How does AI change customer search and decisions? A new shopping path on the Internet
Author
Piotr Rompca
We are no longer looking for sites. We are looking for answers
For many years, searching for information on the Internet was quite predictable. The user entered a phrase into Google, received a list of results, opened several pages and independently compared the found information.
This model still exists, but it is increasingly no longer the primary way to search. The user no longer just wants to find a page. I want to get an answer. He wants to understand the problem, compare possibilities, learn about risks, check opinions and make a decision faster.
The search engine itself is also changing. In the article, we mainly focus on examples such as Google AI Overviews, ChatGPT Search, Perplexity, Gemini and Claude, but a similar trend can also be seen in other AI-based tools. More and more systems not only show URLs, but also act as a response system: interpreting the question, looking for sources, comparing information and preparing a summary.
It’s convenient for the user. Instead of reading five articles on your own, you can get a synthetic answer in just a few seconds.
This is a big change for companies. Online visibility no longer ends with your position in Google and clicking on an organic result. It is increasingly important whether a company, brand, product or content is recognized by an AI system as a reliable source of answers.
This is not just a technological change. It’s a change in the way we make decisions. More and more often, the user does not go through the entire path from searching for information, through comparing offers, to choosing a supplier. Some of this work is done by AI: it organizes information, indicates criteria, narrows the selection, suggests risks and shows brands that are worth checking out further.
For companies, this means that the fight for customer attention begins earlier than on the website. It starts at the stage of response, comparison and recommendation generated by the AI system.
For years, search worked according to a simple pattern
Classic search was based on a simple process. The user entered a query. Google showed a list of results. The user selected several pages, read them and independently assessed which answer was the best.
In such a model, the most important thing was the place on the results list. The higher the page was, the greater the chance of a click. That’s why traditional SEO has focused mainly on positions, keywords, technical optimization, content and links for years.
The example was simple.
The user entered:
“SEO agency online store”
Then he opened several results, compared offers, read the “about us” tabs, checked the projects and tried to assess which company best suited his problem.
In this model, the company fought primarily to rank as high as possible in Google and get clicks. The website itself was the place where the user only learned the details of the offer.
This pattern still works. There are still phrases, organic results, ads, maps, product pages, articles and classic Google entries. The difference is that the user today has more ways to search at his disposal, and the search engine increasingly performs part of the analysis for him.
Today, users increasingly ask a question rather than enter a phrase
Change starts with user behavior itself.
More and more often, he does not enter a short password, but a full question or description of the situation. It no longer just looks for a page that contains a given phrase. He is looking for an answer that will help him solve a problem or make a decision.
Instead of typing:
"B2B online store"may ask:"which online store to choose for a B2B company with ERP integration?"
Instead of typing:
"SEO AI"may ask:“what is the difference between SEO and AI positioning and should my company change anything?”
These are more natural, longer and closer to a conversation with an advisor. The user doesn’t just want to get a list of places to check. He wants to receive a sensible answer, context, comparison and indication of what he should pay attention to.
This changes the way you think about visibility. The keyword phrases themselves are still important, but they no longer describe the entire user journey. You also need to analyze questions, concerns, comparisons, selection criteria and decision-making situations.
For companies, this means that content created exclusively for short phrases will be less and less sufficient. The user searches more broadly. AI responds more broadly. The company's website, brand and content also need to be understood in a broader context.
We organize the basic concepts related to this topic in a separate article: [GEO vs AIO vs AEO. How are new AI search engine positioning different?]
The search engine is no longer just a list of links
The biggest change isn’t just that users are asking longer questions. The way the tools that answer these questions work is also changing.
The classic search engine was primarily a list of links. The user went through the sources and built the answer himself.
AI search works differently. It can collect information from several places, compare it and prepare a summary. The user no longer has to visit each page separately to understand the basics of the topic.
This can be seen in Google AI Overviews, where an AI-generated response may appear above the organic results.
This can be seen in ChatGPT Search, where the user asks a question in the form of a conversation.
This can be seen in Perplexity, which strongly highlights sources and citations.
This can also be seen in Gemini and Claude, used for research, analysis, comparison and work with information.
