Agentic Landmark · Methodology
The Agent Readiness Index
A five-dimension scoring instrument that quantifies a brand's revenue exposure to agentic commerce and locates where revenue is leaking first. The composite runs 0 to 100; higher means more exposed. The instrument is published here because a practice arguing for machine-readable infrastructure should not run an opaque one.
Traffic Dependency
How reliant the business is on organic browsing, SEO-driven discovery, and affiliate traffic. Heavy organic search dependency means immediate exposure, because agents compress click-through behavior: they read, evaluate, and answer without delivering the visit the channel was built to monetize.
Structured Comparability
Whether an agent can meaningfully compare the brand's offering against competitors in a structured evaluation. High comparability means higher disruption risk: clear feature matrices, standardized pricing tiers, and transparent eligibility criteria are exactly the raw material of the comparison table. Agents do not browse; they compare.
Agent Substitutability
Whether an agent could reasonably shortlist or select in this category without meaningful human involvement. Categories where decisions follow clear rules and the purchase is self-serve automate fastest; categories with heavy emotional differentiation or complex human judgment automate last.
Brand Moat Strength
The brand equity that makes consumers resistant to agent-substituted alternatives, scored inversely: a strong moat lowers exposure. The diagnostic question is not whether the brand is well known, but whether the moat is built on genuine product advantage or on behavioral friction. An agent does not have a home screen; it queries every option on every transaction, and friction-based loyalty does not survive that.
Structured Data Maturity
How ready the organization's data and infrastructure are for machine evaluation: API exposure, pricing clarity, schema completeness, machine-readable documentation. Low maturity means agents cannot accurately assess the brand even when they try, producing misrepresentation or omission rather than competitive selection. Immature data does not lose the comparison; it never enters it.
ARI = (TD × 0.25) + (SC × 0.20) + (AS × 0.20) + ((100 − BM) × 0.15) + (SDM × 0.20)
All inputs score 0–100. Brand Moat is inverse: a BM score of 70 contributes (100 − 70) × 0.15 = 4.5 points, not 10.5. A strong moat lowers the composite, because brand strength is the one asset that survives algorithmic intermediation.
The composite places a brand in one of four bands
Low
Structural protections insulate the brand from near-term disruption: a strong moat, low traffic dependency, or low substitutability. Focus: GEO content architecture and schema markup. Monitor and prepare.
Emerging
Agentic displacement is beginning to reach adjacent competitors. The window for proactive investment is open but narrowing. Focus: the structured data layer and order-management API readiness.
Significant
Category dynamics are already shifting and competitive gaps are forming. Delay compounds the cost of remediation. Focus: protocol integration and retrieval infrastructure.
Structural
Revenue impact is measurable or imminent on a 12–24 month horizon. Focus: the full Accelerator program, all tracks simultaneously.
The published layer is not the whole instrument. The working Assessment applies a category exposure overlay that calibrates the dimensions to how agents actually reach your category, supplementary gates for platform-catalog, brand-owned-agent, and white-label exposure, and vertical sub-dimensions where the category demands them. The math above tells you what the score means. The Assessment is what produces yours.
External validation
Boston Consulting Group's 2026 scenario analysis of agentic retail mapped four possible futures and found two imperatives constant across all of them: discoverability, the ability to be found by agents regardless of which system they operate inside, and desirability, brand strength that survives algorithmic intermediation. Those two imperatives map directly onto the instrument: discoverability is what Traffic Dependency and Structured Data Maturity measure, and desirability is what the inverse-scored Brand Moat dimension captures. The ARI was built before that analysis published; the alignment is convergence, not citation.