A customer types your brand name into an AI search box. Not Google. Not your website. An LLM.
They ask, “Is this company legit?”
“Who are their competitors?”
“Is their product safe?”
“Best alternative?”
And the AI answers confidently, instantly, and often without sending the user anywhere else.
That moment is now one of the most influential “touchpoints” in your funnel. But it’s also one of the least visible.
Nearly half of consumers are already using AI to support shopping decisions, and AI-powered search experiences are pushing more queries into “zero-click” outcomes where the answer is the destination.
If you’re not actively monitoring what AI says about your brand, you’re essentially letting a third party narrate your story without an approval process.
This challenge has given rise to Generative Engine Optimization (GEO). Though the name draws comparisons to SEO, the implications are far greater. Brands that ignore GEO risk disappearing from the conversation altogether, while those that embrace it stand to build a revenue channel unlike any they've seen before.
The shift isn’t subtle. AI systems are becoming the interface between buyers and information. In many cases, the buyer won’t even realize what sources the AI used to form an opinion. They’ll just remember the summary.
And the sources can be eclectic. Research on AI answer sourcing has shown heavy reliance on crowdsourced and forum-style content (e.g., Wikipedia and Reddit for some systems, and community platforms for others).
That’s not automatically bad. Sometimes it’s the most candid reflection of sentiment. But it does mean:
If you’re in B2B, this is especially dangerous because brand perception is often built on trust signals like security posture, compliance claims, integration capabilities, customer proof, and competitive positioning. A single wrong statement can derail a deal long before your SDR ever gets a chance to respond.
Most leaders assume the big AI platforms are “mostly accurate.” The reality is messier.
Independent testing has surfaced alarming inaccuracy rates in AI search-style experiences. For example, Josh Bersin highlights testing that found a significant portion of AI answers containing errors, and separate reporting has raised concerns about high error rates in certain AI search tools.
Stack Overflow’s leadership has also cautioned that even grounded systems can still produce output where a meaningful fraction is wrong or off-topic.
And it isn’t just “wrong facts” in abstract. Real-world harm is already documented:
This is the pattern brand leaders need to internalize:
AI systems can be both confident and wrong, and users often can’t tell the difference.
In traditional brand monitoring, the danger was a bad review, a viral post, or a competitor taking a swing at your category narrative.
In AI brand reality, the danger is a summary.
One incorrect sentence, “They don’t support X,” “Their pricing starts at $X,” “They’re known for layoffs,” “They were involved in a lawsuit,” “Their product has been linked to X”, can get repeated across buyer conversations, sales cycles, and committee members with almost no friction.
You may never know it happened. Because the buyer never clicks.
That’s the defining change: the web is turning into inputs, and AI is becoming the output.
McKinsey has noted that only a small percentage of brands are systematically tracking AI search performance today. That means most companies don’t even have baseline visibility into how they show up in AI-mediated discovery.
For years, marketing teams monitored Google rankings, social sentiment, review sites, and analyst mentions.
Now there’s another layer: LLM brand perception.
This includes questions like:
And you can’t manage what you can’t see.
That’s why AI accuracy and brand monitoring are now inseparable disciplines. You’re not just optimizing for keywords anymore. You’re protecting the narrative buyers will receive before they ever enter your funnel.
You don’t need an enterprise program to start. You need consistency and a repeatable method.
Start by documenting the facts that must be correct, especially in B2B:
This becomes the reference point when you audit AI answers.
Most AI monitoring fails because brands ask artificial questions.
Instead, use prompts that mirror buying behavior:
Run these across the models your buyers use (ChatGPT-style tools, AI Overviews-style experiences, etc.) and track the answers over time.
To keep this usable, focus on three signals:
If you do nothing else, do this monthly, then increase frequency if you see volatility.
When you find misinformation, don’t just shrug. Log it, categorize it, and respond systematically.
Ask: Where could the model be getting this? Outdated pages? A forum thread? A competitor comparison post? A misinterpreted press mention?
Then do the blocking and tackling of modern brand control:
This is where SEO and GEO converge: you’re not just ranking pages anymore, you’re shaping the source material AI summarizes.
Many organizations are already experiencing the “AI cleanup tax”. Hours spent each week verifying AI outputs. That’s not a sign you should avoid AI. It’s a sign you need governance.
If your team uses AI for brand statements, competitive claims, or customer-facing answers, define review rules (even lightweight ones). The faster you move, the more you need a safety rail.
Get in early. The brands that move first will be the hardest to catch.
Most of your competitors aren't paying attention yet. The McKinsey data is worth repeating: only a small fraction of brands are systematically tracking their AI search presence today. That gap won't stay open forever.
The brands that implement GEO programs now, get something the late movers won't: a head start. They'll know what good looks like, they'll catch problems faster, and they'll have cleaner source material working in their favor before AI-mediated discovery gets even more crowded.
Not sure where to start? Few agencies know GEO like BOL. We help brands get in ahead of the crowd, with the expertise and confidence to do it right.