Structuring for AI: Optimising Content for AI Overviews & Generative AI

What you’ll be able to do after this playbook: ship AIO-ready pages and measure success

This playbook gives a pragmatic framework for AI Content Structure: how to diagnose where AI Overviews (AIO) are likely to affect clicks, how to restructure pages so generative systems can extract and cite your answers, and how to instrument clear metrics for comparable measurement. Focus areas: intent-based descriptions, visual alignment, JSON‑LD, and multiformat SEO (text, image, video).

Concrete outcomes you will achieve (diagnose AIO risk, structure extractable answers, add JSON‑LD)

• Diagnose AIO risk by query and page
  • Inventory priority non-branded informational queries and flag those that trigger AIO; overlay revenue/lead value to prioritise action.
  • Use query panels and tools with AIO filters to track changes at scale.
• Restructure pages for extractable answers
  • Lead sections with a 1-2 sentence task-focused summary, include short answer blocks (40-80 words), labelled subheads (H3/H4 as questions), and key-value facts for easy quoting.
  • Add images that explicitly illustrate the task/outcome and use descriptive filenames and alt text.
  • Where relevant, include short videos and mark them up (VideoObject / Clip) so key moments can be cited.
• Add and harden JSON‑LD
  • Implement baseline schema (Article/Person/Organisation/Product) and keep structured data aligned with visible content.
  • Avoid relying on deprecated feature types; focus schema on entities and media that power enhancements and understanding.
• Design for citations
  • Prioritise non-branded informational queries you can answer uniquely with data, process or local expertise; format content so it’s attractive to AIO (concise definitions, step lists, verified facts).

Quick success metrics to track (AIO presence %, CTR delta, conversions from AIO visits)

Optimise for visibility inside and around AIO as well as downstream conversions. Track these core metrics:

• AIO presence % – share of target queries where AIO renders and % where your domain is cited; track weekly panels.
• CTR delta vs non-AIO SERPs – compare CTR for matched queries with and without AIO; use query sets and annotations to control for changes.
• Conversions from AIO-exposed visits – tag pages restructured for AIO and monitor GA4 conversions and downstream value; if conversions hold or improve despite lower clicks, your structure is working.
• Defended visibility score – a weighted measure of AIO citations plus Top 3 rankings when AIO is absent.

Short real-world illustration

Organise pages for extractability (concise answers, clear evidence, good schema, and matching visuals) to increase citation opportunities. Success is more citations and protected conversions – not always higher sessions.

What changed in 2024-2025 – why content structure now determines AIO visibility

Google moved generative answers from experiment to mainstream between late 2024 and mid-2025. AI Overviews rolled out widely and AI Mode (conversational, multimodal) has appeared in Labs – both prioritise task-oriented answers and rich media. For ANZ brands, how you structure and label content increasingly decides whether you’re cited, clicked or sidelined.

Snapshot: AI Overviews vs AI Mode vs Classic Search behaviour

• AI Overviews (AIO)
  • Global coverage; presents inline citations and link rails; tends to trigger for informational, longer queries and comparisons.
• AI Mode
  • Conversational, multimodal, supports follow-ups and visual inputs; favours clearly sectioned content, concise answers and assets that map to cards.
• Classic Search
  • Still available (Web filter), but many journeys now start with an AI summary rather than ten blue links.

Immediate KPI impacts to watch

• CTR compression – informational queries are most affected; plan for headwinds and measure defended clicks and conversion yield, not sessions alone.
• Citation consolidation – citations can come from deep pages, so being highly specific and well structured matters more than a homepage rank.
• Quality-of-clicks – those who do click often have higher intent; structure pages to convert the smaller set of visitors.

Bottom line for ANZ teams: prioritise an explicit AI Content Structure now – how you segment, label and visualise information increasingly determines if you’re cited, clicked and converted.

Should you target AI Overviews or steer clear? A practical decision framework

Decide by intent, monetisation and compliance risk. AIO impact varies by category and query type, so build a category-specific plan rather than a generic rule.

Decision matrix: intent, monetisation and compliance trade-offs

• User intent fit
  • Explainers, comparisons and multi-step queries: higher AIO likelihood – good candidates to earn citations if structured well.
  • Brand, “vs” and product terms: more volatile – protect classic listings first.
• Monetisation model
  • Publishers: model ad/venue downside on your keyword set.
  • Lead gen/ecommerce: prioritise pages that convert on device mix (desktop often favours AIO referrals).
• Compliance & brand risk
  • For YMYL topics and safety-critical content, favour protective preview controls (nosnippet/data-nosnippet) and stricter review cadences.

