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.
Common structured data issues that reduce AI visibility
Structured data problems that can undermine AI visibility often come down to inconsistency and weak entity signalling. Examples include disconnected entity relationships (such as content not clearly linked to Author or Organisation), inconsistent or missing entity identifiers (notably @id), and structured data being treated as a one-off implementation rather than a maintained system. Misalignment between markup and visible page content (for example, marking up reviews or ratings that are not shown to users) is flagged as another key risk, alongside schema that becomes stale or contradictory across templates (such as price and availability not matching what is on the page). The overall emphasis is that schema supports AI understanding best when it accurately reflects page reality and reinforces a consistent entity model across the site.
Why we care: AI-driven discovery is placing more weight in entity clarity and consistency signals, so structured data quality is becoming a more practical lever for protecting and improving brand and content representation. Schema is not “set and forget”; it needs maintenance. Companies can use this to tighten technical hygiene by auditing entity connections (Author/Organisation), standardising @id patterns across templates, and putting checks in place so markup stays aligned with on-page content and updated as page attributes change (especially reviews, pricing and availability).
Do comparison posts, roundups and listicles still have any value?
Comparison posts, roundups and listicles stilll have a role, but their value comes from helping users make decisions rather than acting as a repeatable traffic or AI-visibility shortcut. Performance is positioned as stronger when comparisons are genuinely informative, aligned to real user needs (particulary for high-consideration decisions), and built on trust signals and clear differentiation. Conversely, scaled, templated or thin list content created primarily to “win search” is presented as increasingly fragile, because platforms can identify patterns that look manipulative or low-value. Beyond owned content, being included in reputable third-party roundups is highlighted as a meaningful visibility route that can drive exposure, qualified referral traffic and audience growth, especially when those publishers actively promote their content.
Why we care: Comparison and “best of” formats are still being produced at scale, but search and AI experiences are raising the threshold for what gets surfaced, trusted and clicked. Templated list content is becoming more volatile, while genuinely helpful comparisons built around trust, clear differentiation and real decision support are more likely to hold value.
Using Reddit to build organic and AI search visibility
Reddit is positioned as both an insight source and a visibility surface as search platforms and AI assistants increasingly surface community discussions in response to informational and product-led queries. Businesses can use Reddit to identify the language people use, recurring questions, and unmet needs, then translating those insights into on-site content improvements (including FAQS and supporting content) that much better match user intent. The guide also stresses that any brand participation needs to be credible and community-aligned to avoid reputational harm, with the aim of strengthening how the brand is discussed and discovered across search and AI-led journeys.
Why we care: Reddit is showing up more often in both traditional search results and AI-generated answers, which means community discussions can influence what users see (and believe) about brands earlier in the journey. Businesses can mine Reddit for recurring questions and language, feed those insights into content briefs and on-site improvements, and agree on a sensible, brand-safe approach to participation so companies can build visibility without creating reputational risk.
Positioning AI search visibility as a brand channel
AI-driven search visibility can function more like a branding channel than a directly attributable acquisition source, as standard analytics and last click models often do not capture where influence began. This makes it harder to draw a straight line from being referenced in AI results to measurable outcomes like branded search growth or Direct traffic increases, even when AI inclusion is contributing to awareness and consideration. The practical implication is that “success” in AI search should be evaluated as part of a broader discovery and brand impact picture, rather than judged solely on referral traffic.
Why we care: AI search is increasingly shaping what users notice and trust during discovery, but its influence is not consistently visible in referral or Direct traffic reporting. AI visibility is best treated as a brand exposure signal (not a guaranteed traffic driver). This allows a more accurate read on performance, avoid misattribution in reporting, and prioritise the content and entity signals most likely to earn AI mentions over time.
AI referral traffic and “Direct” in GA4: what the data suggests
A recent analysis examined whether traffic orginating from AI tools is being incorrectly recorded as “Direct” in Google Analytics. Across 12 GA4 properties, it found large increases in Direct frequently resemble automated or low-quality sessions rather than genuine user demand, with one example showing very high Direct session volumes but very few engaged sessions. When the lens was narrowed to engaged Direct sessions, the overall pattern was effectivdly flat. The study also reports that users could not consistently isolate “hidden AI” within Direct without explicit user-reported attribution; where AI could be identified within “Direct”, it represented a very small share in the sample. A wider correlation check across hundreds of tests did not show a consistent relationship between AI visibility and subsequent increases in banded search or Direct.
Why we care: This is a useful reminder to be cautious about using Direct traffic as a proxy for AI-led discovery or brand lift. We can use this approach to validate whether Direct spikes reflect real audience behaviour by prioritising engagement signals, checking for anomalous patterns consistent with automated activity, and pressure-testing any narrative that “AI is driving Direct” unless there is stronger attribution (for example user-reported source data).
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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