AI in eCommerce: Practical B2B Use Cases to Boost Efficiency and Growth

AI is both a catalyst and a disruptor. But if you’re in manufacturing or distribution, you’ve probably wondered whether all this AI talk is hype or something tangible you can start benefiting from right now.

Here’s something worth knowing: McKinsey reports that B2B companies are making significantly bigger bets on generative AI, allocating between 11% to 25% of their eCommerce budgets – far outpacing their B2C peers. This echoes broader shifts, where B2B eCommerce budgets overall have expanded dramatically as businesses increasingly recognize digital’s strategic importance.

Yet, despite this enthusiasm, many B2B leaders still struggle to pinpoint exactly how AI in the eCommerce industry can realistically add value. That’s what we’re here to share.

Understanding AI

Let’s quickly demystify AI to ensure we’re speaking the same language.

• Machine Learning (ML): Algorithms that improve through experience, analyzing past data to make accurate predictions or automate decisions. Think of your email spam filter: it continually improves its accuracy by learning from each message it analyzes.
• Generative AI (Gen AI): A type of AI capable of creating new content (text, images, code) from scratch. Think ChatGPT writing responses, DALL-E generating images, or GitHub Copilot suggesting code.
• Natural Language Processing (NLP): NLP is a subset of AI that enables machines to read, understand, interpret, and respond to human language. It’s behind chatbots, sentiment analysis, and text summarization.

Now, how does this apply practically to your everyday commerce operations? Let’s dive in.

Practical AI Use Cases for B2B eCommerce

Order and Quote Automation

Imagine you’re a sales rep: an order comes in as a PDF attachment via email. You open it, carefully reading each line, then painstakingly type each product SKU, quantity, and price into your ERP or order management system. It’s repetitive, mistakes happen easily (one incorrect SKU or misplaced digit can mean hours of troubleshooting), and it takes valuable time away from meaningful customer interactions.

AI transforms this reality.

Now, AI-powered tools automatically extract order details and customer data directly from emailed PDFs and input them accurately into your ERP or eCommerce platform. The tedious manual entry process is replaced by quick verification steps. Your team spends less time on data entry and error correction, and more time on tasks that truly benefit the eCommerce business, like consulting with customers, providing strategic recommendations, and nurturing long-term relationships.

PO Order Recognition in OroCommerce

Inventory Management Optimization: Less Waste, More Speed

Another area ripe for AI improvement is fulfillment. Right now, many manufacturers and distributors struggle with inventory that’s scattered globally, hard to track, and tied up in millions of dollars of capital. Your warehouse team might spend hours calling around, manually checking stock, and making educated guesses about demand, often only to end up overstocked in one location and short in another.

AI technology for inventory optimization offers a way out of this chaos. With AI, your team can gain a unified, real-time snapshot of exactly what’s in stock and what’s moving. No more guessing. Instead, AI precisely forecasts demand, optimizes inventory levels across locations, does pricing optimization and streamlines restocking decisions.

For your warehouse teams, this means greater operational efficiency with fewer days chasing missing stock, fewer delayed orders, and significantly reduced inventory carrying costs.

Product Data: Clean, Accurate, and Instant

Managing product data, especially with thousands or even millions of SKUs, is one of the most resource-intensive parts of B2B eCommerce business. Typically, teams handle mountains of raw product information coming from suppliers or manufacturers, often spread across various formats: PDFs, Excel sheets, or even handwritten documents.

AI tools directly address these challenges through advanced data cleansing and content generation capabilities. Let’s break down exactly how this works.

First, consider the process of data cleaning. AI tools use advanced machine learning algorithms and natural language processing (NLP) to systematically scan through product data, identifying and automatically fixing common issues such as duplicate entries, incorrect product names, and inconsistent formatting. 

For example, if your inventory lists the same product as “Industrial Drill Model X23,” “Drill, Industrial X-200,” and “X-Model Drill,” an AI-driven tool will recognize these variants as identical products. It automatically standardizes naming conventions, merges redundant listings, and eliminates discrepancies in product descriptions.

Content Generation and Translation

But AI doesn’t stop at cleaning. It also dramatically streamlines content creation. AI tools can automatically generate detailed, standardized product descriptions from sparse or raw data inputs.

And if you’re running a global business with products sold in multiple languages, AI tackles another major challenge – translation. For example, a catalog with 25,000 products, each containing 60 fields across 16 languages, means potentially managing millions of translations manually. 

AI automatically translates each new product entry into all required languages instantly upon creation, speeding your path to global markets.

Personalization: Making Every Customer Interaction Count

Many companies still handle personalization superficially limited to simple tactics like recycling generic recommendations. But AI provides B2B eСommerce companies a far richer opportunity to meaningfully engage customers based on their specific context, needs, and behaviors.

