AI Share of Voice: The New Visibility Metric for 2026

Last Updated on 24/11/2025

If you’ve watched your organic traffic flatten or decline over the past year, even while your brand “feels” more visible, you’re not imagining things. Search is shifting from blue links to AI-generated answers.

Users ask a question, and tools like Google’s AI Overviews, ChatGPT, Perplexity, and Copilot summarize the web for them in a single response.

Your brand may be mentioned, even recommended but the click never happens.

Traditional metrics, such as rankings, impressions, and the classic share of voice, no longer tell the whole story. You need a way to measure how often AI systems choose you when they answer your buyers’ questions. That’s where AI’s share of voice comes in.

AI share of voice (AI SOV) tracks your visibility and presence inside AI-generated answers across search and LLM platforms.

Instead of asking, “What position do we hold in the SERPs?”, it asks, “When an AI explains our category, how often does it talk about us compared to our competitors and how prominently?”

For SEO pros, CMOs, SaaS founders, and performance marketers, this isn’t a nice-to-have vanity metric. It’s the missing layer between brand, search, and revenue in a zero-click world. In this guide, we’ll break down exactly what AI share of voice is, how to measure it, and how to deliberately grow it so your brand stays visible even when users never leave the AI answer.

What Is AI Share of Voice?

AI share of voice is a simple idea with big implications:

It’s the percentage of AI-generated answers in your category where your brand is mentioned or recommended, compared to all brands your buyers could choose.

Instead of only asking, “What’s our market share in search?”, AI share of voice asks:
“When people ask an AI about our problem space, how often does it talk about us?”

For CMOs and founders, this is the closest thing to “top-of-mind awareness inside machines”.
For SEOs and growth teams, it becomes a new visibility KPI to sit alongside rankings, traffic, and share of search.

Plain-English Definition (for CMOs & Founders)

AI share of voice (AI SOV) = how visible and recommended your brand is inside AI-generated answers across tools like Google AI Overviews, ChatGPT, Claude, Perplexity, Copilot, and other LLM-powered assistant, compared to your competitors.

Whenever someone asks:

• “Best email marketing tools for SaaS”
• “Top CRM platforms for small business”
• “Which link-building agencies are trusted?”

AI systems generate an answer. If your brand appears in those answers, especially if you’re on the shortlist of recommended options, you’re winning AI share of voice.

If you never appear, or appear less often than key competitors, you’re losing AI SOV, even if your organic rankings still look decent.

AI Share of Voice vs Traditional Share of Voice

Classic share of voice (SOV) usually refers to:

• Your ad spend vs competitors (media SOV)
• Your SERP presence vs competitors (SEO share of voice)
• Your social or PR mentions vs the market

Those are still useful, but they assume a world where:

• Users see multiple ads or results
• Users browse and choose among 10 blue links
• All impressions are roughly equal

That’s not how AI answers work.

In AI-generated responses:

• Users see one consolidated answer
• The AI chooses a handful of brands to highlight
• Many sessions end without a click at all

So classic SOV tells you who’s loudest in spend or SERPs,
While AI’s share of voice tells you who’s being picked as the answer.

You can think of it like this:

• Traditional SOV: “How loud are we in the market?”
• Share of search: “How often are people searching for us by name?”
• AI share of voice: “When an AI explains our category, how often does it choose us as an example, option, or recommendation?”

In a zero-click environment, that last one matters a lot.

A Simple AI Share of Voice Formula

To make AI share of voice actionable, you need a basic way to quantify it.

Start with a set of prompts/queries that represent how your ideal buyers would research your category. For example, if you sell a SaaS analytics tool:

• “best product analytics tools for startups”
• “alternatives to [big competitor] analytics”
• “how to track feature adoption in SaaS”

You then run those prompts across one or more AI platforms (e.g., ChatGPT + Perplexity + Google AI Overviews) and log:

• Does your brand appear in the answer?
• Do your competitors appear?
• How many total brands are mentioned?

At its simplest, your AI share of voice for that query set could be:

AI SOV (%) = (Number of answers where your brand is mentioned) 
             ÷ (Total brand mentions for your category) 
             × 100

Or, more practically, for a first version:

AI SOV (%) = (Number of prompts where you’re included as a recommended brand) 
             ÷ (Total prompts tested) 
             × 100

Example:
You test 20 prompts.
In 9 of them, the AI lists your tool as one of the top recommended options.

