How AI engines describe your brand, why Google and ChatGPT criticize for opposite reasons, and a five-step sentiment audit.
AI brand sentiment decides how an engine describes you once it has already decided to mention you. Getting cited is step one. Getting described as the obvious choice instead of the risky one is what closes the deal, and most B2B teams never look at it.
BrightEdge classified every brand mention across Google AI Overviews and ChatGPT in February 2026 and found that 47.7% of Google's brand references and 54.4% of ChatGPT's landed in the neutral bucket. Most B2B brands sit in that bucket, described in language that gives a buyer nothing to act on.
This guide covers what the 2026 sentiment and accuracy data shows, why the two largest engines criticize brands for opposite reasons, and how to run an AI brand sentiment audit that produces a fix list instead of a dashboard.
AI brand sentiment is the tone an AI engine uses when it names your company in a generated answer: recommended outright, mentioned with caveats, or flagged as a limitation. Citation tracking answers whether you appear. Sentiment tracking answers what the reader takes away.
The two metrics move independently. A page can earn a citation while the surrounding sentence positions you as the budget option someone settles for. BrightEdge's February 2026 analysis put Google AI Overviews at 49.9% positive, 47.7% neutral and 2.3% negative brand mentions, with ChatGPT at 43.9% positive, 54.4% neutral and 1.6% negative.
Read those numbers as a distribution problem rather than a crisis. Almost nobody gets attacked. Roughly half of everybody gets described in flat, interchangeable language that gives a buyer no reason to shortlist them. Moving out of neutral is the actual competition.
Spotlight reached a similar split across 1.8 million brand-mentioning responses in 2026: 80.6% neutral, 18.4% positive, 1% negative.
Rarely, and the rate differs by engine. BrightEdge measured negative brand mentions at 2.3% in Google AI Overviews and 1.6% in ChatGPT, which makes Google 44% more likely to criticize a brand it names. Google's positive-to-negative ratio runs about 21:1 and ChatGPT's about 27:1.
Rarity does not mean low stakes. BrightEdge grouped identifiable negative triggers and found brand controversies and legal issues drove 32% of them, product limitations and compatibility 21%, safety and recalls 17%, service failures and outages 11%, product discontinuation 9%, price and value criticism 8%, and competitive comparisons 3%.
For B2B software, that ranking is unusual news. Only 8% of negativity traces to price criticism and only 3% to head-to-head comparisons. The categories that dominate are operational: an outage postmortem, a deprecated feature, a compliance dispute. Those live in third-party coverage, not on your site.
Monitor your progress with Nobori across all five engines rather than sampling one.
Google reports news. ChatGPT evaluates products. BrightEdge isolated prompts where one engine went negative and the other did not, and found Google 4.5x more likely to surface criticism tied to lawsuits, breaches, recalls and regulatory action, while ChatGPT went negative 3x more often on product evaluation queries about limitations and compatibility.
The engines also disagree about who deserves the blame. On overlapping prompts that carried negative brand sentiment in both, Google and ChatGPT flagged different brands 73% of the time. One engine might criticize the platform while the other criticizes the integration partner in the same scenario.
Industry flips the pattern too. BrightEdge recorded Google as more negative in Electronics (2.5% vs 1.7%) and Education (2.5% vs 1.4%), then found the reverse in Apparel, where ChatGPT ran 3x more negative (0.6% vs 0.2%) because there was less controversy for Google to report.
Tracking one engine gives you half the story, and possibly the wrong half.
In research, before a buyer has a shortlist. BrightEdge found 85.1% of Google's negative-sentiment responses appeared on informational queries and 68.5% of ChatGPT's did the same, against an all-prompt informational baseline of 58.7%.
ChatGPT pushes further down the funnel. It placed 19.4% of its negative sentiment on consideration-stage queries, compared with 1.5% for Google. That is the "which of these three should we buy" moment, and ChatGPT is the engine willing to render a verdict there.
