AI Engine Optimization (AEO) 2025

AEO is the new standard for e-commerce visibility and sales.

Why is AEO so important for your online store?

• AI takes over traffic from search engines. AI (AIO) responses already appear for a large proportion of e-commerce queries and generate significant traffic
• Top 1-3 on Google is not enough. Only a fraction of these items make it to AIO, and most of the sources in AIO do not coincide with the classic TOP3 SEO
• Talk sells. Purchasing via chat/voice/AI is hundreds of billions of dollars a year - it's a viable revenue channel, it's the present day
• Personalization = money. In leaders, recommendations/personalization yield ~35-40% of revenue
• Polish practice. On average, 6% of traffic from AI chats can generate as much as 14% of revenue (case Semly)
• Conclusion: add AEO alongside SEO + Prepare data, push offer to ChatGPT/Gemini and measure share of sales with AI

What to do now?

1. Arrange product data
2. publish them to LLM ecosystems
3. enable shopping assistant
4. measure visibility in AI and revenue impact

Why isn't SEO enough anymore?

• AI shortens the path user, ignoring classic organic results (80% of sources in AI Overview are not SEO leaders)
• 77% of e-commerce managers use AI on a daily basis for campaign automation, analytics, recommendations and personalization
• 29% of retail companies are already building an edge based on big data and insights from AI - better forecasting, inventory, rapid response to trends
• Zero-click is growing and OSR (Organic Search Revenue) is flattening out; regulatory pressure around AIOs in the EU confirms the scale of the phenomenon

What does it realistically command in AEO?

• Share of AI Visibility (SAV): percentage of queries in which the brand appears in AIO/LLM. Today, the "new 1st page" on Google
• Conversation-assisted revenue: revenue share where the first touch is chat/LLM (attribution based on parameters and post-purchase surveys)
• Time-to-Answer (TTA) and Answer Quality Score: latency and completeness of responses (FAQ, price, availability, returns)
• Retention & AOV uplift supported by personalization - confirmed by mcKinsey meta-analyses
• Conversational GMV - growing pie ($290 billion), so even a small share equals material revenue

AEO architecture - from data to visibility

Product data layer

• Canonical feed Google XML + JSON Lines for LLM (keys: productTitle, brand, gtin, mpn, sku, description, price.value/currency, availability, imageUrl, url, shipping, returns, country, language)
• Normalization of units (ml/g/cm), variants, bundles, compatibility with schema.org/Product (JSON-LD)
• Freshness SLA: prices/states min. every 15-60 min. (LLM prefer up-to-date)

AEO-ready content layer

• Descriptions based on intention and use-cases (complete answers, not phrases)
• FAQ/Q&A per product/category (short answer + source)
• Polyglot: same entities (Brand, GTIN) and thesaurus phrases in EN/EN/DE/ES

Trust signals layer

• Reviews (number, freshness, rating), return policy, warranty, local states (LIA), transparency of delivery

Publishing layer to AI ecosystems

• Ingestion endpoints for ChatGPT/Gemini/Perplexity (feed pull / webhook push)
• Category mapping (Google Product Taxonomy) + custom ontology
• Safety & rights: robots/LLM-allow, source policy, UTMs for attribution

Conversational layer

• Purchasing assistant (chat/voice) with grounding in the feed (retrieval), access to prices/states and policies
• Handover to checkout or cart in 1 click (deep link, schema app links)

Observability

• Telemetry: SAV, CTR-to-site with AIO/LLM, % of responses with correct price/stock, change indexing time, inferred share of recommendations

Data from the Polish market

1. Example of a children's goods store - AEO implementation (via Semly) translated into increase in the number of product inquiries in AI models by 130% m/m and an increase in the conversion rate in this channel by 18%
2. Lighting store: the appearance of products in the "See products" section of Google Gemini gave +25% of the value of the average basket
3. Clothing stores: users using personalized AI recommendations spend 30% more time on site, which directly translates into higher conversion rates

How to measure AEO - definitions of KPIs

1. SAV @Top Answer = branded queries in the "main" response / all monitored queries
2. Coverage in AIO/LLM = #indexed SKUs in models / #SKUs in feed
3. Answer Accuracy (price/status) with sampling every N hours
4. Conv-assist rate = orders with first touch LLM - chat / all orders
5. mCAC(AI) = (tool cost + ops) / orders assigned to AEO
6. Uplift AOV/CR in "with chat" vs "without chat" cohorts

30-day implementation plan (battle-tested)

• Day 1-5: data audit (GTIN/MPN/brand completeness ≥ 95%), schema consistency, JSON-LD + JSONL preparation
• Day 6-10: "Answer packs" for top 100 queries (description, FAQ, parameters, 2-3 comparisons of alternatives)
• Day 11-15: publication to LLM (ChatGPT/Gemini) + attribution routing (parameters, post-purchase survey)
• Day 16-20: shopping assistant (chat) with grounding and policies (returns/delivery/pricing)
• Day 21-25: freshness tests (price/status), validation Answer Accuracy > 97%, tests (no SKU, no variant)
• Day 26-30: tuning prompts, organizing categories (taxonomy), dashboard KPIs (SAV, Conv-assist, AOV uplift)

Conversational Commerce and AI's contribution to sales

Global spending on conversational commerce (shopping "via chat," voice, AI) will exceed $290 billion in 2025. For Polish stores, the implementation of AEO by Semly already results in an average of 6% of traffic comes from AI chatbots (ChatGPT, Gemini), responsible for as much as 14% of the store's revenue.

