AI Assistants Become the Main Product Research Source for 15% of Respondents, Poll Reveals

AI assistants now serve as a primary product research channel for about 15% of respondents, according to a September 2026 survey. The findings show how shoppers are combining AI, search engines, social media, and company websites to build answers that rarely come from one place.

A survey of 200 people who had recently searched online for product or service information found that AI has become a regular part of commercial research. AI assistants and chatbots were the main source for 14% of respondents, a figure that rounds to about 15%. Another 11% relied primarily on AI-generated search summaries. Combined, AI-mediated interfaces accounted for 25% of primary-source choices.

That result places AI alongside social media as a major starting point for product research. It also shows why brands face a wider visibility problem. A company may rank in search results and still be absent from the part of the research journey where a buyer decides whether a claim is credible, a price is reasonable, or a product fits a specific need.

The survey data points to a broader change in how people gather commercial information. Search engines remain the most frequently used source, with 56.5% of respondents turning to them during their most recent journey. Social media followed at 48.5%, company websites and apps at 44.5%, and AI assistants or chatbots at 33.5%.

AI does not need to replace search to have a major effect on product discovery. A third of respondents used an AI assistant at some point in the process, while one in four selected an AI-mediated source as the main place they relied on for information.

The breakdown of primary sources was widely distributed:

The 14% figure for AI assistants is close to the 15% described in the headline, while the combined 25% figure captures a larger category: interfaces that summarize, synthesize, or present information through AI.

Other research supports the idea that AI is becoming part of ordinary information gathering, while also showing that it has limits. Pew Research Center reports that searching for information is among the common uses of AI tools. A Gartner survey found that only about one-third of consumers viewed generative AI chatbots as comparable to search engines for learning new information.

The practical reading is simple: AI is already important enough to shape product research, but consumers still use other channels to fill gaps and check what they have found.

AI assistants have become a meaningful part of product research, serving as the primary source for about 15% of respondents and appearing somewhere in the journey for one-third. Yet the survey shows that consumers still move among search engines, social media, company websites, reviews, and AI tools because individual sources rarely answer every practical buying question. Only 39.5% found their main source completely sufficient, with price, comparisons, evidence, freshness, limitations, and fit among the most common gaps. For brands, the opportunity is not simply to appear in search or AI summaries, but to publish complete, current, verifiable information that reduces the need for further research.

The survey found that 75.5% of respondents considered the overall process of finding information very or somewhat easy. Only 8% called it difficult.

That positive response can hide a complicated workflow. Only 39.5% said their main source answered their question completely. Another 41.5% said it answered the question mostly, while 16% said it answered only partly.

People often describe the process as easy because moving between sources has become cheap and fast. A shopper can ask an AI assistant for a summary, search for independent reviews, open a company page for specifications, and check social media for actual user experiences. Each step takes little time. The complete answer still has to be assembled across all of them.

72.5% of respondents used two or more sources during their most recent product or service search, while only 19% relied on a single source.

The most common pattern involved exactly two sources, reported by 50.5% of respondents. That behavior matters for content strategy because the first source often functions as a launch point. It may answer the broad question and create the next, more specific one.

For example, a person searching for accounting software may start with a general product comparison. The next search may focus on inventory management, Shopify integration, pricing by user, migration support, or cancellation terms. A page that handles only the first question leaves the user to solve the rest elsewhere.

Three quarters of respondents identified something missing or unclear in their search results. The largest gap involved price or total cost, cited by 33.5% of the sample.

That result reflects a common weakness in product and service content. A page may describe features in detail while leaving out the information that determines whether a buyer can proceed. Base pricing may appear without implementation fees, usage limits, renewal costs, add-ons, taxes, or cancellation rules.

The other gaps show that users want more than a product description:

These gaps are closely connected. A missing price makes comparison difficult. A missing comparison weakens the value of a product claim. A lack of evidence creates doubt, while outdated information makes even accurate details harder to trust.

The result is a content problem that keyword research alone cannot identify. A page can cover the right topic and still fail to answer the questions that determine a decision.