BrightEdge also identified a mixed-sentiment pattern in roughly 1.4% of brand-mention prompts, where an engine praises one vendor and flags another inside the same answer. Traditional search never did this. A ranked list of ten blue links made no argument about which vendor had a weakness.
Map your own exposure by funnel stage. If your category triggers evaluative language and you have unresolved gaps in a competitor's favorite comparison, ChatGPT will say so at the worst possible moment.
More than most teams assume, and the errors often start on the brand's own site. Profound analyzed over 158,000 claims through its FactCheck product and published the results on 17 August 2026: inaccuracy rates ranged from 4.3% to 6.8% depending on the answer engine.
The source breakdown is the uncomfortable part. Profound found 54% of brands had at least one inaccurate claim citing their own content, and 51% had at least one citing earned media. Your outdated pages feed the engine that then repeats the mistake back to your buyers.
Errors also scatter. The top 10 domains accounted for only 13% of earned media inaccuracies and the top 100 for 40%, which means no single publisher fix moves the needle. For brands with inaccurate coverage, Profound found errors typically traced to just four websites, and those four varied by brand.
No industry list of problem domains will match yours, so the audit has to start from the URLs the engines actually cite when they describe your company.
Because pricing changes faster than anyone updates the pages that describe it. Profound's FactCheck data shows pricing and billing made up 12% of evaluated claims but 24% of all inaccurate claims, six times the share of the next-largest category, eligibility and terms, at 4%.
Among brands with at least ten evaluated pricing claims, pricing was the highest-inaccuracy theme for 66% of them. Some brand sites carried pricing that contradicted the company's own records. Profound also found pricing accounted for 35% of all earned media inaccuracies, spread thin across publishers.
For B2B software this compounds. A stale "starts at $49/seat" line in a 2024 roundup survives in retrieval long after you moved to usage-based tiers, and a buyer who gets that number from ChatGPT arrives at your sales call already anchored wrong.
Fix the owned pages first. Publish current pricing in plain HTML text, date-stamp it, and treat every third-party pricing page as a correction target. Our guide to off-page AEO and third-party mentions covers the outreach mechanics.
Month to month, by several points, with no market event behind it. Previsible calls this LLM perception drift and tracked it in the project management category using Evertune's AI brand score, which measures how likely a model is to recommend a brand unprompted.
Between September and October 2025, Slack dropped 8.10 points and Trello dropped 5.59, while Monday.com fell 0.78 and ClickUp 0.74. Atlassian gained 5.50, Google 3.62 and Microsoft 2.08. Professional services firms including Deloitte (+5.00) and KPMG (+4.00) climbed into a software category they do not sell software in.
Jordan Koene, Previsible's CEO, reads the pattern as category entanglement plus ecosystem advantage: models pull project management into operations, workflow orchestration and IT consulting, and reward brands that appear across many contexts with documentation, integrations and cross-product density.
Smaller vendors moved too. Celoxis gained 5.17 and Workfront 2.38, which shows you can shift model recall without winning classic SEO. Track the stability of your score across months, not a single reading.
The three or four adjectives a buyer needs to hear to shortlist you, chosen before you write anything. Models learn brand attributes from repetition across sources they trust, so "reliable at enterprise scale" only sticks if independent pages say it in those words.
Pick attributes you can prove with artifacts. Uptime history, SOC 2 reports, published benchmarks, named customer logos and migration guides all give an engine something concrete to summarize. Vague claims about being innovative give it nothing, so it defaults to neutral phrasing.
Then check what you currently own. Prompt each engine with "what is [brand] known for" and "what are the drawbacks of [brand]" and read the adjectives it returns. That second prompt is the one teams skip and the one that tells you which objection your sales team is already fighting.
Publish the counter-evidence where third parties will pick it up rather than only on your own blog. Engines weight corroboration, and a claim that appears in one place reads as marketing.
Build a fixed prompt set, run it across every engine, and score the language rather than the citation. Five steps get you a fix list.
