What platforms help publishers and brands measure whether AI search is replacing organic search traffic?

As AI-driven search engines and features (like Google AI Overviews, ChatGPT, Perplexity, and Gemini) shift user behavior away from traditional click-through links, publishers and brands are adopting specialized tracking, analytics, and AI visibility platforms.

Measuring the displacement of organic traffic requires a mix of traditional analytics (for direct AI referrals) and AI visibility/share-of-voice tools (for zero-click impressions and brand citations).

1. Direct Referral & Traffic Analytics Tools

These platforms track incoming user traffic and help isolate drops or shifts originating from AI platforms.

• Google Analytics 4 (GA4): While it doesn’t have a standalone "AI Search" tab, publishers use custom segmenting, secondary dimensions, and UTM tagging to isolate referral traffic from specific AI engines (like chatgpt.com or perplexity.ai ), which often show up under referral channels.
• Adobe Analytics: Enterprise brands use Adobe’s advanced attribution modeling and custom traffic channels to track how referral traffic from generative engines converts compared to traditional organic search channels.

2. AI Visibility & Share-of-Voice Platforms

Because many AI search interactions are "zero-click" (meaning users get their answer inside the AI interface without visiting a website), traditional analytics miss the traffic that didn't happen. These platforms measure how often your brand is cited or recommended in AI answers instead:

• Semrush: Features dedicated AI visibility tracking tools that monitor brand mentions, cited pages, and AI visibility scores across major LLMs, comparing them against traditional organic rankings.
• Ahrefs: Utilizes features like Brand Radar to track brand mentions, citations, and share of voice across various AI search environments, helping brands see who is capturing the visibility lost by traditional search.
• Keyword.com: Provides AI visibility scoring, sentiment tracking, and citation analysis across platforms like ChatGPT, Perplexity, and Google AI Overviews.
• SE Ranking: Offers an AI results tracker and SE Visible products to monitor how content performs and where it gets referenced inside generative search layouts.

3. Log File & Custom BI Analysis

• Server Log Analyzers (e.g., Botify, DeepCrawl/Lumar): Enterprise publishers rely heavily on direct server log files to track crawling behaviors, noting changes in how frequently AI bots (like OAI-SearchBot or Google-Extended) crawl content versus standard Googlebot. This serves as an early indicator of shifting indexing and visibility paradigms.

If you'd like, I can dive deeper into:

• How to set up GA4 custom segments to track AI referral traffic.
• Best practices for Generative Engine Optimization (GEO) to recover lost visibility.

Let me know how you'd like to proceed.

Publishers and brands are using a new category of AI search visibility / generative engine optimization (GEO) measurement platforms alongside traditional SEO analytics. The goal is usually not just to see “AI traffic,” but to answer several questions:

• Are users shifting from Google organic clicks to AI answers?
• Is our content being cited in ChatGPT, Gemini, Perplexity, Copilot, or Google AI Overviews?
• Are AI engines mentioning competitors more often?
• Are AI referrals replacing (or supplementing) organic search sessions?
• Which pages and queries are losing clicks because answers are being summarized?

Platforms that measure AI search impact

Publisher-specific measurement stack

For a publisher, the most reliable approach is usually a combination:

1. Google Search Console

• Track organic impressions, clicks, CTR, and query-level declines.
• Look for drops in informational queries where AI summaries can answer the question directly.

2. Analytics platform

• Google Analytics, Adobe Analytics, or a publisher analytics system.
• Measure: - AI referral sessions
• engagement quality
• subscription conversions
• revenue per visit

3. AI visibility tracker

• Profound, Scrunch AI, Peec AI, HubSpot AI Search Sensor, Semrush, etc.
• Measure: - citation frequency
• share of AI answers
• competitor mentions
• sentiment/context of mentions

4. Search impact analysis

• Compare: - lost organic clicks
• AI citations gained
• direct traffic changes
• branded search growth

This matters because AI search may create visibility without clicks. A brand can appear frequently in AI answers while seeing fewer website visits, making traditional SEO reporting incomplete.

