<\/span><\/h2>\nSpam and scam calls have conditioned users to rely heavily on technology for protection.\u00a0<\/span><\/p>\nModern smartphones flag suspicious calls and traditional search engines have spent decades refining systems to verify contact information for legitimate businesses.<\/span><\/p>\nAs a result, users have learned to trust phone numbers surfaced directly in search results. If a number appears in a prominent answer box, most people assume it is accurate.\u00a0<\/span><\/p>\nThat trust is now being extended, perhaps prematurely to AI-generated search results.<\/span><\/p>\nUnlike traditional search engines, AI models are newer, less transparent and more vulnerable to manipulation, making incorrect contact information not just an inconvenience but a potential security threat.<\/span><\/p>\n<\/span>What Triggered the Concern About AI Phone Number Accuracy?<\/b><\/span><\/h2>\nThe issue gained urgency after an incident surfaced on Reddit showing ChatGPT providing a scam phone number for Chase Bank.\u00a0<\/span><\/p>\n![\"Example](\"https:\/\/7982212.fs1.hubspotusercontent-na1.net\/hub\/7982212\/hubfs\/AI%20Models%20Incorrect%20Phone%20numbers-%20Chase%20Bank%20number%20scam%20ChatGPT.png?width=564&height=776&name=AI%20Models%20Incorrect%20Phone%20numbers-%20Chase%20Bank%20number%20scam%20ChatGPT.png\")
<\/p>\n
This was not a fringe example.\u00a0<\/span><\/p>\nClickstream data indicates that visitors to chase.com use ChatGPT 13% more often than Bing, suggesting that real customers are already relying on AI tools for banking-related information.<\/span><\/p>\nThis raised a critical question: was the Chase incident a rare glitch or a sign of a systemic problem?<\/span><\/p>\n<\/span>Was This Just a One-Off Error?<\/b><\/span><\/h2>\nThe short answer is no. <\/span>An initial internal test looked up contact information for a single brand across six AI models.\u00a0<\/span><\/p>\nFour models returned phone numbers but only one was accurate.\u00a0<\/span><\/p>\nThat result prompted a much larger experiment to determine how widespread the issue really is.<\/span><\/p>\nA scaled analysis examined 178 branded phone-number queries across multiple industries, testing responses from seven AI search platforms including ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Claude, Meta and Gemini.<\/span><\/p>\n![\"AI](\"https:\/\/7982212.fs1.hubspotusercontent-na1.net\/hub\/7982212\/hubfs\/sparktoro-chase-bank-audience.png?width=1200&height=458&name=sparktoro-chase-bank-audience.png\")
<\/p>\n
<\/span>How Often Do AI Models Provide Phone Numbers?<\/b><\/span><\/h2>\nAI systems are highly willing to answer phone-number queries.\u00a0<\/span><\/p>\nAcross all prompts, AI models included a phone number 91% of the time when explicitly asked.<\/span><\/p>\nFrom a system perspective, this makes sense. The models are designed to be helpful and responsive, even when certainty is low.\u00a0<\/span><\/p>\nFrom a user perspective, however, this behavior creates risk because AI does not reliably know whether the information it is providing is correct.<\/span><\/p>\nWhen phone numbers were not included, models typically cited limitations, redirected users to official websites or stated that phone support was unavailable.<\/span><\/p>\n<\/span>Which AI Models Are Most Likely to Show Phone Numbers?<\/b><\/span><\/h2>\nThe likelihood of phone-number inclusion varied slightly by platform.\u00a0<\/span><\/p>\nPerplexity and Google AI Mode provided phone numbers 99% of the time, with ChatGPT close behind at 97%.<\/span><\/p>\nThis consistency reinforces the illusion of reliability.\u00a0<\/span><\/p>\nWhen nearly every AI tool confidently returns a phone number, users naturally assume that accuracy is high.<\/span><\/p>\nUnfortunately, the data suggests otherwise.<\/span><\/p>\n<\/span>How Accurate Are the Phone Numbers AI Models Provide?<\/b><\/span><\/h2>\nAccuracy was measured using three verification methods: alignment with Google Business Profiles, matching citations when sources were referenced and comparison against official customer service pages.<\/span><\/p>\nThe results were uneven. Only 27% of AI-provided phone numbers matched Google Business Profiles, which often list corporate or headquarters numbers rather than customer service lines.\u00a0<\/span><\/p>\nCitation verification showed a much higher accuracy rate at 93%, indicating that when AI models explicitly cited a source, the number usually existed on that page.<\/span><\/p>\nCustomer service page matching, arguably the most relevant benchmark revealed the core problem.\u00a0<\/span><\/p>\nOnly 64% of phone numbers matched official customer service pages, meaning 36% were inaccurate.<\/span><\/p>\n<\/span>Which Industries Are Most Affected?<\/b><\/span><\/h2>\nIndustries where phone support is central to the customer experience were the most likely to have phone numbers surfaced.\u00a0<\/span><\/p>\nTelecom and airlines showed a 99% inclusion rate, while banking, one of the most sensitive categories, had phone numbers displayed 95% of the time.