AI SOV = 9 ÷ 20 × 100 = 45%

That means:
In 45% of AI conversations about your category (based on your sample), you’re present as a credible option.

Later in the article, we’ll make this smarter by weighting position (first vs last in the list), platform (Google vs ChatGPT), and even sentiment. But at this stage, what matters is:

• You move from “I have no idea how AIs talk about us”
• To “I can see where we stand vs competitors in AI answers.”

That’s the core of AI share of voice:
Measure how often you show up. Then deliberately increase it.

Where AI Share of Voice Lives: The New Surfaces of Visibility

AI’s share of voice doesn’t live in just one place. It’s spread across a growing set of AI-powered surfaces that answer questions, recommend tools, and influence buying decisions, often before a user ever hits your website.

For SEOs, CMOs, and SaaS founders, these are the primary areas where AI SOV is most visible.

Google AI Overviews & AI “Modes”

On Google, AI is increasingly the first layer of the answer:

• AI Overviews summarize results directly on the SERP.
• Experimental or region-specific AI modes (search/chat hybrids) let users ask follow-up questions conversationally.

Here, AI might:

• Mention your brand by name
• Cite your content as a source
• List you in a bulleted “top tools” or “options” section

From an AI SOV perspective, you want to know:

• How often do AI Overviews mention or cite you for your core topics?
• Are you consistently appearing when people search “best [category] tools,” “[problem] solutions,” or “[competitor] alternatives”?
• Are your pages being used as sources, even if the user never clicks?

If you’re invisible here, you’re invisible at the exact layer where decisions increasingly start.

Chat-Based LLMs (ChatGPT, Claude, Perplexity, Copilot)

The second major surface is conversational LLMs:

• ChatGPT
• Claude
• Perplexity
• Microsoft Copilot
• Other general-purpose assistants

These tools don’t display 10 blue links; they respond. When a user asks:

• “Which CRM should I use for a 10-person SaaS sales team?”
• “What are the best link-building agencies for B2B?”
• “Compare [your brand] vs [competitor] for startups”

The LLM generates a narrative answer and often:

• Lists specific products or vendors
• Describes pros/cons and ideal use cases
• Sometimes links out to a handful of sources

Your AI share of voice here is:

• How often is your brand named in those answers
• How you’re framed (ideal for X, budget-friendly, enterprise-only, etc.)
• Where you appear in lists (first, middle, after a competitor)

For SaaS and B2B especially, this is the new “shortlist builder.” People ask the AI what to consider before they ever Google you.

Vertical & Embedded AIs (Product, Browser, and Niche Assistants)

The third, often missed surface: embedded AI assistants inside products and platforms your audience already uses.

Examples:

• A browser copilot that summarizes a category and recommends vendors
• A SaaS help-bot suggesting tools and services to integrate with
• Workspace/search assistants inside tools (Notion, Slack, ClickUp, etc.)
• Industry-specific AI tools that ingest docs, blogs, and forums to offer “expert” recommendations

These surfaces may not look like classic “search,” but they still:

• Pull from the open web, docs, forums, and integrations
• Choose which brands to mention
• Influence what busy professionals try first

If you sell to marketers, devs, or operators, these embedded AIs can quietly shape which vendors even get evaluated.

Why This Matters for Strategy

When considering AI share of voice, don’t limit your thinking to “How does Google’s AI Overview talk about us?”

Instead, think:

“In every AI-powered answer my buyers see, search, chat, embedded, how often do we show up, and how are we positioned versus competitors?”

Those are the surfaces where your next customers are already being educated and nudged.

How AI Systems Decide Which Brands to Mention

Source: Exploding Topics

To grow your AI share of voice, you first need to understand how AI systems decide which brands to mention when someone asks a question.

LLMs don’t “pull from thin air.” They’re standing on top of:

• The public web
• Knowledge graphs
• User-generated content (UGC)
• High-authority sources
• Your own site’s structure and signals

Broadly, three categories of signals shape whether you’re mentioned or ignored:

1. Discovery signals: where the AI first learns you exist
2. Authority signals: why it decides to trust and favor you
3. Entity & consistency signals: how clearly it can understand and represent your brand

Let’s break those down.

