Search Optimisation Tools: In-depth Comparison. It’s the closest thing you’ll find right now to a buyer’s guide for this new wave of tools.

If there’s one takeaway from the AI Search Optimization vs SEO conversation in 2025, it’s that we’re no longer talking about an incremental evolution of search. This is a structural shift in how buyers discover, evaluate, and shortlist solutions. Traditional SEO still matters for now. Your site still needs to be technically sound, your core pages still need to rank, and your brand still benefits from visibility in Google’s organic results. But it’s no longer the only, or even the primary, gateway into the buying journey.

The new reality is that AI-driven platforms are answering questions before clicks happen. They’re forming opinions about your brand based on how you’re described across the web not just on your own site. That means your visibility now depends on an ecosystem of touchpoints: review sites, analyst reports, partner listings, industry blogs, press releases, and anywhere else an AI might go to piece together its recommendations.

And the pace of change is relentless. The same tactics that give you a lift this quarter might be outdated by next. That’s why AISO isn’t just a channel to “test”, it’s a muscle your team needs to build, maintain, and refine continuously.

For brands that act early, the upside is enormous. When you understand how these systems pull information, you can place your positioning in the right places, in the right format, and watch your AI visibility compound. The brands that wait? They’ll wake up in a year wondering why their name never makes it into the shortlists buyers are now building without ever touching a traditional search engine.

The good news: you don’t have to guess. Platforms like Chosenly are purpose-built to track how you’re showing up in AI search, pinpoint where you’re missing, and give you a clear playbook for winning those high-intent prompts. If SEO was about owning the SERP, AISO is about owning the answer and in 2025, that’s where the real competitive edge will be decided.

AI Search Optimization is the practice of shaping your brand’s visibility and accuracy in AI-powered search environments such as ChatGPT, Google’s AI Overviews and AI Mode, Gemini, Perplexity, and Claude. Unlike traditional SEO, which focuses on optimizing your own website for rankings, AISO focuses on ensuring that AI systems can find, trust, and recommend you across the entire web. This includes your site, but also extends to PR articles, review platforms, partner websites, and any other source that an AI might reference when answering a query relevant to your business.

The core difference between AI SEO and traditional SEO lies in the scope and the signals. Traditional SEO is keyword-driven and site-centric; it is about getting your pages to rank for specific search terms in Google and other search engines. AI SEO, or AI Search Optimization, is information-driven and ecosystem-centric. It is about creating and distributing unique, accurate, and strategically positioned information so that AI systems present you favorably in their generated answers. While both disciplines care about quality and relevance, the execution is very different.

Generative Engine Optimization (GEO) is a subset of AISO. GEO focuses specifically on optimizing for AI-generated answers. For example, making sure you appear in ChatGPT’s responses to relevant questions. AISO covers that, but also includes optimization for AI Overviews in Google, collaboration with PR teams to seed narratives in trusted sources, managing review site profiles, and influencing the full spectrum of AI-driven search behaviors. In other words, GEO is one piece of the broader AISO puzzle.

AISO does not replace SEO. They are complementary strategies. SEO remains critical for capturing demand in traditional search, especially since Google is still the dominant starting point for many queries. However, as AI-driven search platforms gain adoption, AISO becomes essential for ensuring you are present and well-positioned in those environments as well. Ignoring AISO would be like ignoring mobile optimization when mobile search began to rise, it would leave a growing segment of your audience untouched.

The speed of results depends on the nature of the change and the platforms involved. Simple fixes, such as correcting inaccurate descriptions on a high-authority site or adding unique information to a page that AI already references, can lead to changes in AI-generated answers within a week. More complex initiatives, like building a sustained presence in multiple AI systems across a range of topics, may take several months to fully bear fruit. It is worth noting that the current environment is more responsive than it may be in the future, as platforms are moving toward more caching and delayed updates.