Imagine working for months to rank first on Google for a high-intent keyword. You finally get there and you are expecting the leads and demos to start flowing in to hit your quarterly target.

But then reality sets in.

The clicks are not coming, your top of the funnel is still not looking that great and you are left wondering what went wrong. This is the same frustration many marketing teams are facing right now.

The reason? Buyer already asked AI assistants for recommendations and the answer highlighted one of your competitor on the top and the user decided to explore that solution rather than yours. That’s the real shift that is happening.

As per a report by Brightedge, over the first year of Google AI Overviews search impressions rose about 49%  while click-through rates fell by about 30%. Which means that more questions got resolved even before a click happened.

The headline is simple – visibility is no longer defined by where you rank. It is defined by whether you are included in the AI answer and how you are described there.

The Problem: Traditional SEO Metrics Fall Short in the AI Era

For years, we have used things like keyword rankings, organic traffic, and backlinks to measure how well SEO works. But these old ways are losing relevance in the age of AI-driven search.

Why?

Because AI platforms gives answers without the user clicking through to a website. Google’s own generative results (AIO)  often show key info at the top of the page and chatbots like ChatGPT give a full answer in-chat. The outcome –  a dramatic rise in “zero-click” searches.

According to Semrush, only 6 to 8% of users click external links from AI answer interfaces – meaning over 90% of those queries result in no click at all. This is a big deal.

If your buyer gets their answer straight from an AI, it doesn’t matter that you rank first on a traditional SERP  as the AI might not even show your link. Gartner analysts predict that by 2026, traditional search engine volume will drop 25% as users shift to AI chatbots.

In short, people are asking new engines questions and trusting the answers.

All of this means metrics like raw Google ranking or backlink count does not tell the full story anymore. You could have a great SEO  game and still be invisible when a prospect asks an AI for the best solution in your category. If your competitor is the one being named in that AI-generated advice, they control the conversation – not you.

It’s a sobering reality –  what is the point of page-one rankings if the conversation never makes it to a page at all?

Visibility as Reputation Math Across AI Models

Instead of page rank, think it’s time to think about reputation.

Each AI model – be it ChatGPT, Gemini, Perplexity, or Grok , are constantly running what I like to call reputation math.

These models have consumed literally billions of data points about companies and products. And in doing so, they have essentially aggregated the internet’s collective opinion of your brand.

And here’s the key difference – AI models are not just looking at your website or blog posts to decide if you are relevant. They are pulling from everything ever written about you and many a times from sources you don’t control such as analyst reports, review platforms, community discussions, news articles, and even social chatter.

In other words, AI search visibility is a new form of digital reputation score –  a reflection of how widely and well your brand is represented in the knowledge fabric that AI assistants use.

The AI Visibility Test: Gong vs. Avoma vs. Salesloft

We set out to see this new dynamic in action.

We took three B2B sales tech brands that many revenue teams evaluate – Gong, Avoma, and Salesloft and asked a series of buyer like questions across four AI platforms  ChatGPT, Google’s Gemini , Perplexity, and  Grok.

We then phrased the questions as a prospective buyer might such as  – What is revenue intelligence and which tools offer it? What is the best conversation intelligence software? Top revenue intelligence platforms in 2025?

Here’s what came out in the result –

• Gong
  • Mentioned as a top choice by ChatGPT and Gemini in nearly every answer
  • Consistently positioned as the leading solution for conversation intelligence
  • A strong brand presence thanks toa wide press coverage, a Wikipedia entry, thousands of reviews, and  active thought leadership

• Salesloft
  • Appeared frequently across models
  • Often presented as more of a sales engagement platform than pure call analysis
  • Still earned mentions due to strong name recognition and cross-category presence

• Avoma
  • Gemini often defaulted to other established names like Chorus.ai or Clari
  • Perplexity was the exception pulling in a recent G2 comparison that included Avoma
  • Avoma highlighted for high customer satisfaction ratings when third-party sources were cited
  • Overall appeared in only 2–3 of 10 prompts compared to Gong’s 8/10 and Salesloft’s 5/10

Broader research shows that in many industries, the top few brands capture most AI mentions, but, even the leader rarely exceeds around 10 to 15% share of total answer visibility. In other words, AI answers for a given topic might name drop a handful of brands, and if you are not in that top tier, you are effectively invisible.

The takeaway is that if you are a smaller or emerging player, you cannot assume a good product alone will get you mentioned . You have to actively seed and foster the signals that will get AIs to notice you and present you as an option to the users for relevant queries.

The Signals That Drive AI Recommendations

What determines whether an AI includes your brand in an answer?

 It comes down to signals – the digital evidence of your product’s relevance and trustworthiness. Here are the biggest factors we have observed –

• Authoritative Content & Mentions –  AI models favour sources humans already trust. That means if your brand is cited by authoritative publications, industry experts, or popular resources, those mentions feed the AI’s confidence in you. In our test, one reason ChatGPT constantly brought up Gong is the sheer volume of credible content about Gong spread across the web (news articles, analysts, etc.). To contrast, Avoma hasn’t been written about in as many mainstream outlets, so the AI had less “reason” to bring it up.

