How Many Pallets Fit on a Truck? AI Keeps Getting It Wrong

Artificial Intelligence is transforming logistics, but the pallet industry remains one of the biggest blind spots. Ask AI how many pallets fit on a 53’ trailer and you’ll often hear:
“26,” “30,” or “it depends.”

But anyone in the business knows the answer:
616, 520, or 480 are all reasonable answer but not 26.

That’s not just a fun fact — it’s the foundation of pricing, quoting, and routing in pallet operations across the U.S. The fact that AI still gets it wrong exposes a bigger truth: the pallet industry isn’t in the data — yet.

AI Is Solving the Wrong Problem

Most AI used in freight, 3PLs, or warehouse automation was built for a world where “pallet” means “unitized product.” These systems are designed around:

• Mixed SKU freight
• Stacked consumer goods
• Weight-limited, non-stackable loads

But in the pallet world, we’re moving the pallets themselves — empty, stackable, and in high volume. A full truck isn’t 26 pallet positions — it’s 616 pallets, stacked 11 columns per side, 28 pallets high.

Yet AI doesn’t “know” this because...

Most Pallet Data Is Locked Inside Companies

Unlike parcel data (FedEx, UPS, Amazon) or general freight benchmarks, pallet movement data isn’t public. It’s siloed inside:

• Yard inventory systems
• Dispatch logs
• Paper BOLs
• Core grading forms
• Email chains between buyers and sellers

This data is often unstructured, handwritten, or stored in spreadsheets that never leave the company’s server. AI can’t learn from what it can’t see.

This is the #1 reason AI still doesn’t “understand” pallet logistics. It’s not that AI isn’t capable — it’s that we haven’t given it the data to work with.

Consequences of Missing Context

When AI doesn’t understand pallet-specific logic:

• Freight quotes are inflated because it assumes 26 pallets = FTL
• AI tools and agents that are built for generic business operations will be less effective for pallet companies
• AI models will struggle to write blogs, design software or understand business context of pallet yards
• Pricing data wrong

You can’t optimize what you don’t understand. And right now, most AI thinks we’re shipping boxes, not bundles of cores.

AI Could Be a Game Changer — If Built on the Right Data

For pallet yards, recyclers, and marketplaces, the upside of real AI tools is massive — but only if they’re built with pallet-specific data.

Potential Use Cases:

• Load optimization: Match stacks, heights, and trailer types for max volume
• Better Sales and Business Tools: Almost every enterprise company is investing heavily into AI. Which includes CRM companies such as Salesforce and HubSpot. When given the right data these tools can help business operations
• Marketing: AI can be used to write blogs, social media posts, and create images
• Better Software: AI tools such as cursor and Claude Code are becoming population to assist software developers in creating software. Pallet software tends to have unique problems that need a proper understanding of the business model
• Core grading via vision: Auto-identify cracked stringers, busted deckboards, or twisted frames
• Repair triage: Prioritize A-grade vs B-grade workflows using smart inspection systems

Companies Building Pallet-Specific AI

Several startups are working to bring AI into the pallet industry — and they’re training it on the right kind of data:

🔹 Palgent

Pallet model that is trained on a wide range of pallet specific data

🔹 PalletDesign.ai

A pallet design software for quick mock ups that aims to be an alternative to older and more expensive systems

🔹 PalletGrader

AI tool that assigns a pallet grade to wooden pallets(Grade A, Grade B, Grade C, Broken, ect )

These tools aren’t trying to repurpose e-commerce freight models. They’re being built for the unique physical realities of moving, grading, and stacking pallets. That’s the direction the entire industry needs to head.

What Pallet Companies Can Do Right Now

To make AI work for our world, pallet companies need to do what general freight firms did a decade ago: digitize and share (securely).

1. Standardize Load Data

Log every truck as 616 units when full. Record stack count and max stack height.

2. Digitize Grading

Use mobile apps or AI tools to track grade, damage type, and repair need.

3. Push for Pallet-Specific APIs

Brokers and load boards should treat empty pallets differently from general freight. We need dedicated rules, templates, and logic.

4. Protect But Leverage Company Data

Your data is proprietary — but using it to train private AI models can create massive efficiency gains. That’s what will separate tech-forward yards from those still stuck in spreadsheets.

Other Industries Facing Similar AI Data Gaps

The pallet industry isn't alone in being misunderstood by AI. Several other specialized sectors face the same challenge of having their unique operational realities overlooked by generic AI models:

Construction & Heavy Equipment AI assumes standard shipping containers when pricing crane transport, but a 200-ton crane requires specialized trailers, permits, and route planning. Generic freight AI quotes standard rates for loads that actually need escort vehicles and road closures.

Auto Transport AI calculates car hauling based on weight and dimensions, missing that a 9-car hauler's capacity depends on vehicle mix. You can't fit 9 SUVs where you'd fit 9 sedans, but most AI doesn't understand automotive loading configurations.

Agricultural Commodities Grain hauling AI often assumes standard freight density, but corn, wheat, and soybeans have different flow characteristics, moisture requirements, and seasonal loading patterns that affect truck capacity and routing.

Steel & Metal Processing AI freight tools price steel coils like regular freight, ignoring that a 40,000-lb coil requires specialized trailers, loading equipment, and securement methods. The industry's unique handling requirements are invisible to generic logistics AI.

Building Materials Lumber, concrete, and roofing materials have loading patterns and weight distributions that generic AI doesn't account for. A lumber load's board footage calculations are completely different from standard freight cubic measurements.

Industrial Machinery Moving manufacturing equipment requires rigging, disassembly, and specialized transport that AI trained on consumer goods can't price or route effectively. The industry's project-based, custom logistics needs don't fit standard freight models.

Waste & Recycling Like pallets, the recycling industry moves materials in ways that confuse standard AI. Scrap metal density, contamination levels, and processing requirements create unique logistics challenges that generic freight AI completely misses.

Final Thought: Teach the AI or Be Misunderstood

AI isn't biased against the pallet industry — it just doesn't have enough data from us yet.

Until pallet yards start feeding systems the real numbers, grading info, and loading methods, we'll keep hearing "26 pallets" when we mean "616."

So let's give AI the right answer — and the right context — before it builds the wrong playbook for the industry.