When AI Agents Become the Customer: The New Challenge for Brands

For decades, brands have competed to capture human attention. Advertising, search rankings, emotional storytelling and customer experience have all been designed around a familiar assumption: people discover products, compare alternatives and decide what to buy. But what happens when an AI assistant begins making some of those decisions on their behalf?

The rise of AI agents is introducing a new intermediary into the customer journey. Instead of browsing multiple websites, consumers can increasingly ask intelligent assistants to research products, compare specifications and recommend suitable options. Some emerging systems can also assist with purchases and other transactions. For brands, this creates a new challenge: communicating their value when the first evaluator of a product may be an algorithm rather than a human shopper.

By October 2026, developments in agentic commerce from Google, OpenAI and major payment networks are making this shift increasingly relevant. Yet the implications extend far beyond technology. AI agents and brands are becoming part of a new discussion about discoverability, customer trust, differentiation and loyalty. Businesses must now consider how their products are interpreted by AI systems without losing the human relationships that give their brands lasting value.

When AI Agents Become the Customer, Who Actually Makes the Decision?

The title reflects a significant change in digital commerce, but an important distinction remains: the AI agent is not the customer in the traditional commercial or legal sense.

The customer is still the person or organization making a purchasing decision, authorizing a transaction and ultimately using the product or service.

What changes is the process leading to that decision.

An AI assistant can evaluate information, filter alternatives and present a smaller selection of products based on the user’s requirements. Depending on its capabilities and permissions, it may also perform actions on the customer’s behalf.

Consider someone looking for a new smartphone.

In a conventional buying journey, that person might visit technology websites, watch reviews, compare technical specifications and explore several online stores.

With an AI assistant, the process could begin with a single request:

“Find me a smartphone under €500 with excellent battery life, reliable software support and a good camera.”

The assistant may then compare multiple devices, assess available information and produce a shortlist.

The customer still makes the final decision, but the range of brands considered may already have been narrowed by the AI system.

This introduces a significant marketing question.

If a brand never enters the AI-generated shortlist, will the consumer ever consider it?

The answer depends on the shopping context and the user’s behavior. Nevertheless, the possibility makes AI-mediated discovery an increasingly important part of brand strategy.

The Customer Journey Is Becoming an AI-Mediated Experience

Traditional marketing models describe a customer journey that moves through awareness, consideration, purchase and post-purchase experience.

AI agents do not necessarily eliminate these stages.

Instead, they may alter who performs the activities associated with them.

Awareness: The First Brand Impression May Be Indirect

Brand awareness has traditionally depended on consumers encountering advertising, recommendations, search results or other forms of communication.

In an AI-mediated journey, the user may encounter only a few brands selected by an assistant.

The AI system becomes an intermediary between the consumer and the broader market.

A brand that previously relied on attracting visitors through search engines may therefore face an additional visibility challenge: being represented accurately and relevantly in AI-generated recommendations.

However, this does not mean traditional brand awareness becomes irrelevant.

A consumer may explicitly instruct an AI assistant to compare products from a preferred manufacturer. In that situation, existing brand recognition continues to influence the recommendation process.

AI may change how brands are discovered, but it does not automatically erase the preferences people already hold.

Consideration: Product Comparison Becomes More Compressed

The consideration stage may experience an even greater transformation.

AI assistants can organize product information into concise comparisons based on attributes such as price, compatibility, availability, delivery and technical specifications.

This can reduce the time consumers spend visiting separate websites.

For businesses, however, it creates a new communication problem.

A distinctive brand promise may lose its persuasive power if it is not represented through information the assistant can retrieve and interpret.

For example, a company may emphasize exceptional customer service, but an AI comparison might primarily focus on price and product specifications.

The result is not necessarily incorrect. It may simply fail to communicate everything that makes the brand valuable.

Purchase: The Brand May No Longer Control Every Interaction

When shopping moves into an AI interface, the customer may complete parts of the journey without directly interacting with the retailer’s traditional website.

This can affect the opportunities a business has to communicate its identity, introduce complementary services or explain its policies.

However, the degree of change varies across platforms.

Some AI shopping systems direct customers to retailers to complete their purchases. Others support more deeply integrated transactions under specific conditions.

The emergence of agentic commerce should therefore not be confused with the universal replacement of traditional checkout processes.

Post-Purchase: Human Expectations Do Not Disappear

Once a product has been purchased, the customer still expects delivery, support, clear return conditions and reliable communication.

