AI in wholesale distribution in 2026: what mid-size distributors are doing

Where are mid-size distributors deploying AI first?#

Mid-size distributors are deploying AI first in order intake, quoting, product content and customer service. The four share three traits: high volume, plenty of unstructured text and a right answer that a person can check before anything reaches a customer.

Order intake is often the starting point because the pain is obvious. Customers still send purchase orders as emails, PDFs and spreadsheets, and inside sales reps retype them into the ERP. Tools that read those documents and draft order lines for review cut the retyping without removing the human check.

Broad surveys give context but not a distributor-specific picture. McKinsey's State of AI 2025 survey, published in November 2025 with 1,993 participants across industries and company sizes, found that 88% of respondents report regular AI use in at least one business function, while 23% say their organizations are scaling an agentic AI system. Read those as cross-industry figures, not as a measure of mid-size distributors, many of whom are still at the pilot stage.

Pricing guidance, demand forecasting and agents that act across several systems tend to come later. They depend on trust in years of transaction history and item masters, and that trust takes longer to earn than a successful order-intake pilot.

AI use cases and the records behind them#

Every AI use case in distribution rests on a specific set of records. The table pairs common use cases with those records and a readiness signal you can check this week without a consultant.

How mid-size distributors are buying AI#

Mid-size distributors tend to buy AI rather than build it, because few have a data team. The routes are AI features added to ERPs such as Epicor, Infor, SAP Business One, Acumatica and NetSuite; point tools for order intake, product data or email; and general-purpose assistants licensed for office staff.

That route is fast, but it moves the work rather than removing it. Each tool needs its own connection, its own mapping of item and customer data and its own review of what the vendor does with the data it receives. A distributor running three AI tools has three sets of data terms to track and three places its records now live.

The records question behind every use case#

The records question is the same for every use case in the table: does the history exist, and is it linked? Quotes need outcomes, orders need their exceptions, service emails need resolutions and item masters need their attribute changes. Distributors who kept that history intact through ERP migrations and inbox cleanups have far more to work with than those who did not.

Those records also matter outside the company. Model developers building quoting, order-entry and customer service agents for distribution need real examples of how the work is done, which they cannot easily create themselves. A distributor can license de-identified history to them, keeping ownership and setting the permitted use.

ERP migrations are where that history is most often lost. A replacement system usually takes open orders, active items and current customers, and leaves closed quotes, old exceptions and attribute history behind in the retired system. Export those records with their identifiers before the old license ends.

Sharing data with an AI vendor is not the same as licensing it#

Sharing data with an AI vendor and licensing it to a model developer are different transactions, even when the records are identical. When you connect a tool, data flows to the vendor under its terms, which may allow it to use aggregated or de-identified data to improve its products. When you license, you set the scope, the permitted use and the term, and you are paid for it.

Before connecting any AI tool to the ERP, the shared inbox or the e-commerce platform, get written answers to four questions.

• 1Does the agreement allow the vendor to train models on our data or on data derived from it?
• 2Can we opt out in the contract, and does opting out change pricing or features?
• 3Does the vendor treat our customer pricing and purchase history as confidential?
• 4What happens to our data and any derived data when we leave?

Illustrative: a supplies distributor's first pilot#

Illustrative: a fictional janitorial and packaging supplies distributor runs Acumatica, a shared customer service inbox and a quoting spreadsheet. It pilots an order intake tool on emailed POs from its largest accounts.

The pilot stalls on customer part numbers. Many accounts order using their own item codes, and the cross-reference table is incomplete. The team fills the gaps from past orders and the pilot moves forward. During vendor review, counsel finds a clause letting the vendor use de-identified order data for product improvement and negotiates a contractual opt-out.

The owner also lists the quote and service-email history in the company's data inventory as a separate asset to assess, so any decision to license it stays independent of the tool purchase.

A readiness checklist for a distributor's first AI project#

A readiness checklist keeps a first AI project small enough to measure and honest about its data. Work through it before signing a pilot agreement.

• 1Pick one use case with a baseline you already track, such as order lines keyed by hand or quote turnaround.
• 2Confirm the records behind it exist, are linked and go back far enough to test against.
• 3Fix the cross-references and masters the use case touches before the pilot, not during it.
• 4Review the vendor's data terms before connecting any system.
• 5Keep a human approval step until results match your team's own decisions on past cases.
• 6Record which historical records you hold, so internal AI and any later licensing start from the same inventory.

Where SourceX fits#

SourceX does not sell AI tools to distributors. It helps established distributors license historical operational records to AI developers through the SourceX five-step transaction, starting with a metadata-only fit check. The SourceX Enterprise Data Value Framework describes which drivers, such as uniqueness, domain expertise, human-generated signal and rights, make linked quotes and service threads more valuable than raw invoices. Nothing is shared during the initial assessment, and the distributor approves every step.