By Eric Richmond, CiteMetrix
A chatbot can recommend your brand and help create a purchase. It can also surface a limitation, repeat an outdated detail, or point the buyer toward a competitor.
That two-sided influence is the important finding in new research from Semrush and Exploding Topics.
In their survey of 2,338 adults in the United States, published by Semrush on September 7, 2026, 73.58% of respondents who used AI at least weekly said they had purchased a product based on an organic AI recommendation. At the same time, 57.5% of AI users said they had decided not to buy something based on information from a chatbot.
The same channel produced a purchase decision for 73.58% of weekly AI users, and talked 57.5% of AI users out of one.
That is not simply a funnel.
It is an influence layer with two directions.
AI can create demand, or intercept it
The Semrush survey also found that 65.01% of AI users had at least partially replaced product-related Google searches with AI chatbots.
These figures are not CiteMetrix research, and they should not be treated as universal measures of consumer behavior. They are self-reported responses from one survey. But they are a meaningful directional signal: AI assistants are increasingly involved in product research, brand discovery, and purchase decisions.
The acquisition opportunity is clear. If an AI assistant recommends your product, you may enter a buyer’s consideration set before they ever visit your website.
The risk is less visible.
If an assistant warns a buyer away from your brand, the lost opportunity may never appear in your analytics. There may be no impression in Search Console, no paid click, no abandoned cart, and no “we chose a competitor because the chatbot said…” note in your CRM.
The buyer simply moves on.
The negative path is harder to measure
A chatbot can influence a buyer away from your brand in several ways:
- It can surface negative or mixed reviews.
- It can describe a product limitation you would rather the buyer did not see.
- It can contradict your current positioning.
- It can repeat an outdated price, feature, or policy.
- It can describe a competitor as a better fit.
- It can answer the buyer’s question using a competitor’s source entirely.
- It can omit a critical fact that would have changed the comparison.
Some of these statements may be accurate. Others may be incomplete, stale, misleading, or simply wrong.
From a measurement perspective, they look similar: your brand loses consideration.
Traditional SEO reporting is not designed to capture this. Rankings tell you where a page appears. Traffic tells you who clicked. Conversion reporting tells you what happened after the visitor arrived.
None of those metrics reliably show what an AI assistant said before the visitor decided whether to click, or whether your brand was worth considering at all.
That is why AI visibility programs need to become part of brand knowledge governance: the cross-functional discipline of keeping the facts, claims, positioning, and sources about your brand accurate across the systems buyers use.
Presence is not enough
A brand can be mentioned frequently and still be described unfavorably.
It can be recommended for one use case but misrepresented for another. It can appear in a comparison but be ranked below competitors because the assistant has incomplete information. It can have strong citation visibility while carrying an inaccurate product description.
So AI visibility requires at least three separate questions:
- Are we present?
- How are we being described?
- Is the description accurate?
The first question is citation tracking.
The second is sentiment and perception measurement.
The third is accuracy monitoring against a verified source of truth.
CiteMetrix measures all three. Model Sentiment and Brand Perception show the qualitative language AI platforms use about your brand, including positive, neutral, and unfavorable framing. This is where a high mention count can be put into context.
A citation is not automatically a good outcome.
Measure sentiment as deliberately as visibility
Negative framing should be monitored with the same discipline as positive recommendations.
That does not mean trying to remove every criticism. Buyers need honest information, and a credible brand should not treat every unfavorable statement as a crisis.
The practical question is whether the framing is:
- Accurate and current
- Relevant to the buyer’s question
- Properly contextualized
- Balanced against the strengths and limitations of alternatives
- Based on a source you would recognize and stand behind
CiteMetrix’s Model Sentiment and Brand Perception analyses help identify how AI platforms characterize your brand over time. You can see whether sentiment is shifting on a specific platform, query category, or competitor comparison.
That distinction matters. A brand may have a stable overall visibility score while its perception deteriorates in the exact prompts buyers use before purchasing.
Accuracy requires more than a spot check
Manual testing can uncover an incorrect AI answer. It is not a reliable monitoring system.
The answer may change by platform, query wording, location, or time. A correction made on your website may not immediately change what an AI assistant says. And a fact that is correct today may become outdated after a pricing, product, or policy change.
CiteMetrix’s Accuracy & Hallucination Detection checks AI responses against your verified Brand Facts. It identifies five error types:
- Incorrect Fact
- Outdated Info
- Fabricated Claim
- Misleading Context
- Critical Omission
Each issue receives one of three severity levels:
- Minor
- Moderate
- Critical
The system also separately tracks unverified facts and correct answers. That matters because an unverified statement is not automatically false, and a correct answer should not generate a false alarm.
