Why structured business data is becoming more important in an AI-driven economy
Gartner predicted that more than 80% of enterprises would have used generative AI APIs or deployed generative AI-enabled applications by 2026. AI is now an integral part of daily life for many businesses and consumers.
For businesses, AI-powered tools can cut expenses, streamline operations, and reduce human error. This is especially true in digital commerce, where speed and accuracy are crucial for success. With the right setup, AI agents can become the backbone of an entire e-commerce operation.
Yet, there's a catch: Many e-commerce businesses are drowning in fragmented, non-standardized data scattered across isolated channels. As unified commerce platform DigitBridge explains below, AI agents need clean, unified inputs to recognize patterns and automate workflows.
Why E-Commerce Businesses Struggle with Unstructured Data
Every e-commerce business runs on data. Unlike a traditional brick-and-mortar shop where you can physically count the cash in the register or see the sweaters on the shelf, an e-commerce operation has no physical substrate. If the data is wrong, disconnected, or out of sync, the business breaks.
When said data is pouring in from multiple sources and is processed by different departments and/or tools, it’s tough to keep everything under wraps. Furthermore, different business models (retail, wholesale, dropshipping, etc.), use different channels and tools.
While the digital economy hosts a wide range of business models, retailers and wholesalers cover a massive chunk of the landscape, so the article will focus on their data needs moving forward.
E-Commerce Retailers (B2C)
Modern B2C retailers juggle multiple consumer-facing storefronts to capture impulse attention. To boost online visibility, a single retailer might run up to four different channels:
- Online storefront (Shopify, WooCommerce)
- Online marketplaces (Amazon, eBay, Walmart)
- Social commerce (TikTok Shop, Instagram)
- A warehouse management system (WMS)
Each channel sends data into its own platform. Even though each platform uses structured data (tables of SKUs, prices, quantities, and customer IDs), they use non-standardized structures that do not match.
Consider an e-commerce retailer operating on three channels: Shopify, Amazon, and a WMS.
Data fragmentation looks like this when registering the color of a product:
- Shopify might track a product color as Variant: Blue
- Amazon requires a specific attribute tree like Color_Name: Navy Blue
- The WMS identifies the product strictly by its barcode: UPC_789012
When the retailer tries to sync these systems, the data breaks down or requires manual spreadsheets to reconcile. Because of this, core operations run on mismatched, unstandardized workflows.
Data fragmentation also shows up as misaligned time management:
- A customer buys the last pair of sneakers on TikTok Shop at 2 p.m.
- TikTok syncs with the central database every 30 minutes
- Meanwhile, at 2:10 p.m., a customer buys those same sneakers on the main website
Because the structured data wasn't updated in real time across channels, the business has just oversold stock it doesn't have. This leads to canceled orders, dinged seller ratings on marketplaces, and poor customer experience.
As a result, the data quality is too poor to train or power autonomous AI agents reliably. In fact, industry reports show that multi-channel retailers lose 5% to 15% of their potential annual revenue to stockouts, overselling, and operational errors caused by fragmented data.
Wholesale Businesses (B2B)
Because of the sheer difference in complexity, wholesalers face an entirely different scale of fragmentation.
While retailers juggle individual SKUs and color variants, wholesalers manage multi-tiered hierarchies, like:
- Master cartons and case packs
- Pallet configurations
- Custom contract pricing for specific clients
- Rigid EDI (Electronic Data Interchange) feeds for corporate retailers
Their data flows across legacy ERPs, B2B portals, wholesale marketplaces, and manual sales rep entries. A single product often requires multiple data layers (units, inner packs, master cases, and buyer-specific price sheets).
When these systems fail to sync, the fallout goes far beyond a canceled $30 sneaker order. It could be the reason for a cancelled corporate contract, thousands of dollars in misrouted bulk inventory, and broken supply chain compliance.
Why AI Needs Structured Business Data
The most popular generative AI platforms were built for unstructured data (images, text, videos, etc.). They're great for drafting emails, writing code, or being an advanced search engine. Yet, running complex business operations requires something entirely different.
AI agents can only reliably automate operational workflows if they're fed structured business data. For digital commerce, this means clean product data, exact real-time inventory counts, standardized pricing tiers, accurate warehouse locations, and unified order histories.
Without clean, well-organized data, even the most advanced AI agent won't be able to accurately calculate safety stock, route an order efficiently, or adjust prices dynamically. That's because operational workflows require absolute mathematical precision.
For example, without standardized labels, a product listed as "Navy Shirt - Large" on one site and "BLU-LS" on another looks like two different items to a machine.
Unified Commerce as a Solution
In the race for digital readiness, many e-commerce retailers skipped a few foundational steps.
Before AI-powered tools arrived, poor data quality and system fragmentation were manageable headaches (with the help of human employees). But in an AI-driven economy, that approach needs to adapt.
To support the adoption of AI agents in enterprises of all sizes, digital readiness requires three core elements:
- Unified data infrastructure to create a single source of truth
- Standardized and clean business data across all sales channels in real time
- Interoperability and automation to reduce manual human intervention to a minimum
Unified Platforms Turn AI Insights Into Action
Up until recently, AI was often seen as a silent observer or advisor. Its job was to analyze backend data, generate dashboard insights, or flag stock issues for human operators to fix.
But autonomous AI agents can step in and run the operations themselves, if they have access to structured business data. A unified commerce operations platform can provide the execution layer that AI agents need, because all sales channels, inventory databases, and logistics software are connected into a single source of truth.
Enterprise AI Needs Structured Business Data
Today's AI agents hold enormous potential to automate, optimize, and scale modern business. But if they can't take action based on accurate, clean, up-to-date data, much of this potential is lost.
As businesses step further into an AI-driven economy, they should consider making a strategic investment to address that backend infrastructure.
This story was produced by DigitBridge and reviewed and distributed by Stacker.
