Ecommerce AI RFP Template: Questions, Scorecard and Proof Requirements

Imagine a shopper asks to return a jacket. The assistant finds the order, explains the policy, and replies, “Your return is all set.”

Before anyone marks the demonstration as a success, open the returns platform. Is there a return record? Does it contain the right item? Was the shopper authorized to make the request? If the connection timed out, did the assistant check what happened?

Those details belong in the buying process. They determine what your team will have to fix after launch.

An ecommerce AI RFP template is a structured request for proposal that defines the customer tasks an AI vendor must support, the systems it must connect to, and the evidence required for selection. A useful RFP includes a project brief, vendor questions, a weighted scorecard, commercial assumptions, and measurable pilot acceptance criteria.

The template below gives ecommerce, customer experience, and IT teams a common way to evaluate proposals. It covers chatbots, shopping assistants, and agents that take actions. Its questions, weights, and test scenarios are proposed evaluation tools. Adapt them to your business before sending them to vendors.

Start with the customer tasks you need completed

“Improve customer experience with AI” leaves too much open to interpretation.

Be specific about the business need. Help shoppers find a compatible replacement filter. Explain which promotion applies to their basket. Create an eligible return. Transfer a damaged-order complaint to the right person with the order details attached.

For an AI shopping assistant RFP, the requirements should follow the shopping journey: understanding the request, supporting product discovery, selecting the correct variant, and checking purchase conditions. Alhena’s guide to AI shopping assistants provides the broader context for that scope.

A support deployment needs its own success conditions, including policy accuracy, identity checks, completed actions, and human takeover. If you need both shopping and support, evaluate each workflow separately. A strong product recommendation does not establish that an order cancellation will work.

Bring the relevant stakeholders into a short requirements gathering session before drafting the RFP. Ask ecommerce, support, IT, information security, and finance to identify the customer problems they need solved. Give each use case an owner and agree on the success metrics that will determine whether it works.

If you are still exploring what an AI platform should do, start with a request for information, or RFI. Use the RFP process once you can define the scope and compare concrete proposals. This gives the selection process a clearer structure and helps avoid evaluating a different sales pitch from every supplier.

Copy this project brief into your RFP

Use this opening statement:

[Company] is evaluating an ecommerce AI solution for [priority workflows] across [sales channels, stores, markets, and languages]. The initial deployment must improve [business outcome] from [baseline], while meeting the operational and security requirements below. Vendors must identify what is available today, disclose implementation dependencies, and demonstrate the agreed workflows using approved test data.

Then complete the following fields.

Include representative catalog records and approved policy examples. If essential attributes are missing, document who will supply them. The evaluation should expose data preparation work early enough to include it in the plan and price.

Map the ecommerce platform and connected systems

Ask for a simple architecture diagram showing how the assistant connects to your tech stack. Label the system that owns each fact and the system permitted to change it. The following map is a starting point; confirm the authoritative source in your own environment.

Name the exact ecommerce platform and edition, such as Shopify Plus, Adobe Commerce, Magento Open Source, or Salesforce Commerce Cloud. These are examples to include in your brief, not a statement that every vendor supports every configuration.

For a headless storefront, identify where the interface runs and how authentication, sessions, and API access work. Record any planned platform migration so the vendor can price the work against the architecture you intend to operate.

Require a consistent vendor response

Give every requirement an ID and identify whether it is a mandatory pass condition, a scored item, or an optional capability. If a requirement serves both as a gate and a scored item, mark both roles in advance. Split compound requirements into individual items before scoring them.

Ask vendors to use this response format:

Requirement ID: [ID]
Availability: [Available now / configuration required / custom development / roadmap / unsupported]
How it works: [Description and dependencies]
Evidence: [Document, test, or demonstration reference]
Limitations: [Known gaps and unsupported conditions]
Buyer work: [People, data, access, and estimated effort required]
Cost: [One-time and recurring charges]
Delivery: [Date and owner for anything not currently available]

Request a short executive summary alongside the completed responses. It should identify the proposed scope, the largest dependencies, and any business requirement the vendor cannot meet. Use the same structure for all submissions.