Rerun the same prompt set monthly. A one-time snapshot cannot separate a real shift from normal churn, and citations rotate fast enough to fool you. Our analysis of AI citation half-life and retention explains the baseline volatility you are measuring against.
Yes, and the effect shows up days later rather than in your referral report. Profound joined AI conversation data to browsing behavior for more than 2 million conversations from January to June 2026 and measured what users did in the seven days after an engine introduced a brand they had not asked about.
Visits ran well above each user's own forecasted baseline. Gemini lifted brand site visits from a 2.21% baseline to 5.42%, Google AI Overviews from 4.83% to 7.79%, and ChatGPT from 4.33% to 6.39%. Software was the strongest vertical on Google AI Overviews at a 4.0 point lift, or 128% above baseline.
Attribution hides almost all of it. Profound found 20.5% of downstream visits happened within an hour and 42% within 24 hours, yet only 2.47% of post-exposure visits in June carried a trackable AI referral parameter.
Most of that demand lands as direct and branded traffic days later, so sentiment quality earns budget even when your dashboard cannot draw the line back to the answer that started it.
Four deliverables, in dependency order.
Week 1: baseline and triage. Run the 25-prompt audit across five engines. Count your neutral share and list every factual error with its source URL. Fix owned-site pricing, plan and feature pages the same week, since Profound found 54% of brands feed inaccuracies with their own content.
Week 2: attribute decision. Choose three attributes you will claim and name the artifact that proves each one. Publish the missing artifact, whether that is a security page, a benchmark, or a migration guide with real numbers in it.
Week 3: earned correction. Email the four to six domains carrying wrong facts about you. Lead with the specific error and the correct figure. Profound's data shows the problem domains are brand-specific, so a template blast wastes the send.
Week 4: instrument and rerun. Lock the prompt set, rerun it, and compare. Report three numbers to leadership: neutral share, factual error count, and negative mention count by engine. Track those alongside citation volume from the start. Closing the wider B2B AI visibility gap depends on both.
Nobori tracks how ChatGPT, Google AI Overviews, Perplexity, Gemini and Claude describe your brand, not just whether they link to you. You define the prompt set once, and Nobori runs it on a schedule across every engine, capturing the full response text so you can read the sentence that named you rather than guessing from a citation count.
Sentiment and factual drift show up as changes you can act on. When an engine starts attaching a caveat to your brand or repeating a stale price, Nobori surfaces the response and the cited source, so your team knows whether the fix belongs on your own site, in a publisher's roundup, or in a review profile. Because the same prompt set reruns on a cadence, you can tell a real perception shift apart from the weekly churn that makes one-off checks useless.
The output is a task list. Nobori ranks what to correct by how often the bad claim appears and which engines carry it, so a two-person marketing team can work the highest-impact items first instead of auditing 200 pages by hand.
Is AI brand sentiment the same as social listening? No. Social listening measures what people post about you. AI brand sentiment measures what the model itself says when it answers a question, which reaches every user who asks and persists until the underlying sources change.
Which engine should a B2B team monitor first? Monitor ChatGPT and Google AI Overviews together. BrightEdge found the two flagged different brands on 73% of overlapping negative prompts, so one engine cannot proxy for the other.
Can you get an AI engine to remove a false claim about your brand? Not directly. You change the sources it retrieves. Correct your owned pages, then get the third-party pages carrying the error updated, since Profound traced most brand-specific inaccuracies to about four domains.
How much of a neutral mention rate is normal? Roughly half. BrightEdge recorded 47.7% neutral in Google AI Overviews and 54.4% in ChatGPT, so a neutral-heavy profile is the default rather than a failure. Beating the default is the goal.
Does entity recognition affect sentiment? Yes. An engine that cannot resolve your brand confidently hedges its language. Our guide to entity optimization for AI search covers the identity layer underneath sentiment.
Nobori tracks your brand's visibility and sentiment across ChatGPT, Gemini, Perplexity, Google AI Overviews and Claude, updated daily. See who gets recommended instead of you, which claims are wrong, and what to fix first.
See if AI engines are citing you → nobori.ai