An AI agent may help track an order or initiate a service request, but the underlying responsibility for fulfilling the brand’s promises remains with the relevant businesses.

This is where customer experience may become particularly important.

The assistant may influence the initial choice, but the quality of the actual experience can determine whether the person chooses the brand again.

What the 2026 Research Reveals About Consumer Trust

The development of agentic commerce does not mean consumers are equally comfortable delegating every stage of a purchase.

Recent research reveals meaningful differences between using AI to gather information and trusting it to conduct financial transactions.

Visa: Only 23% Trust Generative AI to Handle Payments

On September 9, 2026, Visa published results from its Visa Trust Index for agentic commerce.

The research, conducted by Harris Poll between May 26 and May 28, surveyed 2,065 adults in the United States.

According to the official Visa announcement, 72% of surveyed consumers had used an AI assistant.

However, only 23% said they trusted generative AI to handle payment transactions on their behalf.

The same research found that 61% of respondents would trust Visa to handle agentic transactions.

These figures measure stated attitudes among the surveyed US population, not actual transaction volumes or global adoption.

Nevertheless, they illustrate an important distinction between trusting an AI system to provide assistance and allowing it to act in financially consequential situations.

For marketers, this suggests that familiarity with AI does not automatically translate into confidence in AI-mediated purchasing.

A brand’s reputation, payment safeguards and customer-facing assurances may remain influential.

Rithum: Incorrect AI Information Can Damage Brand Trust

Another important finding comes from research published by Rithum in partnership with Industry Dive.

The organizations surveyed more than 1,000 online shoppers in the United States and the United Kingdom to examine how AI influences product discovery and purchasing decisions.

According to Rithum’s Consumer Trust & Discovery Report, 58% of respondents said their trust in a brand decreases when AI gives them incorrect information about its products.

The research also found that 16% would abandon the purchase entirely in such circumstances.

These findings raise a difficult question about responsibility.

The incorrect information might originate from outdated product descriptions, inconsistent retailer listings or errors introduced by the AI system itself.

Yet the consumer’s negative experience may still affect their perception of the brand being discussed.

Businesses cannot control every AI-generated response. They can, however, reduce some sources of confusion by maintaining accurate, consistent and accessible product information.

Brand Visibility in the Age of AI Agents: Beyond Traditional SEO

Search engine optimization remains an important component of digital marketing.

However, AI-mediated product discovery introduces additional requirements.

A traditional search result often invites the user to visit a website and evaluate its content directly.

An AI assistant may instead combine information from multiple sources into a recommendation or comparison.

That means a business needs to consider not only whether its website appears in search results, but also whether its products and services can be understood accurately by systems retrieving information on behalf of customers.

From Keywords to Clear Product Attributes

Consider two retailers selling comparable laptops.

The first describes its product using broad marketing language such as “premium performance” and “exceptional productivity.”

The second provides detailed, consistent information about processor specifications, memory, display, battery performance, warranty coverage, connectivity and compatibility.

For a consumer seeking a laptop with specific requirements, the second description supplies more directly usable information.

An AI assistant may be better able to determine whether those details match the user’s request.

This does not guarantee that the second retailer will be recommended.

AI systems differ in their data sources, retrieval methods and ranking mechanisms. Other considerations, including price, availability and relevance, may affect the result.

Nevertheless, precise and verifiable information can reduce ambiguity when a product is evaluated by software rather than solely by a human visitor.

Structured Product Data Is Becoming More Important

In March 2026, OpenAI announced expanded shopping and product-discovery experiences in ChatGPT.

Its official product announcement explained that merchants could provide richer product information through the Agentic Commerce Protocol, helping AI-assisted shopping experiences represent products more accurately.

The announcement described product feeds containing information such as descriptions, pricing and availability.

Google has also expanded its agentic commerce strategy through the Universal Commerce Protocol, developed with retailers and technology partners.

For brands, these developments suggest that reliable product information may need to travel across more systems than the company’s own website.

Catalog accuracy, structured attributes and updated availability can become part of marketing infrastructure.

However, structured data should not be presented as a guaranteed method for securing recommendations.

There is no universal formula that ensures a particular brand will appear first in every AI shopping assistant.

The practical objective is to make accurate brand information accessible through supported and appropriate channels.

Can Brand Differentiation Survive Algorithmic Comparison?

One of the most interesting questions surrounding AI agents is whether they will strengthen competition on measurable attributes such as price, availability and product specifications.

