Something fundamental has shifted in how people decide what to buy.

It used to be straightforward process where a person had a need, they searched for it, they browsed a few options, and they made a decision. Brands that showed up in the right place with the right message won the sale. That model worked well for over two decades.

Today, that same person is just as likely to open ChatGPT or Perplexity, describe their situation in plain language, and receive a synthesised answer that names specific products, compares features, flags concerns, and tells them where to buy, before they ever visit a brand’s website.

Recent data suggests that around 85% of consumers use AI tools weekly when researching products or services. That level of adoption means AI now touches every stage of the purchase process, from the moment a buyer first recognises a need to the final decision of who to buy from.

For brands that sell directly to customers, understanding where AI enters the journey, and how it shapes the decisions buyers make along the way, is quickly becoming one of the most important strategic questions in digital marketing. The brands that get this right will be the ones AI recommends. The ones that ignore it will find themselves increasingly invisible in conversations they never knew were happening.

What is the Buyer’s Journey?

The buyer’s journey describes the process a person goes through from recognising a need to selecting a solution. It has traditionally been divided into three stages: awareness, consideration, and decision. While that structure still holds, the behaviour within each stage has changed considerably.

Buyers no longer move in a straight line. They loop between stages, revisit questions, and rely on external tools to refine their understanding. AI tools have accelerated this behaviour by reducing the effort needed to gather and process information, compressing what used to take hours of research into a single conversation.

Here is a simplified view of the journey today:

Stage	Buyer Goal	Key Factors	Typical Behaviour
Awareness	Recognise a problem or need	Educational content, problem framing, discoverability	Searching broadly, asking questions, reading articles
Consideration	Evaluate possible solutions	Credibility, depth of information, comparison ease, social proof	Researching brands, reading reviews, comparing options
Decision	Choose a vendor or product	Trust validation, risk reduction, simplicity of action	Reading final reviews, checking policies, completing purchase

What matters for brands at each stage is different. In Awareness, you need to be discoverable and relevant. In Consideration, you need to be credible and clear. In Decision, you need to eliminate doubt and make it easy to act. That has always been true. What AI has changed is how buyers move through each stage, and the degree to which an external system is now shaping their path.

The Evolution of the Buyer Journey

The buyer journey has evolved in two significant waves before the AI shift, we are experiencing now, moving from a traditional model through a search-driven era and into the AI-assisted present.

In the traditional era, buyers relied on word of mouth, print advertising, in-store experiences, and salespeople. Discovery was largely passive. Brands pushed messages out and buyers received them. The relationship was one-directional, and brands with the biggest reach tended to win.

The rise of search engines disrupted that entirely. Buyers became active researchers. Google gave them the ability to compare options on their own terms, at their own pace, without talking to a salesperson. Brands responded by investing in SEO, content marketing, and paid search. The buyer was now in charge of the discovery process, though they still had to do the work of synthesising all the information they found themselves.

The AI-assisted era changes that final piece. Buyers still research, but they are increasingly delegating the synthesis work to AI. Rather than reading ten articles and five review pages to form an opinion, they ask an AI tool to do that work for them. The AI reads, compares, summarises, and presents a recommendation, often with citations. The buyer’s effort drops and the AI’s influence over the outcome rises significantly.

For brands, this means the content you publish is no longer just for the humans who visit your website. It is being read, interpreted, and summarised by AI systems that are making judgments about whether your brand is worth mentioning, often before a potential customer ever searches for you directly.

Stage 1: Getting Found Before the Search Begins

Awareness is the moment a potential customer first recognises they have a need. In the past, brands could reliably reach people at that moment through search rankings, paid ads, and well-placed content. Today, a growing number of buyers are having that first moment of recognition inside an AI conversation rather than on a search results page. For brands, being discoverable now means something broader than ranking well. It means being present and credible in the places where AI goes looking for answers.

Shifts in Language, Queries, and Intent Interpretation

The way buyers express a need has changed in a way that most brands have not fully accounted for yet.

In the search era, people compressed their needs into short keyword strings. “Running shoes women.” “Best project management tool.” “Organic dog food.” That compression was a necessity of how search engines worked, and brands built entire content strategies around reverse-engineering those phrases.

