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Useful results depend on the quality of policies and product data, access to order and delivery systems, clear limits on what the assistant can do, and a dependable handoff to a person. The ten uses below follow the customer journey and the work behind the scenes.
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Where AI fits in ecommerce customer support
Ecommerce support AI includes several approaches, not just a chat window. IBM describes rule-based, conversational, messaging, transactional and hybrid chatbots. A rule-based bot follows set flows; a conversational system interprets natural-language requests; messaging bots operate in channels such as messaging apps; transactional bots connect to systems to retrieve information or carry out permitted actions; and hybrid systems combine automation with human support. These categories can overlap. The right choice depends on the task and the data it needs, rather than on the label. IBM’s overview of ecommerce chatbots discusses these forms and uses, including FAQs, order tracking, checkout, product discovery and returns.
Customer-facing automation answers or assists shoppers directly. Agent-assist tools work behind the scenes by summarizing cases, finding approved information or suggesting next steps for a human to review. Shopify documents these as capabilities that support teams may use; they are not available in every product or guaranteed to work equally well across platforms. Shopify’s guide to AI in customer service describes examples including summaries, ticket tags and routing, knowledge retrieval, customer and order context, and suggested next steps.
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Start with work that is repetitive and low-risk. Give the assistant access only to the content and systems it needs, set explicit permissions, and make human escalation part of the workflow. An answer based on a stale return policy or a transactional action without appropriate identity checks can create more work than it saves.
10 practical ways to use AI
1. Answer routine FAQs around the clock
Use an AI assistant to answer questions whose answers are already clear in approved store content: shipping timeframes, business hours, pricing and return policies. Ground responses in a maintained knowledge base rather than expecting the system to infer current rules. For example, it can answer “How long does standard shipping take to California?” if the relevant shipping policy is available and up to date.
Set a clear route to a person when the information is missing, conflicting or does not address the shopper’s situation. Store teams should review frequently missed questions and update the underlying content. This is a practical first deployment because the assistant can provide useful answers without changing an account or order.
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2. Give order-status and delivery updates
For “Where is my order?” or “I haven’t received my stuff yet,” an assistant needs more than a general FAQ. It needs an appropriate connection to current order and shipment data. Account-specific answers also call for identity checks before details are disclosed.
Define which fields it can retrieve and what it should say when tracking data is delayed, unavailable or unclear. A general shipping estimate is not a substitute for the shopper’s actual shipment status. IBM notes that transactional bots are often connected to commerce, CRM and order-management systems; delivery answers likewise depend on the relevant shipment information being accessible. IBM’s ecommerce chatbot guide describes transactional bots and order-tracking use cases.
3. Guide returns and exchanges
An assistant can explain the return policy, check eligibility against the information available, collect the details needed to start a request and route it to the right queue. It should initiate a return or exchange only when the merchant’s rules and system permissions explicitly allow that action.
Make exceptions, disputes, unclear eligibility and sensitive complaints available for human review. The bot should not promise an outcome merely because a shopper asks for it; policy and the actual order must govern what happens. DHL eCommerce reported in 2025 that 71% of surveyed U.S. consumers said they would leave if the return process did not meet their expectations. That finding concerns consumers’ stated expectations, not a measured effect of AI. DHL eCommerce’s 2025 report release gives the survey context.
4. Help shoppers during checkout
During checkout, AI can answer questions about promotions, delivery choices and how to complete a step. These interactions are useful only when the assistant reflects the store’s actual checkout rules and current offers. If a promotion is not confirmed by the store, the assistant must not imply that the shopper qualifies for a discount.
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Delivery options deserve particular care: the assistant should describe the choices actually available for that order and destination, not generic options that may not apply. DHL eCommerce reported that 76% of surveyed U.S. consumers would abandon a purchase if their preferred delivery option were unavailable. This is a 2025 survey finding about stated consumer behavior, not evidence that a support bot increases conversion. DHL eCommerce’s release reports the result and the broader survey scope.
5. Support product discovery
A conversational assistant can ask clarifying questions and surface catalog items matching a shopper’s stated needs. A shopper might say, “I need a laptop for video editing under USD 1,500”; the assistant can narrow results only if price and product attributes are accessible and current.
