Adopting AI in wholesale distribution without losing your people

An AI rollout in wholesale distribution should start with a specific problem in the work, the people who understand it, and a clear way to evaluate the result. Choose a bounded use case, involve daily users, and agree on data handling and human review before expanding.

This guide builds on Hugh Hornsby's conversation with Jason Sullivan, founder of Distro, alongside the people-first perspectives of Randy Chaffee and Mark Hunter. Distro is a Blue Collar Smart sponsor; Sullivan's comments are a vendor's perspective, not an independent product evaluation.

Which workflow should you start with?

Sullivan explains that distributors have developed different processes and systems over many years. There is no universal rollout that fits every branch. Begin by watching the work rather than choosing a tool from a feature list.

Ask the team where repetitive tasks consume time, where information gets copied between systems, and where delays affect a customer. Select one workflow with a clear owner and a result that can be checked. Drafting a response for review is a different level of responsibility from making a customer commitment automatically.

How do you involve experienced employees?

Comfort with AI varies across a team. Sullivan describes the need for live training and continued support, especially for people with a process they already trust. Leadership needs to explain the purpose and remain involved after launch.

Include both an enthusiastic user and an experienced skeptic in the pilot. Let them test real examples, record errors, and explain where the tool misses context. Make it clear who can stop the pilot and how the team returns to the existing process.

What should you ask an AI vendor?

Sullivan recommends putting your IT leader directly in conversation with the vendor's engineering or product leadership. Use that discussion to establish how the system will fit your organization.

• What information leaves our systems, and where is it stored?
• How is our data separated from other customers' data?
• Who has access, and how is that access controlled?
• What happens when the system produces an incorrect result?
• How are incidents communicated and handled?
• What ongoing training and support are included?

Have your technical team evaluate the answers against your actual requirements. A convincing demonstration is not the same as an approved production system.

How should you measure the pilot?

Before the pilot, record the time needed for the existing task, the common errors, and the quality of the customer outcome. During the pilot, include the time spent checking and correcting AI output. A faster first draft is only useful if the final work remains dependable.

Review adoption alongside accuracy. If people avoid the system, find out whether the problem is training, workflow fit, trust, or performance. Expand only after the team can explain where it helps and where human judgment remains necessary.

What should stay human?

Chaffee and Hunter both describe technology as support for people and relationships. Use the time saved to understand customer needs, develop colleagues, and solve the problems that require context. Keep an accountable person behind the promises your business makes.

Before buying another tool, sit beside one person doing the work. Write down one problem they would actually like help solving.