Episode Transcript

Be the Answer: Emarketed's AEO Show

AI Search Reputation Risk Is Now a Marketing Problem

Host: Alex | Duration: 15:03

[0:00] [Intro Music]

[0:09] Alex:

Welcome back to Be the Answer, Emarketed's AEO show. I'm Alex, and today we're tackling something that most marketing teams still don't have on their radar. AI search reputation risk.

Here's the setup. When someone asks ChatGPT, Perplexity, Google's AI Overviews, or Copilot about your business, what comes back? Because whatever that answer is, that's your brand now. Not your homepage. Not your tagline. The answer the AI assembles from dozens of sources across the web. That answer is what a growing number of buyers, patients, and decision-makers see first.

And here's the thing. Most businesses have no idea what AI is saying about them. They've never even checked. So today, I'm going to walk you through exactly how AI search tools form brand perception, why reputation management is now a core marketing function, and I'll give you a practical framework you can use this week to find and fix your biggest reputation risks in AI search.

Let's get into it.

[1:17] Alex:

So first, let's talk about what's actually happening under the hood when someone asks an AI tool about your business.

Traditional search gave you ten blue links. You clicked one, you landed on a website, and you formed your own opinion. AI search is fundamentally different. It doesn't send people to your site. It reads the internet for them and delivers a summary. One answer. One narrative.

And where does that narrative come from? Everywhere.

AI tools pull from your website, sure. But they also pull from Google reviews, Yelp reviews, G2 reviews, Reddit threads, Quora answers, Better Business Bureau pages, news articles, press releases, industry directories, blog posts that mention you, and forum discussions you've probably never seen.

Here's a stat that should get your attention. Research from BrightEdge found that Google's AI Overviews display negative information about brands 44 percent more often than ChatGPT does. For every million queries in industries like retail, electronics, and education, roughly 23,000 would return a negative AI response about a brand. That's not a rounding error. That's a structural problem.

[2:34] Alex:

And Reddit is the biggest wildcard. Data shows Reddit is the single most cited source in generative AI responses, accounting for up to 40 percent of citations in some categories. AI tools love Reddit because it feels authentic. It's unfiltered. It's human. But that also means a two-year-old complaint thread about your billing process or a frustrated customer venting about a bad experience can end up as the lead detail in an AI summary about your company.

Let me give you a real example. Google's AI Overviews once generated a response about a business that included claims about poor professionalism and communication issues. The business had factual data proving otherwise, but AI Overviews had assembled that narrative from a handful of negative forum posts and review comments, ignoring the official responses entirely. That's how this works. AI doesn't fact-check against your version. It synthesizes whatever it finds.

[3:37] Alex:

Now, most businesses that even think about online reputation put it in the PR bucket. Or customer support. Maybe the operations team. Somebody monitors Yelp, somebody responds to Google reviews, and that's considered handled.

But AI search changes the equation completely. Here's why this is now squarely a marketing problem.

First, AI answers happen at the top of the funnel. When someone is researching whether to hire you, buy from you, or visit your practice, they're increasingly asking AI first. A recent survey found that 25 percent of clients now use ChatGPT as a discovery tool when evaluating service providers. If the AI response paints a negative picture, you've lost that prospect before they ever visited your website. That's a marketing problem. That's pipeline.

Second, you can't buy your way out of it. In traditional search, you could run paid ads above negative results. In AI search, there's no ad slot. There's just the answer. And if the answer includes a line about inconsistent service or complaints about pricing, that's what the prospect sees. Period.

[4:53] Alex:

Third, the information compounds. AI tools don't just surface one review. They synthesize patterns. If three Reddit threads mention slow response times and five reviews mention billing confusion, the AI doesn't list each one. It concludes that your business has response time and billing issues. It creates a narrative that's harder to undo than any individual review.

And fourth, this affects every vertical. This isn't just a consumer brand problem. Local businesses, healthcare providers, B2B companies, manufacturers, professional services firms. Every business that gets mentioned anywhere online is now subject to AI narrative risk.

So let me be direct. If your marketing team isn't monitoring what AI tools say about your brand, you have a blind spot in your funnel. And it's growing every quarter.

[5:46] Alex:

Let me make this concrete with examples from three different verticals.

Local business. There's a well-documented pattern where local businesses rank well in traditional Google search but are completely invisible in AI Overviews. Or worse, they show up with a skewed narrative. The common issue is that local business websites have generic service pages, no pricing context, thin staff bios, and buried FAQs. When AI tries to extract clear, concise information about what you do and whether you're trustworthy, it can't find what it needs on your site, so it goes to reviews and forums instead. And when AI Overviews do appear for local searches, organic click-through rates drop by up to 60 percent. So if the AI is pulling from a handful of negative Yelp reviews instead of your carefully crafted service pages, that's what your local market sees.

[6:39] Alex:

Healthcare. This one is serious. Perplexity AI once gave post-open-heart surgery advice that recommended stretches which could actually aggravate sternum repair and delay healing. That's a patient safety issue, but think about it from the provider side too. If an AI tool associates your practice with outdated or inaccurate medical information, or surfaces patient complaints from forums without context, that shapes how new patients evaluate you. Healthcare content that sounds generic or AI-generated itself gets lost in what researchers call the sea of sameness. Your practice might have excellent outcomes and five-star patient satisfaction, but if AI can't extract that clearly, it fills in the gaps with whatever else it finds.