These are not the only examples of such tools, but they clearly show the main direction of change: from the search engine as a list of links to a system that helps build an answer.
The classic model looked like this:
the user enters a query, clicks on three results and compares the information himself.
The AI model looks different:
the user asks a question, the system analyzes the sources and shows the answer along with selected information, citations or recommendations.
This doesn’t mean that websites stop being important. On the contrary. AI systems still need sources. The difference is that the page can be used before the user visits it.
The company no longer competes only for clicks from the results list. It also competes to be a source, quote, example, comparator, or brand mentioned in a response.
What happens behind the scenes of AI responses?
To the user, the AI response looks simple. He enters a question and receives a ready-made text. However, there may be several processes going on behind the scenes.
For many queries, the AI system does not rely solely on its prior knowledge. It can download current information from the Internet, analyze the results and build an answer based on it. This mechanism is often described as RAG, i.e. generating responses supported by downloading data from external sources.
In practice, this means that the model not only “remembers” information, but can also search for it in current sources.
The second important mechanism is breaking the query into smaller parts. The user asks one question, but the system may treat it as a set of several or a dozen auxiliary questions.
Example:
user asks: “which online store system to choose for a B2B company?”
The system can break this question down into multiple topics:
- What are the popular B2B e-commerce platforms?
- when does WooCommerce make sense?
- when will PrestaShop be better?
- when is it worth choosing Magento?
- what is the importance of ERP integrations?
- what influences the cost of implementation?
- what are the limitations of individual solutions?
- What risks should a business owner know?
Only from such fragments is the answer created.
This is an important change. Classic SEO often focused on matching the page to the phrase. Rather, AI search looks for response elements. He needs passages that are clear, specific, credible and placed in a good context.
Therefore, what matters is not only whether the page contains a given phrase. What matters is whether it contains information that can be used to create a meaningful response.
From searching for information to making decisions
The biggest change isn’t just about the user typing in longer questions. More importantly, search is increasingly moving from the information gathering stage to the decision-making stage.
In the past, the user did most of the work themselves. He had to find several websites, open them, compare information, check reviews, write down the company names, go back to Google, refine the question and only then build his own picture of the situation.
Today, part of this process can be taken over by AI.
The user no longer just asks:
“what is SEO?”
He asks more often:
“does SEO still make sense if Google shows AI answers?”
He doesn't just ask:
“what is Magento?”
He asks more often:
“does Magento make sense for a medium-sized store, or is it better to choose PrestaShop?”
These are decision questions. The user doesn’t want the definition itself. Wants to understand options, risks, constraints, costs, selection criteria and next step.
AI fits very well into this stage. It can prepare a comparison, point out arguments, indicate dependencies, provide possible scenarios, organize the topic and narrow the list of options.
In practice, not only the way of looking for information changes, but the entire path to reach a decision. The difference is well demonstrated by comparing the classic model with the AI-supported model:
| stage | classic search model | AI-supported model |
|---|---|---|
| 1 | the user enters a phrase in Google | user describes a problem in Google AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude or another AI system |
| 2 | clicks on several results | the system sorts the topic and shows the selection criteria |
| 3 | reads the pages | the user learns what he should pay attention to |
| 4 | compares offers | AI suggests possible solutions, types of suppliers, tools or brands |
| 5 | checks opinions | the user narrows down the selection |
| 6 | chooses several companies | only then checks specific companies, their websites, opinions and projects |
| 7 | sends an inquiry | contacts selected suppliers |
This means that the customer can visit the company’s website later than before, but more consciously. You may already know the basic differences between the solutions. He may have a list of questions. Can understand the risks. May have an initial comparison of several suppliers.
This is a big sales change for companies. Website, SEO and content no longer work only on the first click. They also work at an earlier stage where AI helps the client understand the market and decide who is worth checking out.
In this model, a company can win or lose the customer's attention before they even visit the website. If the AI system mentions a brand, recalls its content, uses it as a source or shows it as an example, the company goes into the initial decision basket. If it does not appear in the response, it may be dropped from consideration, even though formally it still has a good site and a good offer.
