{"data":"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","generatedBy":"Crater Editor: Layers Edition"}
{"data":"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","generatedBy":"Crater Editor: Layers Edition"}
{"data":"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","generatedBy":"Crater Editor: Layers Edition"}
July 2025
Hey Industry Trailblazers! Last month, we talked about how your shop floor machines are the "eyes and ears" of your future AI. We shifted the perspective from automation as a labor-replacement cost to automation as a data foundation.
But let’s get real. If you’ve spent the last 60 days digitizing your back office and mapping your data leaks, you’re likely asking, “Okay, Kat, where’s the actual ROI?”
In the pallet world, we are experts at measuring the ROI of a new nailing line or a more efficient grinder. We can see the boards per minute. But the ROI of an AI-optimized business process is often invisible until it’s massive.
I like to think of it as the "Friction Tax." Every time an invoice gets stuck, every time a customer service rep has to hunt for a BOL, or every time a yard manager makes a "gut-feel" lumber order based on an incomplete spreadsheet, you are paying a tax on your own efficiency. AI in your business processes is how you stop paying that tax.
When we look for technology that actually pays off, we should look at how it’s being applied in other "heavy" industries where the margins are just as tight as ours. Take Samsara, for example. They aren't just selling dash cams; they’ve transformed fleet safety. By using AI to analyze thousands of hours of driving footage in real-time, they don’t just record accidents—they prevent them by coaching drivers on the fly.
The "payoff" isn't the camera; it’s the dramatic drop in insurance premiums and litigation costs. That is a business process transformation that hits the bottom line immediately.
Or look at Flexport. They’ve taken the incredibly messy process of global freight forwarding and layered an AI agent over it to handle "exception management." Instead of a human being sifting through thousands of emails to find the one container that’s delayed, the AI identifies the outlier and alerts the team only when a human decision is actually needed. They aren't just "moving freight" faster; they’ve eliminated the administrative drag that kills profitability.
Sound familiar? We spend so much of our day "managing the exceptions"—the broken order, the late core delivery, the billing discrepancy. We’ve been taught that this grit is just "part of the job." Here’s the exciting part: It doesn’t have to be. When your software and your shop floor data are connected,
AI can begin to automate the boring stuff. Imagine an AI agent that doesn't just track your inventory but automatically adjusts your procurement strategy based on a shift in lumber futures and your actual real-time yield. The payoff isn't just "saved time"—it's the captured margin that you were previously leaving on the table.
To make this technology actually pay off, you have to look for the "High-Volume, Low-Complexity" tasks. If you have a team member spending four hours a day manually reconciling pallet counts, that is a process begging for a "bridge." That is twenty hours a week of high-level human brainpower being used for $15-an-hour data entry.
A company in China that took this concept to the ultimate extreme: every single employee is required to automate one task using AI every single day, or they face termination. It’s an intense, high-pressure approach that sounds worlds away from our family-owned yards.
Now, I’m not suggesting we start handing out pink slips at the repair line for not using a chatbot, but there is a powerful lesson in that level of commitment to progress. What if we challenged our teams to automate just one task a week? Imagine the compound
Samsara isn't just selling dash cams; it has transformed fleet safety.
interest of a culture where every person is looking for ways to let technology handle the friction.
Your roadmap for May is to conduct a "Friction Audit." Sit with your team and ask: "What is the most annoying, repetitive task you do every day that involves moving data from one place to another?"
Don't look for the biggest problem; look for the most frequent one. Once you find it, look for a "low-code" AI tool or a simple integration that can handle the heavy lifting. The payoff will be immediate, not just in dollars, but in the collective sigh of relief from your team. Stop looking for the "magic pill" tech that solves everything. Start looking for the "bridge" tech that fixes the friction in your daily grind.
Until next time, keep automating the friction, keep leading with precision, and keep bridging the gap!
WPM
May 2026