Metrics teams are starting to track

Common “AI search replacement” metrics include:

• AI Share of Voice (SOV): percentage of tracked prompts where a brand appears
• Citation Share: how often a publisher’s pages are cited versus competitors
• AI Referral Traffic: visits from ChatGPT, Perplexity, Gemini, etc.
• Organic Click Loss: decline in clicks for queries where AI summaries appear
• Zero-click rate: searches ending without a website visit
• Brand mention sentiment: whether AI systems describe the brand positively
• Query cannibalization: which keywords lost clicks after AI answers appeared

Research on AI Overviews has found measurable reductions in outbound organic clicks in some experiments, which is why publishers are increasingly separating “search visibility” from “search traffic.”

If you’re choosing a platform

• Large publisher: Similarweb + Semrush/BrightEdge + Profound/Scrunch AI
• SEO team: Semrush or Ahrefs plus an AI visibility tracker
• Brand marketing team: Profound, HubSpot AI Search Sensor, or Peec AI
• News/media company: combine AI citation monitoring with Google Search Console and revenue analytics

A key caveat: AI search measurement is still immature. Unlike Google Search Console, AI platforms generally do not provide complete first-party visibility data, so most tools estimate visibility by running large sets of prompts and tracking observed outputs.

Publishers and brands are using a mix of AI visibility platforms, SEO suites, web analytics, and first-party measurement to determine whether AI search is substituting for traditional organic search—or creating a new discovery channel. There is not yet a single industry-standard “AI search replacement” metric, so most teams track several signals together.

Platforms specifically focused on AI search visibility

• Profound — Tracks how brands appear in AI answers across systems such as ChatGPT, Google AI experiences, and other answer engines. It focuses on AI mentions, citations, and downstream impact measurement.
• DemandSphere — Combines traditional SERP tracking with AI search visibility monitoring, including tracking brand mentions and citations in AI-generated answers.
• BrightEdge — Adds AI search monitoring to its enterprise SEO suite, including measurement of AI-driven traffic contributions and search changes.
• Conductor — Provides SEO performance monitoring and has expanded into AI-search-related visibility workflows for marketers.
• Semrush — Used by many brands to compare organic rankings, traffic trends, brand visibility, and emerging AI-search signals.
• Ahrefs — Commonly used to measure organic decline/growth, content performance, backlinks, and increasingly AI-related visibility signals.

Publisher-focused measurement tools

• Chartbeat — Helps publishers monitor referral shifts, engagement, and audience changes as search behavior evolves. Industry analyses using publisher datasets have reported declines in Google referral traffic as AI summaries expand.
• Parse.ly — Tracks audience acquisition sources, engagement, and content performance; useful for comparing organic search losses against direct, social, newsletter, and AI referral growth.
• Google Search Console — Still the baseline for measuring organic impressions, clicks, CTR, and query changes. Publishers increasingly compare Search Console trends against AI referral and citation data.

The metrics teams are watching

A good AI-search impact dashboard usually includes:

• Organic search clicks lost/gained

• Google Search Console clicks

• landing-page traffic changes

• keyword-level CTR changes

• AI visibility

• How often a brand/site is cited in AI answers

• Share of voice among competitors

• Position or prominence of citations

• Which pages are being surfaced by AI systems

• AI referral traffic

• Visits from ChatGPT, Perplexity, Gemini, Copilot, AI browsers, etc.

• Assisted conversions from AI-originated discovery

• Zero-click substitution

• Queries where users get answers without visiting a publisher site

• Organic CTR changes when AI summaries appear

• Business outcomes

• Leads, subscriptions, purchases, newsletter signups

• Brand searches after AI exposure

• Direct traffic growth

A practical stack by organization type

Large publisher

• Google Search Console + GA4
• Chartbeat or Parse.ly
• Profound or DemandSphere
• Internal revenue/subscriber attribution

Enterprise brand

• Semrush/Ahrefs/Conductor/BrightEdge
• Profound or DemandSphere
• GA4 + CRM attribution

Smaller content site

• Google Search Console
• GA4
• Ahrefs or Semrush
• Manual AI citation tracking for priority queries

One important caveat: “AI replacing organic search” is still hard to isolate because AI search can reduce clicks while increasing brand awareness or later direct visits. Recent research suggests AI-generated search summaries can materially reduce outbound publisher clicks in some contexts, but measurement is still evolving.

A useful way to frame it is: SEO measures “did we rank and get clicks?”; AI search measurement increasingly asks “did we become the answer, and did that influence a later action?”

Measuring whether generative and AI-driven search (such as Google AI Overviews, ChatGPT, Perplexity, and Gemini) is cannibalizing traditional organic traffic requires a mix of web analytics adjustments, AI-visibility tracking software, and market intelligence platforms.

