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First, verify what actually increased: sessions, users, landing-page views, conversions, or server requests. An “AI” or referral label describes how an analytics system classified an arrival; it does not, on its own, prove that a person visited, intended to shop, or completed a purchase. Compare the analytics reports with your site’s own tagging history and, if available, server or CDN logs before attributing the jump to new demand.
1. Confirm what spiked—and where it appears
Compare the affected dates with the equivalent preceding period, using the same property, data stream, report scope, and dimensions. A jump in sessions is not interchangeable with a jump in users, page views, events, or orders. Also distinguish session-scoped traffic attribution from event-scoped reporting; mixing them can make a change look broader or more specific than it is.
- Check sessions and users, then inspect landing-page views, key events, and completed orders separately.
- Confirm whether the change appears across the whole property or only in one data stream, report, or dimension.
- Record the exact dates the increase began and whether it is a one-day step, a sustained rise, or a reporting-only change.
This establishes what needs explaining. It does not identify the cause; that requires examining the source, pages, and measurement changes.
2. Identify the source and the pages receiving visits
Break the increase down by session source/medium and session default channel group, then compare landing pages, geography, device category, and relevant events. Inspect the actual source or referrer values rather than inferring a vendor from a label such as “AI.” A rise concentrated on one assistant and a few pages differs from a broad increase across your usual entry pages and visitor cohorts.
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- Source and medium: Which referrer hostnames or campaign values changed? Is the traffic classified as AI Assistant, generic Referral, Organic Search, Direct, or Unassigned?
- Landing pages: Are visitors arriving on normal product, category, or editorial pages, or on an unusual URL or a small set of pages?
- Audience and activity: Do geography, device mix, engagement, key events, and orders move in a way consistent with the sessions?
Google Analytics defines referral traffic by its external source. Direct traffic applies when referral-source information is unavailable or when the referring source or search term is configured to be ignored. Neither classification, by itself, verifies that an arrival was a human shopper.
3. Check how GA4 is classifying AI assistants
GA4’s current Default Channel Group includes an AI Assistant channel for recognized referrers such as ChatGPT, Gemini, Deepseek, Copilot, and Grok. Google lists Google AI Overviews and AI Mode under Organic Search, not AI Assistant. The default channel group is not editable, and classification depends on whether the incoming referrer matches a recognized source. Google’s definition may change over time.
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Google’s May 13, 2026 release note described assigning medium ai-assistant, the AI Assistant channel, and campaign (ai-assistant) when a referrer matches a recognized assistant. For the channel’s current listed sources, use Google’s maintained channel definition rather than treating that release note’s examples as a complete or permanent list.
If you use a custom channel group
Google’s custom-channel guidance gives an AI-assistant example that includes Perplexity, among other sources. A custom group can therefore classify sources differently from the default group. Channel rules are evaluated in order: traffic goes into the first channel whose definition it matches. Put the AI-assistant rule above Referral—and above any other rule that could capture the same traffic—and review any example regex against the sources you actually want to include. Do not assume a sample pattern is a complete vendor list.
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4. Look for measurement or site changes at the same time
A sharp change aligned with a technical or reporting update can indicate reclassification or altered measurement rather than a sudden increase in shoppers. Review the timing of changes to analytics tags, consent settings, campaign tagging, routing, domains, checkout flows, and report configuration.
GA4 uses document referrer information and campaign values in traffic-source processing. A direct visit does not necessarily replace an existing referrer, and campaign values received partway through a session are associated with relevant events rather than retroactively changing the existing session’s attribution. When checking a change, compare the affected dimensions and scopes instead of assuming that a new label means a new visit source.
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5. Separate browser sessions from automated requests
Browser analytics and web server or CDN logs observe different layers. A crawler or other automated fetch can appear in request logs without producing a browser analytics session; conversely, analytics sessions are not a complete record of all requests reaching the site. Keep these counts separate rather than adding log requests to human session totals.
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6. Review referral exclusions without hiding the evidence
Check GA4’s unwanted-referral settings for domains that genuinely belong in the transaction flow, such as a third-party payment processor. Google notes that attribution assigned before a domain is excluded can remain relevant for returning users. Do not add an AI-assistant domain merely to make a report look tidier: suppressing that referral information can make the spike harder to diagnose.
7. Report only what the records support
Once the checks are complete, describe the observed change precisely: which channel or source increased, which dates and landing pages were involved, whether measurement changes coincided with it, and whether engagement or orders changed as well. If logs show automated-looking requests, report that evidence separately from browser sessions. If the available records do not establish the cause or revenue impact, leave those questions unresolved rather than calling the increase a demand surge.
For context only, Chartbeat reported that ChatGPT pageviews in its publisher network grew by more than 200% year over year, while AI chatbots still represented less than 1% of pageviews in that network in its Q1 2026 report. Those figures describe Chartbeat’s network—not an ecommerce-specific rate, forecast, or benchmark for an individual site.
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