SEO & AI Industry Update

November Update

Search moved again in November. Google pushed harder into AI-led results, expanded answer-style summaries at the top of results pages, and caused some visible ranking swings as those systems were tuned in real-time search.

For most people, this still feels like “using Google as normal”. In practice, more of the early journey now happens inside AI answers that reference only a small set of sites. That changes how visibility works and why brands need an AI-aware approach alongside traditional SEO.

Below is a joined-up view of what changed in November, why that change matters, and where action is needed versus where the priority is awareness and planning.

Google Is Accelerating Its Shift Towards AI-First Discovery

Google Routes More Searches Into AI Mode With Gemini 3

What’s happening
Google has embedded the Gemini 3 model directly into Search AI Mode. Some complex queries now route straight into AI Mode, so users sometimes skip a traditional blue link results page for those searches.

In AI Mode, results appear as a single explanation with only a few sources cited. Classic results still sit underneath, but the first stage of research for some queries now happens in an AI interface.

Why it matters
Given the speed of the rollout and the deep link into core Search, AI Mode is shifting from experiment to a default layer for complex informational searches. That reduces the space where traditional rankings can show and raises the value of being named as a citation source. For clients, a growing share of early journey discovery now happens in AI interfaces, often without users noticing a switch.

“What to know” Panels Expand Into Standard SERPs

What’s happening
Google has rolled out “What to know” panels across standard results pages. These panels summarise context and key subtopics directly on the page and follow the same answer-first pattern as AI Mode.

Why it matters
Top of funnel information gains more weight. Sites that explain topics clearly and in a structured way are more likely to feed both these panels and AI citations. The real focus is simple, direct answers, strong coverage of key questions, and a predictable content structure.

A Necessary Clarity: AI Visibility Is Not The Same As Traditional Rankings

Although AI answers often draw from pages that rank well in classic search, the overlap is not exact. Some strong ranking pages remain absent from AI Mode, while other sources rise because they respond more clearly or more directly to the question.

Outside Google, the gap is even larger. Other AI tools do not use Google’s ranking systems. Those tools rely on their own retrieval, training data, and partnerships. Traditional SEO still matters, but no longer explains the full picture on visibility. Brands now need an AI-focused layer that aims to be selected and cited inside AI answers, not just listed below.

Market Shift Indicator: AI Mode Reaches Roughly 75 Million Daily Users

Google’s AI Mode Reaches Roughly 75 Million Daily Users

What’s happening
Google reported AI Mode reaching roughly 75 million daily users, with query volume doubling in Q3. AI experiences are now tightly integrated into core Search.

Why it matters
This points to a large behavioural shift towards AI-shaped results, even when people feel they are just “using Google”. It also shows that AI visibility stretches far wider than any one platform. ChatGPT is not the only environment that shapes discovery. Google’s AI layer already shapes how large audiences learn, compare, and shortlist.

This section is about readiness, not a long tactical list. Discovery is moving towards answer first experiences. SEO needs to protect classic demand and also evolve towards citation-focused optimisation, especially for informational and comparison-led journeys.

Volatility Suggests Live Tuning Of AI Triggers And Citations

Ranking Volatility Around Gemini 3 Rollout And Late-Month Waves

What’s happening
High levels of ranking volatility were observed around the Gemini 3 introduction, and another broad wave appeared around 24 November. No single public “core update” label explains the pattern.

The most likely explanation is live tuning of AI Mode triggers, citation choices, and blended layouts.

Why it matters
Monitoring now needs to look beyond classic rank movement and include AI Mode frequency, citation presence, and top of funnel click-through behaviour. Traffic and CTR can shift when AI layers change, even when rankings appear stable.

What marketing teams can do next
This needs clear monitoring, but most teams should not be running heavy manual checks week to week.

Light-lift internal step

• Watch Search Console click-through rate and impressions for your priority informational themes. Treat this as an early warning system.

