Size a directional AI-demand opportunity
Convert supplied Google keyword volume into an editable low, base, and high estimate while keeping modeled demand distinct from measured query counts.
AI Search Volume Estimator
Free, no signup. Nobody publishes per-keyword query counts for ChatGPT, Gemini, or Perplexity — so "AI search volume" isn't a number you can look up, only one you can model. Paste the Google volume you already have and this splits a total "AI pie" across each system's market share, tracked month by month. Google AI Overviews are shown separately (they're still Google searches).
+ add monthly Google-volume history (optional)
Paste month,volume lines (e.g. 2026-01,12000) to
see how the AI-equivalent volume moved as market share shifted. Overrides the single
value above for the months you provide.
Upload or paste a keyword list with a volume column — Keyword Planner,
Ahrefs, or Semrush exports work (columns auto-detected). An optional intent
column is used when present, and month columns (2026-01, 2026-02, …) are
read as a per-keyword volume history.
Runs entirely in your browser — nothing you paste is uploaded or stored. The 'Copy share link' button encodes your inputs into the URL locally. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Default market-share data last reviewed Jul 1, 2026.
Sample report
Say you enter a single commercial keyword with 12,000 Google searches a month:
keyword,volume,intent
best crm software,12000,commercial
how to migrate crm data,2400,informational
salesforce login,90000,navigational…and, for that keyword, the tool returns:
Example data — captured by running this page's own estimator against a 12,000/mo commercial keyword
Google · 2026-05
12K
1 keyword
Total AI (assistants)
2.6K
22% of Google volume
Google AI Overviews
3.6K
still Google searches
Combined search + AI
14.6K
Google + assistants
Estimated search volume by system · 2026-05
- Total AI (2.6K, 22%) is nine systems combined, not just the two or three you might think of first — ChatGPT is the largest single slice (1.4K) but Gemini, Claude, DeepSeek, Grok, Perplexity, and Copilot each add a smaller share. See how the model splits share ↓.
- AI Overviews (3.6K, 30%) is shown apart from Total AI on purpose — those are still Google searches surfacing an AI-generated answer, not third-party assistant demand, so adding them to the "Total AI" figure would double-count against Google's own 12K.
- Changing intent changes every AI figure — informational queries route to assistants at a higher rate than commercial ones, so the same 12K would produce a bigger AI slice under a different intent. Model the underlying market shift with AI search.
How to use it
- Keep Single keyword selected and paste a Google monthly volume (a keyword label is optional). Pick a Search intent — AI usage skews toward informational and research queries, so this materially changes the estimate.
- Or switch to Bulk (CSV / paste) to upload or paste a keyword list with a volume column — Keyword Planner, Ahrefs, and Semrush exports auto-detect. Then press Analyze list.
- Read the four summary stats, the per-system bar chart for the selected month, and the two over-time charts. Change Snapshot month and Region to move the estimate through time and geography.
- Open Assumptions & market-share inputs to edit any number — the search-like share, the total AI pie, each system's ratio, intent modifiers, and the AI Overviews trigger rate. Copy share link to save or share your exact configuration.
Site passport Local context for this saved site
Local data
Saved targets, named lists, and recent check summaries remain only in this browser.
estimated / interpolated month
Estimated monthly search volume by system
Over time
Filled dots are sourced anchor months; the line between them is interpolated. Market share comes from the data file; the volume chart also reflects any Google-volume history you enter.
Per-keyword estimates
How people may reformulate this in AI search
Rate this tool
Assumptions & market-share inputs — every number is editable
AI systems effective ratio = total-AI-pie × market share
Intent modifiers how much each intent shifts to AI
Editing the pie or a system ratio changes the snapshot for the selected month. The over-time charts always use the sourced monthly market-share history.
How this is calculated & where the numbers come from
The model. Each month, a system's effective multiplier is the total AI pie
(AI search-equivalent volume ÷ Google volume) times that system's market share. Then for a
keyword with Google volume V and an intent:
ratio(system, month) = aiPieOfGoogle(month) × marketShare(system, month) system volume = V × ratio × intentModifier × consolidationFactor
Where the pie comes from. The total AI pie is anchored to ChatGPT's stated prompt volume, following the Ahrefs analysis: 2.5B ChatGPT prompts/day × 65% "search-like" ÷ 13.7B Google searches/day ≈ 12% of Google for ChatGPT alone (July 2025). Dividing by ChatGPT's ~54% market share gives a total pie of ~22% by May 2026. The 65% is the biggest lever and is an editable input — OpenAI/Harvard's stricter "pure search" reading is ~24% (which lands the pie near ~8%). A cross-check: Perplexity's ~40M search-native queries/day works out to ~1.3% of the pie, matching its ~1.3% web-visit share.
