AI Search vs SEO in 2025

If your organic clicks are sliding and attribution looks murkier than ever, you’re not alone. In 2025, AI Overviews and answer engines power a surge of zero-click searches (analysts peg many categories near 60%), displacing traditional links by hundreds to thousands of pixels and reshaping how visibility turns into revenue.

What Is AI Search Traffic? What Is SEO (Organic) Traffic?

When traffic dips and reporting gets noisy, clarity starts with definitions you can measure. AI search traffic is exposure and visits earned via AI-generated answers—Google’s AI Overviews, Bing Copilot, Perplexity, Brave, and ChatGPT Browse—and the citations or links those systems include. Traditional SEO traffic is the sessions that arrive from ranking in classic SERP elements (blue links, featured snippets, People Also Ask) and other organic search features.

AI search often answers a query in-line, compressing the click funnel and moving brand discovery earlier in the journey. Traditional organic relies on your listing and snippet winning a click, with CTR shaped by rank position, SERP features, and brand familiarity. The practical takeaway: treat AI search visibility and SEO traffic as related but distinct channels with different KPIs and levers.

Quick working definitions:
AI search traffic: impressions and sessions from AI-generated answers and their citations/links.
SEO (organic) traffic: sessions from standard SERP listings and organic features.
AEO/GEO: Answer/Generative Engine Optimization to influence inclusion in AI answers.
Zero-click: queries resolved on-SERP without a site visit.

AI search traffic (AIOs, answer engines, assistants): how it arrives and why it’s often zero‑click

If your brand is showing up in AI answers but GA4 isn’t spiking, you’re seeing the new reality. AI Overviews, Copilot, and Perplexity synthesize responses and often satisfy intent without a click, so your “win” is frequently a mention, citation, or brand recall rather than an immediate session.

When clicks do occur, they’re usually deeper-funnel actions—price checks, demos, comparisons—or they come from users tapping a cited source to verify the claim. This shifts the shape of your traffic: fewer top-of-funnel sessions, more mid-to-bottom-funnel engagement.

In practice, brands see fewer total sessions but a higher share of converting visits from AI-driven exposure. For example, when a buyer asks “best SOC 2 software vs ISO 27001,” the AI answer may list 3–5 vendors with links; ensuing clicks tend to be evaluative and downstream.

Expect fewer top-of-funnel visits but stronger assisted conversions and shorter time-to-purchase on those that arrive. The takeaway: measure AI search in terms of citations, mentions, and assisted value, not just raw sessions.

Traditional SEO traffic: SERP listings, featured snippets, and on-site engagement

If CTR curves feel flatter, classic SEO is still the engine—but it faces more overhead.

Organic SEO traffic still flows from ranking pages that match intent, structured well enough to earn prominent snippets and compel a click. You compete for blue links, featured snippets, and PAA, then convert through content depth, UX, and internal paths. CTR follows known decay curves, but AI-induced displacement—links pushed 1,000–1,500 pixels down the fold—reduces clicks even for high positions.

This channel remains your workhorse for long-tail queries, evergreen “how-to” content, and branded navigation. A robust internal linking and schema strategy increases your eligibility for snippets and reduces pogo-sticking by delivering direct answers above the fold. The takeaway: classic SEO still drives volume, but protect it with snippet-first formatting and MoFu/BoFu depth that AI is less likely to fully replace.

Sources of traditional SEO traffic to track:
Blue link rankings for target queries
Featured Snippets and PAA appearances
Image/Video/News/Local packs (where relevant)
Discover and Top Stories for timely content

How AI Overviews and Answer Engines Change the Funnel

If your TOFU blog posts are down but demo and quote requests look steadier, your funnel dynamics have shifted. AI systems compress top-of-funnel information needs, while rewarding brands that are credible entities, well-cited, and easy to quote.

The result is fewer exploratory clicks but more qualified discovery and mid-funnel visibility where buyers compare and decide.

Funnel shifts to plan for:
TOFU: more zero-click; prioritize brand education and entity signals
MoFu: comparisons and FAQs surface in AI answers; optimize for inclusion
BoFu: fewer clicks but higher conversion intent; strengthen proof and UX
Post‑click: trust signals and fast paths to action carry more weight

Zero-click reality: displacement, CTR declines, and higher-intent survivors

If CTRs are dropping where AI Overviews appear, that’s consistent with industry patterns. Marketers report meaningful CTR declines where AI Overviews show, with category drops around 15–64% depending on query mix and SERP crowding.