The buyer owns the final acceptance test. Vendors can suggest improvements, but everyone must be evaluated against the same requirement.

For example, “supports returns” should become: “For an authenticated customer with an eligible item, create one return request in [system], then report the confirmed status and reference number.”

That statement tells the evaluator what to inspect.

12 questions to include in your ecommerce AI RFP

Q1. Can the assistant recommend the correct product and variant?

Ask the vendor to work with several requirements at once: compatibility, dimensions, budget, material, availability, or another attribute that matters to your customers.

For a home improvement store, “a filter for model X200” is a different task from “a popular air filter.” Include a product with a similar name that does not fit. Then ask a follow-up question to see whether the original compatibility requirement survives.

Require proof: The shopper’s requirements, the catalog evidence, and the exact products and variants returned, including the selected SKU. Include cases with missing attributes and no suitable match. The assistant should clarify uncertainty or explain the limitation.

Ask how merchandising rules influence the results. A promoted product still needs to satisfy the shopper’s hard requirements. For explainability, request the product facts behind each choice and the reason an alternative was excluded. For on-site AI search and discovery, check that the result grid and conversation agree.

Alhena’s guide to measuring product recommendation accuracy explains how to evaluate individual recommendations and the complete shortlist. Apply those checks to the vendor’s demonstration.

Q2. How does it verify inventory, pricing, and promotions?

Ask which data is retrieved when needed, which is cached, and how updates reach the assistant. Require an answer for each data type, rather than one general claim about being “real time".

Change a test variant’s availability during a conversation. Change the basket so that an offer no longer qualifies. If you use dynamic pricing, test whether the assistant refreshes prices at the agreed decision points and respects the declared freshness limit. Check a different market or currency.

Require proof: Timestamped source changes and subsequent answers, plus the agreed freshness limit for each field. Confirm what happens when current information cannot be retrieved. The ecommerce platform or other authoritative commerce service should validate the final price and promotion eligibility at checkout.

For a fuller test design, use Alhena’s explanation of live inventory, pricing, and promotion data.

Q3. Which actions can it perform, and where are permissions enforced?

Request an operation-level integration list. Reading an order, editing an address, creating a return, and issuing a refund are separate requirements, even when they use the same platform.

For each operation, record the identity check, allowed scope, required confirmation, human approval rule, and destination system.

Document the boundary between the assistant, the ecommerce platform, and the payment gateway. If subscription changes affect recurring payments, test the required approval and resulting billing state. Keep fraud detection and payment authorization responsibilities explicit; an AI conversation should not bypass those controls.

Require proof: One permitted action completed correctly and one prohibited action rejected. Inspect the resulting system records and permission checks.

OWASP’s guidance on excessive agency recommends limiting tool permissions and enforcing authorization in application or downstream controls. Ask the vendor to show where those controls apply in your proposed deployment.

For the payment-related scope, have the responsible team establish the applicable PCI DSS obligations. The PCI Security Standards Council explains that outsourcing payment processing does not remove the merchant’s responsibilities. Require a clear allocation of those responsibilities for the proposed service.

Q4. What happens when an action fails halfway through?

Include a timeout after the destination system accepts a request but before the assistant receives confirmation. Ask how the system checks the result, handles retries, and prevents duplicate actions.

Also test an unavailable integration and a request that remains pending.

Require proof: The conversation, action log, and destination record. The assistant should communicate the confirmed state, distinguish pending work from completed work, and escalate uncertainty where required.

For a return workflow, agree on the exact endpoint. Creating a return authorization, generating a label, and completing a refund are different events. The acceptance test should name the event the customer was promised.

Q5. Who owns the conversation when a human takes over?

Ask what happens when an agent intervenes while the AI is preparing a reply. Check how existing helpdesk rules, automated acknowledgements, and other AI responders interact with the new system.

Define when escalation is mandatory and what happens outside staffed hours.

If the scope includes abandoned cart emails or other automated follow-ups, assign ownership across the assistant and existing marketing tools. Check that a single customer event does not trigger competing messages across sales channels.