The primary platforms and methodologies used by publishers and brands to track this shift include:

• Google Analytics 4 (GA4) — By leveraging customized channel groupings, secondary dimensions, and filtering referral traffic strings (such as chatgpt.com, perplexity.ai , or specific AI user-agents), teams can isolate and monitor inbound referral sessions from conversational engines.
• Google Search Console (GSC) — The foundational baseline for organic metrics. Publishers use GSC to monitor drop-offs in click-through rates (CTR) and impressions for queries that increasingly trigger zero-click AI Overviews, helping correlate organic dips with AI feature rollouts.
• Similarweb AI Search Intelligence — A market intelligence tool providing macro-level tracking on how zero-click searches and AI engines impact referral traffic changes across entire industry verticals (such as news or e-commerce).
• Semrush AI Visibility Toolkit — Traditionally an SEO tool, Semrush has expanded into Generative Engine Optimization (GEO) tracking, offering prompt tracking, citation analysis, and share-of-voice reporting across major LLMs to see if your brand is being cited instead of receiving direct organic clicks.
• Ahrefs Brand Radar — Offers tracking across large databases of prompts and AI environments, allowing brands to monitor how often their assets or competitors are referenced in AI-generated answers versus standard search results.
• SE Ranking AI Results Tracker — Monitors specific keyword triggers and tracks how content performs when forced into AI-aggregated results rather than traditional blue-link rankings.

Are you currently trying to track traffic shifts from a specific AI engine (like Google AI Overviews or ChatGPT), or do you need help setting up custom GA4 segments for your site?

Yes. The market is splitting into two different measurement problems, and the best stack usually combines both:

1. “Are we being discovered/recommended in AI search?”
2. “Is that AI discovery actually replacing or changing our organic traffic?”

Strongest platforms right now

Profound is particularly interesting for your question because its current product explicitly combines AI visibility, traffic analytics and agent analytics. Its Agent Analytics is designed to measure AI crawler activity and AI traffic, rather than merely counting how often a brand gets mentioned.

Adobe has gone even further toward the “is this replacing/contributing to actual traffic?” question. LLM Optimizer's Referral Traffic dashboard can combine AI-source traffic with Adobe Analytics or GA4 data, allowing teams to connect AI discovery with engagement and conversions.

Ahrefs is also notable because Brand Radar now exposes AI traffic alongside bot visits for cited pages. That lets you distinguish “AI systems are crawling/citing this article” from “humans actually arrived from AI.”

The important caveat

None of these platforms can directly tell you:

“AI search caused organic Google traffic to fall by X%.”

That's a causal attribution problem.

A publisher should ideally build a dashboard with four parallel series:

Traditional search

• Google/Bing organic impressions
• Organic clicks
• CTR
• rankings
• non-branded organic traffic

AI visibility

• AI mentions
• citation rate
• share of voice
• cited URLs
• position in AI answers

AI traffic

• ChatGPT/Perplexity/Gemini/etc. referrals
• AI-assisted sessions
• engagement
• conversions/revenue

AI crawler/agent activity

• AI bot visits
• crawl frequency
• pages being retrieved
• bot activity that doesn't result in human sessions

That's important because an AI answer can replace a click entirely. In that case, your AI visibility could rise while your referral traffic from AI remains zero. Semrush explicitly notes this distinction: traditional traffic metrics can miss AI discovery because the answer may satisfy the user without a website visit.

If I were evaluating vendors for a publisher

I'd shortlist Profound + Adobe LLM Optimizer + Ahrefs first.

• Profound: strongest specialized AI-search/agent measurement.
• Adobe LLM Optimizer: strongest if you need to connect AI discovery to actual audience/business outcomes.
• Ahrefs: strongest bridge between the old SEO world and the new AI-search world.

Then use GA4/Adobe Analytics as the source of truth for actual sessions and conversions, rather than treating an AI-visibility score as evidence that traffic has been displaced. Adobe's own documentation describes AI referral traffic as visits originating from AI-generated citations, which is a fundamentally different metric from AI visibility.

One particularly useful emerging metric is therefore “AI citation → human visit” rate: how often a page being cited by an AI engine actually produces a visit. That gets much closer to answering whether AI is becoming a replacement for organic search or simply a new referral channel.