When AI gives a sufficient summary, many users stop there, and only the most motivated click through to evaluate options or verify expertise. That leaves a smaller pool of higher-intent visitors who expect clear proof once they land.

Brands can mitigate losses by targeting queries where AI still defers to experts—compliance details, pricing nuances, and context-rich comparisons. Strengthen your “reason to click” with unique data, calculators, or firsthand experience the model cannot fully reproduce. The takeaway: stop measuring only lost sessions; prioritize the quality and conversion rate of the sessions that remain.

Actions to offset zero-click:
Lead with unique data, frameworks, and calculators
Add comparison matrices and proof assets on-page
Use FAQ and HowTo schema to anchor quotes
Ensure above-the-fold answers plus deeper exploration paths
Track assisted conversions tied to AI-visible pages

Beyond Google: Bing Copilot, Perplexity, Brave, ChatGPT Browse

If Google variability makes planning hard, diversify where your answers can be found. AI search visibility extends beyond Google, and each engine has its own retrieval and citation patterns.

Bing Copilot blends web results and ChatGPT-style summaries. Perplexity and Brave tend to cite more sources, and ChatGPT Browse can include link cards to references. These ecosystems can yield small but high-intent traffic that punches above its weight in conversions.

Treat these engines as emerging referrers and a hedge against Google volatility. Build eligibility by aligning entities, using clean Q&A formatting, and publishing expert-backed content that earns citations across engines. The takeaway: broaden tracking and content formatting to be “answer-friendly” wherever your buyers ask.

Non-Google engines to prioritize:
Bing Copilot for commercial/Microsoft ecosystems
Perplexity for research-heavy and technical queries
Brave Search for privacy-minded audiences
ChatGPT Browse for exploratory and instructional tasks

AI Search Traffic vs SEO Traffic: Side-by-Side Comparison

If budget pressure demands clear tradeoffs, compare channels on what they truly deliver. Use these contrasts to align KPIs with stakeholder expectations and timelines, and to set realistic targets for speed versus compounding growth.

Fast contrasts:
Visibility: AI = citations/mentions; SEO = rankings/snippets
Volume: AI = lower sessions; SEO = higher sessions
Intent: AI = mid-to-high; SEO = full-funnel
Time to impact: AI = faster on new Q&A; SEO = steadier compounding
Attribution: AI = harder; SEO = mature tools
Risk: AI = accuracy and policy; SEO = algorithm and SERP feature shifts

Attribution and visibility signals

If leaders ask “are we visible,” clarify that AI and SEO signal visibility differently. AI visibility is primarily about whether the engine cites you and how prominently it does so.

You track “being quoted” rather than “being ranked,” and that means instrumenting mentions, brand lift, and downstream conversions. For SEO, you still monitor impressions, positions, CTR, and click share by query. Together, they form a fuller picture of influence and acquisition.

In practice, pair SERP spot-checks and tools that log citations with GA4 assisted conversion paths. For Google specifically, you can’t yet filter AI Overviews reliably in GSC at scale; use proxy signals like rank stability with CTR decline and manual SERP audits for key cohorts. The takeaway: define and report AI citations alongside classic SEO rankings so leadership sees both sides of visibility.

Core visibility metrics:
AI: number of citations per query cohort; share of voice in answers; clicks per citation
SEO: impressions, average position, CTR, click share, pixel depth when available

Volume, intent, and conversion patterns

If “less traffic” is being equated with “less revenue,” separate volume from value. AI surfaces fewer clicks but stronger buyer intent, often mid-funnel and below.

Organic SEO continues to supply the breadth of sessions, especially on long-tail how-to queries and branded navigation. The mix you see depends on your query landscape and the density of AI Overviews in your SERPs.

Expect conversion rate and assisted conversion share to run higher on AI-sourced visits, while total volume favors classic listings. Model this explicitly so stakeholders don’t confuse “less traffic” with “less revenue.” The takeaway: judge AI search on revenue-per-visit and influence, not just top-line sessions.

KPI patterns to monitor:
Sessions per 1,000 impressions (AI vs SEO cohorts)
Conversion rate and pipeline value per session
Assisted conversions and path length
New vs returning visitor split by cohort

Time to impact and stability

If you need quick signal, AI inclusion often moves faster than rankings. A well-structured Q&A can appear in AI engines quickly as they refresh indices and embeddings, especially for emergent topics.

