94% of executives are spending big on AI visibility, but most marketers don't know what they're measuring. Here's the framework that separates real impact from vanity metrics.
By Adam Makins · 
Enterprise leaders are throwing money at AI visibility faster than ever. According to recent data, 94% of C-level executives surveyed plan to ramp up spending in 2026. But here’s the problem: a third of marketers have no idea how to actually measure it.
That gap between investment and understanding is costing companies real money. You can’t optimize what you can’t see, and you can’t see what you’re not tracking correctly. The traditional SEO playbook doesn’t work here. You can’t just open Search Console and call it a day.
Before you sign up for any tool, get clear on what success looks like. Most teams fumble this part and end up with reporting that tells them nothing useful.
There are three distinct signals worth tracking separately:
Citations happen when an AI engine links to your website or clearly attributes information to your page. This is the closest thing AI visibility has to traditional SEO, which is why most teams obsess over it first.
Mentions occur when your brand name appears in the response, whether or not there’s a link back. Mentions show your brand lives in the model’s vocabulary on a topic. But they can also deceive you. Your brand can be mentioned as a passing example and still lose the sale.
Recommendations are what actually matters. When someone asks for the best option, does the AI suggest your product, or just acknowledge you exist? Being visible and being recommended are not the same thing. That distinction changes everything about your strategy.
If you only track citations, you’ll celebrate movement that never turns into revenue.
Nothing in AI visibility works without a serious prompt library filled with questions real buyers would actually ask. Start with 10 to 20 prompts that you write yourself. You already know the language your customers use, the objections they raise, and the competitors they consider.
Begin with obvious commercial queries, then expand into comparisons, pain-point searches, and local variations. Add specifics like city names, competitor names, budget levels, and industry filters where they’d realistically shape the answer. ChatGPT and Claude’s own data show how conversational search has become. Generic one-line prompts miss how people actually discover businesses in real life.
Once you have your manual foundation, use AI tools to generate variants and cluster them by intent. Aim for 50 high-quality prompts instead of 300 bloated ones. Too few and you miss the long tail. Too many and you’re tracking noise.
Platforms like Peec, Semrush, Ahrefs, and DataForSEO now track AI visibility directly. What makes them worth the subscription isn’t just data collection. It’s that they make data operational. Good platforms automate daily checks, visualize trends, and surface content gaps.
But here’s the catch: most tracking still depends on search-enabled environments or vendor-specific ways of querying models. The answer a user gets live can look very different depending on whether web search is active and how the system composes the response.
Treat these dashboards as monitoring infrastructure, not ground truth. They’re directionally useful. They’re not a perfect reflection of what every real user sees.
Once a month, run your most commercially important prompts manually across ChatGPT, Claude, and Gemini in fresh chats. You get control over exact phrasing, you can add location directly into queries, and you see full response richness instead of platform summaries.
Save those responses and use a high-reasoning model to analyze them. Track citations, mentions, recommendations, and competitor prominence. Ask for patterns and hypotheses about why certain competitors outperform you on specific prompts. This layer adds the context that automated tools often flatten.
Here’s where most teams fail: they optimize visibility without proving it moves the needle. Track AI referral traffic in Google Analytics, but also add “AI assistant” as an option in your “How did you hear about us?” field. Train your sales team to ask whether leads first discovered you through ChatGPT or Claude.
Watch for indirect signals too. When your recommendation rate improves, do branded searches and demo requests rise? If visibility numbers climb but downstream indicators don’t move, something’s broken in the chain. The best setup is usually hybrid: platform subscriptions for broad patterns, manual checks for prompts that actually feed your pipeline.
Visibility is interesting. Impact is what justifies the work.
Source: Entrepreneur