Require proof: A takeover that reaches the correct queue with the transcript, relevant customer details, and actions already attempted. Verify that competing substantive replies stop and that the shopper receives an accurate expectation about the next response.

Alhena’s guide to reply ownership, routing, and rollback in Gorgias provides a useful operational example. Test equivalent ownership rules in whichever helpdesk you use.

Q6. How are customer data and account boundaries protected?

Request a data-flow diagram covering the vendor, model providers, and other subprocessors. Ask about storage locations, retention, deletion, encryption, administrator access, and audit logs. Clarify how proprietary data is used and whether customer conversations or business records are used for model training.

Have your data privacy and information security leads map applicable obligations, including GDPR and CCPA, to the proposed data use, contractual roles, and operating controls. Request the agreements and evidence needed to assess compliance in your specific deployment.

If personalization or memory is included, ask what is stored, how it is linked to an identity, and how it can be corrected or removed. Include guest sessions and shared-device scenarios.

Require proof: Current security documentation and applicable contractual commitments, plus tests using separate synthetic accounts. A customer supplying someone else’s order reference must not gain access to protected information.

Where your procurement policy requires a SOC 2 report or ISO 27001 certificate, inspect its scope, date, and relevant exceptions. Assess the proposed deployment as well as the vendor’s documentation.

Q7. Can untrusted content change the assistant’s behavior?

OWASP’s prompt injection guidance describes how instructions can arrive through retrieved content and tool outputs as well as direct user messages. Your test plan should cover those routes.

Require proof: In an approved test environment, place a harmless conflicting instruction inside a synthetic product description or support ticket. Confirm that it cannot override store policy, expand permissions, disclose protected data, or trigger an unauthorized action.

Ask how these tests are maintained as integrations and models change. Passing a small test set establishes what happened under those conditions; it does not establish universal resistance to prompt injection.

Q8. Can customers use it reliably at peak demand?

Define a realistic load profile to test scalability and stability, including concurrent conversations and slow dependencies. Ask for time to a useful answer, time to a completed action, and timeout rates. Request median and 95th-percentile latency, with an explanation of which requests are included.

Request a service-level agreement (SLA) that defines uptime, measurement boundaries, exclusions, incident severity, response commitments, and remedies. Keep infrastructure availability separate from latency and successful task completion. The customer experiences all three.

Specify the languages, devices, and accessibility target your business requires. W3C’s WCAG 2.2 provides testable criteria for areas such as keyboard operation, visible focus, and status messages available to assistive technology.

Require proof: Results for the proposed configuration, supported by keyboard and screen-reader checks of the complete customer journey. Include opening and closing the assistant, navigating product cards, and recovering from an error. A few checks supplement, rather than replace, a full accessibility assessment.

Q9. What will implementation require from our team?

Ask vendors to identify the work behind their launch estimate: catalog cleanup, policy approval, integration configuration, authentication, helpdesk routing, analytics, testing, and staff training.

Separate standard configuration from customization and custom development. Ask who maintains connections when an API or business process changes. Include a development environment for testing and any data migration needed for an existing assistant’s approved knowledge or settings.

Require proof: An implementation timeline with named roles, dependencies, estimated buyer effort, and acceptance milestones. Have an operator from your team perform a routine task, such as correcting a policy source or investigating a failed conversation. If the ecommerce platform is also changing, identify which milestones depend on that work.

The useful launch estimate is the one your team can staff.

Q10. How are changes, incidents, and rollback handled?

Ask who can publish a policy change, alter permissions, change a model, or enable a new workflow. Request the approval process, version history, notification policy, and retesting requirements. Make governance practical by assigning responsibility for change approvals, security management, and pausing affected workflows.

Require proof: An operator changes an approved setting, checks its effect, and restores the previous version. Then pauses an affected workflow and shows what happens to conversations already in progress.

NIST’s AI Risk Management Framework recommends documented evaluations under conditions similar to deployment, production monitoring, and plans for incident response and change management. Translate those principles into named responsibilities and observable controls in your RFP.

Q11. How will we verify resolution and revenue claims?

