Gartner expects 40% of enterprise applications to embed AI agents by the end of 2026, up from less than 5% in 2025. Conversational commerce, known as conversational commerce in the specialist literature, is the use of messaging and assistants to guide a customer in real time. The model serves the customer well on a simple purchase. It shows its limits quickly once the catalog gets complex and agentic commerce takes over.
What is conversational commerce and how does it work?
A simple definition of conversational commerce
Conversational commerce covers every sales interaction that happens through a conversation rather than a form or a static product catalog. A customer talks to a brand through a chat, a chatbot, a messaging app such as WhatsApp or a voice agent to find a product, ask a service question or complete a purchase on a complex catalog. The exchange replaces classic browsing with a guided dialogue, one question after another, through to conversion. That conversational experience is exactly the mechanism a conversational commerce solution has to deliver, with an agent able to answer the customer instead of sending them back to a generic support center.
A short history: from live chat to AI assistants
Conversational commerce starts with live chat on ecommerce sites in the early 2010s, when a human advisor answers a customer in real time while they browse. Companies then add rule-based chatbots to absorb the most common questions without pulling an advisor in every time. The arrival of natural language processing and then of generativeartificial intelligence turns those bots into assistants able to understand a request phrased freely rather than a keyword or a predefined button. That technical shift explains why two companies can both talk about conversational commerce while giving the customer a radically different experience.
Conversational commerce and social commerce: what is the difference?
Social commerce is selling directly from a social network, through a buy button under a post or a shop built into the brand's profile. Conversational commerce can happen on those same platforms but it is defined by the nature of the exchange with the customer and not by the channel hosting it. A company can run conversational commerce on its own website, in a mobile app or on a social network, as long as the interaction stays a conversation rather than a single buy click.
The channels of conversational commerce
Companies deploy conversational commerce across several complementary messaging channels, often combined according to the customer they serve and the brand's own priorities:
- Instant messaging, led by WhatsApp : more than 200 million businesses use WhatsApp Business worldwide and 175 million customers contact a business account every day through that messaging app.
- Facebook Messenger and the messaging built into social networks, where the brand meets its customer without making them switch apps.
- SMS, used mostly for order confirmations and after-sales follow-up.
- Voice assistants , available on the merchant site or through a connected speaker at home: voice then becomes a channel for the brand.
- Chat built directly into the brand's mobile app, often paired with the customer's purchase history.
Managing these messaging channels requires the same brand and the same voice at every touchpoint, otherwise the customer gets disoriented moving from one channel to the next.
The role of NLU and artificial intelligence
The technical engine of modern conversational commerce rests on natural language processing , which extracts the real intent behind a freely written message rather than looking for an exact keyword. Generativeartificial intelligence goes further: it writes an original answer fitted to the context of the conversation rather than picking from a pre-written script. Machine learning then refines those answers over time by learning from past conversations, rephrasings and the corrections made by support teams.
A typical conversational journey
A customer opens the chat on a site to ask for advice about a product. This product discovery phase lets the assistant ask two or three questions to pin down the need, the budget and the intended use, then suggest one or two references that match. The mechanism works as well for a physical product as for a service. The customer confirms or refines the recommendation, asks a last question about delivery, then moves on to payment without leaving the conversation. That customer journey compresses into a few exchanges what classic browsing would have spread across several pages and several filters. A brand that wants to stay consistent across the whole journey has to connect that moment to the service history already known about the product and the customer, otherwise the company ends up asking questions it has already asked elsewhere.
The benefits of conversational commerce, by industry
Round-the-clock availability and fewer abandoned carts
A conversational channel answers the customer at any hour, which mechanically reduces the number of unanswered questions at the point of payment. Conversational commerce on WhatsApp reaches conversion rates of 45% to 60%, up to twelve times higher than traditional channels. A question settled live during checkout (shipping cost, delivery time, size in stock) avoids thecart abandonment that often happens when a customer hesitates alone in front of a form with nobody to ask.