Classic SEO can take longer to move, but compounding benefits and internal linking deliver more stability over time. Both channels remain volatile during major updates and index refreshes.

Set expectations by objective: fast discovery and mid-funnel inclusion lean AI; long-term compound growth and category moats lean SEO. The takeaway: ladder quick AI wins into long-term SEO clusters so momentum is both fast and durable.

Timeline guardrails:
AI inclusion tests: 2–6 weeks
SEO ranking movement: 8–24 weeks
Stabilization after updates: 2–4 weeks
Review cadence: biweekly for AI, monthly for SEO

Levers that move the needle (AEO/GEO vs traditional SEO levers)

If teams are unsure where to start, point each lever at its primary outcome. For AI inclusion, focus on entity clarity, concise answers, source citations, author expertise, and structured Q&A markup.

For SEO, continue technical hygiene, topical depth, internal links, and link earning. Backlinks still matter because authority influences both ranking and the likelihood of being quoted.

Pilot both: ship answer-first sections with schema and expert bios while consolidating thin pages into authoritative clusters. The takeaway: AEO/GEO expands your surface area for citations; SEO keeps your content discoverable and durable.

High-impact levers:
AEO/GEO: FAQs, HowTo, schema, entity mapping, expert bios, citations to primary sources
SEO: crawlability, page speed, internal linking, content hubs, quality backlinks

Risk and compliance (YMYL, accuracy, citations)

If you’re in YMYL categories, accuracy isn’t optional—it’s risk management. YMYL categories face higher stakes because AI summaries can misstate treatment, finance, or legal steps.

You must publish medically, financially, or legally reviewed content with clear credentials and update logs. Inaccurate AI citations can create liability and brand risk if your pages are outdated.

Create a source citation policy, require expert review, and publish revision dates and authorship on-page. The takeaway: robust E‑E‑A‑T reduces AI hallucination risk and increases your odds of being cited responsibly.

Governance essentials:
Credentialed authors and reviewers
Fact-check workflow and audit trail
Clear last-updated stamps
Claims linked to primary sources
Rapid corrections policy

Measurement Blueprint: How to Attribute AI Search Traffic

If attribution is blocking investment, standardize definitions and build proxies you can trust. Attribution is the blocker most teams report, so start by standardizing your KPI taxonomy and building proxy signals.

AI answers don’t always pass distinct referrers, so triangulation is required to separate influence from acquisition.

Measurement goals:
Separate AI vs SEO cohorts
Track citations and assisted value
Annotate changes and test cohorts
Build a 30/60/90-day dashboard

Set up KPI taxonomy: impressions, citations/mentions, sessions, assisted conversions

If reporting debates never end, it’s a taxonomy problem. Define visibility as impressions and citations, engagement as sessions and CTR, and economic value as conversions and pipeline influenced.

For AI, treat mentions/citations as a primary KPI and clicks per citation as a secondary KPI. For SEO, keep impressions, position, and CTR as your core. This lets you compare influence and acquisition without mixing signals.

Tie AI-visible URLs to assisted conversions using GA4 conversion paths and “landing page” + “page path” pivoting. Add a “citation visibility score” that weights frequency, order, and number of sources cited per answer. The takeaway: your taxonomy should let you compare influence (AI) to acquisition (SEO) without mixing the two.

Recommended KPIs:
AI: citations per query cohort; clicks per citation; assisted conversions per AI-visible page
SEO: impressions, average position, CTR, sessions, conversions per landing page
Shared: revenue per session, pipeline per session, time to conversion

Google Search Console and GA4: filters, annotations, and pitfalls

If GSC feels blind to AI Overviews, use controlled cohorts to close the gap. GSC remains your source for impressions and CTR, but it doesn’t consistently break out AI Overviews as a search appearance.

Use query cohorts where you see AI Overviews in the SERP and monitor CTR deltas versus similar non-AI queries. In GA4, build explorations by landing page group (AI-formatted Q&A vs standard articles) and track conversion paths.

Annotate AI rollout dates (e.g., AI Overviews expansion, “AI Mode” experiments) and major algorithm updates. Beware of mixing brand and non-brand data; brand CTR masks AI impact. The takeaway: rely on controlled cohorts, annotations, and landing-page segmentation until Google exposes an “AI Overview” search appearance reliably.

