As of 13 August 2026, AI can only partly improve your ecommerce checkout flow.

What to actually do

1. Hand it to a person

   The route this page recommends

   A person who owns the outcome does this end to end, worth it when the failure is dear.

2. Use a tool built for this

   Second choice

3. Do it yourself

   The distant third

   A chat interface, chat-fluent skill, and roughly 1 hour until you can act on the result.

   How to actually do it

   1. Open your analytics or ecommerce platform and export the checkout funnel, including abandonment by step, device and traffic source.
   2. Gather the current checkout screens, field labels, validation messages, delivery information, payment options, support tickets and relevant user research, removing unnecessary personal data.
   3. Paste the business context and evidence into the prompt, and add technical, accessibility, privacy, payment and brand constraints that the proposed flow must respect.
   4. Ask the model to separate evidence from hypotheses and to rank proposed changes by likely user impact, implementation effort and risk.
   5. Open Relume or your existing design tool and turn the selected flow and wireframe description into screens for the current checkout and the proposed alternative.
   6. Compare every drafted label, error message, delivery statement, price and payment claim against the live checkout and current business rules before sharing the prototype.
   7. Ask a colleague from product, engineering, customer support or trading to challenge the assumptions, then run usability checks and an experiment before publishing a material change.
   8. After release, compare the agreed primary and guardrail metrics by device and traffic source, and revert or revise the change if the evidence does not support it.

   Prompt

   Act as a senior ecommerce UX researcher and product strategist. Review the checkout flow described below and identify improvements that are supported by the evidence, not by generic best practice.
   
   Business and checkout context:
   - Business: [business type]
   - Products and customers: [brief description]
   - Checkout platform and payment methods: [details]
   - Current checkout steps: [step-by-step description]
   - Devices and markets: [details]
   - Business constraints: [technical, legal, operational or brand constraints]
   
   Evidence:
   - Funnel data by checkout step: [paste data]
   - Device, browser and traffic-source breakdown: [paste data]
   - Customer research, support tickets or session observations: [paste evidence]
   - Existing experiments and their results: [paste evidence]
   
   Produce:
   1. A concise diagnosis of the biggest likely sources of abandonment, separating evidence from hypotheses.
   2. A prioritised list of improvements, with the affected step, the user problem, the proposed change, expected mechanism, effort, risk and evidence needed.
   3. Alternative versions of any important field labels, error messages or reassurance copy. Do not invent policies, delivery promises, prices or payment options.
   4. A revised checkout flow in plain text, including error and recovery states.
   5. A low-fidelity wireframe description that a designer or prototyping tool can use.
   6. A measurement plan for each proposed change, including the primary metric, guardrail metrics, segment to monitor and what result would justify keeping or rejecting it.
   7. Five questions that must be answered before implementation.
   
   Do not claim that a change will improve conversion without an experiment or reliable supporting evidence. Flag anything that needs an accessibility, privacy, payment-security, legal or engineering review. Keep the recommendations specific to the supplied evidence and say "insufficient evidence" where the inputs do not support a conclusion.

   Open it prefilled in ChatGPT or Claude, or copy it into Gemini, which takes no prefill link.