Therefore, AI search must be treated not only as a change in SEO. This is a change in the customer’s decision-making process.
The brand becomes part of the answer before the customer even enters the website
One of the most important impacts of AI search is changing the role of brands in the decision-making process.
In the classic model, the user first entered a query, then clicked on the result, went to the company’s website and only there learned about the brand. This website was the first place of contact with the company.
In the new model, this contact may occur earlier. The user asks a question in Google AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude or another AI system and receives a response containing the names of companies, products, tools, stores, services or experts.
This means that the brand can enter the customer’s mind before the click.
Example:
user asks:
“which company to choose to implement a B2B online store?”
or:
“which agencies deal with SEO and GEO for e-commerce?”
If the AI system responds by mentioning specific brands or indicating them as examples of companies operating in a given area, the user may treat this as the first signal of trust. He doesn’t have to contact the company yet. He doesn’t even have to go to her website right away. The mere presence of a brand in a response can make people associate it, check it later on Google, compare it with the competition or come back to it when making decisions.
This is very important because AI increasingly acts as a pre-selection filter. It helps the user narrow down the selection, pinpoint criteria, sort out the market, and understand which brands are worth checking out next.
In practice, the customer journey may look like this:
1.
the user asks a question in the AI tool,
2.
the system provides an answer and lists several brands or sources,
3.
the user remembers the company name,
4.
then he enters it in Google,
5.
checks the website, opinions, projects and offers,
6.
compares the company with others,
7.
only then does it send an inquiry or make a purchase decision.
In analytics, such a path may look different than in reality. A company may see a branded, direct or contact form input, but it won’t always see the earlier moment when the user first encountered the brand in the AI response.
Therefore, in the era of AI search, brand becomes even more important. It’s not just about the logo, name or visual identification. It’s about whether AI systems can clearly link a company to a specific category, specialization, service, product, location, reviews and sources of trust.
A company that is well described, consistently present on the network and associated with a specific area is more likely to appear in AI responses as a recognizable point of reference. A company that has a vague description, inconsistent data, and generic content may be passed over, even if it actually does good work.
AI search therefore strengthens the importance of the brand, but understood more broadly than the marketing image. The brand becomes a signal of trust, a context for responses and one of the elements that can influence the customer’s decision before even entering the website.
Brand credibility online is becoming one of the key signals
In AI search, what counts is not only what a company writes about itself on its website. It is increasingly important that a brand’s entire online presence creates a coherent and credible image.
When preparing an answer, the AI system can use various sources: company websites, articles, comparisons, opinions, industry catalogues, company profiles, local data, expert publications, product content, brand mentions and other places that help assess what the company does and whether it can be treated as a valuable source.
Therefore, well-written content on the website itself may not be enough. If a brand is poorly recognizable, poorly described outside its own website, has inconsistent data or lacks external trust signals, the AI system may have problems assessing its credibility.
The brand’s authority online is built, among others, by:
consistent information about the company in various places on the Internet,
clear connection of the brand with a specific specialization,
valuable expert content,
customer opinions and reviews,
mentions in external sources,
case studies and examples of implementation,
presence in industry catalogues, rankings or comparisons,
company, location and contact data consistent across various websites,
recognition of the people, experts or team behind the brand.
This is especially important in services, B2B and e-commerce, where the customer does not make decisions solely based on price. He is looking for confirmation that the company understands his problem, has experience, is active in a given area and can be trusted.
In practice, AI can more easily include a brand that has clear context on the web. If a company regularly publishes specific content, is cited, has consistent descriptions, good opinions, visible implementations and clearly defined specialization, it is easier to assign it to a given category.
Example:
if a user asks about an e-commerce SEO and GEO company, the AI system needs to understand which brands are actually associated with this area. The information itself on one subpage may be too weak. A stronger signal is created when this specialization is confirmed in many places: in blog content, service descriptions, case studies, opinions, external publications and consistent data about the company.
The credibility of a brand therefore affects not only how the user sees it, but also how AI systems can interpret it. A brand that is well-described and consistently present online is more likely to be recognized as a source, example, or company worth including in the response.