For businesses that compete primarily through these characteristics, automated comparison may make competing offers easier for shoppers to evaluate.

But many brands derive their value from characteristics that cannot be reduced to a simple specification sheet.

Design, craftsmanship, reliability, identity, community and customer service can influence the choices people make.

The challenge is communicating these qualities in a context where an assistant may summarize a complex brand proposition in only a few sentences.

The Difference Between Price and Value

Imagine two hypothetical luggage brands.

Brand A sells a suitcase for €120, while Brand B charges €165.

If an AI assistant compares only price, dimensions and weight, Brand A may appear to offer better value.

But suppose Brand B provides a longer warranty, established repair support and easily available replacement parts.

For a customer who travels frequently, those characteristics may justify the higher price.

The challenge for Brand B is not to persuade an algorithm through exaggerated promotional claims.

It is to communicate the relevant advantages through accurate product information, transparent policies and credible supporting evidence.

The example is hypothetical, but the strategic issue applies across many categories.

Brands must become easier to evaluate without reducing their identity to the lowest available price.

Emotional Branding Still Matters

AI agents may filter and summarize product options, but purchasing decisions are not always purely functional.

People choose brands because they identify with their values, appreciate their design language or associate them with positive experiences.

A loyal customer may specifically ask an assistant to find products from a preferred brand.

Someone buying a gift may prioritize reputation, presentation or emotional significance over minor price differences.

These examples demonstrate why agentic commerce does not automatically eliminate emotional branding.

Instead, it may create two complementary communication requirements: brands need clear information for automated comparison and meaningful identities that people continue to recognize and value.

The Risk of Becoming Invisible Before the Customer Arrives

One possible consequence of AI-mediated discovery is that fewer customers will examine every competing offer directly.

If an assistant presents three options from a much larger market, the other businesses may never enter that particular customer’s consideration set.

This creates what could be described as an AI-mediated visibility risk.

The risk is not simply that a website receives fewer visits.

It is that a brand may be excluded from the decision process before a conventional website visit becomes necessary.

For marketers, this challenges the assumption that every meaningful customer journey begins with a search impression, advertisement click or landing-page visit.

Some journeys may begin with a conversation and progress through a recommendation without following that familiar sequence.

This shift is closely connected to the zero-click customer journey, a challenge previously examined by Targeted.gr in “The Zero-Click Customer: What Happens When AI Answers Before the Brand Does“. As AI assistants increasingly influence brand discovery before a website visit occurs, businesses must distinguish between generating measurable traffic and earning a place in the consumer’s consideration set. With agentic commerce, that challenge extends further, as AI systems may not only recommend brands but also participate in the purchasing process.

Why Brand Familiarity May Still Influence AI-Assisted Shopping

An established brand can retain an advantage when consumers specify it in their instructions.

A customer might ask an assistant to find the best offer on a particular product, compare two familiar brands or identify accessories compatible with something they already own.

In these situations, previous advertising, word of mouth and customer experience can continue to influence the request itself.

This creates an important distinction between brand discovery and brand preference.

An AI assistant may change which products are discovered.

But the preferences that shape the customer’s instructions may still reflect years of brand communication and personal experience.

For this reason, businesses should be cautious about abandoning traditional brand-building activities in favor of strategies focused exclusively on AI visibility.

Who Owns the Customer Relationship When AI Becomes the Interface?

A central issue for brands is how customer relationships evolve when an external AI platform mediates discovery and potentially facilitates purchasing.

In traditional e-commerce, a business may control much of the experience through its website, product pages, loyalty programs and customer-service channels.

When a purchase begins inside an AI assistant, some interactions may take place through an interface operated by another company.

This can create new customer-acquisition opportunities, but it also raises questions about how much of the journey the merchant controls.

OpenAI’s 2026 Shopping Changes Offer an Important Example

OpenAI’s March 2026 shopping update illustrates why the future of agentic commerce should not be treated as a single, settled model.

The company explained that its initial Instant Checkout implementation had not provided the degree of flexibility it wanted for merchants.

It therefore shifted its emphasis toward enhanced product discovery while allowing merchants to use their own checkout experiences.

The announcement also introduced a Walmart experience within ChatGPT designed to connect conversational discovery with Walmart’s own retail environment and capabilities.

This demonstrates that AI commerce does not necessarily require retailers to surrender their entire customer relationship.

Different models may coexist, including AI-assisted discovery followed by merchant-controlled checkout, deeper integrations and other experiences with varying levels of automation.