Google AI Overviews are modeled separately — they are still Google searches, just answered with AI, so they are never added into the third-party AI total:
AI Overview–influenced = V × aioTriggerRate × intentModifier
Read this as directional, not precise. No platform publishes per-keyword AI query volume. Headline market shares are sourced (below); the total-pie sizing, smaller-system splits, and per-month interpolation are coarse estimates. AI usage skews toward informational and research queries, so the intent modifiers matter. Adjust every input to match your own data or beliefs.
Default sources
Estimates only. "AI search volume" cannot be measured directly — this tool converts known Google volume into a modeled AI equivalent so you can size the opportunity, compare systems, and see the trend. All computation happens locally in your browser.
This estimator turns the Google keyword volume you already have into a modeled view of demand across AI assistants. For the concepts behind it, see AI search and AI share of voice. To grow that demand, pair it with AI Overviews optimization and answer engine optimization.
How to read the estimate
- Google · [month] — the traditional Google volume you entered, carried through unchanged for the selected snapshot month.
- Total AI (assistants) — the modeled sum across all enabled third-party assistants, with its share of Google volume beneath it. This is the headline "AI search volume" figure.
- Google AI Overviews — a separate estimate of your Google searches that return an AI answer. Shown dashed and never added into Total AI, because they still happen on Google.
- Combined search + AI — Google plus the assistants, for a rough all-in demand picture.
- Per-system bars — the estimate split by ChatGPT, Gemini, Claude, Perplexity, Copilot, DeepSeek, Grok, and Other, each coloured to its brand.
- estimated / interpolated month flag — appears when the snapshot month is not a sourced anchor month, so its market share was interpolated. On the over-time charts, filled dots are sourced anchors and the line between them is interpolated.
Assumptions & methodology
Everything runs client-side — there is no server or API, and nothing you enter is uploaded or stored. For each month, a system's effective multiplier is the total AI pie (AI search-equivalent volume ÷ Google volume) times that system's market share that month; your keyword volume is then multiplied by that, an intent modifier, and a query-consolidation factor. Google AI Overviews are modeled independently as volume × trigger rate × intent modifier so they never double-count assistant demand.
The default pie is anchored to Ahrefs' analysis of ChatGPT prompt volume — about 12% of Google for ChatGPT alone in mid-2025, dividing out to roughly a 22% total pie by 2026 — with a Perplexity cross-check. Market shares are stored per month in a bundled data file, so the estimate can be tracked over time, and every headline source is dated in the methodology panel.
Features
- Single-keyword and bulk modes; bulk auto-detects the volume (and optional intent) column from Keyword Planner, Ahrefs, and Semrush CSV/TSV exports.
- Per-system estimates for eight assistants plus an overall total, with Google AI Overviews shown separately.
- Month-by-month snapshots and two over-time charts (volume, and market share by system), with interpolation between sourced anchor months clearly flagged.
- Optional per-keyword monthly Google-volume history (
month,volumelines) so the trend reflects your own demand movement, not just shifting market share. - Every assumption is editable — search-like share, total pie, each system ratio, intent modifiers, consolidation factor, AI Overview trigger rate, and region.
- CSV export of per-keyword estimates and a shareable link that encodes your full configuration in the URL hash.
Limitations
These are modeled estimates, not measurements. No assistant publishes per-keyword
query volume, so the total-pie sizing, the split across smaller systems, and the month-to-month
interpolation are coarse. The single biggest lever is the search-like share of AI prompts
(0.65 following Ahrefs versus about 0.24 for a stricter pure-search
reading), which alone can roughly halve or double the AI pie. The tool starts from your Google
volume, so it inherits any error in that input, and it does not itself fetch keyword volumes,
measure your rankings, or check whether you actually appear in AI answers. Treat the output as a way
to size and compare opportunity, and adjust the assumptions to your own data before relying on it.
Frequently asked questions
What is AI search volume?
AI search volumeAI search uses large language models and retrieval-augmented generation (RAG) to synthesize an answer from multiple sources rather than returning a ranked list of links. Examples include Google AI Overviews, ChatGPT Search, and Perplexity. is an estimate of how many times a question or topic is asked inside AI assistants like ChatGPT, Gemini, Claude, Perplexity, and Copilot each month. Unlike Google keyword volume, no assistant publishes per-keyword query counts, so it cannot be measured directly — this tool models it by taking a share of the traditional Google search volume you already have. Treat every figure as a directional estimate, not measured data.
How do you estimate AI search volume from Google keyword volume?
For each system, the tool computes an effective multiplier equal to the total "AI pie" (AI search-equivalent volume divided by Google volume for a month) times that system's market share that month. It then multiplies your Google volume by that multiplier, an intent modifier, and a query-consolidation factor. The default pie is anchored to Ahrefs' analysis of ChatGPT prompt volume (about 12% of Google for ChatGPT alone in mid-2025, roughly 22% across all assistants by 2026), and every input is editable so you can match your own assumptions.
