Expert: Chirag Shah, Professor, University of Washington Information School The system did not get the sentiment wrong. It got the identity wrong, and then reported the sentiment of the wrong company with full confidence. This is an entity-resolution failure, and it is the failure mode that gets worse when a search engine stops being a list and starts being an answer. "The Plastics Shed" sells building plastics; other companies sell plastic sheds. NW Select Property Management shares a name shape with a firm that closed years ago. Philippa Main shares a name with agents who work 900 miles away. Each of those is a name-similarity signal that a review-aggregating system has to resolve before it can say anything true, and in these cases it resolved them to the wrong business and mixed the review corpora together. Shah explains why that matters more now than it did in a page of ten blue links: "With the traditional search engine, the user sees multiple things. So if one of them has something wrong, chances are something else would counterbalance it." That counterbalance is gone. What a user gets instead is what he calls "The Answer" - one confident synthesis that may or may not be correct, with no adjacent source for the reader to check it against. For a business, the summary is no longer a result about them. It is the digital storefront, and it is the first impression before anyone reaches their own site. Whitney put the commercial consequence plainly: "If I'm a customer, and I'm looking for a property management company, and I look at the AI overview, I wouldn't call me." Main, a real-estate agent, framed the same arithmetic from the seller's side: "There's so much competition in my industry that if any single thing seems off, someone's just going to call the next person on the list." The correction path is the real asymmetry in this case. The model can assert a negative summary instantly; the business has thumbs-down feedback buttons, support-forum posts, and website tweaks, and the fixes are partial and unstable. Whitney's overview improved after she found a third-party SEO expert through a Google support forum - then periodically reverted to the mistake-filled version. Mansell's improved after a couple of weeks of repeated feedback, and he still could not get his ad spend back: "I'm like a dog with a bone. But I met my match with Google." He summed up the position his own business was put in: "AI gives the common man the knowledge, but it can also ruin the common man." What actually reduces this risk, for both sides of the page: 1. Treat entity identity as infrastructure, not SEO garnish. An accurate, claimed Google Business Profile, Organization and LocalBusiness structured data carrying the legal name, address, phone and sameAs links, and explicit disambiguating copy on the site ("we are the building-plastics supplier in X, not the plastic-shed retailer in Y") give an aggregator the signals it needs to separate two similarly named companies. 2. Watch brand queries, not just rankings. The failure appears when someone searches the business name, so that is the query set to monitor. A wrong overview is invisible in rank-tracking tools. 3. Keep a dated evidence trail. Screenshots, feedback submissions, support-forum threads and the timeline of what changed when. Whitney got help only after she escalated in a support forum; ad-refund and correction requests without a documented trail go nowhere. 4. Push review volume onto assets the business owns. Aggregators are more accurate about entities with dense, unambiguous first-party and third-party sources. Thin review profiles are the ones that get merged with strangers. For users: a summary that makes a reputational or commercial claim about one specific business is a claim about entity identity, and identity claims are exactly what these systems get wrong. Before acting on it, open the underlying reviews and check whether they are about the company you are looking at. The scale argument cuts both ways. As reported, Google processes over five trillion searches a year; at 90% overview accuracy that still leaves hundreds of billions of wrong answers, while Google says the accuracy study behind that framing has "serious holes." Either way, the individual owner lives in the tail, and the tail is not small. Source: https://www.businessinsider.com/google-ai-overviews-aio-causing-chaos-small-businesses-2026-8