Practical steps:
Create GSC query groups: short- vs long-tail; branded vs non-branded; with/without AI Overview presence
Build GA4 audiences for AI-optimized pages; track conversions and assisted paths
Annotate AIO rollouts and site changes; compare 14/28‑day windows
Cross-check with rank/pixel-depth tools to flag displacement

Assistant and engine tracking: Bing Copilot, Perplexity, Brave, ChatGPT Browse

If non-Google referrers look messy, validate them directly and standardize UTMs. Non-Google engines pass more attributable referrers, and you can improve visibility with UTMs when you control links.

Perplexity commonly passes perplexity.ai. Brave Search passes search.brave.com. Bing Copilot often appears as bing.com with specific parameters. ChatGPT Browse may pass chat.openai.com in some flows but can be inconsistent.

Validate referrers by clicking from each engine to your site in a private window and inspecting GA4 real-time and server logs. When possible, encourage users to copy share links with UTMs in your interactive tools and calculators. The takeaway: track known referrers, build UTMs where you can, and use log files to backstop gaps.

What to track and test:
Referrers: perplexity.ai, search.brave.com, bing.com (Copilot), chat.openai.com
UTM patterns for your tool outputs and share links
Log-file cues for assistant crawlers (e.g., PerplexityBot) vs human clicks
Query-level landing pages tied to AI-cited answers

How to Win Both: A Hybrid AI + SEO Content Strategy

If you’re torn between protecting traffic and earning citations, run a dual-track plan. A dual-track approach lets you capture AI answer visibility while safeguarding and growing organic sessions.

Build entity-first content that’s easy to quote and deep enough to convert, then measure both influence and acquisition in one view.

Hybrid pillars:

Entity clarity and schema
Answer-first formatting
Depth pages and internal linking
Authority via links and experts
Continuous measurement and iteration

Entity-first content and schema: FAQs, Q&A, how‑tos, author bios, citations

If AI can’t parse who you are, it won’t cite you. AI and search engines lean on entities—people, organizations, products, and concepts—to connect queries with trustworthy sources.

Make your entity edges unambiguous with Organization, Person, Product, and FAQPage schema. Add author bios with credentials, and cite primary sources. Convert key pages to answer-first layouts with concise definitions followed by depth.

Use clear Q&A blocks for common intents and anchor them with schema and jump links. Link out to reputable references to signal knowledge graph alignment and improve citation likelihood. The takeaway: if the model can parse who you are and what you’re expert in, it’s more likely to quote you.

Must-have schema and structures:
Organization, Person, Product/Service
FAQPage and HowTo where applicable
Article with author and reviewedBy
Breadcrumb and sitelinks searchbox
Pros/cons or comparison sections with headings

Backlinks and authority signals in an AI-first world

If you’re wondering whether links still matter, they do—for humans and models alike. Backlinks still correlate with authority, human trust, and model training signals that value well-referenced sources.

While AI answers reduce clicks, engines favor citing well-linked, reputable domains—especially on YMYL topics. Quality beats quantity; expert mentions, digital PR, and data assets earn the links most likely to influence both rankings and citations.

Design a controlled test: ship two matched Q&A pages; promote one with 5–10 quality links and digital PR, and keep the other as a control. Track citations across engines and compare inclusion rates and order. The takeaway: link earning remains a high-ROI lever for both SEO and AI inclusion when tied to topical authority.

Authority builders to prioritize:

Data studies and original research
Expert roundups with named credentials
High-quality guest features and PR
Industry glossary and pillar pages
Thought-leadership tied to product expertise

Content design for AI retrieval: concise answers + depth pages (chunking, headings)

If your pages are hard to quote, they’re hard to click. AI pulls sentences and short passages, so your pages should chunk content into scannable sections.

Start with a crisp definition or verdict, follow with 2–3 supporting bullets, and then provide rich detail with headings, diagrams, and examples. Give every section a clear H2/H3 and keep paragraphs tight for both skimmers and answer engines.

Pair answer modules with depth resources—calculators, checklists, templates—that create a “why click” moment. Use internal links to drive from answer pages to demo pages, pricing, or case studies without friction. The takeaway: design for in-line citation and for the human who clicks to validate and act.

Design checklist:

Definition/answer in first 100–150 words
5–7 item bullet blocks for comparisons
Jump links and sticky TOC
Visuals with alt text and captions
Prominent next-step CTAs tied to intent

Playbooks by Scenario

If your industry mix changes how AI shows up, adjust with targeted plays. Different industries see different blends of AI vs SEO impact, so tailor content structure, proof assets, and measurement to fit buyer behavior without guessing.