AI-powered product recommendations

AI product recommendation engines analyze actual transaction data and purchasing patterns, not generalized assumptions, to suggest highly relevant products. Imagine a distributor whose customer regularly orders industrial adhesives; rather than waiting for that customer to manually search for related items, the AI automatically presents complementary products like specialty applicators or adhesive removers, genuinely enhancing the buyer’s workflow.

Merchandising

Merchandising also becomes dramatically more effective. AI can dynamically organize your online catalog based on customer intent, purchasing history, and product profitability. 

If a procurement manager at an automotive plant frequently orders high-temperature lubricants, the AI immediately surfaces complementary products like thermal gaskets or protective coatings, placing them prominently on the customer’s personalized homepage and product pages.

Product bundling

AI algorithms can analyze historical sales to accurately predict what products are typically bought together, creating bundles that genuinely resonate with your customers and boost sales and average order value naturally, rather than pushing arbitrary product combinations.

Site search

Even site search transforms from a passive tool into a proactive sales channel. AI-driven search engines not only instantly recognize relevant products based on synonyms and customer-specific terminology but also dynamically reorder products according to each buyer’s past interactions, interests, and purchasing patterns. It’s the difference between a search that merely filters results and one that actively anticipates customer intent and boosts customer satisfaction.

In practical terms, your merchandising team becomes more efficient, your sales team engages more effectively, and your customers benefit from recommendations that genuinely match their business context. Instead of generic interactions, each customer experience feels thoughtful, relevant, and genuinely valuable.

Enhanced Customer Service and Support

Your support team likely spends hours each day answering the same routine questions: checking if items are in stock, confirming compatibility, or troubleshooting basic issues. It’s repetitive, time-consuming, and often leaves complex customer challenges waiting in line.

AI changes this completely. Advanced LLM-powered chatbots powered by business and customer data quickly handle those common inquiries without sounding robotic. They instantly confirm availability, clearly explain product compatibility, and provide accurate answers to routine troubleshooting questions – all while understanding context and previous customer interactions.

See OroCommerce’s integrated AI-powered assistant in action

Natural language processing use case in distribution

Ferguson, a major US distributor, embedded an AI-powered procurement assistant directly into their customer platform. Contractors no longer had to manually check product availability or guess at complementary items; instead, the assistant proactively suggested products based on past orders, local demand trends, and project specifics. 

Artificial intelligence even forecasted seasonal demand spikes, alerting contractors to stock up early on critical supplies like HVAC units during summer months.

Contractors benefited from fewer delays, clearer communication, and confidence that Ferguson understood their needs. Ferguson saw real business results too: average order sizes increased by 12%, and customer satisfaction climbed significantly.

AI Implementation Challenges: Technology Isn’t the Only Barrier

Even with all the clear benefits we’ve outlined, many manufacturers and distributors still hesitate to dive fully into AI. Gartner recently found that almost half (49%) of companies struggle to show clear ROI from their AI investments.

On the surface, businesses cite valid concerns like data privacy, legacy systems, and difficulties integrating new technologies. Those are genuine hurdles, but they’re not what’s truly stopping most companies from moving forward.

The deeper issue is cultural. Artificial intelligence often feels like a threat rather than a partner, especially for teams entrenched in traditional ways of working. Employees wonder: “Will automation make my job obsolete?” Managers worry about disruption, hesitant to dismantle legacy processes they’ve relied on for years.

This mindset can freeze progress, keeping companies trapped in complex, manual workflows, even when simpler, more efficient solutions exist.

Consider, for example, a distributor with a 20-year-old ERP system. Yes, it’s outdated and inefficient, but employees know exactly how it works. Introducing AI-powered automation to streamline operations sounds smart, but it’s seen internally as risky. The real challenge isn’t technical; it’s convincing a team comfortable with familiar inefficiencies to embrace a new approach.

Breaking through this inertia demands more than technology. It requires leadership willing to openly address these concerns, communicate transparently about how AI tools fit into existing roles, and commit to retraining and reskilling.

In short, the companies that see the biggest returns on their AI investments aren’t the ones with the newest tech. They’re the ones brave enough to challenge old habits, simplify unnecessarily complex processes, and foster a culture that actively supports continuous improvement.

Scaling AI in eCommerce: Practical Steps

Addressing cultural barriers is essential – but once your team is on board, you’ll need practical steps to scale AI and machine learning use cases effectively. 

Your Move: Grow with AI in eCommerce

The sooner your eCommerce business thoughtfully addresses internal resistance and takes practical steps toward integrating AI in eCommerce, the sooner you’ll unlock tangible efficiencies and competitive advantages. 

Waiting on the sidelines is riskier than embracing measured change: the businesses already succeeding with AI tools are those who moved deliberately, strategically, and quickly.