Discovery Signals: Where AI First “Sees” Your Brand

Before an AI can recommend you, it has to discover you.

Discovery signals come from places where people talk about, link to, or review you:

• Reddit threads, Quora answers, and niche forums
• Community spaces (Slack groups, Discord, Facebook groups, indie communities)
• Product review platforms (G2, Capterra, Trustpilot, etc.)
• Social media discussions and Q&A threads
• Blog posts, listicles, and comparison articles that mention you alongside others in your category

From an AI’s perspective, if you never show up in those ecosystems, you’re effectively invisible. Even if your own site is solid, a lack of third-party presence limits your AI SOV because:

“One place saying you’re great” < “100 different people and sites talking about you.”

Part of increasing AI’s share of voice is ensuring your brand exists beyond your own domain in places where AIs are trained and/or continuously ingest data.

Authority Signals: Why AI Chooses You Over Competitors

Discovery alone is not enough. AIs also need to decide who to prioritize when multiple brands could answer the same need.

Authority signals include:

• High-quality, in-depth content on your domain for key problems in your category
• Strong backlinks from trusted, niche-relevant websites
• Digital PR mentions in respected publications
• Thought leadership (original research, data reports, frameworks, POV pieces)
• Topical depth – a clear hub of content around your product space, not just one thin landing page

When a model decides who to highlight, it’s effectively asking:

• “Which brands have the deepest, clearest explanations?”
• “Who gets cited the most by other sites?”
• “Whose content looks like a definitive source for this topic?”

That’s why a lot of what we already do in classic SEO (E-E-A-T, topical authority, digital PR) directly influences AI share of voice. You’re not just ranking in SERPs, you’re becoming source material for AI.

Entity & Brand Consistency: How Clearly AI Can Represent You

The third piece is entity clarity, how clearly the AI can understand who you are, what you do, and which topics you “own.”

This comes from:

• A clean, consistent brand name and tagline
• Clear “What is [Brand]?” and “Who is [Brand] for?” copy on your site
• Schema markup (Organization, Product, FAQ, Article, etc.)
• Consistent descriptions across your website, LinkedIn, Crunchbase, directories, and profiles
• Internal linking that reinforces your core topics and product areas

If your brand is fuzzy, generic, or inconsistent (“Are they an agency? A SaaS? A blog?”), AI systems can struggle to position you confidently. If your name collides with unrelated entities (e.g., common dictionary words, band names, unrelated companies), it gets even harder.

When your entity is well-defined:

• AI can map you cleanly to your category
• It “knows” which use cases you’re ideal for
• It can confidently drop your name into recommendations without hallucinating

In other words:

Discovery decides if you’re in the consideration set.
Authority decides how strongly you show up.
Entity clarity decides how accurately you’re described.

All three together drive your AI share of voice.

The Components of AI Share of Voice (What You Actually Measure)

If you’re a CMO, SEO lead, or SaaS founder, you don’t just want a concept; you want a framework you can plug into a sheet or dashboard.

AI share of voice isn’t a single number pulled from a magic tool. It’s a composite of several dimensions of visibility inside AI answers.

You can think of it as made up of six key components:

1. Frequency: how often you’re mentioned
2. Prominence: how strongly you’re recommended
3. Coverage: how many key topics you show up for
4. Citation type: how you’re referenced
5. Sentiment & framing: how you’re talked about
6. Platform mix: where you show up

You can start simple (just use 1–3) and then layer the rest as your tracking matures.

Frequency: How Often You’re Mentioned

This is the core of AI SOV:

Out of all the AI answers in your category, how many times does your brand actually appear?

Examples:

• You test 30 prompts across AI tools – your brand is mentioned in 12
• Or: across those answers, there are 80 total brand mentions – your brand appears 10 times

You can track this as:

• Prompt-based frequency:
  Number of prompts where you’re mentioned / total prompts
• Mention-based frequency:
  Total mentions of your brand / total mentions of all brands

The higher this is, the more present you are in AI-driven consideration.

Prominence: How Strongly You’re Recommended

Not all mentions are equal. Being:

• Named first
• Labeled “best for X”
• Or grouped in a “top 3”

Satters more than being one of 10 names buried in a paragraph.

