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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCustomers are starting some shopping and service journeys with a message or a question to AI, not a search box. Businesses should adapt by making discovery, help, and useful actions work across the channels customers use—not by rushing to launch an autonomous shopping agent. The near-term risks are harder discovery, unnecessary friction, and weaker service when customers ask somewhere else and your business cannot respond.
What conversational commerce means—and what it does not
Conversational commerce is commerce conducted through dialogue: a customer asks a question, gets a relevant response, and can move toward a decision or task. It includes business messaging, AI-assisted product discovery, and conversational customer service. These experiences overlap, but they are not interchangeable—and none necessarily means an AI can purchase on a customer’s behalf.
| Approach | What the customer does | What the business needs to provide |
|---|---|---|
| Business messaging | Messages a business to ask a question, get help, or continue a purchase conversation. | A recognizable business identity, timely responses, access to relevant information, and a clear route to a person when needed. |
| AI-assisted discovery | Asks an AI assistant to find, compare, or recommend products—for example, “find me a waterproof hiking boot under $150.” | Accurate, structured product information that can be found and represented correctly, including attributes such as price, use, and availability. |
| Conversational service | Uses a company chatbot or a third-party AI tool to answer a question or complete a service task. | Connected service information and the ability to take useful, authorized actions—not just generate a plausible answer. |
| Agentic purchasing | Allows an AI agent to take steps toward, or complete, a purchase. | Clear permissions, customer control, trustworthy transaction handling, and reliable product and order data. |
A business can support the first three without letting an AI independently place orders. Treating all four as one “AI commerce” project can lead to premature investment in automation before the information and service foundations are ready.
Why customer journeys are shifting
The clearest signal is not that conventional search or retailer websites have vanished; it is that discovery is spreading across more entry points. In its 30 September 2026 release, Salesforce reported that the rate of shoppers discovering products through brand-owned properties fell 7% between August 2025 and May 2026, while traditional search fell 15% and the rate choosing newer channels—including AI assistants, social-media AI, and delivery apps—rose 38%. These are reported changes in discovery-channel rates, not shares of all shoppers or evidence that newer channels replaced the old ones. Salesforce’s 2026 commerce release also says agentic search as a first step in shopping grew 200% year over year; that growth figure is not the proportion of shoppers using it.
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Salesforce’s analysis of commerce-site behavior in the same 2026 release found AI-chat-referred traffic grew between 150% and 428% year over year in each quarter measured, while overall traffic grew in the single to low double digits in the same comparison. Those are growth rates for referred traffic in the analyzed commerce-site behavior, not a forecast for every retailer or proof that AI referrals convert better.
Other studies indicate what people are trying to do in these environments, but their results should not be combined as if they came from one survey. The National Retail Federation’s 2026 agentic-commerce consumer research reports that 41% of consumers use AI assistants to research products, 33% to look for reviews, and 31% to search for deals; its reviewed page does not expose full methodology. Adobe and Oxford Economics’ 2026 AI and Digital Trends research, fielded in October and November 2025 across an overall sample of 3,000 executives and practitioners and 4,000 customers, found 25% of consumers cite AI-powered platforms as a top source for information, purchasing decisions, and recommendations. Retail and consumer-goods respondents were a subset of that overall research.
Customer-service behavior also extends beyond a company’s own chatbot. Gartner surveyed 3,566 B2B and B2C customers in February and March 2026 and reported that customers were approximately three times more likely to use third-party GenAI tools than company-provided chatbots to resolve service issues. Gartner said third-party GenAI use in service interactions nearly doubled in the prior year, while company-chatbot use remained statistically unchanged since 2022. The implication is not that every company needs a proprietary AI assistant; it is that customers may already be asking questions elsewhere, so your answers, policies, and service pathways need to be accessible and consistent. Gartner’s July 2026 findings also report that 58% of GenAI-using customers had used it to complete a task on their behalf, rising to 74% in B2B settings. These are survey responses, not observed completion rates for every service or shopping task.
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Interest is real, but readiness and comfort are uneven
Messaging is a practical starting point for many businesses because it lets a customer ask for help without requiring a fully agentic transaction. In 2025 fieldwork commissioned by Meta Platforms, Inc. and conducted by Kantar, 73.3% of surveyed consumers across 22 markets said they preferred messaging when communicating with a business. The 11,056 adults surveyed were in the United States, France, Germany, United Kingdom, Spain, India, Mexico, Brazil, Indonesia, Thailand, Vietnam, Malaysia, Philippines, Canada, Argentina, Colombia, Turkey, Singapore, Saudi Arabia, UAE, New Zealand, and Australia; WhatsApp Business reported the findings in 2026. In the same study, 72.4% said they were more likely to purchase from a brand that offers messaging. That is stated likelihood, not measured incremental sales or causal proof. WhatsApp Business’s summary of the Kantar study also reports that 74.6% said they trusted a business more when they could exchange messages with it, and 42.9% believed AI would improve their messaging experience. The latter indicates perceived potential, not a universal preference for AI support.
Adoption figures describe businesses, not consumers. Salesforce’s 2026 release says 28% of commerce organizations in its stated sample use agentic AI today and another 52% plan to adopt it within six months. Plans are not completed deployments, and organizational adoption does not establish customer comfort or business results.
The gap is especially visible around fully agentic experiences. In Adobe and Oxford Economics’ 2026 research, 19% of consumers agreed that AI agents would be their primary way of interacting with brands, compared with 57% of retail and consumer-goods brands expecting that outcome. The study also found 35% of consumers would be comfortable with their personal AI agent working directly with a brand’s AI agent, while 41% of brands expected consumers to be comfortable. Business enthusiasm is therefore not a safe proxy for customer readiness. Adobe’s retail digital trends report provides the study context.
