The conversation around AI in distribution has moved past the pilot stage. Budgets are being committed, vendors are promising transformation, and the stakes on getting it right are higher than ever. Justin Johnson, founder and CEO of Motivate, offers a practical reality check for distributors weighing where AI actually moves the needle and where it just creates motion without results.
His observations on AI in distribution cut across B2B channels, and the patterns he describes show up the same way in HVACR. Messy data, customer-specific pricing, exceptions that outnumber the rules, and the gap between a polished demo and a live deployment. The real question on AI in distribution right now is not whether the tools work. It is whether the business model is set up to capture the return.
AI in Distribution: A Grounded Look from the Field
If you’re running a distribution business right now, you’ve heard the pitch. Probably more than once. Every vendor has a story, every platform promises to transform your operations, and every demo makes it look effortless.
Some of it is legitimate. There are tools out there delivering real results.
But there’s also a lot of product that looks great under controlled conditions and falls apart the moment it touches an actual distribution operation. Messy data, customer-specific pricing, orders coming in six different formats, exceptions that are more rule than exception. The gap between a polished demo and a live deployment can be significant.
That’s what makes this moment worth paying attention to. We’re past the experimentation phase. This is budget. And it deserves the same scrutiny as any other investment you’re expecting a return on.
The efficiency trap
Most AI conversations start with efficiency. Reducing manual work, speeding up order entry, cutting out repetitive tasks. Those are real problems worth solving. But efficiency tends to get treated like it’s the destination when it’s really just the starting point.
Saving time doesn’t create ROI by itself. It creates capacity. The return only materializes if that capacity goes somewhere useful, either lower costs or more revenue. Without that second step, the business just moves a little faster. It doesn’t actually change.
McKinsey has been tracking this pattern for a while now, and the finding keeps repeating: most companies are using AI somewhere, but only a minority are seeing it move the bottom line.
Where it actually makes a difference
The distributors getting real results tend to follow a recognizable pattern.
They start by removing friction. Automating order intake, eliminating rekeying, cleaning up the back office work that eats time without adding value. That part is relatively straightforward, and the operational improvement shows up quickly.
But they don’t stop there. They make a deliberate decision about what to do with the time they’ve recovered. Sales teams get clearer direction on which accounts to prioritize, what those customers are likely to need, and where revenue is quietly walking out the door. Pricing gets more intentional instead of reactive.
That’s the shift. Automation makes the business more efficient. Better decisions make it more profitable. One creates capacity. The other determines what you do with it.
A real example
We worked with an electrical distributor who had a problem most inside sales teams would recognize immediately. The majority of their day was spent rekeying orders, emails, PDFs, spreadsheets, all of it going into the ERP by hand. Everyone knew it was inefficient. It was also just how things had always worked.
They brought in an AI tool to handle order intake and cut processing time roughly in half.
And then not much happened.
Orders moved faster, but the team kept operating the same way. The recovered time got absorbed into the day. Nothing showed up in the numbers.
The change came when leadership made a deliberate call about where that time should go. Reps were given a short daily list of accounts to contact, with specific products flagged based on buying history. Simple in concept, but it changed behavior.
A few months in, they started seeing lift. Higher average order sizes, more reorders from existing customers. Not overnight results, but enough to show up in revenue. The technology didn’t produce that outcome on its own. It created the conditions for it. What made the difference was what the business chose to do next.
The tool sprawl problem
There’s a separate issue worth flagging as more solutions hit the market.
There are now a lot of highly specialized AI tools. One for pricing, one for inventory forecasting, one for customer messaging, one for AP automation. Many of them are genuinely well-built within their lane.
The problem is what happens when you stack them. Instead of simplifying the operation, you end up with more integrations, more data inconsistencies, and systems that don’t talk to each other. Each tool optimizes its own function, but none of them are working toward a shared outcome. That’s where ROI starts to erode, not because the tools don’t work, but because they aren’t aligned around how the business actually makes money.
Why vendor experience matters
Distribution isn’t a clean environment. It never has been. Orders arrive in a dozen formats, pricing is customer-specific, inventory is spread across branches, and exceptions are part of daily life.
Any AI solution has to actually work inside that reality, not a simplified version of it.
This is where the gap between vendors becomes obvious. Some teams have built systems that understand these dynamics and handle them well. Others have designed for ideal conditions that rarely exist in practice. A tool that performs beautifully in a demo can completely unravel once it’s live.
That’s why customer references matter. Not logos on a slide, but real conversations with distributors who are using the system day to day. What changed after go-live? What broke? What results did they actually see? That’s the picture you need before signing anything.
Start with the outcome, not the tool
One of the clearest patterns among distributors getting this right is that they don’t start with technology. They start with a specific outcome. Increase revenue per sales rep. Improve order throughput without adding headcount. Free up working capital stuck in inventory. Then they evaluate whether a given tool actually moves that metric.
That framing changes the conversation. You’re no longer asking whether something is impressive. You’re asking whether it helps you do the specific thing you’re trying to do. That’s a much easier question to answer, and it filters out a lot of noise.
McKinsey’s research puts this in context: only about a third of companies have moved AI beyond pilots into scaled use. The gap between running a proof of concept and actually changing how the business performs is real, and most organizations are still sitting in it.
One final thought
The AI market is going to keep growing. More tools, better demos, more convincing messaging.
The hard part isn’t finding tools that work. It’s finding the ones that will actually change how your business performs.
Because the difference between a smart investment and a sunk cost isn’t whether the technology delivers efficiency.
It’s what happens after the work gets automated.
That’s where the return either shows up, or quietly doesn’t.
About the Author
Justin Johnson, Founder & CEO at Motivate
Justin Johnson is the founder and CEO of Motivate, an AI-driven sales process automation platform for B2B distributors. Motivate empowers inside sales teams to reduce quote and order times by more than 45%, while its patent-pending ecommerce module has been shown to boost distributor online sales by more than 300%. A seasoned tech entrepreneur, Justin has a proven track record in B2B distribution and manufacturing solutions, having successfully built and sold multiple high-growth software companies, including LeadMethod (acquired by Revalize, backed by TA Associates) and MightyRep (acquired by Blue Agave). Justin is a regular contributor to technology and sales process automation thought leadership for B2B manufacturers and distributors.
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