B2B. One industrial products company discovered it was completely invisible in AI search results despite strong traditional search rankings. Meanwhile, competitors with what the company described as lesser product offerings were appearing consistently in AI-driven platforms. The issue wasn't product quality. It was content structure. Their expertise and authority weren't packaged in a way AI could extract and cite. In B2B, where sales cycles are long and buyers do extensive research, being absent or misrepresented in AI search means losing deals you never even knew were on the table.

[8:15] Alex:

Okay, so now you understand the risk. Let's talk about what to do. I'm going to give you a three-step framework you can start using this week. Discover, Diagnose, Fix.

Step one: Discover.

This is the baseline audit. You need to find out what AI is actually saying about your brand right now. And you need to check multiple platforms, because they don't all say the same thing.

Open ChatGPT, Perplexity, Google with AI Overviews enabled, and Microsoft Copilot. Ask each one these prompts:

What do people say about your company? Is your company good at your core service? What are the pros and cons of working with your company? Compare your company to your top competitor.

Write down every claim, positive or negative. Note which sources each AI tool appears to be drawing from. You'll probably be surprised. Some of the information will be outdated. Some will be pulled from sources you've never heard of. Some might be flat-out wrong. That's your starting point.

[9:20] Alex:

Step two: Diagnose.

Now categorize what you found. You're looking for three types of risk.

First, narrative risk. This is when AI tells an inaccurate or unfairly negative story about your brand. Maybe it emphasizes a problem you fixed two years ago, or it pulls from a competitor's comparison page that positions you unfavorably.

Second, absence risk. This is when AI doesn't mention you at all for queries where you should appear. If someone asks best providers in your category in your city, or top providers in your service area, and you're not in the answer, that's a visibility gap that's costing you leads.

Third, source risk. This is when the dominant sources AI cites about you are ones you don't control. If the top signals are Reddit threads, anonymous forum posts, or outdated directory listings, your brand narrative is being written by strangers.

Map each risk you find to a priority level. Anything that directly contradicts reality or could cost you a sale goes to the top.

[10:36] Alex:

Step three: Fix.

Here's where marketing takes ownership. The fix isn't one thing. It's a systematic effort across multiple channels.

For narrative risk, you need to create and publish content that directly addresses the claims AI is making. If AI says your pricing is confusing, publish a clear pricing page with FAQs. If AI says your response times are slow, publish case studies with timeline details. Make the correct information so clear and so well-structured that AI can't miss it.

For absence risk, you need to optimize your content for AI extraction. That means structured data, clear question-and-answer formatting, specific service pages instead of generic ones, and consistent entity information across every directory and platform where you're listed. The data shows that consistent web mentions have the strongest correlation with AI visibility, at 0.664 in one major study.

For source risk, you need to amplify the sources you want AI to find. That means actively building your review volume on the platforms AI trusts most. It means publishing original research and data that positions you as an authority. It means engaging authentically in the communities where your customers talk, including Reddit, industry forums, and LinkedIn. And it means earning press coverage and backlinks from authoritative sites.

One important note. Review volume matters more than you might think. Research shows that high volume on platforms like G2 and TrustRadius correlates strongly with AI citations. AI systems prioritize quantity alongside quality because volume signals market presence. So don't just aim for perfect five-star reviews. Aim for a steady, growing volume of genuine customer feedback.

[12:38] Alex:

This can't be a one-time project. AI search results change as new content is published, new reviews come in, and AI models are updated. So you need to build this into your ongoing marketing operation.

Here's what I'd recommend.

Set a monthly cadence for AI brand audits. Run the same prompts across the same platforms every month. Track changes. Are you showing up more or less? Is the narrative improving or drifting?

Assign ownership. Someone on your marketing team needs to own AI reputation the same way someone owns your social media calendar or your email nurture sequences. This isn't a side project. It's a core function.

Build a response playbook. When you find a problem, whether it's a negative Reddit thread gaining traction or an outdated review that AI keeps citing, you need a documented process for how to respond. That might mean creating new content, requesting review updates, engaging in community discussions, or reaching out to publications for corrections.

And measure the impact. Track whether AI mentions convert differently than traditional search traffic. Monitor which AI platforms are driving awareness and how sentiment shifts over time. Nearly 80 percent of businesses currently struggle to measure AI search impact. Getting ahead of that measurement challenge is a competitive advantage.

[14:02] Alex:

So let me bring this home. AI search reputation risk is real, it's growing, and it's now a marketing problem. Not a PR afterthought. Not a customer service task. A core marketing function that affects your pipeline, your brand perception, and your revenue.

The businesses that figure this out early, the ones that audit what AI says about them, diagnose the risks, and systematically fix the signals, those are the ones that will own their narrative in the age of AI search. The rest will be defined by whatever the internet says about them.

If you're listening to this and thinking, I need help figuring out what AI is saying about my brand and how to fix it, that's exactly what we do at Emarketed. We specialize in Answer Engine Optimization, and reputation risk is a core part of our AEO framework. Head to emarketed.com to learn more or reach out directly.

Thanks for listening to Be the Answer. I'm Alex, and I'll see you on the next one.

[15:03] [Outro Music]

Transcript generated for Be the Answer: Emarketed's AEO Show Episode: AI Search Reputation Risk Is Now a Marketing Problem