When to pilot AIO-focused pages versus protect discovery

• Pilot AIO-focused pages when
  • Intent fit is high, goals are top-of-funnel or thought leadership, and you can differentiate via structure, visuals and data.
  • Device context and formats match (e.g., desktop-heavy categories).
• Protect discovery when
  • Pages drive direct revenue (PDPs, pricing, lead forms) or present compliance/safety risks – use nosnippet, max-snippet or data-nosnippet selectively.
  • Categories show high AIO volatility – prioritise classic SERP assets first.

Operational score & next steps

1. Score each URL on AIO likelihood, revenue sensitivity, compliance risk and differentiation ability (0-3).
   • Pilot if: high AIO likelihood + low revenue/compliance risk + strong differentiation.
   • Protect if: medium/high revenue or compliance risk, or high volatility.
2. Implement AIO-ready variants (tight summaries, schema, visuals) and protective meta on sensitive templates; measure CTR and conversions vs controls.

ZCMarketing builds page-level matrices and test roadmaps for ANZ brands to balance citations and conversion yield.

How to estimate AIO likelihood and SERP volatility by intent

Use predictable signals to prioritise content and monitoring cadence. Map each keyword to an AIO-likelihood score that combines intent, phrasing patterns and category sensitivity.

Checklist: signals that make a query AIO‑likely

• Informational intent – AIO favours explainers, comparisons and how-tos.
• Question words & definition phrasing – queries starting with “how”, “why”, “what” or “vs”.
• Long-tail, multi-part tasks – multi-step or technical tasks are increasingly likely to trigger AIO/AI Mode.
• Comparisons & alternatives – “X vs Y” and “best alternatives” often surface summarised answers.
• Ranking signals – strong organic presence (top 10) increases citation odds, but AIO can also cite deeper pages; treat deep pages as candidates too.

Cadence & mitigations for volatile SERPs

1. Segment by volatility
   • High (daily): travel, entertainment, restaurants during events.
   • Medium (weekly): health, science, law.
   • Event-driven (daily around sales): shopping/product queries.
2. Snapshot metrics
   • AIO present/not present, your citation status, rank overlap, and zero-click trendlines.
3. Quick-update rules
   • Tighten the intro to a 1-2 sentence answer; add bulleted step summaries for how-tos and pros/cons blocks for comparisons.
   • Strengthen evidence with inline citations and schema; add labelled images or short clips to improve scannability.
4. Governance
   • Assign a SERP owner to run weekly AIO checks and trigger content updates within 72 hours when visibility or zero-click risk shifts materially.

Operationalise AIO likelihood scoring, use AIO-aware tooling, and validate with your data before broad changes.

“We see the clicks are of higher quality, because they’re not clicking on a webpage, realising it wasn’t what they want and immediately bailing.”
– Elizabeth (Liz) Reid, VP / Head of Google Search

Which keyword strategy best fits your business model?

Match keyword types and formats to business goals and the way AIO surfaces content. Below are concise playbooks per model with the format-to-conversion mapping.

Ecommerce: capture commercial intent and reduce friction to purchase

• Keywords: product + variant, “best/compare”, problem-led modifiers.
• Structure: Intent-based descriptions on PDPs (size/compatibility upfront), scannable bullets, spec tables.
• Schema & visuals: Product/ProductGroup markup, descriptive images, demo videos (VideoObject) and transcripts.

Local services: win “near me” and suburb intent

• Keywords: service + suburb, problem + suburb, trust qualifiers.
• Structure: Intent-based descriptions (who/what/next steps), visible CTAs, coverage/response info.
• Schema & visuals: LocalBusiness, openingHours, serviceArea; authentic team/vehicle photos and before/after galleries.

B2B/B2C SaaS: compete on comparisons, integrations and calculators

• Keywords: “[category] software”, “best for [role]”, competitor alternatives, ROI/pricing queries.
• Structure: Neutral summary, side-by-side comparisons, integration docs and ROI calculators.
• Schema & visuals: SoftwareApplication, VideoObject, downloadable assets; demo videos and calculators to qualify demand.

