Confident your freight operations are running smoothly?
Correcting these common mistakes will ramp up your freight operations.

Even the most well-run freight operations can fall into these traps. If you’re seeing delays, unexpected costs, or inefficiencies, one of these might be the culprit.

1. Inaccurate data in quotes & booking requests.
   Incorrect weight, dimensions, or HS codes can cause unexpected delays, additional fees, and compliance issues. Small errors at the start can snowball into major disruptions.

2. Lack of a freight pricing strategy.
   Without a good system for managing freight pricing, it’s easy to overlook the best options for a given shipment. This can lead to missing out on better rates, service levels, or faster transit times.

3. Miscommunication with carriers & suppliers.
   Misalignment on schedules and lead times causes last-minute scrambles, missed cutoffs, and unnecessary delays. Clear communication and proactive updates keep things running smoothly.

4. Lack of visibility over shipments.
   Relying on multiple freight partners without a good internal management system makes it challenging to keep track and manage shipments. Without proper tracking, small issues quickly turn into major disruptions.

5. Always choosing the cheapest freight option.
   Sometimes, you get what you pay for. A lower rate can mean slower transit times, unsecured space, or poor communication, leading to delays and unexpected costs.

How many of these are you dealing with right now?

These mistakes aren’t just frustrating, they cost time and money. Fixing these five issues can transform your operations and improve reliability.

What’s your current biggest freight challenge?

Last week, I started testing tools to see if AI could automate data extraction from freight documents—commercial invoices, packing lists, BOLs.

The goal? Less manual data entry, faster processing, fewer errors.

I tested two AI tools: V7Go and Airtable (Others shortlisted were Rossum and Docsumo).

The test to see if AI could extract key info like VAT numbers, HS codes, values etc without manual effort and ultimately use that data for further automations.

Findings: What Worked vs. What Didn’t

✅ Fast & easy upload – AI processed docs in seconds

✅ Basic fields (invoice numbers, dates, totals) were mostly accurate

✅ Clear potential for automation – structured data opens up workflow automation

❌ Not all docs contained the required info – client invoices vary massively

❌ Occasionally pulled the wrong data – e.g., exporter’s VAT instead of importer’s

❌ Still needs human oversight – AI is good, but not 100% reliable

❌ Setup takes effort – the real work is in improving document quality, planning & prompts

Final Thoughts: Is AI Worth It?

• AI definitely has potential to improve freight operations given the right set up.
• The biggest lesson? AI is only as good as the data you give it.
• Next step: Evaluate options for how the final structured data could be best used.

I wrote a more detailed breakdown covering how I tested AI for freight paperwork, what worked, and what didn’t.

I’ll drop the link in the comments if you’re interested.

Anyone else tested AI for freight paperwork?

What’s worked for you—and what hasn’t?

AI is everywhere right now. Apparently, it’s set to transform logistics—cut costs, reduce delays, and make everything more efficient.

But how much of this actually holds up in a real-world freight operation? And how much is just marketing fluff?

I’m always keen to try new tools, so I’m going to find out (and because I’ve set myself the challenge of posting on LinkedIn more this year!).

Over the coming months, I’ll test different AI and no-code tools to see which ones actually help businesses in freight and logistics—rather than just adding more complexity.

✅ Can AI really help secure better freight rates? ✅ Can it predict and prevent delays before they happen? ✅ Does it genuinely make life easier for people managing freight?

I’ll share what works, what doesn’t, and what’s actually worth considering.

If you’ve tried any AI tools in our industry, what’s been useful? And what’s been overhyped?