How Legacy Industries Are Building Scalable Data Foundations for AI-Powered E-Commerce

For decades, legacy industries — from agriculture to heavy equipment to manufacturing — have relied on in-person relationships, dealer networks, and fragmented back-office systems to support their B2B sales. However, today’s buyers expect the same seamless, personalized e-commerce experiences they get as consumers. That expectation is forcing even the most traditional industries to modernize — and fast.

Nowhere is this shift more visible than in sectors like agricultural equipment. With customers who manage multimillion-dollar operations and dealer organizations spanning large territories, the complexity of selling and servicing equipment online goes far beyond a simple digital storefront. What’s needed is a scalable, flexible infrastructure that integrates legacy systems and enables automation, predictive analytics, and artificial intelligence-powered personalization.

Why Modernization Matters Now

The pressure to digitize isn’t just coming from customers. Internal teams are also feeling the strain of outdated systems. Siloed data, disconnected platforms, and manual processes slow down operations, increase the risk of error, and create friction across the entire sales and service lifecycle.

Before generative AI or predictive analytics can drive value, companies must first clean and connect their data. That foundational work is essential to unlocking smarter recommendations, automated quoting, proactive service alerts, and other AI-enabled capabilities that are quickly becoming table stakes in B2B commerce.

How C&B Equipment Transformed its E-Commerce Backbone

A case in point: C&B Equipment, one of the nation’s largest John Deere dealers, recently overhauled its digital infrastructure to meet rising e-commerce demands. Like many in the John Deere ecosystem, C&B operated with data distributed across multiple platforms — including product databases, quoting tools, CRM systems, ERP software, and e-commerce platforms. The result was a patchwork of disconnected systems that made e-commerce inefficient and error-prone.

Rather than rip out its entire tech stack, C&B took a smarter route: build a robust middleware-powered data integration layer using tools to unify and validate information in real time. The company embedded custom business logic to support John Deere’s unique user model — where one customer might manage multiple businesses or farming operations — and optimized content to reflect equipment specs, availability, and pricing across regions.

The outcome is a scalable e-commerce system that not only improves internal efficiency but also delivers a more intuitive, accurate digital experience for C&B’s customers. Tasks that once required manual effort (e.g., reconciling quotes or checking compatibility) can now happen automatically, freeing up teams to focus on higher-value work and allowing customers to self-serve more effectively.

A Replicable Model for the Broader Industry

C&B’s journey offers a blueprint for digital transformation in legacy industries. By addressing data fragmentation first, businesses can modernize without abandoning the systems they rely on. This layered approach enables agility, adding new capabilities like AI recommendations or dynamic pricing, without the disruption of a full platform rebuild.

Other John Deere dealers — and legacy industries more broadly — can take a page from this approach: streamline where it matters most, start with data, and build for scale. The future of B2B e-commerce isn’t about abandoning what works; it’s about evolving it to work smarter.

Best Practices for Scalable Digital Transformation

For companies in legacy industries considering a similar modernization path, these best practices can help lay the foundation for success:

• Prioritize data readiness. Ensure product, customer and pricing data are complete, clean, and consistently structured before pursuing AI or automation.
• Connect existing systems. Use middleware or APIs to integrate platforms instead of replacing them.
• Align with real-world workflows. Customize digital infrastructure to reflect how customers actually operate.
• Automate routine tasks first. Eliminate the manual processes that slow teams down, such as quote generation, inventory lookups, or data entry.
• Build for scale. Design modular systems that support growth and future capabilities like personalization or predictive analytics.

The companies best positioned for tomorrow’s e-commerce landscape will be those that modernize deliberately, build with flexibility, and never lose sight of the customer experience.

Greg Tull is the director of marketing at Classy Llama, a digital consulting agency specializing in e-commerce platforms, AI traction, data management, and digital marketing.