The new power of pre-selection: How AI responses are changing competition and brand value – Creative image on the topic, with AI: Xpert.Digital
Those who become visible too late compete for the places left behind by others
AI helps decide which brands customers even consider
36.7 percent instead of 3.6 percent: The large lead of established brands in AI responses
For many people, the search for a bank, business software, or hotel no longer begins with a long list of results. Increasingly, AI provides a concise selection, categorizes providers, and explains the reasons for its recommendation. This is changing the competitive landscape: companies must not only impress with their offerings but also be considered in the initial selection process.
Data analyzed from January to September 2026 suggests that early visibility in AI-generated responses can be associated with a significant advantage. Brands that appeared in brand-neutral purchasing advice questions in the first half of the year were mentioned in an average of 36.7 percent of the responses to the same questions. In contrast, brands that first appeared in the third quarter reached only 3.6 percent. At the same time, the brands most frequently mentioned at the beginning of the year remained in the lead through September in 62.8 percent of the questions analyzed. Recommendation lists also became shorter: In banking, the average number of mentioned brands fell from 7.6 to 4.7.
The figures show a correlation, but not proof that early investments alone cause a later advantage. Brand awareness, product quality, and the specific question being asked can also play a role. Nevertheless, the results point to an economically significant risk: If AI systems narrow down the selection and consistently consider established providers, it may become more difficult for brands that emerge later to make the shortlist. Therefore, the key is not to be mentioned as often as possible, but to be identifiable as a suitable and demonstrably relevant solution when the right questions are asked.
Why early visibility in AI responses can become a competitive advantage
The competition for customers no longer begins on a company website, a comparison portal, or in a personal sales conversation. It can already be decided in an AI response that narrows down a large market to a smaller selection. When people ask for a suitable bank, business software, or hotel, they don't necessarily receive a comprehensive market overview. Often, they get a few names, a brief explanation, and thus an initial orientation that can influence the further decision-making process.
Economically, this pre-selection is particularly relevant when it becomes entrenched. The data under consideration indicates precisely this: Brands that already appeared in responses to brand-neutral purchasing advice questions in the first half of 2026 were mentioned in an average of 36.7 percent of the responses to the same questions. Brands that were added for the first time in the third quarter reached only 3.6 percent. In 62.8 percent of the questions examined, the brand that had already been most frequently mentioned in January and February remained in the lead in September. At the same time, the recommendation lists shortened. In banking, the average number of mentioned brands fell from 7.6 to 4.7 between January and September.
This leads to a significant economic perspective: AI visibility is perhaps less a short-term advertising opportunity than a reputation-like asset. Those who are convincingly associated with a particular problem solution can maintain this position for a relatively long time. Those who enter the market later must not only gain attention but also disrupt an already established pre-selection.
This development does not mean that markets are permanently closed, nor that early visibility automatically generates sales. However, it does change the conditions under which companies make the shortlist. The decisive competition is thus shifting, in part, from the question of who has the best presentation to the question of who is even considered a relevant option in the first place.
An open search is transformed into a condensed decision template
There is a fundamental difference between a traditional results list and AI-powered buying advice. A results list initially presents search results side by side. A generated response, however, processes this information into a recommendation, a ranking, or a selection of pros and cons. It thus takes over some of the evaluation work that users would otherwise have to do themselves.
This initially translates into a productivity gain. Those who no longer have to open numerous pages, compare features, and reconcile conflicting information save time. Especially in complex markets, a clear summary can improve orientation.
However, this relief comes at a price. Users relinquish some control over which alternatives they are presented with. The decision is not entirely delegated to the system, but its starting point is pre-structured. A provider missing from this initial selection must later enter the decision-making process via another point of contact.
This gives the pre-selection process a function that was previously fulfilled by specialist retailers, consultants, trade media, and comparison portals, among others. AI systems can consolidate this function within a single interface: They explain the need, translate it into selection criteria, and identify suitable providers.
This creates additional competition for companies to be considered. Product quality, price, and service remain crucial. However, they can only be effective if a potential customer even perceives the company as a viable solution. An outstanding product or service that isn't readily visible isn't economically worthless, but it is more difficult to market.
