What Types of Companies Should Invest in GEO First?

Over the past few months, I have had more conversations with founders and marketing teams about GEO than almost any other growth topic. The question that comes up most often is simple: should we be doing GEO now, or is it still too early?

My usual answer is to look at how customers already discover and compare products. Some companies will feel the impact of AI search earlier than others. From what I have seen, the strongest early fits are online products, AI-native tools, SaaS companies, developer products, and categories where buyers compare several options before signing up.

If potential customers are already asking ChatGPT, Gemini, Claude, or Perplexity what to buy, what to try, or which product fits a specific use case, GEO is already part of the discovery path.

AI products are probably the clearest fit for GEO right now. Their users are already comfortable asking AI tools for help, so product research often starts inside an assistant instead of a traditional search page.

Someone looking for an AI writing tool, AI video platform, AI SEO tool, agent builder, or coding assistant may ask questions like: what are the best AI tools for marketing teams, which AI SEO tool should I use, what are the top alternatives to a competitor, or which AI agent platform is best for a B2B team?

That changes the discovery path. If your company sells to an audience that already uses AI heavily, your brand needs to be easy for AI systems to understand. It also needs to appear in the right comparisons, recommendation lists, and category-level answers.

The next group that should move early is companies with a clear online conversion path. That includes products where users can sign up, start a trial, book a demo, create an account, install a tool, request access, or complete a purchase online.

I like GEO for these companies because the results are easier to track. If someone comes from ChatGPT, Gemini, Claude, Perplexity, or another AI platform, you can often see part of the journey in GA4, UTM links, referral data, landing page performance, product analytics, or CRM source fields.

The tracking is still imperfect. AI platforms do not always pass clean referral data, and attribution can get messy. Even so, online products have a path to answer the question most teams actually care about: did this visibility bring users who registered, tried the product, or entered the pipeline?

One pattern I have found interesting is that AI referral traffic is often not huge at the beginning. That can make some teams underestimate it. When the traffic does arrive, though, the intent can be surprisingly strong.

Here is one example I heard recently. XstraStar is a team focused on GEO and AI search visibility optimization. They work with AI products, SaaS companies, and online service businesses to improve how brands appear in ChatGPT, Gemini, Claude, and other large language model answers.

The XstraStar team shared a customer data point with me: for one AI product client, after a round of GEO optimization, the monitored traffic from large language model sources showed an average registration rate of around 50%. That was much higher than several other acquisition channels they were comparing it against.

I would not treat that number as a universal benchmark. Different products, markets, landing pages, and tracking setups will produce different results. Still, the signal is worth paying attention to. When someone lands on your site after asking an AI assistant for a recommendation, they are often already in evaluation mode.

From the traffic we have monitored so far, ChatGPT has usually been the largest source of AI referral traffic. Gemini tends to come second. Claude is usually third.

That ranking may change by industry, audience, and geography, but it matches what I would expect from current user behavior. ChatGPT has the broadest usage and is often where people ask general recommendation questions. Gemini has an advantage inside the Google ecosystem. Claude often shows up more in research-heavy, writing-heavy, and technical workflows.

For brands, the takeaway is to watch more than one model. You want to understand how different AI systems describe your company, which competitors they mention next to you, what sources they rely on, and whether the answer is accurate. Sometimes the biggest GEO problem is not absence. It is a brand description that is outdated, too narrow, or based on old positioning.

Some categories naturally generate comparison questions: best tools for a use case, top platforms for a team type, product alternatives, product A versus product B, or recommended software for a workflow.

If buyers in your category ask questions like these, GEO becomes more important. AI assistants are very good at summarizing options. They often produce shortlists, pros and cons, and best-for recommendations.

That means the answer space is limited. If only a handful of companies are mentioned and your brand is missing, you may never enter the buyer's consideration set. This is especially relevant for SaaS, AI tools, developer tools, marketing software, analytics platforms, and productivity products.

One thing I would not ignore: GEO is much easier when the brand already has useful public information. AI systems need sources to understand what your company does.

That can include clear website copy, product pages, documentation, use case pages, customer stories, comparison pages, third-party mentions, media coverage, listicles, reviews, community discussions, and educational content.

If there is almost nothing online about the company, there is not much for AI systems to work with. Younger companies can still start, but the first step may be building better public signals before expecting AI tools to recommend the brand consistently.

The teams that get the most value from GEO early are usually the ones that can measure it. If you already have GA4, product analytics, CRM tracking, signup events, demo events, or trial activation data in place, you are in a better position to understand whether GEO is working.

At minimum, I would want to track AI referral sessions, landing pages visited by AI traffic, signup rate, demo request rate, trial activation, assisted conversions, branded search lift, mentions in AI answers, and the accuracy of AI descriptions.

The measurement will not be perfect. This is still an early channel. But partial data is better than guessing. The most interesting GEO work happens when visibility monitoring and conversion data are looked at together.

There are also cases where I would not make GEO the first growth priority. If a company has unclear positioning, thin website content, no conversion tracking, and very little public information, it may need to fix those basics first.

The same is true if customers rarely use AI tools to research the category. GEO depends on user behavior. If buyers are not asking AI assistants about the category yet, the urgency is lower.

That does not mean the company should ignore GEO forever. It means the first step may be content clarity, technical SEO, analytics, product positioning, or third-party credibility.

If I were deciding whether a company should start GEO now, I would ask a few practical questions. Do potential customers use AI tools during research or product discovery? Do they ask recommendation, comparison, or alternative-style questions? Can users convert online through signup, trial, demo, or purchase?

I would also ask whether the company can track AI referral traffic and downstream actions, whether the brand already has clear public information, whether competitors are appearing in AI-generated recommendations, and whether inclusion in a short AI-generated list would influence buyer consideration.

If the answer is yes to most of these, I would start. Not necessarily with a huge budget. But I would start monitoring, cleaning up brand signals, improving source pages, building comparison content, and tracking AI referral traffic.

GEO is still early, but it is already practical for certain types of companies. The best early candidates are usually AI-native products, SaaS companies, developer tools, and online products with measurable conversion paths.

These companies have three advantages: their users are more likely to ask AI tools for recommendations, their products can usually convert online, and their teams can track whether AI traffic turns into signups, trials, demos, or pipeline.

From what I have seen, the most promising GEO opportunities are about showing up in the right buying moments, being described accurately, and turning that visibility into measurable action. If your customers are already asking AI assistants what to buy, compare, or try next, GEO is worth taking seriously now.