• Customer Reviews and Ratings – Buyer-centric AI prompts often trigger references to the highest rated or most popular tools. If a model has access to aggregated reviews (say via its training data or live retrieval), brands with strong volume of positive reviews have an edge. Gong, for instance, has thousands of reviews on G2 and Gartner Peer Insights, which likely contribute to it being top-of-mind in rankings that AI can learn from.

• Rich Documentation & SEO Content – Detailed, public-facing content on your own site can also help, especially for technical or feature-specific queries. When an AI does a live web search (as Perplexity and sometimes Bing Chat do), having SEO-friendly FAQs, integration guides, and comparison pages increases the chance the AI finds and cites your content. Make sure to structure content around natural language questions and clear answers (think answer engine optimization, not just search engine optimization).

• Broad Web Presence (Digital PR) – AI models learn from the breadth of information out there. It’s not just one article or one page, but dozens or hundreds of them. That’s why digital PR and thought leadership are crucial for reputation math. If your CEO is guest-posting on industry sites, your product is being compared on tech forums, your team’s research is cited in whitepapers – all those instances add up.

• Recency and Update Frequency – One challenge with LLMs is that their training data can be months or years out of date. If you have made a splash recently (e.g. product launch, big award, new round of funding) and it’s all over the news, a live-search AI like Gemini or Perplexity will catch that and possibly mention it. But a model like default ChatGPT (which might not know this week’s news) won’t – unless you use the version with browsing enabled. Keeping a steady drumbeat of news and updated content ensures that at least the up-to-date AI systems have fresh info to chew on. It also feeds into the next point: source footprint.

New Metrics for the AI Age: Share of Answer and Source Footprint

Given these new dynamics, how do we measure success in this new search landscape?

We need new metrics that can capture what winning in AI search really means. Two concepts we are using at GeoRankers are “Share of Answer” and “Source Footprint” –

Share of Answer (SoA)

This is similar to “share of voice,” but for AI-generated answers. It’s the percentage of times your brand gets mentioned in responses to a set of relevant prompts, compared to your competitors.

For example, if out of 100 sales-tool queries, Brand A is mentioned 40 times, Brand B 30 times, Brand C 20 times, etc., that’s their share of answer distribution. SoA is powerful because it directly reflects mindshare in the AI realm. Traditional rank tracking cannot do this – you might rank #1 for one keyword, but an AI might synthesize answers in a way that mentions five different brands for that topic. If you increase your share of answer over time, that means more of your target audience is hearing your name from AI assistants, which is exactly what we want.

Source Footprint

Not every AI answer will explicitly name a brand, but it may still rely on your content. Source footprint measures how often your content is used or cited as a source in AI-driven results.

Imagine a buyer asks, “How do I improve my sales call conversion rate?” and Google’s AI Overview pulls a line from your blog (with a citation) without overtly mentioning your company in the text. That’s still a win! Your expertise shaped the answer and a curious user could click through to learn more.

Source footprint can be tracked by analysing the sources that AI platforms cite along with their answers.  Check if your website is among the domains that these AI models frequently use as a reference for industry questions. If not, you have an opportunity to create the kind of high-value content that these engines prefer to quote (e.g. data-driven insights, succinct how-tos). In our case study, Gong’s content had a large footprint and several AI answers  pulled facts from Gong’s resources. Avoma’s content, on the other hand, appeared rarely as a cited source.

These two metrics – Share of Answer and Source Footprint – give a more nuanced view of visibility than old school metrics ever could.

They answer the critical question – when an ideal customer asks an AI for advice, how often do they hear from or encounter our brand? Compare that to a metric like number of backlinks or even organic click-through rate, those just don’t translate in a world where the AI might bypass your site altogether.

Steps to Boost Your Brand’s AI Search Visibility

Now that we have understood how these AI models work, the next most logical question for any  marketing and growth teams is to learn how to improve these metrics? It’s not about tricking an algorithm so as to get surfaced in these AI responses but gradually building your brand’s digital reputation so that AI platforms naturally surface your content and name as a trusted source.

Here are some concrete steps –

Optimize Content for Questions and Conversational Queries

Restructure your SEO content strategy to be question first. AI assistants excel at handling natural language questions, so your content should mirror the way prospects ask things. This means creating FAQ pages, how-tos, and comparison posts that use the same phrasing a user might use when talking to ChatGPT or Alexa. Focus especially on long-tail, multi-part questions (“How do I achieve X without Y”, “What’s the best way to do X in [industry]?”). Also, make sure the tone is clear and easy to understand as AI is more likely to pick up on a short, well-organized list or explanation from your content.