This means that building authority is no longer just an element of classic SEO. It becomes part of AI search visibility and one of the factors influencing whether a brand appears earlier in a customer’s decision.
The client comes later, but is often closer to the decision
In the classic model, many users landed on the website at a very early stage. Only there did they learn about the problem, learn concepts, compare variants and check whether a given company could help them.
In the age of AI, some of this education may happen earlier. Before entering the website, the user can ask the AI about the differences between solutions, typical costs, risks, questions for the contractor, comparison of tools or when a given solution makes sense.
The effect is that the customer can visit the website later, but with greater awareness.
You may already know:
what options are available
what should he avoid,
what questions to ask the contractor,
what comparison criteria matter,
which brands or solutions appear most often,
what risks are associated with choosing the cheapest offer,
when it is worth choosing a simpler solution and when a more complex one.
This changes the quality of traffic and queries. Some simple information inputs may disappear because the user will receive the answer directly in the AI. At the same time, the inquiries that ultimately reach the company may be more specific.
The customer may no longer ask:
“what do you do?”
May ask:
“In our case, is it better to develop the current website or prepare a new structure for SEO and AI?”
Or:
“Is category optimization enough for our WooCommerce store, or should we consider a major change in architecture?”
This is a completely different level of sales conversation.
For companies, this means that content and visibility in AI should support not only traffic acquisition, but also customer education before contact. A company that answers decision-making questions well may attract fewer random people, but better prepared interlocutors.
AI search doesn’t just change where a user finds information. It also changes the moment at which they start to trust the brand, compare it with others and mature to make a purchase decision.
No-click searches will become more and more common
One of the most important impacts of AI search is the growing importance of no-click responses.
A user may get a response in Google AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude or another AI tool and not go straight to the website. For simple questions, you may not need to do so. A definition, a short guide, a quick comparison or basic information can be consumed without opening an additional link.
This changes the way we understand visibility.
Until now, many companies measured effectiveness mainly by the number of visits from Google. If traffic increased, it was good. If it fell, there was a problem. In the AI era, the situation is more complex.
The brand may appear in the AI response, but the user will not click immediately. It can remember the name and come back later via brand search. Can go directly to the website. He may only reach out after a few more interactions. Or they may see the company in comparison and consider it as an option, even though the first exposure won’t show up as a session in Google Analytics.
This doesn’t mean that clicks no longer matter. They are still important. However, it is important to distinguish between the loss of accidental information traffic and the loss of valuable sales inquiries.
Not every input has the same value. A user who clicks after prior analysis in AI may be more aware and closer to contact with the company.
Zero-click search will be one of the most important impacts of this change. We describe it in more detail in the article:
The phenomenon of organic traffic loss, i.e. the loss of clicks from traditional organic results in favor of direct generative AI responses
Check out the article
Do the major AI search systems work the same?
No. This is important because in discussions about AI, all tools are often lumped into one bag.
In this article, we focus on the most important examples: Google AI Overviews, ChatGPT Search, Perplexity, Gemini and Claude. This does not mean, however, that these are the only tools of this type. The AI search market is developing more broadly, and subsequent systems increasingly combine classic search, generative responses, source citations and conversation with the user.
These tools may lead the user to a similar goal, an answer, but they do it in different ways. They differ in data sources, the way results are presented, the approach to citation, the timeliness of information and how strongly they rely on the classic search engine index.
| system | how the user uses it | which is important for visibility |
|---|---|---|
| Google AI Overviews | he types a query into Google and sees an AI summary above the results | indexation, authority, content structure, matching intentions, visibility in the Google ecosystem |
| ChatGPT Search | asks a question in a conversation and expects a synthetic answer | clear answers, reliable sources, brand recognition, good topic coverage |
| Perplexity | looking for answers with sources and citations | specificity, topicality, citationality, data, structured content |
| Gemini | uses the Google assistant and ecosystem | information consistency, context, visibility in Google, structured data |
| Claude | uses tools for analysis, research, comparisons and tasks requiring conclusions | trusted sources, brand clarity, structured text, technical readability, consistent expert context |
| other AI Search tools | combine search, generative responses and working with context | quality of sources, timeliness, clear content structure, brand credibility |
For the company, this means that visibility in AI is not a single metric. You can be highly visible on Google, but less present on Perplexity. You can appear on ChatGPT but not be well recognized by Claude. You can have a high organic ranking but not be used as a source in the response.