You can model this with a simple position score, for example:

• 3 points if you’re the first recommended brand
• 2 points if you’re in the main shortlist (top 3–5)
• 1 point if you’re mentioned but not clearly recommended
• 0 if not mentioned

Summing that across prompts gives you a Prominence score that sits next to raw frequency.

Coverage: How Many Key Intents You Own

AI share of voice isn’t just how often you appear; it’s where you appear.

You likely have different intent clusters, like:

• “Best [category] tools”
• “[Category] tools for [segment] (SaaS, SMB, enterprise, agencies)”
• “Alternatives to [competitor]”
• “How to solve [job/problem]”

Coverage asks:

For how many of our high-intent buyer journeys do we appear in AI answers?

If you’re present only in generic “best tools” prompts but absent from competitor comparisons or use-case queries, your AI SOV is fragile.

You can:

• List your 10–20 most important intents
• Track presence vs absence per intent cluster
• Calculate coverage:
  Intents where you appear / total important intents

Citation Type: How You’re Referenced

There’s a difference between:

• A plain-text brand mention
• A linked citation to your site
• Your content is being quoted as the source for part of the AI answer

You can score this simply:

• 3 = Cited with link and/or quoted as a source
• 2 = Mentioned as a recommended option (with or without a link)
• 1 = Mentioned in passing (e.g., “other tools include”)
• 0 = Not present

Over time, you want to see more high-value citations, signals that your site is being used as reference material, not just that your brand name exists.

Sentiment & Framing: How You’re Talked About

An AI can:

• Recommend you strongly
• Position you as “good for budget users”
• Warn about limitations
• Or rank you clearly below key competitors

Even if you’re mentioned, negative or weak framing reduces the practical value of your AI share of voice.

You don’t need super complex NLP here; a simple human review can tag sentiment as:

• +1 Positive (strong recommendation / good fit)
• 0 Neutral (factual, balanced)
• -1 Negative (cautions, downsides, “worse than X”)

Roll this into an average and you’ll know whether your AI SOV is helping or hurting perception.

Platform Mix: Where You Show Up

Ultimately, not all AI surfaces are equally suitable for your audience.

For example:

• Dev tools may care more about ChatGPT + Copilot
• B2B SaaS may care more about Google AI Overviews + Perplexity
• Consumer brands might care more about mobile search and assistant ecosystems

You can either:

• Track AI SOV per platform (ChatGPT vs Perplexity vs Google), or
• Weight platforms differently, e.g.:
  • Google AI = 40%
  • ChatGPT = 30%
  • Perplexity = 20%
  • Others = 10%

This helps you align your AI share of voice with where your buyers actually are.

You don’t need a perfect model from day one. A very practical start is:

1. Frequency (do we show up?)
2. Prominence (how often are we top 3?)
3. Coverage (for how many key intents?)

That alone gives you a powerful AI SOV baseline to improve from.

How to Measure Your AI Share of Voice (From Scrappy to Sophisticated)

You don’t need a dedicated “AI SOV platform” to get started. You can begin with a simple spreadsheet and 20 prompts, then gradually move toward semi-automation or tool-based tracking as your org matures.

Think of this in three levels:

1. Manual baseline: perfect for SEOs, solo marketers, founders
2. Semi-automated: for growing teams that want repeatable tracking
3. Tool-based: for CMOs and larger orgs that need dashboards

Let’s walk through each.

Level 1: Manual Baseline (1–2 Hours, No Dev Needed)

This is where everyone should start. It provides a clear, reality-based snapshot of how AI currently discusses your category.

Step 1: Build a Prompt Set

Create a list of 10–50 prompts that reflect how your ideal buyers research your space. Include:

• “Best [category] tools”
• “[category] tools for [segment] (SaaS, SMB, agencies, enterprise)”
• “Alternatives to [competitor]”
• “How to [core problem] for [market]”
• “Which tool should I use for [job]?”

You want a mix of:

• Category prompts (broad)
• Use-case prompts (specific)
• Competitor prompts (comparison/alternatives)

These become your AI SOV test set.

Step 2: Choose 2–3 AI Platforms to Test

For most B2B/SaaS use cases, a solid combo is:

• Google – AI Overviews / AI mode (where available)
• ChatGPT (GPT-4.x)
• Perplexity or Claude (optional but useful)

You can start with just one, but a multi-platform gives a much better signal.

