AI brand sentiment is how positively, neutrally or negatively AI assistants and AI search tools describe a brand when they mention it. It covers tone, accuracy and whether the AI recommends the brand or steers people elsewhere.
This page covers what AI brand sentiment is, where it comes from, how to measure it with sentiment prompts and simple scoring, and what you can realistically do to improve it.
What is AI Brand Sentiment?
Traditional brand sentiment tracks how people talk about you on social media, in reviews and in the press. AI brand sentiment tracks how AI systems talk about you. When someone asks ChatGPT "Is this software easy to use?" or asks Perplexity "Is this company legit?", the answer carries a tone and a set of claims. That combination is your AI brand sentiment.
It has three parts worth separating. Tone is whether the answer is favorable, neutral or critical. Accuracy is whether the facts are right, including pricing, features, locations and leadership. Positioning is how you are framed relative to competitors, for example as the premium choice, the budget option or the one with poor support.
AI brand sentiment is one dimension of broader AI visibility. Visibility asks whether you show up. Sentiment asks what the AI says once you do.
Why AI Brand Sentiment matters
AI answers increasingly act like a first opinion. A buyer who asks an assistant for a shortlist and reads "known for slow customer support" next to your name may never visit your site to hear your side. Because many of these conversations end without a click, the damage often stays invisible in your analytics.
Sentiment also compounds. AI tools tend to repeat what the sources they draw on say. If several highly cited sources describe a problem you fixed two years ago, the AI may keep presenting it as current.
The limits are real, though. AI answers vary from one run to the next, so a single negative response is not a trend. A study by SparkToro and Gumshoe.ai, reported by Search Engine Journal, found that AI tools almost never returned the same recommendation list twice for the same prompt. Sentiment has to be measured across many runs and platforms before you act on it. And no one, including agencies and tool vendors, can directly edit what a model says.
How AI Brand Sentiment works
Where the opinions come from
An AI model's view of a brand comes from two places. The first is training data: the reviews, articles, forum threads and pages the model learned from. The second is live retrieval: assistants that search the web pull in current pages and summarize them. In retrieval-based answers, the specific pages cited carry a lot of weight. Peec AI's guide to tracking brand sentiment in LLMs highlights review platforms such as Trustpilot and Glassdoor, Reddit threads and outdated news articles as common drivers of how a brand is described.
Sentiment by theme
Sentiment is rarely uniform. A brand might get positive framing on product quality and negative framing on pricing or support. That is why it is more useful to group prompts by theme, such as pricing, ease of use, customer service, reliability and trust, and score each theme separately.
Variation across platforms
Different assistants use different models and retrieve from different sources, so the same brand can be described differently in ChatGPT, Gemini, Claude, Perplexity and Copilot. Sentiment can also vary by country and language.
AI Brand Sentiment in practice: an example
Consider a hypothetical meal-kit company. It runs 30 branded prompts, such as "Is [brand] worth the money?" and "What do customers say about [brand] delivery?", five times each across four AI tools.
Product quality comes back mostly positive. Delivery is mixed. Cancellation is clearly negative: many answers say canceling is difficult, and the cited sources are a three-year-old consumer news article and a popular Reddit thread. The company had simplified its cancellation flow a year earlier, but nothing prominent on the web said so.
The fix is not to argue with the AI. The company publishes a clear, dated help page on how to cancel, updates its FAQ, asks the news outlet whether a correction or update is possible, and responds publicly and honestly in community threads. Three months later, it reruns the same prompts to see whether the framing has shifted.
How to measure and improve AI Brand Sentiment
- Build a set of sentiment prompts. Write branded questions people actually ask, grouped by theme: price, quality, support, trust, alternatives. Keep them separate from unbranded visibility prompts so they do not skew those results.
- Run them repeatedly across platforms. Test each prompt several times in each AI tool your buyers use, since a single answer can be an outlier.
- Score consistently. Label each mention positive, neutral or negative, and flag factual errors separately. A simple, consistent scale beats a complex one applied loosely.
- Trace the sources. For negative or inaccurate answers, note which URLs are cited. These are your highest-leverage targets.
- Fix what you own first. Update your own pages with clear, current, dated facts on the topics that come back negative or wrong.
- Address third-party sources honestly. Request corrections for factual errors, respond to reviews, and earn fresh coverage that reflects how things are now. Do not post fake reviews or astroturf forums; it is unethical, often against platform rules, and tends to backfire.
- Track trends over time. Re-test on a fixed schedule and watch the direction of each theme rather than chasing individual answers.
Common mistakes and misconceptions
"One bad answer means the AI hates our brand." AI responses vary run to run. Measure across many prompts, runs and platforms before drawing conclusions.
"We can just tell the AI to say nicer things." Arguing with a chatbot in your own session does not change what it tells other people. Lasting change comes from improving the sources the AI learns from and retrieves.
"Sentiment is only about tone." Accuracy matters as much as tone. A friendly answer with the wrong price or a discontinued product can hurt you. Some of these errors are a form of AI hallucination, while others are faithfully repeating outdated sources.
"Good reviews will fix everything." Reviews help, but AI tools also draw on press, forums, comparison articles and your own site. A balanced approach across brand mentions in many places works better than focusing on one channel.
AI Brand Sentiment vs. traditional brand sentiment
Traditional brand sentiment measures what people say about you directly, in social posts, reviews and articles. AI brand sentiment measures how AI systems summarize and repeat that conversation. The two are linked, since AI draws on what people publish, but the AI version can lag reality, overweight a few heavily cited sources and present opinions as settled facts.
How Butter approaches AI Brand Sentiment
We start with fundamentals: making sure the facts on your own site are clear, current and consistent, and finding which sources are shaping what AI says about you. We do not promise to change a model's opinion overnight, because no one can.
Our GEO plans start at $399/month and include monthly AI prompt testing, which is where sentiment issues usually surface, along with on-page recommendations to correct them. When the problem sits in third-party coverage, our public relations work focuses on earning accurate, current media coverage.
For teams that want to connect brand health to business results, Butter Analytics brings website performance from 85+ tools into one dashboard, so you can see how traffic and conversions move alongside your AI sentiment work.
Frequently asked questions
How do I find out what ChatGPT says about my brand?
Ask it the questions your customers would ask, such as whether you are worth the price or how your support compares, and repeat each question several times. Record the tone, any factual errors and which sources are cited, then repeat the exercise in other AI tools.
Can I remove negative information about my company from AI answers?
Not directly. You can correct your own content, ask publishers to fix factual errors and earn newer coverage, which can change what AI tools retrieve and cite over time. Accurate criticism is unlikely to disappear and is better addressed by fixing the underlying issue.
Why does AI describe my brand differently on different platforms?
Each AI tool uses its own model, training data and search sources. One may cite a review site while another leans on a news article, which produces different framing.
How often should I track AI brand sentiment?
Monthly is a sensible default for most brands, with extra checks after major news, product changes or a PR issue. Keep your prompt set stable so changes reflect real shifts, not new questions.