AI search has flipped the buying journey in favour of startups

For the past two decades, the online buying journey has historically followed a familiar pattern. A customer would search on Google, trawl through the top results, browse several websites, quickly scan the different options, and gradually narrow down their options while manually assessing reviews, case studies, and other signals of credibility.

Buyers would flick simultaneously between multiple tabs, slowly gathering information from different sources, before formulating a view about which company, product, or service seemed most trustworthy or best suited to their needs. Seldom was this a quick process and depending on the complexity of the purchase, it could take hours, days, or maybe even weeks of research before someone felt confident enough to make a firm decision.

The entire process, however, is now changing rapidly. The emergence of AI search interfaces such as ChatGPT, Gemini, Claude, and Perplexity is compressing what used to be a multi-step research journey into a single interaction.

From digging for information to receiving tailored recommendations

In practical terms, the internet is shifting from a model based on making the same recommendation for all searchers to one actively making personalised recommendations based on what AI knows about the searcher. For buyers, this dramatically reduces the time and effort required to assess different providers.

Instead of comparing options one-by-one across multiple sites, users are increasingly receiving streamlined answers from an AI interface that shares relevant options, clearly articulating the differences between them. The comparison process that once required significant effort is now delivered in a matter of seconds within the AI interface itself. And it’s tailored to the individual.

But for businesses, the implications run far deeper. The mechanisms that determine which companies feature prominently are fundamentally different from those that governed traditional search.

Why traditional SEO favoured incumbents, yet AI search changes the dynamic

For years, online visibility was largely dictated by SEO authority. Businesses that had spent decades building backlinks, publishing content, and strengthening their domain authority typically dominated search results.

That system created a significant structural advantage for behemoths. Even if a startup had a better product or a more innovative solution, competing with companies that had accumulated years of digital authority was extremely difficult. It also often required substantial investment in SEO over long periods of time.

AI search changes that dynamic because it is designed to deliver a direct and specific answer rather than simply providing a list of links. When someone asks an AI assistant which platform they should use or which provider might solve a particular problem, the system typically returns a summarised set of recommendations rather than broadly directing the user to multiple websites. The result is a much shorter path from question to decision, and the attention becomes concentrated on whichever companies the AI surfaces first.

Personalisation creates a new battleground by compressing the buying journey

A further important distinction is that AI search is far more personalised than traditional search. When two people search on Google they tend to see pretty similar results.

However, large language models can generate responses based on context, previous interactions, and user behaviour. The same question can therefore produce markedly different results depending on who is asking, which equates to a very different competitive landscape. Instead of trying to be the most visible company on the internet, startups can now focus on becoming the best answer to a specific buyer’s question.

What makes this shift especially significant is how dramatically it shortens the path from research to decision. Essentially, the comparison work has been done for them as the AI system gathers information from across the internet and condenses it into a distilled answer. This allows the user to swiftly whittle their decision-making process down to one or two options, which fundamentally changes where influence truly sits within the buying process.

Why specialisation favours startups

When high-growth companies succeed, it’s typically because they focus on a specific problem or set of customers. Rather than trying to serve every possible audience, they create products designed for a defined use case or industry. This is where startups often have an edge.

The level of specialisation often aligns well with how AI systems assess relevance. When a model evaluates which company best answers a user’s question, specifics can be a clear and powerful signal. Large incumbents often position themselves broadly because they serve multiple markets and sit across different product categories. They might have a broad appeal, but they are rarely specific.

By contrast, startups frequently communicate much more clearly about the problem they solve and the audience they serve. When an AI model evaluates which company best answers one question, that clarity can make smaller companies the more relevant recommendation.

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How startups can anticipate what the press wants in Q1

Speed also plays a very important role in this new environment. The pace at which a large business moves is more akin to an oil tanker than the speedboat pace of a startup. When it comes to adjusting positioning or updating a website, these decisions can require multiple layers of internal approval, meaning it can take months before new narratives or positioning appear online.

Startups, however, operate very differently. They can move quickly, pivot their messaging, make changes to their website, and publish new content swiftly as their strategy evolves. Because AI systems constantly ingest new information from across the internet, companies that move faster can shape how those systems understand their brand and what problems they solve. Over time, those signals compound.

Structured content, credible online mentions, featuring in authoritative publications, and consistent messaging all help shape how AI systems interpret companies and their expertise. In many ways, reputation signals online are becoming increasingly important in determining which companies appear inside AI-generated answers.

A rare window of opportunity

Perhaps most importantly, the AI search landscape is still very nascent. Traditional SEO has had more than two decades to mature and evolve. In that time, entire industries have formed around understanding and optimising for search engines.

As for AI-driven discovery, it’s only beginning to take shape. The companies that start shaping their visibility now in a considered way can establish a presence before it becomes further saturated.

For early-stage businesses, this creates a rare window of opportunity. Competing with giants in traditional search has long been an uphill battle, however AI search offers something different – the chance to become the best answer for the right customer. It is no longer about simply being the biggest company in the market but rather being the right company for the right buyer.