What the difference between 36.7 and 3.6 percent actually means
The difference between brands mentioned early and late is considerable. There is a difference of 33.1 percentage points between 36.7 and 3.6 percent. Relatively speaking, brands mentioned early were mentioned more than ten times as often.
To illustrate this point: If we were to extrapolate the measured average values to 1,000 comparable response opportunities, they would correspond to approximately 367 mentions for an already established brand and 36 mentions for a later-entering provider. This is not a sales forecast, but rather an illustration of the order of magnitude.
The precise reference point is crucial. The values do not represent market shares, purchase probabilities, or the overall chances of all companies. They refer to the mention of brands in response to the same observed questions. Furthermore, multiple brands can appear in a single answer. Therefore, the mention probabilities of different providers do not necessarily add up to 100 percent.
The timing of appearances also requires precision. Brands that appeared in response to a survey question between January and June are considered early entrants. Late entrants were first mentioned between July and September. A later first appearance does not necessarily mean that the company was only founded or began marketing at that time. It could just as easily be an established brand that had not previously been considered for a particular use case.
It is noteworthy that the gap was similar in all three systems examined. In ChatGPT, 3.1 percent for brands added later compared to 35.6 percent for those present early. In Gemini, the figures were 3.8 percent versus 36.8 percent, and in Google AI Mode, 3.9 percent versus 37.7 percent. This suggests a cross-platform pattern within the analyzed dataset, rather than a peculiarity of a single system.
Why consistency should not be mistaken for evidence of early investment
A large difference between two groups does not yet explain its cause. The figures show a correlation between early mention and later visibility. However, they do not, in themselves, prove that an early optimization measure caused this advantage.
A plausible alternative explanation is that well-known and successful companies tend to appear earlier and are subsequently mentioned more frequently. They may have more customers, more extensive documentation, greater media presence, and more independent reviews. Early visibility would then be partly a reflection of existing strength and not solely its cause.
The relevance of the provider to the specific set of questions being examined can also play a role. A broadly positioned provider is likely to be considered more often for general purchasing advice than a specialist. However, this relationship could be reversed for a narrowly defined technical question.
Furthermore, there is a potential selection bias. Those who appeared in the first half of the year have demonstrated a certain relevance to the respective question up to that point. The group of brands added later, on the other hand, may contain more providers that are only relevant under specific circumstances or appear only temporarily.
Therefore, to make a sound investment decision, it would be crucial to separate the effects of specific measures from such initial differences. This would require, for example, comparable control groups, documented changes, and repeated measurements.
The sensible conclusion is therefore narrower than a blanket "early starter" hypothesis: Already established visibility proves to be significantly more stable over the period studied than a later, initial appearance. This implies an economically relevant risk of waiting, but not yet a guarantee of success for any early expenditures.
Why AI responses can appear dynamic yet still have consistent winners
An AI response can visibly change from week to week. Wording shifts, justifications are weighted differently, and linked pages are replaced. This can easily give companies the impression that each new response opens up a completely new competition.
The consistency of the mentioned brands puts this perception into perspective. In 62.8 percent of the questions, the brand most frequently mentioned at the beginning of the year remained in the lead until September. The relevant leadership status referred to the frequency of mention in a question, not necessarily to the first position within each individual answer.
This distinction is important. A brand can rank first one day and third the next, yet still be considered more frequently than all its competitors for months on end. The linguistic and visual interface fluctuates, while the composition of the preferred supplier base remains relatively stable.
A comparison with a random sample of the observed brands underscores this consistency: Under this simplified assumption of randomness, the same market leader would have been expected in only 14.9 percent of the cases. However, this comparison does not represent a realistic ideal of a perfectly open market. It merely serves to contextualize the observed stability against a random sample.
Economically, this can be interpreted as a form of persistence. The systems don't need to consciously adhere to a brand. It's sufficient that similar questions repeatedly encounter similar information sets, established descriptions, and the same reliable supplier profiles. Then consistent results emerge, even though each answer is reformulated.
What else can be seen in positional stability within chats?
A supplementary analysis of more than six million chats indicates a positional stability of 74.9 percent for the brand in first place. For the brand in fifth place, the corresponding figure is 36.1 percent. The difference is 38.8 percentage points.