AI tools removed that constraint entirely. Buyers can now describe their situation in plain, conversational language. “I have a golden retriever with a sensitive stomach, and I want to switch him to something cleaner without breaking the bank.” That is a context-rich description of a real problem, and AI is built to interpret exactly that kind of input.

This shift has a direct implication for content. Search engines matched words while AI tools map meaning. A product page optimised around “sensitive stomach dog food” will still rank in Google, but it may never surface in an AI response if it does not speak to the actual situation a buyer is describing. Content written around customer problems, in the language customers use, is far more likely to be understood and surfaced by AI than content written purely around keyword targets.

Intent interpretation adds another dimension to this. When someone types a keyword into Google, the intent behind it can be ambiguous. When someone describes a situation to an AI tool, the intent is embedded in the description itself. AI uses that context to deliver a much more precise response, which means brands whose content addresses specific situations and specific customer profiles have a meaningful advantage over those publishing broad, generic category content.

New Discovery Channels

The places where awareness begins have expanded significantly, and not all of them resemble traditional search.

Discovery Channel	Content Type	User Intent
Google Search	Articles, landing pages, listicles	Browse and find options
AI Chat (ChatGPT, Gemini)	Synthesised answers, recommendations	Explain, advise, shortcut research
AI Shopping Tools (Rufus, Perplexity)	Curated picks, comparisons	Find the right product for my situation
Social and Video (TikTok, YouTube)	Short reviews, demos, unboxings	Show me what it is actually like
Voice and Assistants	Conversational answers	Quick answers, hands-free

AI-powered discovery channels are answer-first. A buyer receives a response that already reflects a judgment about what is relevant and credible, rather than browsing a list of results and deciding what to click. If your brand is absent from that response, you are absent from that buyer’s consideration set regardless of where you rank on Google.

Credibility signals matter at this stage in a way they did not before. AI tools draw from sources they assess as authoritative. Consistent brand information across platforms, genuine third-party coverage, well-structured product and content pages, and a clear demonstration of expertise all contribute to whether AI considers your brand worth mentioning at this first stage of the journey.

The ChatGPT Example: Real AI-Led Discovery in Practice

To understand what AI-led awareness looks like in practice, it helps to walk through a real scenario.

A buyer opens ChatGPT and types: “I am trying to eat less processed food, but I work long hours and do not have much time to cook. Where do I even start?” They are describing a lifestyle problem and asking for orientation, with no specific product in mind.

ChatGPT delivers a structured response that might cover meal planning principles, ingredient categories to prioritise, and tools that help with preparation time. Depending on the query, it may reference specific product types or even brand names that fit the context. That response is built from the content ChatGPT has ingested and, with browsing enabled, from live web sources it assesses as credible and relevant.

A food brand that publishes genuinely helpful content around busy, health-conscious lifestyles, written clearly and structured well, has a real chance of being part of that response. A brand whose website only contains product listings and promotional copy is unlikely to feature.

This is how AI-led awareness works in practice. The buyer did not search for a specific brand or product category. The AI surfaced relevant options because the content behind them spoke directly to the situation being described and came from a source credible enough to cite. Brands that build their content strategy around this logic are the ones that will show up in the moments that matter most.

Stage 2: How Buyers Evaluate in the AI Era

Once a buyer knows they have a need, the consideration stage is where they figure out who can meet it. This used to mean visiting multiple websites, reading review pages, watching comparison videos, and gradually forming an opinion over time. It was effortful and cognitively demanding. AI has changed that process significantly, and for brands, the implications run just as deep as the changes reshaping the awareness stage.

The New Research Behaviour

The consideration stage used to demand real effort from buyers. They had to gather information from multiple sources, hold it all in their heads, and synthesise it themselves. Many ended up making decisions based on incomplete research simply because the process became too time-consuming to continue.

AI tools have absorbed much of that effort. A buyer can now describe what they are looking for in a single message and receive a structured, comparative response within seconds. They are getting context, reasoning, and a recommendation tailored to the specific details they provided, rather than a list of links to sort through manually.