<\/span><\/p>\nThis is particularly concerning because financial services were also among the most targeted by scams, increasing the potential harm when incorrect numbers are surfaced.<\/span><\/p>\n<\/span>Which AI Models Are Least Accurate?<\/b><\/span><\/h2>\nAccuracy varied meaningfully by platform. Gemini performed best overall, with an accuracy rate of 89%, while ChatGPT ranked lowest at 68%.<\/span><\/p>\nOne reason appears to be sourcing behavior. Gemini cited more sources per response and leaned heavily on structured third-party databases such as GetHuman.\u00a0<\/span><\/p>\nChatGPT cited fewer sources and showed greater variance in output.<\/span><\/p>\nBoth models relied on brand-owned sources only about 64% of the time, with the remaining information pulled from third-party and user-generated platforms.<\/span><\/p>\n<\/span>Why Are AI Models Getting Phone Numbers Wrong?<\/b><\/span><\/h2>\nThe core issue lies in sourcing.<\/span><\/p>\nAcross the dataset, brand-owned sources were cited 41% of the time, while third-party sources accounted for 59%.\u00a0<\/span><\/p>\nMany of these third-party platforms such as GetHuman, PissedConsumer, BBB.org, and ZoomInfo are vulnerable to outdated data or manipulation.<\/span><\/p>\nWhile some third-party sites showed relatively high accuracy, the reliance on external sources increases the risk of incorrect or misleading information being repeated at scale.<\/span><\/p>\n<\/span>What Does This Look Like for Real Users?<\/b><\/span><\/h2>\nA real-world example illustrates the confusion.\u00a0<\/span><\/p>\nSearching for IHG Hotels yields different phone numbers depending on the platform.<\/span><\/p>\nGoogle\u2019s AI Overview shows a branded vanity number. AI models may return a different toll-free number sourced from a consumer complaint site.\u00a0<\/span><\/p>\nVisiting the official IHG website reveals multiple numbers segmented by country and purpose.<\/span><\/p>\nNone of these numbers are necessarily fraudulent but inconsistency creates friction.\u00a0<\/span><\/p>\nUsers may waste time calling the wrong line, lose confidence in the brand, or abandon their task altogether.<\/span><\/p>\n<\/span>Why This Is a Bigger Problem Than Scams Alone<\/b><\/span><\/h2>\nWhile scams are the most alarming risk, everyday inaccuracies also carry consequences.<\/span><\/p>\n![\"Wil](\"https:\/\/7982212.fs1.hubspotusercontent-na1.net\/hub\/7982212\/hubfs\/wil-scam-number-test.png?width=825&height=1193&name=wil-scam-number-test.png\")
\u00a0<\/span><\/p>\nWhen customers cannot reach support easily, frustration builds.\u00a0<\/span><\/p>\nOver time, that frustration translates into negative brand perception, lower trust and lost revenue.<\/span><\/p>\nInconsistent AI answers erode the assumption that surfaced information is authoritative, undermining confidence in AI search as a whole.<\/span><\/p>\n<\/span>What Can Businesses Do to Reduce Risk?<\/b><\/span><\/h2>\nThe most actionable area for brands is their own customer service infrastructure.\u00a0<\/span><\/p>\nWhile companies cannot control how AI models scrape third-party sites, they can ensure that official customer service pages are clear, consistent and easy to interpret.<\/span><\/p>\nReducing ambiguity, such as multiple undocumented phone numbers or buried contact details helps AI systems latch onto the correct information more reliably.<\/span><\/p>\n<\/span>What Should Users Do Right Now?<\/b><\/span><\/h2>\nFor consumers, the safest approach remains old-fashioned: verify phone numbers directly on official websites, especially for financial, healthcare, or account-related matters.<\/span><\/p>\nUntil AI systems demonstrate stronger verification mechanisms, blind trust in AI-provided contact information carries real risk.<\/span><\/p>\n<\/span>Final Takeaway<\/b><\/span><\/h2>\nAI search tools are fast, convenient, and increasingly influential but this research shows they are not yet dependable for something as basic as a phone number.<\/span><\/p>\nWith a 36% inaccuracy rate for customer service contacts, the issue is no longer theoretical.\u00a0<\/span><\/p>\nIt affects real users, real businesses, and real outcomes.<\/span><\/p>\nUntil AI platforms improve sourcing transparency and verification, accuracy not speed may prove to be the defining challenge of the next phase of AI-powered search.<\/span><\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":"
AI-powered search tools are providing incorrect business phone numbers more than one-third of the time. According to a recent multi-model analysis, exposing users to scams, misinformation and mounting frustration. The findings highlight a growing gap between traditional search safeguards and newer AI search experiences, where authoritative-looking answers may still be wrong. As AI tools increasingly […]<\/p>\n","protected":false},"author":3,"featured_media":6531,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[15],"tags":[],"class_list":["post-6530","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"\n
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