Keep catalog details and availability synchronized, and have the system distinguish verified product facts from uncertain matches. It should not invent specifications, promise stock or present a recommendation as certain when the catalog does not support it. IBM includes product discovery among ecommerce chatbot use cases. IBM’s guide describes that use alongside support and transactional applications.
6. Offer post-purchase help across messaging channels
Common post-purchase workflows can extend beyond website chat to messaging channels customers use. Channel coverage varies by the systems a merchant connects; the experience should preserve enough conversation context for a shopper to continue without repeatedly explaining the issue.
Apply identity checks consistently when a request involves a particular order or account, regardless of channel. Keep a route to an agent, and make sure a handoff carries the conversation and relevant order context rather than starting a disconnected case. IBM describes messaging and hybrid chatbot approaches, including handoffs for complex cases. IBM’s overview outlines those patterns.
7. Summarize conversations for agents
Agent-assist AI can produce a concise case summary covering the customer’s question, relevant history, steps already taken and what remains unresolved. That can reduce the need to reread a long thread, particularly when a case changes hands or arrives after a customer has tried self-service.
Summaries should be treated as working aids, not authoritative records. Agents need access to the original conversation and should be able to correct an inaccurate or incomplete summary. Shopify describes conversation summaries and customer or order context among AI customer-service capabilities. Shopify’s customer-service guide discusses these uses.
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AI can suggest topic tags and urgency levels, then route a case to a relevant queue—for example, delivery, returns or product questions. This can help organize incoming volume, but a wrong classification can delay resolution.
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Give agents a way to correct tags and destinations, and review misroutes as part of ongoing operations. Routing labels should reflect the support team’s actual queues and escalation rules; otherwise automation simply moves work to the wrong place. Shopify describes ticket tagging and routing as potential AI capabilities, not a promise that every system supports them. Shopify’s overview covers these examples.
9. Assist agents with approved answers and next steps
When a customer’s case needs a person, AI can retrieve relevant knowledge-base content and suggest a response or next action. The agent can use that material to work faster while retaining judgment over what is sent.
Keep approval with the human agent for consequential, uncertain or sensitive matters, including policy exceptions and account changes. Suggestions should cite or expose the approved content they rely on so an agent can check whether it applies to the specific order and situation. Shopify describes knowledge retrieval and real-time next-step suggestions as possible support uses. Shopify’s guide provides examples.
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Across tickets and conversations, AI can group recurring questions or complaints to help a team notice unclear policy language, missing product information, delivery issues or repeated reasons for returns. Treat these patterns as leads for investigation, not proof of a root cause: a cluster of complaints may point to a problem, but the underlying cases still need review.
Use the findings to check source material and operational records, then update policies or processes only when the evidence supports a change. Trends may also reveal that a supposedly automated issue needs better data or a clearer escalation path.
How to choose the first workflow
Compare candidate tasks by the consequences of a wrong answer, what information the assistant needs, and how easy it is to reverse an action. A policy FAQ is generally a lower-risk starting point than issuing a refund or changing an account. Transactional tasks require appropriate system access, verified identity and permission boundaries.
| Decision factor | What to establish |
|---|---|
| Task risk and reversibility | What harm could an incorrect answer or action cause, and can the action be undone? |
| Source accuracy | Are the relevant policies, catalog attributes and availability current and approved? |
| Context and integrations | Does the task need order, shipment, CRM or helpdesk information, and can the system access it reliably? |
| Permissions | Can the assistant only explain, or may it initiate a return, cancellation, refund or account change? Which actions require approval? |
| Human handoff | Can a person take over with the conversation history and relevant context intact? |
| Channels and languages | Does the workflow work in the channels customers use, with suitable language coverage? |
| Monitoring and correction | Can the team review incorrect answers, misroutes and agent corrections? |
| Customer control and transparency | Can customers tell when they are interacting with AI and reach a person when needed? What data is used? |
| Operating effort | What integration, content maintenance and ongoing review will the workflow require? |
Trust matters beyond the mechanics of the bot. Visa’s 2025 research surveyed nationally representative online samples in the United States (n=1,600), Australia (n=1,600) and New Zealand (n=500), and examined consumer concerns about transparency and control of shopping data in agentic commerce. Those findings concern agentic commerce broadly, not customer-support chatbots specifically, so they should inform questions about control rather than be treated as a chatbot-preference measurement. Visa’s 2025 consumer study sets out its scope.