This behavioural shift has two important consequences for brands. The first is that buyers are arriving at brand touchpoints later in the consideration process and already more informed. By the time someone visits your website after an AI-assisted research session, they may already have a strong opinion about whether your product fits their needs. The second is that the information AI uses to form those early opinions come entirely from your existing digital presence. Your product pages, your reviews, your published content, and your third-party mentions are the raw material AI works with. If that material is thin, inconsistent, or poorly structured, the picture AI builds of your brand will reflect that.

How AI Evaluates Your Brand

When an AI tool responds to a consideration-stage query, it is making judgments about which sources are credible, which content is relevant to the specific situation described, and which brands are worth including in the response. Understanding what drives those judgments is essential for any brand that wants to show up consistently during this phase.

Clarity matters enormously. AI tools synthesise and compare, which means they need your content to communicate key differentiators clearly and directly. Vague positioning and marketing language that avoids specifics are difficult for AI to work with. Brands that articulate precisely what their product does, who it is for, and how it differs from alternatives give AI more useful material to draw from and are more likely to appear in the responses buyers receive.

Content structure plays a significant role alongside clarity. Well-organised pages with clear headings, specific facts, and concise explanations are far easier for AI to extract from than dense, unstructured prose. This is not about dumbing content down. It is about organising it in a way that serves both human readers and the AI systems increasingly mediating their decisions.

Trust and authority signals remain foundational throughout this process. E-E-A-T, which stands for Experience, Expertise, Authoritativeness, and Trustworthiness, matters to AI systems in much the same way it matters to Google. Content that demonstrates genuine knowledge and is backed by data and third-party credibility is the content that earns citations. Personalisation fit adds a further layer. Modern AI tools tailor recommendations based on the context a buyer provides, which means brands that publish content addressing specific customer segments and specific scenarios are more likely to appear in the personalised responses buyers increasingly receive.

Perplexity Example: How AI Shapes the Evaluation Process

Perplexity has become one of the most important platforms to understand for the consideration stage. Unlike general AI chat tools, Perplexity retrieves live web results and synthesises them in real time, presenting sourced answers alongside clickable citations. For buyers, it functions as a hybrid between a search engine and a research analyst, delivering current, sourced comparisons without requiring the buyer to visit multiple pages.

Consider a buyer evaluating project management tools for a small marketing team. They open Perplexity and ask which tools work best for teams under ten people who need strong client reporting features and a straightforward setup. Perplexity delivers a structured comparison drawing from current reviews, product pages, and industry content, with citations the buyer can follow if they want to go deeper.

For brands in that category, the question is whether their content is the kind Perplexity is likely to cite. That comes down to whether their product pages are specific and well-structured, whether their reviews across third-party platforms are positive and detailed, and whether there is enough authoritative content published about their product to give Perplexity something credible to work with.

The broader lesson applies across all AI consideration tools. Brands that are not actively managing their digital presence and content quality are leaving their representation in the consideration stage to chance. The AI will form an impression of your brand regardless of your involvement. The question is whether that impression is based on content you have deliberately shaped or content others have created about you.

Stage 3: Converting in an AI-Mediated World

The decision stage is where purchase intent either converts or stalls. A buyer has identified their options and formed a preference. What stands between them and a completed purchase is usually doubt. Is this the right choice? Is this brand trustworthy? What happens if it does not work out? AI is increasingly present at this final stage, and its role is less about discovery and more about resolving the hesitations that prevent people from following through.

Confidence Building

Confidence is what closes a sale, and AI has become a significant source of it for modern buyers. Before completing a purchase, a growing number of people turn to AI tools to validate the decision they are already leaning toward. They want reassurance that they are making a sound choice, and AI is well positioned to provide it.

This validation behaviour looks different from earlier stage research. At the consideration stage, buyers gather information broadly. At the decision stage, they ask more specific, risk-oriented questions. Will this product work for my particular situation? What do people say about this brand’s customer service? Are there recurring complaints I should know about before committing? AI tools synthesise reviews, surface recurring feedback patterns, and flag concerns that might be relevant to a buyer’s specific circumstances.