Are Google AI Overviews counted as AI search volume?
No. AI OverviewsAI Overviews are the AI-generated summary box Google shows above or within its regular search results, written by Gemini models from pages retrieved out of Google's normal Search index. It's a Search feature, not a separate platform or index. (and AI Mode) are still Google searches that happen to be answered with AI, so the tool shows them on a separate, dashed line and never folds them into the third-party "Total AI" figure. They are modeled as your Google volume times an editable AI Overview trigger rate times the intent modifier, which keeps Google demand and assistant demand from being double-counted.
Is AI search volume data accurate or real?
It is a model, not a measurement. Headline market shares come from dated, cited sources, but the total-pie sizing, the split across smaller systems, and the month-to-month interpolation are coarse estimates. The single biggest lever is the "search-like share of AI prompts" input (0.65 following Ahrefs versus about 0.24 for a stricter pure-search reading). Read the output as a way to size and compare the opportunity, and adjust the assumptions to your own beliefs before you rely on it.
Does this tool send my keywords anywhere?
No. The estimator is entirely client-side — all parsing and math run in your browser, and nothing you paste or upload is sent to a server or stored. The only network activity is loading the page and its bundled market-share data file. That means you can paste a full keyword export (Google Keyword Planner, Ahrefs, or Semrush) without it leaving your machine.
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Where this tool helps
Common use cases
Compare demand across assistant systems
Split the modeled assistant total across the bundled monthly market-share assumptions without treating a platform slice as first-party keyword data.
Keep AI Overviews separate from assistant demand
Model Google searches that may show an AI answer on their own line so they are not double-counted inside third-party assistant volume.
Audit a keyword portfolio by intent and month
Upload a local CSV with keyword, intent, and optional monthly-volume columns to compare modeled demand and supplied Google history across a portfolio.
Run assumption-sensitive planning scenarios
Edit region, search-like prompt share, total AI pie, system ratios, intent modifiers, consolidation, and AI Overview rate, then share or export the exact scenario.
Watch the full workflow
AI Search Volume Estimator walkthrough
Read the transcript
AI Search Volume Estimator
This beginner walkthrough explains what the estimator models, checks one fictional commercial keyword, separates assistant demand from Google AI Overviews, tests the most important assumption, audits a five-keyword monthly C-S-V, changes the snapshot, and exports the exact scenario.
Step 1
No major AI assistant publishes monthly keyword counts like a traditional keyword tool. This estimator therefore starts with Google volume you already have and applies a dated, editable model to estimate search-equivalent demand across assistants. The output is modeled planning guidance, not measured ChatGPT, Gemini, Claude, or Perplexity demand. All calculations run in your browser.
Step 2
Keep three number types separate. Google volume is the starting value you supply. Total A-I is a modeled estimate across enabled third-party assistants. Google A-I Overviews estimates how many of the supplied Google searches may show an A-I answer, so it stays separate. Combined demand adds Google and assistants only. It does not add A-I Overviews again, because those searches already belong to Google.
Step 3
Use it to compare possible A-I demand across topics, see how a modeled total divides among assistants, keep A-I Overviews separate, review a keyword portfolio by intent and month, or test how planning changes under stricter assumptions. The result can tell you where deeper research may be worthwhile. It cannot tell you the true number of assistant searches.
Step 4
The methodology note says this is a range, not measured keyword demand. The low and high cases change the assumed share of A-I prompts that replace or resemble a Google search, and they include modest uncertainty around system shares. These are planning scenarios. They are not a statistical confidence interval, a platform report, or a forecast guarantee.
Step 5
Enter the fictional keyword best C-R-M software, supply twelve thousand Google searches per month, and choose commercial intent. The results appear automatically. Twelve thousand is the evidence we supplied. Every assistant number is calculated from that input, the selected month and region, the intent modifier, and the editable market assumptions.
Step 6
At the default global May twenty-twenty-six snapshot, Google remains twelve thousand. Total A-I ranges from eight hundred twenty-nine to three thousand, with a base near two thousand six hundred, or twenty-two percent of Google volume. A-I Overviews show three thousand six hundred separately. Combined Google plus assistant demand is fourteen thousand six hundred.
Step 7
The base assistant estimate is divided among enabled systems. ChatGPT is about one thousand four hundred, Gemini about seven hundred thirty-seven, Claude about two hundred forty-three, and the remaining systems receive smaller modeled slices. Every system also has a low and high value. These are allocations from the model, not keyword counts reported by the platforms.
