18 WESTERN PALLET
Your Next Expert Might Be AI Vision
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Hey Industry Trailblazers! We could spend this whole column on the macro debate, AI versus automation versus labor. But I want to zoom into one narrow corner with the biggest ripple effect for all of us: grading and quality control. Ask any forklift driver who's also your quality checker how they decide, mid-shift, whether a pallet's good enough to load. They're moving fast, the delivery window's tight, and they make that call in the few seconds it takes to lift the stack. That's not a knock on them. That's the job. But it also means the same load can get graded differently depending on who's driving, how many hours they've been on shift, and whether they're dialed in or running on autopilot.
The person making the grading call is human, and humans get tired, rushed, distracted, occasionally careless, without meaning to be. Ask the same driver to grade the same pallet at seven in the morning and again at three in the afternoon, and don't be surprised if the answer changes. That inconsistency, not any one person's skill, is exactly what's been costing operators big-box retail accounts long before AI entered the conversation.
The shift underneath this is scaling fast. Goldman Sachs's labor research, studying more than 800 occupations, found entry-level and frontline roles absorbing the sharpest hit from automation, because that work is the most codified, easiest to hand to software or a sensor. Boston Consulting Group's 2026 analysis says roughly half of U.S. jobs will be reshaped by AI in the next few years. This isn’t a new idea. So why wait and see. Operators are moving now and they are the ones fixing this first.
This is where the codified-versus-tacit distinction economists have been drawing, including MIT's David Autor in research the Dallas Fed published this year, gets interesting in a different way than it first looks. Tacit knowledge, the stuff that never fit in a manual, used to be genuinely hard to capture. Every operator's judgment lived and died with that operator. AI vision changes that, not because it needs to learn your yard's specific instincts from scratch, but because it's already learned from enough graders, at enough operations, that the tacit part has effectively become codified. What took one person fifteen years to learn is now something you install.
And you don't need your best person to manage this rollout or spend months training a model. The grading is already figured out, built from countless data points across the industry, closer to plug and play than most of what gets called "AI" right now. The camera goes up, the system starts classifying pallets against a standard trained on millions of prior judgment calls, and within days every unit gets graded the same way, whether it's the first pallet of the shift or the last, whether the driver's fresh or exhausted. Precision and consistency, every time.
None of that makes the person optional, though. Human expertise plus AI will always beat human expertise alone. There's always more to solve. We're just getting started. I'd rather work with someone using AI than someone who isn't, and I think most of your customers agree, whether they'd say it in those words or not. The only person AI replaces is the one who refuses to use it.
Our industry has spent years arguing over whose grading standard is right, peer to peer, supplier to customer, every yard defending its own instincts. But what happens once enough of us run the same kind of AI vision system, feeding it real judgment calls day after day? Does the standardization fight we've been having for decades finally resolve itself, simply because the machines doing the grading all learned from comparable expertise?
Your next assignment before this quarter ends, book a demo with an AI vision company. Ask them directly how fast they can have a working system checking real loads, not months from now, but days. This isn't a build project. It's an install. If you don't know where to start, connect with me and I'll point you toward a few vendors worth a conversation.
Until next time, keep partnering with AI, keep envisioning what's next, and keep bridging the gap!