Scenarios covered:
B2B SaaS
Ecommerce
Nonprofits
Local services & YMYL

B2B SaaS: high-consideration, comparison queries, demos

If comparison traffic is slipping, meet buyers where AI summarizes their choices. SaaS buyers ask evaluative questions AI loves to answer: “X vs Y,” “best for,” “SOC 2 vs ISO,” “alternatives.”

Build objective comparison pages with transparent criteria, customer quotes, and pricing context where possible. Add structured FAQs for deployment, integrations, and security to earn citations and equip sales.

Create demo-focused CTAs on every comparison and alternatives page, and publish implementation guides that AI can cite for technical depth. The takeaway: own the MoFu battlefield with credible comparisons and proof assets that turn citations into demos.

SaaS plays:

“X vs Y” and “Best [category] for [use case]” hubs
Security and compliance FAQ clusters
ROI calculators and TCO guides
Case studies with measurable outcomes
Integration dictionaries and implementation handbooks

Ecommerce: product/category FAQs and reviews

If product discovery feels more zero-click, make PDPs impossible to substitute. AI often resolves basic product queries, but shoppers still click for price, availability, and authentic reviews.

Enrich PDPs with concise FAQs, sizing/compatibility guides, and UGC signals; add HowTo schema for setup or care when relevant. Earn citations with category-level buying guides featuring expert tips and unique testing data.

Use comparison charts and “best for [persona]” sections to encourage clicks from AI summaries. The takeaway: make PDPs and category pages the definitive resource with answer-first modules and trust markers.

Ecommerce plays:

PDP FAQs and HowTo guides
Comparison and “best for” roundups with criteria
UGC review highlights and structured pros/cons
Sizing/fit tools and return policy clarity
Seasonal gift guides and bundles

Nonprofits: branded protection, donation UX, local visibility

If donors see summaries before your site, control the narrative and the path to give. Donors ask mission, impact, and legitimacy questions, and AI will often summarize from your site and third parties.

Secure branded and cause FAQs with clear answers, financial transparency, and third-party ratings. Make the donation path fast and mobile-optimal with visible impact statements and local pages for chapters.

Use GA4 to track donation assists from FAQ and “About” pages that AI frequently cites. The takeaway: protect your brand query space and make the path from AI discovery to donate unbroken.

Nonprofit plays:

Branded FAQ and “Why give” pages with schema
Annual report highlights and impact dashboards
Local chapter pages with NAP consistency
Ratings and accreditation badges (e.g., Charity Navigator)
Donation UX: express checkout and clear designations

Local services & YMYL: accuracy, credentials, and updates

If trust is your differentiator, show credentials and recency at a glance. In YMYL and local, credentials and recency drive trust and inclusion.

Publish medically/legally reviewed pages with reviewer bios, license numbers, and last-updated dates. Add service-area pages with consistent NAP, and ensure policy and safety sections are prominent so AI extracts the right guidance.

Monitor AI answers for accuracy and correct your pages quickly if misinterpreted. The takeaway: lean hard into E‑E‑A‑T and frequent updates to become the safest citation.

Local/YMYL plays:

Reviewer bios with credentials and affiliations
Safety, policy, and eligibility pages
Service-area and location pages with FAQs
Procedure/process checklists with risks and aftercare
Rapid content update cadence for regulatory changes

Budgeting and ROI in the AI Era

If leadership wants to know where the next dollar goes, plan AI and SEO as complementary lines. Model spend and outcomes separately for AI inclusion and SEO compounding, then roll up to an integrated mix with clear timelines, risks, and expected returns.

Budget categories to plan:

Content production and expert review
Link earning and digital PR
Technical/analytics tools
Design/dev for content architecture
Experimentation and research

Cost centers: content, links, tools, analytics

If costs feel front-loaded, that’s normal for refactors and measurement. AI-first content costs include expert time, schema implementation, and Q&A refactors.

Link earning and digital PR drive authority across both channels, while analytics investment covers dashboards, SERP auditing, and log analysis. Expect upfront spend to be heavier in months 1–3 as you refactor pillars and build measurement.

Allocate a reserve for experiments in non-Google engines where early movers still gain outsized visibility. The takeaway: fund AEO/GEO and measurement as first-class line items, not leftovers from SEO.

Example allocation (first 90 days):

40% content and expert review
25% link earning and PR
20% analytics and tools
10% design/dev
5% experiments (Perplexity/Brave/Copilot)

Forecast model: expected ranges for visibility, CTR, and conversion lift

If stakeholders want certainty, frame outcomes as ranges tied to query mix and competition. Set conservative ranges to avoid overpromising.