These figures point in the same direction as the temporal consistency of the most frequently mentioned brands: leading positions appear more robust than those further down the rankings. According to these values, the top position would be slightly more than twice as stable as the fifth-place ranking.
However, this observation should not be equated with the months-long stability of brand leadership. Maintaining a position within a chat context and the frequency of mentions of the same brand over several months are different metrics.
Furthermore, the available data lacks methodological details that would prevent a conclusive interpretation of the chat analysis. Crucially, it would be necessary to know, among other things, which conversation steps were used to compare positions, which platforms were included, how brands were recorded, and whether users explicitly asked for alternatives. Even a very large number of chats cannot replace this information.
Nevertheless, a plausible economic working hypothesis remains: If a supplier is already at the top of a selection process, it can become the point of reference for further inquiries. Users may then ask about its costs, disadvantages, or suitability. This gives the established brand more weight in the subsequent decision-making process, while weaker alternatives can more easily fade into the background.
Shorter recommendation lists make visibility a scarcer commodity
Narrowing the selection is at least as important as maintaining a leading position. In banking, the average number of mentioned brands fell from 7.6 in January to 4.7 in September. This represents a decrease of 2.9 brands, or approximately 38 percent.
Across all sectors studied, the lists of brands narrowed from an average of 7.7 to 5.3. This represents a decline of approximately 31 percent. According to the study, all sectors surveyed and all three systems moved in the same direction.
A shorter list can be beneficial from a user's perspective. It reduces the number of options to be checked and makes the response easier to process. However, it can also reduce the chances of other providers being considered.
It is not crucial whether exactly five or eight names appear. What is economically relevant is that, all other things being equal, fewer companies benefit from the early attention. If, above all, the leading brand is retained while peripheral positions disappear, the relative importance of this leading position increases.
A uniformly distributed market share cannot be calculated from this. A place in a response is neither as valuable as any other nor does it correspond to a later purchase. Nevertheless, this development illustrates a structural shift: Not only is access to the list competitive, but the list itself is also shrinking.
This creates a double challenge for brands that emerge late. They have to compete against established candidates and do so within an increasingly narrow selection space.
How digital reputation becomes an economic asset
The concept of path dependency describes situations in which past developments influence later possibilities. Applied to AI visibility, this does not mean that a system permanently stores every past recommendation or learns directly from every conversation. Such a technical explanation would not be supported by the data under consideration.
Consistency can arise from the surrounding information environment. A well-known brand is mentioned in specialist articles, listed in comparisons, reviewed by customers, and described in detail on its own website. This information can remain available for extended periods and become relevant again when questions arise later.
This creates a body of publicly verifiable reputation. This body can fulfill several economic functions simultaneously: It makes the provider discoverable, facilitates its classification, and provides reasons to consider it for specific requirements.
For companies, this is similar to investing in intangible capital. The expenses are incurred today, while some of the benefits only materialize later. Solid documentation or a compelling reference can have a longer-lasting impact than a short-term campaign.
However, this asset is neither fully controllable nor freely transferable. Its impact depends on what information reaches the respective systems, how they evaluate it, and what questions users ask. Furthermore, reputation can lose value through poor performance, outdated information, or credible counter-evidence.
The economic value of this capital therefore lies not in mere brand recognition. It lies in the robust connection between a brand, a specific need, and verifiable performance characteristics.
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Why your own website is necessary, but not sufficient on its own
One particularly revealing finding concerns the breadth of the information base. If a January response included a brand's own website as well as at least two other pages as evidence on which the brand was mentioned, it appeared again in 77.2 percent of cases in response to the same question in September.
If none of the cited sites mentioned the brand, the corresponding consistency was 40.7 percent. The difference between the two groups is 36.5 percentage points. This also indicates a correlation, not an isolated demonstration of the effectiveness of a specific publication strategy.
The economic mechanism is nevertheless plausible. The company website can clearly explain which services are offered, what conditions apply, and what limitations exist. External information sources can provide additional perspectives and support this assessment.
It is important to distinguish between multiple sources and truly independent evidence. Ten pages that repeat the same advertising copy do not constitute ten independent confirmations. The quality of the information base depends on a traceable origin, factual independence, and verifiable statements.
A company should therefore not strive solely for as many mentions as possible. A smaller number of technically sound publications that address specific requirements and demonstrate verifiable results can be more valuable.