Step 3: Run the Prompts and Log Results

For each prompt on each platform, log:

• Does your brand appear? (Y/N)
• Which competitors appear?
• Where are you listed? (1st, 2nd, “also consider”)
• Quick sentiment tag (+ / 0 / –)

Create columns like:

• Prompt
• Platform
• Our brand mentioned? (0/1)
• Position (0–3 score)
• Competitors mentioned
• Notes (framing, sentiment)

Step 4: Calculate a Simple AI SOV Score

From that data, you can create:

• Presence %
  (# of answers where we appear) / (total answers) x 100
• Average prominence score
  (Using a simple scheme: 3 = leading rec, 2 = in main list, 1 = minor mention, 0 = no mention)
• Coverage %
  (# of key intents where we show up) / (total key intents tested) x 100

This provides a baseline AI share of voice that you can track over time.

Level 2: Semi-Automated Tracking (For Growing Teams)

Once you’ve proven the value of AI SOV internally, the next step is making it repeatable without burning hours every month.

You don’t have to build a full platform. A light layer of automation is enough:

Option A: Custom GPT / Prompt Flows

• Create a custom GPT or workflow that:
  • Accepts a list of prompts
  • Queries them sequentially
  • Extracts brand names mentioned in the answer
• Paste the results into a sheet and calculate SOV by brand.

You still manually review some outputs, but the heavy lifting (running prompts, collecting answers) is automated.

Option B: Browser Automation + Sheets

With basic no-code / low-code tools (Make, Zapier, Playwright, etc.), you can:

• Automate:
  • Opening AI tools
  • Running prompts
  • Copying responses
• Send results to Google Sheets or Airtable
• Run formulas or scripts to:
  • Count brand mentions
  • Score position (based on answer structure / bullet lists)

This lets you:

• Run the same prompt set monthly or quarterly
• Compare AI SOV over time
• See how changes in your content, PR, or community presence affect visibility

6.3 Level 3: Tool-Based AI SOV Tracking (For CMOs & Enterprises)

As AI SOV becomes a core KPI, you’ll see more tools bake it into:

• SEO platforms
• Brand monitoring suites
• “AI visibility” dashboards

What you want from a mature solution is:

• Multi-platform coverage: Google AI, ChatGPT, Perplexity, etc.
• Scheduled runs: weekly/monthly checks, not one-offs
• Competitor comparison: SOV by brand, category, intent cluster
• Sentiment & framing: are you positioned as premium, budget, niche, etc.?
• Integrations: Pipe AI SOV into your BI stack (e.g., Looker, Power BI, HubSpot)

That’s when AI’s share of voice becomes:

• A standard chart in SEO reports
• A slide in board decks
• A lever for budget allocation (e.g., more PR vs more content vs more community)

What Matters Most: Start, Even If It’s Messy

Most teams are still at zero: they have no idea how often AI mentions them.

You don’t need a perfect system. A simple starting point is:

1. 20 prompts
2. 2 AI platforms
3. A single spreadsheet
4. One AI SOV baseline number

From there, you can improve your measurement as you improve your actual visibility.

Benchmarks: What Is a “Good” AI Share of Voice?

Once you calculate your AI share of voice, the first question is always:

“Is this good or are we getting crushed?”

The honest answer: it depends on your category, maturity, and competitive set.
However, we can still establish practical benchmarks and expectations for SEO teams, CMOs, and SaaS founders.

Think in terms of:

• Your stage (early / growth / enterprise)
• Your category size (niche vs crowded)
• Your time horizon (are you just starting AI SOV work?)

Here’s a helpful way to frame it.

Early-Stage SaaS / New Brand

If you’re relatively new, have modest traffic, and limited PR:

• 0–5% AI SOV across your main prompt set is normal
• If you’re present at all for high-intent prompts (“best X tools”), you’re already ahead of many peers
• Your main wins at this stage:
  • Showing up at all for some key prompts
  • Being mentioned positively in comparison / alternative queries

Benchmark goal:

• Get from 0% – 5–10% AI SOV within 6–12 months
• Win visibility on a handful of high-intent prompts, not everything

Growth-Stage SaaS / Established Brand

If you already rank decently in organic search and have some PR or community presence:

• 5–15% AI SOV is a realistic, healthy range across your category prompts
• You’ll often see:
  • You appear in some “best X tools” lists
  • Some competitor prompts mention you as an alternative
  • Stronger presence on one platform (e.g., ChatGPT) but weaker on another (e.g., Google AI Overviews)

Benchmark goal:

• Push to 10–20% AI SOV for:
  • Your core category term prompts
  • Your main use-case prompts
• Improve prominence: not just being mentioned, but consistently in the top 3 recommendations

Enterprise / Category Leaders

If you’re already a market leader or recognized category player:

• You should be aiming for 20–40%+ AI SOV across well-defined category prompts
• In many “best X” or “top X” answers, you should appear by default
• Competitors may still dominate niche or regional prompts

Benchmark goal:

• Maintain lead position for broad category prompts
• Ensure strong presence in:
  • “[competitor] alternatives” prompts
  • “[category] tools for [segment]”
• Monitor sentiment carefully: it’s not enough to appear, you want favorable positioning

More Important Than the Number: The Trend

For AI share of voice, the trend matters more than the absolute number:

• If you go from 2% – 8% in six months, that’s a big strategic win
• If you sit at 15% but flatline or slowly drop while a competitor rises, that’s a warning sign

When you bring this to stakeholders, frame it like:

“Six months ago, we were barely mentioned. Now we’re present in 1 out of 4 key AI answers about our category. Our goal is to hit 1 in 3 by Q4.”

That makes AI SOV:

• Understandable for CMOs and founders
• Clearly linked to broader brand and demand goals
• Something you can attach to specific programs (content, PR, community)

How Often Should You Check AI SOV?

You don’t need daily tracking. For most teams:

• Quarterly is enough for strategic AI SOV
• Monthly, if you’re actively investing in:
  • New content hubs
  • Digital PR campaigns
  • Community/UGC initiatives

What matters is:
You establish a baseline now, and then track whether your work on content, PR, and brand is actually shifting AI’s behavior over time.

Playbook: How to Grow Your AI Share of Voice

Once you’ve measured your AI share of voice and established a baseline, the obvious question is: how do we increase the number?

The good news is you don’t need a totally new playbook.

You need to adapt what you already know from SEO, content, and brand, and aim it at how AI systems discover, trust, and describe you.

At a high level, growing AI SOV means doing three things well: becoming source-worthy, showing up where AIs look for social proof and making it easy for AIs to position you correctly in your category.

Become a Source Worth Quoting

The first lever is to make your site the kind of place an AI model wants to borrow from.

That means building content that doesn’t just rank, it defines ideas, frameworks, and categories.

Instead of ten shallow posts on similar topics, create a few deep, canonical guides that answer an entire question space: definitions, use cases, examples, comparison angles, FAQs, edge cases.

LLMs love clear structure and clarity, so help them out. Use strong intros that clearly define the concept, add FAQs that mirror real questions, sprinkle in concise explanations that could be easily quoted, and support everything with clean headings and internal links.

Schema markup (Organization, Product, FAQ, Article) and a sensible internal linking structure help reinforce which pages are your “pillars” and which topics you legitimately own.

Think less “content calendar” and more “reference library for our category.”

Use PR and Links to Strengthen Authority

The second lever is authority. AI systems don’t just read your site, they look at who else is willing to vouch for you.

That’s where digital PR, strong backlinks, and third-party coverage matter. When respected publications, niche blogs, and industry reports mention and link to you, you’re not just improving classic SEO metrics.

You’re feeding the underlying trust graph that models draw from.

This doesn’t have to mean huge PR budgets. A steady rhythm of guest posts on relevant sites, expert quotes in roundup pieces, participation in industry reports, and a few high-quality digital PR campaigns each year can compound over time.

The more you’re cited as a credible source outside your own domain, the more comfortable AI systems become using you as an example or recommendation inside answers.

Engineer Discovery Through Communities and UGC

The third lever is discovery in the wild. A significant portion of training and retraining data for AI systems originates from user-generated content, including Reddit threads, Q&A sites, reviews, and community discussions.

If you’re never mentioned there, you’re missing a huge slice of the discoverability pie.

You don’t need to spam communities, that backfires fast.

Instead, encourage your team and power users to participate authentically where your audience is already present.

Answer questions in relevant subreddits or Slack/Discord groups, share real use cases, and support people without forcing a pitch every time.