The important shift is not that AI is replacing search; it is that product research is becoming a connected system in which each channel contributes a different kind of evidence. AI may provide a useful summary, social media may reveal real-world experiences, and a company site may hold the authoritative terms, but buyers still have to assemble and verify the answer. Brands should therefore measure content by how many follow-up questions it eliminates, not merely by rankings, clicks, or appearances in AI-generated results.

AI assistants are effective at collecting and organizing information that already exists. They can explain a product category, compare features, summarize reviews, and suggest follow-up questions. Their usefulness depends on the quality, freshness, and completeness of the information available to them.

That creates a clear responsibility for companies. Product information has to be written in a way that can be found, understood, and checked across different systems. A short statement such as a product being easy to use may be repeated by an AI system, but the statement becomes more useful when the source explains:

Google’s guidance on AI features in Search emphasizes that AI features can surface links that help people explore information. That does not guarantee that every source will receive a visit. A study published on arXiv reported that clicks to sources cited in AI Overviews occurred in about 1% of visits in the researchers’ analysis.

This makes source quality and source visibility important even when a user never opens the original page. A brand’s information may influence an answer through a summary, citation, or recommendation. The source still needs to provide enough detail for users who continue the investigation or verify the claim elsewhere.

A complete answer has two parts: information that addresses the question and signals that help the user trust it. The survey found that only 15% of respondents skipped a reliability check.

The most common verification method was comparing the answer with another independent source, used by 32% of respondents. Other checks included:

This behavior explains why a company page often fails to serve as the final destination. A buyer may find the specifications there and then leave to see whether an independent reviewer describes the same performance. They may look for customer complaints, implementation details, or evidence that a claim applies to a business of their size.

The verification stage also affects AI-generated answers. Users may accept an AI summary as a useful first pass, then look for the original documentation or a second source before making a purchase. If a company’s page lacks dates, authorship, references, testing details, or clear terms, it gives the user little help during that second stage.

A useful content page makes checking easier by showing its work. It identifies the source of important claims, explains when information was updated, and separates confirmed specifications from estimates or opinions.

The survey found limited demand for a short, direct answer when people were researching products and services. Only 15.5% selected that format as the most helpful.

A larger group preferred formats that provide context and practical detail:

Together, these richer formats accounted for 58% of preferences. The finding applies especially to decisions involving cost, setup, compatibility, risk, or long-term use. A short answer can identify a product. It rarely explains whether the product will work in a specific situation.

The most useful pages tend to connect information to a decision. A comparison can show which product suits a small team and which one suits a larger organization. A tutorial can reveal the time and technical knowledge required for setup. An example can show how a feature affects an actual workflow.

This does not mean every page needs to become a large guide. It means the format should match the decision. A quick answer works for a simple fact. A purchase decision needs enough explanation to reduce the number of follow-up searches.

The survey does not describe separate audiences for search, social media, company websites, and AI. It describes people moving among them.

Search engines were used by 56.5% of respondents during their research. They also played a recovery role: 31% used a search engine after their first source failed to provide enough information.

Social media has a different strength. It often supplies firsthand experiences, demonstrations, complaints, and informal comparisons. Nearly half of respondents used social platforms, and 24% named social media as their main source. For many buyers, social content helps answer questions about fit and real-world use that product pages leave open.

Company websites remain important because they usually contain the authoritative details: specifications, pricing, terms, documentation, and support information. Their weakness appears when those details are incomplete, difficult to find, or written only for an internal audience.

AI adds another layer by helping users summarize and connect information from several places. It can also generate new questions. A person who asks an assistant for the best project management tool may follow up with questions about data storage, user permissions, integrations, contract terms, or migration.

The journey can be represented as a chain:

Content that supports only the first step leaves the later steps to competitors, reviewers, or unrelated sources.

When the first source failed to provide a complete answer, respondents usually continued the search. Among those who reported an incomplete initial answer, 32.5% checked another source immediately.

Other responses included:

These actions reveal the cost of weak content. The user may still buy from the company, but every missing detail adds another opportunity for a competitor to appear. A follow-up search can expose a competing product. A social media discussion can change the user’s impression. A slow response from the company can cause the decision to be delayed.

The 14.5% who contacted a company directly also represent a practical signal. Support and sales teams often receive the same questions that product content should answer. Repeated questions about total cost, integrations, contract conditions, or setup requirements can show exactly where a page needs revision.