Enterprise: scale authority and meet AI-organised SERPs

• Focus: solution hubs, implementation guides, integration ecosystems and executive topics.
• Approach: standardise schema across products, create solution hubs that are easy to cite, and publish video explainers and community content.

Map formats to conversion goals

• Comparisons: mid-bottom funnel – demos, trials, add-to-cart assists.
• How-tos & implementation guides: mid funnel – pair steps with images/video to earn citations.
• Calculators: bottom funnel – convert evaluation-stage visitors; log events for follow-up.
• Video & 3D/AR: all stages – improves understanding and reduces returns for ecommerce.

Four pillars checklist

• Intent-based descriptions up front.
• Visual alignment (images, captions, video).
• Structured data consistent with visible content.
• Multiformat SEO: text + visual + schema mapped to conversion steps.

ZCMarketing applies these playbooks hands-on for ANZ brands to prioritise content that converts, not just drives clicks.

How to structure a page so AI can extract trustworthy, citable answers

Reusable scaffold: Intent label → TL;DR tiers → canonical answer → steps → evidence

Use this scaffold to make each section easy to chunk and cite while keeping humans in flow.

• Intent label – a 3-6 word tag (for example, Intent: Compare tools | SMB).
• TL;DR tiers
  • Tier 1: one-sentence punchline (≤25 words).
  • Tier 2: 3-5 scannable bullets.
  • Tier 3: short paragraph naming constraints and caveats.
• Canonical answer (2-4 sentences) – self-contained, specific, time-stamped where relevant, and source-linked inline.
• Steps – numbered actions with verbs first; keep them concise.
• Evidence – 1-3 primary sources inline (standards, docs, datasets).

How to write a 2-4 sentence canonical answer that gets cited

1. Lead with the answer – state outcome/definition first.
2. Scope and conditions – name audience, constraints or timeframe.
3. Specifics over adjectives – use figures and standards where possible.
4. Source it inline – include a primary reference inside the answer.

Example: “For [audience], the quickest way to [task] is [method]. It works because [mechanism], assuming [conditions]. Verify with [authority].” Keep surrounding TL;DR bullets and headings aligned so systems can lift either the short answer or the step list as required.

Make it machine-parsable

• Use JSON‑LD matching visible content.
• Place images adjacent to the paragraph you want surfaced and use descriptive alt text and captions.
• Provide watch/read pages for video with VideoObject markup and key moments where appropriate.

Implementation checklist: add an intent tag, TL;DR tiers, a 2-4 sentence canonical answer with an inline source, JSON‑LD and matching visuals for every major section.

What JSON‑LD and schema patterns help systems understand and cite you

Keep JSON‑LD aligned with visible content, validate it, and use it to clarify entities, authorship and dates. Below are template blocks you can adapt for Article, HowTo, Product, ItemList and FAQPage. (Keep code values reflective of the page.)

Article (example)

Include visible dates and author links that match schema values.

HowTo, Product, ItemList, FAQPage

Content block → schema mapping and governance rules

• Visible parity: Mark up only what users can see; avoid hidden or misleading fields.
• Date governance: Show one clear on-page date and mirror it in datePublished/dateModified (ISO 8601).
• Author IDs: Use author as a Person with a canonical profile URL and sameAs links where available.
• Feature availability: Keep stakeholders informed that some rich results have limited visibility; retain markup for machine understanding even if the SERP feature isn’t shown.
• Validation: Put JSON‑LD on the page it describes and validate before publishing.

Operational tip: standardise an AI Content Structure checklist in your CMS templates so writers complete intent descriptions, image fields and author profiles alongside copy.

How to align images and diagrams so generative systems reference them correctly

Visuals are as important as copy for AI experiences. Align the image, caption and markup so machines and people read the same signal.

Image metadata checklist

• Wrap visuals in <figure> with a concise <figcaption> that summarises the key takeaway.
• Alt text: describe the image’s purpose in context (not a keyword list). For complex visuals, keep an alt summary and put full explanation in the caption or body.
• Use ImageObject in JSON‑LD with contentUrl, caption and representativeOfPage where appropriate.
• Retain rights metadata (license/creator) in structured data for reuse and attribution.
• Ensure crawlable image URLs and include fallbacks for <picture>/srcset setups.

