KPI 01 of 07 · Discovery Phase · Merchant-side · Higher is better
Assistant Inclusion Rate
The percentage of AI assistant queries where your brand appears in the recommendation set
Abbreviation: AIRAIR is the foundational metric of agentic commerce visibility. In a world where AI assistants increasingly mediate product discovery, the question is no longer whether your website ranks on page one of Google - it is whether your brand appears at all when an autonomous agent is asked about your category. AIR operationalises this question as a measurable, trackable, improvable number.
The metric draws directly from the Signal Clarity pillar of the Four Pillars of AXD Readiness. Signal Clarity asks whether your business emits structured, machine-readable signals that AI agents can interpret. AIR measures the outcome of that signal emission - whether agents are actually receiving and acting on those signals by including your brand in their recommendations.
AIR is not a single number. It varies significantly across AI surfaces. A brand may score 40% on ChatGPT but 5% on Perplexity. This variation is itself diagnostic - it reveals which surfaces your structured data strategy is reaching and which remain blind to your existence. Reporting AIR as a single aggregate obscures the actionable intelligence that surface-level measurement provides.
Unlike traditional search engine optimisation, where ranking is determined by algorithmic signals that are at least partially documented, agent inclusion is determined by training data, retrieval augmentation, and real-time structured data access. The levers are different. The measurement methodology is different. And the consequences of invisibility are more severe - because an agent that does not include you in its recommendation set does not present you at position eleven. It presents you nowhere.
Where AIR sits in the agentic commerce architecture
AIR sits at the intersection of structured data strategy and agentic commerce protocol adoption. As protocols like Google's Universal Commerce Protocol (UCP) and OpenAI's Agent Commerce Protocol (ACP) mature, they create standardised pathways for agents to discover and recommend products. Businesses that implement these protocols early will see their AIR rise as agent surfaces increasingly rely on protocol-based discovery rather than web scraping.
The protocol layer is not the only driver of AIR. Entity authority in knowledge graphs, comprehensive Schema.org markup, product data coverage, and content freshness all contribute. But protocol adoption creates a structural advantage - it moves your business from passive discoverability (hoping agents find your data) to active participation (publishing your data through channels agents are designed to consume).
Numerator
Agent queries including your brand/product as a recommendation
Denominator
Total relevant agent queries monitored
× 100 = AIR %
Minimum 100 queries per measurement period across at least three AI surfaces (ChatGPT, Perplexity, Gemini, Copilot, etc.).
Measurement protocol
Select 100 or more representative queries across your product categories. These should reflect the natural language patterns that consumers use when asking AI assistants for recommendations - not keyword-optimised search queries, but conversational requests. Run them against a minimum of three AI assistant surfaces at regular intervals (weekly or fortnightly). Record whether your brand appears in the recommendation set for each query.
AIR is not a single number - it varies significantly across AI surfaces. Report per-surface and aggregate. Track longitudinally to identify trends. A rising AIR on one surface with a falling AIR on another reveals where your structured data strategy is working and where it is failing.
Consider automating the measurement process. Manual query testing is appropriate for establishing baseline methodology, but sustained measurement requires programmatic access to AI surfaces or third-party monitoring tools. The goal is consistent, comparable data over time - not a one-off snapshot.
Four levels of agentic visibility
<5%
Invisible to the agentic layer. Agents do not include your brand in recommendations. Structured data, entity authority, and content freshness all require immediate attention.
5-25%
Intermittent visibility. Appearing in some queries but not consistently. Likely present in one AI surface but absent from others. Schema and content gaps are the probable cause.
25-60%
Consistent presence across multiple AI surfaces. Structured data is comprehensive and regularly updated. Entity authority is established in your primary categories.
>60%
Dominant agentic visibility. Your brand is a default recommendation in your category. Structured data is comprehensive, fresh, and semantically rich. Entity graphs are well-connected.
What moves AIR up, down, and sideways
Complete JSON-LD product markup, product data coverage >90%, established entity authority in knowledge graphs, regular content publication with structured data, multi-surface optimisation strategy.
AIR varies significantly across AI surfaces. A brand may score 40% on ChatGPT but 5% on Perplexity. Always measure per-surface and investigate the delta - it reveals which surfaces your structured data strategy is reaching.
Inconsistent entity naming across platforms, missing or incomplete Schema.org markup, absence from major product directories and knowledge bases, stale content with outdated product information.
Why AIR matters commercially
AIR is the leading indicator of agentic commerce readiness. A business with high AIR is visible to the agents that will increasingly mediate consumer purchasing decisions. A business with low AIR is invisible to the agentic layer. The commercial value is direct: as agent-mediated commerce grows, businesses not included in agent recommendation sets will lose market share to those that are.
The economics of agent-mediated discovery differ fundamentally from search engine discovery. In traditional search, a business that ranks on page two still receives some traffic. In agent-mediated discovery, a business that is not included in the recommendation set receives zero traffic from that surface. The distribution is binary rather than graduated - you are either in the recommendation set or you are not.
Investing in AIR now - before agent-mediated commerce reaches mainstream adoption - creates a structural advantage. Businesses that establish entity authority, comprehensive structured data, and protocol integration early will have higher AIR when agent commerce scales. Those that wait will face the significantly harder challenge of building visibility in a mature, competitive agentic landscape.