This results in a changed distribution of roles between corporate communications, trade media, customer testimonials, and product documentation. They are not merely individual channels for increasing reach. Together, they form an information network from which a supplier's market position can be deduced.
The value of older content lies not in its age, but in its continued usefulness
Short-term relevance and long-term relevance are distinct qualities. A new publication can be important because it describes current prices, performance changes, or regulatory requirements. An older article can remain valuable because it provides a particularly good answer to a fundamental question.
The data analyzed shows that much of the cited content on Reddit and YouTube had already been available for some time. Of the cited Reddit posts, 75.2 percent were older than six months, and of the cited YouTube videos, 70.3 percent were older. Only 4.3 percent and 5.9 percent, respectively, were less than 30 days old. Furthermore, Reddit posts older than two years were cited more frequently after four weeks for the same question than posts that were less than a month old.
This does not mean that older content is always preferred, or that publications need a long time to mature before they have an impact. Older content has had more time to be used and shared. Furthermore, particularly helpful posts can remain online longer, while weaker content barely gets noticed.
This has a nuanced impact on companies. Technically sound content should not be hastily replaced by a constant stream of new publications. Often, it makes more sense to carefully update a proven resource and preserve its fundamental value.
Conversely, consistency should not be used to justify outdated information. Especially with regard to prices, safety standards, contracts, or product features, obsolete information can cause significant damage. Therefore, content that remains valuable over the long term requires reliable maintenance.
Why banking makes the economic implications particularly clear
Banking products often combine easily comparable individual features with complex overall conditions. An interest rate is quickly grasped. Account management conditions, time limits, creditworthiness requirements, or suitability for specific customer groups, on the other hand, require more careful consideration.
An AI response can reduce this complexity. However, it can also make a product appear universally suitable, even though its benefits only apply under certain conditions.
When the average bank selection shrinks from 7.6 to 4.7 brands, the quality of the selection criteria becomes increasingly important. A shorter list is not automatically worse. It becomes problematic when essential differences between customer groups or product conditions are lost.
This doesn't necessarily mean a hopeless situation for smaller institutions and specialized providers. They might appear less frequently in general inquiries, but be particularly relevant for precise requirements. A provider for a clearly defined customer group doesn't have to dominate general banking recommendations to be economically successful.
The crucial factor is whether its specific benefits are clearly and reliably documented. General statements about good service or attractive terms are less helpful than clear information about who an offer is suitable for, what conditions apply, and when other solutions would be more appropriate.
This example also illustrates the limitations of simply optimizing visibility. A frequently mentioned brand is not automatically the best choice for every user. What is economically valuable is, above all, an accurate match between supply and demand.
Visibility is a gateway to sales, but not sales
Several steps lie between a brand mention and a business outcome. A user must notice the recommendation, consider it relevant, and follow up. Subsequently, the offer, price, trust, and purchasing process must be convincing.
This distinction is crucial for budget management. An increasing mention rate can be a positive signal. However, it may have little economic impact if the questions in question reveal only a low level of purchase intent or if the company is recommended for the wrong requirements.
Conversely, a relatively infrequent mention in a valuable niche market can be of considerable importance. For complex capital goods or long-term services, a few additional qualified contacts can be more economically valuable than a large number of superficial mentions.
A sensible evaluation approach therefore combines several factors: the frequency of relevant questions, the probability of a suitable recommendation, the resulting qualified contact, the probability of closing the deal, and the expected contribution margin.
Not every metric can be precisely observed. Users might read an AI response and later search directly for the company. A subsequent website visit then doesn't necessarily reveal where the initial selection originated. A sales conversation might also take place weeks after the initial research.
Instead of creating a false sense of precision, companies should combine different information sources. This includes repeated response measurements, website and sales data, as well as verifiable customer accounts of their research process. The crucial factor is not the perfect attribution of every single mention, but a reliable assessment of the additional economic contribution.
For small and medium-sized enterprises (SMEs), technical clarity is more important than general recognition
Large brands can benefit from their existing presence. However, for smaller companies, it's not necessarily advisable to emulate their broad communication strategy. A medium-sized company with a limited budget is unlikely to be able to dominate all the general issues within an industry.