When to escalate

• If CTR drops by a meaningful amount while rankings stay steady, use that as a signal to dig further into AI and panel presence.

What we handle for clients

• We track AI Mode prevalence, citation visibility, and blended layouts on priority queries, then link performance shifts back to layout changes versus content issues.
• We turn those findings into a monthly visibility summary with a short, prioritised action list.
  

Google Reiterated Quality Enforcement On Low-Value AI Content

The “Bad Site” Low-Trust State Is Hard To Reverse

What’s happening
Google has repeated that low-value AI content can push a site into a low-trust state across the full domain. Rewriting with human copy alone does not clear that state. Usefulness drives the signal, not whether a person or a model wrote the copy.

Why it matters
Recovery requires building genuinely useful, interesting, and differentiated content. For agencies and marketing teams, this means stricter editorial control on AI-assisted production, clearer content purpose, and more emphasis on original insight and evidence.

What marketing teams can do next
Two responses make sense. Create content safely. Then review the usefulness of what already exists.

Safe AI production rules

• Only publish content with a clear user purpose, something that helps someone decide, plan, or act.
• Ensure AI-assisted drafts meet a usefulness standard before they ship, not a volume target.

Usefulness assessment and triage

• Start by removing, merging, or rebuilding the weakest 10-20% of content. Focus on pages that are thin, repetitive, or add no differentiated value.
• Prioritise fewer, stronger pages over many shallow ones.

What we handle for clients

• We run usefulness audits with clear “keep, merge, rebuild, remove” outcomes and help define safe operating rules for AI-assisted production.
  

AI Instruction Protocols Are Emerging As A New Visibility Lever

AI Crawling Permissions And Reuse Rights Mature

What’s happening
New protocols for AI crawling permissions and reuse rights are taking shape. These standards mirror robots.txt and meta directives, but for LLM ingestion and citation.

Why it matters
Clear use of these standards will matter for AI visibility. Brands that set permissions in a clean way and provide content that is easy to cite will stand in a stronger position to be indexed and referenced across AI search engines.

This topic sits in 2026 technical roadmaps rather than this quarter’s marketing to-do list.

AI Shopping Is Becoming More Advanced

OpenAI And Perplexity Push Further Into Shopping Experiences

What’s happening
OpenAI and Perplexity are rolling out more advanced shopping experiences. Their goal is to become the default place where users research and choose products, not just ask questions.

Why it matters
To compete in this environment, e-commerce sites need rich, machine-readable product data: complete specifications, availability, price clarity, reviews, and consistent product entities.

Schema and structured data quality will play a major role in gaining AI shopping citations and referral traffic.

What marketing teams can do next
This is actionable, but should be phased.

Light-lift internal step

• Focus on your top revenue products or categories. A starting set of 20 products works well.
• Check that core fields are accurate and consistent. Titles, specifications, price, stock status, and reviews.

Schema and structured data

• Implement and validate Product, Offer, and Review schema so AI systems can reliably interpret the key fields.
• Standardise attributes such as size, colour, material, and technical specs across ranges so products are easy to compare.

What we handle for clients

• We audit structured data coverage, fix schema gaps, and improve product entity consistency across the catalogue.

Final Thought

November made one thing clear. Search is shifting from lists of links to answer first experiences shaped by AI, both inside Google and across other tools. Visibility now depends on whether these systems trust you enough to quote you, not only where you sit on a classic results page.

Usefulness and structure are the levers you can control. Thin or generic AI content risks a low trust label that is hard to shake. Clear, early journey content and clean structured data give AI modes and shopping journeys something safe, specific, and comparable to work with.

The response is not more manual checks for teams that already feel stretched. A more realistic plan is a dual lane approach to visibility. Protect classic SEO performance and, in parallel, build an AI-led strategy that focuses on being selected and cited.

If you want support to set an AI readiness baseline, strengthen early journey content that deserves a citation, or track AI visibility without extra manual work, contact Fingo about Prism at hello@fingo.uk.