New search has no single center. Google is still very important, but users also use other tools. Therefore, visibility must be understood more broadly than just as a position in classic Google results.
SEO is not disappearing, but its role is changing
With the development of AI, many extreme opinions have emerged. One of them is: SEO is dead.
This is too simple and a wrong approach.
SEO still matters because AI systems need accessible, indexed and reliable sources. If the content is not visible to search engines, is poorly organized, lacks authority, or does not respond to user intent, it is difficult to expect it to be frequently used in AI responses.
However, the role of SEO is changing.
In the classic model, the main focus was position and click. In the new SEO model, it becomes part of a larger visibility system. He is still responsible for technique, indexation, structure, speed, content and authority. However, there is a new layer: whether the content is suitable for use as part of a response.
Therefore, this is not the end of SEO. It’s about stopping thinking that SEO is only about phrases, positions and clicks.
The new approach combines several areas:
SEO
i.e. the foundation of visibility and indexation,
AEO
i.e. preparing content for specific answers,
GEO
i.e. visibility in generative responses,
AIO
i.e. presence in AI summaries, especially in Google AI Overviews.
SEO does not lose its importance, but it begins to play a different function in the entire visibility ecosystem. We develop this topic in more detail in the article:
SEO as a complementary part of GEO
Check out the article
What does this change mean for companies?
For companies, the most important consequence is simple: the brand can be noticed, evaluated, compared or omitted before the user even enters the website.
If the AI mentions the company’s name in a response, the user may take it as a first signal of trust. If a company does not appear in the responses, it does not necessarily mean that it does not exist for customers. However, it means that it may fall out of one of the earlier stages of the decision-making process.
A potential customer can ask the AI:
- "which agency will help me improve the store's visibility in Google and AI?"
- "who implements B2B online stores with ERP integration?"
- "how to choose a company for website development?"
- “which company deals with SEO and GEO for e-commerce?”
- "what should you pay attention to when choosing an online store contractor?"
In response, the system can describe the selection criteria, indicate types of suppliers, list risks, recall brands or suggest what questions should be asked to the contractor.
If a company does not have a clear online presence, does not publish specific content, does not demonstrate its specialization and is not described consistently, it may be omitted. Not because it doesn't exist. Because the system doesn't have good enough signals to take it into account in the response.
Companies should therefore treat AI Search as part of the sales and trust-building process. Even if the user does not click on the page immediately, he or she can see the brand, its specialization, content or opinion about it in the AI response.
This changes the way you think about visibility. It’s not just about whether the user entered the site. It is also about whether the company was present at the moment when the user was building his opinion and narrowing down his choice.
This does not mean that every company must immediately analyze all AI tools separately. It is more important to understand the direction of change: the user may encounter a brand, an opinion, a recommendation or a comparison earlier than on the website. Therefore, visibility must be understood more broadly than just organic traffic in Google Analytics.
The most common misconceptions about AI search
There have been a lot of simplifications around AI in search. Some of them lead to bad decisions.
The first myth: “SEO is dead.”
No. SEO is not dead. His role is changing. Pages still need to be indexable, fast, organized and valuable. AI does not eliminate the need for good structure and quality of content.
Second myth: “only ChatGPT matters now.”
Neither. ChatGPT is important, but it is not the entire AI search market. Google, Perplexity, Gemini, Claude, Copilot, YouTube, social media, classic search and other response systems continue to form a larger ecosystem.
The third myth: “it is enough to write texts for AI.”
It’s not enough. Texts alone will not solve the problem if the company has a poor information structure, inconsistent data, lack of authority, technical indexation problems or content disconnected from real customer questions.
The fourth myth: “the website is no longer important.”
The website still matters, but its role is changing. It’s not just a place to click. It can be a source of information that AI uses to respond.
Fifth myth: “you don’t have to fight for clicks anymore.”