Detection is included from Starter. On Professional and above, Hallucination Watch automatically rechecks flagged issues every day until the problem is resolved.
That changes the workflow from “we found an error” to “we know whether the platform stopped repeating it.”
Give AI a dependable source of truth
Accuracy monitoring is only useful if your organization can agree on what is accurate.
CiteMetrix’s Source of Truth creates that foundation. Verified Brand Facts can generate your llms.txt and schema.org markup deterministically, not through AI-written guesses or placeholder text.
The workflow is deliberately controlled:
- Add a fact about your brand.
- Verify it internally.
- Choose whether it should be published.
- Generate the relevant AI-readable expressions.
- Monitor for internal and live drift.
- Check whether the correction changed the AI answer.
The verified-only publish gate is important for cross-functional teams. Marketing may own positioning. Product may own feature details. Legal may review claims. Content operations may manage the website and structured data.
AI visibility is therefore not only an SEO task. It is a coordinated brand knowledge governance process involving marketing, legal, product, and content operations.
CiteMetrix’s Correction Proof closes the loop on the fix, not on the model: it documents the remediation that went out. Updating your website is an action — tracked through to the fix, not to whether the AI answer changes, which nobody can promise.
Find where the negative framing comes from
Once an unfavorable or inaccurate answer is identified, the next question is: what is producing it?
CiteMetrix Citation Gaps show which queries create absent or weak visibility. Source Pages identify the pages AI platforms are using when they form an answer.
Together, these features help distinguish different problems:
- Your brand is missing from a category answer.
- Your brand is present but described using an outdated source.
- A competitor’s page is supplying the comparison language.
- Your own site contains conflicting information.
- A key buyer question has no clear, citable answer from your brand.
This is where remediation becomes more practical. Instead of broadly “optimizing for AI,” your team can identify a specific query, source, claim, or page that needs attention.
If the issue is a competitor comparison, prioritize it. A misdescribed comparison can affect the buyer at the exact moment they are deciding between options.
Track the competitive direction
AI recommendations rarely happen in isolation. Buyers ask for the best option, alternatives, comparisons, and products for a particular use case.
CiteMetrix Share of Voice provides a head-to-head view against named competitors, broken down by AI platform. That helps answer questions such as:
- Which competitor appears more often in category prompts?
- Where are they winning comparisons?
- Which platforms describe them more favorably?
- Are they cited from sources your team has overlooked?
- Is your share improving after a content or source correction?
The goal is not to attack competitors. It is to understand the information environment in which buyers are making decisions.
CiteMetrix Impact then connects visibility movement to business outcomes, helping teams explain whether changes in AI presence, sentiment, or accuracy are connected to meaningful performance.
Use the full closed loop
A useful AI visibility program should follow a monitor-to-fix workflow:
Monitor → Detect → Diagnose → Fix
- Monitor what AI platforms say about your brand.
- Detect negative framing, missing visibility, and factual errors.
- Diagnose the query, platform, source, and error type.
- Fix the underlying content, facts, markup, or source page.
- Track the remediation through to the fix — not to whether the AI answer changed, which nobody can promise.
Every other platform stops at the diagnosis. We don’t.
CiteMetrix brings core capabilities together across 8 AI platforms + Google AI Overview on every plan, including ModelScore, sentiment, accuracy detection, source-of-truth controls, citation gaps, source pages, Share of Voice, Impact, and remediation workflows.
Business plans start at $79 per month, with the platform’s capabilities included rather than split across multiple tools. See the CiteMetrix pricing page for current plan details.
The measurement argument
The Semrush and Exploding Topics survey does not prove that every buyer behaves this way. It measures stated decisions from 2,338 U.S. adults surveyed in July 2026, with different figures based on different respondent groups.
That caveat matters.
The result is not a universal conversion rate or a forecast for your business. It is a directional signal that AI assistants can influence purchase decisions in both directions, and that the negative direction is easy to miss.
If AI can talk a buyer into purchasing, it can also talk one out of purchasing.
The brands prepared for this shift will not measure only whether they were mentioned. They will measure how they were described, whether the description was accurate, which sources shaped the answer, where competitors won the comparison, and whether corrections actually took hold.
See what AI says about your brand, and whether it is helping or intercepting demand → citemetrix.com
Research source: Semrush, “AI chatbots talked 57.5% of AI users out of buying,” published September 7, 2026.
