Welcome to our latest SEO Trends & Insights. Each month, our specialist SEO team analyses the shifting search landscape, algorithm updates, and emerging behaviours to bring you the insights that matter most. Stay ahead with expert commentary, actionable recommendations, and the trends shaping performance right now.
Google may treat scaled AI output as thin content risk: The risk is not the tool, it is scaled pages that add little value and resemble thin content.
Recent research suggests Google is not inherently targeting content simply because it was created with AI. Instead, many mass-produced AI pages tend to share “thin content” characteristics, in other words generic, repetitive, and low-value output, so they often fail to offer original insights, clear differentiation, or genuinely helpful information for users. Overall, this reinforces that usefulness and originality are the deciding factors.
Why we care: AI assisted publishing can scale output faster than editorial standards, which increases the risk of thin or repetitive pages accumulating across a site. That’s why we produce content roadmaps for you that prioritise fewer, higher value pages that demonstrate distinct usefulness and expertise, and we focus QA most heavily on the areas most likely to be produced at volume. This helps protect overall quality signals while allowing the use of AI where it improves efficiency.
Search Console adds Generative AI performance reporting, but it is impressions-led and limited for attribution.
Google has introduced dedicated Generative AI performance reporting in Search Console for Search and Discover, allowing you to see impressions from AI Overviews and AI Mode with breakdowns by page, country, device, and date. The key constraint is that it largely stops at impressions, without queries, clicks, CTR, average position, citation placement, or passage-level detail, so it is better used to spot which URLs are being surfaced in generative experiences rather than to judge performance end-to-end. The article also flags that totals may not reconcile cleanly between charts and tables due to aggregation differences, so analysis needs care when comparing site-wide trends to URL-level counts.
Why we care: This creates an “AI visibility” metric inside the tool SEOs already use, but it is not yet a complete measurement framework. Treat AI impressions as a discovery signal: identify the pages showing up in generative results, audit whether they are the best landing experiences, and then triangulate with classic organic performance and on-site conversion tracking to understand whether AI exposure is translating into value.
Fewer searches send clicks to the open web, while more activity stays within Google-owned properties.
A recent report shows that zero-click behaviour has reached an all-time high, with only around 40% of searches in the U.S. and EU/UK resulting in a click to the open web, while a meaningful share flows to Google-owned properties instead. It also highlights shifts in query mix, with informational searches increasing year-on-year, and notes that AI Mode usage is described as very small in the reported data. The search journey is increasingly resolved on the SERP or within platform ecosystems, changing how discoverability and traffic opportunity should be assessed.
Why we care: If the open-web click pie is shrinking, “rank and receive a click” becomes a less reliable expectation even when visibility is strong. SEO reporting and content planning need to account for outcomes that happen without a site visit (on-SERP consumption and platform journeys), while doubling down on formats and content types that still earn clicks when intent demands deeper comparison, credibility, or task completion.
AI discovery favours familiar brands, while AI attribution remains fragmented and volatile
Recent research shows AI-led discovery is not purely a retrieval problem: models can over-select familiar brands, shaping early consideration even when prompts are not explicitly brand-led. At the same time, AI visibility signals are messy. Systems may use or cite content without naming the brand, link-outs can be limited, and recommendations can change materially after a single follow-up prompt. Where AI platforms do send traffic, it can grow quickly but remain concentrated and volatile, so the landing experiences you expose to AI journeys matter as much as being “included” in answers.
Why we care: The research shows that treating “AI visibility” as one metric will mislead stakeholders. Separate reporting into content usage/citations, brand mentions/recognition, and referrals and conversions, and segment by platform because behaviour varies materially. In parallel, invest in brand consistency to improve shortlist inclusion, and prioritise QA and conversion readiness on the pages most likely to receive AI-led landings, then track those landings independently from broader organic trends to avoid volatility being hidden in aggregate reporting.
Want to understand what these changes actually mean for your organic performance in 2026? Now’s the moment to review how your content, structure and measurement stack hold up in AI-driven search.
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