Which visual type helps each intent

• Compare options: comparison tables with consistent rows and small thumbnails; captions that call out differentiators.
• Explain a process: annotated workflow or numbered diagram in a <figure> with step-mirroring captions.
• Inspect a product: close-ups, lifestyle shots and in-scale images with ImageObject metadata and captions calling out features.
• Summarise a concept: single representative diagram declared as the primary image in schema.

Rule of thumb: decide the user intent, align the visual type to that intent, and express the meaning twice – once for people (caption/body) and once for machines (alt + ImageObject).

How to align internal linking and content types so the right page is cited

Pillar → cluster linking rules and task-based anchor patterns

Structure pillar and cluster pages so task intent is explicit and links are descriptive. Deep, task-focused pages are often selected as AIO sources – ensure they stand alone with concise summaries and unique data.

• Map cluster pages to jobs (learn, compare, choose, buy, troubleshoot) and use descriptive anchors (“compare X vs Y”, “pricing for X”).
• Link each cluster back to its pillar early on the page with a short descriptive anchor.
• Ensure image links have useful alt text describing the destination.
• Make sections scannable so inline links anticipate follow-ups and inline citations look natural to AI experiences.

Use ItemList and BreadcrumbList

• Mark curated hub lists with ItemList so selection criteria and positions are machine-readable.
• Implement BreadcrumbList sitewide to express hierarchy from pillar to leaf; keep on-page lists in the same order as ItemList positions.
• For shopping/comparison clusters, pair ItemList with product/offer schema and accurate imagery to aid citation by AI modules.

Practical next steps: map each pillar to 6-12 task-driven clusters, add BreadcrumbList and ItemList where relevant, and instrument to monitor deep-page citations and impressions.

How to balance the funnel when CTR shrinks but intent quality rises

With impressions increasing and CTRs compressing on many informational queries, rebalance investment toward MOFU/BOFU while keeping TOFU that builds authority for AIO. Focus on capturing higher-quality clicks with contextual offers and tools.

Investment mix by business model (starting points)

• Local services – TOFU 25% / MOFU 40% / BOFU 35%: suburb guides, comparison/process pages, booking & click-to-call optimisation.
• Ecommerce – TOFU 40% / MOFU 40% / BOFU 20%: category guides, comparison tools, PDPs with Product schema and clear pricing.
• B2B/B2C SaaS – TOFU 20% / MOFU 45% / BOFU 35%: problem-led explainers, ROI calculators, integrations and demo funnels.

Monetisation tactics for fewer, higher-quality clicks

• Email capture: contextual offers (templates, size guides) tied to page intent rather than generic pop-ups.
• Interactive tools: calculators and finders that provide immediate value and justify a click.
• Demos & trials: front-load activation and nurture to convert the smaller set of high-intent visitors.
• Short video explainers: use VideoObject and clip markup to surface key moments and improve engagement for visitors who do click.

Focus on converting motivated demand rather than chasing raw click volumes; measure qualified conversions and pipeline influence as primary success criteria.

How to measure and A/B test structural changes for AIO performance

Test structural changes (Intent-based Descriptions, Visual Alignment, Structured Data, Multiformat SEO) and measure their impact on AIO presence, CTR and conversions. Use third-party AIO detectors for presence, Search Console for CTR (with caveats) and GA4 for on-site outcomes.

4-week experiment blueprint

1. Design (Day 0): Randomly split eligible URLs into Control and Variant; stratify by baseline traffic and query mix. Aim for ≥50 URLs per cell and ≥200 tracked keywords per template where possible.
2. Variant changes (Week 1):
   • Add a 40-80 word summary block (Intent-based Description) at the top.
   • Add lead visuals, validate JSON‑LD and embed short explainer video where helpful.
3. Instrumentation (Week 1):
   • Track AIO presence in rank-tracker tools (Semrush/SISTRIX); export daily keyword-level AIO flags.
   • Use Search Console (Web) for CTR/position; note AI Mode/AIO impressions roll into totals and can’t be fully isolated.
   • Create GA4 events for in-viewport exposures (summary_view, lead_image_view) using Intersection Observer.
4. Run (Weeks 2-3): Freeze other changes; monitor data quality.
5. Readout (Week 4): Difference-in-differences on AIO Presence Rate, AIO Citation Rate, Organic CTR and Conversion Rate/Lead Quality.

Implementation notes

• GSC: set Search type to Web and segment by template; annotate rollout dates.
• Rank trackers: enable AI Overview/AI Mode flags and use SERP archives to inspect citations.
• In-viewport tracking: fire GA4 events at meaningful thresholds (e.g., 50% view) and track summary_view sessions.

