How to Improve AI Search Engine Optimization (AI SEO)

I search is changing what “SEO” means for ecommerce. Your goal is no longer just to rank—it’s to be selected and cited in AI answers, and to convert the AI-driven sessions that still click.

1) What “AI SEO” means now (and why clicks aren’t the KPI)

AI Search Engine Optimization (AI SEO) is the practice of making your brand and product information easy for AI systems to retrieve, trust, and reuse—so your pages are more likely to be cited in AI-generated answers and to drive qualified traffic that converts.

The urgency is measurable: in Pew Research Center’s analysis, about 18% of Google searches produced an AI summary. But only 1% of visits to pages with an AI summary resulted in a click on a cited source link inside the summary. Pew also found users ended their browsing session after seeing an AI summary 26% of the time vs. 16% without a summary.

That’s the “zero-click” reality: AI answers absorb intent. So if you’re measuring success mainly by classic organic CTR, you’ll feel like performance is “down” even if rankings are stable.

The upside: being cited still matters. Seer Interactive reports that being cited in AI Overviews is correlated with about 35% more organic clicks and about 91% more paid clicks compared with not being cited.

For commerce operators, the implication is simple: AI SEO is a commerce ops problem. You’re optimizing for:

• Representation accuracy (features, variants, price ranges, availability) so AI doesn’t get your catalog wrong.

• Selection (being cited/used as a source) so you show up inside answers.

• Assisted conversion so the sessions that do click land on pages that match the prompt intent.

2) How AI search engines choose sources (a simple mental model)

Most AI search experiences follow a retrieval + synthesis pattern: they retrieve documents that look relevant and trustworthy, then synthesize an answer. You don’t need to reverse-engineer every model to win—because Google has been explicit that core SEO fundamentals still apply for success in Google’s AI experiences.

A practical mental model: “Can the system confidently lift this?”

• Clear purpose: Is the page obviously about one intent?

• Extractable facts: Are key claims stated plainly (not buried in prose)?

• Trusted entity signals: Is the brand/author/source credible and consistent?

• Freshness + specificity: Does the page answer the prompt with current, concrete details?

What’s different vs. classic SEO

• Phrasing sensitivity and system differences: Controlled experiments show AI search systems can differ in properties like phrasing sensitivity, freshness, and domain diversity.

• “Quotability” wins: AI systems are more likely to reuse content that is chunked into clear, attributable units (definitions, steps, tables) rather than long narrative blocks.

• Selection replaces ranking as the primary objective: You’re optimizing for being chosen as a source inside an answer, not just being position #1.

3) Step-by-step: Improve AI SEO in 30 days (commerce-first plan)

This 4-week plan is designed for ecommerce teams that need operational outputs: better indexability, more extractable intent pages, more accurate product representation, and a measurement loop that tracks citations and assisted revenue.

Week 1: Baseline visibility + fix crawl/index issues

Outcome: You can reliably be crawled and indexed where it matters.

• Confirm Googlebot can access your key pages and that they return a successful status for indexing in AI experiences.

• Create a baseline list of priority prompts (category discovery, “best for” use cases, comparison prompts) and the pages you want cited.

• Capture a “starting snapshot” of whether you appear/cited across platforms (manual checks are enough to start; you’ll systematize in Week 4).

Week 2: Restructure top pages for extractability

Outcome: Your most important pages become easier for AI systems to lift and cite.

• Add a TL;DR answer block at the top of category and guide pages.

• Break “walls of text” into short sections with descriptive subheads.

• Add Q&A and comparison blocks (see Section 5).

Week 3: Product data + entity trust signals

Outcome: AI answers about your products become more accurate and consistent.

• Ensure structured data matches visible content on the page (especially price ranges, variants, and key attributes).

• Standardize naming for products/models/collections across PDPs, category pages, and help content.

• Strengthen “who we are” clarity (About, policies, support, and references) to improve trust and repeatability.

Week 4: Measurement + iteration (prompt sets + citation tracking)

Outcome: You can quantify AI visibility and prioritize fixes.

• Build a prompt set by category (e.g., “best for,” “vs,” “under $X,” “for [use case]”) and track: cited sources, mentioned products, and accuracy.

• Prioritize pages that are close to being cited (appearing but not cited) and pages where representation is wrong (wrong variant/spec).

• Create a weekly “AI visibility” review: what changed, which pages improved, what needs new content blocks.

30-day checklist (printable)

• Access: Googlebot can crawl key pages and they return successful status codes.

• Intent pages: One intent per page; clear purpose in Title/H1.

• Extractability: TL;DR + Q&A + tables on priority pages.

• Structured data alignment: Markup matches visible content.

• Trust: Clear entity signals, citations, consistent business info.

• Measurement: Prompt-level share of voice, citation rate, and accuracy score tracked weekly.

