Agentic Readiness Index (ARI) — Methodology
Measuring tourism operators' preparedness for the AI agent world
Why the ARI exists
AI agents are changing how travelers discover and book. ChatGPT, Perplexity, Google AIO — these systems do not "search" like a human. They read, interpret and recommend based on precise technical signals.
A tourism operator can be 100% independent from OTAs and remain structurally invisible to these new algorithmic intermediaries.
That is the double threat that TPDI and ARI capture together:
- TPDI: dependence on human intermediaries (OTAs, platforms)
- ARI: preparedness for algorithmic intermediaries (AI agents)
Calculation scope
The ARI is calculated on direct operators identified in each market analysis — actors classified as local_strict, local_extended, international_chain or local_operator with an accessible public URL.
Only direct operators are evaluated. Platforms (Viator, Booking.com, etc.) are excluded. The ARI measures the structural and transactional preparedness of operators who choose autonomy.
ARI v2 — Criteria and weighting
ARI v2 measures digital executability: understandable, bookable, informative, current, fast. Five blocks, 100 points.
| Block | Points | What is measured |
|---|---|---|
| Machine-readable structure | 25 | HTTPS, mobile-friendly, Schema.org, transactional schema, sitemap |
| Transactional executability | 25 | Booking engine, Book button, calendar, date params, direct booking |
| Informational completeness | 25 | Price, duration, cancellation policy, languages, location |
| Observable freshness | 15 | Sitemap lastmod, dateModified, dynamic content |
| Technical performance | 10 | PageSpeed > 80, server response time |
| Total | 100 |
Why these five blocks
ARI v2 no longer measures only technical detection. It measures execution: can an AI agent understand the page, book, get essential information, and do so on current and fast data?
Structure (25 pts)
An agent must be able to understand the page: HTTPS, mobile, Schema.org, transactional schema (Offer, price, availability), sitemap. Without these signals, the page remains opaque.
Execution (25 pts)
An agent must be able to book: detected engine, Book button, calendar, date params, direct booking confirmation (non-OTA).
Completeness (25 pts)
Essential information must be present: price, duration, cancellation policy, languages, precise location.
Freshness (15 pts)
Data must appear current: sitemap lastmod < 6 months, dateModified, dynamic content index.
Performance (10 pts)
Speed and stability: PageSpeed > 80, server response time. A slow page is partially read or ignored.
Score interpretation
A high ARI score does not guarantee being recommended by an AI agent. It guarantees not being excluded for technical reasons.
| Score | Level | Meaning |
|---|---|---|
| 0–39 | Low | Operator barely or not detectable by AI agents |
| 40–69 | Medium | Partially executable — structural and freshness gaps detected |
| 70–89 | Good | Well prepared for current agentic indexing |
| 90–100 | Excellent | Optimal infrastructure for the AI agent world |
What ARI does not measure
ARI v2 measures structural and transactional executability. It does not measure:
Semantic content quality
An AI agent needs clear descriptions in natural language. ARI detects the presence of price, duration, languages — not the relevance or richness of content.
External authority
AI agents train on the entire web. An operator cited in articles, reviews, travel blogs has a stronger agentic presence than one present only on their own site. ARI does not measure this dimension.
Technical infrastructure
ARI scores are calculated automatically and cached for 45 days per URL. Enrichment runs every 4 hours. Scores are displayed on public pages of each analyzed market and in the Pro Dashboard.
Our reference covers 60+ systems; automatic detection currently identifies 18+.
Acknowledged limitations
Like the TPDI, the ARI is a snapshot at a specific date. An operator can improve their score between two analyses. Scores evolve with the cache.
Automatic detection has an estimated margin of error of 2-5% for complex cases: multi-language sites, unreferenced booking engines, non-standard JavaScript architectures.
ARI v2 — context
ARI v2 measures digital executability: structure, transaction, completeness, freshness, performance. Methodology updated — March 2026.
ARI is published simultaneously with the TPDI: measuring platform dependence without measuring alternative preparedness would be an incomplete analysis.
That is precisely what the report "The Double Threat — State of Platform Dependence 2026" will document across 1,200 analyzed cities.
Reporting and improvement
Score anomaly, undetected booking engine, misrated site: [email protected]
Any correction is documented and applied in the next enrichment cycle.