Over time, these natural mentions, plus honest reviews on platforms like G2, Capterra, or niche review sites, create a trail of social proof that models can see.

When an AI tries to answer “What tools do people actually use for X?”, you want to show up in that long tail of real conversations.

Optimize for “Recommended Vendor” Style Prompts

Many of the prompts that matter most for revenue sound like: “What’s the best tool for?”, “Which platform should I use for?”, “What are alternatives to?”

Growing AI share of voice means deliberately building assets that help models choose you in exactly these contexts.

That usually means creating honest, useful comparison pages, “best tools” roundups that include competitors, and buyer guides that clearly spell out who you’re for and when you’re not the right fit.

SEOs already know this pattern for traditional search; the difference now is you write and structure those pages as if they’re reference material for a researcher, not just a user. Be explicit about your positioning, ideal customer, strengths, and tradeoffs.

The clearer that narrative is on your own site, the easier it is for an AI to pick it up and reuse it when someone asks, “Which tool is best for [use case]?”

Monitor and Correct AI Narratives

Finally, growing AI share of voice isn’t just about more mentions, it’s about better mentions.

As you start testing prompts regularly, you’ll notice instances where AIs misrepresent you, such as outdated pricing, an incorrect feature set, a wrong category, or comparisons that don’t make sense.

Treat these as bugs in the “model of your brand” and systematically fix them.

You fix them upstream. Update your own site to make the correct information obvious. Ensure core facts (positioning, pricing tier, category) are consistent across profiles and directories.

If a misconception is clearly derived from a specific third-party article or review, consider updating that content through outreach or by publishing a clearer, more authoritative piece on your own domain.

Over time, as models re-ingest fresher and more consistent signals, the way they talk about you will drift toward the reality you’re shaping.

You don’t have to do everything at once. For most teams, the fastest path is to strengthen one or two source-worthy guides, secure a few credible mentions off-site, and seed some authentic community presence. 

Then, rerun your AI SOV measurement in a couple of months. If you’ve moved from “rarely mentioned” to “regularly considered,” you’ll know the playbook is working.

AI Share of Voice in Action: Three Example Scenarios

It’s one thing to talk about AI’s share of voice in theory. It’s another to see how it actually looks when a brand goes from invisible in AI answers to consistently mentioned.

Here are three simple scenarios that mirror the current state of many SaaS companies, agencies, and B2B brands.

Early-Stage SaaS: From 0% to “We’re Finally on the List”

Imagine a small SaaS tool in the analytics space. The team conducts a basic AI SOV check using 20 prompts, such as “best product analytics tools,” “analytics for early-stage SaaS,” and “[big competitor] alternatives,” across ChatGPT and Perplexity.

At baseline, their score is brutal:
They appear in 0 out of 20 prompts. AI recommends their larger competitors every single time. If you looked only at organic rankings and branded search, things might not look terrible but in AI, they effectively don’t exist.

The team decides to do three things over 90 days:

• Publish one authoritative product analytics guide that serves as their primary reference page.
• Land a handful of guest posts and quotes on relevant SaaS and growth blogs.
• Seed a few authentic mentions in founder communities, Slack groups, and Reddit threads where their early adopters already hang out.

When they rerun the same prompt set a few months later, they now appear in 4 out of 20 answers and are explicitly listed as an alternative in a couple of “best X tools” lists.

Their AI SOV is still small in absolute terms, but they’ve increased from 0% to 20% presence, and most importantly, buyers are now at least seeing the brand when they ask AI for recommendations.

Growth-Stage Brand: From “Honorable Mention” to Top 3 Recommendation

Now, take a more established SaaS or agency that already ranks decently and has some PR under its belt. Their AI SOV baseline is mixed: they’re mentioned in about half of the tested prompts, but rarely in the first positions, and often grouped at the end of “other tools include” lists.

To move from “honorable mention” to top-tier recommendation, they focus less on raw volume and more on clarity and authority. They:

• Rebuild a couple of key landing pages as definitive, source-worthy resources with clearer positioning, use-case breakdowns, and updated FAQs.
• Run a focused digital PR campaign around a data report or industry benchmark, earning links and mentions from several strong publications.
• Tighten up their entity and messaging consistency across LinkedIn, review sites, and directories so the brand is always described in the same way.