For marketers, the important question is how to preserve meaningful customer engagement regardless of where the interaction begins.

Customer Experience Must Remain Recognizable Across Channels

A brand promise should remain consistent whether the customer visits the company’s website, encounters a marketplace listing or receives an AI-generated recommendation.

Prices, return policies, shipping conditions and warranty information should not contradict one another without an understandable reason.

An assistant that provides a misleading description of a return policy can create expectations the retailer cannot fulfill.

Likewise, a customer who discovers a product through an AI platform but later receives poor support may associate the entire experience with the retailer’s brand.

Customer experience therefore needs to extend beyond a single branded interface.

It becomes a matter of consistency across the systems, channels and intermediaries involved in the transaction.

Why AI Agents Make Brand Trust More Complicated

Brand trust has traditionally developed through product quality, service consistency, recognizable identities and positive customer experiences.

AI introduces an additional element: the consumer must also consider whether the system presenting the information is reliable.

This creates two interconnected relationships.

The first is the relationship between the customer and the brand.

The second is the relationship between the customer and the AI service helping them choose.

A trusted assistant may influence which businesses a consumer is willing to consider.

At the same time, a trusted retailer may make a customer more comfortable with completing a purchase through an AI-assisted process.

What Happens When AI Gets the Brand Wrong?

Imagine a hypothetical cosmetics retailer whose products are described by an AI assistant as fragrance-free.

A customer selects one of those products based on that description.

The product, however, contains fragrance, and the assistant’s information was inaccurate.

The customer may be disappointed with the retailer, even if the company never made the incorrect claim.

This highlights a difficult reality for businesses: the brand experience can be influenced by information presented through third-party systems.

A retailer cannot guarantee the accuracy of every AI response.

It can improve its own information quality, maintain accessible documentation and provide clear correction and customer-support channels.

The broader objective is to reduce the gap between the company’s actual offer and the offer customers believe they are receiving.

Transparency Can Become a Competitive Advantage

Companies need to communicate not only what they sell but also the conditions under which customers can purchase, use and return their products.

Clear warranty terms, authentic customer feedback, transparent pricing and accessible support information can help consumers evaluate a business.

These signals may also provide useful context for AI-assisted comparisons when supported by the available data and retrieval systems.

Transparency should not be confused with publishing more promotional content.

The aim is to make claims understandable, verifiable and consistent.

The New Role of Reviews, Reputation and Third-Party Evidence

Customer reviews have long influenced online purchasing decisions.

AI-assisted comparison could make trustworthy third-party information even more relevant, particularly when shoppers ask assistants to evaluate the reliability of competing businesses.

However, AI systems do not all use reviews in the same way, and no brand should assume that a particular review platform will determine its visibility.

What remains important is the quality of the underlying evidence.

A high average rating may attract attention, but consumers also care about whether reviews are genuine, recent and relevant to their requirements.

For example, a customer selecting a hotel may care more about repeated complaints concerning cleanliness than about minor differences in overall ratings.

Similarly, someone purchasing expensive electronics may prioritize documented warranty support and after-sales service.

Brands should therefore treat reputation management as a process of improving customer experiences and responding credibly to problems, rather than merely accumulating positive scores.

Fabricated reviews, misleading testimonials and unsupported claims are especially risky because they can undermine both consumer confidence and the reliability of information used by intermediaries.

How Should Brands Prepare for AI-Mediated Customers?

The emergence of agentic commerce does not justify abandoning established marketing principles.

Instead, businesses can adapt their existing strategies to account for a customer journey in which AI may play a more active role.

1. Make Product Information Accurate and Consistent

Brands should examine whether descriptions, specifications, pricing, availability and commercial policies remain consistent across their websites and relevant sales channels.

Conflicting product information can confuse shoppers and automated systems alike.

The objective is to establish a reliable source of product truth and maintain it as offers change.

2. Explain Value Beyond Basic Specifications

Businesses should identify the attributes that distinguish their products from cheaper or more familiar alternatives.

These might include durability, repairability, customer support, customization or particular service benefits.

The claims must be specific and supported.

Descriptions such as “industry-leading quality” offer limited practical value without context or evidence.

3. Build Trust Through Verifiable Experiences

Genuine customer reviews, understandable return policies, realistic delivery commitments and responsive service remain important.

Brands can strengthen these signals by ensuring that promises made during product discovery are fulfilled after purchase.

The customer relationship should be evaluated across the entire experience, not only at the point of conversion.