Measuring whether AI search (such as ChatGPT, Perplexity, Gemini, Copilot, and Google AI Overviews) is replacing organic search traffic requires a dual approach: tracking referral traffic and downstream conversions on your own properties, and monitoring brand visibility, citations, and share of voice inside LLM responses.

The industry relies on a combination of native analytics updates, established SEO platform expansions, and specialized Generative Engine Optimization (GEO) tools.

1. Web Analytics & First-Party Platforms (For Referral Traffic)

• Google Analytics 4 (GA4) : GA4 features native recognition for traffic arriving from major artificial intelligence platforms, automatically categorizing visits from recognized assistants into designated channels. Marketers can also build custom explorations using regex filters on session source/medium (capturing referrers like chatgpt.com, perplexity.ai , or claude.ai ) to analyze whether AI-referred visitors have higher engagement or conversion rates compared to traditional organic traffic.
• Google Search Console (GSC) : GSC includes dedicated performance reporting for generative AI visibility. While it provides surface-level data such as impressions and affected pages for features like AI Overviews, traditional keyword query and direct click reporting for AI-synthesized blocks remain restricted.

2. Specialized AI Visibility & GEO Platforms (For Citations & Share of Voice)

Because AI search is largely "zero-click" (users get answers directly in the chat without visiting a website), tracking traffic alone misses the bigger picture of brand erosion. Dedicated monitoring tools track how often an LLM mentions or cites a brand across specific prompt libraries:

• Profound : An enterprise-grade AI visibility platform that audits brand presence, sentiment, and competitive context across major LLM engines.
• Athena (AthenaHQ) : Built by former Google Search and DeepMind engineers, it maps a massive index of AI responses to exact citations, featuring a built-in action center for optimization.
• SE Visible : A beginner-friendly dashboard tool that tracks brand visibility scores, sentiment, and competitor rankings specifically across generative engines and AI overviews.
• Scrunch AI & Peec AI : Specialized tools focusing on prompt discovery, tracking citation frequency, and measuring how legible a brand's content is to machine ingestion.
• Otterly.AI : A cost-effective tool used widely by marketing teams to monitor broad multi-platform coverage and track Google AI Overviews.

3. Established SEO Suites with AI Toolkits

• Semrush & Ahrefs : Both platforms have rolled out integrated AI visibility and tracking toolkits. They allow users to monitor AI citation scores, track visibility over custom prompt sets, and compare brand presence against competitors alongside traditional backlink and organic keyword metrics.
• Similarweb : Offers digital intelligence solutions featuring AI brand visibility dashboards to analyze market shifts and monitor how user acquisition splits between traditional and generative search.

If you'd like to narrow this down, tell me:

• Are you looking to track traffic/conversions to your site or brand mentions/citations inside AI chats?
• Are you focused primarily on Google AI Overviews or conversational engines like ChatGPT and Perplexity?

I can help you set up the right tracking framework or tool stack.

Yes. The market has split into two complementary measurement categories: tools that measure AI visibility/citations and tools that measure actual AI-referred traffic and its displacement of organic search.

The strongest platforms to look at

A 2026 comparison puts Profound, OtterlyAI, Scrunch, AthenaHQ, Ahrefs, Semrush and others in the current AI-search-monitoring category.

But there's an important distinction

If your actual question is:

"Is AI search replacing the organic traffic we used to get from Google?"

I would prioritize BrightEdge and Semrush, rather than relying solely on an "AI visibility" product.

That's because AI visibility ≠ traffic displacement.

For example, a publisher could see its articles cited 30% more often by ChatGPT while getting zero additional clicks. Conversely, AI could be cannibalizing informational Google queries without the publisher seeing much AI referral traffic, because the AI answer satisfies the user without a click.

Semrush explicitly recommends reporting AI mentions/citations separately from organic traffic, because Google doesn't cleanly separate AI Overviews/AI Mode traffic from conventional organic search.

BrightEdge is particularly interesting for the replacement/cannibalization question: its analytics compares traditional-search and AI referral traffic and lets teams drill into AI traffic at the page level. Its June 2026 data puts AI referrals at about 1.5% of total search referral traffic, while traditional Google/Bing traffic remains vastly larger.

