Agentic Commerce Readiness Checker
Most eCommerce AI projects fail before a model is involved. This scores the five things that actually decide it — and tells you honestly if the answer is fix your data first.
- 15 questions
- Score by dimension
- Which agent to build first
Question
Your Agentic Commerce Readiness Score
Scored across the five dimensions that predict whether an agent ships or stalls
Score by Dimension
Get Your Agent Sequencing Plan
Which agent to build first, what to fix before it, and the blockers we would name in a readiness engagement.
Roadmap on its way!
We will review your dimension scores and send a sequenced plan — including the honest answer if the data layer comes first.
Your Sequencing Plan
Want the ranked version with your systems named?
A fixed-scope readiness engagement ends with a ranked list of candidate workflows and the specific blockers in front of each — not a slide deck.
Book an Agent Readiness Call →Who This Tool Is For
Heads of eCommerce, digital directors and retail technology leaders being asked to do something with AI agents, who want an honest read on whether their store can support one before committing budget. Also useful for engineering leads who suspect the answer is fix the data first and need something to show.
Why We Built This Tool
We published a 64-part field guide on building eCommerce agents on AWS, and the same finding kept surfacing: most projects fail before a model is involved. Orders, inventory, products and vendors disagree about what a SKU is, so the agent produces confident, wrong answers and someone acts on them. This checker front-loads that question. It scores five dimensions — data foundation, catalog legibility, systems access, governance and approval, and operations readiness — and it will tell you to stop and fix the data layer if that is the real answer.
What Problem It Solves
- Board pressure with no scope. Do something with AI is not a project. A dimension score turns it into a specific, defensible next step.
- Picking the impressive agent. Margin agents have the highest ceiling and the lowest readiness. This ranks by data readiness and blast radius instead of by how it sounds in a steering meeting.
- The inbound half is invisible. Catalog legibility scores whether shopping agents in ChatGPT and Gemini can actually parse your products — a question most merchants have not asked.
- Approval paths get designed last. If nobody owns escalation, the agent has no safe failure mode. Better to find that now than in production.
See eCommerce AI Agents on AWS, or read the 64-part field guide behind this scoring.
Frequently Asked Questions
Why does data foundation weigh so heavily?
Because it is the constraint that actually decides the outcome, and it is the one teams skip. An agent asked what to reorder needs orders, inventory, products and vendors to join on a shared identifier. When those systems disagree, the agent produces answers that are fluent, confident and wrong — and because they are fluent, someone acts on them before anyone notices. Fixing join keys is unglamorous and it is usually the whole project.
What if we score low? Is that a sales pitch to hire you?
A low score frequently means the honest first engagement is a data-layer project rather than an AI one, and sometimes it means do nothing yet. We would rather tell you that in week one than in month four. The scoring method and the dimensions are published in full in our field guide, so you can run the exercise internally and act on it without talking to us.
What does the catalog legibility dimension measure?
Whether an AI shopping agent can parse your products. Agents compare on attributes, not adjectives — a product whose weight lives in a prose description rather than a structured field is, to that agent, a product with no weight, and it is excluded from the comparison before quality is assessed. This dimension scores attribute completeness, variant structure, and whether specifications are trapped in marketing copy.
Does a high score mean we should build an agent?
It means you can. Whether you should still depends on whether there is a workflow with real repeated volume and a named owner. A store that is technically ready but has no high-volume repetitive decision does not need an agent — it needs a workflow, which is cheaper, faster and easier to audit.
How is this different from the GenAI Readiness Assessment?
That one scores organisational readiness for generative AI broadly — data maturity, team skills, infrastructure, governance. This one is commerce-specific and asks about join keys between order and inventory systems, catalog attribute structure, WISMO volume, and peak-season discipline. If you are an eCommerce business, start here; if you are evaluating AI adoption across a company, start there.