AI overviews on google facilitate scams by elevating false contact information
We’re seeing a classic problem take on a modern face. AI-generated Google search summaries, what they call “AI Overviews“, are now being weaponized by scammers. They’ve figured out how to manipulate third-party websites, mostly reviews and listing platforms, by posting fake contact information. Google’s algorithms scrape that data, and like clockwork, it’s elevated to the top of your search results. Now it’s disguised as helpful, trustworthy info when it’s anything but.
This isn’t about flaws in AI itself. It’s a failure in the system that determines trust. AI systems are designed to surface popular, consistent, and seemingly relevant data. If malicious actors supply that data in high enough volume and consistency, the AI doesn’t see fraud, it sees relevance. Most users, including those who consider themselves tech-savvy, glance at the top AI answer and assume it’s vetted. That’s a serious problem, especially when it influences real-world behavior and leads to financial loss.
This breakdown is telling us something important, people trust AI-generated summaries more than they trust the fragmented chaos that customer service has become. If you’re running a major company and you’re still hiding your support phone number five layers deep in a mobile app, you’re pushing users toward the open web, where credibility is up for grabs. You’re increasing your customers’ exposure to fraud without even realizing it.
That opens up a strategic question for C-suite teams: are your customers forced to rely on external platforms for critical support info? Because if they are, it’s not just a UX issue, it’s a risk exposure. You don’t want systems outside your control defining your company’s voice or misrepresenting your brand in AI summaries. That leads to scams, dissatisfaction, and reputational damage.
Expect more of this unless platforms and companies close the loop on how contact info is published, verified, and presented. You don’t stop misinformation by patching problems after the fact. You prevent it by building resilient, clear, and consistent communication pipelines controlled by your own systems. Think like a builder, own the architecture, or someone else will.
Limited direct customer service from major companies increases vulnerability to scams
When customers can’t find a phone number to ask a simple question, we’ve got a problem. Many large companies have deprioritized real-time, human support. And while the logic might be operational efficiency, the result is counterproductive. If users can’t reach someone immediately through official channels, they look elsewhere. That “elsewhere” often leads them straight into the trap scammers are setting.
The scam that hit Alex Rivlin, a real estate business owner, is a perfect example. He couldn’t find direct contact info inside the Royal Caribbean mobile app. Like most people, he turned to Google. What he got wasn’t customer support. It was a fraudster who had manipulated third-party search listings. The scammer convinced Rivlin to hand over his credit card details under the false pretense of “waiving” shuttle fees. The result? Unauthorized charges. Avoidable with better access to trustworthy channels.
We need to recognize where the breakdown is happening. When legitimate companies make human contact difficult, users end up placing trust in unverified sources. That’s not an issue of cybersecurity policy, it’s structural customer experience design. The fact that Rivlin considers himself relatively tech-literate shows this isn’t just impacting the uninformed or reckless. The weakness isn’t with the user. It’s in the gap between corporate communication systems and public search platforms.
Senior execs need to review how customers interact with their support infrastructure. If your support strategy is funneling high-value users into open-index search queries, you’re not just creating friction, you’re creating risk. There has to be a balance between scale and reliability, and part of that means ensuring users never have to rely on a Google search to get your phone number.
This is now about liability and brand control. Make contact info visible, accessible, and authenticated. If users are falling victim to fraud due to your systems’ opacity, your company’s reputation is at risk. Fixing that isn’t innovation, it’s maintenance of trust. Do what’s necessary before regulators or public blowback force a rushed response.
Scammers exploit algorithmic credibility to reinforce their fraudulent activities
Scammers aren’t relying on brute force anymore. They’re working the system smarter, writing fake listings, gaming review platforms, and feeding information that search indexes and AI tools interpret as credible. Over time, with enough digital noise and repetition, that false data starts to look legitimate. Not to people, initially, but to the algorithms supplying answers at scale. When those answers surface in AI Overviews or search result summaries, they gain unearned authority.
The core issue here isn’t about AI hallucinations or poor design. It’s about how quickly systems absorb and elevate repeated data points, regardless of their origin. Scammers understand that the technical limits of content verification work in their favor. When algorithms prioritize topical relevance and consistency over source accountability, bad actors take advantage. The volume of false information becomes its own form of legitimacy, at least to the system.
From a leadership perspective, this is a structural flaw in how digital relevance is scored. And that’s important to understand if your business relies on public-facing data. Nothing online stays static. Company details change, services evolve, and contact infrastructure shifts. But the AI layer doesn’t always keep up. Google, for example, told Yahoo! News that it may take time to update its data indexes, even after fake content is removed from source sites. That delay is all scammers need to exploit the gap.
If your company isn’t actively managing how its data appears across the web, especially in third-party ecosystems, then your brand’s representation is subject to manipulation. Relying on platforms to do the cleanup after the fact is reactive, and it’s too slow. Executives should be deploying solutions that keep verified company information continuously synchronized, especially for critical customer touchpoints.
The takeaway isn’t that AI tools are inherently broken. The takeaway is that credibility in AI-originated content is still up for negotiation, and bad actors are ready to claim the space. The faster your organization takes ownership of public-facing accuracy, the less likely it becomes a target or conduit for fraud.
Main highlights
- AI search is amplifying scam risk: Scammers are injecting fake contact information into third-party sites that get surfaced by Google’s AI Overviews. Executives should work with marketing and security teams to ensure verified, accurate contact data is prioritized across public channels.
- Hidden support channels are costing trust: When users can’t easily find direct customer support, they turn to search engines, and land in scam traps. Leaders should reassess support accessibility and make trusted contact options immediately visible to reduce fraud exposure.
- Algorithmic trust is being exploited: Bad actors are gaming search engine credibility signals, making fake data appear authentic in AI-driven results. Business leaders must monitor how their brand is represented online and proactively manage third-party listings to prevent misinformation.