Personalization and the use of conversational data
Every exchange with a customer produces data about their preferences, their objections and their budget, well beyond what a browsing history reveals. That customer data feeds product recommendations better fitted to the customer on later visits, even on a different channel from the one used the first time. The company builds a better customer experience along the customer journey, with no questionnaire to fill in and no preference form to tick. Improving that level of service means being present on the right channel at the right moment, without multiplying the tools that manage customer data.
A faster path to conversion
83% of companies use some form of chatbot or automated messaging in 2026, with adoption higher still in ecommerce, at 78%. The conversational shortcut removes research steps for the customer and pushes straight to conversion by clearing the objection at the moment it arises. The conversion rate climbs when a question gets an immediate answer rather than a pointer to a static FAQ the customer has to work through alone.
Lower customer support costs
Part of the repetitive questions (delivery time, availability of an item, return procedure) can be absorbed by the conversational assistant with no human involvement. Human customer support then concentrates on the complex or sensitive cases, which reduces the volume handled by the help center without degrading service quality on the requests that genuinely need an advisor. A company that wants to be seen as reliable cannot settle for a generic help center for its product.
Retail and ecommerce
In retail, conversational commerce mostly guides a customer through a simple purchase: finding the right size, comparing two close references or tracking an order already placed. The catalog stays limited in the number of variables and the decision is made quickly, which suits a classic chatbot or a first-level conversational AI, without the company needing a more sophisticated platform.
Travel and hospitality
Travel combines dozens of variables (dates, destination, budget, accommodation preferences, family constraints) that make the search hard without conversational guidance. A conversational exchange, by chat or by voice assistant, helps the company frame the request before proposing a personalized product rather than leaving the customer to filter hundreds of results alone on a classic search engine. Voice remains an under-used channel even though customers already use it for other everyday tasks. Kleio sets out how agentic commerce is transforming travel in a dedicated article.
Real estate
A property project commits a high budget and crosses several criteria: location, surface area, financing, availability date. Conversational commerce there mostly qualifies the need before connecting the customer to an advisor rather than closing the sale directly, since that decision is long and rarely finalized in a single session. See also agentic commerce for real estate and agency networks.
Automotive
Choosing a vehicle crosses many technical and financial criteria: engine, mileage, financing option, availability at the dealership. A conversational assistant helps the company shortlist a model from the intended use before a test drive or an appointment at a dealership, a human step customers still expect from an advisor. This level of conversational commerce stays useful even on a technical product such as a car.
Scripted chatbot, conversational AI or agentic commerce: which level of autonomy to choose?
The rule-based scripted chatbot: limited automation
A scripted chatbot answers from a decision tree written in advance by a marketing or support team. It recognizes keywords or buttons rather than a freely phrased intent. That level suits closed questions such as opening hours or the status of an order but it stalls as soon as a request falls outside the script. It cannot negotiate a trade-off, combine several contradictory criteria or act directly on a third-party system such as a CRM or a booking tool.
Conversational commerce powered by NLU and generative AI
The second level understands a request phrased in natural language thanks to NLU and generates an original answer thanks togenerative AI. This level of conversational commerce handles nuance and ambiguity far better, including when the customer changes their mind mid-conversation. It stays reactive though: it answers turn after turn, with no long-running memory and no ability to trigger a concrete action on its own in a CRM, a payment system or a booking tool. The final decision stays entirely with the customer or with a human advisor.
Agentic commerce: autonomous agents that decide and act
The third level is different in kind. An AI agent inside anagentic commerce architecture does not just answer: it plans a sequence of steps, queries several systems in parallel, compares options across several criteria and acts directly for that customer (booking a slot, generating a quote, passing a qualified lead to a salesperson) with no human rephrasing at every intermediate step. Agentic commerce therefore moves the conversation from a simple exchange of questions and answers to autonomous execution of the task the customer was trying to accomplish from the start. A plain conversational commerce platform is no longer enough at that level: it takes a platform built natively for agentic commerce, product by product and customer by customer. The table below puts those three levels side by side on the criteria that really matter to a company that has to choose. Kleio explains that shift in the new era of enterprise sales.
