Executive Summary
Most brands and suppliers’ AI initiatives fail not because of weak models, but because of unprepared data. This white paper outlines five critical actions for building AI-ready commerce data infrastructure: canonical mapping, attribute normalization, parallel processing, resilient ingestion, and comprehensive auditability. Companies that implement these practices see sales teams shift from manual ERP queries to trusting AI-generated quotes, reducing quote generation time by up to 80% while improving accuracy.
5 actions to produce actionable, AI-powered insight from your commerce data
The potential for Artificial Intelligence (AI) in “Business-to-Business (B2B) commerce sales is obvious:
Today, when a rep gets an email asking. “Can you ship 40 units to Dallas by Friday?” they have to open the Enterprise Resource Planning software (ERP), check inventory, check another screen for allocations, ask purchasing about lead times, dig up the right price list, input these details into a Quote template, PDF it, and attach it to an email reply with three caveats because they’re not totally sure.
All of this could be automated with the right AI-powered workflows.
The Barrier to Adoption
We’ve spoken with hundreds of sales leaders at large brands, suppliers, and wholesale distributors over the past year. Their number one barrier to adopting AI to supercharge their sales workflows?
It’s not the strength of the AI models themselves - it’s preparing their data to be readable by AI.
No company’s data attributes or naming conventions are ever finished. They evolve as trends shift, sales teams improve their selling, customer types change, and internal processes mature.
On top of that, ERP Data and Spreadsheets are limited in both scope and detail. PDF catalogs, spec sheets, and marketing materials contain rich images and natural language descriptions that Large Language Models (LLMs), the building blocks of AI, can effectively retrieve information from. That information is valuable, but it cannot be retrieved from traditional ERPs.
The Path Forward
It doesn't make sense to spend significant time and money extracting data from your pre-existing ERP systems. Sometimes starting fresh makes more sense than slapping band-aids on outdated infrastructure. Here are five actions you can take to build a reliable, secure data model that serves your business today, adapts as you evolve, and enables AI to extract insights from all your available data.
A mapping layer that remembers
Canonical Mapping
Before AI can make decisions about your data, it needs to understand the context of your business. Conduit’s Canonical Mapping establishes a shared definition of products, orders, inventory, and customers across every system that touches them, enabling our AI to gain a full window into your business rather than fragmented snapshots from disparate systems.
ERP data rarely stays clean: fields get renamed, data changes over time, and acquisitions introduce new naming conventions that can break downstream workflows. To address this, heterogeneous ERP fields—such as item_id, sku, or part_number—are mapped to a unified canonical commerce model with stable, system-agnostic definitions, such as:
Product.SKU
Inventory.AvailableToPromise
Order.Status
Customer.PriceTier
When Conduit connects to an ERP feed, each incoming field is interpreted in context and mapped to the corresponding canonical concept. Any new fields are automatically classified against the existing model and flagged for review, rather than being silently misfiled or ignored.
A large media distributor running on NetSuite had long struggled with messy data. Previous analytics tools only added confusion, failing to reconcile different products, mismatched fields, and complex availability signals. Their sales reps, however, knew the system inside out— understanding which SKUs were legacy products, which came from acquisitions, and which fields truly mattered. Most tools simply couldn’t replicate that human intuition.
When Universal implemented Conduit, the impact was immediate. Conduit’s canonical mapping unified fields across both catalogs, creating a single, consistent model. Suddenly, product availability, substitutions, and item status were aligned, allowing AI to reason accurately and reliably, rather than compensating for inconsistencies. As a result, the team could:
Make faster, more confident decisions
Reduce errors in order fulfillment
Unlock the full potential of AI-driven workflows
With canonical mapping in place, AI can reason over data instead of spending cycles reconciling it. This enables faster, more confident decisions, reduces fulfillment errors, and ensures outputs reflect how the business actually defines products and availability, without requiring corrections from sales or support.
01
Normalize SKUs and deduplicate attributes into a controlled vocabulary
Normalization
Step one, canonical mapping, defines where fields live and what objects they belong to. Step two, normalization, defines what those fields actually mean. This step ensures that once data is mapped into a shared model, AI can compare, validate, and reason over attributes instead of inferring meaning from inconsistent names.
The Problem: ERP Duplicates
ERP data accumulates near-duplicates over time. Sometimes they’re added to solve edge cases. Sometimes they come from acquisitions or years of “just add a field.”
A common example is size. You’ll see case_size, dial_size, and product_size in the same feed. They’re all trying to answer the same question, how big is it, but the system treats them as unrelated fields, often with different units and different levels of completeness.
The Fix: Normalization rolls these into a single concept like Product.Size, with clear rules about units, valid values, and where size applies. Once those rules exist, AI can compare products and reason about substitutions.
