Web analysts are facing a new challenge: a rapidly growing share of website traffic no longer comes from human visitors, but from AI-powered bots. These AI crawlers are crawling the web on an unprecedented scale, while generative AI is simultaneously changing the rules of search engine optimization.
For businesses, this means that web statistics can become distorted, user engagement can be misinterpreted, and SEO strategies may need to be fundamentally reconsidered.
That’s why, in this article series, we explore the key aspects of AI traffic to help you prepare for what’s ahead.
Part 1 focuses on the two inseparable sides of AI traffic (main link is not working here) : AI crawlers and bots vs. AI SEO.
Part 2 examines the risks to data quality.
Part 3 explains how to identify and filter bot traffic.
Part 4 takes a closer look at benchmarks and new approaches, including Pay-per-Crawl that can help you regain control over your data and content.
Part 5 focuses on AI agents and their impact on web analytics.
With all five parts, you’ll be well equipped to ensure that AI bots and other automated traffic no longer distort your analytics, allowing you to continue making informed, data-driven decisions.
The series at a glance
Coming soon:
Part 3: Filtering AI bot traffic: How to clean up your data in GA4, Adobe & Piano
Part 4: Pay-per-crawl & co.: How companies are responding to AI crawlers
Part 5: AI agents and web analytics: New rules for attribution
AI traffic and AI SEO: We explain the difference between AI crawlers (AI bots that scrape content), AI SEO (AI-powered search engine optimization), and why both are affecting online businesses.
A threat to data quality: Learn how AI bot traffic can distort metrics such as visitor numbers, bounce rate, and conversion rate, and why traditional web analytics are reaching their limits in this area.
Latest studies & figures: Traffic from AI agents grew by around 7,851% in 2025, eight times faster than human traffic. For your analytics, this means that an increasing share of what appears to be user behavior is actually machine-driven, potentially distorting your metrics.
“Pay-per-Crawl” and new models: Learn what Cloudflare’s Pay-per-Crawl initiative is about, under which AI services would pay to crawl content a potential new business model for compensating content creators.
Filtering in analytics tools: Get practical tips for Google Analytics, Adobe Analytics, and Piano Analytics on how to exclude known bot traffic from your reports or analyze it in separate segments.
Best practices from the field: Discover how organizations from news publishers to open-source projects are already responding to the rise of AI bot traffic, using approaches ranging from robots.txt rules and IP blocking to entirely new methods such as Cloudflare’s Bot Labyrinth.
Meaningful AI traffic segmentation: Learn how to divide remaining AI traffic into meaningful segments, for example by bot type or purpose, so you can monitor its impact more accurately and understand why, in some cases, focusing on human users may be more effective than trying to track every single bot.