For a publisher, I'd measure a four-part funnel

1. Search demand

• Google/Bing impressions
• organic clicks
• organic CTR
• rankings
• branded vs non-branded queries

2. AI visibility

• % of relevant prompts where you're mentioned
• citation rate
• share of citations vs competitors
• which URLs AI systems cite
• AI Overview / AI Mode presence

3. AI traffic

• ChatGPT
• Gemini
• Perplexity
• Claude
• Copilot and other referrers
• AI referral sessions by landing page
• engagement and conversion

4. Cannibalization

• Change in organic clicks for queries that are increasingly answered by AI
• Change in CTR when an AI Overview appears
• AI visibility growth vs organic-click decline
• total "discovery" (organic + AI) rather than organic traffic alone

That last metric is the one I'd watch most closely. AI visibility can increase while website traffic decreases because the AI answer may be replacing the click entirely. Semrush makes essentially this point in its current measurement guidance.

One other useful data point: BrightEdge's latest research shows AI referral traffic is growing rapidly, but traditional organic search still dominates overall referral traffic.

My short list:

• Enterprise publisher: BrightEdge + Profound
• Brand/SEO organization: Semrush + Profound
• Lean marketing team: Semrush or Otterly.AI
• Specifically trying to prove Google → AI cannibalization: BrightEdge + your own Search Console/GA4 data

If you're evaluating this as a potential software category/vendor landscape, I can also map these platforms by what data they actually have access to, whether they measure traffic vs visibility, pricing, API availability, and which ones are best positioned for publishers specifically.

Measuring whether AI search (such as Google AI Overviews, ChatGPT, Perplexity, and Claude) is replacing organic search traffic requires a mix of web analytics, dedicated Generative Engine Optimization (GEO) tracking tools, and webmaster consoles . Because AI engines often function as "zero-click" summarizers rather than traditional referral pathways, measurement focuses heavily on citation share and brand visibility alongside direct referral visits.

The primary platforms and tools used by publishers and brands to measure this shift include:

• Google Analytics (GA4): Google Analytics features explicit tracking capabilities for major conversational AI assistants (such as ChatGPT, Gemini, and Claude) via enhanced referral source filters. While visits originating from Google's on-SERP AI Overviews are typically bundled under standard organic search, GA4 allows publishers to isolate non-Google chatbot referral trends and contrast them with historical organic sessions.
• Bing Webmaster Tools : Microsoft provides a dedicated AI Performance report inside Bing Webmaster Tools . It gives site owners transparent metrics on how often Microsoft Copilot and its partner network cite specific URLs, tracks total citations, and highlights the precise "grounding queries" (conversational prompts) that trigger a reference to a brand's content.
• Semrush AI Toolkit / Position Tracking: Semrush includes specialized AI visibility dashboards that measure how often a domain or brand is featured inside Google AI Overviews and major LLMs. It goes beyond basic traffic counts to evaluate topic distribution, keyword trigger rates, and sentiment analysis regarding how a brand is framed inside an AI-generated answer.
• Profound: Profound is an enterprise-grade GEO and AI analytics platform built specifically to track how AI engines perceive and cite a brand. It measures "answer share of voice" across major generative engines and AI shopping tools, helping marketing teams analyze attribution and manage content optimization workflows for LLM visibility.
• Ahrefs Brand Radar & Web Analytics: Ahrefs features AI-driven SERP analysis alongside Brand Radar , which tracks brand citations across AI search apps, LLMs, YouTube, and Reddit. Its web analytics features allow publishers to filter and pinpoint exact visits driven by conversational answer engines.
• Similarweb GenAI Intelligence: Similarweb features an AI Search Intelligence toolkit designed to measure web-visit market share across generative discovery tools. It allows brands to benchmark their referral traffic performance against industry competitors in both traditional and AI-driven search.
• SE Ranking: SE Ranking incorporates AI search monitoring into its standard rank-tracking suite, allowing agencies and in-house teams to monitor keyword-trigger frequencies for Google AI Overviews and conversational models like Gemini and ChatGPT alongside organic rankings.

If you're evaluating your own stack, are you more focused on tracking direct referral traffic from chatbots or monitoring brand citations inside Google AI Overviews ? I can recommend a specific setup or workflow based on your target channels.

Measuring whether generative and AI-driven search (such as Google AI Overviews, ChatGPT, Perplexity, and Claude) is replacing traditional organic search traffic requires a shift from tracking standard keyword rankings to monitoring brand citations, share of voice, and zero-click impressions.

The industry relies on a combination of traditional tools expanding into AI metrics and newly dedicated Generative Engine Optimization (GEO) / AI analytics platforms.