What changes when you do this well
AI can reliably compare products, enforce constraints, and support real substitution logic. Instead of fuzzy “similar item” suggestions, AI produces answers that reflect true equivalence and business rules.
Conduit works with a large flooring brand who does a large volume of custom work. Once attributes were normalized, sales reps could quickly lean on AI to quote effective substitutions for back-ordered custom SKUs.
02
Process in parallel so your data stays current enough to act on
Ingestion Frequency & Parallelism
Commerce workflows are time-sensitive. Availability and lead times change in real-time as new bulk and dropship orders are booked. If ingestion doesn’t run frequently enough, AI is reasoning over stale data, and even well-structured answers are already wrong.
Conduit’s platform solution does not refresh data aggressively without fixing structure first.
Step one, canonical mapping, ensures AI understands what each field represents.
Step two, normalization, ensures those fields carry clear, comparable meanings.
Once both are in place, we start to think about how to refresh data more frequently and parallelly.
Processing in parallel
means breaking large ERP feeds into independent pieces that can update at the same time instead of waiting on one another. Product attributes, inventory by location, open sales orders, and allocations are ingested as separate streams, each updating as soon as new data arrives. A change to inventory does not have to wait for a full product catalog refresh, and a pricing update does not block availability from updating.
What changes when you do this well
AI moves from drafting to execution. Reps trust AI-generated availability, lead times, and quotes without double checking the ERP, and automated AI actions can run without human intervention, freeing teams to focus on revenue instead of verification.
03
Build resilience for the reality of real-world data
Resilience
Data is rarely actually clean or well behaved. Large ERP exports frequently time out, file transfers can fail partway through, and access credentials and server locations change frequently.
A single malformed record or unexpected field can stop an entire data refresh or AI pipeline, forcing the team to rerun exports, patch scripts, or manually fix spreadsheets, which is how automation and AI initiatives can quietly, frequently, and ironically introduce more human effort.
A single malformed record or unexpected field can stop an entire data refresh or AI pipeline, forcing the team to rerun exports, patch scripts, or manually fix spreadsheets, which is how automation and AI initiatives can quietly, frequently, and ironically introduce more human effort.
Resilience is a pre-requisite for any supplier looking to build with AI.
The ingestion layer has to keep moving even when source data behaves badly, which means:
Accepting partial updates
Using AI to extract the fields that matter from messy free-text instead of breaking the pipeline
The Conduit Approach
Conduit’s data ingestion layer relies heavily on LLMs to make sure supplier data is ready to be analyzed by LLMs. Conduit built AI-supported retry behavior so ingestion can recover gracefully under load, high latency, or inconsistent input. The outcome: the ingestion becomes predictable enough that humans stop watching it and AI models can run at any time of day or night.
What changes when you do this well
Your data stays available for analysis and extraction by LLMs even when the source systems misbehave.
04
Add auditability and permissions so AI outputs are governable
Traceability & Governance
This is the step most teams skip, then regret later.
The ingestion layer has to keep moving even when source data behaves badly, which means:
Accepting partial updates
Using AI to extract the fields that matter from messy free-text instead of breaking the pipeline
The Solution: Built-in Audit Trails
Conduit’s ingestion platform captures user-provided reasoning for each catalog-related decision at the moment those decisions are made, so the audit trail is built into the workflow in real-time. What changed and why is recorded as part of ingestion.
Conduit works with a large PE-backed furniture brand that was preparing for an annual leadership meeting. Because a full audit trail was in place, management was able to use AI to identify that a second wave of tariff-related price changes materially affected sales. Leadership could see when the decision was made, who approved it, and how it flowed through to quotes and orders. That led to discussion around how the impact could have been reduced and how similar decisions should be handled going forward.
The ERP as System of Record
This is also where the ERP’s role as system of record becomes non-negotiable. You can enrich and normalize data outside the ERP, but you need traceability back to the source. When a customer challenges a quote, you have to show the path from list price and exceptions to the final number.
What changes when you do this well
Data normalization efforts stop being a black box. Sales leadership can see the exact decisions used to map, normalize, and ingest data, and when they were made. This is critical to building data infrastructure that is future-proof and AI-readable.
05
In Conclusion
If your last AI experiment stalled, there’s a good chance the model wasn’t the real problem.
Commerce data is harder than data from the internet and software platforms used in other industries because it’s live, fragmented, and often held together by human judgment. Availability, pricing, and lead times change throughout the day, while the data itself is split across ERP, WMS, pricing systems, and contracts, with no single source telling the full story. Sales teams quietly bridge those gaps by knowing which numbers to trust and which SKUs are “basically the same,” but AI only works when that tribal knowledge is made explicit and kept up to date.
LLMs can help sales and support teams move faster, but only when the underlying commerce data is structured, normalized, current, and auditable.
