Artificial intelligence (AI) has arrived in wholesale distribution the same way most real change does. Quietly at first. Unevenly. Often without permission.

It didn’t show up with a ribbon-cutting ceremony or a five-year roadmap. It showed up when a salesperson asked ChatGPT to help draft a follow-up email. When a manager asked a tool to summarize a pricing spreadsheet. When marketing experimented with automated product descriptions. In many cases, it showed up before leadership even realized it had entered the building.

For wholesale distributors, the question is no longer whether AI is “real” or whether it will matter. The question is how to think about it in a way that’s practical, grounded and appropriate for a business built on service, trust and thin margins.

That’s the purpose of this article. Not to predict the future. Not to sell software. But to help distribution leaders frame AI clearly enough to make smart decisions over the next 12 to 24 months.

WHY THIS MOMENT IS DIFFERENT

Wholesale distribution has lived through technological cycles before. ERP implementations. Barcoding. E-commerce. Each wave followed a familiar pattern: early adopters experimented, the middle waited for proof and laggards eventually followed once best practices were clear.

AI isn’t following that script.

The pace of this transformation is much faster than those we’ve seen before. The cost of entry is lower. And the gap between organizations that experiment early and those that wait is widening more quickly than most executives expect.

What makes this moment different isn’t that AI is smarter than previous tools. It’s that AI improves continuously and visibly. To more easily paint the picture, understand that any AI tool or platform we use today is the worst version you will experience. Put another way, they will only get better and better. And the organizations that learn how to adapt alongside that improvement gain an advantage that compounds.

In wholesale distribution, where responsiveness, accuracy and product knowledge directly affect customer loyalty, that compounding effect matters.

WHAT AI IS (AND ISN’T)

One reason AI creates confusion is that it gets talked about as a single thing. It’s not.

AI isn’t one platform you install. It’s a family of capabilities that can show up in many places across the business. Some are embedded quietly inside systems you already use. Others sit alongside your existing tools. A few may eventually become strategic differentiators.

Just as important, AI isn’t a replacement for experience or judgment. In distribution, the best outcomes still come from people who understand customers, applications and trade realities. AI’s role is to remove friction, surface insight and accelerate decision-making. When it works, it makes good people more effective. When it’s poorly applied, it creates noise.

That distinction matters.

WHERE AI IS ALREADY SHOWING PRACTICAL VALUE

Most wholesale distributors don’t need moonshots. They need improvements that show up in speed, accuracy and consistency. Early AI use cases tend to cluster in a few familiar areas.

Quoting and responsiveness are often the first. Tools that help sales teams assemble quotes faster, check availability, suggest alternates or standardize follow-ups can reduce cycle time without changing how customers are served. Faster quotes win more work. That’s not theory. It’s observable behavior.

Pricing support is another. AI doesn’t replace pricing strategy, but it can help identify patterns, flag margin leakage and support more disciplined pricing decisions. Especially in environments where cost changes and competitive pressure are constant, this kind of support helps protect margin without forcing blanket rules.

Customer service is a third area. Simple inquiries about order status, availability, documentation or product basics consume an enormous amount of human time. AI-assisted tools can handle a portion of that load, allowing people to focus on exceptions and relationships. The goal isn’t to remove the human element. It’s to preserve it where it matters most.

These same patterns translate directly to HVAC, PVF and waterworks distribution. The workflows differ. The underlying economics don’t.

THE HIDDEN CONSTRAINT: DATA

AI is only as useful as the data it can access.

This is where many distributors encounter friction. Product descriptions that differ by branch. Specifications locked in PDFs. Customer records that are incomplete or inconsistent. ERP shorthand that made sense internally but not to a machine.

None of this is new. But AI is less forgiving of it.

The organizations seeing the best early results aren’t necessarily the most sophisticated. They’re the ones that have invested in basic data discipline. Consistent naming. Clear product hierarchies. Reasonably clean customer and pricing records.

For executives, this reframes the AI conversation. The starting point isn’t tools. It’s readiness. Cleaning up data isn’t glamorous, but it pays dividends regardless of which AI capabilities you adopt later.

THE HUMAN SIDE OF ADOPTION

AI also forces a leadership question that rarely shows up on a balance sheet: trust.

In every organization, there is tribal knowledge. The person who knows which alternates work in the field. The manager who understands how pricing actually gets done. The customer service lead who knows which exceptions matter.

AI doesn’t eliminate that knowledge. It exposes how dependent the organization is on it.

Handled poorly, this creates fear. Handled well, it creates leverage. When knowledge is documented, shared and supported by tools, people become more valuable, not less. The role of leadership is to frame AI as an assistive layer, not a surveillance tool or a replacement strategy.

The companies that struggle aren’t the ones with the wrong software. They’re the ones that never addressed this cultural dimension.

HOW LEADERS SHOULD THINK ABOUT THE NEXT 24 MONTHS

One of the most common mistakes executives make with AI is treating it like a traditional IT project. Long evaluation cycles. Heavy up-front commitment. A belief that the “right” answer will eventually reveal itself.

That approach doesn’t work here.

A better frame is experimentation with guardrails. Choose a narrow use case tied to a real business outcome. Pilot it. Measure what improves. Decide whether to expand, adjust or walk away. Repeat.

This doesn’t require a five-year roadmap. It requires curiosity, discipline and executive engagement.

It also requires restraint. Not every AI capability needs to be adopted. Not every problem needs a technical solution. The winners will be the organizations that stay focused on outcomes rather than novelty.

WHY WE CREATED THE IMARK AI EBOOK

This is the context behind the eBook created specifically for IMARK members.

The goal was not to create another high-level overview or a vendor catalog. It was to provide a practical guide that helps distributor leaders understand:

• What AI actually looks like in distribution
• Where it’s being applied today
• How to think about risk, readiness and sequencing
• Which questions to ask internally before making commitments

The eBook covers strategy, data foundations, real use cases and emerging tools, all through a distribution lens. It’s designed to be read selectively. You don’t need to consume it cover to cover to get value from it.

Most importantly, it’s intended as a reference. Something you can return to as conversations evolve internally and with technology partners.

IMARK is distributing this as a value-add to its members because the conversation is no longer optional. Even choosing not to act is a decision, and leadership teams deserve a clear framework for making it.

A FINAL THOUGHT

AI will not replace relationships in wholesale distribution. It will not change the importance of availability, reliability or trust. What it will change is the pace at which expectations move.

Customers will expect faster answers. Suppliers will expect cleaner data. Employees will expect better tools.

The distributors who thrive won’t be the ones who chase every new capability. They’ll be the ones who stay grounded, experiment thoughtfully and keep learning while others wait for certainty.

That window is open. It won’t stay that way forever.