After two years of AI hype, it can be difficult to split what is “AI aspirational” from what is real. Still, it seems like we are at a point where manufacturers and distributors are entering a more practical phase of AI adoption. Early initiatives often centered on features like chatbots, pilots, and proofs of concept. Now, we are starting to see more opportunities for AI to deeply and directly impact business goals.
For many companies, their existing software platforms are rapidly adding AI capabilities to complete tasks that previously weren’t possible. Ecommerce platforms (e.g. Oro, Adobe Commerce, Salesforce, or BigCommerce), search tools (e.g. Bloomreach, Nosto, Algolia), and marketing platforms (e.g. Marketo, Salesforce and Klaviyo) are all adding AI directly into existing workflows. In other cases, specialized “AI native” applications (e.g. Kaavio) are launching to sit alongside the current stack and refactor how work gets done. This aligns with Cadent Commerce’s AI Adoption Pyramid for Manufacturers and Distributors.
The market is (currently) converging around AI embedded within existing applications rather than standalone chatbots or custom built AI applications. The strongest use cases share three characteristics:
- They start with proprietary workflow context: CRM records, tickets, contracts, HR policies, finance data, project history, or enterprise content
- They take action inside permissioned systems: update records, route cases, draft documents, extract metadata, trigger workflows, or coordinate handoffs
- They preserve enterprise controls: permissions, auditability, human escalation, data residency, privacy, and admin governance are becoming part of the product story
Here are a few B2B AI Use Case examples of how existing platforms are integrating AI to improve B2B operations:
Use case 1: Turn emailed purchase orders into draft orders
Order intake is the logical place to start. Many B2B buyers still send purchase orders by email, PDF, spreadsheet, fax, or portal export. They have their own procurement systems and buying habits. The seller still has to turn that input into a clean order.
OroCommerce’s AI Smart Order shows the pattern. OroCommerce’s documentation says the tool processes offline purchase orders (including PDFs and email attachments) and converts them into digital drafts. OroCommerce also says SmartOrder reduces manual entry “by up to 80%.” This type of automation is best suited to a high-volume process with clear rules and frequent manual cleanup.
Use case 2: Enrich product data and content
Product data remains a common bottleneck for manufacturers and distributors.
Product information is often fragmented across catalogs, specification sheets, PDFs, ERP records, PIM fields, spreadsheets, shared drives, and older web pages.. This can slow ecommerce launches, reduces search quality, weakens product recommendations, and limits sales enablement.
Kaavio is a startup using AI to source, validate, and deploy product content for B2B distributors and manufacturers. They source product data from manufacturer documentation, spec sheets, and industry databases in order to give teams tools to validate, customize, and deploy content.
Feedonomics has also added AI data enrichment for commerce product data. Feedonomics auto-categorizes products, automates SEO and metadata entry, and optimizes product data for answer engines and AI-powered search.
Strong candidates for this type of automation include long-tail SKUs, missing attributes, inconsistent supplier files, thin product pages, and categories where buyers need technical detail before they can buy. Human review still matters, but if the goal is to reduce manual cleanup before review begins, this type of AI supported tool can do a lot of the heavy lifting.
Use case 3: Make search understand how buyers ask
B2B search is often much more complex than B2C search. A buyer may search by SKU, brand, competitor part, product use case, attribute, dimension, industry term, or rough description. Account-specific pricing and catalog visibility can make the same query produce different acceptable results for different customers.
Algolia’s NeuralSearch adds vector search to keyword search, then merges and ranks results so an index can return matches based on concepts and intent. Algolia’s Agent Studio extends that search layer into agent workflows by connecting APIs and actions so agents can retrieve context, reason, and act within governed workflows.
Search quality often determines whether buyers remain in a self-service channel or return to email and phone ordering.
Use case 4: Resolve routine service requests and route exceptions
Support and inside-sales teams handle a mix of routine requests and issues that require judgment. Simple requests include custom pricing requests, order status, tracking, returns, invoices, replacement parts, product availability, and account questions. Judgment calls include technical compatibility, substitutions, pricing exceptions, fitment, and contract-specific handling.
Gorgias, a leading support tool, says AI Agent draws on help center articles, website content, documents, guidance, and commerce data. Its documentation also says AI Agent can take configured actions in connected tools, such as canceling an order, processing a return, or updating a shipping address. Those actions are opt-in and configurable.
For distributors, a service interaction can create a reorder, trigger a sales follow-up, reveal a product content gap, or show a recurring process issue. The operating rule is simple: automate routine work, route exceptions, and keep high-risk actions reviewable.
Use case 5: Analyze RFQs and draft responses
RFQs are a strong use case for AI because the work is repetitive, detailed, and often time-sensitive.
A customer may send an RFQ through an email thread with PDFs, drawings, CAD files, BOMs, delivery dates, quality requirements, and commercial terms. Before an estimator or sales engineer can respond, someone has to extract the important details.That first pass may include part numbers, quantities, materials, finishes, tolerances, revisions, certifications, inspection notes, packaging needs, delivery dates, and exceptions.
Paperless Parts applies AI to its quote workflow. This lets teams forward RFQ emails and attachments into the platform, then uses AI to extract critical data automatically. Its Wingman tool (great name, right?) processes forwarded RFQ emails, extracts attachments, assigns files to line items, identifies the submitting contact, and pulls details such as part numbers, revision numbers, material, and 3D model information. Its RFQ software centralizes pricing, service details, and delivery terms, then uses vetted RFQ content to generate AI-assisted responses with access controls and safeguards.
This is a great output for manufacturers’ sales reps since the system prepares the response, and a person approves it before it goes to the customer. The human reviewer still owns the quote: feasibility, margin, lead time, commercial risk, and customer relationship context.
This use case is valuable because RFQ response speed affects whether a manufacturer gets considered or not. Faster first-pass analysis gives estimators more time to decide what to quote, what to decline, what to clarify, and how to protect margin.
A practical AI adoption plan
Review your current technology stack before purchasing new software or developing a custom application. Your fastest project, and highest ROI, may come from configuring a tool the company already pays for.
For many manufacturers and distributors, the best first projects are draft order creation, product content cleanup, search tuning, support automation, and triggered campaigns.
These workflows sit close to revenue and the customer experience, making their impact easier to measure. They also give teams a practical way to learn what AI should handle, what people should review, and where the underlying data needs work.
Cadent Commerce helps manufacturers and distributors assess their current technology stack, identify high-value AI use cases, and develop a practical adoption roadmap.