Anti-patterns (most common mistakes)

❌ "Rewriting" SEO-content to AEO 1:1 (too slow, inadequate)

❌ None canonical source of truth (price/state) visible to LLM → price hallucinations

❌ FAQs written with marketing instead of concise answers (LLM prefers unambiguity)

❌ No SAV/Accuracy metrics → no feedback loop or optimization

Glossary (AEO 2025)

Basic

• AEO (AI Engine Optimization) - the process of preparing product data, content and trust signals and publishing them to LLM ecosystems and search engine generative layers to increase visibility and sales from AI responses
• AIO (AI Overviews) - google module that generates summarized AI responses to queries (former SGE/AI Snapshots)
• LLM (Large Language Model) - a large language model (e.g., ChatGPT, Gemini) that generates answers based on knowledge and external sources
• Conversational commerce - sales initiated or conducted via chat/voice/AI-assistant

Metrics and KPIs

• SAV (Share of AI Visibility) - share of queries in which the brand appears in the main AI response:
  SAV = (# of queries with visibility in AIO/LLM) / (number of monitored queries)
• AIO/LLM Coverage - catalog coverage in models:
  Coverage = (# of SKUs visible in AIO/LLM) / (# of SKUs in feed)

• Answer Quality Score (AQS) - percentage of AI responses consistent with price/status/policy in control samples:
  AQS = (# of correct answers) / (# of verified answers)

• TTA (Time-to-Answer) - time from inquiry to complete response/handover to cart
• Conv-assist rate (CAR) - share of orders in which the first touch was LLM/chat:
  CAR = (# of orders with 1st LLM touch) / (all orders)

• mCAC(AI) - the marginal cost of acquisition through the AEO channel:
  mCAC(AI) = (cost of tools + ops + AEO content) / (# of orders assigned to AEO)

• Conversational GMV - the value of sales from sessions with conversational interaction
• AOV (Average Order Value) - average order value
• CR (Conversion Rate) - conversion rate
• CAC (Customer Acquisition Cost) - customer acquisition cost
• ROAS/ROMI - return on advertising expenses / marketing investment
• OSR (Organic Search Revenue) - revenue attributed to organic traffic from search engine

Data and formats

• Google XML Product Feed - standard bid file for Google Merchant (expandable under AEO)
• JSON-LD (schema.org/Product) - structured data in a page (Product, Offer, AggregateRating, FAQPage)
• JSONL (JSON Lines) - linear record format (1 product = 1 line) useful for LLM supply
• GTIN/EAN, MPN, SKU global commodity identifier / manufacturer part number / store stock identifier
• Freshness SLA - guaranteed price/status refresh rate (e.g., ≤60 min)
• Answer Packs - short, unambiguous answer blocks (description + 2-3 parameters + source) that LLM can quote 1:1
• Product Knowledge Graph - entity graph (products-brands-parameters-categories) combining identifiers and attributes

AI implementation

• RAG (Retrieval-Augmented Generation) - generation with attached search/retrieval from authoritative store data
• Grounding - "Ground" AI responses in current, trusted sources (feeds, policies, states)
• Embedding - vector representation of text/data used for quick matching of content and products
• Ingestion endpoint - feed pickup/download point through AI ecosystems (pull API, webhook push)
• Taxonomy/Ontology - dictionary of categories and relationships (e.g. Google Product Taxonomy + custom extensions)
• LIA (Local Inventory Ads) - local availability signals (state in stationary store) useful also for AEOs

Attribution and analytics

• UTM - link parameters for source/medium/campaign tracking (e.g. utm_source=chatgpt)
• Post-purchase survey - a short post-purchase survey to validate the impact of AEO ("Where did you first hear about the product?")
• Deep link - link directing directly to the basket/specific variant with attribution parameters
• Handover - controlled transfer from AI assistant to checkout path (e.g., shopping cart with pre-filled SKU)
• Zero-click - the situation in which the user gets the answer without going to the page (especially in AIO/LLM)
• LLM-allow/robots - access policy (robots.txt / meta) clearly allowing selected agents/LLMs to retrieve data
• Observability - a set of logs, indicators and synthetic tests to monitor the visibility, freshness and relevance of AI responses

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