A more promising approach is to focus on those decision-making situations where one's own offering has a clear advantage. These include specific operating conditions, certain company sizes, regional requirements, or demanding technical interfaces.
For example, a provider of intralogistics solutions doesn't need to respond to every general question about warehouse automation. More economically valuable would be a reliable presence when questions arise concerning a specific plant environment, a problematic integration, or a clearly defined modernization need.
The same applies to enterprise software, industrial services, or specialized consulting. The more precisely the requirements are described, the more important concrete experience and verifiable performance limits become.
To achieve this, however, companies must translate their knowledge from internal discussions, presentations, and sales materials into understandable public information. A technical advantage that only the sales department can explain upon request is more difficult to leverage for early digital pre-selection.
The strategic core is therefore not about being mentioned everywhere. It's about being clearly identifiable as a suitable provider when the right questions arise. This limits wasted effort and combines visibility with genuine competitiveness.
When content becomes part of the distribution infrastructure
A reliable public information base is more than just an editorial byproduct. It can take over some of the preliminary work that would otherwise have to be done repeatedly in sales: clarifying requirements, explaining differences, defining limitations, and answering typical objections.
This changes the economic evaluation of specialist content. A carefully prepared article can simultaneously support existing customers, guide new prospects, and improve the automated classification of the company.
Content that goes beyond general advantages is particularly useful. It explains the conditions under which a solution works, the associated costs, and which alternatives should be considered. Transparent limitations can also be valuable because they prevent misinterpretations.
This includes, for example, understandable service descriptions, documented use cases, comprehensible comparison criteria, and regularly updated answers to specific customer questions.
However, this does not justify having as much text as possible. If many pages repeat the same general statements, the amount of information increases without any increase in its value. For readers, the content becomes less clear; for the company, maintenance costs and the risk of objections increase.
The economically meaningful unit is therefore not the published article, but the answered decision question. A smaller, consistent set of information can be more beneficial than a large collection of interchangeable content.
How companies can meaningfully measure the new competition
A single test with a single question is not a reliable measure of success. The answer can depend on the wording, the timing, the course of the conversation, and the system used. Therefore, companies need a repeatable measurement framework.
The process should begin with a selection of real customer questions. These questions must cover different phases of the buying process and various requirements. General guidance, specific problem-solving, and immediate supplier selection should be considered separately.
It is then necessary to distinguish whether a brand is merely mentioned, explicitly recommended, or categorized for an unsuitable purpose. Equally important is the attribute of characteristics ascribed to it. Frequent but factually incorrect mentions are not guaranteed to be successful.
Stability also deserves separate consideration. A brand that regularly appears on important issues has a different starting point than a provider with occasional peak values. Average values alone can mask such differences.
Comparisons between systems should also be conducted carefully. Equally weighted analysis of multiple platforms can reveal whether a pattern is widespread. However, it doesn't automatically indicate its economic significance for a specific target group. For that, it would be necessary to consider which systems potential customers actually use.
The best measurement thus combines technical repeatability with commercial relevance. It answers not only how often the company appears, but whether it is accurately and consistently considered in the crucial matters.
Which investments are justifiable and which are not
The data warrants attention, but not an unlimited increase in spending. Precisely because early visibility is linked to later sustainability, the temptation is strong to portray every short-term measure as an urgent investment.
In contrast, a sound economic decision begins with the expected benefits. Companies should first close those information gaps that are particularly relevant for both customers and digital classification. These include unclear service descriptions, contradictory information, or missing answers to frequently asked selection questions.
Such improvements have an advantage: their benefit is not solely dependent on a specific AI system. They can also support sales, customer service, and traditional search. This reduces the risk of a one-sided platform bet.
Less convincing are measures whose effectiveness is based solely on short-term spikes in visibility. Without a verifiable link to relevant issues, suitable customers, and economic results, their value remains uncertain.
External mentions should not be judged solely on their number. Professional relevance, independence, and sustained accessibility are more important quality indicators than mere quantity.
A step-by-step approach is therefore plausible: First, the most important areas of need and information gaps are identified. This is followed by limited improvements, repeated measurements, and an assessment of the economic impact. Only when a reliable benefit is demonstrated will the allocation of resources be expanded.