4) Nail the “selection signals”: Title, H1, meta, and page purpose

AI systems need to quickly infer what a page is for. Your job is to remove ambiguity. The highest leverage work is aligning the page’s “selection signals”: Title, meta description, H1, and the first screen of content.

Rules that work for ecommerce

• One intent per page: Don’t mix “best running shoes” with “how to clean running shoes” on the same URL.

• Title ↔ H1 alignment: Use the same core phrase and outcome so the page is unmistakable.

• Outcome-driven language: Write for the decision (“best for flat feet,” “best for wide toe box,” “compatible with X”).

• Descriptive subheads: Make sections scannable and extractable.

Examples (ecommerce-ready)

• Category intent page: “Best running shoes for flat feet (2026): 12 picks + fit guide”

• Comparison page: “Brand A vs Brand B: which is better for plantar fasciitis?”

• Use-case landing page: “Best carry-on luggage for international flights: sizes, materials, and top picks”

Do: Write pages that are easy to summarize—clear purpose, short sections, and explicit answers.

Avoid: Keyword stuffing and long, undifferentiated paragraphs that hide the actual decision criteria.

5) Make content extractable: chunking, Q&A, and comparison blocks

“Extractability” is the most practical proxy for AI SEO. If an AI system can lift your answer as a clean chunk, it’s easier to cite and reuse. This aligns with research on Generative Engine Optimization (GEO), which reports that optimization strategies can improve visibility in generative responses by up to 40% in experiments.

Formats AI systems can lift cleanly

• TL;DR answer: 2–4 sentences that directly answer the prompt.

• Definition box: A short definition plus “why it matters” for shoppers.

• Numbered steps: “How to choose” frameworks.

• Pros/cons: Simple tradeoffs.

• Comparison tables: Specs, best-for, price range, materials, warranty, compatibility.

• FAQ blocks: Real questions with direct answers.

Reusable “Best for / Why / Key specs / Proof” template

• Best for: The shopper profile or use case.

• Why: 2–3 decision drivers (comfort, durability, compatibility, etc.).

• Key specs: Bulleted attributes that are easy to quote.

• Proof: Link to policies, documentation, or credible references where applicable.

If you want a commerce-native way to apply GEO thinking, focus on pages that map to prompt archetypes: “best for,” “vs,” “under $X,” “for [condition],” “for [device],” and “alternative to.”

For ecommerce specifically, e-commerce-focused GEO research has introduced a testbed with 7,000+ realistic multi-sentence product queries, highlighting how nuanced product prompts can be. That’s another reason “thin” category pages rarely win: the prompts are detailed, and your pages need structured detail to match.

6) Build entity & trust signals that AI systems repeat

AI systems tend to repeat what they see consistently across the web. So entity clarity isn’t branding fluff—it’s a selection lever.

Pew found that Wikipedia, YouTube, and Reddit accounted for 15% of sources in AI summaries, and .gov sources were 6% of sources in AI summaries vs. 2% in standard results. The takeaway for commerce teams: AI answers often lean on concentrated, high-authority domains, so you need to make your own site more “referenceable” and support it with credible third-party signals.

Trust mini-checklist (commerce edition)

• About page clarity: Who you are, what you sell, where you operate.

• Policy pages: Shipping, returns, warranty, clear and easy to cite.

• Support content: Compatibility, sizing, care instructions, troubleshooting.

• Third-party validation: Where appropriate, cite credible references and documentation.

• Consistency: Keep brand/product naming consistent across your site and external listings.

This isn’t about gaming authority. It’s about reducing uncertainty so an AI system can confidently attribute a claim to your brand.

7) Commerce-specific AI SEO: product data that prevents wrong answers

Generic AI SEO advice often stops at “write better content.” Ecommerce brands have a harder problem: AI assistants frequently answer with product specifics. If your SKU-level facts are messy, you’ll get misrepresented.

Start with a non-negotiable: Google recommends that structured data should match the visible content on the page. For commerce teams, that means your markup and your on-page facts must agree—especially where shoppers (and AI systems) care most: price, variants, compatibility, and warranty.

Publish machine-readable, human-visible product facts

• Price ranges and variant pricing: Make ranges explicit when variants differ.

• Variants: Size, color, capacity, material, bundle contents.

• Compatibility: Devices, models, years, standards.

• Sizing/fit: Measurement tables and “fits like” guidance.

• Warranty and returns: Clear, linkable terms.

• Availability context: When applicable, clarify what “in stock” means (online-only vs store).

Common failure modes (and how to fix them)

• Variant confusion: Fix by adding a variant table and clarifying what changes by variant.

• Outdated pricing: Fix by keeping visible price ranges current and ensuring markup matches.

• Missing compatibility: Fix by publishing compatibility matrices and FAQ entries.

• Ambiguous model names: Fix by standardizing naming and including “also known as” aliases on the page.