Many teams see faster visibility gains in AI citations than rankings, coupled with modest but meaningful conversion lift from MoFu pages. Over 90 days, aim for steady inclusion and increased revenue per session on AI-optimized pages.

Sensitivity-test your model: low, base, high scenarios by query mix and competitive density. The takeaway: sell the plan on efficiency (better sessions, higher intent) rather than sheer volume.

Planning ranges to consider:

AI citations on target queries: 25–50% by day 60; 40–70% by day 90 for well-structured clusters
CTR change on MoFu pages: +5–15% if snippet eligibility improves
Conversion rate lift on AI-visible pages: +10–30% vs baseline
Pipeline influenced by AI-visible pages: +5–10% by day 90

Governance and E‑E‑A‑T Checklist

If accuracy and trust decide whether you’re cited, governance is the safeguard. Accuracy and trust determine whether you’re cited—and whether that citation helps or hurts, especially in regulated spaces.

Systematize governance so updates are fast, traceable, and defensible.

Governance pillars:

Expertise and authorship
Fact-checking and corrections
Source transparency
Update cadence and logs
YMYL safeguards

Authorship, fact‑checking, and update cadence

If expertise isn’t visible, engines and users will assume it’s missing. Every substantive page should list an author with relevant credentials and, where needed, a reviewer with domain authority.

Implement a fact-check workflow that includes source verification and a final QA pass. Publish last-updated dates and maintain an internal change log for auditability.

Set cadences by risk: monthly for YMYL and policy pages; quarterly for evergreen guides; on-demand for breaking updates. The takeaway: visible expertise and recency increase inclusion odds and reduce risk.

Implementation steps:

Add author and reviewedBy schema plus on-page bios
Create a fact-check checklist and require sign-off
Maintain a revision history per URL
Automate recrawl pings after major updates

Source citation policy and accuracy standards

If sourcing feels inconsistent, codify it to build trust with both users and engines. Decide which sources you cite, how you quote them, and when to include primary data or disclaimers.

Link to original research, standards bodies, and regulatory pages; avoid ambiguous secondary summaries. For comparisons, document criteria and maintain neutrality to improve citation likelihood.

Publish an accuracy statement and a corrections policy users can find. The takeaway: consistent, transparent sourcing signals reliability to both users and AI engines.

Policy components:

Source hierarchy (primary > authoritative secondary)
Criteria for comparisons and “best” lists
Disclaimers for YMYL guidance
Public corrections process and contact

FAQs: AI Search Traffic vs SEO Traffic

What is AI search traffic?

Exposure and visits generated from AI answers (e.g., AI Overviews, Copilot, Perplexity) and the citations those answers include.

Which KPIs best indicate AI citation visibility versus traditional rankings?

AI: number of citations, clicks per citation, assisted conversions per AI-visible page; SEO: impressions, position, CTR, sessions.

How do I attribute conversions influenced by AI Overviews or assistant answers?

Segment AI-optimized landing pages in GA4, track assisted conversions and paths, and compare cohorts where SERPs contain AI Overviews vs not; annotate rollout dates.

Do backlinks still matter for AI search in 2025?

Yes; authority and trust drive both rankings and likelihood of citation, especially in YMYL.

What’s the difference between Featured Snippets, People Also Ask, and AI Overviews for traffic?

Snippets/PAA are single-source or short multi-source features that still drive clicks; AI Overviews synthesize multi-source answers and often reduce clicks but increase assisted value.

When should I prioritize AEO/GEO over traditional SEO?

When you need fast MoFu visibility, competitive comparisons, or emerging topics; maintain SEO for compounding long-tail and brand growth.

How does AI search affect branded vs non‑branded traffic?

Branded tends to hold steadier; non-branded TOFU queries see bigger zero-click impact. Protect brand FAQs and invest in MoFu comparisons to capture intent.

Glossary: AIO, AEO, GEO, Entities, Citations

AI Overview (AIO): Google’s generative answer module summarizing results with citations.
Answer Engine Optimization (AEO): Tactics to increase inclusion in AI answers.
Generative Engine Optimization (GEO): Broader term for optimizing content for generative search systems.
Entity: A person, organization, product, or concept recognized by knowledge graphs; clear entities improve retrieval and citation.
Citation: A linked or unlinked reference to your page or brand within an AI-generated answer.