When they re-measure AI SOV the next quarter, something important has shifted.

They’re not just present; they’re now first or second in many category answers and appear more often in “[competitor] alternatives” prompts.

The number of prompts where they’re recommended as a top option has increased, and their internal “prominence score” has climbed sharply, even if the raw mentions haven’t doubled.

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Category Leader: Defending AI Share of Voice Against Upstarts

Finally, consider a category leader, a brand that already dominates paid, organic, and traditional SOV.

When they run their AI SOV baseline, they see strong numbers: they’re included in most “best tools” and “which platform should I use” answers.

However, they also notice a trend: new challenger brands are emerging alongside them more frequently than before.

Instead of assuming their dominance is permanent, the leadership team treats AI SOV as an early warning system.

They notice that in certain segments, like “best tools for startups” or “budget alternatives,” the challengers are mentioned more frequently than they’d like.

In other words, they still own the broad category prompts, but they’re losing ground in specific use-case prompts that matter for future growth.

Their response is strategic, not reactive. They:

• Publish more segment-specific content (e.g., “best for startups,” “best for small teams”) that clarifies who each plan is for.
• Invest in community and creator relationships to ensure their brand remains part of the conversation in user-generated spaces.
• Double down on thought leadership and research to remain the “default example” AI systems reach for when summarizing the category.

Their goal isn’t to move from 30% to 90% AI SOV, that’s unrealistic and unnecessary.

It’s to ensure their share stays stable or grows slightly while challengers don’t quietly overtake them in high-intent slices of the market.

The common thread in all three examples is that nobody changed AI directly. They modified the input content, PR, community, and clarity, then observed how AI behavior changed in response.

That’s the mindset you want your readers to walk away with: AI share of voice isn’t magic, it’s a measurable reflection of the brand, content, and authority you’ve built.

How to Plug AI Share of Voice Into Reporting and Strategy

For AI’s share of voice to matter, it has to live somewhere more useful than a one-off spreadsheet. SEOs, CMOs, and founders need it tied into the same systems they already use to plan, justify, and adjust their work.

The simplest way to start is to treat AI SOV as a visibility KPI that sits alongside rankings, organic traffic, and share of search.

In your regular SEO or growth report, add a small block: your AI SOV baseline, the main prompts/platforms you’re tracking, and how that number has moved since last quarter.

Instead of only saying, “We gained X% more organic clicks,” you can say, “We’re now being recommended in Y% of AI answers about our category,” which is far closer to how discovery actually works today.

From there, connect AI SOV to specific initiatives. If you launch a new content hub, ship a digital PR campaign, or make a significant investment in community, make it explicit that part of the goal is to “improve AI share of voice around [topic].”

That way, when you re-run your AI SOV measurement and see an uptick for those prompts, you can attribute the movement back to those programs. Over time, AI SOV becomes a feedback loop, telling you which levers (content, PR, UGC, brand) are actually changing how AI talks about you.

It also deserves a place in C-level and board conversations. Most non-SEOs don’t care about your ranking for a single keyword, but they immediately understand a statement like: “When AI tools explain our category, we’re mentioned 4 times out of 10; our closest competitor is at 7 out of 10.”

That reframes your work from “traffic and keywords” to “future-proofed visibility and category leadership,” which is exactly the language CMOs and founders think in.

On the planning side, you can use AI share of voice to prioritize roadmaps. If your measurement shows you’re strong in generic “best [category] tools” prompts but weak in “[competitor] alternatives” or specific use-case prompts, that’s a clear signal to invest in comparison content, segment-specific pages, or campaigns that target those slices of demand.

Finally, treat AI SOV as a long-term, directional metric, not a daily scoreboard. Checking it quarterly or monthly is enough. The point isn’t to obsess over every fluctuation; it’s to make sure that as search, chat, and assistants continue to evolve, your brand is steadily becoming more present, more prominent, and more accurately represented wherever AI is answering your buyers’ questions.

Measure the Visibility You’re Actually Getting

Search has already changed. Your buyers aren’t just scrolling through ten blue links anymore they’re asking AI systems what to do, what to buy, and which vendors to trust.

Whether you track it or not, those AIs are already forming opinions about your category and deciding which brands deserve to be mentioned.

AI share of voice is simply a way to make that visible.

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