Semrush says AI search reputation is now an SEO agency priority

On June 15, Semrush framed AI search as a reputation issue, not just a visibility issue. It now does more than surface links. It frames whether a brand looks credible, enterprise-ready, budget-friendly, or risky, and that framing can change a shortlist before a prospect ever reaches a website.

AI search is becoming a reputation layer

AI sentiment analysis evaluates how favorably AI platforms describe a brand and whether those descriptions are accurate. Buyers increasingly use AI systems as research assistants, then act on the language those systems use to characterize vendors. If an answer describes a platform as strong for SMBs and mid-market but weaker for enterprise, the model is doing more than summarizing a feature set. It is shaping the buying context around size, fit, and risk.

The practical implication for agencies is that traditional SEO reporting no longer tells the whole story. Rankings and traffic still matter, but they do not show whether AI outputs are reinforcing the right narratives. A client can rank well and still lose trust if AI search repeatedly frames the brand as limited, incomplete, or not suited for the deal size it wants to win.

What agencies need to measure inside AI answers

Marketers need to inspect both the answer and the web sources that influence it. AI outputs are built from a mix of training signals, public content, and brand mentions scattered across the ecosystem, so the issue is not only what the model says, but why it says it. That pushes agencies toward source audits, prompt monitoring, and correction work that sits alongside technical SEO.

The service opportunity is especially clear in B2B. Agencies can build recurring checks around the prompts buyers actually use at different stages of the funnel, then compare how AI systems describe the client against the positioning the sales team needs. That includes identifying reputation risks, spotting inaccurate comparisons, and correcting repeated mischaracterizations across review sites, comparison pages, and third-party content.

A useful way to think about the work is in three layers:

• Monitor the prompts that surface the client at awareness, comparison, and purchase stages.
• Audit the sources AI systems appear to rely on, including brand mentions and competitive comparison pages.
• Fold the findings into SEO, content, and digital PR so the brand is described more consistently across the web.

Why the risk is not theoretical

BrightEdge, the San Mateo, California company, published a March 5, 2026 analysis of the reputational risk. BrightEdge found Google AI Overviews were 44% more likely than ChatGPT to surface negative brand sentiment overall. BrightEdge also found the opposite pattern near the point of purchase, where ChatGPT concentrated its criticism 13 times more heavily. The study was based on hundreds of millions of AI prompts across apparel, electronics, and education.

Negative sentiment does not appear uniformly across AI products or across the funnel. A buyer could encounter a relatively neutral answer early on and a much harsher one right before purchase, when the brand is closest to conversion. For agencies, that means reputation monitoring has to be stage-aware, not just channel-aware. It also means remediation work should vary by platform, because the same brand may face different sentiment patterns in Google AI Overviews and ChatGPT.

What Ahrefs adds to the picture

Off-site brand activity matters in AI search. In Ahrefs’ analysis of 75,000 brands, brand web mentions had the strongest correlation with AI Overview brand visibility at 0.664. Backlinks were much weaker at 0.218. That does not prove causation, but mentions across the web carry more weight in this environment than many teams still assume.

For agencies, that changes where optimization effort should go. Link building still has a role, but it is no longer enough on its own if the surrounding mention ecosystem is thin, inconsistent, or filled with stale descriptions. Brand web mentions, comparative articles, expert commentary, and product references now have outsized importance because they shape how AI systems describe the company in the first place.

Reputation cleanup can no longer sit outside the SEO brief. If brand mentions are strongly associated with AI Overview visibility, then agencies need to manage not just whether a client is cited, but how it is characterized.

How to turn this into an agency service line

The strongest agencies will package AI reputation work as a measurable offer, not a vague advisory exercise. The concept is clear, the downside risk is measurable, and the underlying signals are identifiable. The opportunity is to sell AI perception management as part of a broader search and reputation retainer.

A workable operating model looks like this:

Executives care less about abstract visibility and more about whether AI search is helping or hurting pipeline. A dashboard that shows sentiment shift, source quality, and brand framing is more useful to a CMO than a report that stops at keyword movement.

Gartner forecast that 30% of brand perception will be shaped by generative AI by 2026, but that figure should not be treated as settled until the underlying Gartner source is verified.