Scoring examples: AIO Presence Score = AIO Presence Rate × AIO Citation Rate × normalised citation position. Consider flat/slightly down CTR with higher conversion a win in many cases.

What governance reduces hallucinations and strengthens provenance signals

Make content unambiguous and verifiable so AI summaries can attribute and quote reliably. Governance balances discoverability with risk controls.

Provenance checklist

• Author & reviewer markup: visible bylines and Article schema with author (Person), sameAs links and profile URLs.
• Single visible date: show one current on-page date and mirror it in datePublished/dateModified (ISO 8601).
• Outbound citations: link to primary sources, especially on YMYL topics.
• Media provenance: adopt Content Credentials (C2PA) where possible and include licensing metadata in ImageObject.
• Schema across formats: keep Article/Video/Product schema up to date for machine readability.

Preview controls and YMYL cadences

• Block previews entirely: use <meta name="robots" content="nosnippet"> to suppress text snippets where extraction risk is unacceptable.
• Limit excerpts: use max-snippet to cap extractable text while keeping the page indexable.
• Exclude sensitive fragments: wrap dosage, pricing or legal text with data-nosnippet.
• YMYL review cadence: set formal review intervals (quarterly for high-risk, six-monthly for evergreen) and show a “Reviewed by” line when updated.

Apply a governance playbook that includes author markup, visible dates, selective preview controls and a YMYL review workflow to reduce downstream hallucination risk while preserving discoverability where it matters.

How to preserve intent signals across languages and regions

hreflang, regional variants and unit/currency handling

Keep locale clusters clean with reciprocal hreflang, same-language canonicals and machine-readable pricing/units so answers aren’t misinterpreted across markets.

• Map variants with BCP47 codes (for example, en-AU, en-NZ) and include an x-default where appropriate.
• Use one canonical per URL and keep it in the same language as the page.
• Publish currency via Offer.priceCurrency (ISO 4217) and numeric price values; reflect local tax norms (AU/NZ GST rules) in visible copy and feeds.
• Localise alt text, captions and declare asset language with inLanguage.

Translate decision rules (not just copy)

• Encode region-specific rules in structured data (MerchantReturnPolicy, OfferShippingDetails, areaServed) rather than relying on translated copy alone.
• Localise wording to search behaviour (for example, “tyres” vs “tires”) so intent-based descriptions match queries.

Keep variants indexable, consistent and accurate; there is no AI-specific schema for AIO – focus on parity and clarity across locales.

How to operationalise AIO-ready structure in your CMS and editorial workflow

Bake intent, visuals, structured data and multiformat SEO into content types so AIO-ready pages ship by default.

Essential CMS fields & template automation

• Intent label: taxonomy field (Informational, Transactional, Local) to drive tone and placement.
• Intent-based description (160-220 chars): one-sentence who/what/when summary for snippets and previews.
• TL;DR tiers: TL;DR-50 (one sentence), TL;DR-150, TL;DR-300 with bullets.
• Canonical answer (1-2 sentences): precise, sourced and placed near the top; mirrored in JSON‑LD.
• Evidence & citations: structured source URL fields that feed a “sources” component.
• Visual fields: hero image (min 1200px) + alt + caption; video fields (URL, thumbnail, duration, transcript).
• Auto-generated JSON‑LD: from CMS fields, kept in parity with visible content.
• Preview controls: page-level robots meta and data-nosnippet toggles.

Editorial QA: schema parity, accessibility and pre-publish linting

1. Schema parity: JSON‑LD must match visible content; validate with Rich Results Test.
2. Accessibility: run WCAG checks (contrast, alt text, headings) with axe-core or WAVE.
3. Pre-publish lint: Rich Results Test passes, preview controls set, JSON‑LD linted.
4. Post-publish: monitor Search Console for clicks/impressions and re-index where needed.

Templates and automation reduce rework and help teams publish AIO-ready pages consistently.

Case studies: structural changes that produced measurable outcomes

Mini-cases and signals to watch

• Ecommerce – DoggieLawn (Shopify Plus): stabilised PDP templates and Product schema; outcome reported: uplift in conversions attributed to clearer product information and rich-result eligibility.

  • Signals: rich-result CTR, add-to-cart rate, revenue per session.

