Conversational AI in B2B is Not a Chatbot
The current narrative around conversational AI is dominated by consumer-facing chatbots answering 'where is my order?'. This model is a poor fit for the realities of B2B commerce, where transactions are complex, high-value, and deeply integrated into buyer procurement systems. The goal is not conversation for its own sake; it is speed, accuracy, and efficiency in processes that are currently manual, slow, and error-prone. Trying to build a generic, all-knowing assistant is a recipe for a high-cost, low-impact project that delivers frustration rather than commercial returns.
For most B2B organisations, especially in sectors like builders merchants or industrial distribution, the biggest drains on operational efficiency are not simple customer queries. They are tasks like re-keying a 200-line order from a PDF, finding a technically compatible alternative for an out-of-stock component, or processing a quote request against a complex trade-account pricing structure. This is where targeted AI agents, not general assistants, can deliver a return inside one financial quarter. Forrester Wave reports increasingly highlight these integrated capabilities as differentiators among platform vendors.
Use Case 1: Agentic Checkout for Complex Orders
The best B2B buyers know exactly what they need, but expressing it in a standard ecommerce interface is inefficient. An agentic checkout allows a user to specify a complex requirement in natural language. For example, a site manager could type: 'I need 20 lengths of 3x2 treated timber, 10 bags of postcrete, and the right fixings for a timber fence, delivered to site B tomorrow'. The system interprets this, resolves the products, quantities, and delivery requirements, and presents a structured basket for confirmation.
The payback here is immediate. It reduces the time professional buyers spend navigating catalogues, saving them hours per week. More importantly, it dramatically reduces the burden on internal sales teams who would otherwise receive this same query via email or phone and have to manually build the order. For businesses we work with in the building materials sector, this manual order processing can consume up to 40% of a sales coordinator's day. Automating it frees them for value-add work like managing exceptions or building relationships. It is a direct and measurable operational saving.
"The goal is not to chat with a computer; it is to get a £50,000 order for building materials from a PDF to the ERP, error-free, in under 60 seconds."
Use Case 2: Parsing Unstructured Procurement Documents
A significant volume of B2B transactions does not start life in a digital format. It begins as a bill of materials in an Excel file, a specification in a PDF, or even a scanned site plan from a contractor. Today, this requires a human to read the document, identify the products and quantities, find them in the commerce system, and add them to a basket. This process is slow, expensive, and a major source of order errors, leading to costly returns and project delays.
An AI agent trained for this task can provide an upload interface. The user submits their file, and the system parses it, matches text strings like 'M10 hex bolt, zinc plated' to specific SKUs, handles quantities, and builds the order in seconds. The user just needs to review and confirm. Our post-project analysis shows this can reduce order entry time from 30 minutes to under two minutes, with error rates falling from over 10% to near zero. A successful implementation here is less about the AI model and more about its tight integration with the PIM and ERP for accurate SKU and stock lookups.
Use Case 3: Expertise as a Service for Discovery and Substitution
A key asset for any specialist distributor or manufacturer is the deep product knowledge held by its most experienced staff. They know which valve is rated for which chemical, which bearing fits a specific machine, or which building material meets a certain fire rating. Conversational AI can codify and scale this expertise. Instead of a simple keyword search, the interface can ask clarifying questions or understand technical parameters expressed in natural language, guiding a less-experienced buyer to the correct part.
This becomes particularly valuable for product substitution. When a specified item is out of stock, a common occurrence in supply chains, the system can analyse its technical attributes and suggest a perfect, form-fit-function replacement. This prevents a lost sale and solves a real problem for a buyer on a tight deadline. This goes far beyond a simple 'customers also bought' recommendation. It requires the AI to have access to rich, structured technical data, but the ROI from saved carts and increased order value is substantial. This is a core focus for our AI for commerce work.