The table sums up a progression many companies go through without always noticing: they believe they have deployed an advanced conversational commerce setup when they have only climbed the first rung of the ladder.
Why classic conversational commerce fails on high-consideration purchases
The complex-catalog trap: 60% of buyers undecided at the start
One figure is enough to frame the problem before any technical solution.
About 60% of customers do not know exactly what they are looking for at the start of a journey on a complex catalog. On a simple catalog , a customer knows what they want and compares two or three options before deciding. On a complex purchase, most customers arrive with no precise need, only an intuition or a budget constraint. Classic conversational commerce is not built to surface that latent need: it answers a question that was asked, it does not build the question with the customer through a progressive, structured exchange.
The limits of a scripted chatbot on long journeys
A customer journey in travel, real estate or automotive often spreads across several sessions, several channels and sometimes several different people inside the same company. A scripted chatbot does not keep that context from one session to the next and cannot cross the budget, timing and personal constraints specific to those three industries. The customer then has to repeat the same information every time contact resumes, which damages the experience rather than smoothing it, the opposite of the channel's original promise.
The 3 maturity stages, from conversational commerce to agentic commerce
Gartner predicts that 60% of brands will use agentic AI to deliver personalized interactions by 2028. That trajectory draws three successive stages for a company: the scripted chatbot that automates a closed answer, conversational commerce enriched by NLU that understands a freely phrased intent andagentic commerce that orchestrates a sequence of actions through to conversion. Conversational commerce is therefore not an endpoint but an intermediate step toward wider autonomy, one the most advanced companies have already started to take. Kleio sets out that trajectory in the next frontier of commerce.
Kleio, the agentic commerce platform that goes beyond conversational commerce
The Knowledge Engine: unifying product, document and customer memory data
The ceiling of classic conversational commerce usually comes down to data: it stays scattered between the product catalog, internal documentation and customer history, in systems that do not talk to each other. Kleio's Knowledge Engine distributes that information across three purpose-built stores (product, document, customer memory) linked by a shared governance layer that controls access and versions. The result measured at Kleio: 99% precision on customer queries and answers in under 3 seconds, with no hallucination, even on a large and frequently updated product catalog.
Thousands of AI agents orchestrated for complex sales journeys
Where a scripted chatbot or a simple conversational AI stops at the answer, Kleio'sAgentic Orchestration deploys thousands of collaboratingAI agents able to run a complex sales journey end to end for each customer: qualifying a need in a few exchanges, comparing a whole product catalog across several criteria, generating a priced quote then passing a qualified lead to a human advisor at the exact moment the conversation calls for it. Kleio sets out why brands need autonomous sales agents rather than plain chatbots.
7 industries, one pre-built business ontology
Kleio builds a Triple Business Ontology specific to each industry, combining function, industry and customer, for 7 verticals including the travel, real estate and automotive cases above. Guillaume Martinaud, CEO ofOrpi, sums up the impact on the property network and its clients: “In three months, we kept our commitment: the platform is live, on Orpi.com and in each of our 1,250 agencies, a tool that leverages the richness of our data to better serve our clients and our 8,000 advisors.” Havas Voyages and Selectour report a comparable finding in travel, where the personalization of classic conversational commerce had already shown its limits against multi-criteria requests.
From demo to production in 8 to 12 weeks
A frequent objection to agentic commerce is the supposed complexity of putting it in place. Kleio deploys a complete platform in 8 to 12 weeks, within a SOC 2 and CCPA compliant framework, with strict tenant isolation. A company therefore moves from a limited conversational chatbot to a genuinely operational AI agent infrastructure in weeks rather than months, without giving up the security expected by companies above 500 million euros in annual revenue.