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- Choose one narrow workflow. Prefer a repeated, low-risk question whose answer comes from dependable policy content or authenticated systems, such as a shipping FAQ or order-status request.
- Prepare the source material. Confirm the approved answer, remove conflicting or outdated policy content, and make sure product or operational data used by the workflow is current.
- Test representative questions. Check common phrasings and edge cases against the answers the store has approved. Include situations where information is missing or contradictory, and confirm the assistant acknowledges uncertainty rather than filling gaps.
- Set identity and permission boundaries. Require suitable identity checks for account-specific information. Specify what the assistant may explain, retrieve or change, and which actions need human approval.
- Design escalation before launch. Define when a person must take over, make that route usable, and test that the agent receives the conversation, prior steps and unresolved question.
- Monitor and correct. Review failed answers, incorrect routing, repeat contacts, customer satisfaction, resolution time and escalation rate. Correct content or rules when a recurring failure points to a source or process problem.
Measure the chosen workflow against its own baseline and operating goals; published figures from different vendors or surveys are not interchangeable benchmarks. In a 2025 survey of 400 ecommerce decision-makers across North America, the U.K. and Europe, Gorgias reported that AI handled 31% of ecommerce customer interactions on average, with respondents expecting 47% within two years. Its 2026 report also cites platform data from more than 16,000 brands and 350 million-plus shopper conversations across channels in 2025. These are Gorgias survey and platform findings, not independent industry-wide measurements. Gorgias’s 2026 report describes their scope.
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What the available figures do—and do not—show
Survey results can provide context for why merchants are exploring AI, but they do not demonstrate that a particular support deployment causes better sales, lower costs or higher satisfaction. DHL eCommerce’s 2025 E-Commerce Trends Report covered 24,000 shoppers across 24 global markets; its U.S. release said 60% of U.S. shoppers wanted retailers to offer AI-powered shopping features. That broad measure is not a chatbot adoption rate or a finding specifically about customer support. DHL eCommerce’s release describes the report and its results.
Shopify attributes to Deloitte’s 2024 year-end generative AI report a finding that 56% of business leaders ranked improved efficiency as their top expected AI benefit. Shopify also cites Zendesk case figures for Lush’s Marvin AI assistant: about five minutes saved per ticket and 360 agent hours saved monthly. The latter is a vendor case example, not a general result that other stores should expect. Shopify’s article supplies that attribution.
AI quality depends on implementation. IBM warns that poorly planned systems, including models trained on inadequate or inappropriate datasets, can create poor commerce experiences. A bot should not be presented as a substitute for human support in every situation: complex, emotional, technical or unusual cases need an effective path to a person. IBM’s discussion of AI in ecommerce addresses implementation risks, while its chatbot guide describes hybrid human handoffs.
Frequently Asked Questions
What is the best first AI use for an ecommerce support team?
A repetitive, low-risk workflow with a reliable answer source—such as an FAQ about shipping or store hours—is a sensible starting point. Order-specific answers need appropriate access to current order or shipment data and identity checks.
Can an AI assistant handle refunds and returns automatically?
It can explain policy and gather information; whether it may initiate a return, exchange or refund depends on the merchant’s rules, integrations and permissions. Keep exceptions and disputed cases available for human review.
Does AI customer support require order-system integration?
Not for every task. A policy FAQ can use approved knowledge content, while order-status answers require relevant, current order and shipment information. The integration should match the data needs of the workflow.
Should AI answer every customer question without an agent?
No. Define escalation for missing or conflicting information, sensitive or consequential requests, and cases the assistant cannot resolve. Human handoff should retain useful conversation context.
Do survey figures prove AI support improves conversion or satisfaction?
No. The cited consumer surveys report attitudes and stated intentions, while vendor reports describe their own survey or platform data. They do not establish that AI support itself causes better commercial or customer outcomes.
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