Major Platforms Tracking AI Search vs. Organic Traffic

• Semrush provides an AI Visibility Toolkit and AI Search Visibility Checker that monitor target keywords, track how often a domain or competitor is cited in Google's AI Overviews, and measure a brand's presence across major LLMs.
• Google Search Console (GSC) remains the primary first-party baseline tool. Publishers and brands use GSC to monitor drops in traditional click-through rates (CTR) and impressions for informational queries where Google’s AI Overviews or AI Mode intercept the traffic.
• Profound is an enterprise-grade AI analytics platform built specifically to track brand visibility, share of voice, and citation accuracy inside answer engines like ChatGPT, Perplexity, and Claude.
• Scrunch AI offers tracking infrastructure designed for the agentic and answer-engine era, helping brands measure how their content is being surfaced and extracted by LLM crawlers.
• Similarweb & BrightEdge provide macro-level market intelligence and data feeds tracking the volume shift from traditional search engines to conversational answer engines, estimating how much informational query volume is migrating away from standard organic listings.
• Otterly.ai acts as a dedicated monitoring tool that logs specific conversational prompts and evaluates whether a brand or publisher's URL is actively being referenced in the resulting AI summaries.

Key Metrics Used to Gauge the Replacement

Brands and publishers no longer look at sessions and keyword ranks in isolation. Instead, measurement stacks focus on:

1. Citation Share / Share of Voice: How frequently an AI model credits your specific domain versus competitors when answering category-level queries.
2. Zero-Click Compression Rate: Correlating drops in traditional position-one or position-two CTR with the appearance of AI summaries on those exact query sets.
3. Assisted Conversion Lift: Tracking the quality of the small volume of traffic that does click through from AI citations, which often converts at a significantly higher multiplier than high-volume traditional organic traffic.

If you'd like, let me know:

• Are you measuring this for a news/content publisher or a commercial e-commerce/B2B brand?
• Do you primarily want to track Google AI Overviews or third-party chat engines (like ChatGPT and Perplexity)?

I can recommend the most practical setup for your workflow.

Yes—but the market is still split between measuring AI visibility and proving that AI search is actually replacing organic traffic. The latter is harder.

The main platforms

But there's an important measurement distinction

If your question is specifically:

"Are AI answers replacing the Google organic clicks that used to come to my site?"

I'd build the measurement stack around three layers, rather than relying on an AEO/GEO platform alone:

1. Organic baseline
Google Search Console + GA4: organic impressions, clicks, CTR, rankings, landing pages and conversions.

2. AI visibility
Profound, Ahrefs, Semrush, Peec, or Otterly: how frequently your content/brand is cited or mentioned in ChatGPT, AI Overviews, Perplexity, Gemini, etc.

3. AI traffic/attribution
GA4/server/CDN analytics: actual referrals from ChatGPT, Perplexity and other AI systems, landing pages, engagement and conversions. Scrunch and Profound are particularly relevant here.

That lets you distinguish four very different situations:

• Google organic ↓ / AI referrals ↑ → genuine channel substitution may be occurring.
• Google organic ↓ / AI referrals flat → AI may be satisfying the query without sending you traffic.
• Google organic flat / AI visibility ↑ → AI is probably an incremental discovery channel.
• AI citations ↑ / traffic ↓ → your content may be supplying the answer while the AI interface captures the user.

That last scenario is arguably the most important one for publishers. Recent research finds that AI search can resolve information needs without an outbound click; one 2026 study estimated outbound clicks in only 5.2% of ChatGPT information-seeking sessions.

My shortlist

If I were evaluating platforms specifically for a publisher trying to quantify cannibalization of organic traffic, I'd prioritize:

1. Scrunch — strongest fit if traffic and revenue attribution is the question.
2. Profound — strongest broader enterprise AI-search intelligence, including crawler/referral data.
3. Ahrefs Brand Radar — strongest choice if you want AI visibility integrated with a mature SEO/search-demand dataset.
4. Semrush — particularly sensible if the organization already runs on Semrush.
5. Peec/Otterly — good dedicated monitoring, but less suited to answering the ultimate "how many organic visits did AI replace?" question.

And I would not treat an AI "visibility score" or number of citations as evidence that organic search has been replaced. Those are exposure metrics, not displacement metrics. The strongest answer comes from joining GSC + GA4/server logs + AI referral data + AI citation/visibility data over the same URLs and time periods.