4. Review AI-Assisted Product Discovery

Marketing teams can conduct structured, repeatable tests to understand how products are represented by different AI services.

For example, a company could examine whether an assistant correctly identifies its warranty terms or compares product attributes accurately.

Such testing should not be confused with definitive market-share measurement.

AI recommendations may vary according to prompts, available information, user context and system updates.

The purpose of these exercises is to discover factual gaps and recurring representation problems rather than assume that a few test prompts accurately describe every customer’s experience.

5. Preserve Human Choice and Support

As AI becomes more involved in shopping, businesses should continue offering clear opportunities for human interaction.

Some consumers may welcome automated comparison while preferring personal confirmation before expensive or sensitive purchases.

Others may want assistance understanding warranty conditions, product compatibility or service limitations.

Brands that accommodate different levels of automation can avoid making customers feel forced into experiences they do not trust.

6. Reconsider Customer Journey Measurement

Traditional marketing dashboards emphasize metrics such as impressions, clicks, sessions and conversions.

These remain relevant, but they may not fully capture journeys influenced by third-party AI assistants.

Where technically and contractually available, businesses can examine attributable referrals from AI services, the quality of incoming traffic, conversion behavior and customer-reported discovery channels.

They should also monitor recurring product-information errors and support issues connected to AI-assisted discovery.

However, no single analytics system should be assumed to identify every AI-mediated interaction.

Direct traffic, unattributed conversions and activity that never reaches the website can limit measurement.

The appropriate response is to improve attribution where possible while clearly acknowledging what cannot yet be measured reliably.

What Happens to Brand Loyalty When AI Continuously Compares Alternatives?

One longer-term question is whether AI agents will make consumers more willing to switch between brands.

An assistant that compares available offers may discover alternatives a customer would otherwise overlook.

For repetitive purchases, such as household supplies or standard accessories, this could make switching between comparable products more convenient.

But the effect will depend on the user’s instructions.

Some consumers may prioritize the lowest price. Others may ask their assistants to reorder familiar products, maintain preferred brands or avoid sellers with uncertain support policies.

Brand loyalty could therefore become another preference that AI systems are asked to respect.

A company that delivers consistent value may remain a preferred choice even when alternatives are easier to compare.

Conversely, brands that rely heavily on consumer inertia rather than meaningful advantages may face additional pressure if shoppers begin delegating comparison tasks more frequently.

These are plausible strategic developments, not established universal outcomes.

The future influence of agents on loyalty will depend on adoption, user preferences, system design and the transparency of recommendations.

Agentic Commerce Is Also Creating a New Economic Question

For brands, access to AI-mediated customers raises questions that extend beyond communication and positioning.

When an AI platform influences product discovery, several organizations may contribute to the resulting transaction: the assistant provider, the retailer, the payment processor and other intermediaries.

The commercial arrangements between those participants could affect acquisition costs, customer access and the distribution of revenue.

Google’s Universal Commerce Protocol and OpenAI’s Agentic Commerce Protocol demonstrate that companies are experimenting with different ways of connecting AI-assisted discovery with retail infrastructure.

But the economics of these arrangements deserve a separate analysis.

That is the focus of the forthcoming Market Insiders article, “The Economics of AI Agent Access: Who Controls the Next Digital Transaction?“, which will explore the business implications of platform access, licensing, transaction control and commercial dependency.

For marketers, the immediate consequence is clear: AI distribution should be evaluated as another customer-acquisition channel, with its own opportunities, limitations and potential dependencies.

It should not be treated as a guaranteed replacement for existing channels.

A Brand Must Be Understandable to AI Without Becoming Defined by AI

The technical challenges surrounding autonomous browsing have already been examined by Athens Pulse in “The Agentic Web Hits a Wall: Why Websites Are Blocking AI Agents“.

That analysis explores why websites restrict agents and how questions of identity, permissions and automated access are shaping the emerging internet.

For brands, the challenge is related but different.

Even when access is technically possible, a business must communicate its value clearly enough to be represented accurately during AI-assisted discovery.

This requires reliable data, meaningful differentiation and consistency between marketing promises and the real customer experience.

It also requires acknowledging that not every consumer will want to delegate the same level of control.

Practical questions about granting permissions, protecting personal information and approving payments will be addressed in the planned Techrow.gr guide, “How to Use AI Browsing Agents Safely: Permissions, Privacy and Payments“.