Clicks are still important, especially for purchasing decisions, services, e-commerce and B2B interactions. The only thing that changes is that some of the impact on the user may happen before the click.
Myth six: “AI will always choose the biggest brands.”
Large brands have the advantage of recognition, but AI systems also need specific, up-to-date and well-organized sources. Smaller companies can be visible if they clearly demonstrate their specialization and build trust in their niche.
Myth seven: “It’s enough to be high in Google.”
A high position helps, but does not always guarantee the presence of AI in the response. The system may choose a different source if it better meets the user’s intent, has a more quotable passage, or is more understandable in a given context.
Myth eight: “you only need to check Google or only ChatGPT.”
It’s not enough. In this article, we focus on Google AI Overviews, ChatGPT Search, Perplexity, Gemini and Claude because they clearly show the different directions of development of AI search. But these are not the only tools of this type. Users can use various answer systems, assistants and AI search engines. Each tool can select sources differently, interpret the question differently and present the answer differently. Therefore, visibility in AI must be treated as an ecosystem, not as one channel.
The ninth myth: “it is enough to have a good offer.”
A good offer is important, but the AI system must still be able to understand it, associate it with a specific category and evaluate it as credible. If a brand is poorly described, has no visible evidence of trust, does not demonstrate specialization and is not present in other sources, it may be omitted at the response stage.
The biggest mistake is extreme. Ignoring AI is risky, but panic is not a strategy either. Companies should calmly analyze the change and gradually adapt visibility, content, SEO, brand authority and analytics.
How should companies think about visibility in new search?
Online visibility is no longer one channel and one indicator. It’s not enough to just look at positions in Google. It is also not enough to check whether there are several entries from ChatGPT.
Companies should think about visibility as a system.
This system includes:
classic SEO,
visibility in Google AI Overviews,
presence in major AI Search tools such as ChatGPT Search, Perplexity, Gemini and Claude,
content answering users' questions,
consistent brand information,
external sources confirming credibility,
opinions, mentions and evidence of trust,
case studies and examples of implementation,
traffic and brand query analytics,
quality of leads,
company recognition in its category.
In the new model, visibility does not depend solely on the content on the website. The brand’s authority throughout the Internet is also important: opinions, mentions, consistent company data, expert publications, case studies, presence in industry sources and a clear connection of the company with a specific specialization.
This does not mean that every company has to invest in everything at once. It means that decisions should result from analysis, not from AI fashion.
First, you need to look at what your company looks like today in the new search ecosystem. Is it visible? Does the AI correctly understand its offer? Are competitors showing up more often? Are the most important topics covered? Is the brand described consistently? Do users search for it directly? Are there reliable signals in the network confirming its specialization?
Only then is it worth planning actions.
We can see what your business looks like today in the new search ecosystem: Google, the main AI Search tools, content, sources, branded queries and trust signals around the brand.
What will be the next stage of this change?
Today’s AI responses are just a transitional stage.
In the beginning, search engines showed links. Then they started showing snippets of the responses. Now AI systems create syntheses, comparisons and recommendations. The next step will be increasingly active AI assistants and agents.
The difference is important.
The AI assistant helps the user understand the topic.
In the future, an AI agent may perform some activities for the user: compare products, check availability, analyze opinions, compare prices, choose a supplier, and in e-commerce even support or complete a purchase.
In B2B services, this may mean more advanced comparison of companies, offers, case studies and competences.
This will be even more clear in e-commerce. AI can analyze product parameters, availability, prices, delivery costs, opinions, returns policy and matching the product to the user’s needs.
Companies that want to be ready for this stage should organize their data, content, offer structure and sources of trust now. Not because tomorrow the entire sales process will be taken over by AI agents. Because the direction of change is clear: from searching for information to supporting decisions and actions.
In e-commerce, this change will go even further, because AI will not only search for information, but also support or automate purchasing decisions. We develop this topic in the article:
Artificial intelligence will do shopping for us. The era of AI agents in e-commerce
Check out the article
If you want to prepare your company for AI-powered search, we can start by analyzing visibility, content, brand authority and where your company may be missed by Google AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude or other response systems.