Ask vendors to define every proposed KPI, including its numerator, denominator, exclusions, and measurement window. Resolve what “deflected,” “resolved,” “AI-assisted,” and “completed” mean before the pilot starts.

Assign ownership of analytics events and validation. If your ecommerce website uses Google Tag Manager, specify who maintains the relevant tags and checks for missing or duplicate events. Verify completed actions against backend records as well as browser events.

Require proof: Reconcile a sample of reported outcomes with the corresponding tickets, orders, or action records. Include abandoned conversations, repeat contacts, refunds, and escalations where relevant.

For revenue claims, ask which visitors were compared and how they entered each group. Comparing shoppers who chose to use the assistant with everyone else can reflect differences that existed before the conversation.

The measurement section below provides definitions you can adapt.

Q12. What is the total cost of ownership, and how can we leave?

Give every vendor the same low, expected, and peak usage scenarios. Ask for itemized estimates under each one, including implementation, integrations, usage, support, and ongoing buyer effort.

Ask whether the pricing model charges for conversations, resolutions, monthly active users, gross merchandise value (GMV), or a combination. Define the unit precisely: a store visitor, an assistant user, and a completed transaction are different measures.

Require proof: A sample invoice calculation and precise billing definitions. Establish how the vendor charges for retries, spam, unresolved requests, human transfers, and conversations that reopen.

Calculate total cost of ownership (TCO) over the same period and workload assumptions for every proposal. Request written commercial terms for termination and price changes. Specify which records and configurations you need to retain, their formats, and any associated fees.

Your AI chatbot vendor evaluation should make the operating cost understandable before the first invoice arrives.

Add B2B ecommerce tests for account-specific buying rules

If you sell to business customers, include a dedicated B2B ecommerce section. Start with the company structure: which users can view prices, request a quote, submit an order, or approve a purchase for each account and location?

Use your actual rules for customer-specific pricing, quantity breaks, minimum order quantities, and payment terms. Where dynamic pricing is part of the business, identify which system calculates the current amount and when the assistant must refresh it.

For example, test bulk ordering with a realistic exception. A buyer requests 120 replacement filters, but their account has access to only certain products and the order exceeds their approval limit. The assistant should identify the permitted item, retrieve the correct account price, and route the purchase for approval when required.

Require proof: An authorized account session, the price and quantity rules applied, the approval decision, and the resulting cart, quote, or order record. Check the ecommerce platform and ERP together where both contribute to the result.

These tests should follow the account model actually configured for the business. Official Shopify documentation on company accounts illustrates why locations, customer permissions, and purchasing terms need explicit treatment. Confirm the features available in your edition and setup.

Record these B2B ecommerce checks under the existing recommendation, action, and security categories. Adjust their importance before evaluation so that the same scorecard remains usable across the shortlist.

Use a weighted scorecard with mandatory pass conditions

Agree on the evaluation criteria and weights before reviewing proposals. This example suits a deployment covering both shopping and support; it is not an industry benchmark.

Enter weighted points in the vendor scorecard. Keep the underlying requirement scores and evidence references in the evaluation record. Record the methodology so another reviewer can reproduce the calculation.

Use the following scale for each individual requirement.

Average the designated scored requirements within each category unless you set different requirement weights in advance. Record mandatory gates separately as pass or fail; exclude gate-only and unscored optional items from the average. Then calculate:

Category points = category weight × category score ÷ 5

A score of 4 in a category worth 20 points contributes 16 points. Add the category points for a total out of 100.

Record the test date, configuration, evidence reference, reviewer, and limitations beside every score. Missing evidence stays visible as unverified. A roadmap promise earns no demonstrated-capability points.

Remove genuinely irrelevant requirements before scoring and apply the same revised weights to every vendor. A vendor’s inability to support a requirement does not make it “not applicable.”

Set the conditions that a high score cannot override

Treat cross-account disclosure, unauthorized actions, duplicate financial actions, unsupported success messages, and failure to honor required human takeover as mandatory failures for the relevant workflows. An untested pass/fail condition remains unresolved.