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Some consumers do signal a switching risk, but it is not universal. Valtech’s 2026 report, The Era of Conversational Commerce, says 19.7% of surveyed consumers would switch brands or abandon the task if a brand did not offer an official AI agent. The same report says 71.5% would consider completing a full purchase inside an AI chat app, including payment. “Would consider” is not observed purchasing behavior, and neither figure proves that a particular business will lose customers if it does not launch an agent. Valtech’s report also finds that 51.1% want AI chat apps to help them finish tasks faster, while 52.4% cite payment-security concerns and 43.9% cite privacy and data-use concerns as barriers. These are findings from Valtech’s study, not universal population estimates.
Make trust part of the experience, not a disclaimer
Customers need to know who they are dealing with, what the system can do, and what happens to their information. A conversational interface that makes progress but obscures identity, data use, or accountability can undermine the confidence it is supposed to build.
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- Explain AI and data use plainly. Tell customers when AI is involved, what information it uses for the current task, and when a conversation may be reviewed or handed to staff. Avoid promising privacy protections your actual systems and policies do not provide.
- Protect transaction steps. Treat identity, payment, and order confirmation as high-consequence moments. Do not ask customers to share sensitive payment details in an unapproved channel; use a secure checkout or payment flow your organization has authorized.
- Keep control with the customer. Make it possible to review recommendations, correct assumptions, stop automation, and confirm consequential actions. Visa’s 2026 Agentic Commerce Consumer Research, covering online shoppers in the United States (n=1,600), Australia (n=1,600), and New Zealand (n=500), found roughly two-thirds used or would use AI shopping agents to save time and find better prices. Nearly nine in ten wanted transparency into agent decisions, and about half said they would stop using agents if control disappeared. These are stated survey responses in those three markets. Visa’s report details the findings.
- Offer a credible human route. Let customers reach a person when automation cannot resolve the request, when they challenge an answer, or when the task is sensitive. Gartner’s 2026 customer survey says customers expect access to a human agent when companies use AI in customer service.
What to prepare before adding more automation
Make product information useful to both people and machines
Start with the facts an assistant needs to answer naturally and accurately: product names, descriptions, prices, availability, size and fit, materials, compatibility, shipping, returns, and other category-specific attributes. Keep those details current and consistent across your commerce system, support content, and product feeds. If your catalog describes a boot as waterproof in one place and water-resistant in another, a conversational answer can turn the inconsistency into a customer-facing mistake.
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Write descriptions that answer real questions, not just internal taxonomy. A customer asking for a waterproof hiking boot under a budget needs the relevant attribute, price, and availability—not a generic paragraph that repeats a product name. Test whether staff can find the same answer in your own sources before expecting an external assistant to represent it well.
Map journeys and customer-preferred channels
Identify where customers currently ask for recommendations, order updates, returns, troubleshooting, and pre-sale advice. Use support transcripts, search queries, messaging volumes, and customer feedback to rank the tasks by frequency, effort, and business value. Do not select a channel solely because a platform offers an AI feature; assess customer reach, accessibility, response expectations, and the team’s ability to support it reliably.
Connect the systems that make an answer actionable
A useful conversation often depends on more than a language model. Check whether the experience can access current catalog and inventory data, order status, customer-service knowledge, and the workflow needed to complete an authorized task. Define which actions an automated system may take, what requires confirmation, and what it must hand off rather than attempt.
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For service, continuity matters: customers should not have to repeat their issue when moving from an automated response to a human. Gartner’s 2026 guidance is to redesign digital support around conversational, action-oriented experiences rather than treat GenAI as a standalone chatbot. That means evaluating the whole task—from question to resolution—not just whether a bot can produce an answer.
A staged plan for adapting
- Choose one customer task. Pick a bounded, recurring need such as finding a compatible product, checking an order, or understanding a return. State the customer problem and the intended outcome before selecting a technology.
- Audit the facts and policies behind that task. Verify that product, order, availability, and policy information is accurate, current, and consistent. Record known exceptions and decide which situations require a person.
- Choose the channel and level of automation. Compare business messaging, an owned conversational experience, and the information you make available to third-party AI discovery. Consider reach, integration, privacy, security, transparency, customer control, human escalation, and the value of the task. There is no single best channel or vendor for every business.
- Pilot with clear boundaries. Make the business identity visible, tell customers when AI is involved, limit permissions to what the pilot needs, and provide an obvious way to correct or escalate. Begin with recommendations or low-risk support before enabling consequential actions.
- Measure the task, not the novelty. Track completion and abandonment, time or effort to resolution, answer accuracy, escalation rates, customer satisfaction, and relevant commercial outcomes. Compare against a baseline and account for changes in traffic or channel mix; a high conversation count alone does not show that the experience helped.
- Expand only where results and safeguards hold. Fix failure patterns, update data and workflows, and retest. Add new channels or permissions only when the customer benefit, operating capability, and risk controls are clear.
How to decide what to prioritize first
For most teams, the sensible order is to improve the information customers and assistants rely on, make existing support easier to reach, and pilot one useful conversational task. Consider deeper agentic purchasing only when product data, transaction security, customer permissions, and recovery paths are dependable—and when customers actually want that degree of automation.
The strategic choice is not simply whether to “have an AI agent.” It is whether customers can make progress when they bring a question to your business, a messaging channel, or an AI-assisted discovery experience. Adapt the journey to those starting points while keeping answers accurate, actions authorized, and responsibility visible.