For brands, this means the quality and volume of reviews across all platforms directly influences AI-assisted confidence building. A brand with strong, detailed, and consistent reviews across Google, Trustpilot, and relevant industry platforms is far more likely to receive a favourable AI summary than a brand with sparse or mixed feedback. Managing your review presence is no longer purely a reputation exercise. It is a direct input into how AI represents your brand at the moment a buyer is deciding whether to trust you.

The accuracy of your product information carries equal weight. If an AI tool summarises your product based on your published content and a buyer arrives on your page to find something different from what they were led to expect, the trust AI helped build disappears immediately. Consistency between what AI communicates about your brand and what buyers find when they arrive is one of the most important and most overlooked factors in AI-era conversion.

Reducing Friction in the Purchase Decision

Even a confident buyer can be lost if the path to purchase is unnecessarily complicated. Friction at the decision stage has always been a conversion problem, and AI is beginning to address it in ways that have meaningful implications for how brands structure the purchase experience.

AI tools are increasingly capable of taking buyers from a recommendation directly toward a transaction. Perplexity Shopping surfaces products with pricing and purchase links embedded in the response. Amazon Rufus guides buyers through product selection and surfaces relevant options without requiring them to navigate category pages manually. The number of steps between wanting a product and buying it is shrinking, and brands that make that path as clear and direct as possible are well positioned to benefit.

Friction also lives in the form of unanswered questions. A buyer who cannot quickly find information about returns, shipping times, sizing, or compatibility may abandon the process entirely rather than go looking for answers. AI tools frequently surface this kind of practical information as part of their decision-stage responses, which means brands need to ensure it is published clearly, consistently, and in a format, AI can readily extract and present. The brands that perform best at the decision stage are the ones that have already anticipated the questions buyers ask at the final moment and made those answers easy to find.

Thrive Market Example: The Rise of Automated Decisions

Thrive Market, the online organic grocery and lifestyle retailer, offers one of the most instructive examples of where AI-assisted decision making is heading. Rather than waiting for members to search for products, Thrive Market’s AI anticipates needs based on past purchase behaviour, dietary preferences, household profile, and location. It proactively surfaces products that fit each member’s specific situation, often before the member has consciously identified a need.

The result is a decision experience that requires very little active effort from the buyer. The work of searching, comparing, and evaluating has been largely absorbed by the system. Members encounter recommendations that already feel relevant and considered, which reduces the cognitive load of deciding and increases the likelihood of conversion without the buyer ever entering a traditional research process.

For brands, the Thrive Market model points to a broader shift underway across retail and e-commerce. AI is moving from a reactive tool that responds to queries toward a proactive one that anticipates behaviour. The decision stage, which has historically been the moment brands work hardest to influence, is increasingly being shaped by systems that form their recommendations long before the buyer consciously enters that final phase.

The practical implication is that the data signals surrounding your brand, covering product attributes, review quality, structured information, and consistency across platforms, all feed into how AI systems like this make their recommendations. Brands that maintain a rich, accurate, and well-structured digital presence are the ones these systems will surface consistently.

Why Smart Brands Are Investing in Search and AI at the Same Time

One of the most persistent misconceptions in digital marketing right now is that AI is killing search. The data does not support that conclusion.

Research shows that 77% of consumers use AI tools and traditional search engines together, relying on each for different parts of the research process. Search engines remain the preferred tool when buyers want to browse visually, compare prices across retailers, find a specific website, or identify local options. AI tools come into their own when buyers want to synthesise information, receive a tailored recommendation, understand a complex topic quickly, or shortcut a research process they would otherwise find time-consuming.

Task	Preferred Tool	Why
Browse product categories	Search (Google, Bing)	Visual results, familiar experience
Get a personalised recommendation	AI (ChatGPT, Perplexity)	Conversational, synthesised
Compare two specific products	AI	Faster, more contextual synthesis
Find the cheapest price	Search	Price comparison tools, shopping tab
Understand a complex topic	AI	Explanatory, context-aware
Read full reviews	Search	Links to complete review content

Understanding how buyers use both tools across the journey helps brands allocate their efforts more intelligently. Search drives traffic and provides the primary sources that AI draws from. AI organises and interprets that information and presents it to buyers in a form that is easier to act on. A strong presence in search feeds a strong presence in AI, and the two reinforce each other over time.

