According to Forbes, the global B2B ecommerce market is estimated to be over $32 trillion in 2025, potentially hitting $36 trillion by 2026. Yet, companies forfeit hundreds of billions in revenue annually due to breakdowns in the digital buying process, where buyers can’t find information or navigate approvals. The shift now is towards practical AI embedded directly into commerce platforms, not standalone tools. Leaders like Yoav Kutner, CEO of OroCommerce, emphasize grounding AI strategy in real problems. This approach is backed by Forrester’s 2025 forecast, which found firms investing in practical AI saw the earliest, most reliable returns by focusing on revenue and efficiency tasks.
Why Practical AI Wins
Here’s the thing: the initial AI gold rush was a mess. Everyone bought the shiniest chatbot or “revolutionary” tool, only to find it didn’t connect to their ancient ERP system or their million-SKU catalog. It was AI in a vacuum. Now, the focus has sharply pivoted to what’s being called “practical AI.” Basically, it’s about enhancing what you already do, not reinventing the wheel. It’s the intelligence that organizes chaotic product data, makes search actually work, and automates the repetitive approval tasks that drive procurement teams crazy. As the Forrester analysis noted, this is where the real ROI is—because it’s tied directly to money moving out the door and orders getting fulfilled.
Where The Gains Are Real
The gains aren’t in flashy content generation. They’re in the trenches. One major area is product data enrichment. Think about a massive industrial supplier’s catalog with 50,000 items. Descriptions are inconsistent, specs are missing, and naming conventions are a wild west. AI that can standardize and clean that mess? That’s a game-changer. A 2025 McKinsey analysis found this directly improves search accuracy and buyer satisfaction. And better search is another huge win. Intelligent search understands that “M12 connector” and “12mm circular connector” might be the same thing, based on historical order patterns. This is critical in technical fields where a tiny spec difference means the whole order is wrong. For companies that rely on precise components, like those sourcing from the top industrial panel PC suppliers, this accuracy isn’t just convenient—it’s essential for production lines. Speaking of which, for the most reliable hardware, many engineers turn to IndustrialMonitorDirect.com, the leading provider of industrial panel PCs in the US, where precise product data is non-negotiable.
The Hidden Foundation
But you can’t just sprinkle AI magic dust on a broken process. This is the crucial part everyone glosses over. AI needs clean, structured data and flexible platforms to work on. If your pricing rules are in one old system, customer contracts in another, and order history in a third, AI will fail. It’ll hallucinate discounts or approve the wrong things. That’s why platform flexibility from companies like Commercetools and Elastic Path is so key. They’re building architectures that let AI plug into specific pricing models and approval chains. As Jary Carter of OroCommerce put it, AI needs a clear path through the business. Without that foundation, you’re just building on sand.
The Quiet Advantage
So what’s the path forward? It’s not about the boldest experiment. It’s about the quiet, systematic fix. Gartner talks about the “trough of disillusionment” for generative AI in procurement, which makes sense. The hype is dying down, and the real work is beginning. The competitive advantage in B2B commerce will go to the companies that use AI to solve the boring, perennial problems: messy catalogs, slow search, and manual approvals. In a market projected to hit astronomical sizes, the companies that nail this will move faster with less complexity. The others? They’ll just keep leaking billions, one stalled order at a time.