Block live rollout of the affected scope until the issue is corrected and retested. Specify who approves progression. A useful overall score helps compare vendors that meet your minimum conditions; it cannot excuse a failed condition.

Ask for proof that another reviewer can inspect

For each important workflow, require a compact evidence record containing:

1. The requirement ID, test case, and expected result.
2. The configuration and relevant catalog or policy version.
3. The complete conversation, including clarification and error messages.
4. The action log, with sensitive information removed.
5. The destination-system outcome or another agreed verification source.
6. The pass or fail decision, reviewer, and unresolved limitations.

For document-based requirements, inspect the relevant report, agreement, or operating procedure. A security report and a return-processing test answer different questions.

Use the same approved scenarios, setup allowances, and workload assumptions across shortlisted vendors. Keep some test cases unseen until evaluation, while disclosing the required capabilities and acceptance criteria. Repeat critical cases with different wording and conversation paths.

The following scenarios are illustrative tests, not reported customer incidents.

Define the outcomes before measuring them

A completion rate needs a denominator your team can defend. So does a revenue claim.

Verified action completion

Use this proposed metric for workflows that execute actions:

Verified action completion rate = unique eligible requests completed correctly by AI without human assistance and confirmed in the destination system ÷ all unique eligible requests assigned to AI.

Define eligibility before the pilot. Keep failed attempts, timeouts, and unresolved eligible requests in the denominator. Count retries against the same request once, and report human-assisted completions separately. Where a workflow intentionally requires human approval, report those completions as a separate category and set an appropriate target. Required approval is not a failed task.

For example, suppose AI receives 100 eligible requests. It completes 70 correctly, people help complete another 10, and 20 remain unsuccessful. Autonomous verified completion is 70%. The human-assisted outcomes belong in a separate line.

Also report coverage: the share of incoming requests assigned to AI within the agreed channels, workflows, and measurement period. Break coverage and completion down by workflow. This makes a narrow successful deployment distinguishable from one handling a broader workload.

Track appropriate refusals and escalations separately. A correct decision to decline an unauthorized request should not create pressure to bypass the control.

Support resolution and operating cost

Agree on what resolves each issue and how repeat contacts or reopened tickets affect that status. Customer silence alone should not establish that a problem was solved.

Define the observation window, evidence source, and treatment of unresolved cases. If reporting satisfaction, show the response count and response rate alongside the score.

For cost per verified resolution, divide the total cost of the defined workflow by its verified resolved cases over the same period. Include relevant vendor charges, human escalation work, and ongoing administration. Keep the scope consistent across vendors.

Attributed revenue and incremental revenue

Attributed revenue connects purchases to an interaction under a chosen rule. Incremental revenue estimates what changed because customers received the AI experience.

Where feasible, test incremental impact with a randomized holdout and compare all visitors assigned to each group. Set the primary metric, randomization unit, sample-size plan, and measurement window before launch. Report uncertainty and account for cancellations or returns where they affect the chosen outcome. Alhena’s guide to measuring incremental revenue with holdout tests explains the purpose of the comparison.

Case studies can help you ask better questions. Alhena’s published Tatcha case study reports conversion at three times the site average and a 38% increase in average order value (AOV). Those reported results describe a particular deployment; the published figures alone do not establish randomized incremental lift or predict another store’s outcome.

Ask every vendor for the population, baseline, period, and method behind its results, including Alhena.

Turn the RFP into a proof of concept and pilot plan

Carry the selected requirements into implementation. Use a proof of concept (POC) to resolve important technical uncertainties for each use case before expanding to live traffic. Each workflow needs a defined endpoint, a measurable pass threshold, a verification source, and an owner.

Use this acceptance statement:

For [workflow], the vendor must meet [threshold] across [approved cases or traffic] during [measurement window], verified through [source]. The pilot pauses if [mandatory failure] occurs. [Owner] approves restart after [correction and retest]. Expansion requires [decision criteria and approver].

Start with synthetic or appropriately prepared test records. Progress to controlled live traffic after the relevant security, integration, and operational conditions pass. Choose the test volume and duration around the workflows and outcomes you need to evaluate.