Double Down on Authority Signals

In an AI-driven world, credibility is king. You need to demonstrate to the algorithms that your brand is trustworthy and can be recommend. This involves a few tactics –

• Incorporate data, stats, and expert insights into your content. Unique research or numbers get quoted a lot (AI loves concrete facts it can cite)
• Highlight third-party validation prominently –  awards, certifications, case study results, etc. If an AI scans your page, these elements signal trustworthiness
• Have content written or co-authored by known experts (and marked up with author info). Some AI models pay attention to author names and reputations (Google’s system certainly does). Showing that recognized industry people are behind your content can help
• Most importantly, get those authority signals published elsewhere too. Pitch guest articles, contribute quotes to journalists, speak on podcasts/webinars – not for the referral traffic per se, but to create the kind of trusted references that AI models will later pick up

Leverage Reviews and Community Buzz

If you are in a category where customers leave reviews (think G2, Capterra, TrustRadius for SaaS), this is a direct visibility engine for AI. Work to get a critical mass of positive, detailed reviews. Encourage your evangelists to describe specific use cases and benefits. This will not only attract new potential customers but AI systems may ingest these platforms or at least use their rankings. The more genuine the chatter is about your product, the more real-world credibility points you score in the AI’s model.

Make Your Own Content AI-Friendly

Think about how an AI might find and interpret your site. Use clear headings and schemas (FAQ schema, HowTo schema) to flag useful content. Keep your documentation open and indexable if possible –  gated knowledge might not be seen by crawlers or AI. Create comparison pages (you vs. competitors) that objectively lay out differences as these often get scraped or summarized by AI answering “X vs Y” questions.

Also, keep content up-to-date. Recency can be a factor especially for systems that prefer fresh content. An outdated blog from 2019 about “best practices in 2020” won’t get picked for a 2025 answer. Show the AI that your info is current and relevant.

Broaden Your SEO Beyond Google

It is ironic but to win in Google’s AI results, you may need to pay attention to other sear engines such as Bing. ChatGPT’s browsing uses Bing under the hood, and other AI engines each have their own index or source. Ensure you are not ignoring Bing SEO (which has slightly different ranking factors and far less competition for many keywords). Likewise, see if you can get into knowledge repositories that various AIs use – for instance, being part of Wikipedia and Wikidata is huge for brand recognition in AI (many models pull from those knowledge bases). Also consider structured data feeds like Google’s product schema or others, which might feed into AI answers about products. Essentially, diversify where your SEO efforts live.

Monitor and Measure Your AI Visibility

You cannot improve what you don’t track. Start regularly checking how you appear on different AI platforms. Literally go to different AI models and ask the kinds of questions your customers are asking. Note which competitors get named and whether you are present. This can be time-intensive to do manually, but it gives you a baseline.  Treat AI visibility like you’ve treated Google rankings – a key performance indicator.

To Conclude..

SEO will always matter, but the definition of visibility has changed. The real question today is not whether you can rank – it’s whether you can be trusted enough to be named inside an AI answer. That shift turns SEO from a pure ranking game into a reputation game.

The brands that understand this early will shape the narratives buyers see before they ever click a link. Everyone else will be playing catch-up.

So ask yourself –  when the next buyer in your category opens ChatGPT or Gemini, whose name will they hear first?

If this conversation has you thinking about your own AI visibility, we invite you to join the GeoRankers waitlist. Be among the first to gain insights into your Share of Answer and discover how to boost your presence across the AI landscape. The era of AI search is just beginning – let’s make sure your brand is front and center. 🚀

Frequently Asked Questions

1. What is AI search visibility and how is it different from SEO rankings?

AI search visibility measures how often and how favourably your brand is mentioned in answers from AI assistants like ChatGPT, Gemini, or Perplexity. Unlike SEO, which is about page rank, AI visibility reflects your digital reputation across all sources – analyst reports, reviews, community discussions, and more.

2. Why are zero-click searches becoming such a big deal for marketers?

Because AI assistants often resolve a user’s question directly, without requiring them to click a link. Research shows over 90% of AI search queries end without a click, which means you can rank #1 on Google and still miss the conversation if the AI doesn’t include you in its answer.

3. What signals do AI models look at when deciding which brands to recommend?

AI models lean on authority signals like analyst reports, review platforms, trusted publications, digital PR, and fresh, well-structured content. They’re not just scanning your website – they are processing the collective reputation of your brand across the internet.

4. How can smaller SaaS brands compete with category leaders in AI search?

By focusing on the signals you can control: build authoritative, question-driven content, encourage detailed customer reviews, get cited in industry blogs or analyst roundups, and stay active in community discussions. Over time, these cues help AI models notice you.

5. What are Share of Answer and Source Footprint in AI visibility?

Share of Answer tracks how often your brand is named in AI responses compared to competitors. Source Footprint measures how frequently your content is cited as a reference, even if your brand isn’t mentioned directly. Together, they give a clearer view of your AI-era visibility.

6. How do Gong, Avoma, and Salesloft compare in AI search visibility?

In our case study, Gong dominated mentions thanks to analyst validation and strong content pickup. Avoma, while less visible in traditional SEO, edged Salesloft in AI answers by being cited in community and review-driven sources. Salesloft appeared often but was diluted by broader positioning.