The relationship between these perspectives reveals the broader significance of the agentic web: technical access, commercial incentives, consumer safety and brand trust are interconnected, but they represent distinct challenges.

Conclusion: The Customer Remains Human, Even When AI Shapes the Journey

The rise of AI agents is introducing a new layer between businesses and the people they want to reach.

Consumers may increasingly rely on intelligent assistants to research products, compare alternatives and prepare purchasing decisions. For brands, this creates new questions about visibility, differentiation and the degree of control they retain over the customer experience.

Research published in 2026 suggests that consumer attitudes remain nuanced. Many people are familiar with AI assistance, yet trust becomes more selective when financial transactions and sensitive actions are involved.

Brands therefore need more than a strategy for appearing in AI-generated recommendations.

They need accurate product information, credible promises, transparent policies and customer experiences that remain consistent across different channels.

AI may become an increasingly influential participant in the buying journey, but it does not remove the human expectations that make brands valuable.

The challenge for businesses is not to replace human-centered branding with algorithm-centered marketing. It is to ensure that their value remains visible, understandable and trustworthy, regardless of whether a customer discovers it personally or through an AI assistant.

Frequently Asked Questions About AI Agents and Brands (FAQ)

What does it mean when AI agents become the customer?

The phrase describes an emerging situation in which AI assistants perform parts of the customer journey on behalf of people. They may research products, compare alternatives and assist with transactions. The actual customer remains the person or organization authorizing and using the purchase.

How are AI agents changing the customer journey?

AI agents can reduce the number of websites consumers need to visit by gathering information and presenting relevant options in one interface. They may influence awareness, product consideration and purchasing decisions, while users retain varying degrees of control depending on the service and task.

Why should brands care about AI shopping assistants?

AI shopping assistants may influence which products consumers discover and compare. If a brand’s information is incomplete, inaccurate or difficult to interpret, it may be represented poorly in an AI-generated recommendation. Brands should therefore consider product-information quality alongside traditional marketing activities.

Will AI agents replace traditional digital marketing?

Not necessarily. Traditional advertising, brand awareness, customer relationships and search visibility remain relevant. AI agents may create additional discovery channels and change how some customers compare products, but they do not automatically eliminate existing marketing channels.

What is the relationship between AI agents and SEO?

SEO helps websites make their information discoverable and understandable through search systems. AI-assisted discovery may also benefit from accurate content and structured product information. However, each AI system has its own retrieval and recommendation processes, and conventional SEO practices do not guarantee inclusion in AI-generated results.

Can a brand guarantee that AI assistants will recommend its products?

No. AI recommendations can depend on customer instructions, product attributes, availability, data sources and system-specific processes. Brands can improve the accuracy and availability of their information but cannot guarantee a particular recommendation or ranking across all platforms.

How does agentic commerce affect brand loyalty?

AI agents may make comparing and switching between products easier. However, customers can also instruct assistants to prioritize familiar brands, trusted retailers or established preferences. The long-term effect on loyalty will depend on consumer behavior and how AI shopping services are designed.

What role does customer trust play in agentic commerce?

Customer trust influences whether people are comfortable using AI for information, product selection and transactions. Research from Visa in 2026 showed a considerable difference between general familiarity with AI assistants and trust in their ability to handle payments. Transparent policies, reliable experiences and appropriate consumer controls remain important.

Why is accurate product information important for AI shopping?

AI assistants often rely on available product details when comparing options. Inaccurate specifications, outdated prices or conflicting warranty terms can produce misleading recommendations. Maintaining consistent, verifiable information can improve the quality of information available to both customers and AI systems.

What happens if AI gives consumers incorrect information about a brand?

Consumers may form inaccurate expectations or make unsuitable purchasing decisions. Research from Rithum found that many respondents reported reduced trust in brands when AI provided incorrect product information. Businesses should maintain reliable product documentation and clear channels for correcting misinformation.

Can small businesses compete in AI-mediated product discovery?

Potentially, yes. Smaller brands can differentiate themselves through specialized products, clear information, authentic customer experiences and distinctive value propositions. However, AI visibility is not guaranteed, and the opportunities may vary depending on platform rules, integration requirements and market competition.

What should marketing teams prioritize in 2026?

Marketing teams should prioritize accurate product data, meaningful differentiation, credible customer reviews, consistent service policies and an understanding of how their brands appear in AI-assisted discovery. They should also maintain human-centered customer experiences and avoid assuming that automated recommendations can replace every aspect of brand building.