Keep functional acceptance separate from business impact. You may establish that an assistant creates returns correctly before you have enough evidence to estimate revenue lift.

Before selecting the vendor, settle open implementation dependencies, acceptance obligations, support responsibilities, and commercial assumptions. Include a reference check with a deployment that has comparable catalog complexity, integrations, and operating requirements. Ask that team what work remained after launch. Reconcile the pilot findings with the proposed SLAs and total cost before approval.

The final decision should leave your operators with a system they can run and evidence they can revisit when something changes.

Frequently asked questions

What should an ecommerce AI RFP include?

Include business objectives, priority workflows, catalog and platform requirements, permitted actions, data protection, implementation responsibilities, reporting definitions, pricing assumptions, and pilot acceptance criteria. Ask vendors to provide evidence for each requirement and separate current capabilities from planned features.

How is an AI shopping assistant RFP different from a chatbot RFP?

An AI shopping assistant RFP should test product and variant selection, shopper requirements, inventory and promotion accuracy, and the steps that help a customer buy. A support chatbot RFP usually emphasizes policy answers, issue resolution, identity checks, and escalation. Combined deployments need both sets of tests.

RFI vs. RFP: which should come first?

Use an RFI when you need to understand the available approaches and clarify your business needs. Use an RFP when you can provide a defined scope, comparable questions, and a decision method. An established brief lets you move directly into the RFP process; an exploratory request does not need a full procurement exercise.

What should a B2B ecommerce AI RFP include?

Add company accounts, buyer permissions, negotiated prices, quantity rules, quote handling, and purchase approvals where relevant. A B2B ecommerce assistant should respect the same account boundaries as the underlying ecommerce platform. Test both an authorized purchase path and a request that must be rejected or sent for approval.

What should an RFP test for AI search and personalization?

For on-site AI search, test whether the assistant understands a query, preserves constraints across follow-up questions, and shows eligible products. For personalization, test permitted context, identity boundaries, and ways to correct stored preferences. Alhena’s guide to conversational search and product discovery explains the customer experience these tests should cover.

Do we need to replace our ecommerce platform to add an AI assistant?

This RFP can evaluate an assistant for your existing ecommerce platform. Ask how the proposed ecommerce solution connects to the store, whether it requires changes to checkout, and which components need replacement. Evaluate any platform change as a separate dependency with its own cost, acceptance conditions, and owner.

How do you score AI chatbot vendors fairly?

Set requirements, weights, and acceptance tests before reviewing proposals. Score individual requirements against the same evidence-based scale, calculate weighted category points, and keep mandatory pass conditions separate. Record missing evidence and limitations so reviewers can explain the result.

What proof should a vendor provide for an AI action?

Require the test request, conversation, relevant action log, and an independently inspectable result in the destination system. For a return, that might be one valid return record linked to the correct customer and items. The assistant’s confirmation message is only one part of the evidence.

Should roadmap features count in the scorecard?

Keep them outside the demonstrated-capability score. If a planned feature is essential, document its delivery date, dependencies, acceptance test, and commercial consequence if it is unavailable. Reevaluate it when working evidence exists.

How long should an ecommerce AI proof of concept or pilot run?

Run it long enough to test the agreed workflows, important exceptions, and operating conditions. For conversion or revenue, determine the required traffic and duration from the baseline, variability, and effect you need to detect. A short functional pilot may leave business impact unresolved.

Can a mid-market business use this RFP template?

Yes. Keep the questions relevant to the work you intend to automate and remove capabilities outside the initial scope. Preserve the evidence requirements, account checks, handoff tests, and commercial definitions. A shorter RFP can streamline selection when every retained question has a clear purpose.

Evaluate Alhena against the same requirements

Alhena’s AI Shopping Assistant and Support Concierge address shopping and support workflows across the customer journey. Evaluate the specific configuration, integrations, and permissions proposed for your business using this scorecard.

Bring a difficult product question, an awkward order exception, and the conditions that require a human. Ask to see the complete path from the customer’s message to the final system record.

Book an Alhena demo with your priority workflows and proof requirements.