The Biggest Mistakes Logistics Companies Make When Implementing AI (And How to Avoid Them)

AI has been pitched as the cure-all, white glove solution for logistics inefficiency. But here’s the truth: most AI deployments in logistics fail.

Why? Because logistics is messy. It’s an industry run off unstructured data, multi-step workflows, and tribal knowledge that lives inside operator’s heads. If you add in the fact that most companies have their own unspoken rules, it’s no surprise that over 70% of deployments fail.

So, what actually goes wrong, and how can other companies avoid the same fate?

Mistake #1: Forgetting That AI Needs to Reason

Most logistics AI fails because it can’t reason through edge cases. The key is finding AI that can build “memories” from your SOPs, rules, and workflows so it can fill in the blanks and handle multi-step processes without breaking down or hallucinating.

The right AI should act like a seasoned operator who remembers the unwritten rules and applies them consistently. This way, when Customer A (who always ships hazmat) forgets to flag it in the request, AI can make accurate inferences to fill in the blanks.

Mistake #2: Dropping AI Into Chaos

AI can’t thrive in disorganized data. Throwing it into scattered systems and telling it to “figure it out” is like hiring a new operations lead and giving them zero training. The fix is simple: teach your AI the ropes.

Document SOPs, spell out the do’s and don’ts, and give it the same onboarding you’d give any other employee. Contrary to popular belief, AI is not all knowing; it needs to be trained to succeed.

Mistake #3: Buying “Off-the-Shelf” AI

Off-the-shelf AI might work in industries with standardized workflows. Logistics isn’t one of them. Every operation has unique processes, exceptions, and relationship-driven communication.

That’s why the right AI solution can’t just be plug-and-play, it needs to adapt to your company. Think customization, not copy-paste.

Mistake #4: Trying to Automate Everything at Once

AI works best when it starts small. Instead of attempting a company-wide overhaul, pick one high-impact workflow like order entry or shipment visibility and start there.

It’s the same logic as hiring department heads; you don’t hire marketing, sales, HR, and engineering leadership all at once. You start where the need is highest, train thoroughly, and expand from there.

The Bottom Line

So what’s the bottom line? AI can transform logistics, but only if it’s deployed the right way.

Do your research. Prepare and organize your data. Start small, prove results, and build upward.

Pallet creates AI workers for logistics companies. Its agent, CoPallet, automates repetitive tasks like order entry, shipment visibility, quoting, document processing, and more. It plugs directly into TMS/WMS/ERP systems with no rip-and-replace required. Learn more about their work at www.pallet.com