Summary: Search changes from a list of results to an answer system
Search doesn’t disappear. It changes form.
Users increasingly ask questions instead of entering short phrases. Google and AI tools are increasingly creating answers rather than just showing a list of links. Sources are still needed, but their role is changing.
AI search is changing not only how we find information, but also how we make decisions. Users increasingly often start with a question, get a comparison, get to know the selection criteria, see brands and only then go to specific pages.
A website, article, product description, case study, opinion, brand mention or external source can become part of the response created by AI.
Clicks still matter, but they are no longer the only measure of influence. The brand may appear in the response, be remembered, compared or checked later in a brand search.
SEO is still needed. However, it is not enough to just think about phrases, positions and movement. Visibility in the AI era requires a broader approach: question-answering content, consistent company information, trusted sources, technical accessibility, brand authority, and presence monitoring across various AI systems.
Companies that understand this change earlier will be better prepared for the next stages: no-click search, AI recommendations and agents that will support users in making decisions.
FAQ
Czy AI zmienia sposób wyszukiwania informacji?
Yes. Users increasingly ask complete questions and expect ready-made answers, comparisons or recommendations. The search engine is no longer just a list of links, but is increasingly becoming a response system.
Jak AI wpływa na decyzje zakupowe klientów?
AI can help the user compare solutions, understand risks, narrow down choices, and identify brands or sources that are worth checking out. Thanks to this, the customer can visit the company’s website later, but more consciously and closer to the purchase decision. For companies, this means that visibility in AI affects not only traffic, but also the earlier stages of trust building and supplier selection.
Czy Google nadal ma znaczenie w erze AI?
Yes. Google is still very important, but the way information is presented is changing. In addition to classic organic results, there are AI-generated responses that can reduce the number of clicks but increase the importance of brand visibility as a source.
Czy narzędzia AI zastąpią Google?
It is not known how exactly the shares of individual tools will change. It is more likely that users will use several ways to search: Google, AI Overviews, ChatGPT Search, Perplexity, Gemini, Claude, other AI search engines, YouTube, social media and branded searches. Therefore, companies should not think about one channel, but about the entire visibility ecosystem.
Dlaczego marka jest ważna w wyszukiwaniu AI?
The brand may appear in the AI response before the user enters the company’s website. If the system mentions the name of a company, product or service as an example or recommendation, the user can later check the brand on Google, compare it with the competition and only then make a purchase decision. Therefore, in the era of AI, not only the position of the website is important, but also the recognition, consistency and trust in the brand in various sources.
Co oznacza autorytet marki w wyszukiwaniu AI?
Brand authority means that the company is described consistently and reliably in various places on the Internet. Not only the content on the website is important, but also opinions, external mentions, case studies, expert publications, company data, industry profiles and a clear link between the brand and a specific specialization. The easier it is for AI systems to understand what a company does and why it can be trusted, the more likely the brand will be included in the response.
Czy Claude ma znaczenie w wyszukiwaniu AI?
Yes, although Claude should not be treated as a classic search engine. It is one of the AI tools used for research, analysis, comparison, working with documents and making decisions. In this article, we treat Claude as one of the main examples of AI systems, along with Google AI Overviews, ChatGPT Search, Perplexity and Gemini.
Czy SEO nadal ma sens?
Yes. SEO is still needed because AI systems use available, indexed and reliable sources. However, the goal changes: it is not only about position and clicks, but also about visibility in replies, citations and recommendations.
Czym jest wyszukiwanie bez kliknięcia?
This is where the user gets the answer directly from Google or the AI tool and doesn’t have to go to the website. This does not mean that the brand does not gain visibility. However, it means that not every exposure will be visible as a session in analytics.
Czy AI będzie podejmować decyzje za użytkowników?
In some cases, AI already helps users compare options, choose solutions and shorten the decision-making process. In the future, this impact may be greater, especially in e-commerce and services, where the user needs to compare many parameters.
Co firmy powinny zrobić najpierw?
First, they should understand how their customers ask questions, how the brand appears in new search systems and whether there are reliable signals on the web confirming its specialization. Only later is it worth making changes to the content, SEO, website, analytics and visibility strategy.
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