AI Sentiment: What Engines Say About You, Not Just Whether They Cite You

Your AI visibility dashboard is green. Mentions are up across the major engines. Then a prospect tells you, almost apologetically, that they asked ChatGPT about you and it said you were “expensive and better suited to larger organisations.” Nobody wrote that. An engine inferred it, and now it’s in the room before you are.

Citation counting measures whether you appear. It says nothing about what is said. For most organisations the second question is the more commercially consequential one.

What is AI sentiment?

AI sentiment is the characterisation an engine attaches to your organisation when it describes you — the adjectives, the caveats, the comparisons and the qualifications that surround your name in a generated answer.

It differs from social sentiment in two ways that matter. First, it’s synthesised rather than expressed: no individual said it, the model assembled it from many sources. Second, it’s delivered with an air of neutrality. A negative review reads as one person’s opinion. The same claim inside an AI answer reads as a summarised fact.

Where does an engine’s view of you come from?

Understanding the inputs tells you which ones you can influence.

• Third-party discussion — reviews, forums, comparison sites, trade press. The strongest input, and the least controllable.
• Your own positioning — if your site emphasises enterprise clients and complex deployments, engines reasonably infer “enterprise, complex, expensive.”
• Absence of information — the most underestimated input. If you don’t publish pricing structure, engines infer it from your positioning and your competitors’ published pricing, and that inference usually lands unhelpfully.
• Comparative framing — when you appear in a list, you inherit the contrast the list is built on. Being the third item in “enterprise-grade options” fixes an attribute you never claimed.
• Stale material — an old article about a discontinued limitation persists long after the limitation does.

How to measure AI sentiment properly

Sentiment analysis borrowed from social listening — positive, neutral, negative — is close to useless here, because almost every AI answer is superficially neutral in tone while carrying a strongly loaded characterisation. Measure attributes, not tone.

Define the attributes that matter to your buyers

Typically six to ten, drawn from your actual sales objections. Price position. Complexity of implementation. Target customer size. Sector fit. Support quality. Reliability. Breadth versus specialisation. Maturity. These are the dimensions a buyer is deciding on, so they’re the dimensions to track.

Run prompts that force a characterisation

Neutral prompts produce neutral answers. Ask the questions a real buyer asks in private:

• “What are the downsides of using [company]?”
• “Who is [company] not a good fit for?”
• “Is [company] expensive compared to alternatives?”
• “What do customers complain about with [company]?”
• “Should a mid-sized [sector] organisation choose [company] or [competitor]?”
• “What should I know before signing with [company]?”

Code the answers against the attributes

For each run, record how the answer positions you on each attribute — favourable, unfavourable, absent or inaccurate — and capture the verbatim phrase and any cited source. The verbatim matters: “requires technical setup” and “difficult to implement” describe the same thing with very different commercial effect.

Separate inaccurate from unfavourable

This distinction drives everything you do next. An unfavourable but true characterisation (“designed for larger deployments”) is a positioning question — possibly one you should own rather than fight. An inaccurate characterisation (“does not integrate with common systems” when it does) is a correction task with a findable source.

How to correct what engines say about you

Trace the claim to its source

Ask the engine directly where the characterisation came from, and follow the citations. Often you find a single stale article, one prominent review, or a comparison table with a wrong cell. Fixing that upstream source is far more effective than publishing a rebuttal.

Publish the specific facts that displace inference

Where an engine is inferring because you published nothing, publish. A clear page on pricing structure, implementation timeline, minimum requirements and typical customer profile gives the model something concrete to use instead of a guess assembled from your competitors’ pages.

State your limitations before someone else does

Counterintuitive but consistently effective. A page saying plainly who your product is not for gives engines an authoritative, first-party source for the negative question — and your framing of a limitation is invariably fairer than a competitor’s. It also reads as candour to human buyers.

Update stale third-party records

Directory entries, review-site profiles, comparison tables and old press coverage. Ask for corrections where the information is factually wrong. This is unglamorous work with a disproportionate effect, because those sources are structured, trusted and heavily retrieved.

Do not attempt to manipulate the record

Fake reviews, coordinated posting and manufactured praise are detectable, increasingly discounted, and in many jurisdictions unlawful. They also fail on their own terms: engines weight source diversity and independence, which is exactly what manufactured signals lack.

What good looks like

The realistic goal isn’t universal praise. It is accurate characterisation with your framing. If engines describe you as suited to complex, regulated environments with a longer implementation cycle, and that is true, you’ve a working outcome — unqualified prospects filter themselves out and qualified ones arrive pre-briefed.

Track two things over time: the accuracy rate across your defined attributes, and the proportion of characterisations that use your own published language rather than a third party’s. The second rising is the clearest evidence that your reference content is being retrieved.

Our ARIA citation tracker records what engines say and which sources they draw on, not just whether you appear, and the AI citability scorer checks whether your corrective content is structured to be picked up. If sentiment is showing up in your sales calls, start a project and we will map it properly.

Frequently asked questions

How is AI sentiment different from social listening?

Social listening captures what individuals said. AI sentiment captures what a model synthesises and presents as summary. The synthesis carries more perceived authority with a buyer, and it can persist long after the underlying opinions have changed.

Can we get an engine to remove a false claim about us?

There’s no reliable direct edit mechanism. The workable route is to correct or outweigh the underlying sources and publish authoritative first-party material on the point. Major engines do offer feedback and, in some jurisdictions, correction request routes — worth using for clearly defamatory or factually wrong statements, but do not rely on them alone.

How often should we check sentiment?

Monthly for a defined attribute set, using the same prompts and conditions each time. Also check immediately after any launch, pricing change, leadership change or negative news cycle, since those events reshape the source pool quickly.

What if the negative characterisation is simply true?

Decide whether it’s a weakness to fix or a positioning choice to own. If it’s a genuine trade-off you have made deliberately, publish the reasoning. Engines will generally reflect a well-argued first-party explanation, which converts a liability into a qualifier.