• Local services – Photo & services optimisation: updating authentic, intent-matching photos improved clicks-to-call and clicks-to-site in location tests.

  • Signals: GBP clicks-to-call, direction requests, local-pack rankings.

• B2B SaaS – Dynamic Mockups: intent-templated pages (comparison hubs, integrations) delivered large traffic and sign-up gains by matching job-to-be-done queries.

  • Signals: AIO citation share, CTR delta on informational vs branded terms, trial/demo conversion and pipeline influence.

Which metrics to track per case

• Citation share: frequency your domain/page is cited in AIO for target intents.
• CTR delta: CTR when AIO is present vs absent for matched queries.
• Conversions & pipeline: ecommerce: revenue per session; local: calls/bookings; SaaS: trial/demo starts and pipeline influence.
• Multiformat metrics: video impressions/clicks and image search clicks where applicable.

These case examples show that structural work – concise answers, schema parity and good visuals – drives measurable outcomes even in an AI-shaped SERP landscape.

“Focus on your visitors and provide them with unique, satisfying content.”
– Google Search Central, Google Search Team (Google Search Central blog post, May 21, 2025)

What to stop doing right now: quick fixes and anti-patterns to avoid

Avoid structural mistakes that are costlier in an AI-shaped SERP. Fix high-impact issues first and remove common anti-patterns.

High-impact quick fixes

• Add a 1-line canonical answer above the fold – plain-English, who/what/when statement to reduce paraphrase risk.
• Align H1, <title> and og:title so Google sees consistent titles.
• Validate JSON‑LD parity before publishing – schema must reflect visible content.
• Use WebSite markup to stabilise brand display and include clear article metadata (headline, date, author).
• Prefer <img> elements for important visuals and use descriptive, non-stuffed alt text.

Governance rules & anti-patterns

• Make core facts single-source fields in the CMS (pricing, specs, NAP) to avoid drift between page and schema.
• Ban keyword-stuffed alt text, avoid hidden marked-up content, and don’t use CSS backgrounds for critical images.
• Enforce recency hygiene for time-sensitive content and require an owner for updates.
• Avoid thin, intent-agnostic programmatic pages created purely for keywords; focus on helpful, task-matched content.

These quick fixes and governance rules protect visibility in AI Overviews while keeping pages conversion-ready.

Key sources and stats to cite when defending your strategy

Use authoritative guidance and recent studies to set realistic expectations for AIO impact. Cite dates and methodology when presenting stats to stakeholders.

Authoritative references

• Google Search Central: AI features guidance, structured data policies and preview controls (use as the source of truth for indexing and markup rules).
• Industry studies: use recent analyses that report AIO presence, CTR deltas and zero-click behaviour; when citing, include sample size, date range and scope (branded vs non-branded, device mix).

How to attribute quotes and stats responsibly

• Always timestamp sources and link to the primary reference.
• Note methodology: sample size, data window and whether metrics are global or region-specific.
• Clarify scope: branded vs non-branded, desktop vs mobile and how zero-click is defined.

Localise references and call out ANZ-specific implications when presenting to stakeholders in Australia and New Zealand.

Your 10-minute recap and decision tool to prioritise next steps

AIO-ready checklist to ship now

• Canonical answer first (40-60 words) – precise, intent-matched summary at the top.
• Intent-based descriptions under H2/H3 to clarify the task.
• JSON‑LD matching visible content (Article/Product/Video) – validate with Rich Results Test.
• Visual alignment: images next to the text they explain, descriptive alt text and captions; transcripts for video.
• Measurement: annotate changes and monitor AIO presence, CTR and qualified conversions at the query-set level.

Quick decision table (summary)

• Generate high-intent leads (SaaS): focus on comparisons, integrations and calculators; AIO stance: lead with canonical answer and evidence.
• Grow ecommerce revenue: focus on product pages, variants and rich-result eligibility; AIO stance: verify specs and front-load decision facts.
• Win local demand: service + suburb pages with intent summaries and local schema; AIO stance: state service scope and cite licences/proof.
• Build category awareness: glossary hubs and explainers; AIO stance: expect higher AIO presence and prioritise citations and neutral definitions.

10-minute actions: add canonical answers to top pages, fill missing alt text, drop baseline JSON‑LD, add a couple of authoritative citations and annotate the changes in analytics. For a 30-90 day plan, standardise templates, ship videos and measure query-set outcomes.