8) Technical controls: indexing, previews, and AI crawlers

Indexing and crawlability (non-negotiable for Google AI experiences)

If you want to show up in Google’s AI experiences, you need the basics: Google recommends ensuring Googlebot can access your pages and that pages return a successful status for indexing in AI experiences.

Preview/snippet controls (use intentionally)

Google also recommends using snippet/preview controls (such as nosnippet, max-snippet, noindex) intentionally to manage how content can be shown in AI experiences.

For commerce brands, the tradeoff is strategic: restricting previews can reduce how much of your content appears in AI experiences, which can reduce selection/citations. On the other hand, there may be legal, licensing, or brand reasons to limit reuse. Treat this as governance, not an SEO tweak.

AI crawlers (e.g., GPTBot) and robots.txt governance

OpenAI documents GPTBot and how publishers can control access (including via robots.txt). Whether to allow or block depends on your distribution strategy and risk tolerance.

Decision tree

• Want citations and discovery? Ensure indexability and allow previews where appropriate.

• Want to restrict reuse? Use preview controls intentionally and understand the visibility tradeoffs.

• Concerned about model training or crawling? Review GPTBot guidance and implement a policy-based robots.txt approach.

9) Platform playbooks: Google AI Overviews vs ChatGPT/Perplexity/Claude/Gemini

The mechanics differ by platform, but the winning pattern is consistent: make your pages accessible, extractable, and accurate—and validate performance with prompt-based testing.

a) Google AI experiences (AI Overviews and beyond)

• Anchor on fundamentals: Google says core SEO fundamentals still apply for AI experiences.

• Technical access: Ensure Googlebot can access pages and they return successful status for indexing.

• Structured data accuracy: Ensure structured data matches visible content.

• Preview controls: Use snippet/preview controls intentionally.

b) Perplexity

Perplexity is widely experienced as citation-forward. The practical play is to publish pages that are easy to quote: TL;DR blocks, clear claims with supporting references, and comparison tables. Differences across AI systems (including phrasing sensitivity and domain diversity) have been documented in controlled experiments, so validate with multiple prompt variants.

c) ChatGPT

For product discovery prompts, focus on entity clarity and product facts: “best for” blocks, compatibility matrices, and variant clarity. If you’re making crawler policy decisions, use GPTBot guidance to control access via robots.txt based on governance needs.

d) Gemini

Treat Gemini like a Google-aligned experience: prioritize indexability, accurate structured data matching visible content, and strong page purpose signals.

Platform validation table

10) Measure what matters: AI visibility, citations, and assisted revenue

AI search changes measurement because it changes clicking behavior. Seer Interactive reports that when an AI Overview appears, organic CTR fell from 1.76% to 0.61% in its dataset, and paid CTR fell from 19.7% to 6.34% on queries with an AI Overview. Seer’s analysis covered 3,119 informational queries and included 25.1 million organic impressions and 1.1 million paid impressions.

Meanwhile, AI-referred traffic is becoming commercially relevant. Adobe reports that traffic from generative AI sources to U.S. retail sites rose about 1,300% year over year from Nov 1 to Dec 31, 2024. Adobe also reports AI-referred visitors showed 8% higher engagement, viewed 12% more pages per visit, and had a 23% lower bounce rate than visitors from non-AI sources.

A measurement framework for ecommerce AI SEO

• Prompt-level share of voice: For a defined prompt set, how often are you mentioned?

• Citation rate: How often is your domain cited as a source?

• Accuracy score: When you’re mentioned, are features/specs/variants correct?

• AI referral conversion rate: Conversion rate for sessions referred from AI sources.

• Assist rate: AI touch → later purchase (requires analytics discipline and attribution choices).

Remember: citations are not just vanity. Seer reports citation is correlated with better click outcomes (about 35% more organic clicks and about 91% more paid clicks for cited brands).

11) Operationalize with Nudge: turn AI visibility into shoppable funnels

Most AI SEO advice stops at “create content that might get cited.” Nudge’s angle is different: AI SEO is a commerce ops loop: visibility, representation, and conversion.

• Monitor AI search visibility: Track how your brand appears across AI discovery experiences with Nudge’s AI search visibility platform.

• Fix representation gaps with better product experiences: Improve how shoppers (and AI-referred sessions) understand products.

• Convert AI-driven intent with prompt-aligned funnels: Build shoppable, intent-matched landing experiences with Nudge shoppable funnels.

Concrete use cases by ICP

• Mid-market DTC: Reduce paid dependence by winning more AI discovery, then routing AI-driven clicks into prompt-aligned landing pages that convert.

• Enterprise DTC: Improve spec/variant accuracy and post-click relevance with prompt-mapped funnels.

• Retailers with multi-brand catalogs: Create scalable category and use-case landing pages that map to real shopping prompts, and benchmark visibility across AI experiences.

Ready to improve AI Search Visibility & Conversion?

Use Nudge to win discovery in AI answers and convert that intent with shoppable, prompt-aligned funnels!