Step 8
Google A-I Overviews and A-I Mode still begin with a Google search. The tool estimates an affected share using an editable trigger rate and the chosen intent, but it never adds that number inside Total A-I. Keeping the lines separate prevents the same Google demand from being counted once as Google volume and again as third-party assistant volume.
Step 9
The tool creates deterministic reformulations such as alternatives and versus comparisons. They can help a writer think about questions and comparison formats. They are templates based on the supplied phrase and intent. They are not observed assistant prompts, autocomplete suggestions, first-party query logs, or proof that any variation has search demand.
Step 10
The trend charts apply each bundled monthly market snapshot to the keyword. In single mode, Google volume stays flat unless you supply monthly history, while the modeled assistant total and system mix change. Filled points mark bundled source months. Lines between those points are interpolated. Neither one is measured historical keyword demand from an assistant.
Step 11
Every major assumption is visible: region, search-like share of A-I prompts, total A-I compared with Google, system ratios, the A-I Overview trigger rate, intent modifiers, and query consolidation. The values are editable so you can test your own beliefs or evidence. Reset restores the bundled defaults, which makes each experiment reversible.
Step 12
Change search-like share from the broad point-six-five reading to the stricter point-two-four reading. The selected A-I pie falls from twenty-two percent of Google to about eight-point-one percent, and the base assistant estimate drops from roughly two thousand six hundred to nine hundred seventy-six. Testing this sensitivity is more honest than presenting one default as settled truth.
Step 13
The stricter scenario keeps the same twelve-thousand Google input and the same commercial intent. Only the search-like assumption changes, so Total A-I falls while A-I Overviews remain at three thousand six hundred. Whenever you use a result, record the source volume, snapshot, region, intent, and assumptions. Without them, the number cannot be reproduced or interpreted.
Step 14
Switch to Bulk and upload the fictional C-R-M portfolio. The local parser loads five keywords and recognizes four monthly Google-volume columns from February twenty-twenty-five through May twenty-twenty-six, plus an intent column. The file remains in this browser. It is not uploaded, stored, or sent to an external keyword or assistant service.
Step 15
At the latest month, the supplied Google volumes total one hundred eleven thousand two hundred across five keywords. Modeled Total A-I ranges from three thousand two hundred to eleven thousand six hundred, with a base of ten thousand one hundred. A-I Overviews are thirteen thousand eight hundred separately. Google plus assistants totals one hundred twenty-one thousand three hundred.
Step 16
The table preserves each keyword, intent, supplied Google volume, assistant split, Total A-I, and A-I Overview estimate. Salesforce login has the largest Google input but uses the smaller navigational modifier. How to migrate C-R-M data uses the larger informational modifier. These are editable modeling choices, and they materially change the output for each row.
Step 17
Change the snapshot to February twenty-twenty-five. The tool now uses that month’s supplied Google-volume column and bundled market model. Google totals one hundred two thousand two hundred, while the base assistant scenario falls to about three thousand three hundred. This is scenario history built from explicit inputs and dated assumptions, not measured assistant query history.
Step 18
With monthly columns present, the chart contains two moving inputs. The fictional Google volumes change from month to month, and the bundled assistant-market model changes too. Use the chart to compare full scenarios over time. Do not attribute the whole movement to A-I adoption unless you hold the underlying Google volume constant.
Step 19
Export C-S-V downloads one header and all five keyword rows. The verified file records the keyword, intent, month, supplied Google volume, each assistant estimate, Total A-I, and A-I Overviews. Copy share link can preserve the current configuration in a U-R-L. Save either handoff with the assumptions so another reviewer can reproduce the scenario.
Step 20
Open How this is calculated to inspect the formulas, caveat, source labels, and review date. The latest selectable snapshot is May twenty-twenty-six. Source transparency lets you challenge or replace assumptions. It does not turn estimated market shares, smaller-system splits, and interpolated months into precise per-keyword measurement.
Step 21
The model inherits any error in the Google volume you supply. It cannot fetch assistant demand, measure rankings, observe citations, verify visibility, or predict traffic and conversions. Small-system splits and interpolated months are coarse. Use the output to compare opportunities, then validate priorities with real assistant captures, analytics, referrals, Search Console data, conversions, and business outcomes.
Model the opportunity. Then measure the real outcome.
Export the scenario with its source volume, month, intent, and assumptions. Compare reasonable low and high readings instead of relying on one point estimate. Then measure actual assistant visibility, citations, referrals, conversions, Search Console performance, and business outcomes separately.
Common issues & how to fix them
- Information AI demand is modeled rather than measured Fix: Treat the output as a scenario range, replace defaults with documented assumptions, and validate decisions against observed first-party demand data.
- Warning Estimated demand range is wide Fix: Treat the output as a scenario range, replace defaults with documented assumptions, and validate decisions against observed first-party demand data.