AI for checkout conversion

The short answer

Checkout abandonment is usually not a design problem. It is an unanswered question at the moment of commitment: will this arrive in time, can I pay this way, what happens if it is wrong, is this the right size or plan for me.

AI moves the number when it answers that specific question in that specific moment, with real data. It does not move the number by adding a chat bubble to a page that already fails to answer it. We built this into the purchase flow at Carrefour and online sales rose 35%.

Where the decision actually sits

Find the step where intent is highest and completion drops hardest. That is a measurable place, not an opinion, and it is usually narrower than the team assumes: one shipping estimate, one payment rejection, one product variant.

Instrument before you build. If you cannot see the drop per step, per segment and per device, you are guessing at which moment to intervene, which is the most common way this work produces nothing. The framing is in how to prioritize AI use cases.

What breaks today

The baseline is almost always static: a generic delivery promise, a payment error with a code instead of a reason, a size chart nobody reads, a support link that opens a form and a two day wait.

None of those are unreasonable individually. Together they hand the buyer a reason to leave and no reason to stay, at the exact moment they were ready to pay.

What the AI actually does

Concretely, at that step: it resolves the question against live systems. Real stock and real delivery windows for that postcode. The actual reason a card failed and the specific alternative that will work. The right variant based on what this buyer already owns and what similar buyers returned.

The value is not conversational fluency. It is a correct, specific answer in under a second, drawn from systems that already hold the answer and were never exposed at the point of decision.

The data it needs

Inventory and fulfilment at the granularity of the promise you are making. Payment failure reasons, not just a boolean. Order history, returns and the reasons behind them. And permissions, because this is a logged in buyer with a real account.

Reachability is the constraint that kills this project when it is missed. A nightly stock export cannot support a real time delivery promise, and discovering that in month three is expensive. Check it first, per data readiness.

Guardrails at the point of payment

This is money and it is a promise. The system must never invent a delivery date, a price, a discount or a stock level. Anything quoted to the buyer comes from the system of record or is not said at all.

That is a retrieval and grounding requirement, not a prompt instruction, and it needs the deterministic layer described in guardrails. A hallucinated delivery date is a support ticket, a refund and a customer who does not come back.

Latency is part of the product here

A buyer with a card in hand will not wait four seconds for an answer. If the response cannot arrive inside the moment of hesitation, it does not matter how good it is.

Set the budget from the flow and design inside it, streaming the first useful sentence rather than a preamble. The mechanics are in latency.

How you measure it

Completion rate at the specific step, for the segment that saw the intervention, against a holdout that did not. Not total site conversion, which moves for a dozen unrelated reasons and will not attribute anything.

Then watch the downstream numbers, because a system that closes more orders and raises returns or chargebacks has moved a metric without creating value. That distinction is the whole argument of measuring AI ROI.

How to sequence it

One question, one step, one segment, behind a flag, with a holdout from day one. Prove it moves the number, then widen.

The failure mode is building an assistant for the entire funnel and being unable to say what it did. Narrow scope is what makes the result legible, and it is how we build production AI, end to end.