Platform dependency increases with the importance of recommendations
If AI-driven responses take over a larger share of early purchase advice, economic dependence on the intermediary platforms can increase. Companies then not only rely on their information being accessible, but also on it being processed accurately and given appropriate consideration.
This dependency differs from a direct customer relationship. A platform can change presentation formats, data access, or selection mechanisms. For individual companies, it is often difficult to understand why a recommendation shifts.
This results in a strategic conflict of objectives. Being present in AI responses can open up new opportunities. At the same time, it would be risky to base all customer acquisition efforts on this approach.
Direct relationships therefore retain their value. These include existing customer contacts, professional networks, events, and in-house information offerings. They enable demand even when visibility in another system changes.
A robust strategy thus combines external discoverability with internal customer loyalty. It treats AI recommendations as an important access point to the market, but not as a replacement for product performance, sales, and trust.
Conversely, this creates a responsibility for platform operators. The more their pre-selection influences economic opportunities, the more important transparent criteria, clear labeling of commercial influences, and the proper handling of erroneous information become.
Whether the competition becomes closer also depends on the precision of the questions
The observed stability can encourage a concentration of attention. If familiar brands appear repeatedly and recommendation lists become shorter, fewer providers make it into the early selection.
However, this does not necessarily prove a corresponding concentration of actual sales. Visibility, demand, and market share are different factors. Furthermore, a highly personalized or precisely worded question may attract different providers than a general, standard question.
Herein lies an important counterforce. General awareness is particularly helpful when the problem is described broadly. The more precisely requirements, budget, location, or technical conditions are defined, the greater the importance of specialized providers can become.
Therefore, for the competitiveness of small businesses, it is not only relevant whether the systems can accommodate new brands. Equally important is whether they can adequately differentiate between different needs.
A market can appear concentrated at the level of general recommendations and yet remain open in specialized submarkets. Conversely, it would be problematic if the same few brands were favored regardless of the specific requirement.
A thorough competitive analysis would therefore need to consider not only average mention rates but also the diversity of recommendations across different user profiles and use cases. Only then could it be assessed whether AI primarily provides guidance or systematically reduces the visible market diversity.
Three developments will determine how long the lead lasts
The observed patterns suggest several possible future scenarios. One would be the further consolidation of existing leadership positions. This would be particularly likely if established information repositories dominate, lists remain short, and new providers are rarely and only rarely permanently integrated.
A second scenario would involve a more targeted segmentation based on need. General questions would continue to generate well-known brands, while detailed requirements would lead to a wider range of recommendations. In this case, specialists could occupy economically relevant niches without achieving the general visibility of large providers.
A third possibility would be a renewed opening of the selection process. New data access, modified systems, or a greater consideration of current product information could make the composition of the recommendations more flexible.
The existing data is insufficient to reliably predict any of these trends. It describes a period from January to September 2026 and the questions observed within that timeframe. This is too little to make a general statement about all markets and future systems.
This suggests a sensible middle ground for companies. They should take the current stability seriously, but not base their strategy on its indefinite continuation. Investments that deliver benefits under various scenarios are particularly resilient: technical clarity, verifiable performance indicators, up-to-date information, and stable customer relationships.
The decisive advantage arises from demonstrable relevance
The economic significance of AI responses lies not only in the creation of another communication channel. It lies in the fact that a system can refine the initial selection process and thus influence which providers are even considered further.
The data reveals a clear lead for established brands, a high degree of consistency in leading positions, and a shortening of recommendation lists. At the same time, it leaves open how much of this lead is attributable to previous investments and how much to brand awareness, product strength, or the composition of the questions.
A sound business strategy must consider both sides. Waiting can be costly if relevant competitors consolidate their position. Untargeted spending can be equally expensive if it only generates mentions without improving the quality of demand.
The viable approach, therefore, does not consist of producing as many texts or mentions as possible. Companies must demonstrate in a comprehensible way which problems they offer a suitable solution for, under what conditions this solution is convincing, and what evidence supports it.
Early visibility can facilitate this process. However, it only becomes truly valuable when it is based on solid performance. In the emerging competition for AI-supported pre-selection, the loudest brand doesn't necessarily have the best long-term starting point, but rather the one whose relevance to a specific need is most clearly identifiable and verifiable.
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