For brands, this means visibility needs to exist across both layers. Ranking in search remains important, but so does being represented accurately and favourably by the AI systems that are now summarising your brand to potential buyers. Treating these as separate and competing priorities misses the point. They are interconnected, and a coherent strategy addresses both.

Future-Proof Your Brand for the AI Era

The shift toward AI-assisted buying is not a short-term trend. It reflects a fundamental change in how people interact with information and make decisions. Brands that adapt early will have a clearer path to maintaining visibility and relevance as that shift continues to accelerate.

Treat AI Visibility as Its Own Channel

AI visibility is a distinct channel with its own signals and success metrics. A page that ranks well on Google does not automatically get cited by AI tools, and building for one does not guarantee presence in the other. Brands need to assess where and how they are appearing in AI-generated responses and build a deliberate strategy around strengthening that presence.

This means identifying the questions your target customers are asking AI tools, not just the keywords they are searching, and ensuring your content provides the clearest and most authoritative answer to those questions.

Secure the Verification Chain

When AI tools recommend a brand or product, that recommendation is based on information gathered from across the web. Brands with a strong, consistent, and authoritative digital presence, covering their own website, third-party review platforms, industry publications, and structured data, are more likely to be cited accurately and favourably.

This means keeping product information consistent across all platforms, actively managing your review presence, and ensuring your content demonstrates genuine expertise. Inconsistency in your data signals, whether through different product names, outdated descriptions, or conflicting specifications, creates confusion for AI systems and reduces the likelihood that your brand is represented accurately when it matters most.

Create Content That Serves Both Search and AI

The content principles that serve AI visibility and traditional search overlap significantly. Both reward clear and well-structured pages with descriptive headings, specific and factual information presented concisely, genuine expertise backed by evidence, FAQ-format content that directly answers common questions, and schema markup that helps machines understand what your content covers.

Where they differ is in emphasis. Search has historically rewarded depth and keyword coverage. AI rewards directness, clarity, and the ability to extract a clean, useful answer quickly. Writing that serves both means being thorough where depth genuinely adds value and concise where clarity matters more.

Generative Engine Optimisation (GEO)

Generative Engine Optimisation is the practice of optimising content to appear in AI-generated responses. It is an emerging discipline, but its core principles are already clear. AI tools prioritise content that answers questions directly, so leading with the answer and adding context afterward tends to perform better than building toward a conclusion. Structured formatting, including headers, lists, and tables, makes content easier for AI to extract and present. Topical authority matters too. AI systems favour sources that cover a subject consistently and in depth over those that touch it occasionally. And content that references credible data and authoritative sources signals reliability to both AI systems and the readers they serve.

Answer Engine Optimisation (AEO)

Where GEO focuses on appearing in generated responses more broadly, Answer Engine Optimisation focuses on becoming the specific source that AI tools cite when answering a direct question. The principles that support AEO include publishing structured FAQ content that reflects the way buyers actually phrase questions to AI tools, implementing schema markup across FAQ, HowTo, and Product content types to make pages more machine-readable, placing concise and authoritative answers at the top of relevant pages before longer supporting explanations, and building a presence on credible third-party sites so that AI tools have external sources to draw from when referencing your brand.

Both GEO and AEO are still developing as disciplines, but the brands investing in them now will carry a meaningful advantage as AI becomes an even more central part of how buyers research and decide.

Ready to Take Your Brand to the Next Level?

The buyer journey is no longer shaped by a single channel or platform. It moves through a network of tools that guide people from initial curiosity to final decision, and AI is now present at every point along that path. It shapes how problems get defined, how solutions get evaluated, and how purchases get made.

For brands, this creates real pressure to be visible, credible, and well-represented in places that did not exist a few years ago. The good news is that the foundation required to show up well in AI, clear content, strong reviews, consistent information, and genuine authority, is the same foundation that drives performance across every other digital channel.

At YBO, we work with brands that sell directly to their customers, helping them build the content strategy, digital presence, and technical foundation needed to show up where modern buyers are looking. If you want to understand how AI tools are currently representing your brand, or if you are ready to build a visibility strategy that works across both search and AI, we would love to talk.